[
  {
    "path": ".git-blame-ignore-revs",
    "content": "# Switch to ruff for formatting\n5ec353baae51d6f23ac4d0a1050b03da9ed2d804\n"
  },
  {
    "path": ".gitattributes",
    "content": "demo-notebooks/** linguist-vendored\n"
  },
  {
    "path": ".gitignore",
    "content": "# Binaries for programs and plugins\n*.exe\n*.exe~\n*.dll\n*.so\n*.dylib\n\n# workdir for history rewrite/repo sync\n.github_sync\n\n# Test binary, built with `go test -c`\n*.test\n\n# Output of the go coverage tool, specifically when used with LiteIDE\n*.out\n\n*.swp\n*.swo\n/**/.DS_Store\n\n/**/__pycache__/\n*.pyc\n*.whl\n\n*~\n#~\n/**/cmake_build/\n/**/build/\n/**/bin/\n!bin/\n\n/**/linux/version.sh\n/**/macos/version.sh\n/**/windows/version.sh\nlinux-x64/version.sh\n\n/**/src/frontend/node_modules\n/**/src/frontend/dist\n\n/**/src/goutils/lib\n\n/**/pkg\n/**/src/dependencies\n/**/src/frontend/package-lock.json\n/**/src/monitor/src/github.com/segmentio\n/**/src/monitor/src/github.com/shirou\n/**/src/monitor/src/github.com/StackExchange\n/**/src/monitor/src/github.com/go-ole\n/**/src/monitor/src/github.com/xtgo\n/**/src/monitor/src/golang.org\n/**/src/monitor/src/gonum.org\n/**/src/goutils/include/libgoutils.h\n/**/src/goutils/src/github.com\n/**/src/goutils/src/golang.org/\n/**/src/engine\n/**/src/poco-*\n/**/src/xgboost\n/**/src/mariadb-connector-c\n/**/vcpkg\n/**/rcedit\n/**/range-v3\n/**/src/documentation/docs/api/**\n/**/src/documentation/html/**\n/docker/notebooks/**\n/docker/projects/**\n/docker/python-api/**\n/**/src/arrow\n/**/src/utf8proc \n/**/src/vcpkg \n/**/python-api/getml/.getML/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# Vim swap files\n*.swp\n*.swo\n\n.DS_Store\n\n# Emacs temporary files\n*~\n\n# jetbrains environment\n.idea/\n.vscode/*\n**/.vscode\n\n# Vim project config directory\n**/.vim/\n\nproxy_url.sh\ngetml-infra-service-key.json\n\n.ccache\n"
  },
  {
    "path": ".gitlab-ci.yml",
    "content": ".job_template: &job_definition\n  image: python:$VERSION\n  cache:\n    when: always\n    paths:\n      - .cache/pip\n  parallel:\n    matrix:\n      - VERSION: [\"3.8\", \"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n  variables:\n    PIP_CACHE_DIR: \"$CI_PROJECT_DIR/.cache/pip\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == 'merge_request_event'\n      changes:\n        - \"src/python-api/**/*\"\n    - if: $CI_COMMIT_REF_PROTECTED == \"true\"\n    - if: $CI_COMMIT_TAG\n\n.default_scripts: &setup_python_venv\n    - cd $CI_PROJECT_DIR/src/python-api\n    - pip install hatch\n    - hatch config set dirs.env.virtual .venv \n    - hatch env create\n    - source \"$(hatch env find)/bin/activate\"\n\nstages:\n  - dummy\n  - lint\n  - import-test\n\ndummy:\n  stage: dummy\n  needs: []\n  rules:\n    - if: $CI_PIPELINE_SOURCE == 'merge_request_event'\n  script:\n    - echo \"I run to pass pipeline validation in merge requests for non python parts\"\n\nlint:\n  stage: lint\n  <<: *job_definition\n  needs: []\n  script:\n    - *setup_python_venv\n    - EXIT_STATUS=0\n    - ruff check . || EXIT_STATUS=$?\n    - ruff format --check . || EXIT_STATUS=$?\n    - if [ \"$EXIT_STATUS\" != \"0\" ]; then exit 1; fi\n\nimport-test:\n  stage: import-test\n  <<: *job_definition\n  needs: [\"lint\"]\n  script:\n    - *setup_python_venv\n    - pytest ./tests/unit/test_import_all.py\n"
  },
  {
    "path": "BUILD.md",
    "content": "# BUILD.md\n\n## Building getML from Source\n\ngetML is a complex software package, and to simplify the build process, we use Docker for our build environment. This guide will walk you through setting up, building, and interacting with getML. Whether you are targeting a single platform or building for multiple architectures, follow the instructions below to compile getML from source.\n\n### Prerequisites\n\n- [Docker](https://docs.docker.com/get-started/get-docker) installed and running on your system.\n- [Docker Buildx](https://github.com/docker/buildx/blob/master/README.md#installing) installed for multi-platform builds.\n- Optionally, ensure [QEMU](https://docs.docker.com/build/building/multi-platform/#qemu) is set up for cross-platform builds.\n\nVerify if Docker Buildx is installed by running:\n\n```bash\ndocker buildx version\n```\n\n### Enabling Multi-Platform Support\n\n#### Docker Desktop\n\n1. Open **Settings**.\n2. In the **General** tab, enable **Use containerd for pulling and storing images**.\n\n#### Docker Engine (non-Docker Desktop)\n\n1. Add the following to `/etc/docker/daemon.json`:\n\n   ```json\n   {\n     \"features\": {\n       \"containerd-snapshotter\": true\n     }\n   }\n   ```\n\n2. Restart Docker:\n\n   ```bash\n   sudo systemctl restart docker\n   ```\n\nFor more details, refer to Docker's [multi-platform guide](https://docs.docker.com/build/building/multi-platform/#enable-the-containerd-image-store).\n\n---\n\n## Quick Start: Local Development\n\nTo simplify development and manage build processes, we provide a script-based interface. Begin by navigating to the `scripts` directory and using the `getml` wrapper to initiate the build process:\n\n```bash\n./scripts/build\n```\n\n### Available Subcommands\n\n| Subcommand  | Description                                                  |\n| ----------- | ------------------------------------------------------------ |\n| `build`     | Build utilities                                               |\n| `help`      | Show help for available commands                              |\n\nWithin the `build` subcommand, you can build various components of getML:\n\n```bash\n./scripts/build <subcommand> [options]\n```\n\n| Subcommand               | Description                                                                              |\n|--------------------------|------------------------------------------------------------------------------------------|\n| `all`                    | Build the entire package, including CLI, Engine, Python API, and tarball archive.        |\n| `cli`                    | Build the CLI (Command Line Interface).                                                  |\n| `engine`                 | Build the C++ Engine.                                                                    |\n| `package`                | Export runnable Engine + CLI package.                                                    |\n| `python`                 | Package the Python API.                                                                  |\n| `python-copy-artifacts`  | Package the Python API, copying engine build artifacts.                                  |\n| `docker`                 | Build the Docker runtime image.                                                          |\n| `docker-copy-artifacts`  | Build the Docker runtime image, copying the engine build artifacts.                      |\n| `archive`                | Create a tarball (`.tar.gz`) archive of the package.                                     |\n| `archive-copy-artifacts` | Create a tarball (`.tar.gz`) archive of the package, copying the engine build artifacts. |\n\n#### Common Build Options\n\n> [!NOTE]\n> Sometimes, the CMake build preset is cached in dangling state. [If you want to reset the CMake build preset](src/engine/README.md#build-using-docker), set `GETML_CMAKE_FRESH_FRESET=true`:\n> \n> ```bash\n> GETML_CMAKE_FRESH_PRESET=true ./scripts/build <subcommand> [options]\n> ```\n\n| Option         | Description                                                               |\n| -------------- | ------------------------------------------------------------------------- |\n| `-b <args>`    | Specify build arguments (e.g., `-b VERSION=1.5.0`) to be passed to Docker. |\n| `-h`           | Display help information.                                                 |\n| `-o <path>`    | Set the output path (default: `build`).                                   |\n| `-p <platform>`| Set the target platform (`linux/amd64`, `linux/arm64`), default: native.   |\n\nFor most cases, you may want to build the Engine:\n\n```bash\n./scripts/build engine\n```\n\nIf you are building the entire package for distribution:\n\n```bash\n./scripts/build package\n```\n\nThis command will generate all the required artifacts, which will be placed in the output folder. To create a compressed tarball for distribution, use the `archive` subcommand:\n\n```bash\n./scripts/build archive\n```\n\n---\n\n## Multi-platform Builds\n\nBy default, the target is only built for the native platform (`BUILDPLATFORM`). If you want to also build the target for the foreign platform you need to explicitly set the target's [`platform` argument](https://docs.docker.com/reference/cli/docker/buildx/build/#platform). The `build` wrapper supplies the shorthand option `-p` to do so. E.g. for the whole package:\n\n```bash\n./scripts/build package -p linux/arm64,linux/amd64\n```\n\nFor specifics on multiplatform builds regarding the Docker runtime, see [runtime/README.md](./runtime/README.md#multiplatform-builds).\n\n---\n\n## Advanced: Interacting Directly with Docker\n\nIf you prefer interacting directly with Docker, the build pipeline uses multi-stage Docker builds, orchestrated with [Docker Bake](https://docs.docker.com/build/bake/). There are two main `Dockerfile`s:\n\n- **CLI and Python packaging**: [`./Dockerfile`](./Dockerfile)\n- **Engine and dependencies**: [`./src/engine/Dockerfile`](./src/engine/Dockerfile)\n\nTo build specific targets using Docker Bake:\n\n```bash\nVERSION=1.5.0 docker buildx bake engine\n```\n\nTo override specific build arguments:\n\n```bash\ndocker buildx bake engine --set engine.args.VERSION=1.5.0\n```\n\nFor more information, refer to Docker's [Bake documentation](https://docs.docker.com/reference/cli/docker/buildx/bake/).\n\n---\n\n## Running the Docker Image\n\nAfter building the runtime, you can run the Docker image:\n\n```bash\ndocker run -it getml/getml -p 1708-1733:11708-11733\n```\n\n---\n\n## Troubleshooting\n\n- **Docker Build Fails**: Ensure that Docker and Docker Buildx are installed and properly configured. For multi-platform builds, confirm that QEMU is set up for cross-platform emulation.\n- **Missing Artifacts**: Ensure that all required build artifacts for the target platforms are present in the output directory before running a multi-platform build.\n- **Permission Errors**: If you encounter permission issues, try running commands with `sudo`, or ensure your user has proper permissions to interact with Docker.\n\nFor additional help, consult the [getML documentation](https://getml.com) or raise an [issue](https://github.com/getml/getml-community/issues) in the repository.\n"
  },
  {
    "path": "CHANGELOG.md",
    "content": "# Release Notes\n\n## For getML Enterprise & Community editions \n\n### 1.5.1   <small>May 15, 2025</small>\n#### Features\n- Expose arrow streams\n- Preserve metadata (roles) of getml dataframes when converting to pandas, arrow, parquet\n- Support python 3.13\n#### Developer-focused\n- More efficient build chain\n- Expose `GETML_CMAKE_FRESH_PRESET`\n- Use uv for env mangement\n- Introduce mise\n#### Bug fixes\n- Fix slow parquet writing (by using the exposed arrow stream instead of materializing batches as arrow tables on the engine side)\n- Fix XGBoost deprecation warnings\n- Support numpy>=2.0.0\n- Fix parquet reader for parquet files containing custom metadata\n- Set unit when setting time_stamp role directly in `from_...` factories\n\n\n### 1.5.0   <small>Sep 24, 2024</small>\n#### Features\n- Overhaul and better integration of API documentation and web page:\n    - Switch from [sphinx](https://www.sphinx-doc.org/en/master/) to [mkdocs](https://www.mkdocs.org/)\n    - Restructuring of [User Guide](https://getml.com/latest/user_guide/), multiple amendments to documentation \n- Introduce strict typing regiment for [feature learning aggregations](https://getml.com/latest/reference/feature_learning/aggregations/) and [loss functions](https://getml.com/latest/reference/feature_learning/loss_functions)\n- Clean up and maintenance of [example notebooks](https://getml.com/latest/examples/), make them executable in [Colab](https://colab.google/)\n- More informative progress bar and status updates using [rich](https://github.com/Textualize/rich?tab=readme-ov-file)\n- Completely reworked IO\n    - Leveraging [PyArrow](https://arrow.apache.org/docs/python/index.html) to improve reliability, speed and maintainability\n- Introduce [reflect-cpp](https://github.com/getml/reflect-cpp) for parsing and de/serialization\n- Introduce overhauled getML Docker runtime [available from Docker Hub](https://hub.docker.com/r/getml/getml), allowing for easy setup\n  - See [docker-related section of the new getML documentation](https://getml.com/latest/install/packages/docker/) for details\n#### Developer-focused\n- Complete rework of the build pipeline (docker and linux native)\n    - Introduce [CCache](https://ccache.dev/), [conan](https://conan.io/), [vcpkg](https://vcpkg.io/en/)\n    - User multi-stage docker builds leveraging buildx and buildkit\n    - Centralized `VERSION`\n- [Ruff](https://docs.astral.sh/ruff/) for linting and formatting\n- [Hatch](https://hatch.pypa.io/latest/) for python package management\n#### Bug fixes\n- Generalization of [`Placeholder.join`](https://getml.com/latest/reference/data/placeholder/#getml.data.Placeholder.join)'s `on` argument \n- Improved timestamp handling\n- Slicing improvements\n    - Slicing of `DataFrames` returned wrong results: Remove short circuit for slices with upper bound\n    - Introduce set semantics for slicing of `DataFrame` (return empty collections instead of erroring)\n- Fix displaying of parameter lists with values that exceed the presentable width\n- Fix displaying of [`DataFrames`](https://getml.com/latest/reference/data/data_frame/) with one row or less\n- Fix progress bar output on Google Colab\n\n### 1.4.0\t<small>Oct 17, 2023</small>\n- Accelerated feature learning through [Fastboost](https://getml.com/latest/reference/feature_learning/fastboost)\n- Improved modelling on huge datasets through [ScaleGBMClassifier](https://getml.com/latest/reference/predictors/scale_gbm_classifier) and [ScaleGBMRegressor](https://getml.com/latest/reference/predictors/scale_gbm_regressor)\n- Advanced trend aggregations using [EWMATrend aggregations](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.EWMA_1S)\n- Faster JSON parsing using YYJSON\n\n### 1.3.2\t<small>Jan 26, 2023</small>\n- Minor bugfixes\n\n### 1.3.1\t<small>Dec 20, 2022</small>\n- Implement `tqdm` for progress bars\n- Minor bugfixes\n\n### 1.3.0\t<small>Aug 28, 2022</small>\n- Use websockets instead of polling\n- Size [threshold](https://getml.com/latest/reference/pipeline.Features.to_sql) for better visualization of feature code\n- Faster reading of memory-mapped data, relevant for all feature learners and predictors\n- Introduce [CategoryTrimmer](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.CategoryTrimmer) as preprocessor\n\n### 1.2.0\t<small>May 20, 2022</small>\n- Support for [SQL transpilation](https://getml.com/latest/reference/pipeline/dialect/): TSQL, Postgres, MySQL, BigQuery, Spark\n- Support for memory mapping\n\n### 1.1.0\t<small>Nov 21, 2021</small>\n- Enhance data processing by introducing Spark (e.g. [spark_sql](https://getml.com/latest/reference/pipeline/dialect/#getml.pipeline.dialect.spark_sql)) and Arrow (e.g. [from_arrow()](https://getml.com/latest/reference/data/data_frame/#getml.data.DataFrame.from_arrow))\n- Integrate Vcpkg for dependency management\n- Improve code transpilation for seasonal variables\n- Better control of predictor training and hyperparamter optimization through introduction of early stopping (e.g. in [ScaleGBMClassifier](https://getml.com/latest/reference/predictors/scale_gbm_classifier/))\n- Introduce [TREND](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.TREND) aggregation\n- Better progress logging\n\n### 1.0.0\t<small>Sep 23, 2021</small>\n- Introduction of [Containers](https://getml.com/latest/reference/data/container/) for data storage\n- Complete overhaul of the API including [Views](https://getml.com/latest/reference/data/view/), [StarSchema](https://getml.com/latest/reference/data/star_schema/), [TimeSeries](https://getml.com/latest/reference/data/time_series/)\n- Add [subroles](https://getml.com/latest/reference/data/subroles/) for fine grained data control\n- Improved model evaluation through [Plots](https://getml.com/latest/reference/pipeline/plots/) and [Scores](https://getml.com/latest/reference/pipeline/scores_container/) container\n- Introduce [slicing](https://getml.com/latest/reference/data/view/#getml.data.View.where) of Views\n- Add [datetime()](https://getml.com/latest/reference/data/time/#getml.data.time.datetime) utility\n\n### 0.16.0 <small>May 25, 2021</small>\n- Add the [Mapping](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.Mapping) and [TextFieldSplitter](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.TextFieldSplitter) preprocessors\n\n### 0.15.0 <small>Feb 23, 2021</small>\n- Add the [Fastprop](https://getml.com/latest/reference/feature_learning/fastprop/) feature learner\n- Overhaul the way RelMT and Relboost generate features, making them more efficient\n\n### 0.14.0 <small>Jan 18, 2021</small>\n- Significant improvement of  project management:\n    -  [project.restart()](https://getml.com/latest/reference/project/#getml.project.attrs.restart), [project.suspend()](https://getml.com/latest/reference/project/#getml.project.attrs.suspend), and [project.switch()](https://getml.com/latest/reference/project/#getml.project.attrs.switch)\n    - multiple project support\n- Add custom `__getattr__` and `__dir__` methods to DataFrame, enabling column retrieval through autocomplete\n\n### 0.13.0 <small>Nov 13, 2020</small>\n- Introduce new feature learner: \n    - RelMTModel [now [RelMT](https://getml.com/latest/reference/feature_learning/relmt/)], \n    - RelMTTimeSeries [now integrated in [TimeSeries](https://getml.com/latest/reference/data/time_series/)]\n\n### 0.12.0 <small>Oct 1, 2020</small>\n- Extend dataframe handling: [delete()](https://getml.com/latest/reference/data/data_frame/#getml.data.DataFrame.delete), [exists()](https://getml.com/latest/reference/data/#getml.data.exists)\n- Data set provisioning: [load_air_pollution()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_air_pollution), [load_atherosclerosis()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_atherosclerosis), [load_biodegradability()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_biodegradability), [load_consumer_expenditures()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_consumer_expenditures), [load_interstate94()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_interstate94), [load_loans()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_loans), [load_occupancy()](https://getml.com/latest/reference/datasets/datasets/#getml.datasets.load_occupancy)\n- High-level hyperopt handlers: [tune_feature_learners()](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.tune_feature_learners), [tune_predictors()](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.tune_predictors)\n- Improve pipeline functionality: [delete()](https://getml.com/latest/reference/pipeline/#getml.pipeline.delete), [exists()](https://getml.com/latest/reference/pipeline/#getml.pipeline.exists), [Columns](https://getml.com/latest/reference/pipeline/columns/)\n- Introduce preprocessors: [EmailDomain](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.EmailDomain), [Imputation](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.Imputation), [Seasonal](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.Seasonal), [Substring](https://getml.com/latest/reference/preprocessors/#getml.preprocessors.Substring)\n\n### 0.11.1 <small>Jul 13, 2020</small>\n- Add pipeline functionality: [Pipeline](https://getml.com/latest/reference/pipeline/pipeline/), [list_pipelines()](https://getml.com/latest/reference/pipeline/#getml.pipeline.list_pipelines), [Features](https://getml.com/latest/reference/pipeline/features/), [Metrics](https://getml.com/latest/reference/pipeline/metrics/), [SQLCode](https://getml.com/latest/reference/pipeline/sql_code/), [Scores](https://getml.com/latest/reference/pipeline/scores_container/)\n- Better control of hyperparameter optimization: [burn_in](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.burn_in), [kernels](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.kernels), [optimization](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.optimization)\n- Handling of time stamps: [time](https://getml.com/latest/reference/data/time/)\n- Improve database I/O: [connect_odbc()](https://getml.com/latest/reference/database/database/#getml.database.connect_odbc.connect_odbc), [copy_table()](https://getml.com/latest/reference/database/database/#getml.database.copy_table.copy_table), [list_connections()](https://getml.com/latest/reference/database/database/#getml.database.list_connections.list_connections), [read_s3()](https://getml.com/latest/reference/data/data_frame/#getml.data.DataFrame.read_s3), [sniff_s3()](https://getml.com/latest/reference/database/database/#getml.database.sniff_s3.sniff_s3)\n- Enable S3 access: [set_s3_access_key_id()](https://getml.com/latest/reference/data/access/#getml.data.access.set_s3_access_key_id), [set_s3_secret_access_key()](https://getml.com/latest/reference/data/access/#getml.data.access.set_s3_secret_access_key)\n- New Feature Learner: MultirelTimeSeries, RelboostTimeSeries [now both integrated in [TimeSeries](https://getml.com/latest/reference/data/time_series/)]\n\n### 0.10.0 <small>Mar 17, 2020</small>\n- Add [XGBoostClassifier](https://getml.com/latest/reference/predictors/xgboost_classifier/) and [XGBoostRegressor](https://getml.com/latest/reference/predictors/xgboost_regressor/) for improved predictive power\n- Overhaul of documentation \n    - Introduction of \"getML in one minute\" (now [Quickstart](https://getml.com/latest/user_guide/quick_start/)) and \"How to use this guide\" (now [User Guide](https://getml.com/latest/user_guide/))\n    - Introduction of User Guide (now [Concepts](https://getml.com/latest/user_guide/concepts/)) to include data annotation, feature engineering, hyperparameter optimization and more\n- Integration with additional databases like [Greenplum](https://getml.com/latest/reference/database/database/#getml.database.connect_greenplum.connect_greenplum), [MariaDB](https://getml.com/latest/reference/database/database/#getml.database.connect_mariadb.connect_mariadb), [MySQL](https://getml.com/latest/reference/database/database/#getml.database.connect_mysql.connect_mysql), and extended [PostgreSQL](https://getml.com/latest/reference/database/database/#getml.database.connect_postgres.connect_postgres) support\n\n### 0.9.1\t<small>Mar 17, 2020</small>\n- Include hotfix for new domain getml.com\n\n### 0.9\t<small>Dec 9, 2019</small>\n- Rework hyperopt design and handling, added [load_hyperopt()](https://getml.com/latest/reference/hyperopt/#getml.hyperopt.load_hyperopt.load_hyperopt)\n- Improved dataframe handling: add [to_placeholder()](https://getml.com/latest/reference/data/data_frame/#getml.data.DataFrame.to_placeholder) and [nrows()](https://getml.com/latest/reference/data/data_frame/#getml.data.DataFrame.nrows)\n\n### 0.8\t<small>Oct 22, 2019</small>\n- Rename Autosql to [Multirel](https://getml.com/latest/reference/feature_learning/multirel/)\n- Boolean and categorical columns: Add support for boolean columns and operators, along with enhanced categorical column handling.\n- Introduce API improvements: fitting, saving/loading of models, data transformation\n- Add support for various aggregation functions such as [MEDIAN](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.MEDIAN), [VAR](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.VAR), [STDDEV](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.STDDEV), and [COUNT_DISTINCT](https://getml.com/latest/reference/feature_learning/aggregations/#getml.feature_learning.aggregations.COUNT_DISTINCT)\n- Move from closed beta to [pip](https://pypi.org/project/getml/)\n- Introduce basic hyperopt algorithms: [LatinHypercubeSearch](https://getml.com/latest/reference/hyperopt/latin/), [RandomSearch](https://getml.com/latest/reference/hyperopt/random/)\n"
  },
  {
    "path": "Dockerfile",
    "content": "ARG OUTPUT_DIR\nARG VERSION\nARG PACKAGE_NAME=\"getml-community-$VERSION-$TARGETARCH-$TARGETOS\"\nARG BUILD_OR_COPY_ARTIFACTS=\"build\"\n\nFROM --platform=$BUILDPLATFORM golang:1.22 AS cli-build\nARG TARGETOS\nARG TARGETARCH\nARG VERSION\nWORKDIR /cli/src\nCOPY src/getml-app/src .\n\nRUN --mount=type=cache,target=/go/pkg/mod/ \\\n    CGO_ENABLED=0 GOOS=${TARGETOS} GOARCH=${TARGETARCH} go build -o /cli/build/getml-cli -ldflags \"-X main.version=$VERSION\" . \\\n    && mkdir /cli/release \\\n    && cp /cli/build/getml-cli /cli/release/getml-cli\n\nFROM scratch AS cli\nARG PACKAGE_NAME\nCOPY --from=cli-build /cli/release/getml-cli $PACKAGE_NAME/getML\n\nFROM scratch AS export\nARG PACKAGE_NAME\nCOPY --from=cli / .\nCOPY --from=engine-package . .\nCOPY LICENSE.txt INSTALL.md $PACKAGE_NAME\nCOPY src/package-build-imports $PACKAGE_NAME\n\nFROM --platform=$BUILDPLATFORM python:3.11-slim AS python-base\nARG TARGETPLATFORM\nARG BUILDPLATFORM\nARG PACKAGE_NAME\nARG VERSION\nWORKDIR /python-api/src\nRUN pip install hatch\nCOPY src/python-api/ .\nRUN echo $VERSION > getml/VERSION\nRUN hatch build -t wheel\nRUN mkdir -p /python-api/src/getml/.getML\n\nFROM python-base AS python-base-arm64\nARG WHEEL_PLATFORM=\"manylinux_2_28_aarch64\"\n# HACK: Remove the any platform wheel on one of the branches\n# to avoid multi-platform artifact name collision\nRUN if [ \"$TARGETPLATFORM\" != \"$BUILDPLATFORM\" ]; then rm -rf dist/*.whl; fi\n\nFROM python-base AS python-base-amd64\nARG WHEEL_PLATFORM=\"manylinux_2_28_x86_64\"\nRUN if [ \"$TARGETPLATFORM\" != \"$BUILDPLATFORM\" ]; then rm -rf dist/*.whl; fi\n\nFROM python-base-$TARGETARCH AS python-build-artifacts\nCOPY --from=export /$PACKAGE_NAME getml/.getML/$PACKAGE_NAME\n\nFROM python-base-$TARGETARCH AS python-copy-artifacts\nARG OUTPUT_DIR\nCOPY $OUTPUT_DIR/$PACKAGE_NAME getml/.getML/$PACKAGE_NAME\n\nFROM python-${BUILD_OR_COPY_ARTIFACTS}-artifacts AS python-build\nRUN hatch build -t wheel\n\nFROM scratch AS python\nCOPY --from=python-build python-api/src/dist python-api\n\nFROM alpine AS archive-build-artifacts\nARG PACKAGE_NAME\nCOPY --from=export / /\n\nFROM alpine AS archive-copy-artifacts\nARG PACKAGE_NAME\nARG OUTPUT_DIR\nCOPY $OUTPUT_DIR/$PACKAGE_NAME $PACKAGE_NAME\n\nFROM archive-${BUILD_OR_COPY_ARTIFACTS}-artifacts AS archive-export\nRUN mkdir /out\nRUN tar czf /out/$PACKAGE_NAME.tar.gz $PACKAGE_NAME\nWORKDIR /out\nRUN sha256sum $PACKAGE_NAME.tar.gz > $PACKAGE_NAME.tar.gz.sha256\n\nFROM scratch AS archive\nCOPY --from=archive-export /out/ .\n\nFROM scratch AS all\nCOPY --from=export / .\nCOPY --from=python / .\nCOPY --from=archive / .\n"
  },
  {
    "path": "INSTALL.md",
    "content": "## Table of Contents\n\n* [Overview](#overview)\n* [Installation](#installation)\n  * [Python API](#python-api)\n  * [Engine](#engine)\n    * [Linux](#engine-linux)\n    * [Docker (macOS, Windows, Linux)](#engine-docker)\n* [Deinstallation](#deinstallation)\n  * [Python API](#deinstallation-python-api)\n  * [Linux](#deinstallation-linux)\n* [Compiling from Source](#compiling-from-source)\n\n## Overview <a name=\"overview\"></a>\n\nThere are three components of the getML Community edition:\n\n1. The Python API | Helps you interact with the getML Engine\n2. The getML Engine | The C++ backend of getML\n3. The CLI | A command line interface to install and interact with the Engine\n\nOn Linux, the getML Community edition binary of the Engine is shipped with the Python API.\nOn macOS and Windows, you will have to run the Engine in a Docker container.\nWe are working on providing native support for macOS and Windows in the near future.\nThe Python API is available for all platforms that support Python 3.8 or above.\n\n## Installation <a name=\"installation\"></a>\n\n### Python API <a name=\"python-api\"></a>\n\nThe Python API can be installed from the Python Package Index by executing the following command in a terminal:\n\n```bash\npip install getml\n```\n\nAlternatively, if you want to install the API from the source,\nyou will have to install the Engine separately unless you install the wheel file provided by following the steps in the [Compiling from Source](#compiling-from-source) section.\n\n```bash\npip install -e ./src/python-api # path to getml python-api\n```\n\n### Engine <a name=\"engine\"></a>\n\nEven though the Community edition of the Engine is shipped with the Python API on Linux platforms,\nin some cases it might be preferred to install it separately. For example, if you want to use the [Enterprise edition](https://.....) of the Engine.\n\n#### Linux <a name=\"engine-linux\"></a>\n\nPlease execute the following commands, replacing `ARCH` with either `x64` or `arm64`, depending on your architecture.\nIf you are unsure, `x64` is probably the right choice.\nYou can also use `uname -m` to figure out the architecture.\nIf it says something like `aarch64` or `arm64`, you need to use [`arm64`](https://static.getml.com/download/1.5.0/getml-1.5.0-arm64-community-edition-linux.tar.gz), otherwise go with [`x64`](https://static.getml.com/download/1.5.0/getml-1.5.0-x64-community-edition-linux.tar.gz).\n\n```bash\n# Download the tar file of the Engine\nwget https://static.getml.com/download/1.5.0/getml-community-1.5.0-<arch>-linux.tar.gz\n\n# Extract the tar file\ntar -xzf getml-community-1.5.0-<arch>-linux.tar.gz\n\n# Change directory \ncd getml-community-1.5.0-<arch>-linux\n\n# Install the Engine\n./getML install\n```\n\nThe output of the `install` command will tell you where the Engine has been installed.\nIt will look something like this:\n\n```bash\ngetml@laptop src % ./getML install        \nInstalling getML...\nCould not install into '/usr/local': mkdir /usr/local/getML: permission denied\nGlobal installation failed, most likely due to missing root rights. Trying local installation instead.\nInstalling getML...\nSuccessfully installed getML into '/Users/getml/.getML/getml-community-1.5.0-arm64-linux'.\nInstallation successful. To be able to call 'getML' from anywhere, add the following path to PATH:\n/home/getml/.getML/getml-community-1.5.0-arm64-linux\n```\n\nTo run the Engine, execute:\n\n```bash\n./getML\n```\n\nIf the Engine was installed to the user home directory, you can add the installation directory to your PATH if you want to call the getML CLI from anywhere.\n\n```bash\nexport PATH=$PATH:/path/to/getml-community-1.5.0-<arch>-linux\n```\n\nTo make the changes permanent, you will have to add the line to your `.bashrc` or `.bash_profile` file.\n\n#### Docker (macOS, Windows, Linux) <a name=\"engine-docker\"></a>\n\nBefore the installation, make sure your system has [Docker](https://www.docker.com/) installed and running. On Linux, make sure that you follow these post-installation steps to run Docker without root rights/sudo: <https://docs.docker.com/engine/install/linux-postinstall/>\n\nWe offer two ways to start the Docker container. One is to use the provided docker-compose file and the other is to use the `docker run` command.\n\nThe docker-compose file is the recommended way to start the container and will be used in the following as it will automatically mount the necessary volumes, map the ports on the host system, and start the container.\n\nFirst, download the docker-compose file in your project directory by executing the following command in a terminal:\n\n```bash\ncurl -LO https://raw.githubusercontent.com/getml/getml-community/1.5.0/runtime/docker-compose.yml\n```\n\nBy executing the following command, the getML service will be started. Further, a local directory `getml` will be created if it doesn't exist yet and mounted into the container. It will contain the files of the project. Also, the ports required for the Python API to communicate with the Engine will be mapped to the host system.\n\n```bash\ndocker compose up\n```\n\nThe docker compose file can also be piped into the docker compose command directly.\n\n```bash\ncurl -L https://raw.githubusercontent.com/getml/getml-community/1.5.0/runtime/docker-compose.yml | docker-compose up -f -\n```\n\nTo shut down the service after you are done, press `Ctrl+c`.\n\n## Deinstallation <a name=\"deinstallation\"></a>\n\n### Python API <a name=\"deinstallation-python-api\"></a>\n\n> :warning: **Project data might be deleted**: If you have not installed the Engine separately and have not set the home directory to a custom location on Engine launch, the project data will be deleted when you uninstall the Python API.\n\nTo uninstall the Python API, execute the following command in a terminal:\n\n```bash\npip uninstall getml\n```\n\n### Linux <a name=\"deinstallation-linux\"></a>\n\n> :warning: **Project data might be deleted**: If you have not set the home directory to a custom location on Engine launch, the project data will be deleted when you remove the `.getML` directory.\n\nYou will have to remove the folder `.getML` from your home directory. To do so, execute the following command in a terminal:\n\n```bash\nrm -r $HOME/.getML\n```\n\n## Compiling from Source <a name=\"compiling-from-source\"></a>\n\nRefer to [BUILD.md](BUILD.md) for instructions on how to compile from source and build the getML Suite.\n"
  },
  {
    "path": "LICENSE.txt",
    "content": "Elastic License 2.0 (ELv2)\n\nElastic License\n\nAcceptance\nBy using the software, you agree to all of the terms and conditions below.\n\nCopyright License\nThe licensor grants you a non-exclusive, royalty-free, worldwide, non-sublicensable, non-transferable license to use, copy, distribute, make available, and prepare derivative works of the software, in each case subject to the limitations and conditions below.\n\nLimitations\nYou may not provide the software to third parties as a hosted or managed service, where the service provides users with access to any substantial set of the features or functionality of the software.\n\nYou may not move, change, disable, or circumvent the license key functionality in the software, and you may not remove or obscure any functionality in the software that is protected by the license key.\n\nYou may not alter, remove, or obscure any licensing, copyright, or other notices of the licensor in the software. Any use of the licensor’s trademarks is subject to applicable law.\n\nPatents\nThe licensor grants you a license, under any patent claims the licensor can license, or becomes able to license, to make, have made, use, sell, offer for sale, import and have imported the software, in each case subject to the limitations and conditions in this license. This license does not cover any patent claims that you cause to be infringed by modifications or additions to the software. If you or your company make any written claim that the software infringes or contributes to infringement of any patent, your patent license for the software granted under these terms ends immediately. If your company makes such a claim, your patent license ends immediately for work on behalf of your company.\n\nNotices\nYou must ensure that anyone who gets a copy of any part of the software from you also gets a copy of these terms.\n\nIf you modify the software, you must include in any modified copies of the software prominent notices stating that you have modified the software.\n\nNo Other Rights\nThese terms do not imply any licenses other than those expressly granted in these terms.\n\nTermination\nIf you use the software in violation of these terms, such use is not licensed, and your licenses will automatically terminate. If the licensor provides you with a notice of your violation, and you cease all violation of this license no later than 30 days after you receive that notice, your licenses will be reinstated retroactively. However, if you violate these terms after such reinstatement, any additional violation of these terms will cause your licenses to terminate automatically and permanently.\n\nNo Liability\nAs far as the law allows, the software comes as is, without any warranty or condition, and the licensor will not be liable to you for any damages arising out of these terms or the use or nature of the software, under any kind of legal claim.\n\nDefinitions\nThe licensor is the entity offering these terms, and the software is the software the licensor makes available under these terms, including any portion of it.\n\nyou refers to the individual or entity agreeing to these terms.\n\nyour company is any legal entity, sole proprietorship, or other kind of organization that you work for, plus all organizations that have control over, are under the control of, or are under common control with that organization. control means ownership of substantially all the assets of an entity, or the power to direct its management and policies by vote, contract, or otherwise. Control can be direct or indirect.\n\nyour licenses are all the licenses granted to you for the software under these terms.\n\nuse means anything you do with the software requiring one of your licenses.\n\ntrademark means trademarks, service marks, and similar rights.\n"
  },
  {
    "path": "README.md",
    "content": "\n<p align=\"center\" style=\"text-align: center;\">\n    <img width=\"400\" style=\"width: 50% !important; max-width: 400px;\" src=\"assets/getml_logo_dark.png#gh-dark-mode-only\" />\n    <img width=\"400\" style=\"width: 50% !important; max-width: 400px;\" src=\"assets/getml_logo.png#gh-light-mode-only\" />\n</p>\n\n<p align=\"center\" style=\"text-align: center;\">\n        <a href=\"https://getml.com/latest/contact\" target=\"_blank\">\n        <img src=\"https://img.shields.io/badge/schedule-a_meeting-blueviolet.svg\" /></a>\n        <a href=\"mailto:hello@getml.com\" target=\"_blank\">\n        <img src=\"https://img.shields.io/badge/contact-us_by_mail-orange.svg\" /></a>\n        <a href=\"LICENSE.txt\" target=\"_blank\">\n        <img src=\"https://img.shields.io/badge/LICENSE-ELv2-green\" /></a>\n</p>\n\n# getML - Automated Feature Engineering for Relational Data and Time Series\n\n## Introduction\n\ngetML is a tool for automating feature engineering on relational data and time series. It includes a specifically customized database Engine for this very purpose.\n\nThis results in a speedup between _60_ to _1000_ times (see [Benchmarks](#benchmarks)) over other open-source tools like [featuretools](https://www.featuretools.com) and [tsfresh](https://tsfresh.com) for automated feature engineering. Also check out our [demonstrational notebooks](https://getml.com/latest/examples) to see more comparisons.\n\n## Table of Contents\n\n* [Introduction](#introduction)\n* [Table of Contents](#table-of-contents)\n* [Quick Start](#quick-start)\n* [Key benefits of using getML](#key-benefits-of-using-getml)\n  * [Features generate by getML](#features-generate-by-getml)\n  * [Documentation](#documentation)\n* [Benchmarks](#benchmarks)\n* [Demo notebooks](#demo-notebooks)\n* [Example](#example)\n* [Release Notes](#release-notes)\n* [Development](#development)\n\n## Quick Start\n\nAs getML is available on [PyPI](https://pypi.org/project/getml), you can install it simply via\n\n```bash\npip install getml\n```\n\n> [!NOTE]\n> To get started on macOS and Windows, you first need to [start the getML docker service](https://getml.com/latest/install/packages/docker/):\n> ```sh\n> # run the lastest version\n> curl -L https://raw.githubusercontent.com/getml/getml-community/refs/heads/main/runtime/docker-compose.yml | docker compose -f - up\n> \n> # run a specific version, e.g. 1.5.1\n> # curl https://raw.githubusercontent.com/getml/getml-community/1.5.1/runtime/docker-compose.yml | docker compose -f - up\n> ```\n\nCheck out [the Example](#example) and the [demonstrational notebooks](https://getml.com/latest/examples) to get started with getML. A [detailed walkthrough guide](https://getml.com/latest/user_guide/walkthrough) and [the documentation](https://getml.com/latest) will also help you on your way with getML.\n\nTo learn, how to build and contribute to getML, check out [BUILD.md](BUILD.md) for instructions on how to build getML from source.\n\n## Key benefits of using getML\n\nOne big key feature over other tools like [featuretools](https://www.featuretools.com), [tsfresh](https://tsfresh.com) and [prophet](https://facebook.github.io/prophet) is the runtime performance. Our own implementation of propositionalization, FastProp (short for fast propositionalization), reaches improvements of about _60_ to _1000_ times faster run times (see specifically [FastProp Benchmarks](https://getml.com/latest/examples/enterprise-notebooks/fastprop_benchmark) within our notebooks). This leads to faster iterations for data scientists, giving them more time to tweak variables to achieve even better results.\n\nFastProp is not only faster, but can also provide an increased accuracy.\n\nFor even better accuracy, getML provides advanced algorithms in its [professional and Enterprise feature-sets](https://getml.com/latest/enterprise/benefits), namely [Multirel](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-multirel), [Relboost](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-relboost), [Fastboost](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-fastboost) and [RelMT](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-relmt).\n\nThe standard version includes [preprocessors](https://getml.com/latest/user_guide/concepts/preprocessing) (like CategoryTrimmer, EmailDomain, Imputation, Mapping, Seasonal, Substring, TextFieldSplitter), [predictors](https://getml.com/latest/user_guide/concepts/predicting) (like LinearRegression, LogisticRegression, XGBoostClassifier, XGBoostRegressor) and [hyperparameter optimizer](https://getml.com/latest/user_guide/concepts/hyperopt) (like RandomSearch, LatinHypercubeSearch, GaussianHyperparameterSearch).\n\nIt also gives access to [the getML Monitor](https://getml.com/latest/user_guide/concepts/getml_suite/#monitor-concepts), which provides valuable information about projects, pipelines, features, important columns, accuracies, performances, and more. Those information give insights and help understand and improve the results.\n\ngetML can [import data from various sources](https://getml.com/latest/user_guide/concepts/importing_data) like CSV, Pandas, JSON, SQLite, MySQL, MariaDB, PostgreSQL, Greenplum, ODBC.\n\nWhile the standard version is open source, can be run on your local machine, and gets basic support via EMail and via this repository, it must not be used for productive purposes. The [professional and Enterprise versions](https://getml.com/latest/enterprise/benefits) in contrast allows productive uses, gets also support via phone and chat, offers training sessions, as well as on-premise and cloud hosting, and export and deployment features. Get in [contact via email](mailto:hello@getml.com) or directly [schedule a meeting](https://getml.com/latest/contact).\n\n### Features generate by getML\n\ngetML generates features for relational data and time series. These include, but are not limited to:\n\n* **Various aggregations**, i.e. average, sum, minimum, maximum, quantiles, exponentially weighted moving average, trend, exponentially weighted trends, ...\n* **Aggregations within a certain time frame**, i.e. maximum in the last 30 days, minimum in the last 7 days\n* **Seasonal factors from time stamps**, such as month, day of the week, hour, ...\n* **Seasonal aggregations**, i.e. maximum for the same weekday as the prediction point, minimum for the same hour as the prediction point, ...\n\nIn other words, it generates the kind of features you would normally build manually. But it automatically generates thousands of features and then automatically picks the best, saving you a lot of manual work.\n\n### Documentation\n\nCheck out the full documentation on <https://getml.com/latest>\n\n## Benchmarks\n\nWe evaluated the performance of [getML's FastProp algorithm](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-fastprop) against five other open-source tools for automated feature engineering on relational data and time series: [_tsflex_](https://github.com/predict-idlab/tsflex), [_featuretools_](https://www.featuretools.com), [_tsfel_](https://github.com/fraunhoferportugal/tsfel), [_tsfresh_](https://github.com/blue-yonder/tsfresh) and [_kats_](https://github.com/facebookresearch/Kats). The datasets used include:\n\n1. Air Pollution\n    * Hourly data on air pollution and weather in Beijing, China.\n2. Interstate94\n    * Hourly data on traffic volume on the Interstate 94 from Minneapolis to St. Paul.\n3. Dodgers\n    * Five-minute measurements of traffic near Los Angeles, affected by games hosted by the LA Dodgers.\n4. Energy\n    * Ten-minute measurements of the electricity consumption of a single household.\n5. Tetouan\n    * Ten-minute electricity consumption of three different zones in Tetouan City, north Morocco\n\nThe plots shown below contain the _runtime per feature_ calculated relative to the runtime per feature of the fastest approach. The fastest approach turns out to be the getML's FastProp, so it gets a value 1.\n\nWe observe, that for all datasets, the features produced by the different tools are quite similar, but **getML is 60-1000 times faster** than other open-source tools.\n\n<p align=\"center\">\n    <img style=\"width: 80%\" src=\"assets/benchmarks_plot_linear.png\" />\n</p>\n\nIn fact, the speed-up is so big that we need a logarithmic scale to even see the bar for getML.\n\n<p align=\"center\">\n    <img style=\"width: 80%\" src=\"assets/benchmarks_plot_log.png\" />\n</p>\n\nTo reproduce those results, refer to the [benchmarks folder](benchmarks) in this repository.\n\n## Demo notebooks\n\nTo experience getML in action, the following example notebooks are provided in the [demo-notebooks](demo-notebooks) directory:\n\n| Notebook                                                      | Prediction Type  | Population Size | Data Type   | Target  | Domain           | Difficulty | Comments                                        |\n| ------------------------------------------------------------- | ---------------- | --------------- | ----------- |-------- | ---------------- | ---------- | ----------------------------------------------- |\n| [adventure_works.ipynb](demo-notebooks/adventure_works.ipynb) | Classification   | 19,704          | Relational  | Churn   | Customer loyalty | Hard       | Good reference for a complex data model         |\n| [formula1.ipynb](demo-notebooks/formula1.ipynb)               | Classification   | 31,578          | Relational  | Win     | Sports           | Medium     |                                                 |\n| [interstate94.ipynb](demo-notebooks/interstate94.ipynb)       | Regression       | 24,096          | Time Series | Traffic | Transportation   | Easy       | Good notebook to get started on time series     |\n| [loans.ipynb](demo-notebooks/loans.ipynb)                     | Classification   | 682             | Relational  | Default | Finance          | Easy       | Good notebook to get started on relational data |\n| [robot.ipynb](demo-notebooks/robot.ipynb)                     | Regression       | 15,001          | Time Series | Force   | Robotics         | Medium     |                                                 |\n| [seznam.ipynb](demo-notebooks/seznam.ipynb)                   | Regression       | 1,462,078       | Relational  | Volume  | E-commerce       | Medium     |                                                 |\n\nFor an extensive list of demonstrational and benchmarking notebooks, have a look at [our docs examples section](https://getml.com/latest/examples) or [the notebook source repository](https://github.com/getml/getml-demo).\n\n## Example\n\nHere is how you can build a complete Data Science pipeline for a time series problem with seasonalities with just a few lines of code:\n\n```python\nimport getml\n\ngetml.engine.launch()\ngetml.set_project(\"interstate94\")\n\n# Load the data.\ntraffic = getml.datasets.load_interstate94(roles=False, units=False)\n\n# Set the roles, so getML knows what you want to predict\n# and which columns you want to use.\ntraffic.set_role(\"ds\", getml.data.roles.time_stamp)\ntraffic.set_role(\"holiday\", getml.data.roles.categorical)\ntraffic.set_role(\"traffic_volume\", getml.data.roles.target)\n\n# Generate a train/test split using 2018/03/15 as the cutoff date.\nsplit = getml.data.split.time(traffic, \"ds\", test=getml.data.time.datetime(2018, 3, 15))\n\n# Set up the data:\n# - We want to predict the traffic volume for the next hour.\n# - We want to use data from the seven days before the reference date.\n# - We want to use lagged targets (autocorrelated features are allowed).\ntime_series = getml.data.TimeSeries(\n    population=traffic,\n    split=split,\n    time_stamps=\"ds\",\n    horizon=getml.data.time.hours(1),\n    memory=getml.data.time.days(7),\n    lagged_targets=True,\n)\n\n# The Seasonal preprocessor extracts seasonal\n# components from the time stamps.\nseasonal = getml.preprocessors.Seasonal()\n\n# FastProp extracts features from the time series.\nfast_prop = getml.feature_learning.FastProp(\n    loss_function=getml.feature_learning.loss_functions.SquareLoss,\n    num_threads=1,    \n    num_features=20,\n)\n\n# Use XGBoost for the predictions (it comes out-of-the-box).\npredictor = getml.predictors.XGBoostRegressor()\n\n# Combine them all in a pipeline.\npipe = getml.pipeline.Pipeline(\n    tags=[\"memory: 7d\", \"horizon: 1h\", \"fast_prop\"],\n    data_model=time_series.data_model,\n    preprocessors=[seasonal],\n    feature_learners=[fast_prop],\n    predictors=[predictor],\n)\n\n# Fit on the train set and evaluate on the testing set.\npipe.fit(time_series.train)\npipe.score(time_series.test)\npredictions = pipe.predict(time_series.test)\n```\n\nTo see the full example, check out the Interstate94 notebook ([interstate94.ipynb](https://getml.com/latest/examples/community-notebooks/interstate94)).\n\n## Release Notes\nSee [CHANGELOG.md](CHANGELOG.md) for release notes.\n\n## Development\n### Python venv\nTo create the virtual environment for the [python api](src/python-api) just run\n`uv sync` from anywhere in the project. If you have \n[mise hooked into your shell](https://mise.jdx.dev/getting-started.html#activate-mise) the \nenvironment will be activated automatically whenever you your prompt is reloaded\ninside the project. If you don't have mise hooked into your shell, you can drop\ninto a shell populated with the environment by running `mise en`.\n\n> [!NOTE]\n> mise doesn't pick up the environment automatically after creating the environment with `uv sync`.\n> To create and activate the environment in one go run:\n> ```sh\n> uv sync && mise en\n> ```\n\n### Building getml\nRefer to [BUILD.md](BUILD.md) for build instructions.\n"
  },
  {
    "path": "VERSION",
    "content": "1.5.1\n"
  },
  {
    "path": "benchmarks/Dockerfile",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nFROM python:3.8.18\n\nRUN apt-get update \\\n    && apt-get install -y \\\n    locales \\\n    && echo \"en_US.UTF-8 UTF-8\" > /etc/locale.gen \\\n    && locale-gen\n\nRUN useradd getml\nUSER getml\nWORKDIR /home/getml\n\nENV PATH=\"/home/getml/.local/bin:${PATH}\"\n\nRUN pip install --user --upgrade pip\n\nCOPY requirements.for-pystan.txt requirements.for-fbprophet.txt requirements.for-kats.txt requirements.txt ./\nRUN pip install --user -r requirements.for-pystan.txt \\\n    && pip install --user -r requirements.for-fbprophet.txt \\\n    && pip install --user -r requirements.for-kats.txt \\\n    && pip install --user -r requirements.txt\n \nARG VERSION_NUMBER\nARG GETML_VERSION\n\nWORKDIR /home/getml\nRUN mkdir /home/getml/.getML\n\nRUN mkdir engine | curl https://static.getml.com/download/$VERSION_NUMBER/$GETML_VERSION.tar.gz | tar -xz --strip-components=1 -C engine -f - ${GETML_VERSION}\nRUN curl https://static.getml.com/download/$VERSION_NUMBER/python.tar.gz | tar -xzf -\nWORKDIR /home/getml/python\nRUN pip3 install .\n\nCOPY --chown=getml:getml . /home/getml/benchmarks/\nWORKDIR /home/getml/benchmarks\nCMD [ \"/home/getml/benchmarks/run_benchmarks.sh\" ]"
  },
  {
    "path": "benchmarks/README.md",
    "content": "# Benchmarks\n\n## About\n\nThe benchmarks evaluate the performance of getML's FastProp algorithm against five other open-source libraries for automated feature engineering on relational data and time series.\n\nData-Sets:\n\n<details>\n  <summary>air_pollution</summary>\n\n  The dataset contains hourly data on air pollution and weather in Beijing, China. The challenge is to predict the pm2.5 concentration for the next hour.\n\n  S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia,“On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario,” Sensors and Actuators B: Chemical, vol. 129, no. 2, pp. 750–757, 2008. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925400507007691\n\n  A detailed demonstration of how to handle this data-set can be found in the [getML-demo repository](https://github.com/getml/getml-demo?tab=readme-ov-file#descriptions)\n</details>\n<details>\n  <summary>dodgers</summary>\n\n  The dataset contains five-minute measurements of traffic near Los Angeles. The traffic volume can be affected by a game hosted by the LA Dodgers in the nearby stadium, but not to the extent that it is very obvious to spot such an event in the data. The LA Dodgers are a popular baseball team from Los Angeles.  The challenge is to predict the traffic volume for the next five-minute interval.\n\n  A. Ihler, J. Hutchins, and P. Smyth, “Adaptive event detection with time-varying poisson processes,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 207–216.\n\n  A detailed demonstration of how to handle this data-set can be found in the [getML-demo repository](https://github.com/getml/getml-demo?tab=readme-ov-file#descriptions)\n</details>\n<details>\n  <summary>energy</summary>\n\n  The dataset contains measurements of the electricity consumption of a single household in ten-minute-intervals. The challenge is to predict the energy consumption of all household appliances for the next ten-minute interval.\n</details>\n<details>\n  <summary>interstate94</summary>\n\n  The dataset contains hourly data on traffic volume on the Interstate 94 from Minneapolis to StPaul. The challenge is to predict the traffic volume for the next hour.\n\n  A detailed demonstration of how to handle this data-set can be found in the [getML-demo repository](https://github.com/getml/getml-demo?tab=readme-ov-file#descriptions)\n</details>\n<details>\n  <summary>tetouan</summary>\n\n  The dataset contains the electricity consumption of three different zones in Tetouan City, north Morocco measured in ten-minute intervals. The challenge is to predict the electricity consumption in Zone 1 for the next ten-minute interval.\n\n  A. Salam and A. El Hibaoui, “Comparison of machine learning algorithms for the power consumption prediction:-case study of tetouan city–,” in 2018 6th International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2018, pp. 1–5.\n</details>\n\nLibraries:\n\n* [getML fastprop](https://getml.com/latest/user_guide/concepts/feature_engineering/#feature-engineering-algorithms-fastprop)\n* [tsflex](https://github.com/predict-idlab/tsflex)\n* [featuretools](https://www.featuretools.com)\n* [tsfel](https://github.com/fraunhoferportugal/tsfel)\n* [tsfresh](https://github.com/blue-yonder/tsfresh)\n* [kats](https://github.com/facebookresearch/Kats)\n\n## Build\n\nBuild the image with necessary Python version and libraries installed\n\n```bash\n$ docker compose build\n```\n\n> [!IMPORTANT]\n> Because of the used libraries, this benchmarks only run on the x86_64 architecture.\n\n## Run\n\nRun the benchmarks inside a container based on the build image\n\n```bash\n$ docker compose up benchmarks\n```\n\n## View logs\n\nThe logs written by the benchmarks are written to the terminal in the [Run](#run) step. They can be recalled later via\n\n```bash\n$ docker compose logs benchmarks\n```\n\nFor better reading and scrolling use for example\n\n```bash\n$ docker compose logs benchmarks 2>&1 | sed 's/\\r/\\n/g' | less\n```\n\n## Clean\n\nRemove the container, its images, logs and volumes\n\n```bash\n$ docker compose down --volumes --rmi all\n```\n\n> [!NOTE]\n> *--rmi* for removing images does currently not work with podman compose.\n> Images have to be removed separately for example via `podman image prune --all`\n"
  },
  {
    "path": "benchmarks/benchmark_plots.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"616dcd3b-7498-4a0d-8369-dc6b528f044e\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from matplotlib.ticker import MultipleLocator\\n\",\n    \"from brokenaxes import brokenaxes\\n\",\n    \"import seaborn as sns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"04e5d265-f72d-4500-8985-f18caf94b571\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>features</th>\\n\",\n       \"      <th>runtime</th>\\n\",\n       \"      <th>runtime/feat.</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>fastprop</td>\\n\",\n       \"      <td>453</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.949</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>tsflex</td>\\n\",\n       \"      <td>259</td>\\n\",\n       \"      <td>63.5</td>\\n\",\n       \"      <td>99.3</td>\\n\",\n       \"      <td>0.944</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>featuretools</td>\\n\",\n       \"      <td>125</td>\\n\",\n       \"      <td>85.5</td>\\n\",\n       \"      <td>309.7</td>\\n\",\n       \"      <td>0.948</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>tsfel</td>\\n\",\n       \"      <td>378</td>\\n\",\n       \"      <td>254.3</td>\\n\",\n       \"      <td>304.8</td>\\n\",\n       \"      <td>0.308</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>tsfresh</td>\\n\",\n       \"      <td>84</td>\\n\",\n       \"      <td>47.7</td>\\n\",\n       \"      <td>257.0</td>\\n\",\n       \"      <td>0.781</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>kats</td>\\n\",\n       \"      <td>189</td>\\n\",\n       \"      <td>482.7</td>\\n\",\n       \"      <td>1156.7</td>\\n\",\n       \"      <td>0.349</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      algorithm  features  runtime  runtime/feat.     R2\\n\",\n       \"0      fastprop       453      1.0            1.0  0.949\\n\",\n       \"1        tsflex       259     63.5           99.3  0.944\\n\",\n       \"2  featuretools       125     85.5          309.7  0.948\\n\",\n       \"3         tsfel       378    254.3          304.8  0.308\\n\",\n       \"4       tsfresh        84     47.7          257.0  0.781\\n\",\n       \"5          kats       189    482.7         1156.7  0.349\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_pollution = pd.read_csv('paper_data.txt', skiprows=545, skipfooter=(733-552),\\n\",\n    \"                       engine='python', sep=' ')\\n\",\n    \"df_pollution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"38ff3423-fa3b-407c-9a99-7b4028774869\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>features</th>\\n\",\n       \"      <th>runtime</th>\\n\",\n       \"      <th>runtime/feat.</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>fastprop</td>\\n\",\n       \"      <td>125</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.979</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>tsflex</td>\\n\",\n       \"      <td>36</td>\\n\",\n       \"      <td>18.8</td>\\n\",\n       \"      <td>59.3</td>\\n\",\n       \"      <td>0.979</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>featuretools</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>74.1</td>\\n\",\n       \"      <td>157.9</td>\\n\",\n       \"      <td>0.972</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>tsfel</td>\\n\",\n       \"      <td>54</td>\\n\",\n       \"      <td>97.1</td>\\n\",\n       \"      <td>224.8</td>\\n\",\n       \"      <td>0.876</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>tsfresh</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>20.7</td>\\n\",\n       \"      <td>215.8</td>\\n\",\n       \"      <td>0.172</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>kats</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>141.1</td>\\n\",\n       \"      <td>653.3</td>\\n\",\n       \"      <td>0.941</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      algorithm  features  runtime  runtime/feat.     R2\\n\",\n       \"0      fastprop       125      1.0            1.0  0.979\\n\",\n       \"1        tsflex        36     18.8           59.3  0.979\\n\",\n       \"2  featuretools        59     74.1          157.9  0.972\\n\",\n       \"3         tsfel        54     97.1          224.8  0.876\\n\",\n       \"4       tsfresh        12     20.7          215.8  0.172\\n\",\n       \"5          kats        27    141.1          653.3  0.941\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_interstate = pd.read_csv('paper_data.txt', skiprows=554, skipfooter=(733-561),\\n\",\n    \"                       engine='python', sep=' ')\\n\",\n    \"df_interstate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"ff6d5b05-3133-45ee-acca-ec1ea5c2d9ab\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>features</th>\\n\",\n       \"      <th>runtime</th>\\n\",\n       \"      <th>runtime/feat.</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>fastprop</td>\\n\",\n       \"      <td>207</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.795</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>tsflex</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>14.8</td>\\n\",\n       \"      <td>70.9</td>\\n\",\n       \"      <td>0.802</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>featuretools</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>57.3</td>\\n\",\n       \"      <td>201.1</td>\\n\",\n       \"      <td>0.794</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>tsfel</td>\\n\",\n       \"      <td>54</td>\\n\",\n       \"      <td>74.5</td>\\n\",\n       \"      <td>285.5</td>\\n\",\n       \"      <td>0.000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>tsfresh</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>14.8</td>\\n\",\n       \"      <td>255.6</td>\\n\",\n       \"      <td>0.667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>kats</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>95.6</td>\\n\",\n       \"      <td>732.6</td>\\n\",\n       \"      <td>0.001</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      algorithm  features  runtime  runtime/feat.     R2\\n\",\n       \"0      fastprop       207      1.0            1.0  0.795\\n\",\n       \"1        tsflex        38     14.8           70.9  0.802\\n\",\n       \"2  featuretools        59     57.3          201.1  0.794\\n\",\n       \"3         tsfel        54     74.5          285.5  0.000\\n\",\n       \"4       tsfresh        12     14.8          255.6  0.667\\n\",\n       \"5          kats        27     95.6          732.6  0.001\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_dodgers = pd.read_csv('paper_data.txt', skiprows=580, skipfooter=(733-587),\\n\",\n    \"                       engine='python', sep=' ')\\n\",\n    \"df_dodgers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"c6054cbd-52d6-4059-a5c6-408de3d07d9f\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>features</th>\\n\",\n       \"      <th>runtime</th>\\n\",\n       \"      <th>runtime/feat.</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>fastprop</td>\\n\",\n       \"      <td>1273.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.171</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>tsflex</td>\\n\",\n       \"      <td>1058.0</td>\\n\",\n       \"      <td>87.1</td>\\n\",\n       \"      <td>108.6</td>\\n\",\n       \"      <td>0.114</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>featuretools</td>\\n\",\n       \"      <td>367.0</td>\\n\",\n       \"      <td>77.7</td>\\n\",\n       \"      <td>269.7</td>\\n\",\n       \"      <td>0.100</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>tsfel</td>\\n\",\n       \"      <td>1566.0</td>\\n\",\n       \"      <td>72.0</td>\\n\",\n       \"      <td>58.5</td>\\n\",\n       \"      <td>0.114</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>tsfresh</td>\\n\",\n       \"      <td>348.0</td>\\n\",\n       \"      <td>59.3</td>\\n\",\n       \"      <td>217.0</td>\\n\",\n       \"      <td>0.108</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>kats1</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      algorithm  features  runtime  runtime/feat.     R2\\n\",\n       \"0      fastprop    1273.0      1.0            1.0  0.171\\n\",\n       \"1        tsflex    1058.0     87.1          108.6  0.114\\n\",\n       \"2  featuretools     367.0     77.7          269.7  0.100\\n\",\n       \"3         tsfel    1566.0     72.0           58.5  0.114\\n\",\n       \"4       tsfresh     348.0     59.3          217.0  0.108\\n\",\n       \"5         kats1       NaN      NaN            NaN    NaN\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_energy = pd.read_csv('paper_data.txt', skiprows=589, skipfooter=(733-596),\\n\",\n    \"                       engine='python', sep=' ')\\n\",\n    \"df_energy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"089598c0-9daa-4d54-a970-7d7b33668eaa\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>features</th>\\n\",\n       \"      <th>runtime</th>\\n\",\n       \"      <th>runtime/feat.</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>fastprop</td>\\n\",\n       \"      <td>412</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.997</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>tsflex</td>\\n\",\n       \"      <td>296</td>\\n\",\n       \"      <td>69.8</td>\\n\",\n       \"      <td>85.1</td>\\n\",\n       \"      <td>0.996</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>featuretools</td>\\n\",\n       \"      <td>136</td>\\n\",\n       \"      <td>88.1</td>\\n\",\n       \"      <td>266.9</td>\\n\",\n       \"      <td>0.981</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>tsfel</td>\\n\",\n       \"      <td>432</td>\\n\",\n       \"      <td>275.6</td>\\n\",\n       \"      <td>262.8</td>\\n\",\n       \"      <td>0.983</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>tsfresh</td>\\n\",\n       \"      <td>96</td>\\n\",\n       \"      <td>52.9</td>\\n\",\n       \"      <td>227.0</td>\\n\",\n       \"      <td>0.940</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>kats</td>\\n\",\n       \"      <td>216</td>\\n\",\n       \"      <td>556.5</td>\\n\",\n       \"      <td>1061.5</td>\\n\",\n       \"      <td>0.978</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      algorithm  features  runtime  runtime/feat.     R2\\n\",\n       \"0      fastprop       412      1.0            1.0  0.997\\n\",\n       \"1        tsflex       296     69.8           85.1  0.996\\n\",\n       \"2  featuretools       136     88.1          266.9  0.981\\n\",\n       \"3         tsfel       432    275.6          262.8  0.983\\n\",\n       \"4       tsfresh        96     52.9          227.0  0.940\\n\",\n       \"5          kats       216    556.5         1061.5  0.978\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_tetouan = pd.read_csv('paper_data.txt', skiprows=617, skipfooter=(733-624),\\n\",\n    \"                       engine='python', sep=' ')\\n\",\n    \"df_tetouan\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"105c4ad1-79a5-4050-95c2-11b3888709af\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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wADMlNgGZWDagaVtwwN32IiIiIBIES4jxwzv1oZovxEuK3nXPrAfzdImKBzs657RHarQXWmtk04Bfn3Cshh+/GS1jPdM6t8sueN7P/Avea2Tjn3H9yEeYgvDXOIiIiInIE+lN8/trlP1+Zh2UOPYBPgd/N7Pi0BzAfiAHa5LK/iUDTsMeVuexDREREpNjTDHH++jfQD3gBGGNmH+Etr3jDOXc4m7aNgdOAbVkcr5abQJxzW4GtoWVmlpsuRERERAJBCXE+cs4lm1kboB3QBbgEuAZYYGYdnXOpR2heAm9d8eNZHP8lX4MVEREREUAJ8dFwEQu9meCP/MfdZjYUeBQvSZ5/hP7WAOWcc0eqky/m3X0hCQkJBX0aERERkSJBa4jzbp//XDGtwMwqR6i3xH+Oy6a/GcB5ZtYp/IC/HZt+eREREREpAEqy8u57//lRM5sOHAQuMrPmwGxgA96630HAr8Bn2fT3BHAF8J6ZTfH7Lws0A7oD9fjf9m4iIiIikk+UEOeRc+5bM3sIGIi3VrgEcDPe3r83AcfjJbCLgOHOud3Z9PenmV0IDMXbceJGYA/e2uHhwBHbi4iIiEjeKCE+Cs65kcDIsOJ/5aBdxO0enHN78RLioXlpLyIiIiK5pzXEIiIiIhJoSohFREREJNCUEIuIiIhIoCkhFhEREZFAU0IsIiIiIoGmhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQiIiIiEmhKiEVEREQk0JQQi4iIiEigKSEWERERkUBTQiwiIiIigaaEWEREREQCTQmxiIiIiASaEmIRERERCTQlxCIiIiISaEqIRURERCTQSkY7ADn2XhvxNSdU3hLtMKQA3TapfbRDEBERKTI0QywiIiIigaaEWEREREQCTQmxiIiIiASaEmIRERERCTQlxCIiIiISaEqIRURERCTQtO1aALX4diSN4+KiHYYUoJ9O8Z6brPwpuoGIiIgUAZohFhEREZFAU0IsIiIiIoGmhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQiIiIiEmjadi2A/to/hvha+tIfC0t7L412CCIiIpINzRCLiIiISKApIRYRERGRQFNCLCIiIiKBpoRYRERERAJNCbGIiIiIBJoSYhEREREJNO29FUBv/7qZhAMx0Q6jcErcHe0IRERE5BjTDLGIiIiIBJoSYhEREREJNC2ZEBERKWYOHz7Mli1bSElJ4fDhw9EORyRdiRIliIuLo3r16pQoUXjmZZUQi4iIFCOHDx9m48aNJCcnExMTQ0xMDGYW7bBEcM5x4MABkpOTSUlJoU6dOoUmKc5zQmxm5wBPAacDZYAznXNL8imuQDKzKUBb51y9KIciIiJF1JYtW0hOTqZy5cpUq1ZNybAUKs45tm7dys6dO9myZQsnnHBCtEMC8riG2MxKAa8DlYG7gF7AhnyMCzOraWaJZnZGfvabhzjO9+OoGM04REREciIlJYWYmBglw1IomRnVqlUjJiaGlJSUaIeTLq8zxA2BukB/59wL+RhPqJrAcGA9sKSAzpET5/txTAF2RTGOfNMh5XFi99eNdhiF099mRzuCQmn9mC7RDkFEcujw4cNaJiGFmpkRExNTqNa353XhRjX/eVc+xXHMmFm8mRWOBSsiIiIFQMmwFHaF7Xs014mhv851kf/ydTNzZrbQP3aKmb1hZjvNbL+ZfWdmV4S1r2xmY81sqZntNbM9ZjbHzE4PqdMW+NZ/+ZJ/Dmdmffzj6/04wmNbmBZLWj9+u2vNbKSZbQL+BI7zj7c0sw/MbLeZ/Wlmi8ysVUj7ROAJ/+W6kDjq+cdLmtlDZrbGzFL8uEaZWVyE2AaZ2XK/3n/N7NmcLMPwY//ezP7wx2qpmd2ZXTsRERERyZm8LJl4HtgEDAWexktct5hZAvC5f2wMsA+4GnjbzK5yzr3lt28AdMVbg7wOqA4MABaZ2anOuf8CPwHDgIeBycCnftsv8hAvwEPAAWAsEAccMLP2wBzge2AEcBjoCywws9bOuW+AN4GTgOvw1kpv9/vb5j+/APQG3gDGAS2BB4AmQLe0k/uJ9XBgPvAccDJwK3COmbVyzh2MFLSZdQBeAz4C7veLmwCt8G5ozJKZVQOqhhU3PFIbERERkSDKdULsnPvSnwEdCnzqnHsDwMzmAxuBc5xzKX7ZROAz4DEgLSFeCpzknEtfOGJm04CVwM3AI865LWY2By8h/tI590peL9AXD5ztnEv2z2fAJOBjoLNzzvnlzwPLgZFAR+fcj2a2GC8hfts5tz4k5tPxkuEXnHP9/eKJZrYVuMfM2jnnPjazqnhJ8of+uQ777VcCzwA9gZeyiLsLsAfo5JxLzeU1D8JLwkVERALJzBg+fDiJiYnRDgWAPn36sHDhQtavX59eVhAxTpkyhb59+7Ju3Trq1auXb/0WZ/myltbMKgPtgRlAeTM73syOB6oAc4HGZlYLwDmXEpIUxphZFWAv8DNwVn7EE8HUtGTYdwbQGHgVqBISb1m82dg2OVhnfKn/PD6sfJz/nHYX0sVALPBk6C8BwD/xkt0j3a20y4+pQzaxRDIRaBr2uDIP/YiIiBQ6EydOxMxo2bJlgZ1j4cKFmFn6o1SpUjRo0IAbb7yRtWvXFth5c2rUqFG8/fbb0Q6jWMivD+ZoBBjwiP+IpBqwyU8078SbwawPxITU2ZFP8YRbF/a6sf889QhtKgC/H+F4XbxlFqtDC51zv5nZLv84Ic8/h9U7YGZrQ45HMhFv2ckcf/3zh8AM59wHR2iT1v9WYGtoWWFbwC4iIsdWvUKyk05+7FyTlJREvXr1+Oabb1i9ejWNGjXKVCc5OZmSJY8+1Rk8eDDnnHMOBw8eZPHixUyePJnZs2ezdOlSatasedT959WoUaPo3r07Xbt2zVDeq1cvrr32WuLiMt3SJFnIr90W0voZizebGemRljgOxZtV/QRvuUAn//jyXMTjsiiPyaI8Oex12nnuPUK8e48ylqPmJ7VnAFcAs4B2eMnxkRJ5ERGRYm3dunV88cUXjB8/nqpVq5KUlBSxXnx8fLYJ8b59+7I9X+vWrenZsyd9+/ZlwoQJjB07lp07dzJ1auH87zgmJob4+HhNhOVCfs0Qp/3d4KBzbn42dbsDHzvnbg4t9Hdc2B5SdKRE83egYoTyuiGxHMka/3lPDuLNKo4NeIl1Y7ybAAEws+p+bBtC6oF3I93akHqxeDPkRzy/c+4A8C7wrj+7PhEYYGaPOOdWH6ltVubdfSEJCQl5aSoiIhJ1SUlJVKpUiS5dutC9e3eSkpIYPjzzbTPh63MTExMZMWIEy5cvZ+TIkcyZM4d69erxww8/5Or87du3B7zEPM3EiRN59tlnWb16NVWqVKFbt248+uijVKxYMVd9R1pnHBq7f9tTerI7derU9MS8d+/eTJkyJcs1xDmJsW3btmzfvp0ZM2Zw22238fXXX1OpUiXuvPNO7rvvvlxdS1GSLzPE/kzmQrxELdNn8Pk3lqVJxVteEXq8B1ArrFnar2wVI5xyDXCun1Sm9XEZcGIOQ/7e7+MeMyuXTbxZxfG+/zwkrPxu/znt71Lz8Xa4GGwZf1W7GW9ZRpZ/v/LXV6fz1yD/6L/U30FERCSQkpKS+Mtf/kJsbCzXXXcdq1at4ttvv82+oa9Hjx78+eefjBo1iv79+2ffIMyaNd68WpUq3n/TiYmJ3HbbbdSsWZNx48Zx1VVX8fzzz9OxY0cOHoy4kdRRmzZtGnFxcbRu3Zpp06Yxbdo0BgwYkGX93MT4+++/c8kll3D66aczbtw4TjnlFO6//37mzJlTINdSGOTXDDHAbXg7Siw1s3/izYZWB84DagNp+wy/Bwwzs5fwtlFrBtxA5pndNXg3lQ00sz/wEtOvnXPr8LY76w58YGYz8LYT68n/Zn6PyDl32Mz64W27ttyPZRNeUt4O72a3y/3q3/vPj5rZdOAg8K5z7v/8pQu3+LPbi4AWeDtPvO2c+9g/1zYzG42348MHZjYLb7Z4EN6WdUfaQeMF/4bFBcCveDPgd+B9ct9PR2gnIiJSLH3//fesXLmSCRMmAHDBBRdQu3ZtkpKSOOecc3LUx+mnn86rr76a43P+8ccfbN++nYMHD/LDDz9w5513YmZcddVVbNu2jdGjR9OxY0fmzJlDiRLeXOMpp5zC7bffziuvvELfvn1zf6HZ6NmzJwMHDqRBgwb07NnziHVzG+N///tfXn75ZXr16gXAzTffTN26dXnxxRfp3Llzvl9LYZBvn9jmnFsBnI0349kHeBYYiHfj2cMhVUfh7cTQCW8v3bPwdlr4T1h/B/GSy1S8LdJeAy70j80F/oq3R/CTeEn3ZXhJY07jXei3+w64HZjgx/0b8I+Qet/i7WN8Ot7HN7/G//b37YeX6J7jx9EeGA1cG3auRP8cdfy+r8bbX7ljVnsQ+14B9uMlzxPxxuPfhGzfJiIiEiRJSUlUr16ddu3aAd7SgWuuuYbp06eTmpqzHUoHDhyYq3PedNNNVK1alZo1a9KlSxf27dvH1KlTOfvss5k/fz4HDhxgyJAh6YkmQP/+/TnuuOOYPTv6NzLmNsZy5cplSLJjY2Np0aJFodhZo6DkaYbYTyYzrdR2zq3FS9qO1DYFuMd/hGoboe4svJvJIvUznsxbnrUNqxMxzpDjS4CrjhSvX28k3t7E4eWH8JL9hzM1ylz3WbxfEo5Up0/Y65nAzOz6FhERCYLU1FSmT59Ou3btMqzfbdmyJePGjeOjjz6iY8eO2fZTv379XJ132LBhtG7dmpiYGI4//niaNGmSfrPehg3erUInn3xyhjaxsbE0aNAg/Xg05TbG2rVrZ7ohr1KlSvz4448UV/m5ZEJERESkwCxYsIDNmzczffp0pk+fnul4UlJSjhLi0qVL5+q8zZo14+KLL85Vm7zKameInM5+54eYmMibdqXd0FccKSEWERGRIiEpKYlq1arx7LOZ/+D65ptv8tZbbzFp0qRcJ7xHo25d7+MEfv75Zxo0aJBefuDAAdatW5frRLpSpUrs2rUrU3mkmeacbquW3zEWR0qIRUREpNBLTk7mzTffpEePHnTv3j3T8Zo1a/Laa68xa9YsrrnmmmMW18UXX0xsbCxPP/00l1xySXqS+uKLL7J79266dMndh5A0bNiQ3bt38+OPP3LaaacBsHnzZt56661MdcuWLRsxeS7oGIujfLupTkRERKSgzJo1iz/++IMrrrgi4vFzzz33iB/SUVCqVq3KAw88wAcffMAll1zCs88+y+DBg7njjjs455xzst0BIty1115L2bJl6datG0899RSjR4+mZcuWnHTSSZnqNm/enPnz5zN+/HimT5/O119/fUxiLI40QywiIhIQ+fGRydGSlJREfHw8HTp0iHi8RIkSdOnShaSkJHbs2JG+R/CxkJiYSNWqVXnmmWe46667qFy5MrfccgujRo2iVKlSueqrSpUqvPXWW9x9993cd9991K9fn9GjR7Nq1SoWL16coe748eO55ZZbePDBB0lOTqZ37960bNmywGMsjqw4L5CWjMwsAVi2bNkyfVKdiEgxlbY1VuhaUZHCJiffp8uXL6dp06YATZ1zywsyHi2ZEBEREZFAU0IsIiIiIoGmhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQiIiIiEmhKiEVEREQk0JQQi4iIiEigKSEWERERkUBTQiwiIiIigaaEWEREREQCTQmxiIiIiARayWgHIMfeayO+5oTKW6IdRia3TWof7RBERKSQmzJlCn379uXbb7/l7LPPzte+V6xYwYwZM+jTpw/16tXL175zatSoUZx66ql07do1T+3z6xrmzZvHiBEjWLx4MXFxcVx00UWMHTv2iH2uWbOGhIQEUlJSCuTrU5CUEIuIiARFYoVoR+BJ3B3tCCJasWIFI0aMoG3btlFNiLt3735UCfHRXsN7773HlVdeyVlnncWYMWPYs2cPTz31FBdccAE//PADVatWjdjurrvuomTJkqSkpOTpvNGkJRMiIiIiBWjfvn3RDiFX7r//fho0aMDnn3/O4MGDefDBB5k/fz6bN29mzJgxEdvMnTuXuXPnctdddx3jaPOHEmIREREpsvr06UO5cuXYtGkTXbt2pVy5clStWpV77rmH1NTUDHWnT59O8+bNKV++PMcddxzNmjXjqaeeArylGD169ACgXbt2mBlmxsKFC9Pbz5kzh9atW1O2bFnKly9Ply5dWL58ecR41qxZw6WXXkr58uW54YYbAFi1ahVXXXUVNWrUID4+ntq1a3Pttdeye7c3Y25m7Nu3j6lTp6afv0+fPgBs2LCBQYMGcfLJJ1O6dGmqVKlCjx49WL9+ffq58+Madu7cyYoVK+jWrRuxsbHp5aeffjpNmjRh+vTpmb4GBw8e5M477+TOO++kYcOGOfmyFTpaMiEiIiJFWmpqKp06daJly5aMHTuW+fPnM27cOBo2bMitt94KeGtir7vuOi666CIee+wxAH766Sc+//xz7rzzTtq0acPgwYN5+umnGTp0KE2aNAFIf542bRq9e/emU6dOPPbYY/z5558899xz6csIQpcnHDp0iE6dOnHBBRcwduxYypQpw4EDB+jUqRMpKSnccccd1KhRg02bNvHee++xa9cuKlSowLRp0+jXrx8tWrTglltuAUhPML/99lu++OILrr32WmrXrs369et57rnnaNu2LStWrKBMmTL5cg1pyx1Kly6daZzLlCnD8uXL+e2336hRo0Z6+ZNPPsnvv//Ogw8+yJtvvplvX9djSQmxiIiIFGn79+/nmmuu4aGHHgJg4MCBnHXWWbz44ovpCfHs2bM57rjjmDt3LjExMZn6aNCgAa1bt+bpp5+mQ4cOtG3bNv3Y3r17GTx4MP369WPy5Mnp5b179+bkk09m1KhRGcpTUlLo0aMHo0ePTi9bsmQJ69at4/XXX6d79+7p5cOGDUv/d8+ePRk4cCANGjSgZ8+eGeLr0qVLhnYAl19+Oeeddx4zZ86kV69e+XIN1atXp2LFinz++ecZzrVjxw5WrFgBwKZNm9IT4t9++41HHnmEsWPHctxxx2Ua16JCSyZERESkyBs4cGCG161bt2bt2rXprytWrMi+ffuYN29ervueN28eu3bt4rrrrmP79u3pj5iYGFq2bMnHH3+cqU1aIp6mQgXvhsa5c+fy559/5jqG0BnbgwcPsmPHDho1akTFihVZvHhxvl1DiRIlGDBgAB999BEPPPAAq1at4vvvv+fqq6/mwIEDACQnJ6f3m7beuF+/frm+psJEM8QB1OLbkTSOi4t2GJn8dErO6zZZ+VPBBSIiIkVKfHx8pp0PKlWqxO+//57+etCgQcyYMYPOnTtTq1YtOnbsyNVXX80ll1ySbf+rVq0CoH37yNuDhs+MlixZktq1a2coq1+/PnfffTfjx48nKSmJ1q1bc8UVV9CzZ8/0ZPlIkpOTGT16NC+99BKbNm3COZd+LG0Ncn5dw8MPP8z27dt5/PHH02+i69ixIzfffDOTJk2iXLlyAHz11VdMmzaNjz76iBIlivYcqxJiERERKdIiLYEIV61aNZYsWcLcuXOZM2cOc+bM4aWXXuLGG29k6tSpR2x7+PBhwFuDG7p2Nk3JkhnTqbi4uIgJ4rhx4+jTpw/vvPMOH374IYMHD2b06NF89dVXmRLocHfccQcvvfQSQ4YM4bzzzqNChQqYGddee216fPl1DbGxsbzwwgs8+uij/PLLL1SvXp2TTjqJ66+/nhIlStCoUSMA7rvvPlq3bk39+vXTb+7bvn07AJs3b2bjxo3UqVMn29gKAyXEIiIiEgixsbFcfvnlXH755Rw+fJhBgwbx/PPP89BDD9GoUSPMLGK7tBvbqlWrxsUXX3xUMTRr1oxmzZrx4IMP8sUXX9CqVSsmTZrEyJEjAbKM4Y033qB3796MGzcuvWz//v3s2rUrQ738vIbq1atTvXp1wLtxceHChbRs2TJ9hnjjxo1s2LCB+vXrZ2p7xRVXUKFChUzxFVZKiEVERKTY27FjB1WqVEl/XaJECU477TSA9J0VypYtC5ApievUqRPHHXcco0aNol27dpQqVSrD8W3btmX5YRVp9uzZQ5kyZTLMxDZr1owSJUpk+CCLsmXLRkwiY2JiMiyTAJgwYUKmreUK6hrGjh3L5s2bmTBhQnrZ5MmTM62HXrBgARMmTGDs2LGcckou1kJGmRJiERERKfb69evHzp07ad++PbVr12bDhg1MmDCBM844I31bsjPOOIOYmBgee+wxdu/eTVxcHO3bt6datWo899xz9OrVi7POOotrr72WqlWrsnHjRmbPnk2rVq145plnjnj+BQsWcPvtt9OjRw9OOukkDh06xLRp04iJieGqq65Kr9e8eXPmz5/P+PHjqVmzJvXr16dly5ZcdtllTJs2jQoVKnDqqafy5ZdfMn/+/AxJfn5dwyuvvMLMmTNp06YN5cqVY/78+cyYMYN+/fpliLVjx46ZrjMtEb/wwgv10c0iIiIihUnPnj2ZPHkyEydOZNeuXdSoUYNrrrmGxMTE9PW+NWrUYNKkSYwePZqbb76Z1NRUPv74Y6pVq8b1119PzZo1GTNmDE888QQpKSnUqlWL1q1b07dv32zPf/rpp9OpUyfeffddNm3aRJkyZTj99NOZM2cO5557bnq98ePHc8stt/Dggw+SnJxM7969admyJU899RQxMTEkJSWxf/9+WrVqxfz58+nUqVOG8+THNZx00kns3LmTRx55hOTkZE4++WQmTZqUvjdycWTh0+9SfJlZArDsnXr1C+UuE7mhXSZERCJL22qsQYMGUY5EJGs5+T5dvnw5TZs2BWjqnFueZcV8oBniAPpr/xjia0XnS7+099KonFdEREQkK0V70zgRERERkaOkhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQiIiIiEmhKiEVEREQk0LTtWgC9/etmEg7EFPyJEncX/DlEREREjpJmiEVEREQk0JQQi4iIiEigKSEWERERkUBTQiwiIiIigaaEOB+ZWXUze8PMdpiZM7MhZtbW/3fbaMcnIiIiuZOYmIiZRTsMKWDaZSKMmZ0PdASedM7tymXzfwCdgBHAb8B3QI18DVBERCSPmk1tFu0QAFjae2me206ZMoW+ffumv46Li6Ny5co0a9aMLl260LdvX8qXL58fYUqAKCHO7HxgODAF2JXLtu2Bd5xzY9MKzKzQJcQdUh4ndn/dgj/R32bnSzfrx3TJl35ERKT4ePjhh6lfvz4HDx7kt99+Y+HChQwZMoTx48cza9YsTjvttGiHKEWIEuL8VY3cJ9EiIiKSS507d+bss89Of/3AAw+wYMECLrvsMq644gp++uknSpcuHcUIj87+/fuJjY2lRAmtbj0WNMohzCwReMJ/uc5f++vMrJ6ZdTCzz8xsl5ntNbOfzWyU366PmTnAgNvS2mVzrpZm9oGZ7TazP81skZm1CjnexMySzezlsHYXmFmqmT2WrxcvIiJSxLVv356HHnqIDRs28Morr6SXL1iwgNatW1O2bFkqVqzIlVdeyU8//ZSp/WeffcY555xDfHw8DRs25Pnnn494nuTkZAYPHszxxx9P+fLlueKKK9i0aRNmRmJiYoa6mzZt4qabbqJ69erExcWRkJDAv/71rwx1Fi5ciJkxffp0HnzwQWrVqkWZMmXYs2cPBw8eZMSIETRu3Jj4+HiqVKnCBRdcwLx5845+wCSdZogzehM4CbgOuAvY7peXBd4DfgSGASlAIyAtgf0E6AVMA+YBGZLYcGbWHpgDfI+33vgw0BdYYGatnXPfOOd+MrOHgCfM7A3n3CwzK4u3lGOlH8eRzlENqBpW3PCIVy8iIlLE9erVi6FDh/Lhhx/Sv39/5s+fT+fOnWnQoAGJiYkkJyczYcIEWrVqxeLFi6lXrx4AS5cupWPHjlStWpXExEQOHTrE8OHDqV69eqZz9OnThxkzZtCrVy/OPfdcFi1aRJcumZf3bdmyhXPPPRcz4/bbb6dq1arMmTOHm2++mT179jBkyJAM9R955BFiY2O55557SElJITY2lsTEREaPHk2/fv1o0aIFe/bs4bvvvmPx4sV06NChIIYwkJQQh3DO/Whmi/ES4redc+sBzGwIEAt0ds5tj9BuLbDWzKYBvzjnXgmvk8a8W1UnAR/7/Tm//HlgOTAS76Y+gPHAlcBkM/scL3muC5znnEvJ5nIG4a2FFhERCYzatWtToUIF1qxZA8C9995L5cqV+fLLL6lcuTIAXbt25cwzz2T48OFMnToVgGHDhuGc49NPP6VOnToAXHXVVTRrlvFGxMWLFzNjxgyGDBnCP/7xDwAGDRpE3759+b//+78Mdf/+97+TmprK0qVLqVKlCgADBw7kuuuuIzExkQEDBmRY1rF//36+++67DGWzZ8/m0ksvZfLkyfk5TBJGSyZyZpf/fKWZHe2YnQE0Bl4FqpjZ8WZ2PN4s9EdAm7RzOOcOA32AcngzyoOA0c6573JwnolA07DHlUcZu4iISKFXrlw5/vjjDzZv3sySJUvo06dPejIMcNppp9GhQwfef/99AFJTU5k7dy5du3ZNT4YBmjRpQqdOnTL0/cEHHwBeEhzqjjvuyPDaOcfMmTO5/PLLcc6xffv29EenTp3YvXs3ixcvztCmd+/emdY9V6xYkeXLl7Nq1ao8jobkhBLinPk38DnwArDFzKab2dV5TI4b+89TgW1hj35AHFAhrbJzbg2QCJyDN4P8SE5O4pzb6pxbHvoA1uQhXhERkSJl7969lC9fng0bNgBw8sknZ6rTpEkTtm/fzr59+9i2bRvJyck0btw4U73wths2bKBEiRLUr18/Q3mjRo0yvN62bRu7du1i8uTJVK1aNcMjbdu4rVu3ZmgT3id4u2ns2rWLk046iWbNmnHvvffy448/5mAUJDe0ZCIHnHPJZtYGaAd0AS4BrsFb89vROZeai+7Skuh7gSVZ1Nkb9jptCUVNoAreHsd5Nu/uC0lISDiaLkRERAqlX3/9ld27d2dKUI+1w4cPA9CzZ0969+4dsU741nCRdsVo06YNa9as4Z133uHDDz/khRde4B//+AeTJk2iX79++R94QCkhzizi7hD+8oWP/MfdZjYUeBQvSZ6fi/7TZmn3OOeybWdmA4EOwN+BB4Dn0dIHERGRiKZNmwZAp06dqFvX23P/559/zlRv5cqVHH/88ZQtW5b4+HhKly4dcVlCeNu6dety+PBh1q1bl2FGefXq1RnqVa1alfLly5OamsrFF198VNdUuXJl+vbtS9++fdm7dy9t2rQhMTFRCXE+0pKJzPb5zxXTCsyscoR6S/znuFz2/z1eUnyPmZULP2hmVUP+XR9vG7iZzrlRwD3AFWZ2Yy7PKSIiUuwtWLCARx55hPr163PDDTdwwgkncMYZZzB16lR27dqVXm/ZsmV8+OGHXHrppQDExMTQqVMn3n77bTZu3Jhe76effmLu3LkZzpG2pnjixIkZyidMmJDhdUxMDFdddRUzZ85k2bJlmWLdtm1bjq5px44dGV6XK1eORo0akZKS3b31khuaIc7se//5UTObDhwELjKz5sBsYAPeB3AMAn4FPstN5865w2bWD+8mueVm9hKwCaiFN9u8B7jc343iX0AycKvf9nkzuwp4yszmO+f+e3SXKiIiUjTNmTOHlStXcujQIbZs2cKCBQuYN28edevWZdasWcTHxwPwxBNP0LlzZ8477zxuvvnm9G3XKlSokGHP4BEjRvDBBx/QunVrBg0axKFDh5gwYQIJCQkZ1uw2b96cq666iieffJIdO3akb7v2yy+/AOD99+0ZM2YMH3/8MS1btqR///6ceuqp7Ny5k8WLFzN//nx27tyZ7XWeeuqptG3blubNm1O5cmW+++473njjDW6//fZ8GkkBvLsg9cj4AB7ES3ZT8ZZQ3AS8jZe4pvjPrwKNw9o54JmwsrZ+eduw8jOAmXh7He8H1uPdvNfePz7Yb/eXsHYnAruB2Xm4rgTALVu2zImISPG0Zs0at2bNmmiHUWBeeukl5///6AAXGxvratSo4Tp06OCeeuopt2fPnkxt5s+f71q1auVKly7tjjvuOHf55Ze7FStWZKq3aNEi17x5cxcbG+saNGjgJk2a5IYPH+68dOl/9u3b52677TZXuXJlV65cOde1a1f3888/O8CNGTMmQ90tW7a42267zZ144omuVKlSrkaNGu6iiy5ykydPTq/z8ccfO8C9/vrrmWIaOXKka9GihatYsaIrXbq0O+WUU9yjjz7qDhw4kNchLBRy8n26bNmytK9zgivg3M+cO+IHqkkxYmYJwLJly5bppjoRkWJq7dq1ADRo0CDKkQTLkiVLOPPMM3nllVe44YYboh1OoZeT79Ply5fTtGlTgKbO2y2rwGgNsYiIiEguJCcnZyp78sknKVGiBG3atIlCRHK0tIZYREREJBcef/xxvv/+e9q1a0fJkiWZM2cOc+bM4ZZbbuHEE0+MdniSB0qIRURERHLh/PPPZ968eTzyyCPs3buXOnXqkJiYyN///vdohyZ5pIRYREREJBc6dOhAhw4doh2G5COtIRYRERGRQFNCLCIiIiKBpoRYRERERAJNCbGIiIiIBJoSYhEREREJNCXEIiIiIhJoSohFREREJNCUEIuIiIhIoCkhFhERkSJjypQpmFmWj6+++iraIUoRpE+qExERCYifTmkS7RAAaLLyp6Pu4+GHH6Z+/fqZyhs1anTUfUvwKCEWERGRIqdz586cffbZUY1h3759lC1bNqoxSP7QkgkREREpVtavX4+ZMXbsWCZPnkzDhg2Ji4vjnHPO4dtvv81Uf+XKlXTv3p3KlSsTHx/P2WefzaxZszLUSVuqsWjRIgYNGkS1atWoXbt2+vFnn32WBg0aULp0aVq0aMGnn35K27Ztadu2LQB79+6lbNmy3HnnnZnO/+uvvxITE8Po0aPzdyAkxzRDLCIiIkXO7t272b59e4YyM6NKlSrpr1999VX++OMPBgwYgJnx+OOP85e//IW1a9dSqlQpAJYvX06rVq2oVasWf/vb3yhbtiwzZsyga9euzJw5k27dumU4x6BBg6hatSrDhg1j3759ADz33HPcfvvttG7dmrvuuov169fTtWtXKlWqlJ40lytXjm7duvHvf/+b8ePHExMTk97na6+9hnOOG264oUDGSrKnhFhERESKnIsvvjhTWVxcHPv3709/vXHjRlatWkWlSpUAOPnkk7nyyiuZO3cul112GQB33nknderU4dtvvyUuLg7wkt4LLriA+++/P1NCXLlyZT766KP0hPbAgQM89NBDnHPOOSxYsICSJb3U6rTTTqNPnz4ZZpFvvPFGkpKSmDdvHpdcckl6+SuvvEKbNm2oU6dOfgyN5IGWTIiIiEiR8+yzzzJv3rwMjzlz5mSoc80116QnwwCtW7cGYO3atQDs3LmTBQsWcPXVV/PHH3+wfft2tm/fzo4dO+jUqROrVq1i06ZNGfrs379/htnd7777jh07dtC/f//0ZBjghhtuyHBu8JL4mjVrkpSUlF62bNkyfvzxR3r27HmUIyJHQzPEIiIiUuS0aNEi25vqwmdc0xLU33//HYDVq1fjnOOhhx7ioYceitjH1q1bqVWrVvrr8J0tNmzYAGTe3aJkyZLUq1cvQ1mJEiW44YYbeO655/jzzz8pU6YMSUlJxMfH06NHjyNeixQsJcQiIiJSLIXO5IZyzgFw+PBhAO655x46deoUsW54olu6dOmjiunGG2/kiSee4O233+a6667j1Vdf5bLLLqNChQpH1a8cHSXEIiIiEkgNGjQAoFSpUhHXJOdE3bp1AW+2uV27dunlhw4dYv369Zx22mkZ6jdt2pQzzzyTpKQkateuzcaNG5kwYUIer0Dyi9YQi4iISCBVq1aNtm3b8vzzz7N58+ZMx7dt25ZtH2effTZVqlThn//8J4cOHUovT0pKSl+aEa5Xr158+OGHPPnkk1SpUoXOnTvn/SIkX2iGWERERIqcOXPmsHLlykzl559/PiVK5Hy+79lnn+WCCy6gWbNm9O/fnwYNGrBlyxa+/PJLfv31V/7v//7viO1jY2NJTEzkjjvuoH379lx99dWsX7+eKVOm0LBhQ8wsU5vrr7+e++67j7feeotbb701fQs4iR4lxCIiIgGRHx+ZXFgMGzYsYvlLL72U/mEYOXHqqafy3XffMWLECKZMmcKOHTuoVq0aZ555ZpbnCHf77bfjnGPcuHHcc889nH766cyaNYvBgwcTHx+fqX716tXp2LEj77//Pr169cpxrFJwLG1huRR/ZpYALFu2bBkJCQnRDkdERApA2pZiaetjJToOHz5M1apV+ctf/sI///nPTMe7devG0qVLWb16dRSii76cfJ8uX76cpk2bAjR1zi0vyHi0hlhERETkKOzfv5/wCcaXX36ZnTt3Rpyt3rx5M7Nnz9bscCGiJRMiIiIiR+Grr77irrvuokePHlSpUoXFixfz4osv0rRp0wz7C69bt47PP/+cF154gVKlSjFgwIAoRi2hlBCLiIiIHIV69epx4okn8vTTT7Nz504qV67MjTfeyJgxY4iNjU2vt2jRIvr27UudOnWYOnUqNWrUiGLUEkoJsYiIiMhRqFevHrNmzcq2Xp8+fejTp0/BByS5pjXEIiIiIhJoSohFREREJNCUEIuIiIhIoCkhFhEREZFAU0IsIiIiIoGmhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQiIiJS6JlZjh4LFy7Mtq+JEycyZcqUAo9Zig59Up2IiEhAPDtwQbRDAOC2Se1z3WbatGkZXr/88svMmzcvU3mTJk2y7WvixIkcf/zx+tQ4SaeEWERERAq9nj17Znj91VdfMW/evEzlInmhJRMiIiJSLBw+fJgnn3yShIQE4uPjqV69OgMGDOD3339Pr1OvXj2WL1/OokWL0pdZtG3bNv342rVr6dGjB5UrV6ZMmTKce+65zJ49O8N5pkyZgpmxfv36DOULFy7MtGzj008/pUePHtSpU4e4uDhOPPFE7rrrLpKTkzO07dOnD+XKlWPTpk107dqVcuXKUbVqVe655x5SU1PzbYwkMs0Qi4iISLEwYMAApkyZQt++fRk8eDDr1q3jmWee4YcffuDzzz+nVKlSPPnkk9xxxx2UK1eOv//97wBUr14dgC1btnD++efz559/MnjwYKpUqcLUqVO54ooreOONN+jWrVuuY3r99df5888/ufXWW6lSpQrffPMNEyZM4Ndff+X111/PUDc1NZVOnTrRsmVLxo4dy/z58xk3bhwNGzbk1ltvPfoBkiwV2YTYzM4BngJOB8oAZzrnlkQ1KBEREYmKzz77jBdeeIGkpCSuv/769PJ27dpxySWX8Prrr3P99dfTtWtXHnzwQY4//vhMyy3GjBnDli1b+PTTT7ngggsA6N+/P6eddhp33303V155JSVK5O6P64899hilS5dOf33LLbfQqFEjhg4dysaNG6lTp076sf3793PNNdfw0EMPATBw4EDOOussXnzxRSXEBaxILpkws1LA60Bl4C6gF7AhH/u/3syG5Fd/IiIiUrBef/11KlSoQIcOHdi+fXv6o3nz5pQrV46PP/442z7ef/99WrRokZ4MA5QrV45bbrmF9evXs2LFilzHFZoM79u3j+3bt3P++efjnOOHH37IVH/gwIEZXrdu3Zq1a9fm+rySO0V1hrghUBfo75x7oQD6vx5oCjxZAH2LiIhIPlu1ahW7d++mWrVqEY9v3bo12z42bNhAy5YtM5Wn7VyxYcMGmjZtmqu4Nm7cyLBhw5g1a1aGtcwAu3fvzvA6Pj6eqlWrZiirVKlSpnaS/4pqQpz23b4rmkHklpmVcc79Ge04REREipvDhw9TrVo1kpKSIh4PTzSPhplFLA+/+S01NZUOHTqwc+dO7r//fk455RTKli3Lpk2b6NOnD4cPH85QPyYmJt9ilNwpcgmxmU0BevsvX/e/KRcBg4G7gTZATbxk+X3gXufcjpD25YFHgK7ACcBu4P+A+51zi81sIXChX9f5zTY45+qZWVvgY+BavLXLNwHlgY+A25xz/wk5z0LgeD/WfwBnA5OBIWZWDRgNXAZUAH4Gxjvnpoa0rwesA+4FUoEheL8IfOOfa1muB09ERKSYatiwIfPnz6dVq1YZlilEklVCW7duXX7++edM5StXrkw/Dt6sLcCuXbsy1NuwIePqzaVLl/LLL78wdepUbrzxxvTyefPmHfli5JgrimuInwdG+f9+Gm/98KNAB6AB8BJwBzAdL3F93zJ+508CbgVmAoOAsUAykLaT96PAEmC733cvvGQ01N+BLsBjfgwdgPlmFv4TWAWY4/c3BPjYr7PQ7zcJL+HdDUwxszsjXO+NeMn+s3hJdFNggZlVjzw8IiIiwXP11VeTmprKI488kunYoUOHMiSvZcuWzZTMAlx66aV88803fPnll+ll+/btY/LkydSrV49TTz0V8JJvgE8++SS9XmpqKpMnT87QX9qMr3Muvcw5x1NPPZX7C5QCVeRmiJ1zX5pZHDAU+NQ59waAmX3mnBsXWtfMvgJeAy4APvWLuwD/dM79NaTq4yH9zzOzTUAl59wrWYRRGWjinPvDP89iYAbQHy9BTlMDGOicez4kpjvxku+ezrkkv2wS3iz3SDP7V1q/vkZAY+fcJr/uB8DXwP14M+IR+bPQ4X8faphVfRERkaLswgsvZMCAAYwePZolS5bQsWNHSpUqxapVq3j99dd56qmn6N69OwDNmzfnueeeY+TIkTRq1Ihq1arRvn17/va3v/Haa6/RuXNnBg8eTOXKlZk6dSrr1q1j5syZ6TtMJCQkcO655/LAAw+wc+dOKleuzPTp0zl06FCGmE455RQaNmzIPffcw6ZNmzjuuOOYOXOm1gQXQkUuIc6Kcy59h2sziwfKAV/5RWfxv4R4F9DSzGo65/6bx9O9HJa0vgFsBi4lY0KcgjdjHepS4De8RD0t9oNm9rRfdiHwXkj9t9OSYb/uN2b2td9Plgkx3uz38BxfkYiISBE3adIkmjdvzvPPP8/QoUMpWbIk9erVo2fPnrRq1Sq93rBhw9iwYQOPP/44f/zxBxdeeCHt27enevXqfPHFF9x///1MmDCB/fv3c9ppp/Huu+/SpUuXDOdKSkpiwIABjBkzhooVK3LzzTfTrl07OnTokF6nVKlSvPvuuwwePJjRo0cTHx9Pt27duP322zn99NOP2bhI9ix0Gr+oCFnL2yNkhrgyXgJ4Lf+76S7NcOfcw369q4GpQCzwPd4645edc+l7mpjZe0BT51y9LM57k3PupbBjnwDVnHOn+K8XAic65xqG1VsJbHXOtQkrPx1vacXtzrlnQ9YQP+ycGx5W92Xgaudc/BHGKKsZ4neWLVtGQkJCVk1FRKQIS9uiq0GDBlGORCRrOfk+Xb58edquHk2dc8sLMp5iM0OMt2ThfOAJvMRyL94a6Q8IWSvtnJthZp8C3YCOeGt47zezvzjn5uRzTMnZVykYzrmtQIY9ZrK6iUBEREQkyIriTXWZmFkl4CJgjHNuuHPuLefcPCDiTtbOuc3OuYnOua5AfWAH3o1y6VWyOWXjsPMb3lrf9TkIdwPQ2MzCx/6UkONZnst3Ug7PJSIiIiLZKBYJMd62ZADhU6BDQl+YWYyZVQgt82dS/wvEhRTvw9sOLSs3+tu3pemOt4VbTmaY38e72e6akLhK4u2MsRfv5rpQXc2sVkjdFkDLHJ5LRERERLJRLJZMOOf2+Gt47/M/1nkT3nKI+mFVywO/mtkbeHsP7wUuBs4BQned+B64xszGA98Ce51z74Yc3wl8ZmYvAdXxEu/VwD9zEO5kYADeNmvN8WZ6uwOtgCFhN+vh9/uZmT2Hl7QPwZvRfhwREREROWrFIiH2XQ9MAG7Dmyn+EOiMN/ub5k9gIl6y/Be8GfLVwCDn3HMh9SYCZwB9gbvwljGEJsSjgNOAB/jfB3MMysmn0Dnnkv2b88bgfWjHcXgfzNHXOTclQpOXgcNk/GCO251zm7M7l4iIiIhkr0gmxM65hYQtj/C3JvtLhOoWUucAcJ//OFL/+4AbjlDlkHNuKN5eyFn10fYIx7bifcpdjjjnxgPjc1pfRESCrSjuICXB4pwrVDf7F5c1xCIiIgKUKFGC1NRUJcVSaDnnSE1NTf+gk8Kg8EQiIiIiRy0uLo7U1FS2bt2qpFgKHeccW7duJTU1lbi4uOwbHCNFcsmEiIiIRFa9enVSUlLYuXMnu3fvJiYmplD9aVqCK21mODU1ldKlS1O9evVoh5ROCXEuRFq7XIDnWn+sziUiIsVHiRIlqFOnDlu2bCElJYXDhw9HOyQRwPuAsNjYWOLi4qhevXqhWjKhhFhERKSYKVGiBCeccEK0wxApMgpPai4iIiIiEgVKiEVEREQk0JQQi4iIiEigKSEWERERkUBTQiwiIiIigaaEWEREREQCTduuBUsswOrVq6Mdh4iIiMgRheQrsQV9LtPHOgaHmfUCXo52HCIiIiK5cKVzblZBnkAzxMHyi//cHVgZzUDyYBnQNNpB5FJD4B3gSmBNlGPJLY33sVXUxltjfWxpvI8tjfexdaTxjgVOBBYVdBBKiINlr/+80jm3PKqR5JKZURRj9q0pirEXxZh9Gu8CprE+tjTex5bG+9jKwXj/cCzi0E11UlSMiHYAAaPxPrY03seOxvrY0ngfWxrvPFJCLEWCcy4x2jEEicb72NJ4Hzsa62NL431sabzzTgmxiIiIiASaEuJg2Yb355Rt0Q4kIDTex5bG+9jRWB9bGu9jS+N9bBWK8da2ayIiIiISaJohFhEREZFAU0IsIiIiIoGmhFhEREREAk0JsYiIiIgEmhJiEREREQk0JcQBYGZxZvaYmf3XzJLN7Gsz6xDtuIoKMzvHzJ4xs+Vmts/MNprZDDM7KULdJmb2gZntNbOdZjbNzKpGqFfCzO4zs3Vmtt/MfjSz647NFRU9ZvZ3M3NmtizCsfPN7DMz+9PMfjOzp82sXIR6+jk4AjM7y8xm+d+3f5rZMjMbHFZHY50PzKyxmU03s1/9sVxpZsPMrExYPY13LphZOTMb4b8H7/TfM/pkUTff36tz2mdxkZPx9sevj//e8h///9BlZvagmcVn0e/NZvaTP96rzOyOLOrV8v8v3mVme8zsHTNrkOcLcs7pUcwfwGvAQeAJ4BbgC//1BdGOrSg8gDeAzcDTQD/gQeA3YC/QNKRebbx9FFcDg4GhwE5gCRAb1udowAGTgf7Ae/7ra6N9vYXt4Y/rPn+8l4UdOwNIBhYDA4GRwH5gToR+9HOQ9Rh3BFKAr4C7/O/JMcDjGut8H+sTgd+B9cDf/PF5yf/5f0fjfVRjW88fxw3Ax/6/+0Sol+/v1bnps7g8cjLeQDm//Evg7/4Y/gtI9dtYWP0Bfv03/Lov+6/vj9DvL8AW4D7/fWsj8B+gSp6uJ9oDqkfBPoAW/jfTPSFl8f4P7RfRjq8oPIDzI7xJNvb/c3olpGwi8CdQJ6TsYn/8bwkpqwUcAJ4JKTPgE/+HOSba11yYHsB04CNgIZkT4veB/wLHhZT188e8Y0iZfg6yHt/j8H7BexMocYR6Guv8Ge+h/vgkhJVP9csrabzzPLZxQA3/32eTdUKc7+/VOe2zOD1yMt5ALHB+hLbD/PoXh5SVBrYD74XVfQVvQqRSSNl9fvtzQspOAQ4Bo/JyPVoyUfx1x/tNbHJagXNuP/AicJ6ZnRitwIoK59wXzrkDYWWrgOVAk5Diq/B+kDeG1JuP91vs1SH1rgRK4b2BptVzwHN4swzn5fc1FFVm1gbve3hIhGPHAR3wfinZE3LoZbw3z9Ax189B1q4HqgN/d84dNrOyZpbh/waNdb46zn/eEla+GTgMHNB4541zLsU591sOqhbEe3VO+yw2cjLezrkDzrkvIhx6y38O/T+0HVCFkPH2PQuUBbqElHUHvnXOfRtyrpV4kyd5Gm8lxMXfmcAvYW+qAN/4z2cc23CKBzMzvCRiu/+6FlAN+C5C9W/wvg5pzsRbAvBThHqE1Q0sM4sBJgAvOOeWRqjSDChJ2Jj7v7wsIfOY6+cgsouBPUAtM/sZL+HaY2bPhazx01jnn4X+84tmdoaZnWhm1wC3Ak875/ah8S4wBfFencs+xVPDf94eUpY2TuHj+D3eL4tp410COC1CPfDGu6GZlc9tQEqIi78T8GYewqWV1TyGsRQnN+D9Oe3f/usT/OesxrqymcWF1N3izzSE1wN9TdIMBOoCD2VxPLsxrxlWVz8HkTXGS77eAebizXT9C2/8X/LraKzziXPuA7zv6Q7AD3jrHqcDE5xzd/nVNN4FpyDeq3PTp3juw/tFfE5I2QlAqnNua2hF/xfBHfxvvCvjLdfI1+/7krltIEVOabybZcLtDzkuuWBmp+D9CedLvHV/8L9xzG6sU9DXJFtmVgV4GHjEObcti2rZjXnpsLoa88jKAWWASc65tF0l3jSzWGCAmQ1DY53f1uOtQ52J9x99F2Comf3mnHsGjXdBKoj36tz0GXhmNhTvL1ODnHO7Qg6VxluzHUno931OxztXlBAXf8l4v0mFiw85LjlkZjWA2cBuoLtzLtU/lDaOORlrfU2yNxLvDu0JR6iT3Zgnh9XVmEeWdu2vhZW/infH93l4NwuBxvqomdm1eOt9T3LO/eoXv+n/GfgxM3sNfW8XpIJ4r85Nn4HmLw8aCbzonHsu7HAy3k14kYR+3xfIeGvJRPG3mf/9OSdUWtl/j2EsRZqZVcD7805F4BLnXOjYpf2ZJqux3umcSwmpW8NfhxxeDwL+NTGzxnhbRz0N1DSzemZWD++NrpT/ujLZj3n410c/B5GlXXv4TV5pf7ashMY6Pw0CfghJhtPMwpupPxONd0EqiPfq3PQZWP7e2C/jTSoNjFBlMxBjZtXC2sXi3WyXNt478WaH8/X7Xglx8bcEOMm/azlUy5Djkg3/5qJ3gZOAy5xzK0KPO+c24e1BeXaE5i3IOM5L8P7jaxJWT18TTy2896angXUhj5Z4478Ob8ueZXhb7GQYc//N8wwyj7l+DiL73n+uFVaetgZvGxrr/FQdiIlQXsp/LonGu8AUxHt1LvsMJDNribezxHfA1c65QxGqLfGfw8fxbLz/E5YAOOcOA0sj1APva7PWOfdHbmNUQlz8vYH35ntLWoG/uL8v8LVz7j/RCqyo8Hc7+Dfen457OOe+zKLqTOCy0G2OzOwivCTu9ZB67+Btmj8opJ7h/ca8CW9T/SBbBnSL8FiOdwNSN7w/t+0G5gM9w+4o7oW3LjZ0zPVzkLUZ/vPNYeX98JKyhRrrfPULcKZl/qTL6/DupP9R413gCuK9Oqd9Bo6ZNcGbFV6PN6GU1XKGBXizv7eGld+Kt2xrdkjZG8A5ZpaeFJvZyUB78jjelvnmSSluzGwGXhLxD7zN2nvj/dZ6kXPuk2jGVhSY2ZPAnXgzxDPCjzvnXvHrnYh31/gu4Cm8/7juBX7F2zw8/U9mZva4f2wy8C3QFe/Gmhucc68W2MUUYWa2EDjeOdc0pOwsvP+UVuCNZW3gr8AnzrlOYe31c5AFM3sRuAnv+3sR0BboAYx2zg3162is84G/t/YCvJvpnvGfLwM6420x2N+vp/HOAzO7HW9ZW028ROpNvPdl8Hby2F0Q79W56bM4yW688X7JW473F6iheL9IhFoTOslkZoPwblp/A2/Xm9bAjXj7pI8KqVfeP095YCzeLy534/1yeMYRbsbOWl4+zUOPovXAW3v5BN76nP14+/R1inZcReWBt2+oy+oRVjfB/yHeh/fxrK8A1SP0WQJ4AO835hS8WdEbon2thflBhE+q88svAD7Hu4liK16SUT5CPf0cZD22pYDh/vfjAWAVMERjXWDj3QLvk+g2++P9M16yUFLjfdRju/4I79f1Qurl+3t1TvssTo/sxpv/fbxzVo8pEfrsD6z0x3s13gczWYR6tfFmg3cDf+BNWjXK67VohlhEREREAk1riEVEREQk0JQQi4iIiEigKSEWERERkUBTQiwiIiIigaaEWEREREQCTQmxiIiIiASaEmIRERERCTQlxCIiIiISaEqIRURERCTQlBCLiIiISKApIRYRKcLMzJlZYrTjEBEpypQQi4hkw8z6+Iln2uOQmW0ysylmVusYnP/Sopb0mlkzf6xaHKHOwrBxDX2cUkBxDTKzPgXRt4gUXSWjHYCISBEyDFgHxAPnAn2AC8ysqXNufwGe91LgNiAxwrHSwKECPHdedQG2At9mU+9X4IEI5f/N94g8g4DtwJQC6l9EiiAlxCIiOTfHOfed/+8XzGw7cD9wBTAjGgEVcCJ+NC7FGy+XTb3dzrlXjkVABcXMDIh3ziVHOxYRyRstmRARybtP/eeGaQX+MoCF4RX95RXrQ17X85cG3GNmt5jZGjNLMbNvzeyc0HZ4s8OELikIOZ5hDbGZJfplJ5nZK2a228y2mdkj5jnRzN4xsz1m9puZ/TVCrHFmNsLMVvsx/cfMHjezuJwMiplVBM4HZuekfjZ95SgWM+trZgvMbKtfb4WZ3RpWZz2QAFwYMpYL/WOJoeMa0iZtuUy90H7M7D0z62Rm3wHJwIC0azezJ/04U/y47zcz/X8rUohphlhEJO/q+c+/H0Uf1wPlgecBB9wHvGlmDZxzB/3ymkAHoFcu+v038BPwN7zlCw8CO/EStwV4M9s3AGPN7Fvn3CcAfuI2C7gAmOz30Qy4CzgJ6JqDc3fyr+XDHNSNMbPjw8r2O+f25jKWW4Hlfv1DwOXARDMr4Zx71q8zBJgA7AUe9cu25CDGSE4GXsP7+vwT+NnMygCLgFp++Ua8XwxGAyf45xeRQkgJsYhIzlXwk7d4oCUwHEgB3juKPusAjZ1zvwOY2c/AO3hJ5XvOuS/N7BegQy6XFnzjnEubtZwMrAfGAQ845x7zy1/DW6t7E/CJ3+564GLgQufcZ2mdmdkyYJKZne+c+yKbc3cBPnfO7c5BnKcA28LKpuKtz85NLBeGLVl4xsw+AO4GngVwzr1tZiOB7fmwTKMRcIlzbm5IXA/i/bXgTOfcKr/4eTP7L3CvmY1zzv3nKM8rIgVACbGISM7ND3u9HujpnPv1KPr8d1oy7EtbhtHgKPoEeCHtH865VP9P+7WBF0PKd/kJeOi5euDNxK4Mm7ld4D+3A7JMiP1Z3UuAJ3IY53qgf1hZ2g11OY4lNBk2swpAKbzZ2k5mViGHyXlurAtNhkPi/RT4PSze+Xgz9W2ApHyOQ0TygRJiEZGcuw34BaiAN6vaBm+G+GhsDH3hnPvdu0eLSvnZL7AbbynC9gjlVUJeNwaakHnWNk21bM57DlCVnK8f3uecC/9FI9exmFkrYARwHlAmrF4FvOvMT+silDUGTiPvYyciUaKEWEQk575J22XCzN4GPgNeNbOTnXN7/ToOsAhtY7LoMzWL8kh95EakfnNyrhLAUrylBpFk9yf/S4H1zrkV2dTLiRzFYmYNgY+AlX7d/wAH/FjuImc3kGe1G0ZWX7dIO0qUAOYBj2fR5pccxCEiUaCEWEQkD/xlCA8AHwO3A2P8Q78TeblD3aM53VG0za01wOnARznYMi2SLsD7xziWy4E44ArnXPrMuJm1i1A3q37S1nBXdM7tCinPzddtDVDuCDPeIlJIaRsYEZE8cs4tBL4BhphZvF+8BjjFzKqm1TOz04FWR3GqfX4/FY+ij5yagbdLQvi6XsystJmVzaqhmVUHziIftlvLZSxpM98WcrwC0DdCn/uAihHK1/jPbUL6KAv0zmW855lZpwjxVjQzTUKJFFL64RQROTpPAK/j7YowCfgX3p/t55rZi3jrRgfibQl2XB7P8b3//LSZzQVSnXPTjyboI5gGXI23i0M74HO8ZQOn+OWdgO+yaHspsB9v1vxYxvIh3hKJd83seaAcXhK9FW+7s1DfA7f6O0KsBrY65xb4fWwEXjSzJ/CS7Jvw1gPXyWG8T+B9SMt7/v7R3wNl8baK6463TV/4Gm4RKQSUEIuIHJ038WYX7zGzfzrnfjKzG4GHgfHACrz9g68H2h7FOSYA1wI98WZCCyQhds4dNrOueGtvbwS6AX8Ca4GnOPI62EuBj/PrE9tyGotz7mcz6w6MBMYCvwHP4SWz/wrr9mG8ZRD34e3/vAhY4Jw7aGbdgInAI34fT+ItpXgph/H+aWYXAkPxdpy4Edjjxzmc/L+xT0TyieVtiZiIiMj/+MsBduDtczwx2vGIiOSG1hCLiEh+qAz8A3gr2oGIiOSWZohFREREJNA0QywiIiIigaaEWEREREQCTQmxiIiIiASaEmIRERERCTQlxCIiIiISaEqIRURERCTQlBCLiIiISKApIRYRERGRQFNCLCIiIiKBpoRYRERERAJNCbGIiIiIBJoSYhEREREJtP8Hc6N57yz8mLUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_axis = np.arange(len(df_pollution['algorithm']))\\n\",\n    \"\\n\",\n    \"height = 0.12\\n\",\n    \"\\n\",\n    \"plt.figure(dpi=120)\\n\",\n    \"\\n\",\n    \"plt.barh(x_axis, df_pollution['runtime/feat.'], height=height, label='Air Pollution')\\n\",\n    \"plt.barh(x_axis + height, df_interstate['runtime/feat.'], height=height, label='Insterstate94')\\n\",\n    \"plt.barh(x_axis + (2*height), df_dodgers['runtime/feat.'], height=height, label='Dodgers')\\n\",\n    \"plt.barh(x_axis + (3*height), df_energy['runtime/feat.'], height=height, label='Energy')\\n\",\n    \"plt.barh(x_axis + (4*height), df_tetouan['runtime/feat.'], height=height, label='Tetouan')\\n\",\n    \"\\n\",\n    \"plt.gca().xaxis.set_minor_locator(MultipleLocator(100))\\n\",\n    \"\\n\",\n    \"plt.yticks(x_axis, df_pollution['algorithm'])\\n\",\n    \"plt.xlabel('Runtime / Feature')\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"db8f9e5c-74e5-4ca8-ab42-8c42fd1d6541\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_axis = np.arange(len(df_pollution['algorithm']))\\n\",\n    \"\\n\",\n    \"width = 0.1\\n\",\n    \"\\n\",\n    \"plt.figure(dpi=120)\\n\",\n    \"\\n\",\n    \"plt.bar(x_axis, df_pollution['runtime/feat.'], width=width, label='Air Pollution')\\n\",\n    \"plt.bar(x_axis + width, df_interstate['runtime/feat.'], width=width, label='Insterstate94')\\n\",\n    \"plt.bar(x_axis + (2*width), df_dodgers['runtime/feat.'], width=width, label='Dodgers')\\n\",\n    \"plt.bar(x_axis + (3*width), df_energy['runtime/feat.'], width=width, label='Energy')\\n\",\n    \"plt.bar(x_axis + (4*width), df_tetouan['runtime/feat.'], width=width, label='Tetouan')\\n\",\n    \"\\n\",\n    \"plt.gca().yaxis.set_minor_locator(MultipleLocator(100))\\n\",\n    \"\\n\",\n    \"plt.xticks(x_axis, df_pollution['algorithm'])\\n\",\n    \"plt.ylabel('Runtime / Feature')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.savefig('../assets/benchmarks_plot.png');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"33037788-b647-451f-91f6-967a502de159\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x480 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_axis = np.arange(len(df_pollution['algorithm']))\\n\",\n    \"\\n\",\n    \"width = 0.1\\n\",\n    \"\\n\",\n    \"plt.figure(dpi=120)\\n\",\n    \"\\n\",\n    \"baxes = brokenaxes(ylims=((1,400),(600,1200)))\\n\",\n    \"\\n\",\n    \"baxes.bar(x_axis, df_pollution['runtime/feat.'], width=width, label='Air Pollution')\\n\",\n    \"baxes.bar(x_axis + width, df_interstate['runtime/feat.'], width=width, label='Insterstate94')\\n\",\n    \"baxes.bar(x_axis + (2*width), df_dodgers['runtime/feat.'], width=width, label='Dodgers')\\n\",\n    \"baxes.bar(x_axis + (3*width), df_energy['runtime/feat.'], width=width, label='Energy')\\n\",\n    \"baxes.bar(x_axis + (4*width), df_tetouan['runtime/feat.'], width=width, label='Tetouan')\\n\",\n    \"\\n\",\n    \"# plt.gca().yaxis.set_minor_locator(MultipleLocator(100))\\n\",\n    \"\\n\",\n    \"baxes.set_xticks(x_axis, df_pollution['algorithm'])\\n\",\n    \"baxes.set_ylabel('Runtime / Feature')\\n\",\n    \"\\n\",\n    \"baxes.legend(loc='upper left');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"3d738228-ae06-4269-914b-f51732baaca3\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_axis = np.arange(len(df_pollution['algorithm']))\\n\",\n    \"\\n\",\n    \"width = 0.1\\n\",\n    \"\\n\",\n    \"plt.figure(dpi=120)\\n\",\n    \"\\n\",\n    \"plt.bar(x_axis, df_pollution['runtime/feat.'], width=width, label='Air Pollution')\\n\",\n    \"plt.bar(x_axis + width, df_interstate['runtime/feat.'], width=width, label='Insterstate94')\\n\",\n    \"plt.bar(x_axis + (2*width), df_dodgers['runtime/feat.'], width=width, label='Dodgers')\\n\",\n    \"plt.bar(x_axis + (3*width), df_energy['runtime/feat.'], width=width, label='Energy')\\n\",\n    \"plt.bar(x_axis + (4*width), df_tetouan['runtime/feat.'], width=width, label='Tetouan')\\n\",\n    \"\\n\",\n    \"plt.xticks(x_axis, df_pollution['algorithm'])\\n\",\n    \"plt.ylabel('Runtime / Feature')\\n\",\n    \"plt.yscale('log')\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"1bf3712c-cdec-42c4-81c9-c76270baf702\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_axis = np.arange(len(df_pollution['algorithm']))\\n\",\n    \"\\n\",\n    \"width = 0.1\\n\",\n    \"\\n\",\n    \"plt.figure(dpi=120)\\n\",\n    \"\\n\",\n    \"plt.bar(x_axis, 1/df_pollution['runtime/feat.'], width=width, label='Air Pollution')\\n\",\n    \"plt.bar(x_axis + width, 1/df_interstate['runtime/feat.'], width=width, label='Insterstate94')\\n\",\n    \"plt.bar(x_axis + (2*width), 1/df_dodgers['runtime/feat.'], width=width, label='Dodgers')\\n\",\n    \"plt.bar(x_axis + (3*width), 1/df_energy['runtime/feat.'], width=width, label='Energy')\\n\",\n    \"plt.bar(x_axis + (4*width), 1/df_tetouan['runtime/feat.'], width=width, label='Tetouan')\\n\",\n    \"\\n\",\n    \"plt.xticks(x_axis, df_pollution['algorithm'])\\n\",\n    \"plt.ylabel('Features/Runtime')\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"aace4760-5bc5-4860-b70f-f46a92748ccf\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>getml</th>\\n\",\n       \"      <th>tsflex</th>\\n\",\n       \"      <th>featuretools</th>\\n\",\n       \"      <th>tsfel</th>\\n\",\n       \"      <th>tsfresh</th>\\n\",\n       \"      <th>kats</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Air_Pollution</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>99.3</td>\\n\",\n       \"      <td>309.7</td>\\n\",\n       \"      <td>304.8</td>\\n\",\n       \"      <td>257.0</td>\\n\",\n       \"      <td>1156.7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Interstate94</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>59.3</td>\\n\",\n       \"      <td>157.9</td>\\n\",\n       \"      <td>224.8</td>\\n\",\n       \"      <td>215.8</td>\\n\",\n       \"      <td>653.3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Dodgers</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>70.9</td>\\n\",\n       \"      <td>201.1</td>\\n\",\n       \"      <td>285.5</td>\\n\",\n       \"      <td>255.6</td>\\n\",\n       \"      <td>732.6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Energy</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>108.6</td>\\n\",\n       \"      <td>269.7</td>\\n\",\n       \"      <td>58.5</td>\\n\",\n       \"      <td>217.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Tetouan</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>85.1</td>\\n\",\n       \"      <td>266.9</td>\\n\",\n       \"      <td>262.8</td>\\n\",\n       \"      <td>227.0</td>\\n\",\n       \"      <td>1061.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"algorithm     getml tsflex featuretools  tsfel tsfresh    kats\\n\",\n       \"Air_Pollution   1.0   99.3        309.7  304.8   257.0  1156.7\\n\",\n       \"Interstate94    1.0   59.3        157.9  224.8   215.8   653.3\\n\",\n       \"Dodgers         1.0   70.9        201.1  285.5   255.6   732.6\\n\",\n       \"Energy          1.0  108.6        269.7   58.5   217.0     NaN\\n\",\n       \"Tetouan         1.0   85.1        266.9  262.8   227.0  1061.5\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"indices = ['Air_Pollution', 'Interstate94', 'Dodgers', 'Energy', 'Tetouan']\\n\",\n    \"\\n\",\n    \"columns = df_pollution['algorithm'].copy()\\n\",\n    \"columns.loc[0] = 'getml'\\n\",\n    \"\\n\",\n    \"df_runtime_per_feat = pd.DataFrame(index=indices, \\n\",\n    \"                                   columns=columns)\\n\",\n    \"\\n\",\n    \"data_frames = [df_pollution, df_interstate, df_dodgers, df_energy, df_tetouan]\\n\",\n    \"\\n\",\n    \"for name, df in zip(indices, data_frames):\\n\",\n    \"    df_runtime_per_feat.loc[name, :] = df['runtime/feat.'].values\\n\",\n    \"\\n\",\n    \"df_runtime_per_feat\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"3858416f-2a9e-458f-bb11-d56fe239b6c8\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>algorithm</th>\\n\",\n       \"      <th>getml</th>\\n\",\n       \"      <th>tsflex</th>\\n\",\n       \"      <th>featuretools</th>\\n\",\n       \"      <th>tsfel</th>\\n\",\n       \"      <th>tsfresh</th>\\n\",\n       \"      <th>kats</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Air_Pollution</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>99.3</td>\\n\",\n       \"      <td>309.7</td>\\n\",\n       \"      <td>304.8</td>\\n\",\n       \"      <td>257.0</td>\\n\",\n       \"      <td>1156.7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Interstate94</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>59.3</td>\\n\",\n       \"      <td>157.9</td>\\n\",\n       \"      <td>224.8</td>\\n\",\n       \"      <td>215.8</td>\\n\",\n       \"      <td>653.3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Dodgers</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>70.9</td>\\n\",\n       \"      <td>201.1</td>\\n\",\n       \"      <td>285.5</td>\\n\",\n       \"      <td>255.6</td>\\n\",\n       \"      <td>732.6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Energy</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>108.6</td>\\n\",\n       \"      <td>269.7</td>\\n\",\n       \"      <td>58.5</td>\\n\",\n       \"      <td>217.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Tetouan</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>85.1</td>\\n\",\n       \"      <td>266.9</td>\\n\",\n       \"      <td>262.8</td>\\n\",\n       \"      <td>227.0</td>\\n\",\n       \"      <td>1061.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"algorithm     getml tsflex featuretools  tsfel tsfresh    kats\\n\",\n       \"Air_Pollution   1.0   99.3        309.7  304.8   257.0  1156.7\\n\",\n       \"Interstate94    1.0   59.3        157.9  224.8   215.8   653.3\\n\",\n       \"Dodgers         1.0   70.9        201.1  285.5   255.6   732.6\\n\",\n       \"Energy          1.0  108.6        269.7   58.5   217.0     NaN\\n\",\n       \"Tetouan         1.0   85.1        266.9  262.8   227.0  1061.5\\n\",\n       \"                0.0    0.0          0.0    0.0     0.0     0.0\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_runtime_per_feat.loc['', :] = np.zeros(6)\\n\",\n    \"df_runtime_per_feat\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"8c85f567-6280-45ea-8fb4-1993d9bb8423\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1200x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(dpi=120, figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"df_runtime_per_feat.head(5).plot(y=columns, \\n\",\n    \"                         kind='bar',\\n\",\n    \"                         ax=ax,\\n\",\n    \"                         ylabel='Runtime / Feature',\\n\",\n    \"                         xlabel='Dataset',\\n\",\n    \"                         legend=True,\\n\",\n    \"                         rot=45,\\n\",\n    \"                         grid=True\\n\",\n    \"                         );\\n\",\n    \"\\n\",\n    \"handles, labels = ax.get_legend_handles_labels()\\n\",\n    \"lgd = ax.legend(handles, labels, bbox_to_anchor=(0.85,0.98))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"787a24d3-580b-4992-9af4-e5e4fc4c9177\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1200x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(dpi=120, figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"df_runtime_per_feat.plot(y=columns, \\n\",\n    \"                         kind='bar',\\n\",\n    \"                         ax=ax,\\n\",\n    \"                         logy=True,\\n\",\n    \"                         ylabel='Runtime / Feature',\\n\",\n    \"                         xlabel='Dataset',\\n\",\n    \"                         legend=True,\\n\",\n    \"                         rot=45,\\n\",\n    \"                         grid=True\\n\",\n    \"                         );\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"5468310c-73c6-43ef-8fb0-a34e500c633d\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Solarize_Light2',\\n\",\n       \" '_classic_test_patch',\\n\",\n       \" '_mpl-gallery',\\n\",\n       \" '_mpl-gallery-nogrid',\\n\",\n       \" 'bmh',\\n\",\n       \" 'classic',\\n\",\n       \" 'dark_background',\\n\",\n       \" 'fast',\\n\",\n       \" 'fivethirtyeight',\\n\",\n       \" 'ggplot',\\n\",\n       \" 'grayscale',\\n\",\n       \" 'seaborn',\\n\",\n       \" 'seaborn-bright',\\n\",\n       \" 'seaborn-colorblind',\\n\",\n       \" 'seaborn-dark',\\n\",\n       \" 'seaborn-dark-palette',\\n\",\n       \" 'seaborn-darkgrid',\\n\",\n       \" 'seaborn-deep',\\n\",\n       \" 'seaborn-muted',\\n\",\n       \" 'seaborn-notebook',\\n\",\n       \" 'seaborn-paper',\\n\",\n       \" 'seaborn-pastel',\\n\",\n       \" 'seaborn-poster',\\n\",\n       \" 'seaborn-talk',\\n\",\n       \" 'seaborn-ticks',\\n\",\n       \" 'seaborn-white',\\n\",\n       \" 'seaborn-whitegrid',\\n\",\n       \" 'tableau-colorblind10']\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.style.available\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"68055eac-681c-48c7-b77a-55b2a785bb9e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sns.set()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"1bb6b218-f920-493a-8d92-cc4da5da9140\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1200x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.style.use('fivethirtyeight')\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(dpi=120, figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"df_runtime_per_feat.head(5).plot(y=columns, \\n\",\n    \"                         kind='bar',\\n\",\n    \"                         ax=ax,\\n\",\n    \"                         ylabel='Runtime / Feature',\\n\",\n    \"                         xlabel='Dataset',\\n\",\n    \"                         legend=True,\\n\",\n    \"                         rot=45,\\n\",\n    \"                         grid=True\\n\",\n    \"                         );\\n\",\n    \"\\n\",\n    \"handles, labels = ax.get_legend_handles_labels()\\n\",\n    \"lgd = ax.legend(handles, labels, bbox_to_anchor=(0.85,0.98))\\n\",\n    \"\\n\",\n    \"fig.suptitle('getML vs other tools: linear scale')\\n\",\n    \"\\n\",\n    \"plt.tight_layout()\\n\",\n    \"\\n\",\n    \"plt.savefig('../assets/benchmarks_plot_linear.png');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"0426a0c3-c640-4b46-9f55-66ed6fd9a896\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def plot(style):\\n\",\n    \"    \\n\",\n    \"    plt.style.use(style)\\n\",\n    \"                  \\n\",\n    \"    fig, ax = plt.subplots(dpi=120, figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"    df_runtime_per_feat.plot(y=columns, \\n\",\n    \"                             kind='bar',\\n\",\n    \"                             ax=ax,\\n\",\n    \"                             logy=True,\\n\",\n    \"                             ylabel='Runtime / Feature',\\n\",\n    \"                             xlabel='Dataset',\\n\",\n    \"                             legend=True,\\n\",\n    \"                             rot=45,\\n\",\n    \"                             grid=True\\n\",\n    \"                             );\\n\",\n    \"    \\n\",\n    \"    handles, labels = ax.get_legend_handles_labels()\\n\",\n    \"    lgd = ax.legend(handles, labels)\\n\",\n    \"    \\n\",\n    \"    fig.suptitle('getML vs other tools: logarithmic scale')\\n\",\n    \"    \\n\",\n    \"    plt.tight_layout()\\n\",\n    \"\\n\",\n    \"    plt.savefig('../assets/benchmarks_plot_log.png');\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"e857127b-5580-4a9a-9598-b9899071e7cb\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1200x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot('fivethirtyeight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"4636267d-5ca4-449e-a10a-610392a35f6d\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# plot('ggplot')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"6621d98f-25ff-408f-87ff-e4d045ce43e3\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# plot('tableau-colorblind10')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d360f5d5-c74e-432c-aa37-310ae4a68f47\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "benchmarks/benchmarks/__init__.py",
    "content": "from . import drivers\nfrom .benchmarks import (\n    benchmark_air_pollution,\n    benchmark_dodgers,\n    benchmark_energy,\n    benchmark_interstate94,\n    benchmark_tetuan,\n)\n"
  },
  {
    "path": "benchmarks/benchmarks/benchmarks.py",
    "content": "\"\"\"\nContains the main benchmark functions\n\"\"\"\n\nimport logging\nimport os\nfrom typing import Callable, Sequence\nfrom urllib import request\n\nimport getml\nimport pandas as pd  # type: ignore\n\nfrom .demo import Benchmark\nfrom .utils import log_run, project\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s %(message)s\")\n\n\ndef _load_csv(fname: str, url: str) -> pd.DataFrame:\n    if not os.path.exists(fname):\n        fname, _ = request.urlretrieve(url + fname, fname)\n    return pd.read_csv(fname)\n\n\ndef _zero_padding(date_time: str) -> str:\n    hour = date_time.split(\" \")[1].split(\":\")[0]\n    hour = \"0\" + hour if len(hour) == 1 else hour\n    return (\n        date_time.split(\" \")[0]\n        + \" \"\n        + hour\n        + \":\"\n        + date_time.split(\" \")[1].split(\":\")[1]\n    )\n\n\ndef benchmark_air_pollution(\n    benchmark_drivers: Sequence[Callable], runs: int = 1\n) -> Benchmark:\n    ds_name = \"air_pollution\"\n    time_stamp = \"date\"\n    frequency = getml.data.time.hours(1)\n    horizon = getml.data.time.hours(1)\n    memory = getml.data.time.hours(12)\n\n    observer = Benchmark(ds_name)\n\n    for run in range(1, runs + 1):\n        with project(f\"__benchmark_{ds_name}\"):\n            air_pollution = getml.datasets.load_air_pollution()\n\n            air_pollution = air_pollution[\n                ~air_pollution[\"pm2.5\"].is_null() & ~air_pollution[\"pm2.5\"].is_inf()\n            ]\n\n            print(air_pollution)\n\n            split = getml.data.split.time(\n                population=air_pollution,\n                time_stamp=time_stamp,\n                test=getml.data.time.datetime(2014, 1, 1),\n            )\n\n            time_series = getml.data.TimeSeries(\n                population=air_pollution,\n                split=split,\n                alias=air_pollution.name,\n                time_stamps=time_stamp,\n                horizon=horizon,\n                memory=memory,\n                lagged_targets=True,\n            )\n\n            for driver in benchmark_drivers:\n                with log_run(driver, ds_name, run, runs):\n                    try:\n                        observer = driver(\n                            observer=observer,\n                            time_series=time_series,\n                            min_chunksize=int(memory // frequency),\n                            num_features=1000,\n                            frequency=frequency,\n                        )\n                    except Exception as exp:\n                        print(f\"Failed {exp}\")\n    return observer\n\n\ndef benchmark_dodgers(\n    benchmark_drivers: Sequence[Callable], runs: int = 1\n) -> Benchmark:\n    ds_name = \"dodgers\"\n    source = \"https://archive.ics.uci.edu/ml/machine-learning-databases/event-detection/Dodgers.data\"\n    time_stamp = \"ds\"\n    frequency = getml.data.time.minutes(5)\n    horizon = getml.data.time.minutes(5)\n    memory = getml.data.time.hours(2)\n\n    observer = Benchmark(ds_name)\n\n    for run in range(1, runs + 1):\n        with project(f\"__benchmark_{ds_name}\"):\n            dodgers_pd = pd.read_csv(source, header=None, names=[\"ds\", \"y\"])\n            dodgers_pd.ds = pd.to_datetime(dodgers_pd.ds)\n            dodgers_pd = dodgers_pd[dodgers_pd.y > 0]\n            dodgers = getml.DataFrame.from_pandas(\n                dodgers_pd,\n                name=\"dodgers\",\n                roles={\n                    getml.data.roles.target: [\"y\"],\n                    getml.data.roles.time_stamp: [\"ds\"],\n                },\n            )\n\n            split = getml.data.split.time(\n                population=dodgers,\n                time_stamp=time_stamp,\n                test=getml.data.time.datetime(2005, 8, 20),\n            )\n\n            time_series = getml.data.TimeSeries(\n                population=dodgers,\n                split=split,\n                alias=dodgers.name,\n                time_stamps=time_stamp,\n                horizon=horizon,\n                memory=memory,\n                lagged_targets=True,\n            )\n\n            for driver in benchmark_drivers:\n                with log_run(driver, ds_name, run, runs):\n                    try:\n                        observer = driver(\n                            observer=observer,\n                            time_series=time_series,\n                            min_chunksize=int(memory // frequency),\n                            num_features=1000,\n                            frequency=frequency,\n                        )\n                    except Exception as exp:\n                        print(f\"Failed {exp}\")\n\n    return observer\n\n\ndef benchmark_energy(benchmark_drivers: Sequence[Callable], runs: int = 1) -> Benchmark:\n    ds_name = \"energy\"\n    time_stamp = \"date\"\n    frequency = getml.data.time.minutes(10)\n    horizon = getml.data.time.minutes(10)\n    memory = getml.data.time.hours(2)\n\n    observer = Benchmark(ds_name)\n\n    url = \"https://raw.githubusercontent.com/LuisM78/Appliances-energy-prediction-data/master/\"\n\n    data_frame = pd.concat(\n        [_load_csv(\"training.csv\", url), _load_csv(\"testing.csv\", url)]\n    )\n    data_frame.sort_values(by=[\"date\"])\n\n    for run in range(1, runs + 1):\n        with project(f\"__benchmark_{ds_name}\"):\n            energy: getml.DataFrame = getml.DataFrame.from_pandas(data_frame, \"energy\")\n            energy.set_role(\"date\", getml.data.roles.time_stamp)\n            energy.set_role(\"Appliances\", getml.data.roles.target)\n            energy.set_role(energy.roles.unused_float, getml.data.roles.numerical)\n\n            split = getml.data.split.time(\n                population=energy,\n                time_stamp=time_stamp,\n                test=getml.data.time.datetime(2016, 4, 1),\n            )\n\n            time_series = getml.data.TimeSeries(\n                population=energy,\n                split=split,\n                alias=\"energy\",\n                time_stamps=time_stamp,\n                horizon=horizon,\n                memory=memory,\n                lagged_targets=True,\n            )\n\n            for driver in benchmark_drivers:\n                with log_run(driver, ds_name, run, runs):\n                    try:\n                        observer = driver(\n                            observer=observer,\n                            time_series=time_series,\n                            min_chunksize=int(memory // frequency),\n                            num_features=2000,\n                            frequency=frequency,\n                        )\n                    except Exception as exp:\n                        print(f\"Failed {exp}\")\n\n    return observer\n\n\ndef benchmark_interstate94(benchmark_drivers: Sequence[Callable], runs=1) -> Benchmark:\n    ds_name = \"interstate94\"\n    time_stamp = \"ds\"\n    frequency = getml.data.time.hours(1)\n    horizon = getml.data.time.hours(1)\n    memory = getml.data.time.hours(24)\n\n    observer = Benchmark(ds_name)\n\n    for run in range(1, runs + 1):\n        with project(f\"__benchmark_{ds_name}\"):\n            traffic = getml.datasets.load_interstate94()\n            traffic = traffic.drop(traffic.roles.categorical)\n\n            split = getml.data.split.time(\n                traffic, time_stamp, test=getml.data.time.datetime(2018, 3, 15)\n            )\n\n            time_series = getml.data.TimeSeries(\n                population=traffic,\n                split=split,\n                alias=traffic.name,\n                time_stamps=time_stamp,\n                horizon=horizon,\n                memory=memory,\n                lagged_targets=True,\n            )\n\n            for driver in benchmark_drivers:\n                with log_run(driver, ds_name, run, runs):\n                    try:\n                        observer = driver(\n                            observer=observer,\n                            time_series=time_series,\n                            min_chunksize=int(memory // frequency),\n                            num_features=1000,\n                            frequency=frequency,\n                        )\n                    except Exception as exp:\n                        print(f\"Failed {exp}\")\n\n    return observer\n\n\ndef benchmark_tetuan(benchmark_drivers: Sequence[Callable], runs: int = 1) -> Benchmark:\n    ds_name = \"tetuan\"\n    time_stamp = \"DateTime\"\n    frequency = getml.data.time.minutes(10)\n    horizon = getml.data.time.minutes(10)\n    memory = getml.data.time.hours(2)\n\n    observer = Benchmark(ds_name)\n\n    url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00616/\"\n\n    raw_data = _load_csv(\"Tetuan%20City%20power%20consumption.csv\", url)\n    raw_data[\"DateTime\"] = [\n        _zero_padding(date_time) for date_time in raw_data[\"DateTime\"]\n    ]\n\n    for run in range(1, runs + 1):\n        with project(f\"__benchmark_{ds_name}\"):\n            tetuan: getml.DataFrame = getml.data.DataFrame.from_pandas(\n                raw_data, \"tetuan\"\n            )\n            tetuan.set_role(\n                \"DateTime\", getml.data.roles.time_stamp, time_formats=[\"%n/%e/%Y %H:%M\"]\n            )\n            tetuan.set_role([\"Zone 1 Power Consumption\"], getml.data.roles.target)\n            tetuan.set_role(tetuan.roles.unused_float, getml.data.roles.numerical)\n\n            split = getml.data.split.time(\n                population=tetuan,\n                time_stamp=time_stamp,\n                test=getml.data.time.datetime(2017, 11, 1),\n            )\n\n            time_series = getml.data.TimeSeries(\n                population=tetuan,\n                split=split,\n                alias=\"tetuan\",\n                time_stamps=time_stamp,\n                horizon=horizon,\n                memory=memory,\n                lagged_targets=True,\n            )\n\n            for driver in benchmark_drivers:\n                with log_run(driver, ds_name, run, runs):\n                    try:\n                        observer = driver(\n                            observer=observer,\n                            time_series=time_series,\n                            min_chunksize=int(memory // frequency),\n                            num_features=2000,\n                            frequency=frequency,\n                        )\n                    except Exception as exp:\n                        print(f\"Failed {exp}\")\n\n    return observer\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/__init__.py",
    "content": "from .benchmark import Benchmark\nfrom .cesiumml_builder import CesiumMLBuilder\nfrom .ft_time_series_builder import FTTimeSeriesBuilder\nfrom .kats_builder import KATSBuilder\nfrom .load import load_or_query, load_or_retrieve\nfrom .tsfel_builder import TSFELBuilder\nfrom .tsflex_builder import TsflexBuilder\nfrom .tsfresh_builder import TSFreshBuilder\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/add_original_columns.py",
    "content": "import numpy as np\n\n\ndef _add_original_columns(original_df, df_selected, cutoff=0):\n    cutoff = max(0, cutoff)\n    for colname in original_df.columns:\n        df_selected[colname] = np.asarray(original_df[colname])[cutoff:]\n    return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/benchmark.py",
    "content": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass, field\nfrom datetime import timedelta\nfrom time import perf_counter\nfrom typing import Any, Dict, Generator, List, Optional\n\nimport numpy as np\nfrom getml import Pipeline\nfrom getml.pipeline.metrics import _all_metrics\n\n\n@dataclass\nclass BenchmarkData:\n    ds_name: str\n    lib_name: str\n    num_features: List[int] = field(default_factory=list)\n    runtimes: List[Optional[timedelta]] = field(default_factory=list)\n    scores: List[Dict[str, float]] = field(default_factory=list)\n\n\n@dataclass\nclass Row:\n    ds_name: str\n    lib_name: str\n    num_features: int\n    runtime: Any\n    score: Dict[str, Any]\n\n    def to_list(\n        self, best_runtime: timedelta, best_runtime_per_feature: timedelta\n    ) -> List[Any]:\n        return [\n            self.ds_name,\n            self.lib_name,\n            self.num_features,\n            f\"{self.runtime}\",\n            f\"{self.runtime/self.num_features}\",\n            f\"{self.runtime/best_runtime}\",\n            f\"{(self.runtime/self.num_features)/best_runtime_per_feature}\",\n            {\n                name: f\"{val:.3f}\"\n                for name, val in self.score.items()\n                if name in _all_metrics\n            },\n        ]\n\n\ndef _make_row(data: BenchmarkData) -> Row:\n    runtimes = [runtime for runtime in data.runtimes if runtime is not None]\n    runtime = sum(runtimes, timedelta(0)) / float(len(runtimes))\n    return Row(\n        ds_name=data.ds_name,\n        lib_name=data.lib_name,\n        num_features=data.num_features[0],\n        runtime=runtime,\n        score=data.scores[0],\n    )\n\n\nclass Benchmark:\n    def __init__(self, ds_name: str) -> None:\n        self.ds_name = ds_name\n        self._data: Dict[str, Any] = {}\n\n    @contextmanager\n    def __call__(self, lib_name: str) -> Generator[None, None, None]:\n        with self.benchmark(lib_name):\n            yield\n\n    def __getattr__(self, lib_name) -> BenchmarkData:\n        try:\n            return self._data[lib_name]\n        except KeyError:\n            return super().__getattribute__(lib_name)\n\n    def __repr__(self) -> str:\n        template = \"{:15}  {:12}  {:>8}  {:14}  {:14}  {:20}  {:25} {}\"\n\n        rows = [_make_row(benchmark) for benchmark in self._data.values()]\n\n        best_runtime = min((row.runtime for row in rows))\n        best_runtime_per_feature = min((row.runtime / row.num_features for row in rows))\n\n        headers = [\n            \"ds_name\",\n            \"lib_name\",\n            \"num_feat\",\n            \"runtime\",\n            \"runtime/feat\",\n            \"runtime (normalized)\",\n            \"runtime/feat (normalized)\",\n            \"scores\",\n        ]\n\n        rows_as_list = [\n            row.to_list(best_runtime, best_runtime_per_feature) for row in rows\n        ]\n\n        head = template.format(*headers)\n        body = \"\\n\".join([template.format(*row) for row in rows_as_list])\n\n        return f\"{head}\\n{body}\"\n\n    @contextmanager\n    def _benchmark_runtime(self, lib_name: str) -> Generator[Benchmark, None, None]:\n        begin = perf_counter()\n        yield self\n        end = perf_counter()\n        self._data[lib_name].runtimes.append(timedelta(seconds=end - begin))\n        print(f\"Time taken: {self._data[lib_name].runtimes[-1]}\")\n\n    @contextmanager\n    def benchmark(self, lib_name: str) -> Generator[Benchmark, None, None]:\n        if lib_name not in self._data:\n            self._data[lib_name] = BenchmarkData(\n                ds_name=self.ds_name, lib_name=lib_name\n            )\n        with self._benchmark_runtime(lib_name) as timer:\n            yield timer\n\n    @property\n    def runtimes(self) -> Dict[str, List]:\n        return {lib_name: data.runtimes for lib_name, data in self._data.items()}\n\n    @property\n    def scores(self) -> Dict[str, List]:\n        return {lib_name: data.scores for lib_name, data in self._data.items()}\n\n    def write_scores(self, lib_name: str, pipe: Pipeline, num_features: int) -> None:\n        self._data[lib_name].num_features.append(num_features)\n        self._data[lib_name].scores.append(dict(pipe.scores[-1]))\n        print(f\"Performance achieved: {self._data[lib_name].scores[-1]}\")\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/cesiumml_builder.py",
    "content": "\"\"\"\nWrapper around CesiumML\n\"\"\"\n\nimport datetime\nimport time\nimport warnings\nfrom typing import Generator, List, NamedTuple\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom cesium import featurize  # type: ignore\nfrom cesium.features import feature_categories  # type: ignore\nfrom pandas.api.types import is_numeric_dtype  # type: ignore\nfrom scipy.stats import pearsonr  # type: ignore\n\nfrom .add_original_columns import _add_original_columns\nfrom .print_time_taken import _print_time_taken\nfrom .remove_target_column import _remove_target_column\n\n# ------------------------------------------------------------------\n\n\nclass _ChunkMaker(NamedTuple):\n    \"\"\"\n    Helpers class to create chunks of data frames.\n    \"\"\"\n\n    data_frame: pd.DataFrame\n    id_col: pd.Series\n    time_col: str\n    horizon: pd.Timedelta\n    memory: pd.Timedelta\n\n    def make_chunk(self, current_id: str, now: pd.Timedelta) -> pd.DataFrame:\n        \"\"\"\n        Generates a chunk of the data frame that\n        contains all rows within horizon and memory.\n\n        Used by roll_data_frame.\n        \"\"\"\n        begin = now - self.horizon - self.memory\n        end = now - self.horizon\n        return self.data_frame[\n            (self.id_col == current_id)\n            & (self.time_col > begin)\n            & (self.time_col <= end)\n        ]\n\n\n# ------------------------------------------------------------------\n\n\ndef _roll_data_frame(\n    data_frame: pd.DataFrame,\n    column_id: str,\n    time_stamp: str,\n    horizon: pd.Timedelta,\n    memory: pd.Timedelta,\n) -> Generator[pd.DataFrame, None, None]:\n    \"\"\"\n    Returns a generator that contains pd.DataFrame chunks\n    to be aggregated.\n    \"\"\"\n    id_col = data_frame[column_id]\n    time_col = pd.to_datetime(data_frame[time_stamp])\n    chunk_maker = _ChunkMaker(data_frame, id_col, time_col, horizon, memory)\n    return (\n        chunk_maker.make_chunk(row[column_id], pd.to_datetime(row[time_stamp]))\n        for _, row in data_frame.iterrows()\n    )\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunk(\n    chunk: pd.DataFrame, column_id: str, time_stamp: str, aggregations: List[str]\n) -> pd.DataFrame:\n    orig_colnames = [col for col in chunk.columns if col not in (time_stamp, column_id)]\n    times = [np.asarray(chunk[time_stamp])] * len(orig_colnames)\n    values = [np.asarray(chunk[col]) for col in orig_colnames]\n    if chunk.shape[0] > 1:\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            fset_cesium = featurize.featurize_time_series(\n                times=times, values=values, errors=None, features_to_use=aggregations\n            )\n            data = fset_cesium.values.flatten()\n    else:\n        data = [np.nan] * len(orig_colnames) * len(aggregations)\n    colnames = [\n        agg + \"(\" + cname + \")\" for cname in orig_colnames for agg in aggregations\n    ]\n    return pd.DataFrame({key: [value] for (key, value) in zip(colnames, data)})\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunks(\n    rolled: Generator[pd.DataFrame, None, None],\n    column_id: str,\n    time_stamp: str,\n    aggregations: List[str],\n) -> pd.DataFrame:\n    aggregated_chunks: List[pd.DataFrame] = [\n        _aggregate_chunk(chunk, column_id, time_stamp, aggregations) for chunk in rolled\n    ]\n    return pd.concat(aggregated_chunks, ignore_index=True).reset_index()\n\n\n# ------------------------------------------------------------------\n\n\nclass CesiumMLBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on CesiumML.\n\n    Args:\n        aggregations: The aggregations to use.\n\n        num_features: The (maximum) number of features to build.\n\n        horizon: The prediction horizon to use.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n    \"\"\"\n\n    cadence_aggs: List[str] = feature_categories[\"Cadence/Error\"]\n    general_aggs: List[str] = feature_categories[\"General\"]\n    periodic_aggs: List[str] = feature_categories[\"Lomb-Scargle (Periodic)\"]\n    all_aggs = cadence_aggs + general_aggs + periodic_aggs\n\n    def __init__(\n        self,\n        num_features: int,\n        horizon: pd.Timedelta,\n        memory: pd.Timedelta,\n        column_id: str,\n        time_stamp: str,\n        target: str,\n        aggregations: List[str] = general_aggs,\n        allow_lagged_targets: bool = False,\n        n_jobs=0,\n        min_chunksize=0,\n    ):\n        self.aggregations = aggregations\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.allow_lagged_targets = allow_lagged_targets\n\n        self._runtime = None\n        self.fitted = False\n        self.max_depth = 2\n\n        self.num_features_generated = 0\n        self.selected_features: List[int] = []\n\n    def _extract_features(self, data_frame):\n        data_frame = data_frame.reset_index()\n        del data_frame[\"index\"]\n        rolled = _roll_data_frame(\n            data_frame, self.column_id, self.time_stamp, self.horizon, self.memory\n        )\n        df_extracted = _aggregate_chunks(\n            rolled, self.column_id, self.time_stamp, self.aggregations\n        )\n        for col in df_extracted:\n            if is_numeric_dtype(df_extracted[col]):\n                df_extracted[col][df_extracted[col].isna()] = 0\n        return df_extracted\n\n    def _select_features(self, data_frame, target):\n        colnames = np.asarray(data_frame.columns)\n        print(\"Selecting the best out of \" + str(len(colnames)) + \" features...\")\n        colnames = np.asarray(\n            [\n                col\n                for col in colnames\n                if is_numeric_dtype(data_frame[col])\n                and np.var(np.asarray(data_frame[col])) > 0.0\n            ]\n        )\n        correlations = np.asarray(\n            [np.abs(pearsonr(target, data_frame[col]))[0] for col in colnames]\n        )\n        correlations[np.isnan(correlations) | np.isinf(correlations)] = 0.0\n\n        self.selected_features = colnames[np.argsort(correlations)][::-1][\n            : self.num_features\n        ]\n        self.num_features_generated = data_frame.shape[1]\n        return data_frame[self.selected_features]\n\n    def fit(self, data_frame):\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(\"CesiumML: Trying features...\")\n        begin = time.time()\n        target = np.asarray(data_frame[self.target])\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = self._select_features(df_extracted, target)\n        df_selected = _add_original_columns(data_frame, df_selected)\n        end = time.time()\n        _print_time_taken(begin, end)\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return df_selected\n\n    @property\n    def runtime(self):\n        if self.fitted:\n            return self._runtime\n\n    def transform(self, data_frame):\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = df_extracted[self.selected_features]\n        df_selected = _add_original_columns(data_frame, df_selected)\n        return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/ft_time_series_builder.py",
    "content": "import datetime\nimport time\n\nimport featuretools as ft\nimport numpy as np\nimport pandas as pd\nfrom pandas.api.types import is_numeric_dtype\nfrom scipy.stats import pearsonr\n\nfrom .add_original_columns import _add_original_columns\nfrom .print_time_taken import _print_time_taken\nfrom .remove_target_column import _remove_target_column\n\n# ------------------------------------------------------------------\n\n\nclass _ChunkMaker:\n    \"\"\"\n    Helpers class to create chunks of data frames.\n    \"\"\"\n\n    def __init__(self, data_frame, id_col, time_col, horizon, memory):\n        self.data_frame = data_frame\n        self.id_col = id_col\n        self.time_col = time_col\n        self.horizon = horizon\n        self.memory = memory\n\n    def make_chunk(self, current_id, now, index):\n        \"\"\"\n        Generates a chunk of the data frame that\n        contains all rows within horizon and memory.\n\n        Used by roll_data_frame.\n        \"\"\"\n        begin = now - self.horizon - self.memory\n        end = now - self.horizon\n        chunk = self.data_frame[\n            (self.id_col == current_id)\n            & (self.time_col > begin)\n            & (self.time_col <= end)\n        ]\n        chunk[\"_featuretools_join_key\"] = int(index)\n        return chunk\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_entity_set(data_frame, rolled, time_stamp):\n    relationships = [\n        (\"population\", \"_featuretools_index\", \"peripheral\", \"_featuretools_join_key\")\n    ]\n\n    dataframes = {\n        \"population\": (\n            data_frame,\n            \"_featuretools_index\",\n            time_stamp,\n        ),\n        \"peripheral\": (\n            rolled,\n            \"_featuretools_index\",\n            time_stamp,\n        ),\n    }\n\n    return ft.EntitySet(\"self-join-entity-set\", dataframes, relationships)\n\n\n# ------------------------------------------------------------------\n\n\ndef _roll_data_frame(data_frame, column_id, time_stamp, horizon, memory):\n    \"\"\"\n    Duplicates data so that it matches the format\n    required by featuretools.\n    \"\"\"\n    id_col = data_frame[column_id]\n    time_col = pd.to_datetime(data_frame[time_stamp])\n    chunk_maker = _ChunkMaker(data_frame, id_col, time_col, horizon, memory)\n    chunks = [\n        chunk_maker.make_chunk(row[column_id], pd.to_datetime(row[time_stamp]), index)\n        for index, row in data_frame.iterrows()\n    ]\n    rolled = pd.concat(chunks, ignore_index=True).reset_index()\n    rolled[\"_featuretools_index\"] = np.arange(rolled.shape[0])\n    return rolled\n\n\n# ------------------------------------------------------------------\n\n\nclass FTTimeSeriesBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on featuretools.\n\n    Args:\n\n        num_features: The (maximum) number of features to build.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n    \"\"\"\n\n    all_primitives = ft.list_primitives()\n    agg_primitives = all_primitives[all_primitives.type == \"aggregation\"].name.tolist()\n    trans_primitives = all_primitives[all_primitives.type == \"transform\"].name.tolist()\n\n    def __init__(\n        self,\n        num_features,\n        horizon,\n        memory,\n        column_id,\n        time_stamp,\n        target,\n        allow_lagged_targets=False,\n        min_chunksize=0,\n        n_jobs=1,\n    ):\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.allow_lagged_targets = allow_lagged_targets\n        self.n_jobs = n_jobs\n\n        self._runtime = None\n        self.fitted = False\n        self.max_depth = 2\n\n        self.num_features_generated = 0\n        self.selected_features = []\n\n    def _extract_features(self, data_frame):\n        data_frame = data_frame.reset_index()\n        del data_frame[\"index\"]\n        rolled = _roll_data_frame(\n            data_frame, self.column_id, self.time_stamp, self.horizon, self.memory\n        )\n\n        data_frame[\"_featuretools_index\"] = np.arange(data_frame.shape[0])\n\n        entityset = _make_entity_set(data_frame, rolled, self.time_stamp)\n        df_extracted, _ = ft.dfs(\n            entityset=entityset,\n            agg_primitives=self.agg_primitives,\n            target_dataframe_name=\"population\",\n            max_depth=self.max_depth,\n            ignore_columns={\n                \"peripheral\": [\n                    self.column_id,\n                    \"index\",\n                    \"join_key\",\n                    \"_featuretools_join_key\",\n                    \"_featuretools_index\",\n                ]\n            },\n            n_jobs=self.n_jobs,\n        )\n\n        for col in df_extracted:\n            if is_numeric_dtype(df_extracted[col]):\n                df_extracted[col][df_extracted[col].isna()] = 0\n\n        return df_extracted\n\n    def _select_features(self, data_frame, target):\n        colnames = np.asarray(data_frame.columns)\n        colnames = np.asarray(\n            [\n                col\n                for col in colnames\n                if is_numeric_dtype(data_frame[col])\n                and np.var(np.asarray(data_frame[col])) > 0.0\n            ]\n        )\n        correlations = np.asarray(\n            [np.abs(pearsonr(target, data_frame[col]))[0] for col in colnames]\n        )\n        correlations[np.isnan(correlations) | np.isinf(correlations)] = 0.0\n\n        self.selected_features = colnames[np.argsort(correlations)][::-1][\n            : self.num_features\n        ]\n        self.num_features_generated = data_frame.shape[1]\n        return data_frame[self.selected_features]\n\n    def fit(self, data_frame):\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(\"featuretools: Trying features...\")\n        begin = time.time()\n        target = np.asarray(data_frame[self.target])\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = self._select_features(df_extracted, target)\n        df_selected = _add_original_columns(data_frame, df_selected)\n        end = time.time()\n        _print_time_taken(begin, end)\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return df_selected\n\n    @property\n    def runtime(self):\n        if self.fitted:\n            return self._runtime\n\n    def transform(self, data_frame):\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = df_extracted[self.selected_features]\n        df_selected = _add_original_columns(data_frame, df_selected)\n        return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/kats_builder.py",
    "content": "\"\"\"\nWrapper around KATS.tsfeatures\n\"\"\"\n\nimport datetime\nimport itertools as it\nimport logging\nimport time\nimport warnings\nfrom contextlib import contextmanager\nfrom typing import Dict, Generator, List, NamedTuple, Optional, Union\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom kats.consts import TimeSeriesData\nfrom kats.tsfeatures.tsfeatures import TsFeatures\nfrom pandas.api.types import is_numeric_dtype  # type: ignore\nfrom scipy.stats import pearsonr\n\nfrom .add_original_columns import _add_original_columns\nfrom .print_time_taken import _print_time_taken\nfrom .remove_target_column import _remove_target_column\n\nKATSAggs = Dict[str, List[str]]\n\nALL_AGGS: KATSAggs = TsFeatures().feature_group_mapping\n\n\n@contextmanager\ndef disable_logging(highest_level=logging.CRITICAL):\n    previous_level = logging.root.manager.disable\n\n    logging.disable(highest_level)\n\n    try:\n        yield\n    finally:\n        logging.disable(previous_level)\n\n\n# ------------------------------------------------------------------\n\n\nclass _ChunkMaker(NamedTuple):\n    \"\"\"\n    Helpers class to create chunks of data frames.\n    \"\"\"\n\n    data_frame: pd.DataFrame\n    id_col: pd.Series\n    time_col: pd.Series\n    horizon: pd.Timedelta\n    memory: pd.Timedelta\n    min_chunksize: int\n\n    def make_chunk(self, current_id: str, now: pd.Timedelta) -> pd.DataFrame:\n        \"\"\"\n        Generates a chunk of the data frame that\n        contains all rows within horizon and memory.\n\n        Used by roll_data_frame.\n        \"\"\"\n        begin = now - self.horizon - self.memory\n        end = now - self.horizon\n        chunk = self.data_frame[\n            (self.id_col == current_id)\n            & (self.time_col > begin)\n            & (self.time_col <= end)\n        ]\n        return (\n            chunk\n            if len(chunk) >= self.min_chunksize\n            else pd.DataFrame(columns=self.data_frame.columns)\n        )\n\n\ndef _flatten_aggs(aggs: KATSAggs, domains: Optional[List[str]] = None) -> List[str]:\n    domains = domains or list(aggs.keys())\n    return list(it.chain(*aggs.values()))\n\n\ndef _get_aggs_by_domain(aggs: KATSAggs, domain: str) -> KATSAggs:\n    return {name: aggs for name, aggs in ALL_AGGS.items() if name == domain}\n\n\n# ------------------------------------------------------------------\n\n\ndef _roll_data_frame(\n    data_frame: pd.DataFrame,\n    column_id: str,\n    time_stamp: str,\n    horizon: pd.Timedelta,\n    memory: pd.Timedelta,\n    min_chunksize: int,\n) -> Generator[pd.DataFrame, None, None]:\n    \"\"\"\n    Returns a generator that contains pd.DataFrame chunks\n    to be aggregated.\n    \"\"\"\n    id_col = data_frame[column_id]\n    time_col = pd.to_datetime(data_frame[time_stamp])\n    chunk_maker = _ChunkMaker(\n        data_frame, id_col, time_col, horizon, memory, min_chunksize\n    )\n    return (\n        chunk_maker.make_chunk(row[column_id], pd.to_datetime(row[time_stamp]))\n        for _, row in data_frame.iterrows()\n    )\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunk(\n    chunk: pd.DataFrame,\n    column_id: str,\n    time_stamp: str,\n    aggregations: KATSAggs,\n) -> pd.DataFrame:\n    orig_colnames = [col for col in chunk.columns if col not in (time_stamp, column_id)]\n    data = []\n    for col in orig_colnames:\n        colnames = [f\"{name} ({col})\" for name in _flatten_aggs(aggregations)]\n        if chunk.shape[0] > 1:\n            chunk_ts = TimeSeriesData(\n                chunk[[col, time_stamp]].rename(columns={time_stamp: \"time\"})\n            )\n            with warnings.catch_warnings():\n                warnings.simplefilter(\"ignore\")\n                with disable_logging():\n                    features_chunk = pd.DataFrame(\n                        data=[\n                            TsFeatures(\n                                selected_features=_flatten_aggs(aggregations)\n                            ).transform(chunk_ts)\n                        ],\n                    )\n                    features_chunk.columns = colnames\n        else:\n            features_chunk = pd.DataFrame(columns=colnames)\n        data.append(features_chunk)\n\n    return pd.concat(data, axis=1)\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunks(\n    rolled: Generator[pd.DataFrame, None, None],\n    column_id: str,\n    time_stamp: str,\n    aggregations: KATSAggs,\n) -> pd.DataFrame:\n    aggregated_chunks: List[pd.DataFrame] = [\n        _aggregate_chunk(chunk, column_id, time_stamp, aggregations) for chunk in rolled\n    ]\n    return (\n        pd.concat(aggregated_chunks, ignore_index=True)\n        .reset_index()\n        .drop(\"index\", axis=1)\n    )\n\n\n# ------------------------------------------------------------------\n\n\nclass KATSBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on TSFEL.\n\n    Args:\n        aggregations: The aggregations to use.\n\n        num_features: The (maximum) number of features to build.\n\n        horizon: The prediction horizon to use.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n    \"\"\"\n\n    stl_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"stl_features\")\n    level_shift_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"level_shift_features\")\n    holt_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"holt_params\")\n    hw_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"hw_params\")\n    statistics_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"statistics\")\n    cusum_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"cusum_detector\")\n    robust_stat_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"robust_stat_detector\")\n    bocp_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"bocp_detector\")\n    outlier_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"outlier_detector\")\n    trend_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"trend_detector\")\n    nowcasting_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"nowcasting\")\n    seasonalities_aggs: KATSAggs = _get_aggs_by_domain(ALL_AGGS, \"seasonalities\")\n\n    all_aggs: KATSAggs = {\n        **stl_aggs,\n        **level_shift_aggs,\n        **holt_aggs,\n        **hw_aggs,\n        **statistics_aggs,\n        **cusum_aggs,\n        **robust_stat_aggs,\n        **bocp_aggs,\n        **outlier_aggs,\n        **trend_aggs,\n        **nowcasting_aggs,\n        **seasonalities_aggs,\n    }\n\n    default_aggs: KATSAggs = {\n        **statistics_aggs,\n        **cusum_aggs,\n        **seasonalities_aggs,\n    }\n\n    def __init__(\n        self,\n        num_features: int,\n        horizon: pd.Timedelta,\n        memory: pd.Timedelta,\n        column_id: str,\n        time_stamp: str,\n        target: str,\n        aggregations: Optional[KATSAggs] = None,\n        allow_lagged_targets: bool = False,\n        min_chunksize: int = 0,\n        n_jobs: int = 1,\n    ) -> None:\n        self.aggregations = aggregations or self.default_aggs\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.allow_lagged_targets = allow_lagged_targets\n\n        self._runtime = None\n        self.fitted = False\n        self.max_depth = 2\n        self.min_chunksize = min_chunksize\n\n        self.num_features_generated = 0\n        self.selected_features: List[int] = []\n\n    def _extract_features(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        data_frame = data_frame.reset_index()\n        del data_frame[\"index\"]\n        rolled = _roll_data_frame(\n            data_frame,\n            self.column_id,\n            self.time_stamp,\n            self.horizon,\n            self.memory,\n            self.min_chunksize,\n        )\n        df_extracted = _aggregate_chunks(\n            rolled, self.column_id, self.time_stamp, self.aggregations\n        )\n        for col in df_extracted:\n            try:\n                df_extracted[col][df_extracted[col].isna()] = 0.0\n                df_extracted[col][np.isinf(df_extracted[col])] = 0.0\n            except TypeError:\n                pass\n        return df_extracted\n\n    def _select_features(\n        self, data_frame: pd.DataFrame, target: Union[pd.Series, np.ndarray]\n    ) -> pd.DataFrame:\n        colnames = np.asarray(data_frame.columns)\n        print(\"Selecting the best out of \" + str(len(colnames)) + \" features...\")\n        colnames = np.asarray(\n            [col for col in colnames if np.var(np.asarray(data_frame[col])) > 0.0]\n        )\n        correlations = np.asarray(\n            [np.abs(pearsonr(target, data_frame[col]))[0] for col in colnames]\n        )\n        correlations[np.isnan(correlations) | np.isinf(correlations)] = 0.0\n        self.selected_features = colnames[np.argsort(correlations)][::-1][\n            : self.num_features\n        ]\n        self.num_features_generated = data_frame.shape[1]\n        return data_frame[self.selected_features]\n\n    def fit(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(f\"KATS: Trying features...\")\n        begin = time.time()\n        target = np.asarray(data_frame[self.target])\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        cutoff = len(target) - len(df_extracted)\n        df_selected = self._select_features(df_extracted, target[cutoff:])\n        df_selected = _add_original_columns(\n            df_for_extraction,\n            df_selected,\n            cutoff=df_for_extraction.shape[0] - df_selected.shape[0],\n        )\n        end = time.time()\n        _print_time_taken(begin, end)\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return df_selected\n\n    @property\n    def runtime(self) -> Union[None, datetime.timedelta]:\n        if self.fitted:\n            return self._runtime\n\n    def transform(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = df_extracted[self.selected_features]\n        cutoff = len(df_for_extraction) - len(df_selected)\n        df_selected = _add_original_columns(\n            df_for_extraction,\n            df_selected,\n            cutoff=df_for_extraction.shape[0] - df_selected.shape[0],\n        )\n        return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/load.py",
    "content": "from pathlib import Path\n\nimport getml\n\n\ndef load_or_query(conn, name, **kwargs):\n    \"\"\"\n    Loads the data from disk (the project folder) if present, if not, queries it from\n    the database associated with `conn`.\n\n    `kwargs` are passed to `getml.data.DataFrame.from_db`.\n    \"\"\"\n\n    if not getml.data.exists(name):\n        print(f\"Querying {name!r} from {conn.dbname!r}...\")\n        df = getml.DataFrame.from_db(name=name, table_name=name, conn=conn, **kwargs)\n        df.save()\n    else:\n        print(f\"Loading {name!r} from disk (project folder).\")\n        df = getml.data.load_data_frame(name)\n\n    print()\n\n    return df\n\n\ndef load_or_retrieve(csv_file, name=None, **kwargs):\n    \"\"\"\n    Loads the data from disk (the project folder) if present, if not, retrieves the\n    and reads the `csv_file`.\n\n    `kwargs` are passed to `getml.data.DataFrame.from_csv`.\n\n    If no name is supplied, the df's name is inferred from the filename.\n    \"\"\"\n\n    if name is None:\n        name = Path(csv_file).stem\n\n    if not getml.data.exists(name):\n        df = getml.DataFrame.from_csv(fnames=csv_file, name=name, **kwargs)\n        df.save()\n    else:\n        print(f\"Loading {name!r} from disk (project folder).\")\n        df = getml.data.load_data_frame(name)\n\n    print()\n\n    return df\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/print_time_taken.py",
    "content": "def _print_time_taken(begin, end):\n    seconds = end - begin\n\n    hours = int(seconds / 3600)\n    seconds -= float(hours * 3600)\n\n    minutes = int(seconds / 60)\n    seconds -= float(minutes * 60)\n\n    seconds = round(seconds, 6)\n\n    print(\"Time taken: \" + str(hours) + \"h:\" + str(minutes) + \"m:\" + str(seconds))\n\n    print(\"\")\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/remove_target_column.py",
    "content": "import numpy as np\nimport pandas as pd\n\n\ndef _remove_target_column(data_frame: pd.DataFrame, target: str) -> pd.DataFrame:\n    colnames = np.asarray(data_frame.columns)\n    if target not in colnames:\n        return data_frame\n    colnames = colnames[colnames != target]\n    return data_frame[list(colnames)]\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/rolling.py",
    "content": "\"\"\"\nUtilties for rolling data frames.\n\"\"\"\n\nimport warnings\nfrom typing import Generic, Iterator, NamedTuple, Optional, TypeVar\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.lib.stride_tricks import as_strided\nfrom typing_extensions import TypeGuard\n\nIndexT = TypeVar(\"IndexT\", pd.Timestamp, int)\nRangeT = TypeVar(\"RangeT\", pd.Timedelta, int)\n\n\ndef _is_int(val: object) -> TypeGuard[int]:\n    return isinstance(val, int)\n\n\ndef _is_timedelta(val: object) -> TypeGuard[pd.Timedelta]:\n    return isinstance(val, (pd.Timedelta))\n\n\nclass _ChunkMaker(NamedTuple):\n    \"\"\"\n    Helpers class to create chunks of data frames.\n    \"\"\"\n\n    data_frame: pd.DataFrame\n    horizon: RangeT\n    memory: RangeT\n    min_chunksize: int\n\n    def make_chunk(self, now: IndexT) -> pd.DataFrame:\n        \"\"\"\n        Generates a chunk of the data frame that\n        contains all rows within horizon and memory.\n\n        Used by roll_data_frame.\n        \"\"\"\n\n        index_0 = self.data_frame.index[0]\n\n        begin = now - self.horizon - self.memory\n        end = now - self.horizon\n\n        if isinstance(self.data_frame.index, pd.RangeIndex):\n            begin = max(index_0, begin)\n            end = max(index_0, end)\n\n        # we want to exclude the lower bound we use a hard (iloc) slice to make this\n        # independent from the frequency, so we have to ensure that the chunk actually\n        # contains the lower bound\n        if _is_timedelta(self.memory):\n            if not begin < index_0:\n                return self.data_frame[begin:end][1:]\n\n        return self.data_frame[begin:end]\n\n\n# ------------------------------------------------------------\n\n\ndef _roll_data_frame(\n    data_frame: pd.DataFrame,\n    time_stamp: Optional[str] = None,\n    horizon: RangeT = 1,\n    memory: RangeT = 10,\n    min_chunksize: int = 1,\n) -> Iterator[pd.DataFrame]:\n    \"\"\"\n    Returns an iterator that yields pd.DataFrame chunks\n    to be aggregated.\n    \"\"\"\n    if len(data_frame) < min_chunksize + 1:\n        raise ValueError\n\n    if time_stamp:\n        data_frame = data_frame.set_index(time_stamp)\n\n    chunk_maker = _ChunkMaker(data_frame, horizon, memory, min_chunksize)\n\n    # when working with slices, pandas' .loc includes its upper bound while .iloc does\n    # not. we need to correct for that.\n    if isinstance(data_frame.index, pd.DatetimeIndex):\n        # the following indirection is nessecary because we cannot correct the value of\n        # memory inside make_chunk (to exclude one of the bounds) without knowing the\n        # time stamp's frequency. we shift the index by 1 to exclude the upper bound\n        # (at the cost of including one observation beyond the lower bound). the shift\n        # is necessery to allow the index run though its end. within make_chunk, we will\n        # chop off the first observation if appropriate\n        offset = data_frame.index[min_chunksize - 1] + horizon\n        return (chunk_maker.make_chunk(now) for now in data_frame[offset:].index)\n\n    else:\n        return (\n            chunk_maker.make_chunk(i)\n            for i in range(min_chunksize + horizon, len(data_frame) + 1)\n        )\n\n\n# ------------------------------------------------------------\n\n\ndef _roll_strided(arr: np.ndarray, horizon: int, memory: int) -> np.ndarray:\n    stride = arr.strides[0]\n    arr_strided = as_strided(\n        arr,\n        shape=(len(arr) - memory - horizon, memory + horizon),\n        strides=(stride, stride),\n        writeable=False,\n    )\n    if horizon > 0:\n        return arr_strided[:, horizon:]\n    return arr_strided\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/tsfel_builder.py",
    "content": "\"\"\"\nWrapper around TSFEL\n\"\"\"\n\nimport datetime\nimport time\nimport warnings\nfrom typing import Dict, Iterator, List, Optional, TypedDict, Union\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom pandas.api.types import is_numeric_dtype  # type: ignore\nfrom tsfel.feature_extraction.calc_features import calc_window_features\nfrom tsfel.feature_extraction.features_settings import (\n    get_features_by_domain,  # type: ignore\n)\n\nfrom .print_time_taken import _print_time_taken\nfrom .rolling import _roll_data_frame\n\n\nclass TSFELAgg(TypedDict):\n    \"\"\"\n    A typed dict holding meta data for a certain TSFEL aggregation.\n    \"\"\"\n\n    complexity: str\n    description: str\n    function: str\n    parameters: Union[str, Dict[str, Union[str, int]]]\n    n_features: int\n    use: str\n    tag: Union[str, List[str]]\n\n\nTSFELAggs = Dict[str, Dict[str, TSFELAgg]]\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunk(\n    chunk: pd.DataFrame,\n    column_id: str,\n    time_stamp: str,\n    aggregations: TSFELAggs,\n    n_jobs: Optional[int],\n) -> pd.DataFrame:\n    orig_colnames = [col for col in chunk.columns if col not in (time_stamp, column_id)]\n\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\")\n        return calc_window_features(\n            signal_window=chunk[orig_colnames],\n            dict_features=aggregations,\n            window_size=len(chunk),\n            verbose=0,\n            fs=100,\n            n_jobs=n_jobs,\n        )\n\n\n# -------------------------------------------------------------------\n\n\ndef _aggregate_chunks(\n    rolled: Iterator[pd.DataFrame],\n    column_id: str,\n    time_stamp: str,\n    aggregations: TSFELAggs,\n    n_jobs: Optional[int],\n) -> pd.DataFrame:\n    aggregated_chunks: List[pd.DataFrame] = [\n        _aggregate_chunk(chunk, column_id, time_stamp, aggregations, n_jobs)\n        for chunk in rolled\n        if len(chunk) > 5\n    ]\n    return pd.concat(aggregated_chunks, ignore_index=True).reset_index(drop=True)\n\n\n# -------------------------------------------------------------------\n\n\ndef _flatten_aggs(\n    aggs: TSFELAggs, domains: Optional[List[str]] = None\n) -> Dict[str, TSFELAgg]:\n    domains = domains or list(aggs.keys())\n    return {\n        name: {**values, **{\"domain\": domain}}\n        for domain, agg in aggs.items()\n        for name, values in agg.items()\n        if domain in domains\n    }\n\n\n# ------------------------------------------------------------------\n\n\n# TODO: Complete? Check for additional params\ndef _infer_required_obs(aggs: TSFELAggs) -> int:\n    try:\n        n_coeff = int(_flatten_aggs(aggs)[\"LPCC\"][\"parameters\"][\"n_coeff\"])\n    except KeyError:\n        n_coeff = 1\n\n    try:\n        d = int(_flatten_aggs(aggs)[\"ECDF\"][\"parameters\"][\"d\"])\n    except KeyError:\n        d = 1\n\n    return max(n_coeff, d)\n\n\n# ------------------------------------------------------------------\n\n\nclass TSFELBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on TSFEL.\n\n    Args:\n        num_features: The (maximum) number of features to build.\n\n        horizon: The prediction horizon to use.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n\n        aggregations: The aggregations to use.\n\n        allow_lagged_targets: Whether to build features based on lagged targets\n\n        min_chunksize: The minimum size of chunks to aggregate over\n\n    \"\"\"\n\n    statistical_aggs: TSFELAggs = get_features_by_domain(\"statistical\")\n    temporal_aggs: TSFELAggs = get_features_by_domain(\"temporal\")\n    # spectral_aggs: TSFELAggs = tsfel.get_features_by_domain(\"spectral\")\n    # all_aggs: TSFELAggs = tsfel.get_features_by_domain()\n    all_aggs: TSFELAggs = {**statistical_aggs, **temporal_aggs}\n\n    def __init__(\n        self,\n        num_features: int,\n        horizon: pd.Timedelta,\n        memory: pd.Timedelta,\n        column_id: str,\n        time_stamp: str,\n        target: str,\n        aggregations: Optional[TSFELAggs] = None,\n        allow_lagged_targets: bool = False,\n        min_chunksize: Optional[int] = None,\n        n_jobs: Optional[int] = None,\n    ) -> None:\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.aggregations = aggregations or self.all_aggs\n        self.allow_lagged_targets = allow_lagged_targets\n        self.min_chunksize = min_chunksize or _infer_required_obs(self.aggregations)\n        self.n_jobs = None if n_jobs == 1 else n_jobs\n\n        self._runtime = None\n        self.fitted = False\n\n        self.num_features_generated = 0\n        self.selected_features: List[int] = []\n\n    def _extract_features(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        data_frame = data_frame.select_dtypes(\n            include=[np.number, np.datetime64]\n        ).reset_index(drop=True)\n        rolled = _roll_data_frame(\n            data_frame, self.time_stamp, self.horizon, self.memory, self.min_chunksize\n        )\n        extracted = _aggregate_chunks(\n            rolled, self.column_id, self.time_stamp, self.aggregations, self.n_jobs\n        )\n        for col in extracted:\n            if is_numeric_dtype(extracted[col]):\n                extracted[col][extracted[col].isna()] = 0\n        return extracted\n\n    def _select_features(\n        self, data_frame: pd.DataFrame, target: Union[pd.Series, np.ndarray]\n    ) -> pd.DataFrame:\n        print(f\"Selecting the best out of {data_frame.shape[1]} features...\")\n\n        if not self.selected_features:\n            data_frame = data_frame.loc[:, (data_frame != data_frame.iloc[0]).any()]\n            correlations = data_frame.corrwith(pd.Series(target)).abs()\n            correlations = correlations.replace([np.inf, np.nan], 0)\n            correlations = correlations.sort_values(ascending=False)\n            self.selected_features = correlations.index[: self.num_features].tolist()\n\n        self.num_features_generated = data_frame.shape[1]\n        return data_frame[self.selected_features]\n\n    def fit(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(\"TSFEL: Trying features...\")\n        begin = time.time()\n        grouped = data_frame.groupby(self.column_id)\n        extracted = []\n        other = []\n        for _, group in grouped:\n            to_extract = group if self.allow_lagged_targets else group.drop(self.target)\n            extracted.append(self._extract_features(to_extract))\n            other.append(group[self.min_chunksize :])\n        extracted = pd.concat(extracted)\n        other = pd.concat(other).reset_index(drop=True)\n        selected = self._select_features(extracted, other[self.target])\n        selected = pd.concat([selected, other], axis=1)\n        end = time.time()\n        _print_time_taken(begin, end)\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return selected\n\n    @property\n    def runtime(self) -> Union[None, datetime.timedelta]:\n        if self.fitted:\n            return self._runtime\n\n    def transform(self, data_frame):\n        \"\"\"\n        Transforms the raw data into a set of features.\n        \"\"\"\n\n        return self.fit(data_frame)\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/tsfel_builder_roll.py",
    "content": "\"\"\"\nWrapper around TSFEL\n\"\"\"\n\nimport datetime\nimport time\nimport warnings\nfrom typing import Dict, Generator, List, NamedTuple, Optional, TypedDict, Union\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nimport tsfel\nfrom pandas.api.types import is_numeric_dtype  # type: ignore\nfrom scipy.stats import pearsonr  # type: ignore\n\nfrom .add_original_columns import _add_original_columns\nfrom .print_time_taken import _print_time_taken\nfrom .remove_target_column import _remove_target_column\nfrom .rolling import _roll_data_frame\n\n\nclass TSFELAgg(TypedDict):\n    \"\"\"\n    A typed dict holding meta data for a certain TSFEL aggregation.\n    \"\"\"\n\n    complexity: str\n    description: str\n    function: str\n    parameters: Union[str, Dict[str, Union[str, int]]]\n    n_features: int\n    use: str\n    tag: Union[str, List[str]]\n\n\nTSFELAggs = Dict[str, Dict[str, TSFELAgg]]\n\n# ------------------------------------------------------------------\n\n\ndef _flatten_aggs(\n    aggs: TSFELAggs, domains: Optional[List[str]] = None\n) -> Dict[str, TSFELAgg]:\n    domains = domains or list(aggs.keys())\n    return {\n        name: {**values, **{\"domain\": domain}}\n        for domain, agg in aggs.items()\n        for name, values in agg.items()\n        if domain in domains\n    }\n\n\n# ------------------------------------------------------------------\n\n\n# TODO: Complete? Check for additional params\ndef _infer_required_obs(aggs: TSFELAggs) -> int:\n    try:\n        n_coeff = int(_flatten_aggs(aggs)[\"LPCC\"][\"parameters\"][\"n_coeff\"])\n    except KeyError:\n        n_coeff = 1\n\n    try:\n        d = int(_flatten_aggs(aggs)[\"ECDF\"][\"parameters\"][\"d\"])\n    except KeyError:\n        d = 1\n\n    return max(n_coeff, d)\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunk(\n    chunk: pd.DataFrame,\n    column_id: str,\n    time_stamp: str,\n    aggregations: TSFELAggs,\n) -> pd.DataFrame:\n    orig_colnames = [col for col in chunk.columns if col not in (time_stamp, column_id)]\n    if chunk.shape[0] > 1:\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            data = tsfel.time_series_features_extractor(\n                aggregations,\n                chunk[orig_colnames],\n                verbose=0,\n            ).values.T.flatten()\n    else:\n        data = []\n    colnames = [\n        agg + \"(\" + cname + \")\"\n        for cname in orig_colnames\n        for agg in _flatten_aggs(aggregations).keys()\n    ]\n    return pd.DataFrame({key: [value] for (key, value) in zip(colnames, data)})\n\n\n# ------------------------------------------------------------------\n\n\ndef _aggregate_chunks(\n    rolled: Generator[pd.DataFrame, None, None],\n    column_id: str,\n    time_stamp: str,\n    aggregations: TSFELAggs,\n) -> pd.DataFrame:\n    aggregated_chunks: List[pd.DataFrame] = [\n        _aggregate_chunk(chunk, column_id, time_stamp, aggregations) for chunk in rolled\n    ]\n    return pd.concat(aggregated_chunks, ignore_index=True).reset_index()\n\n\n# ------------------------------------------------------------------\n\n\nclass TSFELBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on TSFEL.\n\n    Args:\n        aggregations: The aggregations to use.\n\n        num_features: The (maximum) number of features to build.\n\n        horizon: The prediction horizon to use.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n    \"\"\"\n\n    statistical_aggs: TSFELAggs = tsfel.get_features_by_domain(\"statistical\")\n    temporal_aggs: TSFELAggs = tsfel.get_features_by_domain(\"temporal\")\n    spectral_aggs: TSFELAggs = tsfel.get_features_by_domain(\"spectral\")\n    all_aggs: TSFELAggs = tsfel.get_features_by_domain()\n\n    def __init__(\n        self,\n        num_features: int,\n        horizon: pd.Timedelta,\n        memory: pd.Timedelta,\n        column_id: str,\n        time_stamp: str,\n        target: str,\n        aggregations: Optional[TSFELAggs] = None,\n        allow_lagged_targets: bool = False,\n    ) -> None:\n        self.aggregations = aggregations or self.all_aggs\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.allow_lagged_targets = allow_lagged_targets\n        self.min_chunksize = _infer_required_obs(self.aggregations)\n\n        self._runtime = None\n        self.fitted = False\n        self.max_depth = 2\n\n        self.num_features_generated = 0\n        self.selected_features: List[int] = []\n\n    def _extract_features(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        data_frame = data_frame.reset_index()\n        del data_frame[\"index\"]\n        rolled = _roll_data_frame(\n            data_frame,\n            self.time_stamp,\n            self.horizon,\n            self.memory,\n            self.min_chunksize,\n        )\n        df_extracted = _aggregate_chunks(\n            rolled, self.column_id, self.time_stamp, self.aggregations\n        )\n        for col in df_extracted:\n            if is_numeric_dtype(df_extracted[col]):\n                df_extracted[col][df_extracted[col].isna()] = 0\n        return df_extracted\n\n    def _select_features(\n        self, data_frame: pd.DataFrame, target: Union[pd.Series, np.ndarray]\n    ) -> pd.DataFrame:\n        colnames = np.asarray(data_frame.columns)\n        print(\"Selecting the best out of \" + str(len(colnames)) + \" features...\")\n        colnames = np.asarray(\n            [\n                col\n                for col in colnames\n                if is_numeric_dtype(data_frame[col])\n                and np.var(np.asarray(data_frame[col])) > 0.0\n            ]\n        )\n        correlations = np.asarray(\n            [np.abs(pearsonr(target, data_frame[col]))[0] for col in colnames]\n        )\n        correlations[np.isnan(correlations) | np.isinf(correlations)] = 0.0\n\n        self.selected_features = colnames[np.argsort(correlations)][::-1][\n            : self.num_features\n        ]\n        self.num_features_generated = data_frame.shape[1]\n        return data_frame[self.selected_features]\n\n    def fit(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(\"TSFEL: Trying features...\")\n        begin = time.time()\n        target = np.asarray(data_frame[self.target])\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        cutoff = len(target) - len(df_extracted)\n        df_selected = self._select_features(df_extracted, target[cutoff:])\n        df_selected = _add_original_columns(data_frame[cutoff:], df_selected)\n        end = time.time()\n        _print_time_taken(begin, end)\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return df_selected\n\n    @property\n    def runtime(self) -> Union[None, datetime.timedelta]:\n        if self.fitted:\n            return self._runtime\n\n    def transform(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else _remove_target_column(data_frame, self.target)\n        )\n        df_extracted = self._extract_features(df_for_extraction)\n        df_selected = df_extracted[self.selected_features]\n        df_selected = _add_original_columns(data_frame, df_selected)\n        return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/tsflex_aggregations.py",
    "content": "\"\"\"\nCustom implementations of FastProp aggregations\nfor tsflex.\n\"\"\"\n\nimport functools\nfrom typing import Any, Callable, List, Tuple\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom scipy.stats import kurtosis, mode, skew  # type: ignore\n\n\ndef _avg(arr) -> float:\n    if len(arr) == 0:\n        return 0.0\n    return np.mean(arr)\n\n\ndef _count_above_mean(arr) -> float:\n    if len(arr) == 0:\n        return 0.0\n\n    # Needed to catch numerical instability\n    # issues\n    if np.unique(arr).shape[0] == 1:\n        return 0.0\n\n    mean = np.mean(arr)\n    filtered = [0.0 for v in arr if v > mean]\n    return float(len(filtered))\n\n\ndef _count_below_mean(arr) -> float:\n    if len(arr) == 0:\n        return 0.0\n\n    # Needed to catch numerical instability\n    # issues\n    if np.unique(arr).shape[0] == 1:\n        return 0.0\n\n    mean = np.mean(arr)\n    filtered = [0.0 for v in arr if v > mean]\n    return float(len(filtered))\n\n\ndef _count_distinct(arr) -> float:\n    return float(len(np.unique(arr)))\n\n\ndef _count_distinct_over_count(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    count_distinct = float(len(np.unique(arr)))\n    return count_distinct / float(len(arr))\n\n\ndef _count_minus_count_distinct(arr) -> float:\n    if len(arr) == 0:\n        return 0.0\n    count_distinct = float(len(np.unique(arr)))\n    return float(len(arr)) - count_distinct\n\n\ndef _is_earlier(tup1, tup2):\n    if tup2[0] < tup1[0]:\n        return tup2\n    return tup1\n\n\ndef _first(arr: pd.Series) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return arr[0]\n\n\ndef _kurtosis(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n\n    # There appear to be numerical instability\n    # issues with the scipy implementation.\n    if np.unique(arr).shape[0] == 1:\n        return 0.0\n\n    return kurtosis(arr, fisher=False)\n\n\ndef _last(arr: pd.Series) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return arr[-1]\n\n\ndef _make_tuples(arr, descending=False) -> List[Tuple[float, float]]:\n    # TSFlex returns numpy arrays, so we have\n    # to assume a constant stride.\n    if descending:\n        return list(zip((float(len(arr) - i) for i in range(len(arr))), arr))\n    return list(zip((float(i) for i in range(len(arr))), arr))\n\n\ndef _max(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return np.max(arr)\n\n\ndef _median(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return np.median(arr)\n\n\ndef _min(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return np.min(arr)\n\n\ndef _mode(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    result = mode(arr)[0][0]\n    try:\n        return float(result)\n    except ValueError:\n        return np.nan\n\n\ndef _num_max(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    maximum = np.max(arr)\n    filtered = [0.0 for v in arr if v == maximum]\n    return float(len(filtered))\n\n\ndef _num_min(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    maximum = np.min(arr)\n    filtered = [0.0 for v in arr if v == maximum]\n    return float(len(filtered))\n\n\ndef _quantile(arr, quantile) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return float(np.quantile(arr, quantile, interpolation=\"linear\"))\n\n\ndef _q1(arr) -> float:\n    return _quantile(arr, 0.01)\n\n\ndef _q5(arr) -> float:\n    return _quantile(arr, 0.05)\n\n\ndef _q10(arr) -> float:\n    return _quantile(arr, 0.1)\n\n\ndef _q25(arr) -> float:\n    return _quantile(arr, 0.25)\n\n\ndef _q75(arr) -> float:\n    return _quantile(arr, 0.75)\n\n\ndef _q90(arr) -> float:\n    return _quantile(arr, 0.9)\n\n\ndef _q95(arr) -> float:\n    return _quantile(arr, 0.95)\n\n\ndef _q99(arr) -> float:\n    return _quantile(arr, 0.99)\n\n\ndef _skew(arr) -> float:\n    if not len(arr) == 0:\n        return np.nan\n\n    # There appear to be numerical instability\n    # issues with the scipy implementation.\n    if np.unique(arr).shape[0] == 1:\n        return 0.0\n\n    return skew(arr)\n\n\ndef _sum(arr) -> float:\n    if len(arr) == 0:\n        return 0.0\n    return np.sum(arr)\n\n\ndef _stddev(arr) -> float:\n    if not len(arr) == 0:\n        return np.nan\n    return np.std(arr)\n\n\ndef _time_since_first_maximum(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n\n    def _is_better(tup1, tup2):\n        if tup2[1] == tup1[1]:\n            if tup2[0] > tup1[0]:\n                return tup2\n            return tup1\n        if tup2[1] < tup1[1]:\n            return tup2\n        return tup1\n\n    tuples = _make_tuples(arr)\n\n    return functools.reduce(_is_better, tuples, tuples[0])[0]\n\n\ndef _time_since_first_minimum(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n\n    def _is_better(tup1, tup2):\n        if tup2[1] == tup1[1]:\n            if tup2[0] < tup1[0]:\n                return tup2\n            return tup1\n        if tup2[1] < tup1[1]:\n            return tup2\n        return tup1\n\n    tuples = _make_tuples(arr)\n\n    return functools.reduce(_is_better, tuples, tuples[0])[0]\n\n\ndef _time_since_last_maximum(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n\n    def _is_better(tup1, tup2):\n        if tup2[1] == tup1[1]:\n            if tup2[0] > tup1[0]:\n                return tup2\n            return tup1\n        if tup2[1] > tup1[1]:\n            return tup2\n        return tup1\n\n    tuples = _make_tuples(arr)\n\n    return functools.reduce(_is_better, tuples, tuples[0])[0]\n\n\ndef _time_since_last_minimum(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n\n    def _is_better(tup1, tup2):\n        if tup2[1] == tup1[1]:\n            if tup2[0] > tup1[0]:\n                return tup2\n            return tup1\n        if tup2[1] < tup1[1]:\n            return tup2\n        return tup1\n\n    tuples = _make_tuples(arr)\n\n    return functools.reduce(_is_better, tuples, tuples[0])[0]\n\n\ndef _trend(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    tuples = _make_tuples(arr, descending=True)\n    mean_x = np.mean([v[0] for v in tuples])\n    mean_y = np.mean([v[1] for v in tuples])\n    sum_xx = np.sum([(v[0] - mean_x) * (v[0] - mean_x) for v in tuples])\n    if sum_xx == 0.0:\n        return mean_y\n    sum_xy = np.sum([(v[0] - mean_x) * (v[1] - mean_y) for v in tuples])\n    beta = sum_xy / sum_xx\n    return mean_y - beta * mean_x\n\n\ndef _var(arr) -> float:\n    if len(arr) == 0:\n        return np.nan\n    return np.var(arr)\n\n\ndef _variation_coefficient(arr) -> float:\n    mean = np.mean(arr)\n    if mean == 0.0:\n        return np.nan\n    return np.var(arr) / mean\n\n\ndef _ewma(arr, half_life: float) -> float:\n    tuples = _make_tuples(arr, descending=True)\n    weights = [np.exp(v[0] / half_life) for v in tuples]\n    sum_weights = np.sum(weights)\n    if sum_weights == 0.0:\n        return np.nan\n    sum_all = np.dot([v[1] for v in tuples], weights)\n    return sum_all / sum_weights\n\n\ndef _make_ewma_aggs(freq: float) -> List[Callable[[Any], float]]:\n    t1s = 1.0 / (np.log(0.5) * freq)\n    t1m = t1s * 60.0\n    t1h = t1m * 60.0\n    t1d = t1h * 24.0\n    t7d = t1d * 7.0\n    t30d = t1d * 30.0\n    t90d = t1d * 90.0\n    t365d = t1d * 365.0\n\n    def _ewma_t1s(arr) -> float:\n        return _ewma(arr, t1s)\n\n    def _ewma_t1m(arr) -> float:\n        return _ewma(arr, t1m)\n\n    def _ewma_t1h(arr) -> float:\n        return _ewma(arr, t1h)\n\n    def _ewma_t1d(arr) -> float:\n        return _ewma(arr, t1d)\n\n    def _ewma_t7d(arr) -> float:\n        return _ewma(arr, t7d)\n\n    def _ewma_t30d(arr) -> float:\n        return _ewma(arr, t30d)\n\n    def _ewma_t90d(arr) -> float:\n        return _ewma(arr, t90d)\n\n    def _ewma_t365d(arr) -> float:\n        return _ewma(arr, t365d)\n\n    return [\n        _ewma_t1s,\n        _ewma_t1m,\n        _ewma_t1h,\n        _ewma_t1d,\n        _ewma_t7d,\n        _ewma_t30d,\n        _ewma_t90d,\n        _ewma_t365d,\n    ]\n\n\ndef make_fastprop_aggregations(frequency: float) -> List[Callable[[Any], float]]:\n    return [\n        _avg,\n        _count_above_mean,\n        _count_below_mean,\n        _count_distinct,\n        _count_distinct_over_count,\n        _count_minus_count_distinct,\n        _first,\n        _kurtosis,\n        _last,\n        len,\n        _max,\n        _median,\n        _min,\n        _mode,\n        _num_max,\n        _num_min,\n        _q1,\n        _q5,\n        _q10,\n        _q25,\n        _q75,\n        _q90,\n        _q95,\n        _q99,\n        _skew,\n        _sum,\n        _stddev,\n        _time_since_first_maximum,\n        _time_since_first_minimum,\n        _time_since_last_maximum,\n        _time_since_last_minimum,\n        _trend,\n        _var,\n        _variation_coefficient,\n    ] + _make_ewma_aggs(frequency)\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/tsflex_builder.py",
    "content": "\"\"\"\nWrapper around tsflex\n\"\"\"\n\nimport datetime\nimport time\nimport warnings\nfrom typing import Callable, List, Optional, Union, cast\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom pandas.api.types import is_numeric_dtype  # type: ignore\nfrom tsflex.features import FeatureCollection, MultipleFeatureDescriptors\nfrom tsflex.features.function_wrapper import FuncWrapper\n\nfrom .print_time_taken import _print_time_taken\nfrom .tsfel_builder import TSFELBuilder\nfrom .tsflex_aggregations import make_fastprop_aggregations\n\n# ------------------------------------------------------------------\n\n\ndef _wrap_aggregation(func) -> Callable:\n    return FuncWrapper(\n        func=func,\n        output_names=func.__name__,\n    )\n\n\n# ------------------------------------------------------------------\n\n\nclass TsflexBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on tsflex.\n\n    Args:\n        num_features: The (maximum) number of features to build.\n\n        horizon: The prediction horizon to use.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n\n        aggregations: The aggregations to use.\n\n        allow_lagged_targets: Whether to build features based on lagged targets\n\n        min_chunksize: The minimum size of chunks to aggregate over\n\n        n_jobs: TODO\n    \"\"\"\n\n    def __init__(\n        self,\n        num_features: int,\n        horizon: pd.Timedelta,\n        memory: pd.Timedelta,\n        column_id: str,\n        time_stamp: str,\n        target: str,\n        strides: pd.Timedelta,\n        aggregations: Optional[List[Callable]] = None,\n        allow_lagged_targets: bool = False,\n        min_chunksize: int = 0,\n        n_jobs: int = 1,\n    ) -> None:\n        self.num_features = num_features\n        self.horizon = horizon\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.strides = strides\n        self.aggregations = aggregations or make_fastprop_aggregations(\n            float(strides.total_seconds())\n        )\n        self.allow_lagged_targets = allow_lagged_targets\n        self.min_chunksize = min_chunksize\n        self.n_jobs = n_jobs\n\n        self._runtime = None\n        self.fitted = False\n\n        self.selected_features: List[int] = []\n        self.num_features_generated = 0\n\n    def _extract_features(self, data_frame: pd.DataFrame) -> pd.DataFrame:\n        fc = FeatureCollection(\n            MultipleFeatureDescriptors(\n                functions=[_wrap_aggregation(agg) for agg in self.aggregations],\n                series_names=[\n                    col for col in data_frame.columns if col != self.column_id\n                ],\n                windows=self.memory,\n                strides=self.strides,\n            )\n        )\n\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            extracted = cast(\n                pd.DataFrame, fc.calculate([data_frame], return_df=True, n_jobs=1)\n            )\n\n        for col in extracted:\n            if is_numeric_dtype(extracted[col]):\n                extracted[col][extracted[col].isna()] = 0\n\n        return extracted\n\n    def _select_features(\n        self, data_frame: pd.DataFrame, target: Union[pd.Series, np.ndarray]\n    ) -> pd.DataFrame:\n        print(f\"Selecting the best out of {data_frame.shape[1]} features...\")\n\n        data_frame = data_frame.loc[:, (data_frame != data_frame.iloc[0]).any()]\n        correlations = data_frame.corrwith(pd.Series(target)).abs()\n        correlations = correlations.replace([np.inf, np.nan], 0)\n        correlations = correlations.sort_values(ascending=False)\n\n        self.num_features_generated = data_frame.shape[1]\n        self.selected_features = correlations.index[: self.num_features].tolist()\n        return data_frame[self.selected_features]\n\n    def fit(\n        self, data_frame: pd.DataFrame, selection: Optional[List[str]] = None\n    ) -> pd.DataFrame:\n        \"\"\"\n        Fits the DFS on the data frame and returns\n        the features for the training set.\n        \"\"\"\n        print(\"tsflex: Trying features...\")\n        begin = time.time()\n        data_frame = data_frame.set_index(self.time_stamp)\n        grouped = data_frame.groupby(self.column_id)\n        extracted = []\n        other = []\n        for _, group in grouped:\n            to_extract = group if self.allow_lagged_targets else group.drop(self.target)\n            extracted.append(self._extract_features(to_extract))\n            other.append(group[self.min_chunksize :])\n        extracted = pd.concat(extracted)\n        other = pd.concat(other)\n        if selection:\n            selected = extracted[selection]\n        else:\n            selected = self._select_features(extracted, other[self.target])\n        selected = pd.concat([selected, other], axis=1)\n        selected = selected[selected[\"id_\"] == selected[\"id_\"]]\n        end = time.time()\n        _print_time_taken(begin, end)\n\n        self.fitted = True\n        self._runtime = datetime.timedelta(seconds=end - begin)\n        return selected\n\n    @property\n    def runtime(self) -> Union[None, datetime.timedelta]:\n        if self.fitted:\n            return self._runtime\n\n    def transform(\n        self, data_frame: pd.DataFrame, data_frame_full: Optional[pd.DataFrame] = None\n    ):\n        \"\"\"\n        Transforms the raw data into a set of features.\n        \"\"\"\n\n        if not self.fitted:\n            raise RuntimeError\n\n        if data_frame_full is not None:\n            split_ix = np.argmax(\n                data_frame_full[self.time_stamp] == data_frame[self.time_stamp][0]\n            )\n            data_frame = data_frame_full[split_ix - self.min_chunksize :]\n\n        return self.fit(data_frame, selection=self.selected_features)\n"
  },
  {
    "path": "benchmarks/benchmarks/demo/tsfresh_builder.py",
    "content": "\"\"\"\nUtility wrapper around tsfresh.\n\"\"\"\n\nimport datetime\nimport gc\nimport time\nfrom typing import List, Optional\n\nimport numpy as np\nimport pandas as pd\nimport tsfresh\nfrom scipy.stats import pearsonr\nfrom tsfresh.utilities.dataframe_functions import roll_time_series\n\nfrom .add_original_columns import _add_original_columns\nfrom .print_time_taken import _print_time_taken\n\n\ndef _ts_to_float(col):\n    epoch = datetime.datetime(1970, 1, 1)\n    return [(t - epoch).total_seconds() for t in col]\n\n\nclass TSFreshBuilder:\n    \"\"\"\n    Scikit-learn-style feature builder based on TSFresh.\n\n    Args:\n\n        num_features: The (maximum) number of features to build.\n\n        memory: How much back in time you want to go until the\n                feature builder starts \"forgetting\" data.\n\n        column_id: The name of the column containing the ids.\n\n        time_stamp: The name of the column containing the time stamps.\n\n        target: The name of the target column.\n    \"\"\"\n\n    def __init__(\n        self,\n        num_features,\n        memory,\n        column_id,\n        time_stamp,\n        target,\n        horizon=0,\n        allow_lagged_targets=False,\n        min_chunksize: int = 0,\n        n_jobs: int = 1,\n    ):\n        self.num_features = num_features\n        self.memory = memory\n        self.column_id = column_id\n        self.time_stamp = time_stamp\n        self.target = target\n        self.horizon = horizon\n        self.allow_lagged_targets = allow_lagged_targets\n\n        self._runtime = None\n\n        self.num_features_generated = 0\n        self.selected_features: List[int] = []\n\n    def _extract_features(self, data_frame):\n        # https://github.com/blue-yonder/tsfresh/issues/716#issuecomment-649755423\n        df_numerical = data_frame.select_dtypes(include=[np.number])\n\n        df_numerical[self.time_stamp] = _ts_to_float(data_frame[self.time_stamp])\n\n        df_rolled = roll_time_series(\n            df_numerical,\n            column_id=self.column_id,\n            column_sort=self.time_stamp,\n            max_timeshift=self.memory,\n        )\n\n        df_rolled = df_rolled[\n            [col for col in df_rolled.columns if col != self.column_id]\n        ]\n\n        extracted_minimal = tsfresh.extract_features(\n            df_rolled,\n            column_id=\"id\",\n            column_sort=self.time_stamp,\n            default_fc_parameters=tsfresh.feature_extraction.MinimalFCParameters(),\n            n_jobs=1,\n        )\n\n        extracted_index_based = tsfresh.extract_features(\n            df_rolled,\n            column_id=\"id\",\n            column_sort=self.time_stamp,\n            default_fc_parameters=tsfresh.feature_extraction.settings.IndexBasedFCParameters(),\n            n_jobs=1,\n        )\n\n        extracted_features = pd.concat(\n            [extracted_minimal, extracted_index_based], axis=1\n        )\n        del extracted_minimal\n        del extracted_index_based\n\n        gc.collect()\n\n        extracted_features[np.isnan(extracted_features)] = 0.0\n\n        extracted_features[np.isinf(extracted_features)] = 0.0\n\n        return extracted_features\n\n    def _remove_target_column(self, data_frame):\n        colnames = np.asarray(data_frame.columns)\n\n        if self.target not in colnames:\n            return data_frame\n\n        colnames = colnames[colnames != self.target]\n\n        return data_frame[colnames]\n\n    def _select_features(self, data_frame, target):\n        print(\n            \"Selecting the best out of \" + str(len(data_frame.columns)) + \" features...\"\n        )\n\n        df_selected = tsfresh.select_features(data_frame, target)\n\n        colnames = np.asarray(df_selected.columns)\n\n        correlations = np.asarray(\n            [np.abs(pearsonr(target, df_selected[col]))[0] for col in colnames]\n        )\n\n        # [::-1] is somewhat unintuitive syntax,\n        # but it reverses the entire column.\n        self.selected_features = colnames[np.argsort(correlations)][::-1][\n            : self.num_features\n        ]\n\n        self.num_features_generated = data_frame.shape[1]\n        return df_selected[self.selected_features]\n\n    def fit(self, data_frame):\n        \"\"\"\n        Fits the features.\n        \"\"\"\n        begin = time.time()\n\n        target = np.asarray(data_frame[self.target])\n\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else self._remove_target_column(data_frame)\n        )\n\n        df_extracted = self._extract_features(df_for_extraction)\n\n        df_selected = self._select_features(df_extracted, target)\n\n        del df_extracted\n        gc.collect()\n\n        df_selected = _add_original_columns(data_frame, df_selected)\n\n        end = time.time()\n\n        self._runtime = datetime.timedelta(seconds=end - begin)\n\n        _print_time_taken(begin, end)\n\n        return df_selected\n\n    @property\n    def runtime(self):\n        return self._runtime\n\n    def transform(self, data_frame):\n        \"\"\"\n        Transforms the raw data into a set of features.\n        \"\"\"\n        df_for_extraction = (\n            data_frame\n            if self.allow_lagged_targets\n            else self._remove_target_column(data_frame)\n        )\n\n        df_extracted = self._extract_features(df_for_extraction)\n\n        df_selected = df_extracted[self.selected_features]\n\n        del df_extracted\n        gc.collect()\n\n        df_selected = _add_original_columns(data_frame, df_selected)\n\n        return df_selected\n"
  },
  {
    "path": "benchmarks/benchmarks/drivers/__init__.py",
    "content": "from .drive_getml import drive_getml\nfrom .drive_wrappers import (\n    drive_featuretools,\n    drive_kats,\n    drive_tsfel,\n    drive_tsflex,\n    drive_tsfresh,\n)\n"
  },
  {
    "path": "benchmarks/benchmarks/drivers/drive_getml.py",
    "content": "import logging\nfrom datetime import timedelta\nfrom typing import Any, Union\n\nfrom getml.data.time_series import TimeSeries\nfrom getml.feature_learning.fastprop import FastProp\nfrom getml.feature_learning.loss_functions import SquareLoss\nfrom getml.pipeline.pipeline import Pipeline\nfrom getml.predictors.xgboost_regressor import XGBoostRegressor\nfrom getml.preprocessors import Seasonal\n\nfrom benchmarks.demo import Benchmark\n\n\ndef drive_getml(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: timedelta,\n    num_features: int = 1000,\n    min_chunksize: int = 0,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with getml.\n    \"\"\"\n\n    fast_prop = FastProp(\n        aggregation=FastProp.agg_sets.All,\n        loss_function=SquareLoss,\n        num_features=num_features,\n        num_threads=n_jobs,\n    )\n\n    pipe_fl = Pipeline(\n        feature_learners=[fast_prop],\n        data_model=time_series.data_model,\n        tags=[\"fastprop\", \"paper\", \"benchmark\"],\n    )\n\n    features = {}\n\n    pipe_fl.check(time_series.train)\n\n    with observer.benchmark(\"fastprop\"):\n        pipe_fl.fit(time_series.train)\n        features[\"train\"] = pipe_fl.transform(\n            time_series.train, df_name=\"features_train\"\n        )\n\n    features[\"test\"] = pipe_fl.transform(time_series.test, df_name=\"features_test\")\n\n    if min_chunksize > 0:\n        features[\"train\"] = features[\"train\"][min_chunksize:]\n\n    pipe_pr = Pipeline(\n        tags=[\"prediction\", \"fastprop\"],\n        predictors=XGBoostRegressor(),\n    )\n\n    pipe_pr.fit(features[\"train\"])\n    pipe_pr.score(features[\"test\"])\n\n    num_features = len(\n        pipe_pr.features.filter(lambda feat: feat.name.startswith(\"feature_\"))\n    )\n\n    observer.write_scores(\"fastprop\", pipe_pr, num_features)\n\n    return observer\n"
  },
  {
    "path": "benchmarks/benchmarks/drivers/drive_wrappers.py",
    "content": "from datetime import timedelta\nfrom typing import Optional, Type, Union\n\nimport numpy as np\nimport pandas as pd  # type: ignore\nfrom getml.data import Container, DataFrame, TimeSeries, View, roles\nfrom getml.data.columns.columns import StringColumnView\nfrom getml.pipeline.pipeline import Pipeline\nfrom getml.predictors.xgboost_regressor import XGBoostRegressor\n\nfrom benchmarks.demo import (\n    Benchmark,\n    CesiumMLBuilder,\n    FTTimeSeriesBuilder,\n    KATSBuilder,\n    TSFELBuilder,\n    TsflexBuilder,\n    TSFreshBuilder,\n)\n\n\ndef _drive_wrapper(\n    wrapper: Type[\n        Union[FTTimeSeriesBuilder, TSFreshBuilder, TSFELBuilder, TsflexBuilder]\n    ],\n    lib_name: str,\n    observer: Benchmark,\n    time_series: TimeSeries,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: Optional[int] = 1,\n    transform_horizon_memory: bool = True,\n    strides: Optional[pd.Timedelta] = None,\n) -> Benchmark:\n    dfs_pd = time_series.container.to_pandas()\n\n    horizon = (\n        pd.Timedelta(time_series.horizon, unit=\"S\")\n        if transform_horizon_memory\n        else time_series.horizon\n    )\n    memory = (\n        pd.Timedelta(time_series.memory, unit=\"S\")\n        if transform_horizon_memory\n        else time_series.memory\n    )\n    kwargs = {\"strides\": strides} if strides is not None else {}\n    wrapper_instance = wrapper(\n        horizon=horizon,\n        memory=memory,\n        column_id=\"id_\",\n        time_stamp=time_series.time_stamps,\n        target=time_series.population.roles.target[0],\n        num_features=num_features,\n        allow_lagged_targets=time_series.lagged_targets,\n        min_chunksize=min_chunksize,\n        n_jobs=n_jobs,\n        **kwargs,\n    )\n\n    features_pd = {}\n\n    with observer.benchmark(lib_name):\n        features_pd[\"train\"] = wrapper_instance.fit(dfs_pd[\"train\"].assign(id_=1))\n\n    features_pd[\"test\"] = wrapper_instance.transform(dfs_pd[\"test\"].assign(id_=1))\n\n    xgboost = XGBoostRegressor()\n    pipe_pr = Pipeline(tags=[\"prediction\", lib_name], predictors=[xgboost])\n\n    features = {\n        subset: DataFrame.from_pandas(\n            feat_df_pd,\n            name=subset,\n            roles={\n                k: v\n                for k, v in time_series.population.roles.to_dict().items()\n                if \"unused\" not in k\n            },\n        )\n        for subset, feat_df_pd in features_pd.items()\n    }\n\n    for subset in features:\n        features[subset].set_role(features[subset].roles.unused, roles.numerical)\n\n    pipe_pr.fit(features[\"train\"])\n    pipe_pr.score(features[\"test\"])\n\n    observer.write_scores(lib_name, pipe_pr, wrapper_instance.num_features_generated)\n\n    return observer\n\n\ndef drive_featuretools(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: float,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with\n    featuretools.\n    \"\"\"\n\n    return _drive_wrapper(\n        FTTimeSeriesBuilder,\n        \"featuretools\",\n        observer,\n        time_series,\n        min_chunksize,\n        num_features,\n        n_jobs,\n    )\n\n\ndef drive_kats(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: float,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with KATS.\n    \"\"\"\n\n    return _drive_wrapper(\n        KATSBuilder,\n        \"kats\",\n        observer,\n        time_series,\n        min_chunksize,\n        num_features,\n        n_jobs,\n    )\n\n\ndef drive_tsfresh(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: float,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with\n    tsfresh.\n    \"\"\"\n    new_time_series = TimeSeries(\n        population=time_series.population,\n        split=time_series.split,\n        alias=time_series.alias,\n        time_stamps=time_series.time_stamps,\n        horizon=int(time_series.horizon / frequency),\n        memory=int(time_series.memory / frequency),\n        lagged_targets=time_series.lagged_targets,\n    )\n    return _drive_wrapper(\n        TSFreshBuilder,\n        \"tsfresh\",\n        observer,\n        new_time_series,\n        num_features,\n        n_jobs,\n        transform_horizon_memory=False,\n    )\n\n\ndef drive_tsfel(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: float,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with tsfel.\n    \"\"\"\n\n    return _drive_wrapper(\n        TSFELBuilder,\n        \"tsfel\",\n        observer,\n        time_series,\n        min_chunksize,\n        num_features,\n        n_jobs,\n    )\n\n\ndef drive_tsflex(\n    observer: Benchmark,\n    time_series: TimeSeries,\n    frequency: float,\n    min_chunksize: int = 0,\n    num_features: int = 1000,\n    n_jobs: int = 1,\n) -> Benchmark:\n    \"\"\"\n    A common interface for benchmarking propositionalization on time series with tsflex.\n    \"\"\"\n    strides = pd.Timedelta(seconds=frequency)\n    print(min_chunksize)\n    return _drive_wrapper(\n        TsflexBuilder,\n        \"tsflex\",\n        observer,\n        time_series,\n        min_chunksize,\n        num_features,\n        n_jobs,\n        strides=strides,\n    )\n"
  },
  {
    "path": "benchmarks/benchmarks/pyproject.toml",
    "content": "[tool.mypy]\ningnore_errors = true\n\n[tool.isort]\nmulti_line_output = 3\nprofile = \"black\"\nsections = ['FUTURE', 'STDLIB', 'THIRDPARTY', 'GETML', 'FIRSTPARTY', 'LOCALFOLDER']\nknown_getml = ['getml']\n"
  },
  {
    "path": "benchmarks/benchmarks/run_all.py",
    "content": "\"\"\"\nRuns the benchmark scripts\n\"\"\"\n\nfrom .benchmarks import benchmark_interstate94\nfrom .drivers import (\n    drive_featuretools,\n    drive_getml,\n    drive_tsfel,\n    drive_tsflex,\n    drive_tsfresh,\n)\n\nall_drivers = [drive_getml, drive_tsflex]\n\nbenchmark_interstate94(all_drivers)\n"
  },
  {
    "path": "benchmarks/benchmarks/utils.py",
    "content": "import logging\nfrom contextlib import contextmanager\nfrom typing import Callable, Generator\n\nfrom getml.engine import delete_project, set_project\n\n\n@contextmanager\ndef project(name: str) -> Generator[None, None, None]:\n    \"\"\"\n    A context manager that ensures proper setup and teardown of a project and prevents caching.\n    \"\"\"\n    set_project(name)\n    yield\n    delete_project(name)\n\n\n@contextmanager\ndef log_run(\n    driver: Callable, ds_name: str, run: int, runs: int\n) -> Generator[None, None, None]:\n    \"\"\"\n    A context manager for logging benchmark runs.\n    \"\"\"\n    logging.info(\n        \"Benchmarking %s on %s (run %i/%i)...\",\n        driver.__name__[6:],\n        ds_name,\n        run,\n        runs,\n    )\n    yield\n    logging.info(\"Finished.\")\n"
  },
  {
    "path": "benchmarks/docker-compose.yml",
    "content": "services:\n  benchmarks:\n    build:\n      context: ./\n      dockerfile: Dockerfile\n      platforms:\n        - \"linux/amd64\"\n      args:\n        - VERSION_NUMBER=$VERSION_NUMBER\n        - GETML_VERSION=$GETML_VERSION\n    networks:\n      - benchmarks_network\n    ports:\n      - \"1709:1709\"\n\nnetworks:\n  benchmarks_network:"
  },
  {
    "path": "benchmarks/paper_data.txt",
    "content": "FASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 1\r\nFastProp: A Customized Database Engine for Fast\r\nPropositionalization\r\nPatrick Urbanke, Soeren Nikolaus\r\nAbstract—We present the design of a database engine that has\r\nbeen customized for automated feature engineering on relational\r\ndata and time series. Using public-domain datasets, we demon-\r\nstrate that an implementation of a static propositionalization\r\nalgorithm based on this database engine is at least about 60\r\ntimes and up to 1000 times faster than implementations based\r\non standard in-memory database engines. This speedup is com-\r\nparable to the speedup attained by introducing GPUs to training\r\nneural networks, which is considered to be a revolutionary step\r\nin the deep learning literature [1], [2].\r\nIndex Terms—Automated Feature Engineering, Feature Learn-\r\ning, Propositionalization, Relational Learning, Time Series\r\nI. INTRODUCTION\r\nA\r\nUTOMATED feature engineering on relational data and\r\ntime series describes the process of automatically gen-\r\nerating features that can be inserted into standard machine\r\nlearning algorithms from relational data and time series. This\r\nis an important topic: Automated feature engineering promises\r\nto drastically reduce the time spent on data preparation, thus\r\nsubstantially decreasing the costs of applied machine learning\r\nprojects.\r\nIn the relational learning literature, the process of generating\r\nfeatures from relational data and time series for the purpose\r\nof inserting them into a standard machine learning algorithm\r\nis referred to as propositionalization [3]. Popular implementa-\r\ntions include Deep Feature Synthesis [4], tsfresh [5], TSFEL\r\n[6] and tsflex [7]. More specifically, these implementations\r\ncan be categorized as static propositionalization approaches.\r\nUnlike some other propositionalization algorithms, they do not\r\napply machine learning paradigms such as decision trees or\r\ngraphical models to the domain of relational learning. Instead,\r\nthey generate a large number of features using simple static\r\nrules. The best features are then selected using standard feature\r\nselection approaches.\r\nWe argue that implementation speed is a key measure for\r\nstatic propositionalization algorithms. As indicated, the idea\r\nof such algorithms is to generate a large amount of features\r\nto then evaluate which of these features are effective for the\r\nproblem domain. Being able to generate more features per\r\nsecond will enable us to generate more features overall, which\r\nis highly likely to improve the predictive accuracy.\r\nIn this study, we present the design of a database engine\r\nthat has been customized for automated feature engineering\r\non relational data. We demonstrate that an implementation of\r\na static propositionalization algorithm based on this database\r\nengine is at least about 60 times and up to 1000 times faster\r\nthan implementations based on existing in-memory database\r\nsystems. This speedup is about the same as the speedup\r\nattained by introducing GPUs to neural networks which is\r\nconsidered to be a revolutionary step in the deep learning\r\nliterature [1], [2]. We argue that the speedup attained through\r\ncustomized database systems has the potential to be similarly\r\nimportant for the applicability of relational learning.\r\nThe remainder of this study is organized as follows: In\r\nSection 2, we introduce literature related to automated feature\r\nengineering on relational data and time series. In Section 3, we\r\nintroduce the proposed algorithm. In Section 4, we evaluate\r\nthe algorithms against existing implementation of the static\r\npropositionalization approach. Section 5 concludes this study.\r\nII. RELATED LITERATURE\r\nA. Relational Learning\r\nRelational learning refers to learning from data that is\r\neither laid out in an explicit relational structure, like in an\r\nrelational database, or has a complex internal structure that\r\nis relational [8]. There are different approaches to learning\r\nfrom relational structures that can be classified into one of\r\nthose two categories: Inductive logic programming (ILP) is\r\nthe classical approach to learning from internal (relational)\r\nstructures. Propositionalization is set in the context of learning\r\nfrom explicit relational structures, like multi-relational data\r\nmining or statistical relational learning.\r\nB. Propositionalization\r\nFrom a classical machine learning or database/data mining\r\nperspective, approaches dealing with flat tables are classified\r\nas propositional, where approaches dealing with multiple\r\ntables are classified as relational. The term propositional\r\noriginates from classical formal logic and deals with simple\r\npropositions in attribute-value form [9]. The logical equivalent\r\nto relational representations if first-order logic [10]. Proposi-\r\ntionalization is the process of transforming first-order logic\r\nor relational representations into propositional ones [3]. From\r\na process perspective, propositionalization can be viewed as\r\nan intermediate step in the machine learning pipeline, where\r\ndata is transformed into intermediate representations which\r\nare then provided as inputs to propositional learners (machine\r\nlearning algorithms). Among machine learning pracitioners\r\npropositionalization is know as feature engineering, refer to\r\n[11], [12], or [13], [14] for more recent usages of the term.\r\nThe two most important reasons for using propositionaliza-\r\ntion (in contrast to learning from relational data directly) are\r\nto reduce complexity, to reduce learning times and to be able\r\nto use well-established machine learning algorithms [15].\r\nThe simplest approach to propositionalization is static\r\npropositionalization, which involves generating a large amount\r\nof features based on a predetermined rule set and then usingFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 2\r\nsome feature selection methods to keep the relevant portion.\r\nThis approach is also known as expand-reduce [16]. Using\r\nstatic propositionalization is a good approach to address the\r\nproblem of information loss inherent to propositionalization\r\nalgorithms [17].\r\nC. Implementations\r\nDeep feature synthesis [4] is a static propositionalization\r\nalgorithm that applies a pre-defined set of transformations fol-\r\nlowed by a feature selection and hyperparameter optimization.\r\nOneBM builds features by traversing a joining path – defined\r\nby a sequence of primary keys and tables – and applying\r\na pre-defined set of aggregation functions to the resulting\r\nprojection. LazyBum [17] is a lazy version of OneBM that\r\ntries to avoid unnecessary computations. Approaches in the\r\nclassical ILP setting are POLKA [18] and RELAGGS [19].\r\nFORF [20] uses ensemble learning to learn a set of first-order\r\nlogic query augmented by aggregation functions. NFOIL [21]\r\nintegrates na¨ıve baise learning into ILP-based rule learning.\r\nMODL [22] is an example for a probabalistic approach to\r\nstatic propositinalization. Wordifaction [23] is special case\r\nof static propositionalization that applies simple text mining\r\ntechniques to a bag-of-words representation of relational data.\r\nMRDTL [24]–[26] is a supervised tree-based algorithm that\r\nuses surrogate criteria to learn an optimal set of conditional\r\naggregation.\r\nAs discussed in [reference to discussion about framing time\r\nseries as relational problems], time series can be understood\r\nas special case of relational data, where one observation is\r\nrelated to historical observation of itself and other attributes.\r\nCesiumML is [27] one of the first libraries that applies\r\npropositionalization to time series. Seglearn [28] is the first\r\nlibrary that utilized sliding window segmentation. tsfresh [5]\r\nis a popular library for time series propositonalization that\r\ncomes with a large set of pre-defined aggregations and a\r\nfeature selection technique. TSFEL [6] is another time series\r\npropositinalization library with a special focus on spectral\r\nfeatures. tsflex [7] is a meta time-series propositinalization\r\nlibrary that comes with interfaces for applying Seqlearn,\r\ntsfresh, or TSFEL aggregations.\r\n1) Propositionalization:\r\n1) Static Propositionalization\r\na) Relational\r\n⊠ Featuretools/Deep feature synthesis [4]\r\n⊠ OneBM [29]\r\n⊠ Lazy Bum [17]\r\n⊠ POLKA [18]\r\n⊠ RELAGGS [19]\r\n⊠ FORF [20]\r\n⊠ MODL [22]\r\n⊠ Wordification [23]\r\n⊠ MRDTL [24]–[26]\r\nb) Time Series Feature Extraction\r\n⊠ tsfresh [5]\r\n⊠ Seglearn [28]\r\n⊠ tsfel [6]\r\n⊠ tsflex [7]\r\n⊠ cesium ml [27]\r\nD. Contribution\r\nAs we have seen above, propositionalization is a well-\r\nresearched approach for which numerous implementations\r\nexist. However, researchers have paid comparatively little\r\nattention to an efficient implementation of propositionalization\r\nalgorithms. The purpose of this study is to contribute to the\r\nclosing of this gap.\r\nWe design a database Engine that is customized for propo-\r\nsitionalization. We demonstate that propositionalization algo-\r\nrithms implemented on top of this Engine are at least 60 times\r\nand up to 1000 times faster than implementations cited above.\r\nThis speedup is about the same as the speedup attained by\r\nintroducing GPUs to deep learning which is considered to be\r\na major milestone in the deep learning literature [1], [2]. We\r\nargue that the speedup attained through customized database\r\nsystems has the potential to be similarly important for the\r\napplicability of relational learning.\r\nIII. PROPOSED ALGORITHM\r\nA. Terminology\r\nWe begin by introducing terminology.\r\nA relational learning problem contains exactly one table\r\nwhich defines the statistical population and contains the target\r\nvariables. We refer to such a table as a population table.\r\nFurthermore, there is at least one table that is joined onto the\r\npopulation table. Such a table is referred to as a peripheral\r\ntable. Note that a peripheral table might be identical to the\r\npopulation table, particularly for time series problems. Such a\r\nscenario is called a self join.\r\nWhen joining a peripheral table onto a population table,\r\nwe have to find the rows in the peripheral table that match a\r\nspecific row in the population table. Such rows are referred\r\nto as matches. Matches are determined by several factors. A\r\ncommon scenario is to join the two tables based on a particular\r\nkey, called a join key.\r\nMoreover, in relational learning problems, we often use time\r\nstamps to limit the number of matches. In predictive analytics,\r\nit is common to limit the rows in the peripheral tables to a\r\nsubset where a time stamp in the peripheral table is smaller\r\nthan the a time stamp in the population table in order to avoid\r\nusing the data from the future [30]. We refer to said time stamp\r\nin the population table as a reference date.\r\nNote that every row in the population table has its own set\r\nof matches and that these matches might overlap.\r\nAfter we have identified the matches, we need a value to be\r\naggregated. A value to be aggregated can simply be a value\r\nin a column in the peripheral table, but it could also be a\r\ncombination of several column. For instance, it is a common\r\nscenario to calculate the aggregate time since since something\r\noccured. For instance, we might want to the time since the last\r\ntransaction a customer made. In order to accomplish this, we\r\nwould substract the reference date from all transaction dates\r\nand then find the minimum.\r\nFurthermore, we might want to add a condition to our\r\nfeature. The means that would only consider those matches forFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 3\r\nour aggregation for which the condition is true. This is par-\r\nticularly important for identifying seasonalites. For instance,\r\nwe might want to calculate the average traffic volume on the\r\nsame weekday on the same hour of the day as the reference\r\ndate.\r\nWe might also want to define a prediction horizon. The\r\nhorizon is the amount of time we want to predict into the\r\nfuture. For instance, we might want to predict the traffic\r\nvolume in the next hour or 24 hours from now.\r\nLikewise, we might want to instruct the algorithm not to use\r\ndata that is older than a specified amount of time in relation\r\nto the reference data. We refer to this amount of time as\r\nthe memory of the algorithm. For instance, we might want\r\nconfigure the algorithm such that it only aggregates data from\r\nthe last seven days before the reference date and disregard\r\neverything before that.\r\nIn time series problems in particular, it is very common to\r\nhave both a horizon and memory, with a fixed amount time\r\nbetween them. We refer to such a scenario as sliding windows.\r\nB. Optimizations\r\nWe propose the following optimizations to increase the\r\nperformance of static propositionalization approaches:\r\n1) Indexing join keys: We reduce the time it takes to find\r\nthe appropriate matches by creating an index over the join\r\nkey in the peripheral table. Such an index is implemented as a\r\nhash map, which maps each unique join key value onto a list of\r\nindices indicating the rows in the peripheral tables containing\r\nthat unique join key value.\r\n2) Encoding join keys: Join keys are often strings. Com-\r\nparing and hashing strings is more expensive than comparing\r\nand hashing integers. We therefore map every unique join key\r\nvalue onto a unique integer. The join key columns then contain\r\nthe integers instead of the corresponding strings. The encoding\r\nis kept globally, meaning that the same string corresponds to\r\nthe same integer for all of the join keys in our data set.\r\n3) Indexing time stamps: When we have a time series\r\nproblem with sliding windows we often have to find all rows in\r\nbetween some upper and lower bound. We therefore index the\r\ntime stamps using a red-black tree with the time stamp value\r\nas the key and the corresponding row indices as the value.\r\nUnlike a hash map, a red-black tree also enables us to find all\r\nvalues smaller or greater than a particular value, which makes\r\nit a useful instrument for sliding windows. How we build such\r\nan index is expressed in pseudo-code in Figure 1. How it can\r\nbe used to efficiently find the matching rows is expressed in\r\npseudo-code in Figure 2.\r\n4) Row-wise generation of feature sets: Despite the op-\r\ntimizations described in the previous subsections, finding\r\nmatches is still a very expensive operation. However, simply\r\ncalculating the matches for all peripheral tables and then\r\nstoring them could be prohibitively expensive in terms of\r\nmemory usage, particularly for large cross joins. The libraries\r\nused for reference therefore find the matches for every fea-\r\nture separately, which results in significant redundancies. We\r\ncircumvent this problem by row-wise generation: Instead of\r\ncalculating each feature seperately, we calculate a batch of\r\n▷ Evaluates whether t1 is smaller than t2\r\n▷ t1 (Tuple[int, float]): A tuple consisting of a join key\r\nand a time stamp\r\n▷ t2 (Tuple[int, float]): A tuple consisting of a join key\r\nand a time stamp\r\n1: procedure ISSMALLER(t1, t2)\r\n2: if t1.joinKey = t2.joinKey then\r\n3: return t1.timeStamp < t2.timeStamp\r\n4: end if\r\n5: return t1.joinKey < t2.joinKey\r\n6: end procedure\r\n7:\r\n▷ Generates an index consisting that maps join keys and\r\ntime stamps to row numbers.\r\n▷ table (Table): The table to index. We the join key in\r\nquestion is denoted jk and the time stamp ts.\r\n8: procedure MAKETIMESTAMPINDEX(table)\r\n9: t ← []\r\n10: for i = 0; i < n; i ← i + 1 do\r\n11: t ← t + [((table.jk[i], table.ts[i]), i)]\r\n12: end for\r\n▷ Generates a red-black trees, sorting them by using\r\nIsSmaller.\r\n13: return TOREDBLACKTREE(t, IsSmaller)\r\n14: end procedure\r\nFig. 1. Procedure for generating a time stamp index.\r\n▷ Returns a range of all of the row numbers in per that\r\ncorrespond to row r in pop.\r\n▷ r (int): Signifies a row number in pop.\r\n▷ pop (Table): The population table.\r\n▷ join (Join): A struct describing the join. Includes per,\r\nthe peripheral table used for this join.\r\n1: procedure FINDMATCHES(r, pop, join)\r\n2: lower ← pop.ts[r] − join.horizon − join.memory\r\n3: upper ← pop.ts[r] − join.horizon\r\n4: tsIndex ← join.per.tsIndex\r\n▷ UpperBound is a function that returns the first element\r\ngreater than the given element. It can be relatively easily\r\nimplemented on a red-black-tree.\r\n5: begin ← UPPERBOUND(tsIndex,(pop.jk[r], lower))\r\n6: end ← UPPERBOUND(tsIndex,(pop.jk[r], upper))\r\n▷ MakeRange returns a range over all of the row numbers\r\nin the time stamp index between begin and end.\r\n7: return MAKERANGE(begin, end)\r\n8: end procedure\r\nFig. 2. Procedure to find matches using the time stamp index\r\nfeatures (usually 100 at a time) row-by-row. This allows us\r\nto find the matches for a particular row in the population\r\ntable and use them to calculate the values for many features\r\nat a time, significantly reducing the amount of redundant\r\noperations. This idea is expressed in pseudo-code in Figures\r\n3 and 4.\r\n5) Memoization: In static propositionalization, we often\r\napply different aggregations to the same set of values. EvenFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 4\r\n▷ Generates the features column-wise.\r\n▷ pop (Table): The population table.\r\n▷ joins (List[Join]): A list of classes describing the joins.\r\nIncludes per, the peripheral table used for this join.\r\n▷ features (List[Feature]): A list of classes describing the\r\nfeatures, both in abstract form as well as the underlying\r\ndata.\r\n1: procedure GENERATECOLUMNWISE(pop, joins,\r\nfeatures)\r\n2: for f in features do\r\n3: for r = 0; r < pop.nrows; r ← r + 1 do\r\n4: m ← FINDMATCHES(r, pop, f.join)\r\n5: f[r] ← AGGREGATE(pop, m, f)\r\n6: end for\r\n7: end for\r\n8: end procedure\r\nFig. 3. Column-wise generation of feature sets. This is the approach used by\r\nmost of the benchmarking libaries. Note that that FindMatches, a relatively\r\nexpensive operation, is called more often than for row-wise generation. Due\r\nto memory constraints, storing the results of FindMatches for repeated use is\r\nnot an option in column-wise generation.\r\n▷ Generates the features row-wise.\r\n▷ pop (Table): The population table.\r\n▷ joins (List[Join]): A list of classes describing the joins.\r\nIncludes per, the peripheral table used for this join.\r\n▷ features (List[Feature]): A list of classes describing the\r\nfeatures, both in abstract form as well as the underlying\r\ndata.\r\n1: procedure GENERATEROWWISE(pop, joins, features)\r\n2: for r = 0; r < pop.nrows; r ← r + 1 do\r\n3: m ← []\r\n4: for j in joins do\r\n5: m ← m + [FINDMATCHES(r, pop, j)]\r\n6: end for\r\n7: for f in features do\r\n8: f[r] ← AGGREGATE(pop, m, f)\r\n9: end for\r\n10: end for\r\n11: end procedure\r\nFig. 4. Row-wise generation of feature sets. The pseudo-code is simplified.\r\nIn practice, we do not calculate the entire feature set as once, but in batches\r\nof 100 features at a time.\r\nwhen we have the matches, we still have to retrieve the values\r\nto be aggregated which might not be located adjacent to one\r\nanother, leading to frequent cache misses. We therefore mem-\r\norize the values to be aggregated in a separate vector and then\r\napply the different aggregations to that vector. In functional\r\nprogramming, a pattern such as this is called memoization.\r\nThis idea is expressed in pseudo-code in Figure 5.\r\n6) Caching: We use columnar storage for both our data and\r\nthe generated features. This serves to speed up aggregation, but\r\nit creates a problem for the row-wise generation of the data, as\r\nthe index is forced jump from feature to feature as we generate\r\nthe row, leading to many cache misses. In order to overcome\r\nthis, we also use a caching technique, expressed in Figure 6.\r\n▷ Tests whether features f1 and f2 are based\r\n▷ on the same values.\r\n1: procedure HAVESAMEVALUES(f1, f2)\r\n2: return f1.dataUsed = f2.dataUsed ∧ f1.perCol =\r\nf2.perCol ∧ f1.popCol = f2.popCol ∧ f1.per =\r\nf2.per ∧ f1.cond = f2.cond\r\n3: end procedure\r\n4:\r\n▷ Executes the aggregation using the memoization tech-\r\nnique.\r\n▷ pop (Table): The population table.\r\n▷ m (List[int]): A list indicating the matching rows in the\r\nperipheral table.\r\n▷ f (Feature): A class describing the feature, both in\r\nabstract form as well as the underlying data.\r\n5: mem ← None\r\n6: procedure AGGREGATE(pop, m, f)\r\n7: if ¬mem ∨ ¬HAVESAMEVALUES(f, mem.f) then\r\n8: mem.f ← f\r\n▷ GetValues returns an array of values\r\n▷ from pop based on the abstract\r\n▷ metadata that is also used in HaveSameValues.\r\n9: mem.vals ← GETVALUES(pop, m, f)\r\n10: end if\r\n▷ AggregateVals aggregates the values\r\n▷ according to the aggration function\r\n▷ defined in f.\r\n11: return AGGREGATEVALS(f, mem.vals)\r\n12: end procedure\r\nFig. 5. Memoization\r\nC. Implementation\r\nWe recognize that it is possible to implement some of\r\nthe optimizations using existing database frameworks. For\r\ninstance, tsflex uses pandas time stamps as indices to achieve\r\nsimilar optimizations as we describe with the time stamp\r\nindex, albeit without the join keys [7]. However, to the best\r\nof our knowledge, it is not possible to implement all of\r\nthe optimizations described above using existing database\r\nframeworks. We therefore implement a customized database\r\nengine in C++ which is specifically designed for the purpose\r\nof relational learning and makes use of all of the opimizations\r\ndescribed above.\r\nIV. EVALUATION\r\nA. Approach\r\nWe evaluate the proposed algorithm by benchmarking it\r\nagainst five open-source implementations of static proposition-\r\nalization on five different public-domain datasets.\r\nThe five open-source benchmark implementations are the\r\nfollowing featuretools [4], tsfresh [5], TSFEL [6], tsflex [7]\r\nand KATS [31]. These particular implementations have been\r\nchosen based on their popularity, availablity and quality of\r\nimplementation.\r\nSince all of the benchmark implementations with the sole\r\nexception of featuretools only support time series and notFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 5\r\n▷ Calculates a new row in the feature set.\r\n▷ r (int): The row number\r\n▷ pop (Table): The population table.\r\n▷ joins (List[Join]): A list of classes describing the joins.\r\nIncludes per, the peripheral table used for this join.\r\n▷ features (List[Feature]): A list of classes describing the\r\nfeatures, both in abstract form as well as the underlying\r\ndata.\r\n▷ c (Array[float]): The cache we use to store the data.\r\n1: procedure MAKEROWWITHCACHING(r, pop, joins,\r\nfeatures, c)\r\n2: m ← []\r\n3: for j in joins do\r\n4: m ← m + [FINDMATCHES(r, pop, j)]\r\n5: end for\r\n6: cacheSize ← LENGTH(c)\r\nLENGTH(f eatures)\r\n7: m ← r mod cacheSize\r\n8: n ← LENGTH(features)\r\n9: for i = 0; i < n; i ← i + 1 do\r\n10: c[m ∗ n + i] ← AGGREGATE(pop, joins, m, f)\r\n11: end for\r\n12: if (r + 1) mod cacheSize = 0 then\r\n13: for i = 0; i < n; i ← i + 1 do\r\n14: for j = 0; j < cacheSize; j ← j + 1 do\r\n15: k ← r + 1 − cacheSize + j\r\n16: features[i][k] ← c[j ∗ n + i]\r\n17: end for\r\n18: end for\r\n19: end if\r\n20: end procedure\r\nFig. 6. Row-wise generation of feature sets with caching. This is a simpli-\r\nfication, because we assume that the number of rows can be exactly divided\r\nby the number of lines in c, which is usually not the case.\r\nrelational data in general, our analysis is limited to time series\r\nproblems.\r\nThe benchmark datasets considered are the following:\r\n1) Air pollution [32] The dataset contains hourly data on air\r\npollution and weather in Beijing, China. The challenge\r\nis to predict the pm2.5 concentration for the next hour.\r\n2) Dodgers [33] The dataset contains five-minute measure-\r\nments of traffic near Los Angeles. The traffic volume\r\ncan be affected by a game hosted by the LA Dodgers\r\nin the nearby stadium, but not to the extent that it is\r\nvery obvious to spot such an event in the data. The LA\r\nDodgers are a popular baseball team from Los Angeles.\r\nThe challenge is to predict the traffic volume for the\r\nnext five-minute interval.\r\n3) Energy The dataset contains measurements of the elec-\r\ntricity consumption of a single household in ten-minute-\r\nintervals. The challenge is to predict the energy con-\r\nsumption of all household appliances for the next ten-\r\nminute interval.\r\n4) Interstate94 The dataset contains hourly data on traffic\r\nvolume on the Interstate 94 from Minneapolis to StPaul. ˙\r\nThe challenge is to predict the traffic volume for the next\r\nhour.\r\n5) Tetouan [34] The dataset contains the electricity con-\r\nsumption of three different zones in Tetouan City, Mor-\r\nocco measured in ten-minute intervals. The challenge is\r\nto predict the electricity consumption in Zone 1 for the\r\nnext ten-minute interval.\r\nWith the exception of tsflex, the different implementations\r\ncontain their own set of aggregations, which only partially\r\nintersect with the aggregations we have implemented for the\r\npurposes of this benchmark. This makes it difficult to conduct\r\na fair comparison of the overall runtime of the different\r\nimplementations.\r\nTo overcome this difficulty, we have chosen the runtime\r\nper generated feature as the key metric by which we compare\r\nthe different implementations. We justify this approach on the\r\nbasis of the following arguments:\r\n1) The main idea of the static propositionalization algo-\r\nrithm is to produce a large number of features in a\r\nshort period of time. Therefore, the number of features\r\ngenerated per second is a key metric by which such\r\napproaches should be evaluated.\r\n2) If algorithm A produces 10 features per second and\r\nalgorithm B produces 20 features per second, it would\r\nbe reasonable to say that algorithm B is twice as fast\r\nas algorithm A. Therefore, the speed of static proposi-\r\ntionalization algorithms should be measured in terms of\r\ngenerated features per second or its inverse, runtime per\r\ngenerated feature.\r\nFor the case of tsflex, which does not provide its own set\r\nof aggregations, we use the exact same aggregations we have\r\nimplemented for our own approach, which makes the results\r\nvery comparable. Note that the overall number of generated\r\nfeatures still differs, because tsflex considers fewer values to be\r\naggregated. For instance, our approach would also consider the\r\ndifference between the time stamp “now” and the time stamp\r\nof the instances we are aggregating as an additional value to\r\nbe aggregated.\r\nIn addition to the runtime, we also consider the quality of\r\nthe generated features. For this purpose, we train an untuned\r\ngradient boosting regressor on the generated features. We then\r\nevaluate key metrics for predictive accuracy on a testing set.\r\nThe key metrics are root mean squared error (RMSE), mean\r\nabsolute error (MAE) and the predictive r-squared. All of\r\nthese metrics are commonly used for regression problems in\r\na machine learning context.\r\nB. Results\r\nThis section contains the results of our evaluation. For each\r\nof the five benchmark datasets and the algorithm considered,\r\nwe present the following metrics:\r\n• Number of features: The number of features generated by\r\nthe algorithm.\r\n• Runtime: The overall runtime taken relative to the runtime\r\nof the fastest approach.\r\n• Runtime/feat.: The overall runtime per feature relative to\r\nthe runtime of the fastest approach.\r\n• R-squared: Even though we have measured three different\r\nmetrics, we only choose to present the preditive R2 dueFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 6\r\nTABLE I\r\nRESULTS FOR THE AIR POLLUTION DATASET\r\nalgorithm features runtime runtime/feat. R2\r\nfastprop 453 1.0 1.0 0.949\r\ntsflex 259 63.5 99.3 0.944\r\nfeaturetools 125 85.5 309.7 0.948\r\ntsfel 378 254.3 304.8 0.308\r\ntsfresh 84 47.7 257.0 0.781\r\nkats 189 482.7 1156.7 0.349\r\nTABLE II\r\nRESULTS FOR THE INTERSTATE94 DATASET\r\nalgorithm features runtime runtime/feat. R2\r\nfastprop 125 1.0 1.0 0.979\r\ntsflex 36 18.8 59.3 0.979\r\nfeaturetools 59 74.1 157.9 0.972\r\ntsfel 54 97.1 224.8 0.876\r\ntsfresh 12 20.7 215.8 0.172\r\nkats 27 141.1 653.3 0.941\r\nto space restrictions. We have done so after determining\r\nthat the three metrics considered coincide.\r\nResults are shown in Tables I, II, III, IV and V.\r\nWe find that the proposed algorithm is at least about 60\r\ntimes faster than any of the benchmark algorithms in terms of\r\nruntime per feature. In some cases, it is over 1000 times faster\r\nthan the benchmark algorithm.\r\nThe predictive accuracy is comparable to tsflex, and con-\r\nsiderably better than all other benchmark algorithms on all of\r\nthe datasets considered. As we configured tsflex to mimic the\r\naggregations we use for our own approach, it is not surprising\r\nthat the predictive accuracy is comparable.\r\nV. CONCLUSION\r\nIn this study, we have designed a customized database\r\nengine for automated feature engineering on relational data\r\nand time series. Our goal was to improve the speed of static\r\npropositionalization approaches.\r\nTABLE III\r\nRESULTS FOR THE DODGERS DATASET\r\nalgorithm features runtime runtime/feat. R2\r\nfastprop 207 1.0 1.0 0.795\r\ntsflex 38 14.8 70.9 0.802\r\nfeaturetools 59 57.3 201.1 0.794\r\ntsfel 54 74.5 285.5 0.00\r\ntsfresh 12 14.8 255.6 0.667\r\nkats 27 95.6 732.6 0.001\r\nTABLE IV\r\nRESULTS FOR THE ENERGY DATASET\r\nalgorithm features runtime runtime/feat. R2\r\nfastprop 1273 1.0 1.0 0.171\r\ntsflex 1058 87.1 108.6 0.114\r\nfeaturetools 367 77.7 269.7 0.100\r\ntsfel 1566 72.0 58.5 0.114\r\ntsfresh 348 59.3 217.0 0.108\r\nkats1\r\n– – – –\r\nThroughout this study, we have argued that features gener-\r\nated per second (or its inverse runtime per feature) is a key\r\nmeasure for static propositionalization approaches. The results\r\nfrom our benchmarks support this argument. Even though our\r\nimplementation was consistently faster than other approaches\r\nin terms of overall runtime, it generated more features in\r\nthat shorter amount of time. This enabled us to consistently\r\noutperform the benchmark algorithms in terms of predictive\r\naccuracy, with the exception of tsflex which we have set\r\nhave up to apply the same aggregations and therefore yield\r\ncomparable results in terms of predictive accuracy.\r\nWe have demonstrated that a static propositionalization\r\napproach implemented using this customized database engine\r\nis at least about 60 times and up to 1000 times faster\r\nthan implementations on non-customized database engines.\r\nThis speedup is about the same as the speedup attained by\r\nintroducing GPUs to the deep learning literature [1]. We\r\ntherefore conclude that using customized database enginesFASTPROP: A CUSTOMIZED DATABASE ENGINE FOR FAST PROPOSITIONALIZATION 7\r\nTABLE V\r\nRESULTS FOR THE TETOUAN DATASET\r\nalgorithm features runtime runtime/feat. R2\r\nfastprop 412 1.0 1.0 0.997\r\ntsflex 296 69.8 85.1 0.996\r\nfeaturetools 136 88.1 266.9 0.981\r\ntsfel 432 275.6 262.8 0.983\r\ntsfresh 96 52.9 227.0 0.940\r\nkats 216 556.5 1061.5 0.978\r\ncould potentially revolutionize the applicability of relational\r\nlearning approaches.\r\nREFERENCES\r\n[1] R. Raina, A. Madhavan, and A. Y. Ng, “Large-scale deep unsupervised\r\nlearning using graphics processors,” in Proceedings of the 26th annual\r\ninternational conference on machine learning, 2009, pp. 873–880.\r\n[2] S. Mittal and S. Vaishay, “A survey of techniques for optimizing deep\r\nlearning on gpus,” Journal of Systems Architecture, vol. 99, p. 101635,\r\n2019.\r\n[3] S. Kramer, N. Lavrac, and P. 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Van Hoecke,\r\n“Tsflex: Flexible time series processing & feature extraction,”\r\narXiv:2111.12429 [cs, eess, stat], Dec. 2021.\r\n[8] J. Struyf and H. Blockeel, “Relational Learning,” in Encyclopedia of\r\nMachine Learning and Data Mining, C. Sammut and G. I. Webb, Eds.\r\nBoston, MA: Springer US, 2017.\r\n[9] “Propositional Logic,” in Encyclopedia of Machine Learning and Data\r\nMining, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US,\r\n2017.\r\n[10] P. A. Flach, “First-Order Logic,” in Encyclopedia of Machine Learning\r\nand Data Mining, C. Sammut and G. I. Webb, Eds. Boston, MA:\r\nSpringer US, 2017.\r\n[11] S. Scott and S. Matwin, “Feature engineering for text classification,” in\r\nICML, vol. 99. Citeseer, 1999, pp. 379–388.\r\n[12] G. Forman, “Feature engineering for a gene regulation prediction task,”\r\nACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 106–107, Dec.\r\n2002.\r\n[13] P. 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Blockeel, “LazyBum: Decision Tree\r\nLearning Using Lazy Propositionalization,” in Inductive Logic Program-\r\nming, ser. Lecture Notes in Computer Science, D. Kazakov and C. Erten,\r\nEds. Cham: Springer International Publishing, 2020, pp. 98–113.\r\n[18] A. J. Knobbe, M. D. Haas, and A. Siebes, “Propositionalisation and\r\naggregates,” in In Proceedings of the Fifth European Conference on\r\nPrinciples of Data Mining and Knowledge Discovery, 2001.\r\n[19] M.-A. Krogel and S. Wrobel, “Transformation-based learning using\r\nmultirelational aggregation,” in International Conference on Inductive\r\nLogic Programming. Springer, 2001, pp. 142–155.\r\n[20] A. Van Assche, C. Vens, H. Blockeel, and S. Dzeroski, “First order ˇ\r\nrandom forests: Learning relational classifiers with complex aggregates,”\r\nMachine Learning, vol. 64, no. 1, pp. 149–182, Sep. 2006.\r\n[21] N. Landwehr, K. Kersting, and L. D. Raedt, “Integrating Naive Bayes\r\nand FOIL,” The Journal of Machine Learning Research, vol. 8, pp. 481–\r\n507, Dec. 2007.\r\n[22] M. Boulle, C. Charnay, and N. Lachiche, “A scalable robust and ´\r\nautomatic propositionalization approach for Bayesian classification of\r\nlarge mixed numerical and categorical data,” Machine Learning, vol.\r\n108, no. 2, pp. 229–266, 2019.\r\n[23] M. Perovsek, A. Vavpeti ˇ c, J. Kranjc, B. Cestnik, and N. Lavra ˇ c,ˇ\r\n“Wordification: Propositionalization by unfolding relational data into\r\nbags of words,” Expert Systems with Applications, vol. 42, no. 17, pp.\r\n6442–6456, Oct. 2015.\r\n[24] A. J. Knobbe, A. Siebes, and D. Van Der Wallen, “Multi-relational\r\ndecision tree induction,” in European Conference on Principles of Data\r\nMining and Knowledge Discovery. Springer, 1999, pp. 378–383.\r\n[25] H. A. Leiva, “MRDTL: A multi-relational decision tree learning algo-\r\nrithm,” 2002.\r\n[26] A. Atramentov, H. Leiva, and V. Honavar, “A multi-relational decision\r\ntree learning algorithm–implementation and experiments,” in Interna-\r\ntional Conference on Inductive Logic Programming. Springer, 2003,\r\npp. 38–56.\r\n[27] A. Crellin-Quick, “Cesium: Open-Source Platform for Time Series\r\nInference,” cesium-ml, Jan. 2022.\r\n[28] D. M. Burns and C. M. Whyne, “Seglearn: A Python Package for\r\nLearning Sequences and Time Series,” Journal of Machine Learning\r\nResearch, vol. 19, no. 83, pp. 1–7, 2018.\r\n[29] H. T. Lam, J.-M. Thiebaut, M. Sinn, B. Chen, T. Mai, and O. Alkan,\r\n“One button machine for automating feature engineering in relational\r\ndatabases,” arXiv:1706.00327 [cs], Jun. 2017.\r\n[30] G. Shmueli and O. R. Koppius, “Predictive analytics in information\r\nsystems research,” MIS quarterly, pp. 553–572, 2011.\r\n[31] F. I. D. S. Team, “Kats,” https://github.com/facebookresearch/Kats, 2021.\r\n[32] S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia,\r\n“On field calibration of an electronic nose for benzene estimation in\r\nan urban pollution monitoring scenario,” Sensors and Actuators B:\r\nChemical, vol. 129, no. 2, pp. 750–757, 2008. [Online]. Available:\r\nhttps://www.sciencedirect.com/science/article/pii/S0925400507007691\r\n[33] A. Ihler, J. Hutchins, and P. Smyth, “Adaptive event detection with time-\r\nvarying poisson processes,” in Proceedings of the 12th ACM SIGKDD\r\ninternational conference on Knowledge discovery and data mining,\r\n2006, pp. 207–216.\r\n[34] A. Salam and A. El Hibaoui, “Comparison of machine learning al-\r\ngorithms for the power consumption prediction:-case study of tetouan\r\ncity–,” in 2018 6th International Renewable and Sustainable Energy\r\nConference (IRSEC). IEEE, 2018, pp. 1–5."
  },
  {
    "path": "benchmarks/requirements.for-fbprophet.txt",
    "content": "-r requirements.for-pystan.txt\nconvertdate==2.4.0\nholidays==0.23\nLunarCalendar==0.0.9\npandas==1.3.5\npystan==2.19.1.1\ntqdm==4.66.2"
  },
  {
    "path": "benchmarks/requirements.for-kats.txt",
    "content": "-r requirements.for-fbprophet.txt\nfbprophet==0.7.1\npackaging==21.3"
  },
  {
    "path": "benchmarks/requirements.for-pystan.txt",
    "content": "Cython==3.0.9\nNumPy==1.21.6"
  },
  {
    "path": "benchmarks/requirements.txt",
    "content": "-r requirements.for-kats.txt\nkats==0.2.0\ntsfresh==0.18\nfeaturetools==1.12.1\ntsflex==0.3.0\ncesium==0.12.1\ntsfel==0.1.4"
  },
  {
    "path": "benchmarks/run_benchmarks.py",
    "content": "\"\"\"\nRuns the benchmark scripts\n\"\"\"\n\nfrom typing import Callable, Sequence\n\nfrom benchmarks import (\n    benchmark_air_pollution,\n    benchmark_dodgers,\n    benchmark_energy,\n    benchmark_interstate94,\n    benchmark_tetuan,\n)\nfrom benchmarks.drivers import (\n    drive_featuretools,\n    drive_getml,\n    drive_kats,\n    drive_tsfel,\n    drive_tsflex,\n    drive_tsfresh,\n)\n\nRUNS = 1\n\ndrivers_without_kats: Sequence[Callable] = [\n    drive_getml,\n    drive_tsflex,\n    drive_featuretools,\n    drive_tsfel,\n    drive_tsfresh,\n]\n\n\nall_drivers: Sequence[Callable] = drivers_without_kats + [drive_kats]\n\n\nair_pollution = benchmark_air_pollution(all_drivers, runs=RUNS)\nprint(air_pollution)\ninterstate94 = benchmark_interstate94(all_drivers, runs=RUNS)\nprint(interstate94)\ndodgers = benchmark_dodgers(all_drivers, runs=RUNS)\nprint(dodgers)\nenergy = benchmark_energy(drivers_without_kats, runs=RUNS)\nprint(energy)\ntetuan = benchmark_tetuan(all_drivers, runs=RUNS)\nprint(tetuan)\n\nprint()\nprint()\n\nprint(air_pollution)\nprint(interstate94)\nprint(dodgers)\nprint(energy)\nprint(tetuan)\n"
  },
  {
    "path": "benchmarks/run_benchmarks.sh",
    "content": "#!/bin/sh\ncd /home/getml/engine\n./getML --install --home-directory=/home/getml/storage &\n\ncd /home/getml/benchmarks\npython3.8 run_benchmarks.py"
  },
  {
    "path": "demo-notebooks/Dockerfile",
    "content": "FROM python:3.11.9\n\nRUN apt-get update \\\n    && apt-get install -y \\\n    locales \\\n    && echo \"en_US.UTF-8 UTF-8\" > /etc/locale.gen \\\n    && locale-gen\n\nRUN useradd getml\nUSER getml\nWORKDIR /home/getml\n\nCOPY --chown=getml:getml --chmod=0777 ./requirements.txt /home/getml/requirements.txt\n\nENV PATH=\"/home/getml/.local/bin:$PATH\"\nRUN python3.11 \\\n    -mpip install \\\n    -r /home/getml/requirements.txt\n\nCOPY --chown=getml:getml . /home/getml/demo/\n\nEXPOSE 1709 8888\nCMD [ \"/home/getml/.local/bin/jupyter\", \"lab\", \"--ip='*'\", \"--port=8888\", \"--notebook-dir='/home/getml/demo'\" ]"
  },
  {
    "path": "demo-notebooks/README.md",
    "content": "# Usage\n\nThe provided notebooks can be checked and used in different ways. \n\n  * [Reading Online](#reading-online)\n  * [Experimenting Locally](#experimenting-locally)\n    * [Using Docker](#using-docker)\n    * [On the Machine (Linux/x64 & arm64)](#on-the-machine-linuxx64--arm64)\n\n## Reading Online \n\nAs github renders the notebooks, they can each be viewed by just opening and scrolling through them. For convenience, the output of each cells execution is included.\n\n## Experimenting Locally\n\nTo experiment with the notebooks, such as playing with different pipelines and predictors, it is best to run them on a local machine. Linux users with an x64 or arm64 architecture can choose from one of the options provided below. Soon, we will offer a simple, container-based solution compatible with all major systems (Windows, Mac) and will also support ARM-based architectures.\n\n### Using Docker\n\nThere are a `docker-compose.yml` and a `Dockerfile` for easy usage provided.\n\nSimply clone this repository and command to start the `notebooks` service. The image, it depends on, will be build if it is not already available.\n\n```\n$ git clone https://github.com/getml/getml-community.git\n$ cd getml-community/demo-notebooks\n$ docker compose up notebooks\n```\n\nTo open Jupyter Lab in the browser, look for the following lines in the output and copy-paste it in your browser:\n\n```\nOr copy and paste one of these URLs:\n\nhttp://localhost:8888/lab?token=<generated_token>\n```\n\n> [!NOTE]  \n> Using alternatives to [Docker Desktop](https://www.docker.com/products/docker-desktop) like  \n> * [Podman](https://podman.io),  \n> * [Podman Desktop](https://podman-desktop.io) or  \n> * [Rancher Desktop](https://rancherdesktop.io) with a container engine like dockerd(moby) or containerd(nerdctl)\n> \n> allows bind-mounting the notebooks in a user-writeable way (this might need to include `userns_mode: keep-id`) instead of having to `COPY` them in. In combination with volume-binding `/home/getml/.getML/logs` and `/home/getml/.getML/projects`, runs and changes can be persisted across containers.\n\n### On the Machine (Linux/x64 & arm64)\n\nAlternatively, getML and the notebooks can be run natively on the local Linux machine by having certain software installed, like Python and some Python libraries, Jupyter-Lab and the getML Engine. The [getML Python library](https://github.com/getml/getml-community) provides an Engine version without [Enterprise features](https://getml.com/latest/enterprise/benefits).\n\nThe following commands will set up a Python environment with necessary Python libraries and Jupyter-Lab\n\n```\n$ git clone https://github.com/getml/getml-community.git\n$ cd getml-community/demo-notebooks\n$ pipx install hatch\n$ hatch env create\n$ hatch shell\n$ pip install -r requirements.txt\n$ jupyter-lab\n```\n\nWith the last command, Jupyter-Lab should automatically open in the browser. If not, look for the following lines in the output and copy-paste it in your browser:\n\n```\nOr copy and paste one of these URLs:\n\nhttp://localhost:8888/lab?token=<generated_token>\n```"
  },
  {
    "path": "demo-notebooks/adventure_works.ipynb",
    "content": "{\n    \"cells\": [\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"# AdventureWorks - Predicting customer churn\\n\",\n                \"\\n\",\n                \"In this notebook, we will demonstrate how getML can be used for a customer churn project using a synthetic dataset of a fictional company.\\n\",\n                \"\\n\",\n                \"Summary:\\n\",\n                \"\\n\",\n                \"- Prediction type: __Classification model__\\n\",\n                \"- Domain: __Customer loyalty__\\n\",\n                \"- Prediction target: __churn__ \\n\",\n                \"- Population size: __19704__\\n\",\n                \"\\n\",\n                \"_Author: Dr. Patrick Urbanke_\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {\n                \"tags\": [\n                    \"remove_cell_on_docs\"\n                ]\n            },\n            \"source\": [\n                \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/adventure_works.ipynb\\\">\\n\",\n                \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n                \"</a>\"\n            ]\n        },\n        {\n            \"attachments\": {},\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## Background\\n\",\n                \"\\n\",\n                \"[AdventureWorks](https://github.com/Microsoft/sql-server-samples/releases/tag/adventureworks) is a fictional company that sells bicycles. It is used by Microsoft to showcase how its MS SQL Server can be used to manage business data. Since this dataset resembles a real-world customer database and it is open-source, we will use it to showcase how getML can be used for a classic customer churn project (real customer databases are not easily available for the purposes of showcasing and benchmarking, for reasons of data privacy).\\n\",\n                \"\\n\",\n                \"The dataset has been downloaded from the [CTU Prague relational learning repository](https://relational-data.org/dataset/AdventureWorks) (Motl and Schulte, 2015).\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## Analysis\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"Let's get started with the analysis and set up your session:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 1,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"Note: you may need to restart the kernel to use updated packages.\\n\",\n                        \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n                        \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912122459.log.\\n\",\n                        \"\\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'adventure_works'</span>.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Connected to project \\u001b[32m'adventure_works'\\u001b[0m.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                }\n            ],\n            \"source\": [\n                \"%pip install -q \\\"getml==1.5.0\\\" \\\"pandas~=2.2\\\" \\n\",\n                \"\\n\",\n                \"import numpy as np\\n\",\n                \"import pandas as pd\\n\",\n                \"\\n\",\n                \"import getml\\n\",\n                \"\\n\",\n                \"getml.engine.launch()\\n\",\n                \"getml.set_project('adventure_works')\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 1. Loading data\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 1.1 Download from source\\n\",\n                \"\\n\",\n                \"We begin by downloading the data:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 2,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/plain\": [\n                            \"Connection(dbname='AdventureWorks2014',\\n\",\n                            \"           dialect='mysql',\\n\",\n                            \"           host='relational.fel.cvut.cz',\\n\",\n                            \"           port=3306)\"\n                        ]\n                    },\n                    \"execution_count\": 2,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"conn = getml.database.connect_mariadb(\\n\",\n                \"    host=\\\"relational.fel.cvut.cz\\\",\\n\",\n                \"    dbname=\\\"AdventureWorks2014\\\",\\n\",\n                \"    port=3306,\\n\",\n                \"    user=\\\"guest\\\",\\n\",\n                \"    password=\\\"ctu-relational\\\"\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"conn\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 3,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [],\n            \"source\": [\n                \"def load_if_needed(name):\\n\",\n                \"    \\\"\\\"\\\"\\n\",\n                \"    Loads the data from the relational learning\\n\",\n                \"    repository, if the data frame has not already\\n\",\n                \"    been loaded.\\n\",\n                \"    \\\"\\\"\\\"\\n\",\n                \"    if not getml.data.exists(name):\\n\",\n                \"        data_frame = getml.DataFrame.from_db(\\n\",\n                \"            name=name,\\n\",\n                \"            table_name=name,\\n\",\n                \"            conn=conn\\n\",\n                \"        )\\n\",\n                \"        data_frame.save()\\n\",\n                \"    else:\\n\",\n                \"        data_frame = getml.data.load_data_frame(name)\\n\",\n                \"    return data_frame\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 4,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [],\n            \"source\": [\n                \"product = load_if_needed(\\\"Product\\\")\\n\",\n                \"sales_order_detail = load_if_needed(\\\"SalesOrderDetail\\\")\\n\",\n                \"sales_order_header = load_if_needed(\\\"SalesOrderHeader\\\")\\n\",\n                \"sales_order_reason = load_if_needed(\\\"SalesOrderHeaderSalesReason\\\")\\n\",\n                \"special_offer = load_if_needed(\\\"SpecialOffer\\\")\\n\",\n                \"store = load_if_needed(\\\"Store\\\")\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 5,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">   ProductID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">    MakeFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">FinishedGoodsFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SafetyStockLevel</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ReorderPoint</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">DaysToManufacture</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ProductSubcategoryID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ProductModelID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Name                 </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ProductNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Color        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">StandardCost </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ListPrice    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Size         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SizeUnitMeasureCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">WeightUnitMeasureCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Weight       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ProductLine  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Class        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Style        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SellStartDate      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SellEndDate  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">DiscontinuedDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">    unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">        unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">  unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string        </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string        </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">750</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Adjustable Race</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">AR-5381</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">694215B7-08F7-4C0D-ACB1-D734BA44...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">750</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Bearing Ball</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BA-8327</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">58AE3C20-4F3A-4749-A7D4-D568806C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB Ball Bearing</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BE-2349</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">9C21AED2-5BFA-4F18-BCB8-F11638DC...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Headset Ball Bearings</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BE-2908</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">ECFED6CB-51FF-49B5-B06C-7D8AC834...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">316</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Blade</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BL-2036</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">E73E9750-603B-4131-89F5-3DD15ED5...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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           \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        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           \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>499</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">995</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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  \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">500</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">375</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">96</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">ML Bottom Bracket</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB-8107</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">44.9506</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">101.2400</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">G</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">168.00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">71AB847F-D091-42D6-B735-7B0C2D82...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>500</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">996</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">500</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">375</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">97</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">HL Bottom Bracket</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB-9108</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">53.9416</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">121.4900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">G</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">170.00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">H</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">230C47C5-08B2-4CE3-B706-69C0BDD6...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>501</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">997</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">31</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Road-750 Black, 44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BK-R19B-44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">343.6496</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">539.9900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">19.77</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">44CE4802-409F-43AB-9B27-CA534218...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>502</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">998</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">31</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Road-750 Black, 48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BK-R19B-48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">343.6496</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">539.9900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">20.13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">3DE9A212-1D49-40B6-B10A-F564D981...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>503</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">999</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                          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       <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"   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class=\\\"unused_string\\\">BK-R19B-52</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">343.6496</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">539.9900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">52</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">20.42</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">AE638923-2B67-4679-B90E-ABBAB17D...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    504 rows x 25 columns<br />\\n\",\n                            \"    memory usage: 0.17 MB<br />\\n\",\n                            \"    name: Product<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name      ProductID       MakeFlag   FinishedGoodsFlag   SafetyStockLevel   ...   SellStartDate         SellEndDate  \\n\",\n                            \"role   unused_float   unused_float        unused_float       unused_float   ...   unused_string         unused_string\\n\",\n                            \"   0              1              0                   0               1000   ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   1              2              0                   0               1000   ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   2              3              1                   0                800   ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   3              4              0                   0                800   ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   4            316              1                   0                800   ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"                ...            ...                 ...                ...         ...                   ...          \\n\",\n                            \" 499            995              1                   1                500   ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 500            996              1                   1                500   ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 501            997              1                   1                100   ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 502            998              1                   1                100   ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 503            999              1                   1                100   ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \"\\n\",\n                            \"name   DiscontinuedDate   rowguid                            ModifiedDate       \\n\",\n                            \"role   unused_string      unused_string                      unused_string      \\n\",\n                            \"   0   NULL               694215B7-08F7-4C0D-ACB1-D734BA44...   2014-02-08 09:01:36\\n\",\n                            \"   1   NULL               58AE3C20-4F3A-4749-A7D4-D568806C...   2014-02-08 09:01:36\\n\",\n                            \"   2   NULL               9C21AED2-5BFA-4F18-BCB8-F11638DC...   2014-02-08 09:01:36\\n\",\n                            \"   3   NULL               ECFED6CB-51FF-49B5-B06C-7D8AC834...   2014-02-08 09:01:36\\n\",\n                            \"   4   NULL               E73E9750-603B-4131-89F5-3DD15ED5...   2014-02-08 09:01:36\\n\",\n                            \"       ...                ...                                ...                \\n\",\n                            \" 499   NULL               71AB847F-D091-42D6-B735-7B0C2D82...   2014-02-08 09:01:36\\n\",\n                            \" 500   NULL               230C47C5-08B2-4CE3-B706-69C0BDD6...   2014-02-08 09:01:36\\n\",\n                            \" 501   NULL               44CE4802-409F-43AB-9B27-CA534218...   2014-02-08 09:01:36\\n\",\n                            \" 502   NULL               3DE9A212-1D49-40B6-B10A-F564D981...   2014-02-08 09:01:36\\n\",\n                            \" 503   NULL               AE638923-2B67-4679-B90E-ABBAB17D...   2014-02-08 09:01:36\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"504 rows x 25 columns\\n\",\n                            \"memory usage: 0.17 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 5,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"product\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 6,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"  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right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>  name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesOrderDetailID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">    OrderQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">   ProductID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SpecialOfferID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">CarrierTrackingNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">UnitPrice    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">UnitPriceDiscount</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">LineTotal    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"     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               \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2024.9940</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2024.994000</td>\\n\",\n                            \"          \\n\",\n            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    \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2024.9940</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">6074.982000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">7ABB600D-1E77-41BE-9FE5-B9142CFC...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43659</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">778</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2024.9940</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2024.994000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">475CF8C6-49F6-486E-B0AD-AFC6A50C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43659</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">771</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2039.9940</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2039.994000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">04C4DE91-5815-45D6-8670-F462719F...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43659</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">772</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2039.9940</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2039.994000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">5A74C7D2-E641-438E-A7AC-37BF2328...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"         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class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>121312</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75122</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"      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    <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">878</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">21.9800</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">21.980000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">8CAD6675-18CC-4F47-8287-97B41A8E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>121313</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75122</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">121314</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">712</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">8.9900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">8.990000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">84F1C363-1C50-4442-BE16-541C59B6...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>121314</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75123</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">121315</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">878</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">21.9800</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">21.980000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C18B6476-429F-4BB1-828E-2BE5F82A...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>121315</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75123</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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       \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">879</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">159.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">159.000000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">75A89C6A-C60A-47EA-8A52-B52A9C43...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>121316</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75123</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">121317</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">712</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">8.9900</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">8.990000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">73646D26-0461-450D-8019-2C6C8586...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    121317 rows x 11 columns<br />\\n\",\n                            \"    memory usage: 21.60 MB<br />\\n\",\n                            \"    name: SalesOrderDetail<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"  name   SalesOrderID   SalesOrderDetailID       OrderQty      ProductID   ...   UnitPrice       UnitPriceDiscount\\n\",\n                            \"  role   unused_float         unused_float   unused_float   unused_float   ...   unused_string   unused_string    \\n\",\n                            \"     0          43659                    1              1            776   ...   2024.9940       0.0000           \\n\",\n                            \"     1          43659                    2              3            777   ...   2024.9940       0.0000           \\n\",\n                            \"     2          43659                    3              1            778   ...   2024.9940       0.0000           \\n\",\n                            \"     3          43659                    4              1            771   ...   2039.9940       0.0000           \\n\",\n                            \"     4          43659                    5              1            772   ...   2039.9940       0.0000           \\n\",\n                            \"                  ...                  ...            ...            ...         ...             ...              \\n\",\n                            \"121312          75122               121313              1            878   ...   21.9800         0.0000           \\n\",\n                            \"121313          75122               121314              1            712   ...   8.9900          0.0000           \\n\",\n                            \"121314          75123               121315              1            878   ...   21.9800         0.0000           \\n\",\n                            \"121315          75123               121316              1            879   ...   159.0000        0.0000           \\n\",\n                            \"121316          75123               121317              1            712   ...   8.9900          0.0000           \\n\",\n                            \"\\n\",\n                            \"  name   LineTotal       rowguid                            ModifiedDate       \\n\",\n                            \"  role   unused_string   unused_string                      unused_string      \\n\",\n                            \"     0   2024.994000     B207C96D-D9E6-402B-8470-2CC176C4...   2011-05-30 22:00:00\\n\",\n                            \"     1   6074.982000     7ABB600D-1E77-41BE-9FE5-B9142CFC...   2011-05-30 22:00:00\\n\",\n                            \"     2   2024.994000     475CF8C6-49F6-486E-B0AD-AFC6A50C...   2011-05-30 22:00:00\\n\",\n                            \"     3   2039.994000     04C4DE91-5815-45D6-8670-F462719F...   2011-05-30 22:00:00\\n\",\n                            \"     4   2039.994000     5A74C7D2-E641-438E-A7AC-37BF2328...   2011-05-30 22:00:00\\n\",\n                            \"         ...             ...                                ...                \\n\",\n                            \"121312   21.980000       8CAD6675-18CC-4F47-8287-97B41A8E...   2014-06-29 22:00:00\\n\",\n                            \"121313   8.990000        84F1C363-1C50-4442-BE16-541C59B6...   2014-06-29 22:00:00\\n\",\n                            \"121314   21.980000       C18B6476-429F-4BB1-828E-2BE5F82A...   2014-06-29 22:00:00\\n\",\n                            \"121315   159.000000      75A89C6A-C60A-47EA-8A52-B52A9C43...   2014-06-29 22:00:00\\n\",\n                            \"121316   8.990000        73646D26-0461-450D-8019-2C6C8586...   2014-06-29 22:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"121317 rows x 11 columns\\n\",\n                            \"memory usage: 21.60 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 6,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_detail\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 7,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th> name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">RevisionNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      Status</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">OnlineOrderFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">  CustomerID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesPersonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\"> TerritoryID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">BillToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ShipToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ShipMethodID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CreditCardID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CurrencyRateID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">OrderDate          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">DueDate            </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ShipDate           </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SalesOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">PurchaseOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">AccountNumber </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">CreditCardApprovalCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SubTotal     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">TaxAmt       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Freight      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">TotalDue     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Comment      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">  unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header 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class=\\\"left\\\">29825</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">279</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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    <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">16281</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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    \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO43659</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">PO522145787</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000676</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">105041Vi84182</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">20565.6206</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1971.5149</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">616.0984</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">23153.2339</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">79B65321-39CA-4115-9CBA-8FE0903E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43660</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n             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unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">279</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 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        \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5618</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                   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                 \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1294.2529</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">124.2483</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">38.8276</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1457.3288</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n               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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \" 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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">517</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"    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       \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">85274Vi6854</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">32726.4786</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">3153.7696</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">985.5530</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">36865.8012</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D91B9131-18A4-4A11-BC3A-90B6F53E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    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             \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              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         \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000227</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">125295Vi53935</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">28832.5289</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2775.1646</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">867.2389</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">32474.9324</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4A1ECFC0-CC3A-4740-B028-1C50BB48...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"        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    \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO43663</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">PO18009186470</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000510</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">45303Vi22691</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">419.4589</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">40.2681</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">12.5838</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">472.3108</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">9B1E7A40-6AE0-4AD3-811C-A6495185...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-06-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                    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      \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6761</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75119</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-011981</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">429826Vi35166</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">42.2800</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">3.3824</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1.0570</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">46.7194</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">9382F1C9-383A-435F-9449-0EECEA21...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>31461</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75120</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                       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unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">28374</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">28374</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            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\\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75120</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-018749</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">929849Vi46003</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">84.9600</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">6.7968</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2.1240</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">93.8808</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">AE6A4FCF-FF73-4CD4-AF2C-5993D00D...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>31462</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75121</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">15251</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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  \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">26553</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">26553</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14220</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75121</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-015251</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">529864Vi73738</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">74.9800</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">5.9984</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1.8745</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">82.8529</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D7395C0E-00CB-4BFA-A238-0D6A9F49...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>31463</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75122</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                        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unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14616</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14616</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">18719</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75122</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-015868</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">330022Vi97312</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">30.9700</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2.4776</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.7743</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">34.2219</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4221035A-4159-492F-AF40-4363A64F...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>31464</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \" 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          <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14024</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14024</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                           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         \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">10084</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-12 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-07 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75123</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-018759</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">230370Vi51970</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">189.9700</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">15.1976</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4.7493</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">209.9169</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D54752FF-2B54-4BE5-95EA-3B72289C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-07-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    31465 rows x 26 columns<br />\\n\",\n                            \"    memory usage: 12.56 MB<br />\\n\",\n                            \"    name: SalesOrderHeader<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \" name   SalesOrderID   RevisionNumber         Status   OnlineOrderFlag   ...   Freight         TotalDue        Comment      \\n\",\n                            \" role   unused_float     unused_float   unused_float      unused_float   ...   unused_string   unused_string   unused_string\\n\",\n                            \"    0          43659                8              5                 0   ...   616.0984        23153.2339      NULL         \\n\",\n                            \"    1          43660                8              5                 0   ...   38.8276         1457.3288       NULL         \\n\",\n                            \"    2          43661                8              5                 0   ...   985.5530        36865.8012      NULL         \\n\",\n                            \"    3          43662                8              5                 0   ...   867.2389        32474.9324      NULL         \\n\",\n                            \"    4          43663                8              5                 0   ...   12.5838         472.3108        NULL         \\n\",\n                            \"                 ...              ...            ...               ...         ...             ...             ...          \\n\",\n                            \"31460          75119                8              5                 1   ...   1.0570          46.7194         NULL         \\n\",\n                            \"31461          75120                8              5                 1   ...   2.1240          93.8808         NULL         \\n\",\n                            \"31462          75121                8              5                 1   ...   1.8745          82.8529         NULL         \\n\",\n                            \"31463          75122                8              5                 1   ...   0.7743          34.2219         NULL         \\n\",\n                            \"31464          75123                8              5                 1   ...   4.7493          209.9169        NULL         \\n\",\n                            \"\\n\",\n                            \" name   rowguid                            ModifiedDate       \\n\",\n                            \" role   unused_string                      unused_string      \\n\",\n                            \"    0   79B65321-39CA-4115-9CBA-8FE0903E...   2011-06-06 22:00:00\\n\",\n                            \"    1   738DC42D-D03B-48A1-9822-F95A67EA...   2011-06-06 22:00:00\\n\",\n                            \"    2   D91B9131-18A4-4A11-BC3A-90B6F53E...   2011-06-06 22:00:00\\n\",\n                            \"    3   4A1ECFC0-CC3A-4740-B028-1C50BB48...   2011-06-06 22:00:00\\n\",\n                            \"    4   9B1E7A40-6AE0-4AD3-811C-A6495185...   2011-06-06 22:00:00\\n\",\n                            \"        ...                                ...                \\n\",\n                            \"31460   9382F1C9-383A-435F-9449-0EECEA21...   2014-07-06 22:00:00\\n\",\n                            \"31461   AE6A4FCF-FF73-4CD4-AF2C-5993D00D...   2014-07-06 22:00:00\\n\",\n                            \"31462   D7395C0E-00CB-4BFA-A238-0D6A9F49...   2014-07-06 22:00:00\\n\",\n                            \"31463   4221035A-4159-492F-AF40-4363A64F...   2014-07-06 22:00:00\\n\",\n                            \"31464   D54752FF-2B54-4BE5-95EA-3B72289C...   2014-07-06 22:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"31465 rows x 26 columns\\n\",\n                            \"memory usage: 12.56 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 7,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_header\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 8,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th> name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesReasonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\"> unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43697</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43697</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">9</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"    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\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">9</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">43703</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27642</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75119</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27643</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75120</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27644</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75121</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27645</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75122</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27646</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75123</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    27647 rows x 3 columns<br />\\n\",\n                            \"    memory usage: 1.22 MB<br />\\n\",\n                            \"    name: SalesOrderHeaderSalesReason<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \" name   SalesOrderID   SalesReasonID   ModifiedDate       \\n\",\n                            \" role   unused_float    unused_float   unused_string      \\n\",\n                            \"    0          43697               5   2011-05-30 22:00:00\\n\",\n                            \"    1          43697               9   2011-05-30 22:00:00\\n\",\n                            \"    2          43702               5   2011-05-31 22:00:00\\n\",\n                            \"    3          43702               9   2011-05-31 22:00:00\\n\",\n                            \"    4          43703               5   2011-05-31 22:00:00\\n\",\n                            \"                 ...             ...   ...                \\n\",\n                            \"27642          75119               1   2014-06-29 22:00:00\\n\",\n                            \"27643          75120               1   2014-06-29 22:00:00\\n\",\n                            \"27644          75121               1   2014-06-29 22:00:00\\n\",\n                            \"27645          75122               1   2014-06-29 22:00:00\\n\",\n                            \"27646          75123               1   2014-06-29 22:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"27647 rows x 3 columns\\n\",\n                            \"memory usage: 1.22 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 8,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_reason\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 9,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SpecialOfferID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      MinQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      MaxQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Description                     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">DiscountPct  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Type                </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Category     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">StartDate          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">EndDate            </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">  unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string       </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-01 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-11-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0290C4F5-191F-4337-AB6B-0A2DDE03...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-03-31 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">11</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount 11 to 14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0200</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D7542EE7-15DB-4541-985C-5CC27AEF...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">15</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">24</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount 15 to 24</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.0500</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4BDBCC01-8CF7-40A9-B643-40EC5B71...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">25</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">40</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount 25 to 40</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.1000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">41</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">60</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount 41 to 60</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.1500</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-05-31 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">677E1D9D-944F-4E81-90E8-47EB0A82...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>11</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">12</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LL Road Frame Sale</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.3500</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Excess Inventory</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-07-14 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C0AF1C89-9722-4235-9248-3FBA4D9E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>12</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Touring-3000 Promotion</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.1500</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">New Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-08-29 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">5061CCE4-E021-45A8-9A75-DFB36CBB...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>13</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Touring-1000 Promotion</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.2000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">New Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-08-29 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>14</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">15</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Half-Price Pedal Sale</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.5000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Seasonal Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Customer</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-07-14 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-08-14 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">03E3594D-6EBB-46A6-B8EE-A9289C0C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-06-13 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>15</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">16</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Mountain-500 Silver Clearance Sa...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0.4000</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Discontinued Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-03-31 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">EB7CB484-BCCF-4D2D-BF73-521B2001...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-28 23:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    16 rows x 11 columns<br />\\n\",\n                            \"    memory usage: 0.00 MB<br />\\n\",\n                            \"    name: SpecialOffer<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name   SpecialOfferID         MinQty         MaxQty   Description                        ...   Category     \\n\",\n                            \"role     unused_float   unused_float   unused_float   unused_string                      ...   unused_string\\n\",\n                            \"   0                1              0           nan    No Discount                        ...   No Discount  \\n\",\n                            \"   1                2             11            14    Volume Discount 11 to 14           ...   Reseller     \\n\",\n                            \"   2                3             15            24    Volume Discount 15 to 24           ...   Reseller     \\n\",\n                            \"   3                4             25            40    Volume Discount 25 to 40           ...   Reseller     \\n\",\n                            \"   4                5             41            60    Volume Discount 41 to 60           ...   Reseller     \\n\",\n                            \"                  ...            ...            ...   ...                                      ...          \\n\",\n                            \"  11               12              0           nan    LL Road Frame Sale                 ...   Reseller     \\n\",\n                            \"  12               13              0           nan    Touring-3000 Promotion             ...   Reseller     \\n\",\n                            \"  13               14              0           nan    Touring-1000 Promotion             ...   Reseller     \\n\",\n                            \"  14               15              0           nan    Half-Price Pedal Sale              ...   Customer     \\n\",\n                            \"  15               16              0           nan    Mountain-500 Silver Clearance Sa...   ...   Reseller     \\n\",\n                            \"\\n\",\n                            \"name   StartDate             EndDate               rowguid                            ModifiedDate       \\n\",\n                            \"role   unused_string         unused_string         unused_string                      unused_string      \\n\",\n                            \"   0   2011-05-01 00:00:00   2014-11-30 00:00:00   0290C4F5-191F-4337-AB6B-0A2DDE03...   2011-03-31 22:00:00\\n\",\n                            \"   1   2011-05-31 00:00:00   2014-05-30 00:00:00   D7542EE7-15DB-4541-985C-5CC27AEF...   2011-04-30 22:00:00\\n\",\n                            \"   2   2011-05-31 00:00:00   2014-05-30 00:00:00   4BDBCC01-8CF7-40A9-B643-40EC5B71...   2011-04-30 22:00:00\\n\",\n                            \"   3   2011-05-31 00:00:00   2014-05-30 00:00:00   504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...   2011-04-30 22:00:00\\n\",\n                            \"   4   2011-05-31 00:00:00   2014-05-30 00:00:00   677E1D9D-944F-4E81-90E8-47EB0A82...   2011-04-30 22:00:00\\n\",\n                            \"       ...                   ...                   ...                                ...                \\n\",\n                            \"  11   2013-05-30 00:00:00   2013-07-14 00:00:00   C0AF1C89-9722-4235-9248-3FBA4D9E...   2013-04-29 22:00:00\\n\",\n                            \"  12   2013-05-30 00:00:00   2013-08-29 00:00:00   5061CCE4-E021-45A8-9A75-DFB36CBB...   2013-04-29 22:00:00\\n\",\n                            \"  13   2013-05-30 00:00:00   2013-08-29 00:00:00   1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...   2013-04-29 22:00:00\\n\",\n                            \"  14   2013-07-14 00:00:00   2013-08-14 00:00:00   03E3594D-6EBB-46A6-B8EE-A9289C0C...   2013-06-13 22:00:00\\n\",\n                            \"  15   2014-03-31 00:00:00   2014-05-30 00:00:00   EB7CB484-BCCF-4D2D-BF73-521B2001...   2014-02-28 23:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"16 rows x 11 columns\\n\",\n                            \"memory usage: 0.00 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 9,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"special_offer\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 10,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">BusinessEntityID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">SalesPersonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Name                          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Demographics                    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">    unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\"> unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                 </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">292</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">279</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Next-Door Bike Store</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A22517E3-848D-4EBE-B9D9-7437F343...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">294</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">276</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Professional Sales and Service</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">B50CA50B-C601-4A13-B07E-2C63862D...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">296</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">277</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Riders Company</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">337C3688-1339-4E1A-A08A-B54B2356...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">298</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">275</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">The Bike Mechanics</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">7894F278-F0C8-4D16-BD75-213FDBF1...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">300</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">286</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Nationwide Supply</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C3FC9705-A8C4-4F3A-9550-EB2FA4B7...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>696</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1988</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">282</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Retreat Inn</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">EA21EC81-1BFA-4A07-9B4D-73D9852A...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>697</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1990</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">281</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Technical Parts Manufacturing</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C8E3C4ED-8F58-4DB2-B600-E0CD11D9...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>698</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1992</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">277</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Totes &amp; Baskets Company</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CE860B58-643C-4567-BFD8-06E97969...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>699</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1994</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">277</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">World of Bikes</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0C10F2B6-A13A-440C-9C25-5B28D482...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>700</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2051</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">275</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A Bicycle Association</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">82237172-D3FE-4A95-82EF-636F6552...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    701 rows x 6 columns<br />\\n\",\n                            \"    memory usage: 0.38 MB<br />\\n\",\n                            \"    name: Store<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name   BusinessEntityID   SalesPersonID   Name                             Demographics                    \\n\",\n                            \"role       unused_float    unused_float   unused_string                    unused_string                   \\n\",\n                            \"   0                292             279   Next-Door Bike Store             <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"   1                294             276   Professional Sales and Service   <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"   2                296             277   Riders Company                   <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"   3                298             275   The Bike Mechanics               <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"   4                300             286   Nationwide Supply                <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"                    ...             ...   ...                              ...                             \\n\",\n                            \" 696               1988             282   Retreat Inn                      <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \" 697               1990             281   Technical Parts Manufacturing    <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \" 698               1992             277   Totes & Baskets Company          <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \" 699               1994             277   World of Bikes                   <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \" 700               2051             275   A Bicycle Association            <StoreSurvey xmlns=\\\"http://schem...\\n\",\n                            \"\\n\",\n                            \"name   rowguid                            ModifiedDate       \\n\",\n                            \"role   unused_string                      unused_string      \\n\",\n                            \"   0   A22517E3-848D-4EBE-B9D9-7437F343...   2014-09-12 09:15:07\\n\",\n                            \"   1   B50CA50B-C601-4A13-B07E-2C63862D...   2014-09-12 09:15:07\\n\",\n                            \"   2   337C3688-1339-4E1A-A08A-B54B2356...   2014-09-12 09:15:07\\n\",\n                            \"   3   7894F278-F0C8-4D16-BD75-213FDBF1...   2014-09-12 09:15:07\\n\",\n                            \"   4   C3FC9705-A8C4-4F3A-9550-EB2FA4B7...   2014-09-12 09:15:07\\n\",\n                            \"       ...                                ...                \\n\",\n                            \" 696   EA21EC81-1BFA-4A07-9B4D-73D9852A...   2014-09-12 09:15:07\\n\",\n                            \" 697   C8E3C4ED-8F58-4DB2-B600-E0CD11D9...   2014-09-12 09:15:07\\n\",\n                            \" 698   CE860B58-643C-4567-BFD8-06E97969...   2014-09-12 09:15:07\\n\",\n                            \" 699   0C10F2B6-A13A-440C-9C25-5B28D482...   2014-09-12 09:15:07\\n\",\n                            \" 700   82237172-D3FE-4A95-82EF-636F6552...   2014-09-12 09:15:07\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"701 rows x 6 columns\\n\",\n                            \"memory usage: 0.38 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 10,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"store\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 1.2 Prepare data for getML\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"getML requires that we define *roles* for each of the columns.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 11,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">ProductID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">MakeFlag   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">ProductSubcategoryID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">ProductModelID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">SafetyStockLevel</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">ReorderPoint</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">StandardCost</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">ListPrice</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">FinishedGoodsFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">DaysToManufacture</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Name                 </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ProductNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Color        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Size         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SizeUnitMeasureCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">WeightUnitMeasureCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Weight       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ProductLine  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Class        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Style        </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SellStartDate      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SellEndDate  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">DiscontinuedDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\"> join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">       numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">   numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">   numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string        </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string        </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">0</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">750</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Adjustable Race</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">AR-5381</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">694215B7-08F7-4C0D-ACB1-D734BA44...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">0</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">750</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Bearing Ball</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BA-8327</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">58AE3C20-4F3A-4749-A7D4-D568806C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">3</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB Ball Bearing</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BE-2349</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">9C21AED2-5BFA-4F18-BCB8-F11638DC...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">4</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">0</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Headset Ball Bearings</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BE-2908</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">ECFED6CB-51FF-49B5-B06C-7D8AC834...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">316</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">800</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">600</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Blade</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BL-2036</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2008-04-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">E73E9750-603B-4131-89F5-3DD15ED5...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        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\"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"  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                       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            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                     \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>499</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">995</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">96</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">500</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">375</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">44</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.9506</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">101</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.24</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">ML Bottom Bracket</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB-8107</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">G</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">168.00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">71AB847F-D091-42D6-B735-7B0C2D82...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>500</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">996</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">97</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">500</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">375</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">53</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.9416</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">121</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.49</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">HL Bottom Bracket</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BB-9108</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">G</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">170.00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">H</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">230C47C5-08B2-4CE3-B706-69C0BDD6...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>501</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">997</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">343</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.6496</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">539</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.99</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Road-750 Black, 44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BK-R19B-44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">44</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">19.77</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">44CE4802-409F-43AB-9B27-CA534218...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>502</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">998</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">343</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.6496</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">539</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.99</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Road-750 Black, 48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BK-R19B-48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">48</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">20.13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">3DE9A212-1D49-40B6-B10A-F564D981...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>503</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">999</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">75</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">343</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.6496</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">539</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.99</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Road-750 Black, 52</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">BK-R19B-52</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Black</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">52</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">LB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">20.42</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">R</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">L</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">U</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-05-30 00:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">AE638923-2B67-4679-B90E-ABBAB17D...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-08 09:01:36</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    504 rows x 25 columns<br />\\n\",\n                            \"    memory usage: 0.16 MB<br />\\n\",\n                            \"    name: Product<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name   ProductID   MakeFlag      ProductSubcategoryID   ProductModelID   ...   SellStartDate         SellEndDate  \\n\",\n                            \"role    join_key   categorical   categorical            categorical      ...   unused_string         unused_string\\n\",\n                            \"   0           1   0             nan                    nan              ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   1           2   0             nan                    nan              ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   2           3   1             nan                    nan              ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   3           4   0             nan                    nan              ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"   4         316   1             nan                    nan              ...   2008-04-30 00:00:00   NULL         \\n\",\n                            \"             ...   ...           ...                    ...                    ...                   ...          \\n\",\n                            \" 499         995   1             5                      96               ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 500         996   1             5                      97               ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 501         997   1             2                      31               ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 502         998   1             2                      31               ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \" 503         999   1             2                      31               ...   2013-05-30 00:00:00   NULL         \\n\",\n                            \"\\n\",\n                            \"name   DiscontinuedDate   rowguid                            ModifiedDate       \\n\",\n                            \"role   unused_string      unused_string                      unused_string      \\n\",\n                            \"   0   NULL               694215B7-08F7-4C0D-ACB1-D734BA44...   2014-02-08 09:01:36\\n\",\n                            \"   1   NULL               58AE3C20-4F3A-4749-A7D4-D568806C...   2014-02-08 09:01:36\\n\",\n                            \"   2   NULL               9C21AED2-5BFA-4F18-BCB8-F11638DC...   2014-02-08 09:01:36\\n\",\n                            \"   3   NULL               ECFED6CB-51FF-49B5-B06C-7D8AC834...   2014-02-08 09:01:36\\n\",\n                            \"   4   NULL               E73E9750-603B-4131-89F5-3DD15ED5...   2014-02-08 09:01:36\\n\",\n                            \"       ...                ...                                ...                \\n\",\n                            \" 499   NULL               71AB847F-D091-42D6-B735-7B0C2D82...   2014-02-08 09:01:36\\n\",\n                            \" 500   NULL               230C47C5-08B2-4CE3-B706-69C0BDD6...   2014-02-08 09:01:36\\n\",\n                            \" 501   NULL               44CE4802-409F-43AB-9B27-CA534218...   2014-02-08 09:01:36\\n\",\n                            \" 502   NULL               3DE9A212-1D49-40B6-B10A-F564D981...   2014-02-08 09:01:36\\n\",\n                            \" 503   NULL               AE638923-2B67-4679-B90E-ABBAB17D...   2014-02-08 09:01:36\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"504 rows x 25 columns\\n\",\n                            \"memory usage: 0.16 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 11,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"product.set_role(\\\"ProductID\\\", getml.data.roles.join_key)\\n\",\n                \"product.set_role([\\\"MakeFlag\\\", \\\"ProductSubcategoryID\\\", \\\"ProductModelID\\\"], getml.data.roles.categorical)\\n\",\n                \"product.set_role([\\\"SafetyStockLevel\\\", \\\"ReorderPoint\\\", \\\"StandardCost\\\", \\\"ListPrice\\\"], getml.data.roles.numerical)\\n\",\n                \"\\n\",\n                \"product\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 12,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>  name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">               ModifiedDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesOrderDetailID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">ProductID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SpecialOfferID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\"> OrderQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">UnitPrice</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">UnitPriceDiscount</th>\\n\",\n                            \"        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                  \"            <th class=\\\"sub-header numerical\\\">        numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string        </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">  unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th 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               \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">776</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2024</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n                            \"                >.994</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"      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<td class=\\\"unused_string\\\">4911-403C-98</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">B207C96D-D9E6-402B-8470-2CC176C4...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td 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class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">159</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n                            \"                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\"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">159</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          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                   \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">121317</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">712</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n                            \"                >.99</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n                            \"                >.99</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">73646D26-0461-450D-8019-2C6C8586...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    121317 rows x 11 columns<br />\\n\",\n                            \"    memory usage: 14.08 MB<br />\\n\",\n                            \"    name: SalesOrderDetail<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"  name                  ModifiedDate   SalesOrderID   SalesOrderDetailID   ProductID   ...   UnitPrice   UnitPriceDiscount\\n\",\n                            \"  role                    time_stamp       join_key             join_key    join_key   ...   numerical           numerical\\n\",\n                            \"  unit   time stamp, comparison only                                                   ...                                \\n\",\n                            \"     0           2011-05-30 22:00:00          43659                    1         776   ...    2024.994                   0\\n\",\n                            \"     1           2011-05-30 22:00:00          43659                    2         777   ...    2024.994                   0\\n\",\n                            \"     2           2011-05-30 22:00:00          43659                    3         778   ...    2024.994                   0\\n\",\n                            \"     3           2011-05-30 22:00:00          43659                    4         771   ...    2039.994                   0\\n\",\n                            \"     4           2011-05-30 22:00:00          43659                    5         772   ...    2039.994                   0\\n\",\n                            \"                                 ...            ...                  ...         ...            ...                    ...\\n\",\n                            \"121312           2014-06-29 22:00:00          75122               121313         878   ...      21.98                    0\\n\",\n                            \"121313           2014-06-29 22:00:00          75122               121314         712   ...       8.99                    0\\n\",\n                            \"121314           2014-06-29 22:00:00          75123               121315         878   ...      21.98                    0\\n\",\n                            \"121315           2014-06-29 22:00:00          75123               121316         879   ...     159                       0\\n\",\n                            \"121316           2014-06-29 22:00:00          75123               121317         712   ...       8.99                    0\\n\",\n                            \"\\n\",\n                            \"  name   LineTotal   CarrierTrackingNumber   rowguid                         \\n\",\n                            \"  role   numerical   unused_string           unused_string                   \\n\",\n                            \"  unit                                                                       \\n\",\n                            \"     0    2024.994   4911-403C-98            B207C96D-D9E6-402B-8470-2CC176C4...\\n\",\n                            \"     1    6074.982   4911-403C-98            7ABB600D-1E77-41BE-9FE5-B9142CFC...\\n\",\n                            \"     2    2024.994   4911-403C-98            475CF8C6-49F6-486E-B0AD-AFC6A50C...\\n\",\n                            \"     3    2039.994   4911-403C-98            04C4DE91-5815-45D6-8670-F462719F...\\n\",\n                            \"     4    2039.994   4911-403C-98            5A74C7D2-E641-438E-A7AC-37BF2328...\\n\",\n                            \"            ...      ...                     ...                             \\n\",\n                            \"121312      21.98    NULL                    8CAD6675-18CC-4F47-8287-97B41A8E...\\n\",\n                            \"121313       8.99    NULL                    84F1C363-1C50-4442-BE16-541C59B6...\\n\",\n                            \"121314      21.98    NULL                    C18B6476-429F-4BB1-828E-2BE5F82A...\\n\",\n                            \"121315     159       NULL                    75A89C6A-C60A-47EA-8A52-B52A9C43...\\n\",\n                            \"121316       8.99    NULL                    73646D26-0461-450D-8019-2C6C8586...\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"121317 rows x 11 columns\\n\",\n                            \"memory usage: 14.08 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 12,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_detail.set_role([\\\"SalesOrderID\\\", \\\"SalesOrderDetailID\\\", \\\"ProductID\\\", \\\"SpecialOfferID\\\"], getml.data.roles.join_key)\\n\",\n                \"sales_order_detail.set_role([\\\"OrderQty\\\", \\\"UnitPrice\\\", \\\"UnitPriceDiscount\\\", \\\"LineTotal\\\"], getml.data.roles.numerical)\\n\",\n                \"sales_order_detail.set_role(\\\"ModifiedDate\\\", getml.data.roles.time_stamp)\\n\",\n                \"\\n\",\n                \"sales_order_detail\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 13,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th> name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">SalesReasonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">    join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical  </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">43697</td>\\n\",\n                            \"          \\n\",\n           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     \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27642</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">75119</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27643</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">75120</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27644</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">75121</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27645</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">75122</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>27646</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">75123</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-06-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    27647 rows x 3 columns<br />\\n\",\n                            \"    memory usage: 1.00 MB<br />\\n\",\n                            \"    name: SalesOrderHeaderSalesReason<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \" name   SalesOrderID   SalesReasonID   ModifiedDate       \\n\",\n                            \" role       join_key   categorical     unused_string      \\n\",\n                            \"    0          43697   5               2011-05-30 22:00:00\\n\",\n                            \"    1          43697   9               2011-05-30 22:00:00\\n\",\n                            \"    2          43702   5               2011-05-31 22:00:00\\n\",\n                            \"    3          43702   9               2011-05-31 22:00:00\\n\",\n                            \"    4          43703   5               2011-05-31 22:00:00\\n\",\n                            \"                 ...   ...             ...                \\n\",\n                            \"27642          75119   1               2014-06-29 22:00:00\\n\",\n                            \"27643          75120   1               2014-06-29 22:00:00\\n\",\n                            \"27644          75121   1               2014-06-29 22:00:00\\n\",\n                            \"27645          75122   1               2014-06-29 22:00:00\\n\",\n                            \"27646          75123   1               2014-06-29 22:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"27647 rows x 3 columns\\n\",\n                            \"memory usage: 1.00 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 13,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_reason.set_role(\\\"SalesOrderID\\\", getml.data.roles.join_key)\\n\",\n                \"sales_order_reason.set_role(\\\"SalesReasonID\\\", getml.data.roles.categorical)\\n\",\n                \"\\n\",\n                \"sales_order_reason\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 14,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                  StartDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                    EndDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SpecialOfferID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">Category   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">Description                     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">Type                </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   MinQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">DiscountPct</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      MaxQty</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">      join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical                     </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">  numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">              </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">                                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">                    </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-01</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-11-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">No Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0290C4F5-191F-4337-AB6B-0A2DDE03...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-03-31 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount 11 to 14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">11</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.02</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">14</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D7542EE7-15DB-4541-985C-5CC27AEF...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">3</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount 15 to 24</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">15</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.05</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">24</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">4BDBCC01-8CF7-40A9-B643-40EC5B71...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">4</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount 25 to 40</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">25</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.1</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">40</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount 41 to 60</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Volume Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">41</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.15</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">60</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">677E1D9D-944F-4E81-90E8-47EB0A82...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2011-04-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>11</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-07-14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">12</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">LL Road Frame Sale</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Excess Inventory</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.35</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C0AF1C89-9722-4235-9248-3FBA4D9E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>12</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-08-29</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Touring-3000 Promotion</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">New Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.15</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">5061CCE4-E021-45A8-9A75-DFB36CBB...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>13</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-08-29</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Touring-1000 Promotion</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">New Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.2</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-04-29 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>14</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-07-14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2013-08-14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">15</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Customer</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Half-Price Pedal Sale</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Seasonal Discount</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.5</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">03E3594D-6EBB-46A6-B8EE-A9289C0C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2013-06-13 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>15</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-03-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-05-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">16</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Reseller</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Mountain-500 Silver Clearance Sa...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Discontinued Product</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.4</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">EB7CB484-BCCF-4D2D-BF73-521B2001...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-02-28 23:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    16 rows x 11 columns<br />\\n\",\n                            \"    memory usage: 0.00 MB<br />\\n\",\n                            \"    name: SpecialOffer<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name                     StartDate                       EndDate   SpecialOfferID   Category      ...      MinQty\\n\",\n                            \"role                    time_stamp                    time_stamp         join_key   categorical   ...   numerical\\n\",\n                            \"unit   time stamp, comparison only   time stamp, comparison only                                  ...            \\n\",\n                            \"   0                    2011-05-01                    2014-11-30                1   No Discount   ...           0\\n\",\n                            \"   1                    2011-05-31                    2014-05-30                2   Reseller      ...          11\\n\",\n                            \"   2                    2011-05-31                    2014-05-30                3   Reseller      ...          15\\n\",\n                            \"   3                    2011-05-31                    2014-05-30                4   Reseller      ...          25\\n\",\n                            \"   4                    2011-05-31                    2014-05-30                5   Reseller      ...          41\\n\",\n                            \"                               ...                           ...              ...   ...                       ...\\n\",\n                            \"  11                    2013-05-30                    2013-07-14               12   Reseller      ...           0\\n\",\n                            \"  12                    2013-05-30                    2013-08-29               13   Reseller      ...           0\\n\",\n                            \"  13                    2013-05-30                    2013-08-29               14   Reseller      ...           0\\n\",\n                            \"  14                    2013-07-14                    2013-08-14               15   Customer      ...           0\\n\",\n                            \"  15                    2014-03-31                    2014-05-30               16   Reseller      ...           0\\n\",\n                            \"\\n\",\n                            \"name   DiscountPct         MaxQty   rowguid                            ModifiedDate       \\n\",\n                            \"role     numerical   unused_float   unused_string                      unused_string      \\n\",\n                            \"unit                                                                                      \\n\",\n                            \"   0          0              nan    0290C4F5-191F-4337-AB6B-0A2DDE03...   2011-03-31 22:00:00\\n\",\n                            \"   1          0.02            14    D7542EE7-15DB-4541-985C-5CC27AEF...   2011-04-30 22:00:00\\n\",\n                            \"   2          0.05            24    4BDBCC01-8CF7-40A9-B643-40EC5B71...   2011-04-30 22:00:00\\n\",\n                            \"   3          0.1             40    504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...   2011-04-30 22:00:00\\n\",\n                            \"   4          0.15            60    677E1D9D-944F-4E81-90E8-47EB0A82...   2011-04-30 22:00:00\\n\",\n                            \"             ...              ...   ...                                ...                \\n\",\n                            \"  11          0.35           nan    C0AF1C89-9722-4235-9248-3FBA4D9E...   2013-04-29 22:00:00\\n\",\n                            \"  12          0.15           nan    5061CCE4-E021-45A8-9A75-DFB36CBB...   2013-04-29 22:00:00\\n\",\n                            \"  13          0.2            nan    1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...   2013-04-29 22:00:00\\n\",\n                            \"  14          0.5            nan    03E3594D-6EBB-46A6-B8EE-A9289C0C...   2013-06-13 22:00:00\\n\",\n                            \"  15          0.4            nan    EB7CB484-BCCF-4D2D-BF73-521B2001...   2014-02-28 23:00:00\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"16 rows x 11 columns\\n\",\n                            \"memory usage: 0.00 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 14,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"special_offer.set_role([\\\"SpecialOfferID\\\"], getml.data.roles.join_key)\\n\",\n                \"special_offer.set_role([\\\"MinQty\\\", \\\"DiscountPct\\\"], getml.data.roles.numerical)\\n\",\n                \"special_offer.set_role([\\\"Category\\\", \\\"Description\\\", \\\"Type\\\"], getml.data.roles.categorical)\\n\",\n                \"special_offer.set_role([\\\"StartDate\\\", \\\"EndDate\\\"], getml.data.roles.time_stamp)\\n\",\n                \"\\n\",\n                \"special_offer\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 15,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                       test</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesPersonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">BusinessEntityID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Name                          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Demographics                    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">ModifiedDate       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">     join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">    unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                 </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">             </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                              </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">279</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">292</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Next-Door Bike Store</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A22517E3-848D-4EBE-B9D9-7437F343...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">276</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">294</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Professional Sales and Service</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">B50CA50B-C601-4A13-B07E-2C63862D...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">277</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">296</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Riders Company</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">337C3688-1339-4E1A-A08A-B54B2356...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">275</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">298</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">The Bike Mechanics</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">7894F278-F0C8-4D16-BD75-213FDBF1...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">286</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">300</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Nationwide Supply</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C3FC9705-A8C4-4F3A-9550-EB2FA4B7...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>696</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">282</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1988</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Retreat Inn</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">EA21EC81-1BFA-4A07-9B4D-73D9852A...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>697</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">281</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1990</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Technical Parts Manufacturing</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C8E3C4ED-8F58-4DB2-B600-E0CD11D9...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>698</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">277</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1992</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Totes &amp; Baskets Company</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">CE860B58-643C-4567-BFD8-06E97969...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>699</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">277</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1994</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">World of Bikes</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">0C10F2B6-A13A-440C-9C25-5B28D482...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>700</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">275</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2051</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A Bicycle Association</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">&lt;StoreSurvey xmlns=&quot;http://schem...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">82237172-D3FE-4A95-82EF-636F6552...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">2014-09-12 09:15:07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    701 rows x 7 columns<br />\\n\",\n                            \"    memory usage: 0.38 MB<br />\\n\",\n                            \"    name: Store<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name                          test   SalesPersonID   BusinessEntityID   Name                          \\n\",\n                            \"role                    time_stamp        join_key       unused_float   unused_string                 \\n\",\n                            \"unit   time stamp, comparison only                                                                    \\n\",\n                            \"   0           2014-09-12 09:15:07             279                292   Next-Door Bike Store          \\n\",\n                            \"   1                          NULL             276                294   Professional Sales and Service\\n\",\n                            \"   2           2014-09-12 09:15:07             277                296   Riders Company                \\n\",\n                            \"   3                          NULL             275                298   The Bike Mechanics            \\n\",\n                            \"   4                          NULL             286                300   Nationwide Supply             \\n\",\n                            \"                               ...             ...                ...   ...                           \\n\",\n                            \" 696                          NULL             282               1988   Retreat Inn                   \\n\",\n                            \" 697           2014-09-12 09:15:07             281               1990   Technical Parts Manufacturing \\n\",\n                            \" 698                          NULL             277               1992   Totes & Baskets Company       \\n\",\n                            \" 699                          NULL             277               1994   World of Bikes                \\n\",\n                            \" 700           2014-09-12 09:15:07             275               2051   A Bicycle Association         \\n\",\n                            \"\\n\",\n                            \"name   Demographics                       rowguid                            ModifiedDate       \\n\",\n                            \"role   unused_string                      unused_string                      unused_string      \\n\",\n                            \"unit                                                                                            \\n\",\n                            \"   0   <StoreSurvey xmlns=\\\"http://schem...   A22517E3-848D-4EBE-B9D9-7437F343...   2014-09-12 09:15:07\\n\",\n                            \"   1   <StoreSurvey xmlns=\\\"http://schem...   B50CA50B-C601-4A13-B07E-2C63862D...   2014-09-12 09:15:07\\n\",\n                            \"   2   <StoreSurvey xmlns=\\\"http://schem...   337C3688-1339-4E1A-A08A-B54B2356...   2014-09-12 09:15:07\\n\",\n                            \"   3   <StoreSurvey xmlns=\\\"http://schem...   7894F278-F0C8-4D16-BD75-213FDBF1...   2014-09-12 09:15:07\\n\",\n                            \"   4   <StoreSurvey xmlns=\\\"http://schem...   C3FC9705-A8C4-4F3A-9550-EB2FA4B7...   2014-09-12 09:15:07\\n\",\n                            \"       ...                                ...                                ...                \\n\",\n                            \" 696   <StoreSurvey xmlns=\\\"http://schem...   EA21EC81-1BFA-4A07-9B4D-73D9852A...   2014-09-12 09:15:07\\n\",\n                            \" 697   <StoreSurvey xmlns=\\\"http://schem...   C8E3C4ED-8F58-4DB2-B600-E0CD11D9...   2014-09-12 09:15:07\\n\",\n                            \" 698   <StoreSurvey xmlns=\\\"http://schem...   CE860B58-643C-4567-BFD8-06E97969...   2014-09-12 09:15:07\\n\",\n                            \" 699   <StoreSurvey xmlns=\\\"http://schem...   0C10F2B6-A13A-440C-9C25-5B28D482...   2014-09-12 09:15:07\\n\",\n                            \" 700   <StoreSurvey xmlns=\\\"http://schem...   82237172-D3FE-4A95-82EF-636F6552...   2014-09-12 09:15:07\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"701 rows x 7 columns\\n\",\n                            \"memory usage: 0.38 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 15,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"store.set_role([\\\"SalesPersonID\\\"], getml.data.roles.join_key)\\n\",\n                \"\\n\",\n                \"store[\\\"test\\\"] = store[\\\"ModifiedDate\\\"].update(getml.data.random() > 0.5, \\\"NULL\\\")\\n\",\n                \"\\n\",\n                \"store.set_role([\\\"SalesPersonID\\\"], getml.data.roles.join_key)\\n\",\n                \"store.set_role([\\\"test\\\"], getml.data.roles.time_stamp)\\n\",\n                \"\\n\",\n                \"store\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 16,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th> name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                  OrderDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                    DueDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                   ShipDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">               ModifiedDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">CustomerID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesPersonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">TerritoryID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">RevisionNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">OnlineOrderFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">SalesPersonIDCat</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">TerritoryIDCat</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">ShipMethodID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  SubTotal</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   TaxAmt</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  Freight</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  TotalDue</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      Status</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">BillToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ShipToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CreditCardID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CurrencyRateID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SalesOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">PurchaseOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">AccountNumber </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">CreditCardApprovalCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Comment      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">    join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">     join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">   join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical    </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical     </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\"> numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\"> numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">   unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">   unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">  unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"    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                         \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">               </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">               </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          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         \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">             </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">                                </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-12</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">29825</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">43659</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">279</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">8</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">0</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">279</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">20565</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.6206</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      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                \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.2339</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">985</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          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 \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">16281</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO43659</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">PO522145787</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000676</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">105041Vi84182</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">79B65321-39CA-4115-9CBA-8FE0903E...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-05-30 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-12</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">2011-06-06 22:00:00</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">29672</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">43660</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">279</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">8</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">0</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">279</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">5</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1294</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.2529</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">124</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.2483</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">38</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.8276</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1457</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.3288</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">921</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">921</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5618</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO43660</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">PO18850127500</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000117</td>\\n\",\n                            \"          \\n\",\n                            \"        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              \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">nan</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">6</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">8</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n             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      \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">10084</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO75123</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-018759</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">230370Vi51970</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D54752FF-2B54-4BE5-95EA-3B72289C...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    31465 rows x 28 columns<br />\\n\",\n                            \"    memory usage: 8.34 MB<br />\\n\",\n                            \"    name: SalesOrderHeader<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \" name                     OrderDate                       DueDate                      ShipDate                  ModifiedDate\\n\",\n                            \" role                    time_stamp                    time_stamp                    time_stamp                    time_stamp\\n\",\n                            \" unit   time stamp, comparison only   time stamp, comparison only   time stamp, comparison only   time stamp, comparison only\\n\",\n                            \"    0           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    1           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    2           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    3           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    4           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"                                ...                           ...                           ...                           ...\\n\",\n                            \"31460           2014-06-29 22:00:00                    2014-07-12                    2014-07-07           2014-07-06 22:00:00\\n\",\n                            \"31461           2014-06-29 22:00:00                    2014-07-12                    2014-07-07           2014-07-06 22:00:00\\n\",\n                            \"31462           2014-06-29 22:00:00                    2014-07-12                    2014-07-07           2014-07-06 22:00:00\\n\",\n                            \"31463           2014-06-29 22:00:00                    2014-07-12                    2014-07-07           2014-07-06 22:00:00\\n\",\n                            \"31464           2014-06-29 22:00:00                    2014-07-12                    2014-07-07           2014-07-06 22:00:00\\n\",\n                            \"\\n\",\n                            \" name   ...   PurchaseOrderNumber   AccountNumber    CreditCardApprovalCode   Comment         rowguid                         \\n\",\n                            \" role   ...   unused_string         unused_string    unused_string            unused_string   unused_string                   \\n\",\n                            \" unit   ...                                                                                                                   \\n\",\n                            \"    0   ...   PO522145787           10-4020-000676   105041Vi84182            NULL         \\n\",\n                            \"    1   ...   PO18850127500         10-4020-000117   115213Vi29411            NULL         \\n\",\n                            \"    2   ...   PO18473189620         10-4020-000442   85274Vi6854              NULL         \\n\",\n                            \"    3   ...   PO18444174044         10-4020-000227   125295Vi53935            NULL         \\n\",\n                            \"    4   ...   PO18009186470         10-4020-000510   45303Vi22691             NULL         \\n\",\n                            \"              ...                   ...              ...                      ...             ...                             \\n\",\n                            \"31460   ...   NULL                  10-4030-011981   429826Vi35166            NULL         \\n\",\n                            \"31461   ...   NULL                  10-4030-018749   929849Vi46003            NULL         \\n\",\n                            \"31462   ...   NULL                  10-4030-015251   529864Vi73738            NULL         \\n\",\n                            \"31463   ...   NULL                  10-4030-015868   330022Vi97312            NULL         \\n\",\n                            \"31464   ...   NULL                  10-4030-018759   230370Vi51970            NULL         \\n\",\n                            \"\\n\",\n                            \"    0   79B65321-39CA-4115-9CBA-8FE0903E...\\n\",\n                            \"    1   738DC42D-D03B-48A1-9822-F95A67EA...\\n\",\n                            \"    2   D91B9131-18A4-4A11-BC3A-90B6F53E...\\n\",\n                            \"    3   4A1ECFC0-CC3A-4740-B028-1C50BB48...\\n\",\n                            \"    4   9B1E7A40-6AE0-4AD3-811C-A6495185...\\n\",\n                            \"31460   9382F1C9-383A-435F-9449-0EECEA21...\\n\",\n                            \"31461   AE6A4FCF-FF73-4CD4-AF2C-5993D00D...\\n\",\n                            \"31462   D7395C0E-00CB-4BFA-A238-0D6A9F49...\\n\",\n                            \"31463   4221035A-4159-492F-AF40-4363A64F...\\n\",\n                            \"31464   D54752FF-2B54-4BE5-95EA-3B72289C...\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"31465 rows x 28 columns\\n\",\n                            \"memory usage: 8.34 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 16,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_header[\\\"SalesPersonIDCat\\\"] = sales_order_header[\\\"SalesPersonID\\\"]\\n\",\n                \"sales_order_header[\\\"TerritoryIDCat\\\"] = sales_order_header[\\\"TerritoryID\\\"]\\n\",\n                \"\\n\",\n                \"sales_order_header.set_role([\\\"CustomerID\\\", \\\"SalesOrderID\\\", \\\"SalesPersonID\\\", \\\"TerritoryID\\\"], getml.data.roles.join_key)\\n\",\n                \"sales_order_header.set_role(\\n\",\n                \"    [\\\"RevisionNumber\\\", \\\"OnlineOrderFlag\\\", \\\"SalesPersonIDCat\\\", \\\"TerritoryIDCat\\\", \\\"ShipMethodID\\\"], \\n\",\n                \"    getml.data.roles.categorical)\\n\",\n                \"sales_order_header.set_role([\\\"SubTotal\\\", \\\"TaxAmt\\\", \\\"Freight\\\", \\\"TotalDue\\\"], getml.data.roles.numerical)\\n\",\n                \"sales_order_header.set_role([\\\"OrderDate\\\", \\\"DueDate\\\", \\\"ShipDate\\\", \\\"ModifiedDate\\\"], getml.data.roles.time_stamp)\\n\",\n                \"\\n\",\n                \"sales_order_header\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"We must also define customer churn. In this case, we define customer churn as a customer not making another purchase within 180 days of his or her last purchase.\\n\",\n                \"\\n\",\n                \"Thus, the churn variable is defined as follows:\\n\",\n                \"\\n\",\n                \"- 0, if another purchase by the same customer has been made within 180 days after `OrderDate`\\n\",\n                \"- 1, if no purchase by the same customer has been made within 180 days after `OrderDate`\\n\",\n                \"- NULL, if `max(OrderDate) - OrderDate <= 180 days`\\n\",\n                \"\\n\",\n                \"NULL targets can not be used in our analysis.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 17,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th> name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                  OrderDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                    DueDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                   ShipDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">               ModifiedDate</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">CustomerID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesOrderID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">SalesPersonID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">TerritoryID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"target\\\"> churn</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">RevisionNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">OnlineOrderFlag</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">SalesPersonIDCat</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">TerritoryIDCat</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">ShipMethodID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  SubTotal</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   TaxAmt</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  Freight</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  TotalDue</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">      Status</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">BillToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">ShipToAddressID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CreditCardID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">CurrencyRateID</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">SalesOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">PurchaseOrderNumber</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">AccountNumber </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">CreditCardApprovalCode</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">Comment      </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">rowguid                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">    join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">     join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">   join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header target\\\">target</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical    </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical     </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\"> numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\"> numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">   unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">   unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">  unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string      </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\"> unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">          </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n            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        \"            <th class=\\\"sub-header numerical\\\">          </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">          </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"         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\"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1971</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.5149</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">616</span\\n\",\n                            \"              ><span 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\"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"    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\"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO43659</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">PO522145787</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4020-000676</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">105041Vi84182</td>\\n\",\n             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       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.4765</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2673</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n                            \"                >.0613</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">27090</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">27090</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">16083</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">10770</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">SO63362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">10-4030-015692</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">242864Vi83167</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">3151D31F-4020-41B2-87F3-B6732869...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    19704 rows x 29 columns<br />\\n\",\n                            \"    memory usage: 5.39 MB<br />\\n\",\n                            \"    name: SalesOrderHeaderRefined<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \" name                     OrderDate                       DueDate                      ShipDate                  ModifiedDate\\n\",\n                            \" role                    time_stamp                    time_stamp                    time_stamp                    time_stamp\\n\",\n                            \" unit   time stamp, comparison only   time stamp, comparison only   time stamp, comparison only   time stamp, comparison only\\n\",\n                            \"    0           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    1           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    2           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    3           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"    4           2011-05-30 22:00:00                    2011-06-12                    2011-06-07           2011-06-06 22:00:00\\n\",\n                            \"                                ...                           ...                           ...                           ...\\n\",\n                            \"19699           2013-12-30 23:00:00                    2014-01-12                    2014-01-07           2014-01-06 23:00:00\\n\",\n                            \"19700           2013-12-30 23:00:00                    2014-01-12                    2014-01-07           2014-01-06 23:00:00\\n\",\n                            \"19701           2013-12-30 23:00:00                    2014-01-12                    2014-01-07           2014-01-06 23:00:00\\n\",\n                            \"19702           2013-12-30 23:00:00                    2014-01-12                    2014-01-07           2014-01-06 23:00:00\\n\",\n                            \"19703           2013-12-30 23:00:00                    2014-01-12                    2014-01-07           2014-01-06 23:00:00\\n\",\n                            \"\\n\",\n                            \" name   ...   PurchaseOrderNumber   AccountNumber    CreditCardApprovalCode   Comment         rowguid                         \\n\",\n                            \" role   ...   unused_string         unused_string    unused_string            unused_string   unused_string                   \\n\",\n                            \" unit   ...                                                                                                                   \\n\",\n                            \"    0   ...   PO522145787           10-4020-000676   105041Vi84182            NULL         \\n\",\n                            \"    1   ...   PO18850127500         10-4020-000117   115213Vi29411            NULL         \\n\",\n                            \"    2   ...   PO18473189620         10-4020-000442   85274Vi6854              NULL         \\n\",\n                            \"    3   ...   PO18444174044         10-4020-000227   125295Vi53935            NULL         \\n\",\n                            \"    4   ...   PO18009186470         10-4020-000510   45303Vi22691             NULL         \\n\",\n                            \"              ...                   ...              ...                      ...             ...                             \\n\",\n                            \"19699   ...   NULL                  10-4030-020826   1142084Vi17039           NULL         \\n\",\n                            \"19700   ...   NULL                  10-4030-024114   NULL                     NULL         \\n\",\n                            \"19701   ...   NULL                  10-4030-011417   242387Vi34223            NULL         \\n\",\n                            \"19702   ...   NULL                  10-4030-018125   1242859Vi61993           NULL         \\n\",\n                            \"19703   ...   NULL                  10-4030-015692   242864Vi83167            NULL         \\n\",\n                            \"\\n\",\n                            \"    0   79B65321-39CA-4115-9CBA-8FE0903E...\\n\",\n                            \"    1   738DC42D-D03B-48A1-9822-F95A67EA...\\n\",\n                            \"    2   D91B9131-18A4-4A11-BC3A-90B6F53E...\\n\",\n                            \"    3   4A1ECFC0-CC3A-4740-B028-1C50BB48...\\n\",\n                            \"    4   9B1E7A40-6AE0-4AD3-811C-A6495185...\\n\",\n                            \"19699   41278FBB-3DD8-488B-AEA5-8BF4A6F1...\\n\",\n                            \"19700   749532D4-BFF7-4FEC-9F77-396C4A96...\\n\",\n                            \"19701   F92FA2A3-73E1-4DD0-987C-99D1C87E...\\n\",\n                            \"19702   3A3758BF-CDFA-4740-9104-87BD5A08...\\n\",\n                            \"19703   3151D31F-4020-41B2-87F3-B6732869...\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"19704 rows x 29 columns\\n\",\n                            \"memory usage: 5.39 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 17,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"sales_order_header_pd = sales_order_header[[\\\"OrderDate\\\", \\\"CustomerID\\\", \\\"SalesOrderID\\\"]].to_pandas()\\n\",\n                \"\\n\",\n                \"repeat_purchases = sales_order_header_pd.merge(\\n\",\n                \"    sales_order_header_pd[[\\\"OrderDate\\\", \\\"CustomerID\\\"]],\\n\",\n                \"    on=\\\"CustomerID\\\",\\n\",\n                \"    how=\\\"left\\\",\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"repeat_purchases = repeat_purchases[\\n\",\n                \"    repeat_purchases[\\\"OrderDate_y\\\"] > repeat_purchases[\\\"OrderDate_x\\\"]\\n\",\n                \"]\\n\",\n                \"\\n\",\n                \"repeat_purchases = repeat_purchases[\\n\",\n                \"    repeat_purchases[\\\"OrderDate_y\\\"] - repeat_purchases[\\\"OrderDate_x\\\"] > pd.Timedelta('180 days')\\n\",\n                \"]\\n\",\n                \"\\n\",\n                \"repeat_purchases = repeat_purchases.groupby(\\\"SalesOrderID\\\", as_index=False).aggregate({\\\"CustomerID\\\": \\\"max\\\"})\\n\",\n                \"\\n\",\n                \"repeat_purchase_ids = {sid: True for sid in repeat_purchases[\\\"SalesOrderID\\\"]}\\n\",\n                \"\\n\",\n                \"cut_off_date = max(sales_order_header_pd[\\\"OrderDate\\\"]) - pd.Timedelta('180 days')\\n\",\n                \"\\n\",\n                \"churn = np.asarray([\\n\",\n                \"    np.nan if order_date >= cut_off_date else 0 if order_id in repeat_purchase_ids else 1 \\n\",\n                \"    for (order_date, order_id) in zip(sales_order_header_pd[\\\"OrderDate\\\"], sales_order_header_pd[\\\"SalesOrderID\\\"])\\n\",\n                \"])\\n\",\n                \"\\n\",\n                \"sales_order_header[\\\"churn\\\"] = churn\\n\",\n                \"\\n\",\n                \"sales_order_header = sales_order_header[~sales_order_header.churn.is_nan()].to_df(\\\"SalesOrderHeaderRefined\\\")\\n\",\n                \"\\n\",\n                \"sales_order_header.set_role(\\\"churn\\\", getml.data.roles.target)\\n\",\n                \"\\n\",\n                \"sales_order_header\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 18,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.numerical {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.numerical {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  td.char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>train</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>train</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>train</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>test</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>train</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td>...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    infinite number of  rows<br />\\n\",\n                            \"    \\n\",\n                            \"    type: StringColumnView<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"          \\n\",\n                            \" 0   train\\n\",\n                            \" 1   train\\n\",\n                            \" 2   train\\n\",\n                            \" 3   test \\n\",\n                            \" 4   train\\n\",\n                            \"     ...  \\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"infinite number of  rows\\n\",\n                            \"type: StringColumnView\"\n                        ]\n                    },\n                    \"execution_count\": 18,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"split = getml.data.split.random(train=0.8, test=0.2)\\n\",\n                \"split\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 19,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<div style='margin-top: 15px;'>\\n\",\n                            \"<div style='float: left; margin-right: 50px;'>\\n\",\n                            \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n                            \"    <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">subset</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">name                   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">rows   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">type</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">test</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SalesOrderHeaderRefined</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">unknown</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">View</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">train</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SalesOrderHeaderRefined</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">unknown</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">View</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"</div>\\n\",\n                            \"<div style='float: left;'>\\n\",\n                            \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n                            \"    <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">alias             </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">name                       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\">  rows</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">type     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">product</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">Product</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">504</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_detail</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SalesOrderDetail</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">121317</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_header</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SalesOrderHeaderRefined</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">19704</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_reason</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SalesOrderHeaderSalesReason</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">27647</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">special_offer</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SpecialOffer</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">16</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">store</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">Store</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">701</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"</div>\\n\",\n                            \"</div>\"\n                        ],\n                        \"text/plain\": [\n                            \"population\\n\",\n                            \"    subset   name                      rows      type\\n\",\n                            \"0   test     SalesOrderHeaderRefined   unknown   View\\n\",\n                            \"1   train    SalesOrderHeaderRefined   unknown   View\\n\",\n                            \"\\n\",\n                            \"peripheral\\n\",\n                            \"    alias                name                            rows   type     \\n\",\n                            \"0   product              Product                          504   DataFrame\\n\",\n                            \"1   sales_order_detail   SalesOrderDetail              121317   DataFrame\\n\",\n                            \"2   sales_order_header   SalesOrderHeaderRefined        19704   DataFrame\\n\",\n                            \"3   sales_order_reason   SalesOrderHeaderSalesReason    27647   DataFrame\\n\",\n                            \"4   special_offer        SpecialOffer                      16   DataFrame\\n\",\n                            \"5   store                Store                            701   DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 19,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"container = getml.data.Container(population=sales_order_header, split=split)\\n\",\n                \"\\n\",\n                \"container.add(\\n\",\n                \"    product=product,\\n\",\n                \"    sales_order_detail=sales_order_detail, \\n\",\n                \"    sales_order_header=sales_order_header, \\n\",\n                \"    sales_order_reason=sales_order_reason,\\n\",\n                \"    special_offer=special_offer,\\n\",\n                \"    store=store,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"container\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 2. Predictive modeling\\n\",\n                \"\\n\",\n                \"We loaded the data and defined the roles and units. Next, we create a getML pipeline for relational learning.\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.1 Define relational model\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 20,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n                            \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n                            \"            <div style=\\\"height:540px;width:1160px;position:relative;\\\"><svg height=\\\"530\\\" width=\\\"1150\\\"><rect y=\\\"0\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">sales_order_header</text><rect x=\\\"551\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"10\\\" x2=\\\"567.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"10\\\" x2=\\\"583.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"26.0\\\" x2=\\\"599\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"42.0\\\" x2=\\\"599\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"110\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"183.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">product</text><rect x=\\\"51\\\" y=\\\"120\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"120\\\" x2=\\\"67.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"120\\\" x2=\\\"83.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"136.0\\\" x2=\\\"99\\\" y2=\\\"136.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"152.0\\\" x2=\\\"99\\\" y2=\\\"152.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">special_offer</text><rect 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x2=\\\"583.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"466.0\\\" x2=\\\"599\\\" y2=\\\"466.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"482.0\\\" x2=\\\"599\\\" y2=\\\"482.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"440\\\" x=\\\"1000\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"513.8\\\" x=\\\"1075.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"1051\\\" y=\\\"450\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"1067.0\\\" y1=\\\"450\\\" x2=\\\"1067.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"1083.0\\\" y1=\\\"450\\\" x2=\\\"1083.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"1051\\\" y1=\\\"466.0\\\" x2=\\\"1099\\\" y2=\\\"466.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"1051\\\" y1=\\\"482.0\\\" x2=\\\"1099\\\" y2=\\\"482.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"153.0\\\" x2=\\\"573.0\\\" y2=\\\"153.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"151.0\\\" x2=\\\"573.0\\\" y2=\\\"210\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 220 567.0, 210 579.0, 210 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"120.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"150.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >ProductID = ProductID</tspan><tspan y=\\\"160.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Relationship: many-to-one</tspan></text><line x1=\\\"150\\\" y1=\\\"263.0\\\" x2=\\\"490\\\" y2=\\\"263.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 263.0 490, 257.0 490, 269.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"230.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"260.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >SpecialOfferID = SpecialOfferID</tspan><tspan y=\\\"270.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Relationship: many-to-one</tspan></text><line x1=\\\"650\\\" y1=\\\"43.0\\\" x2=\\\"1073.0\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"1073.0\\\" y1=\\\"41.0\\\" x2=\\\"1073.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"1073.0, 440 1067.0, 430 1079.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"749.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"30.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >CustomerID = CustomerID</tspan><tspan y=\\\"40.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >OrderDate &lt;= OrderDate</tspan><tspan y=\\\"50.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 days</tspan><tspan y=\\\"60.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >Lagged targets allowed</tspan></text><line x1=\\\"650\\\" y1=\\\"263.0\\\" x2=\\\"1073.0\\\" y2=\\\"263.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"1073.0\\\" y1=\\\"261.0\\\" x2=\\\"1073.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"1073.0, 440 1067.0, 430 1079.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"230.0\\\" x=\\\"749.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"265.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >SalesOrderID = SalesOrderID</tspan></text><line x1=\\\"650\\\" y1=\\\"373.0\\\" x2=\\\"1073.0\\\" y2=\\\"373.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"1073.0\\\" y1=\\\"371.0\\\" x2=\\\"1073.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"1073.0, 440 1067.0, 430 1079.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"340.0\\\" x=\\\"749.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"375.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >SalesOrderID = SalesOrderID</tspan></text><line x1=\\\"650\\\" y1=\\\"483.0\\\" x2=\\\"990\\\" y2=\\\"483.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"1000, 483.0 990, 477.0 990, 489.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"450.0\\\" x=\\\"749.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"485.0\\\" x=\\\"824.0\\\" font-size=\\\"7pt\\\" >SalesPersonID = SalesPersonID</tspan></text></svg></div>\\n\",\n                            \"            </div>\\n\",\n                            \"\\n\",\n                            \"            <div style='margin-top: 15px;'>\\n\",\n                            \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n                            \"            <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">data frames                     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">staging table                   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">population</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_detail, product, special_offer</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SALES_ORDER_DETAIL__STAGING_TABLE_2</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_header</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SALES_ORDER_HEADER__STAGING_TABLE_3</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">sales_order_reason</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">SALES_ORDER_REASON__STAGING_TABLE_4</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">store</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">STORE__STAGING_TABLE_5</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"            </div>\\n\",\n                            \"            \"\n                        ],\n                        \"text/plain\": [\n                            \"population:\\n\",\n                            \"  columns:\\n\",\n                            \"  - RevisionNumber: categorical\\n\",\n                            \"  - OnlineOrderFlag: categorical\\n\",\n                            \"  - SalesPersonIDCat: categorical\\n\",\n                            \"  - TerritoryIDCat: categorical\\n\",\n                            \"  - ShipMethodID: categorical\\n\",\n                            \"  - ...\\n\",\n                            \"\\n\",\n                            \"  joins:\\n\",\n                            \"  - right: 'sales_order_header'\\n\",\n                            \"    on: (population.CustomerID, sales_order_header.CustomerID)\\n\",\n                            \"    time_stamps: (population.OrderDate, sales_order_header.OrderDate)\\n\",\n                            \"    relationship: 'many-to-many'\\n\",\n                            \"    horizon: 86400.0\\n\",\n                            \"    lagged_targets: True\\n\",\n                            \"  - right: 'sales_order_detail'\\n\",\n                            \"    on: (population.SalesOrderID, sales_order_detail.SalesOrderID)\\n\",\n                            \"    relationship: 'many-to-many'\\n\",\n                            \"    lagged_targets: False\\n\",\n                            \"  - right: 'sales_order_reason'\\n\",\n                            \"    on: (population.SalesOrderID, sales_order_reason.SalesOrderID)\\n\",\n                            \"    relationship: 'many-to-many'\\n\",\n                            \"    lagged_targets: False\\n\",\n                            \"  - right: 'store'\\n\",\n                            \"    on: (population.SalesPersonID, store.SalesPersonID)\\n\",\n                            \"    relationship: 'many-to-many'\\n\",\n                            \"    lagged_targets: False\\n\",\n                            \"\\n\",\n                            \"sales_order_header:\\n\",\n                            \"  columns:\\n\",\n                            \"  - RevisionNumber: categorical\\n\",\n                            \"  - OnlineOrderFlag: categorical\\n\",\n                            \"  - SalesPersonIDCat: categorical\\n\",\n                            \"  - TerritoryIDCat: categorical\\n\",\n                            \"  - ShipMethodID: categorical\\n\",\n                            \"  - ...\\n\",\n                            \"\\n\",\n                            \"sales_order_detail:\\n\",\n                            \"  columns:\\n\",\n                            \"  - SalesOrderID: join_key\\n\",\n                            \"  - SalesOrderDetailID: join_key\\n\",\n                            \"  - ProductID: join_key\\n\",\n                            \"  - SpecialOfferID: join_key\\n\",\n                            \"  - OrderQty: numerical\\n\",\n                            \"  - ...\\n\",\n                            \"\\n\",\n                            \"  joins:\\n\",\n                            \"  - right: 'product'\\n\",\n                            \"    on: (sales_order_detail.ProductID, product.ProductID)\\n\",\n                            \"    relationship: 'many-to-one'\\n\",\n                            \"    lagged_targets: False\\n\",\n                            \"  - right: 'special_offer'\\n\",\n                            \"    on: (sales_order_detail.SpecialOfferID, special_offer.SpecialOfferID)\\n\",\n                            \"    relationship: 'many-to-one'\\n\",\n                            \"    lagged_targets: False\\n\",\n                            \"\\n\",\n                            \"product:\\n\",\n                            \"  columns:\\n\",\n                            \"  - MakeFlag: categorical\\n\",\n                            \"  - ProductSubcategoryID: categorical\\n\",\n                            \"  - ProductModelID: categorical\\n\",\n                            \"  - ProductID: join_key\\n\",\n                            \"  - SafetyStockLevel: numerical\\n\",\n                            \"  - ...\\n\",\n                            \"\\n\",\n                            \"special_offer:\\n\",\n                            \"  columns:\\n\",\n                            \"  - Category: categorical\\n\",\n                            \"  - Description: categorical\\n\",\n                            \"  - Type: categorical\\n\",\n                            \"  - SpecialOfferID: join_key\\n\",\n                            \"  - MinQty: numerical\\n\",\n                            \"  - ...\\n\",\n                            \"\\n\",\n                            \"sales_order_reason:\\n\",\n                            \"  columns:\\n\",\n                            \"  - SalesReasonID: categorical\\n\",\n                            \"  - SalesOrderID: join_key\\n\",\n                            \"  - ModifiedDate: unused_string\\n\",\n                            \"\\n\",\n                            \"store:\\n\",\n                            \"  columns:\\n\",\n                            \"  - SalesPersonID: join_key\\n\",\n                            \"  - test: time_stamp\\n\",\n                            \"  - BusinessEntityID: unused_float\\n\",\n                            \"  - Name: unused_string\\n\",\n                            \"  - Demographics: unused_string\\n\",\n                            \"  - ...\"\n                        ]\n                    },\n                    \"execution_count\": 20,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"dm = getml.data.DataModel(sales_order_header.to_placeholder(\\\"population\\\"))\\n\",\n                \"\\n\",\n                \"dm.add(getml.data.to_placeholder(\\n\",\n                \"    product=product,\\n\",\n                \"    sales_order_detail=sales_order_detail, \\n\",\n                \"    sales_order_header=sales_order_header, \\n\",\n                \"    sales_order_reason=sales_order_reason,\\n\",\n                \"    special_offer=special_offer,\\n\",\n                \"    store=store,\\n\",\n                \"))\\n\",\n                \"\\n\",\n                \"\\n\",\n                \"dm.population.join(\\n\",\n                \"    dm.sales_order_header,\\n\",\n                \"    on=\\\"CustomerID\\\",\\n\",\n                \"    time_stamps=\\\"OrderDate\\\",\\n\",\n                \"    lagged_targets=True,\\n\",\n                \"    horizon=getml.data.time.days(1),\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm.population.join(\\n\",\n                \"    dm.sales_order_detail,\\n\",\n                \"    on=\\\"SalesOrderID\\\",\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm.population.join(\\n\",\n                \"    dm.sales_order_reason,\\n\",\n                \"    on=\\\"SalesOrderID\\\", \\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm.population.join(\\n\",\n                \"    dm.store,\\n\",\n                \"    on=\\\"SalesPersonID\\\", \\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm.sales_order_detail.join(\\n\",\n                \"    dm.product,\\n\",\n                \"    on=\\\"ProductID\\\",\\n\",\n                \"    relationship=getml.data.relationship.many_to_one,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm.sales_order_detail.join(\\n\",\n                \"    dm.special_offer,\\n\",\n                \"    on=\\\"SpecialOfferID\\\",\\n\",\n                \"    relationship=getml.data.relationship.many_to_one,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"dm\\n\",\n                \"\\n\",\n                \" \"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.2 getML pipeline\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"<!-- #### 2.1.1  -->\\n\",\n                \"__Set-up the feature learner & predictor__\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 21,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [],\n            \"source\": [\n                \"fast_prop = getml.feature_learning.FastProp(\\n\",\n                \"    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\\n\",\n                \"    num_threads=1,    \\n\",\n                \"    num_features=400,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"predictor = getml.predictors.XGBoostClassifier(n_jobs=1)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Build the pipeline__\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 22,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre>Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=[],\\n\",\n                            \"         include_categorical=True,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['product', 'sales_order_detail', 'sales_order_header',\\n\",\n                            \"                     'sales_order_reason', 'special_offer', 'store'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['fast_prop'])</pre>\"\n                        ],\n                        \"text/plain\": [\n                            \"Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=[],\\n\",\n                            \"         include_categorical=True,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['product', 'sales_order_detail', 'sales_order_header',\\n\",\n                            \"                     'sales_order_reason', 'special_offer', 'store'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['fast_prop'])\"\n                        ]\n                    },\n                    \"execution_count\": 22,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe1 = getml.Pipeline(\\n\",\n                \"    tags=['fast_prop'],\\n\",\n                \"    data_model=dm,\\n\",\n                \"    feature_learners=[fast_prop],\\n\",\n                \"    predictors=[predictor],\\n\",\n                \"    include_categorical=True,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"pipe1\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.3 Model training\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 23,\n            \"metadata\": {\n                \"scrolled\": true,\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">type</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">label                 </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">message                         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">INFO</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and SALES_ORDER_REASON__STAGING_TABLE_4 over &#x27;SalesOrderID&#x27; and &#x27;SalesOrderID&#x27;, there are no corresponding entries for 33.769352% of entries in &#x27;SalesOrderID&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">INFO</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and STORE__STAGING_TABLE_5 over &#x27;SalesPersonID&#x27; and &#x27;SalesPersonID&#x27;, there are no corresponding entries for 84.941548% of entries in &#x27;SalesPersonID&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\"\n                        ],\n                        \"text/plain\": [\n                            \"    type   label                    message                         \\n\",\n                            \"0   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\\n\",\n                            \"1   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\"\n                        ]\n                    },\n                    \"execution_count\": 23,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe1.check(container.train)\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 24,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">To see the issues in full, run <span style=\\\"color: #800080; text-decoration-color: #800080; font-weight: bold\\\">.check</span><span style=\\\"font-weight: bold\\\">()</span> on the pipeline.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"To see the issues in full, run \\u001b[1;35m.check\\u001b[0m\\u001b[1m(\\u001b[0m\\u001b[1m)\\u001b[0m on the pipeline.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  FastProp: Trying 563 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\\n\",\n                        \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:15\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Trained pipeline.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"Time taken: 0:00:17.669831.\\n\",\n                        \"\\n\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre>Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=[],\\n\",\n                            \"         include_categorical=True,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['product', 'sales_order_detail', 'sales_order_header',\\n\",\n                            \"                     'sales_order_reason', 'special_offer', 'store'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['fast_prop', 'container-9Cswke'])</pre>\"\n                        ],\n                        \"text/plain\": [\n                            \"Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=[],\\n\",\n                            \"         include_categorical=True,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['product', 'sales_order_detail', 'sales_order_header',\\n\",\n                            \"                     'sales_order_reason', 'special_offer', 'store'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['fast_prop', 'container-9Cswke'])\"\n                        ]\n                    },\n                    \"execution_count\": 24,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe1.fit(container.train)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.4 Model evaluation\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 25,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">set used</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">target</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">accuracy</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">    auc</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">cross entropy</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"datetime\\\">2024-09-12 12:25:24</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">train</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">churn</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.9152</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.9723</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.2187</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"datetime\\\">2024-09-12 12:25:25</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">test</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">churn</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.9162</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.9699</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.2265</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\"\n                        ],\n                        \"text/plain\": [\n                            \"    date time             set used   target   accuracy       auc   cross entropy\\n\",\n                            \"0   2024-09-12 12:25:24   train      churn      0.9152    0.9723          0.2187\\n\",\n                            \"1   2024-09-12 12:25:25   test       churn      0.9162    0.9699          0.2265\"\n                        ]\n                    },\n                    \"execution_count\": 25,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe1.score(container.test)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.5 Features\\n\",\n                \"\\n\",\n                \"The most important features look as follows:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 26,\n            \"metadata\": {},\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/markdown\": [\n                            \"```sql\\n\",\n                            \"DROP TABLE IF EXISTS \\\"FEATURE_1_307\\\";\\n\",\n                            \"\\n\",\n                            \"CREATE TABLE \\\"FEATURE_1_307\\\" AS\\n\",\n                            \"SELECT MIN( t1.\\\"orderdate\\\" - t2.\\\"t4__startdate\\\" ) AS \\\"feature_1_307\\\",\\n\",\n                            \"       t1.rowid AS rownum\\n\",\n                            \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n                            \"INNER JOIN \\\"SALES_ORDER_DETAIL__STAGING_TABLE_2\\\" t2\\n\",\n                            \"ON t1.\\\"salesorderid\\\" = t2.\\\"salesorderid\\\"\\n\",\n                            \"GROUP BY t1.rowid;\\n\",\n                            \"```\"\n                        ],\n                        \"text/plain\": [\n                            \"'DROP TABLE IF EXISTS \\\"FEATURE_1_307\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_307\\\" AS\\\\nSELECT MIN( t1.\\\"orderdate\\\" - t2.\\\"t4__startdate\\\" ) AS \\\"feature_1_307\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"SALES_ORDER_DETAIL__STAGING_TABLE_2\\\" t2\\\\nON t1.\\\"salesorderid\\\" = t2.\\\"salesorderid\\\"\\\\nGROUP BY t1.rowid;'\"\n                        ]\n                    },\n                    \"execution_count\": 26,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe1.features.to_sql()[pipe1.features.sort(by=\\\"importances\\\")[0].name]\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 27,\n            \"metadata\": {},\n            \"outputs\": [],\n            \"source\": [\n                \"getml.engine.shutdown()\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 3. Conclusion\\n\",\n                \"\\n\",\n                \"In this notebook, we successfully demonstrated the process of predicting customer churn for AdventureWorks, a fictional company that sells bicycles, using the getML library. The key steps we covered include:\\n\",\n                \"\\n\",\n                \"1. Background and Data Preparation:\\n\",\n                \"\\n\",\n                \"    - Introduced AdventureWorks and the purpose of the analysis.\\n\",\n                \"    - Loaded and prepared the data from the AdventureWorks database, including various tables such as Product, SalesOrderDetail, SalesOrderHeader, and more.\\n\",\n                \"\\n\",\n                \"2. Data Visualization and Preparation:\\n\",\n                \"\\n\",\n                \"    - Defined roles for the dataset columns to prepare them for modeling.\\n\",\n                \"    - Ensured the data was in the correct format for getML to process.\\n\",\n                \"\\n\",\n                \"3. Predictive Modeling:\\n\",\n                \"\\n\",\n                \"    - Created a relational model using getML to capture the relationships between different tables.\\n\",\n                \"    - Built a getML pipeline for feature learning and prediction.\\n\",\n                \"\\n\",\n                \"4. Model Training and Evaluation:\\n\",\n                \"\\n\",\n                \"    - Trained the model on the prepared data.\\n\",\n                \"    - Evaluated the model's performance to predict customer churn.\\n\",\n                \"    \\n\",\n                \"By leveraging getML's capabilities, we efficiently handled the complexities of relational data and built a robust classification model to predict customer churn. This approach can be extended to other customer databases and prediction tasks, providing valuable insights and accurate forecasts.\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## References\\n\",\n                \"\\n\",\n                \"Motl, Jan, and Oliver Schulte. \\\"The CTU prague relational learning repository.\\\" arXiv preprint arXiv:1511.03086 (2015).\"\n            ]\n        }\n    ],\n    \"metadata\": {\n        \"jupytext\": {\n            \"encoding\": \"# -*- coding: utf-8 -*-\",\n            \"formats\": \"ipynb,py:percent,md\"\n        },\n        \"kernelspec\": {\n            \"display_name\": \"Python 3 (ipykernel)\",\n            \"language\": \"python\",\n            \"name\": \"python3\"\n        },\n        \"language_info\": {\n            \"codemirror_mode\": {\n                \"name\": \"ipython\",\n                \"version\": 3\n            },\n            \"file_extension\": \".py\",\n            \"mimetype\": \"text/x-python\",\n            \"name\": \"python\",\n            \"nbconvert_exporter\": \"python\",\n            \"pygments_lexer\": \"ipython3\",\n            \"version\": \"3.10.12\"\n        },\n        \"toc\": {\n            \"base_numbering\": 1,\n            \"nav_menu\": {},\n            \"number_sections\": false,\n            \"sideBar\": true,\n            \"skip_h1_title\": false,\n            \"title_cell\": \"Table of Contents\",\n            \"title_sidebar\": \"Contents\",\n            \"toc_cell\": false,\n            \"toc_position\": {},\n            \"toc_section_display\": true,\n            \"toc_window_display\": true\n        },\n        \"vscode\": {\n            \"interpreter\": {\n                \"hash\": \"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1\"\n            }\n        }\n    },\n    \"nbformat\": 4,\n    \"nbformat_minor\": 4\n}"
  },
  {
    "path": "demo-notebooks/docker-compose.yml",
    "content": "services:\n  notebooks:\n    build:\n      context: ./\n      dockerfile: Dockerfile\n    networks:\n      - notebooks_network\n    ports:\n      - \"1709:1709\"\n      - \"8888:8888\"\n\nnetworks:\n  notebooks_network:"
  },
  {
    "path": "demo-notebooks/formula1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Formula 1 - Predicting the winner of a race\\n\",\n    \"\\n\",\n    \"In this notebook we will use getML to predict the winner of a Formula 1 race.\\n\",\n    \"\\n\",\n    \"Summary:\\n\",\n    \"\\n\",\n    \"- Prediction type: __Classification model__\\n\",\n    \"- Domain: __Sports__\\n\",\n    \"- Prediction target: __Win__ \\n\",\n    \"- Population size: __31578__\\n\",\n    \"\\n\",\n    \"_Author: Dr. Patrick Urbanke_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [\n     \"remove_cell_on_docs\"\n    ]\n   },\n   \"source\": [\n    \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/formula1.ipynb\\\">\\n\",\n    \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"</a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Background\\n\",\n    \"\\n\",\n    \"We would like to develop a prediction model for Formula 1 races, that would allow us to predict the winner of a race before the race has started.\\n\",\n    \"\\n\",\n    \"We use dataset of all Formula 1 races from 1950 to 2017. The dataset includes information such as the time taken in each lap, the time taken for pit stops, the performance in the qualifying rounds etc.\\n\",\n    \"\\n\",\n    \"The dataset has been downloaded from the [CTU Prague relational learning repository](https://relational-data.org/dataset/ErgastF1) (Motl and Schulte, 2015).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get started with the analysis and set up your session:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Note: you may need to restart the kernel to use updated packages.\\n\",\n      \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n      \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912122602.log.\\n\",\n      \"\\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'formula1'</span>.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Connected to project \\u001b[32m'formula1'\\u001b[0m.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%pip install -q \\\"getml==1.5.0\\\" \\\"matplotlib~=3.9\\\"\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline  \\n\",\n    \"\\n\",\n    \"import getml\\n\",\n    \"\\n\",\n    \"getml.engine.launch()\\n\",\n    \"getml.engine.set_project('formula1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Loading data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.1 Download from source\\n\",\n    \"\\n\",\n    \"We begin by downloading the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Connection(dbname='ErgastF1',\\n\",\n       \"           dialect='mysql',\\n\",\n       \"           host='relational.fel.cvut.cz',\\n\",\n       \"           port=3306)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"conn = getml.database.connect_mariadb(\\n\",\n    \"    host=\\\"relational.fel.cvut.cz\\\",\\n\",\n    \"    dbname=\\\"ErgastF1\\\",\\n\",\n    \"    port=3306,\\n\",\n    \"    user=\\\"guest\\\",\\n\",\n    \"    password=\\\"ctu-relational\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"conn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def load_if_needed(name):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Loads the data from the relational learning\\n\",\n    \"    repository, if the data frame has not already\\n\",\n    \"    been loaded.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    if not getml.data.exists(name):\\n\",\n    \"        data_frame = getml.data.DataFrame.from_db(\\n\",\n    \"            name=name,\\n\",\n    \"            table_name=name,\\n\",\n    \"            conn=conn\\n\",\n    \"        )\\n\",\n    \"        data_frame.save()\\n\",\n    \"    else:\\n\",\n    \"        data_frame = getml.data.load_data_frame(name)\\n\",\n    \"    return data_frame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"driverStandings = load_if_needed(\\\"driverStandings\\\")\\n\",\n    \"drivers = load_if_needed(\\\"drivers\\\")\\n\",\n    \"lapTimes = load_if_needed(\\\"lapTimes\\\")\\n\",\n    \"pitStops = load_if_needed(\\\"pitStops\\\")\\n\",\n    \"races = load_if_needed(\\\"races\\\")\\n\",\n    \"qualifying = load_if_needed(\\\"qualifying\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th> name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">driverStandingsId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      points</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        wins</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">positionText </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n      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class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">22</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">22</td>\\n\",\n       \"          \\n\",\n       \"        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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">814</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    31578 rows x 7 columns<br />\\n\",\n       \"    memory usage: 1.85 MB<br />\\n\",\n       \"    name: driverStandings<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name   driverStandingsId         raceId       driverId         points       position           wins   positionText \\n\",\n       \" role        unused_float   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string\\n\",\n       \"    0                   1             18              1             10              1              1   1            \\n\",\n       \"    1                   2             18              2              8              2              0   2            \\n\",\n       \"    2                   3             18              3              6              3              0   3            \\n\",\n       \"    3                   4             18              4              5              4              0   4            \\n\",\n       \"    4                   5             18              5              4              5              0   5            \\n\",\n       \"                      ...            ...            ...            ...            ...            ...   ...          \\n\",\n       \"31573               68456            982            835              8             16              0   16           \\n\",\n       \"31574               68457            982            154             26             13              0   13           \\n\",\n       \"31575               68458            982            836              5             18              0   18           \\n\",\n       \"31576               68459            982             18              0             22              0   22           \\n\",\n       \"31577               68460            982            814              0             23              0   23           \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"31578 rows x 7 columns\\n\",\n       \"memory usage: 1.85 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"driverStandings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      number</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">driverRef    </th>\\n\",\n       \"        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 \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">44</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">hamilton</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HAM</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Lewis</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Hamilton</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1985-01-07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">British</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Lew...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">nan</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">heidfeld</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HEI</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Nick</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Heidfeld</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1977-05-10</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">German</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Nic...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">rosberg</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ROS</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Nico</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Rosberg</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1985-06-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">German</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Nic...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">alonso</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ALO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Fernando</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Alonso</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1981-07-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Spanish</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Fer...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">nan</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">kovalainen</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">KOV</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Heikki</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Kovalainen</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1981-10-19</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Finnish</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Hei...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>835</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">837</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">88</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">haryanto</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HAR</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Rio</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Haryanto</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1993-01-22</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Indonesian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Rio...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>836</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">838</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">vandoorne</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">VAN</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Stoffel</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Vandoorne</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1992-03-26</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Belgian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Sto...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>837</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">839</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">31</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ocon</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">OCO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Esteban</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Ocon</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1996-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">French</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Est...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>838</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">stroll</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">STR</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Lance</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Stroll</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1998-10-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Canadian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Lan...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>839</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">841</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">36</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">giovinazzi</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">GIO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Antonio</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Giovinazzi</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1993-12-14</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Italian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Ant...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    840 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.13 MB<br />\\n\",\n       \"    name: drivers<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name       driverId         number   driverRef       code            forename        surname         dob          \\n\",\n       \"role   unused_float   unused_float   unused_string   unused_string   unused_string   unused_string   unused_string\\n\",\n       \"   0              1            44    hamilton        HAM             Lewis           Hamilton        1985-01-07   \\n\",\n       \"   1              2           nan    heidfeld        HEI             Nick            Heidfeld        1977-05-10   \\n\",\n       \"   2              3             6    rosberg         ROS             Nico            Rosberg         1985-06-27   \\n\",\n       \"   3              4            14    alonso          ALO             Fernando        Alonso          1981-07-29   \\n\",\n       \"   4              5           nan    kovalainen      KOV             Heikki          Kovalainen      1981-10-19   \\n\",\n       \"                ...            ...   ...             ...             ...             ...             ...          \\n\",\n       \" 835            837            88    haryanto        HAR             Rio             Haryanto        1993-01-22   \\n\",\n       \" 836            838             2    vandoorne       VAN             Stoffel         Vandoorne       1992-03-26   \\n\",\n       \" 837            839            31    ocon            OCO             Esteban         Ocon            1996-09-17   \\n\",\n       \" 838            840            18    stroll          STR             Lance           Stroll          1998-10-29   \\n\",\n       \" 839            841            36    giovinazzi      GIO             Antonio         Giovinazzi      1993-12-14   \\n\",\n       \"\\n\",\n       \"name   nationality     url                             \\n\",\n       \"role   unused_string   unused_string                   \\n\",\n       \"   0   British         http://en.wikipedia.org/wiki/Lew...\\n\",\n       \"   1   German          http://en.wikipedia.org/wiki/Nic...\\n\",\n       \"   2   German          http://en.wikipedia.org/wiki/Nic...\\n\",\n       \"   3   Spanish         http://en.wikipedia.org/wiki/Fer...\\n\",\n       \"   4   Finnish         http://en.wikipedia.org/wiki/Hei...\\n\",\n       \"       ...             ...                             \\n\",\n       \" 835   Indonesian      http://en.wikipedia.org/wiki/Rio...\\n\",\n       \" 836   Belgian         http://en.wikipedia.org/wiki/Sto...\\n\",\n       \" 837   French          http://en.wikipedia.org/wiki/Est...\\n\",\n       \" 838   Canadian        http://en.wikipedia.org/wiki/Lan...\\n\",\n       \" 839   Italian         http://en.wikipedia.org/wiki/Ant...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"840 rows x 9 columns\\n\",\n       \"memory usage: 0.13 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"drivers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right 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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">58</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">108699</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:48.699</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    420369 rows x 6 columns<br />\\n\",\n       \"    memory usage: 23.96 MB<br />\\n\",\n       \"    name: lapTimes<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name         raceId       driverId            lap       position   milliseconds   time         \\n\",\n       \"  role   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string\\n\",\n       \"     0              1              1              1             13         109088   1:49.088     \\n\",\n       \"     1              1              1              2             12          93740   1:33.740     \\n\",\n       \"     2              1              1              3             11          91600   1:31.600     \\n\",\n       \"     3              1              1              4             10          91067   1:31.067     \\n\",\n       \"     4              1              1              5             10          92129   1:32.129     \\n\",\n       \"                  ...            ...            ...            ...            ...   ...          \\n\",\n       \"420364            982            840             54              8         107528   1:47.528     \\n\",\n       \"420365            982            840             55              8         107512   1:47.512     \\n\",\n       \"420366            982            840             56              8         108143   1:48.143     \\n\",\n       \"420367            982            840             57              8         107848   1:47.848     \\n\",\n       \"420368            982            840             58              8         108699   1:48.699     \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"420369 rows x 6 columns\\n\",\n       \"memory usage: 23.96 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lapTimes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: 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{\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        stop</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         lap</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n      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>\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">16</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23227</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:28:24</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.227</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      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0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">36</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23199</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:59:29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.199</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">841</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">22994</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:27:41</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">22.994</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">841</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"     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 <td class=\\\"unused_string\\\">17:51:32</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">25.098</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">841</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              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      \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:29:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.716</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"  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0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:29:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.134</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6066</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"       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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">37403</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:06:43</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">37.403</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6067</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          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  \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">25584</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:13:16</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">25.584</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6069</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">26</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">29412</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:05:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.412</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    6070 rows x 7 columns<br />\\n\",\n       \"    memory usage: 0.44 MB<br />\\n\",\n       \"    name: pitStops<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name         raceId       driverId           stop            lap   milliseconds   time            duration     \\n\",\n       \"role   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string   unused_string\\n\",\n       \"   0            841              1              1             16          23227   17:28:24        23.227       \\n\",\n       \"   1            841              1              2             36          23199   17:59:29        23.199       \\n\",\n       \"   2            841              2              1             15          22994   17:27:41        22.994       \\n\",\n       \"   3            841              2              2             30          25098   17:51:32        25.098       \\n\",\n       \"   4            841              3              1             16          23716   17:29:00        23.716       \\n\",\n       \"                ...            ...            ...            ...            ...   ...             ...          \\n\",\n       \"6065            982            839              6             38          29134   21:29:07        29.134       \\n\",\n       \"6066            982            840              1              1          37403   20:06:43        37.403       \\n\",\n       \"6067            982            840              2              2          29294   20:10:07        29.294       \\n\",\n       \"6068            982            840              3              3          25584   20:13:16        25.584       \\n\",\n       \"6069            982            840              4             26          29412   21:05:07        29.412       \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"6070 rows x 7 columns\\n\",\n       \"memory usage: 0.44 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pitStops\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">       round</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">   circuitId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">name                    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">date         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">time         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">url                             </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string           </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Australian Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-03-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">06:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/200...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Malaysian Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-04-05</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">09:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/200...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">17</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Chinese Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-04-19</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">07:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/200...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Bahrain Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-04-26</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">12:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/200...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Spanish Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-05-10</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">12:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/200...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>971</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">984</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">16</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">22</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Japanese Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-10-08</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">05:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">https://en.wikipedia.org/wiki/20...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>972</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">985</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">17</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">69</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">United States Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-10-22</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">19:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">https://en.wikipedia.org/wiki/20...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>973</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">986</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">32</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Mexican Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-10-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">19:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">https://en.wikipedia.org/wiki/20...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>974</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">987</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">19</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Brazilian Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-11-12</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">16:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">https://en.wikipedia.org/wiki/20...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>975</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">988</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">20</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">24</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Abu Dhabi Grand Prix</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-11-26</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">https://en.wikipedia.org/wiki/20...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    976 rows x 8 columns<br />\\n\",\n       \"    memory usage: 0.15 MB<br />\\n\",\n       \"    name: races<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name         raceId           year          round      circuitId   name                       date            time         \\n\",\n       \"role   unused_float   unused_float   unused_float   unused_float   unused_string              unused_string   unused_string\\n\",\n       \"   0              1           2009              1              1   Australian Grand Prix      2009-03-29      06:00:00     \\n\",\n       \"   1              2           2009              2              2   Malaysian Grand Prix       2009-04-05      09:00:00     \\n\",\n       \"   2              3           2009              3             17   Chinese Grand Prix         2009-04-19      07:00:00     \\n\",\n       \"   3              4           2009              4              3   Bahrain Grand Prix         2009-04-26      12:00:00     \\n\",\n       \"   4              5           2009              5              4   Spanish Grand Prix         2009-05-10      12:00:00     \\n\",\n       \"                ...            ...            ...            ...   ...                        ...             ...          \\n\",\n       \" 971            984           2017             16             22   Japanese Grand Prix        2017-10-08      05:00:00     \\n\",\n       \" 972            985           2017             17             69   United States Grand Prix   2017-10-22      19:00:00     \\n\",\n       \" 973            986           2017             18             32   Mexican Grand Prix         2017-10-29      19:00:00     \\n\",\n       \" 974            987           2017             19             18   Brazilian Grand Prix       2017-11-12      16:00:00     \\n\",\n       \" 975            988           2017             20             24   Abu Dhabi Grand Prix       2017-11-26      17:00:00     \\n\",\n       \"\\n\",\n       \"name   url                             \\n\",\n       \"role   unused_string                   \\n\",\n       \"   0   http://en.wikipedia.org/wiki/200...\\n\",\n       \"   1   http://en.wikipedia.org/wiki/200...\\n\",\n       \"   2   http://en.wikipedia.org/wiki/200...\\n\",\n       \"   3   http://en.wikipedia.org/wiki/200...\\n\",\n       \"   4   http://en.wikipedia.org/wiki/200...\\n\",\n       \"       ...                             \\n\",\n       \" 971   https://en.wikipedia.org/wiki/20...\\n\",\n       \" 972   https://en.wikipedia.org/wiki/20...\\n\",\n       \" 973   https://en.wikipedia.org/wiki/20...\\n\",\n       \" 974   https://en.wikipedia.org/wiki/20...\\n\",\n       \" 975   https://en.wikipedia.org/wiki/20...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"976 rows x 8 columns\\n\",\n       \"memory usage: 0.15 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"races\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: 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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">20</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:45.570</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    7397 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.66 MB<br />\\n\",\n       \"    name: qualifying<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name      qualifyId         raceId       driverId   constructorId         number       position   q1           \\n\",\n       \"role   unused_float   unused_float   unused_float    unused_float   unused_float   unused_float   unused_string\\n\",\n       \"   0              1             18              1               1             22              1   1:26.572     \\n\",\n       \"   1              2             18              9               2              4              2   1:26.103     \\n\",\n       \"   2              3             18              5               1             23              3   1:25.664     \\n\",\n       \"   3              4             18             13               6              2              4   1:25.994     \\n\",\n       \"   4              5             18              2               2              3              5   1:25.960     \\n\",\n       \"                ...            ...            ...             ...            ...            ...   ...          \\n\",\n       \"7392           7415            982            825             210             20             16   1:43.756     \\n\",\n       \"7393           7416            982             13               3             19             17   1:44.014     \\n\",\n       \"7394           7417            982            840               3             18             18   1:44.728     \\n\",\n       \"7395           7418            982            836              15             94             19   1:45.059     \\n\",\n       \"7396           7419            982            828              15              9             20   1:45.570     \\n\",\n       \"\\n\",\n       \"name   q2              q3           \\n\",\n       \"role   unused_string   unused_string\\n\",\n       \"   0   1:25.187        1:26.714     \\n\",\n       \"   1   1:25.315        1:26.869     \\n\",\n       \"   2   1:25.452        1:27.079     \\n\",\n       \"   3   1:25.691        1:27.178     \\n\",\n       \"   4   1:25.518        1:27.236     \\n\",\n       \"       ...             ...          \\n\",\n       \"7392   NULL            NULL         \\n\",\n       \"7393   NULL            NULL         \\n\",\n       \"7394   NULL            NULL         \\n\",\n       \"7395   NULL            NULL         \\n\",\n       \"7396   NULL            NULL         \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"7397 rows x 9 columns\\n\",\n       \"memory usage: 0.66 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"qualifying\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.2 Prepare data for getML\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raceId</th>\\n\",\n       \"      <th>year</th>\\n\",\n       \"      <th>round</th>\\n\",\n       \"      <th>circuitId</th>\\n\",\n       \"      <th>name</th>\\n\",\n       \"      <th>date</th>\\n\",\n       \"      <th>time</th>\\n\",\n       \"      <th>url</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2009.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>Australian Grand Prix</td>\\n\",\n       \"      <td>2009-03-29</td>\\n\",\n       \"      <td>06:00:00</td>\\n\",\n       \"      <td>http://en.wikipedia.org/wiki/2009_Australian_G...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>2009.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>Malaysian Grand Prix</td>\\n\",\n       \"      <td>2009-04-05</td>\\n\",\n       \"      <td>09:00:00</td>\\n\",\n       \"      <td>http://en.wikipedia.org/wiki/2009_Malaysian_Gr...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>2009.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>17.0</td>\\n\",\n       \"      <td>Chinese Grand Prix</td>\\n\",\n       \"      <td>2009-04-19</td>\\n\",\n       \"      <td>07:00:00</td>\\n\",\n       \"      <td>http://en.wikipedia.org/wiki/2009_Chinese_Gran...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>2009.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>Bahrain Grand Prix</td>\\n\",\n       \"      <td>2009-04-26</td>\\n\",\n       \"      <td>12:00:00</td>\\n\",\n       \"      <td>http://en.wikipedia.org/wiki/2009_Bahrain_Gran...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>2009.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>Spanish Grand Prix</td>\\n\",\n       \"      <td>2009-05-10</td>\\n\",\n       \"      <td>12:00:00</td>\\n\",\n       \"      <td>http://en.wikipedia.org/wiki/2009_Spanish_Gran...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>971</th>\\n\",\n       \"      <td>984.0</td>\\n\",\n       \"      <td>2017.0</td>\\n\",\n       \"      <td>16.0</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>Japanese Grand Prix</td>\\n\",\n       \"      <td>2017-10-08</td>\\n\",\n       \"      <td>05:00:00</td>\\n\",\n       \"      <td>https://en.wikipedia.org/wiki/2017_Japanese_Gr...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>972</th>\\n\",\n       \"      <td>985.0</td>\\n\",\n       \"      <td>2017.0</td>\\n\",\n       \"      <td>17.0</td>\\n\",\n       \"      <td>69.0</td>\\n\",\n       \"      <td>United States Grand Prix</td>\\n\",\n       \"      <td>2017-10-22</td>\\n\",\n       \"      <td>19:00:00</td>\\n\",\n       \"      <td>https://en.wikipedia.org/wiki/2017_United_Stat...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>973</th>\\n\",\n       \"      <td>986.0</td>\\n\",\n       \"      <td>2017.0</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>32.0</td>\\n\",\n       \"      <td>Mexican Grand Prix</td>\\n\",\n       \"      <td>2017-10-29</td>\\n\",\n       \"      <td>19:00:00</td>\\n\",\n       \"      <td>https://en.wikipedia.org/wiki/2017_Mexican_Gra...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>974</th>\\n\",\n       \"      <td>987.0</td>\\n\",\n       \"      <td>2017.0</td>\\n\",\n       \"      <td>19.0</td>\\n\",\n       \"      <td>18.0</td>\\n\",\n       \"      <td>Brazilian Grand Prix</td>\\n\",\n       \"      <td>2017-11-12</td>\\n\",\n       \"      <td>16:00:00</td>\\n\",\n       \"      <td>https://en.wikipedia.org/wiki/2017_Brazilian_G...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>975</th>\\n\",\n       \"      <td>988.0</td>\\n\",\n       \"      <td>2017.0</td>\\n\",\n       \"      <td>20.0</td>\\n\",\n       \"      <td>24.0</td>\\n\",\n       \"      <td>Abu Dhabi Grand Prix</td>\\n\",\n       \"      <td>2017-11-26</td>\\n\",\n       \"      <td>17:00:00</td>\\n\",\n       \"      <td>https://en.wikipedia.org/wiki/2017_Abu_Dhabi_G...</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>976 rows × 8 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     raceId    year  round  circuitId                      name        date  \\\\\\n\",\n       \"0       1.0  2009.0    1.0        1.0     Australian Grand Prix  2009-03-29   \\n\",\n       \"1       2.0  2009.0    2.0        2.0      Malaysian Grand Prix  2009-04-05   \\n\",\n       \"2       3.0  2009.0    3.0       17.0        Chinese Grand Prix  2009-04-19   \\n\",\n       \"3       4.0  2009.0    4.0        3.0        Bahrain Grand Prix  2009-04-26   \\n\",\n       \"4       5.0  2009.0    5.0        4.0        Spanish Grand Prix  2009-05-10   \\n\",\n       \"..      ...     ...    ...        ...                       ...         ...   \\n\",\n       \"971   984.0  2017.0   16.0       22.0       Japanese Grand Prix  2017-10-08   \\n\",\n       \"972   985.0  2017.0   17.0       69.0  United States Grand Prix  2017-10-22   \\n\",\n       \"973   986.0  2017.0   18.0       32.0        Mexican Grand Prix  2017-10-29   \\n\",\n       \"974   987.0  2017.0   19.0       18.0      Brazilian Grand Prix  2017-11-12   \\n\",\n       \"975   988.0  2017.0   20.0       24.0      Abu Dhabi Grand Prix  2017-11-26   \\n\",\n       \"\\n\",\n       \"         time                                                url  \\n\",\n       \"0    06:00:00  http://en.wikipedia.org/wiki/2009_Australian_G...  \\n\",\n       \"1    09:00:00  http://en.wikipedia.org/wiki/2009_Malaysian_Gr...  \\n\",\n       \"2    07:00:00  http://en.wikipedia.org/wiki/2009_Chinese_Gran...  \\n\",\n       \"3    12:00:00  http://en.wikipedia.org/wiki/2009_Bahrain_Gran...  \\n\",\n       \"4    12:00:00  http://en.wikipedia.org/wiki/2009_Spanish_Gran...  \\n\",\n       \"..        ...                                                ...  \\n\",\n       \"971  05:00:00  https://en.wikipedia.org/wiki/2017_Japanese_Gr...  \\n\",\n       \"972  19:00:00  https://en.wikipedia.org/wiki/2017_United_Stat...  \\n\",\n       \"973  19:00:00  https://en.wikipedia.org/wiki/2017_Mexican_Gra...  \\n\",\n       \"974  16:00:00  https://en.wikipedia.org/wiki/2017_Brazilian_G...  \\n\",\n       \"975  17:00:00  https://en.wikipedia.org/wiki/2017_Abu_Dhabi_G...  \\n\",\n       \"\\n\",\n       \"[976 rows x 8 columns]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"racesPd = races.to_pandas()\\n\",\n    \"racesPd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We actually need some set-up, because the target variable is not readily available. The `wins` column in `driverStandings` is actually the accumulated number of wins over a year, but what we want is a boolean variable indicated whether someone has one a particular race or not.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th> name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">driverStandingsId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      points</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        wins</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">       round</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         win</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">positionText </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">date         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">10</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          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      \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2008</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2008-03-16</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"        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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2008</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">3</td>\\n\",\n       \"          \\n\",\n       \"        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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"      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 \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2008</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"        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style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">5</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2008-03-16</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n      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class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">13</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            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\"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">836</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n  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      \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">22</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>31577</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">68460</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">814</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    31578 rows x 11 columns<br />\\n\",\n       \"    memory usage: 3.21 MB<br />\\n\",\n       \"    name: driver_standings<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name   driverStandingsId         raceId       driverId         points   ...           year          round            win\\n\",\n       \" role        unused_float   unused_float   unused_float   unused_float   ...   unused_float   unused_float   unused_float\\n\",\n       \"    0                   1             18              1             10   ...           2008              1              1\\n\",\n       \"    1                   2             18              2              8   ...           2008              1              0\\n\",\n       \"    2                   3             18              3              6   ...           2008              1              0\\n\",\n       \"    3                   4             18              4              5   ...           2008              1              0\\n\",\n       \"    4                   5             18              5              4   ...           2008              1              0\\n\",\n       \"                      ...            ...            ...            ...                  ...            ...            ...\\n\",\n       \"31573               68456            982            835              8   ...           2017             14              0\\n\",\n       \"31574               68457            982            154             26   ...           2017             14              0\\n\",\n       \"31575               68458            982            836              5   ...           2017             14              0\\n\",\n       \"31576               68459            982             18              0   ...           2017             14              0\\n\",\n       \"31577               68460            982            814              0   ...           2017             14              0\\n\",\n       \"\\n\",\n       \" name   positionText    date         \\n\",\n       \" role   unused_string   unused_string\\n\",\n       \"    0   1               2008-03-16   \\n\",\n       \"    1   2               2008-03-16   \\n\",\n       \"    2   3               2008-03-16   \\n\",\n       \"    3   4               2008-03-16   \\n\",\n       \"    4   5               2008-03-16   \\n\",\n       \"        ...             ...          \\n\",\n       \"31573   16              2017-09-17   \\n\",\n       \"31574   13              2017-09-17   \\n\",\n       \"31575   18              2017-09-17   \\n\",\n       \"31576   22              2017-09-17   \\n\",\n       \"31577   23              2017-09-17   \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"31578 rows x 11 columns\\n\",\n       \"memory usage: 3.21 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"driverStandingsPd = driverStandings.to_pandas()\\n\",\n    \"\\n\",\n    \"driverStandingsPd = driverStandingsPd.merge(\\n\",\n    \"    racesPd[[\\\"raceId\\\", \\\"year\\\", \\\"date\\\", \\\"round\\\"]],\\n\",\n    \"    on=\\\"raceId\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"previousStanding = driverStandingsPd.merge(\\n\",\n    \"    driverStandingsPd[[\\\"driverId\\\", \\\"year\\\", \\\"wins\\\", \\\"round\\\"]],\\n\",\n    \"    on=[\\\"driverId\\\", \\\"year\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"isPreviousRound = (previousStanding[\\\"round_x\\\"] - previousStanding[\\\"round_y\\\"] == 1.0)\\n\",\n    \"\\n\",\n    \"previousStanding = previousStanding[isPreviousRound]\\n\",\n    \"\\n\",\n    \"previousStanding[\\\"win\\\"] = previousStanding[\\\"wins_x\\\"] - previousStanding[\\\"wins_y\\\"]\\n\",\n    \"\\n\",\n    \"driverStandingsPd = driverStandingsPd.merge(\\n\",\n    \"    previousStanding[[\\\"raceId\\\", \\\"driverId\\\", \\\"win\\\"]],\\n\",\n    \"    on=[\\\"raceId\\\", \\\"driverId\\\"],\\n\",\n    \"    how=\\\"left\\\",\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"driverStandingsPd[\\\"win\\\"] = [win if win == win else wins for win, wins in zip(driverStandingsPd[\\\"win\\\"], driverStandingsPd[\\\"wins\\\"])]\\n\",\n    \"\\n\",\n    \"driver_standings = getml.data.DataFrame.from_pandas(driverStandingsPd, \\\"driver_standings\\\")\\n\",\n    \"\\n\",\n    \"driver_standings\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also need to include the date of the race to `lapTimes` and `pitStops`, because we cannot use this data for the race we would like to predict. We can only take lap times and pit stops from previous races.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         lap</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">milliseconds</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">time         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">date         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">13</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">109088</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:49.088</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-03-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            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      \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">11</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">91600</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:31.600</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-03-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n     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<span class=\\\"left\\\">2009</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:32.129</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2009-03-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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     \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">54</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \" 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\"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:47.528</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>420365</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">55</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"            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               >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:47.512</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>420366</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                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style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">108143</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:48.143</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>420367</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">57</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          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\\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:47.848</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>420368</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">58</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">108699</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:48.699</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    420369 rows x 8 columns<br />\\n\",\n       \"    memory usage: 35.31 MB<br />\\n\",\n       \"    name: lap_times<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name         raceId       driverId            lap       position   milliseconds           year   time         \\n\",\n       \"  role   unused_float   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string\\n\",\n       \"     0              1              1              1             13         109088           2009   1:49.088     \\n\",\n       \"     1              1              1              2             12          93740           2009   1:33.740     \\n\",\n       \"     2              1              1              3             11          91600           2009   1:31.600     \\n\",\n       \"     3              1              1              4             10          91067           2009   1:31.067     \\n\",\n       \"     4              1              1              5             10          92129           2009   1:32.129     \\n\",\n       \"                  ...            ...            ...            ...            ...            ...   ...          \\n\",\n       \"420364            982            840             54              8         107528           2017   1:47.528     \\n\",\n       \"420365            982            840             55              8         107512           2017   1:47.512     \\n\",\n       \"420366            982            840             56              8         108143           2017   1:48.143     \\n\",\n       \"420367            982            840             57              8         107848           2017   1:47.848     \\n\",\n       \"420368            982            840             58              8         108699           2017   1:48.699     \\n\",\n       \"\\n\",\n       \"  name   date         \\n\",\n       \"  role   unused_string\\n\",\n       \"     0   2009-03-29   \\n\",\n       \"     1   2009-03-29   \\n\",\n       \"     2   2009-03-29   \\n\",\n       \"     3   2009-03-29   \\n\",\n       \"     4   2009-03-29   \\n\",\n       \"         ...          \\n\",\n       \"420364   2017-09-17   \\n\",\n       \"420365   2017-09-17   \\n\",\n       \"420366   2017-09-17   \\n\",\n       \"420367   2017-09-17   \\n\",\n       \"420368   2017-09-17   \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"420369 rows x 8 columns\\n\",\n       \"memory usage: 35.31 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lapTimesPd = lapTimes.to_pandas()\\n\",\n    \"\\n\",\n    \"lapTimesPd = lapTimesPd.merge(\\n\",\n    \"    racesPd[[\\\"raceId\\\", \\\"date\\\", \\\"year\\\"]],\\n\",\n    \"    on=\\\"raceId\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"lap_times = getml.data.DataFrame.from_pandas(lapTimesPd, \\\"lap_times\\\")\\n\",\n    \"\\n\",\n    \"lap_times\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">    driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        stop</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         lap</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">milliseconds</th>\\n\",\n       \"        \\n\",\n       \"     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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2011</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:28:24</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.227</td>\\n\",\n     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               >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">22994</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2011</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:27:41</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">22.994</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n     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    \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:51:32</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">25.098</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">841</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n      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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">16</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23716</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2011</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:29:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.716</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"  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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">38</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">29134</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:29:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.134</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6066</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \" 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      \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"       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>\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:06:43</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">37.403</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6067</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              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class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">29294</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:10:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.294</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6068</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">25584</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:13:16</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">25.584</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6069</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">982</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">840</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">26</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">29412</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2017</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:05:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.412</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    6070 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.60 MB<br />\\n\",\n       \"    name: pit_stops<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name         raceId       driverId           stop            lap   milliseconds           year   time            duration     \\n\",\n       \"role   unused_float   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string   unused_string\\n\",\n       \"   0            841              1              1             16          23227           2011   17:28:24        23.227       \\n\",\n       \"   1            841              1              2             36          23199           2011   17:59:29        23.199       \\n\",\n       \"   2            841              2              1             15          22994           2011   17:27:41        22.994       \\n\",\n       \"   3            841              2              2             30          25098           2011   17:51:32        25.098       \\n\",\n       \"   4            841              3              1             16          23716           2011   17:29:00        23.716       \\n\",\n       \"                ...            ...            ...            ...            ...            ...   ...             ...          \\n\",\n       \"6065            982            839              6             38          29134           2017   21:29:07        29.134       \\n\",\n       \"6066            982            840              1              1          37403           2017   20:06:43        37.403       \\n\",\n       \"6067            982            840              2              2          29294           2017   20:10:07        29.294       \\n\",\n       \"6068            982            840              3              3          25584           2017   20:13:16        25.584       \\n\",\n       \"6069            982            840              4             26          29412           2017   21:05:07        29.412       \\n\",\n       \"\\n\",\n       \"name   date         \\n\",\n       \"role   unused_string\\n\",\n       \"   0   2011-03-27   \\n\",\n       \"   1   2011-03-27   \\n\",\n       \"   2   2011-03-27   \\n\",\n       \"   3   2011-03-27   \\n\",\n       \"   4   2011-03-27   \\n\",\n       \"       ...          \\n\",\n       \"6065   2017-09-17   \\n\",\n       \"6066   2017-09-17   \\n\",\n       \"6067   2017-09-17   \\n\",\n       \"6068   2017-09-17   \\n\",\n       \"6069   2017-09-17   \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"6070 rows x 9 columns\\n\",\n       \"memory usage: 0.60 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pitStopsPd = pitStops.to_pandas()\\n\",\n    \"\\n\",\n    \"pitStopsPd = pitStopsPd.merge(\\n\",\n    \"    racesPd[[\\\"raceId\\\", \\\"date\\\", \\\"year\\\"]],\\n\",\n    \"    on=\\\"raceId\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"pit_stops = getml.data.DataFrame.from_pandas(pitStopsPd, \\\"pit_stops\\\")\\n\",\n    \"\\n\",\n    \"pit_stops\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"getML requires that we define *roles* for each of the columns.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th> name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\">                       date</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">  raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">    year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">   win</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\"> position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">driverStandingsId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      points</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        wins</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">       round</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">positionText </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">     unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> unit</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">        </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">        </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">        </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">      </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">                 </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">             </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2008-03-16</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">18</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2008</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"               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  \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">23</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">68460</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    31578 rows x 11 columns<br />\\n\",\n       \"    memory usage: 2.49 MB<br />\\n\",\n       \"    name: driver_standings<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name                          date     raceId   driverId       year   ...   driverStandingsId         points           wins\\n\",\n       \" role                    time_stamp   join_key   join_key   join_key   ...        unused_float   unused_float   unused_float\\n\",\n       \" unit   time stamp, comparison only                                    ...                                                  \\n\",\n       \"    0                    2008-03-16         18          1       2008   ...                   1             10              1\\n\",\n       \"    1                    2008-03-16         18          2       2008   ...                   2              8              0\\n\",\n       \"    2                    2008-03-16         18          3       2008   ...                   3              6              0\\n\",\n       \"    3                    2008-03-16         18          4       2008   ...                   4              5              0\\n\",\n       \"    4                    2008-03-16         18          5       2008   ...                   5              4              0\\n\",\n       \"                                ...        ...        ...        ...                       ...            ...            ...\\n\",\n       \"31573                    2017-09-17        982        835       2017   ...               68456              8              0\\n\",\n       \"31574                    2017-09-17        982        154       2017   ...               68457             26              0\\n\",\n       \"31575                    2017-09-17        982        836       2017   ...               68458              5              0\\n\",\n       \"31576                    2017-09-17        982         18       2017   ...               68459              0              0\\n\",\n       \"31577                    2017-09-17        982        814       2017   ...               68460              0              0\\n\",\n       \"\\n\",\n       \" name          round   positionText \\n\",\n       \" role   unused_float   unused_string\\n\",\n       \" unit                               \\n\",\n       \"    0              1   1            \\n\",\n       \"    1              1   2            \\n\",\n       \"    2              1   3            \\n\",\n       \"    3              1   4            \\n\",\n       \"    4              1   5            \\n\",\n       \"                 ...   ...          \\n\",\n       \"31573             14   16           \\n\",\n       \"31574             14   13           \\n\",\n       \"31575             14   18           \\n\",\n       \"31576             14   22           \\n\",\n       \"31577             14   23           \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"31578 rows x 11 columns\\n\",\n       \"memory usage: 2.49 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"driver_standings.set_role(\\\"win\\\", getml.data.roles.target)\\n\",\n    \"driver_standings.set_role([\\\"raceId\\\", \\\"driverId\\\", \\\"year\\\"], getml.data.roles.join_key)\\n\",\n    \"driver_standings.set_role(\\\"position\\\", getml.data.roles.numerical)\\n\",\n    \"driver_standings.set_role(\\\"date\\\", getml.data.roles.time_stamp)\\n\",\n    \"\\n\",\n    \"driver_standings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"categorical\\\">nationality</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"categorical\\\">driverRef  </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">      number</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">code         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">forename     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">surname      </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">dob          </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">url                             </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string                   </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">British</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">hamilton</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">44</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HAM</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Lewis</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Hamilton</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1985-01-07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Lew...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">German</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">heidfeld</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">nan</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HEI</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Nick</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Heidfeld</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1977-05-10</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Nic...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">German</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">rosberg</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ROS</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Nico</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Rosberg</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1985-06-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Nic...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">4</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Spanish</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">alonso</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">14</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ALO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Fernando</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Alonso</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1981-07-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Fer...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">5</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Finnish</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">kovalainen</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">nan</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">KOV</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Heikki</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Kovalainen</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1981-10-19</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Hei...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>835</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">837</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Indonesian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">haryanto</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">88</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">HAR</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Rio</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Haryanto</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1993-01-22</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Rio...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>836</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">838</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Belgian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">vandoorne</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">VAN</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Stoffel</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Vandoorne</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1992-03-26</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Sto...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>837</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">839</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">French</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">ocon</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">31</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">OCO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Esteban</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Ocon</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1996-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Est...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>838</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Canadian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">stroll</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">STR</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Lance</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Stroll</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1998-10-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Lan...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>839</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">841</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">Italian</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">giovinazzi</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">36</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">GIO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Antonio</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">Giovinazzi</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1993-12-14</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">http://en.wikipedia.org/wiki/Ant...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    840 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.11 MB<br />\\n\",\n       \"    name: drivers<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name   driverId   nationality   driverRef           number   code            forename        surname         dob          \\n\",\n       \"role   join_key   categorical   categorical   unused_float   unused_string   unused_string   unused_string   unused_string\\n\",\n       \"   0          1   British       hamilton               44    HAM             Lewis           Hamilton        1985-01-07   \\n\",\n       \"   1          2   German        heidfeld              nan    HEI             Nick            Heidfeld        1977-05-10   \\n\",\n       \"   2          3   German        rosberg                 6    ROS             Nico            Rosberg         1985-06-27   \\n\",\n       \"   3          4   Spanish       alonso                 14    ALO             Fernando        Alonso          1981-07-29   \\n\",\n       \"   4          5   Finnish       kovalainen            nan    KOV             Heikki          Kovalainen      1981-10-19   \\n\",\n       \"            ...   ...           ...                    ...   ...             ...             ...             ...          \\n\",\n       \" 835        837   Indonesian    haryanto               88    HAR             Rio             Haryanto        1993-01-22   \\n\",\n       \" 836        838   Belgian       vandoorne               2    VAN             Stoffel         Vandoorne       1992-03-26   \\n\",\n       \" 837        839   French        ocon                   31    OCO             Esteban         Ocon            1996-09-17   \\n\",\n       \" 838        840   Canadian      stroll                 18    STR             Lance           Stroll          1998-10-29   \\n\",\n       \" 839        841   Italian       giovinazzi             36    GIO             Antonio         Giovinazzi      1993-12-14   \\n\",\n       \"\\n\",\n       \"name   url                             \\n\",\n       \"role   unused_string                   \\n\",\n       \"   0   http://en.wikipedia.org/wiki/Lew...\\n\",\n       \"   1   http://en.wikipedia.org/wiki/Nic...\\n\",\n       \"   2   http://en.wikipedia.org/wiki/Nic...\\n\",\n       \"   3   http://en.wikipedia.org/wiki/Fer...\\n\",\n       \"   4   http://en.wikipedia.org/wiki/Hei...\\n\",\n       \"       ...                             \\n\",\n       \" 835   http://en.wikipedia.org/wiki/Rio...\\n\",\n       \" 836   http://en.wikipedia.org/wiki/Sto...\\n\",\n       \" 837   http://en.wikipedia.org/wiki/Est...\\n\",\n       \" 838   http://en.wikipedia.org/wiki/Lan...\\n\",\n       \" 839   http://en.wikipedia.org/wiki/Ant...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"840 rows x 9 columns\\n\",\n       \"memory usage: 0.11 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"drivers.set_role(\\\"driverId\\\", getml.data.roles.join_key)\\n\",\n    \"drivers.set_role([\\\"nationality\\\", \\\"driverRef\\\"], getml.data.roles.categorical)\\n\",\n    \"\\n\",\n    \"drivers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\">                       date</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">  raceId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">driverId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">    year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">      lap</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">milliseconds</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\"> position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"     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class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2009</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"         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class=\\\"unused_string\\\">1:31.067</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2009-03-29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2009</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">92129</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n     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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:47.848</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>420368</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">58</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">108699</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:48.699</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    420369 rows x 8 columns<br />\\n\",\n       \"    memory usage: 25.64 MB<br />\\n\",\n       \"    name: lap_times<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name                          date     raceId   driverId       year         lap   milliseconds    position   time         \\n\",\n       \"  role                    time_stamp   join_key   join_key   join_key   numerical      numerical   numerical   unused_string\\n\",\n       \"  unit   time stamp, comparison only                                                                                        \\n\",\n       \"     0                    2009-03-29          1          1       2009           1         109088          13   1:49.088     \\n\",\n       \"     1                    2009-03-29          1          1       2009           2          93740          12   1:33.740     \\n\",\n       \"     2                    2009-03-29          1          1       2009           3          91600          11   1:31.600     \\n\",\n       \"     3                    2009-03-29          1          1       2009           4          91067          10   1:31.067     \\n\",\n       \"     4                    2009-03-29          1          1       2009           5          92129          10   1:32.129     \\n\",\n       \"                                 ...        ...        ...        ...         ...            ...         ...   ...          \\n\",\n       \"420364                    2017-09-17        982        840       2017          54         107528           8   1:47.528     \\n\",\n       \"420365                    2017-09-17        982        840       2017          55         107512           8   1:47.512     \\n\",\n       \"420366                    2017-09-17        982        840       2017          56         108143           8   1:48.143     \\n\",\n       \"420367                    2017-09-17        982        840       2017          57         107848           8   1:47.848     \\n\",\n       \"420368                    2017-09-17        982        840       2017          58         108699           8   1:48.699     \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"420369 rows x 8 columns\\n\",\n       \"memory usage: 25.64 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lap_times.set_role([\\\"raceId\\\", \\\"driverId\\\", \\\"year\\\"], getml.data.roles.join_key)\\n\",\n    \"lap_times.set_role([\\\"lap\\\", \\\"milliseconds\\\", \\\"position\\\"], getml.data.roles.numerical)\\n\",\n    \"lap_times.set_role(\\\"date\\\", getml.data.roles.time_stamp)\\n\",\n    \"\\n\",\n    \"lap_times\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid 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      >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">23227</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:28:24</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.227</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">841</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">1</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2011</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">36</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">23199</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:59:29</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">23.199</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">841</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2011</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">22994</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:27:41</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">22.994</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">841</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2011</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">25098</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">17:51:32</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">25.098</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2011-03-27</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">841</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2011</td>\\n\",\n       \"          \\n\",\n       \"       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style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6065</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">839</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">38</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">29134</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:29:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.134</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6066</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">37403</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:06:43</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">37.403</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6067</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            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      \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">20:10:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.294</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>6068</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">25584</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"       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<th>6069</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2017-09-17</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">840</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">2017</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">26</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">29412</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">21:05:07</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">29.412</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    6070 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.46 MB<br />\\n\",\n       \"    name: pit_stops<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name                          date     raceId   driverId       year         lap   milliseconds        stop   time         \\n\",\n       \"role                    time_stamp   join_key   join_key   join_key   numerical      numerical   numerical   unused_string\\n\",\n       \"unit   time stamp, comparison only                                                                                        \\n\",\n       \"   0                    2011-03-27        841          1       2011          16          23227           1   17:28:24     \\n\",\n       \"   1                    2011-03-27        841          1       2011          36          23199           2   17:59:29     \\n\",\n       \"   2                    2011-03-27        841          2       2011          15          22994           1   17:27:41     \\n\",\n       \"   3                    2011-03-27        841          2       2011          30          25098           2   17:51:32     \\n\",\n       \"   4                    2011-03-27        841          3       2011          16          23716           1   17:29:00     \\n\",\n       \"                               ...        ...        ...        ...         ...            ...         ...   ...          \\n\",\n       \"6065                    2017-09-17        982        839       2017          38          29134           6   21:29:07     \\n\",\n       \"6066                    2017-09-17        982        840       2017           1          37403           1   20:06:43     \\n\",\n       \"6067                    2017-09-17        982        840       2017           2          29294           2   20:10:07     \\n\",\n       \"6068                    2017-09-17        982        840       2017           3          25584           3   20:13:16     \\n\",\n       \"6069                    2017-09-17        982        840       2017          26          29412           4   21:05:07     \\n\",\n       \"\\n\",\n       \"name   duration     \\n\",\n       \"role   unused_string\\n\",\n       \"unit                \\n\",\n       \"   0   23.227       \\n\",\n       \"   1   23.199       \\n\",\n       \"   2   22.994       \\n\",\n       \"   3   25.098       \\n\",\n       \"   4   23.716       \\n\",\n       \"       ...          \\n\",\n       \"6065   29.134       \\n\",\n       \"6066   37.403       \\n\",\n       \"6067   29.294       \\n\",\n       \"6068   25.584       \\n\",\n       \"6069   29.412       \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"6070 rows x 9 columns\\n\",\n       \"memory usage: 0.46 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pit_stops.set_role([\\\"raceId\\\", \\\"driverId\\\", \\\"year\\\"], getml.data.roles.join_key)\\n\",\n    \"pit_stops.set_role([\\\"lap\\\", \\\"milliseconds\\\", \\\"stop\\\"], getml.data.roles.numerical)\\n\",\n    \"pit_stops.set_role(\\\"date\\\", getml.data.roles.time_stamp)\\n\",\n    \"\\n\",\n    \"pit_stops\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  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class=\\\"join_key\\\">qualifyId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\"> position</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">   number</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">constructorId</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">q1           </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">q2           </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">q3           </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    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<span class=\\\"left\\\">19</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">94</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:45.059</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>7396</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">982</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">828</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">7419</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">20</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1:45.570</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    7397 rows x 9 columns<br />\\n\",\n       \"    memory usage: 0.57 MB<br />\\n\",\n       \"    name: qualifying<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name     raceId   driverId   qualifyId    position      number   constructorId   q1              q2              q3           \\n\",\n       \"role   join_key   join_key    join_key   numerical   numerical    unused_float   unused_string   unused_string   unused_string\\n\",\n       \"   0         18          1           1           1          22               1   1:26.572        1:25.187        1:26.714     \\n\",\n       \"   1         18          9           2           2           4               2   1:26.103        1:25.315        1:26.869     \\n\",\n       \"   2         18          5           3           3          23               1   1:25.664        1:25.452        1:27.079     \\n\",\n       \"   3         18         13           4           4           2               6   1:25.994        1:25.691        1:27.178     \\n\",\n       \"   4         18          2           5           5           3               2   1:25.960        1:25.518        1:27.236     \\n\",\n       \"            ...        ...         ...         ...         ...             ...   ...             ...             ...          \\n\",\n       \"7392        982        825        7415          16          20             210   1:43.756        NULL            NULL         \\n\",\n       \"7393        982         13        7416          17          19               3   1:44.014        NULL            NULL         \\n\",\n       \"7394        982        840        7417          18          18               3   1:44.728        NULL            NULL         \\n\",\n       \"7395        982        836        7418          19          94              15   1:45.059        NULL            NULL         \\n\",\n       \"7396        982        828        7419          20           9              15   1:45.570        NULL            NULL         \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"7397 rows x 9 columns\\n\",\n       \"memory usage: 0.57 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"qualifying.set_role([\\\"raceId\\\", \\\"driverId\\\", \\\"qualifyId\\\"], getml.data.roles.join_key)\\n\",\n    \"qualifying.set_role([\\\"position\\\", \\\"number\\\"], getml.data.roles.numerical)\\n\",\n    \"\\n\",\n    \"qualifying\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Predictive modeling\\n\",\n    \"\\n\",\n    \"We loaded the data and defined the roles and units. Next, we create a getML pipeline for relational learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  td.char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>test</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    infinite number of  rows<br />\\n\",\n       \"    \\n\",\n       \"    type: StringColumnView<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"          \\n\",\n       \" 0   train\\n\",\n       \" 1   train\\n\",\n       \" 2   train\\n\",\n       \" 3   test \\n\",\n       \" 4   train\\n\",\n       \"     ...  \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"infinite number of  rows\\n\",\n       \"type: StringColumnView\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"split = getml.data.split.random(train=0.8, test=0.2)\\n\",\n    \"split\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.1 Define relational model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\\n\",\n       \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n       \"            <div style=\\\"height:540px;width:660px;position:relative;\\\"><svg height=\\\"530\\\" width=\\\"650\\\"><rect y=\\\"0\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">driver_standings</text><rect x=\\\"51\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"10\\\" x2=\\\"67.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"10\\\" x2=\\\"83.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"26.0\\\" x2=\\\"99\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"42.0\\\" x2=\\\"99\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"110\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"183.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">lap_times</text><rect x=\\\"51\\\" y=\\\"120\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"120\\\" x2=\\\"67.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"120\\\" x2=\\\"83.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"136.0\\\" x2=\\\"99\\\" y2=\\\"136.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"152.0\\\" x2=\\\"99\\\" y2=\\\"152.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">pit_stops</text><rect x=\\\"51\\\" y=\\\"230\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"230\\\" x2=\\\"67.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"230\\\" x2=\\\"83.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"246.0\\\" x2=\\\"99\\\" y2=\\\"246.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"262.0\\\" x2=\\\"99\\\" y2=\\\"262.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"330\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"403.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">qualifying</text><rect x=\\\"51\\\" y=\\\"340\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"340\\\" x2=\\\"67.0\\\" y2=\\\"388\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"340\\\" x2=\\\"83.0\\\" y2=\\\"388\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"356.0\\\" x2=\\\"99\\\" y2=\\\"356.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"372.0\\\" x2=\\\"99\\\" y2=\\\"372.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"440\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"513.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">drivers</text><rect x=\\\"51\\\" y=\\\"450\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"450\\\" x2=\\\"67.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"450\\\" x2=\\\"83.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"466.0\\\" x2=\\\"99\\\" y2=\\\"466.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"482.0\\\" x2=\\\"99\\\" y2=\\\"482.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"440\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"513.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"551\\\" y=\\\"450\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"450\\\" x2=\\\"567.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"450\\\" x2=\\\"583.0\\\" y2=\\\"498\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"466.0\\\" x2=\\\"599\\\" y2=\\\"466.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"482.0\\\" x2=\\\"599\\\" y2=\\\"482.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"43.0\\\" x2=\\\"573.0\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"41.0\\\" x2=\\\"573.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 440 567.0, 430 579.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"30.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >driverId = driverId</tspan><tspan y=\\\"40.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >date &lt;= date</tspan><tspan y=\\\"50.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 days</tspan><tspan y=\\\"60.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Lagged targets allowed</tspan></text><line x1=\\\"150\\\" y1=\\\"153.0\\\" x2=\\\"573.0\\\" y2=\\\"153.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"151.0\\\" x2=\\\"573.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 440 567.0, 430 579.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"120.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"145.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >driverId = driverId</tspan><tspan y=\\\"155.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >date &lt;= date</tspan><tspan y=\\\"165.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 days</tspan></text><line x1=\\\"150\\\" y1=\\\"263.0\\\" x2=\\\"573.0\\\" y2=\\\"263.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"261.0\\\" x2=\\\"573.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 440 567.0, 430 579.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"230.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"255.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >driverId = driverId</tspan><tspan y=\\\"265.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >date &lt;= date</tspan><tspan y=\\\"275.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 days</tspan></text><line x1=\\\"150\\\" y1=\\\"373.0\\\" x2=\\\"573.0\\\" y2=\\\"373.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"371.0\\\" x2=\\\"573.0\\\" y2=\\\"430\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 440 567.0, 430 579.0, 430 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"340.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"365.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >driverId = driverId</tspan><tspan y=\\\"375.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >raceId = raceId</tspan><tspan y=\\\"385.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Relationship: many-to-one</tspan></text><line x1=\\\"150\\\" y1=\\\"483.0\\\" x2=\\\"490\\\" y2=\\\"483.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 483.0 490, 477.0 490, 489.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"450.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"480.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >driverId = driverId</tspan><tspan y=\\\"490.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Relationship: many-to-one</tspan></text></svg></div>\\n\",\n       \"            </div>\\n\",\n       \"\\n\",\n       \"            <div style='margin-top: 15px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n       \"            <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">data frames                    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">staging table                   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">population, qualifying, drivers</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">driver_standings</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DRIVER_STANDINGS__STAGING_TABLE_2</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">lap_times</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">LAP_TIMES__STAGING_TABLE_3</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">pit_stops</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">PIT_STOPS__STAGING_TABLE_4</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"            </div>\\n\",\n       \"            \\n\",\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\\n\",\n       \"<div style='margin-top: 15px;'>\\n\",\n       \"<div style='float: left; margin-right: 50px;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">subset</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name            </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">rows   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">driver_standings</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">driver_standings</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"<div style='float: left;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name            </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\">  rows</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">driver_standings</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">31578</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">lap_times</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">420369</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">pit_stops</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">6070</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">qualifying</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">7397</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">drivers</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">840</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"data model\\n\",\n       \"\\n\",\n       \"  population:\\n\",\n       \"    columns:\\n\",\n       \"    - raceId: join_key\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - year: join_key\\n\",\n       \"    - win: target\\n\",\n       \"    - date: time_stamp\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"    joins:\\n\",\n       \"    - right: 'driver_standings'\\n\",\n       \"      on: \\n\",\n       \"      - (population.driverId, driver_standings.driverId)\\n\",\n       \"      time_stamps: (population.date, driver_standings.date)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      horizon: 86400.0\\n\",\n       \"      lagged_targets: True\\n\",\n       \"    - right: 'lap_times'\\n\",\n       \"      on: \\n\",\n       \"      - (population.driverId, lap_times.driverId)\\n\",\n       \"      time_stamps: (population.date, lap_times.date)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      horizon: 86400.0\\n\",\n       \"      lagged_targets: False\\n\",\n       \"    - right: 'pit_stops'\\n\",\n       \"      on: \\n\",\n       \"      - (population.driverId, pit_stops.driverId)\\n\",\n       \"      time_stamps: (population.date, pit_stops.date)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      horizon: 86400.0\\n\",\n       \"      lagged_targets: False\\n\",\n       \"    - right: 'qualifying'\\n\",\n       \"      on: \\n\",\n       \"      - (population.driverId, qualifying.driverId)\\n\",\n       \"      - (population.raceId, qualifying.raceId)\\n\",\n       \"      relationship: 'many-to-one'\\n\",\n       \"      lagged_targets: False\\n\",\n       \"    - right: 'drivers'\\n\",\n       \"      on: \\n\",\n       \"      - (population.driverId, drivers.driverId)\\n\",\n       \"      relationship: 'many-to-one'\\n\",\n       \"      lagged_targets: False\\n\",\n       \"\\n\",\n       \"  driver_standings:\\n\",\n       \"    columns:\\n\",\n       \"    - raceId: join_key\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - year: join_key\\n\",\n       \"    - position: numerical\\n\",\n       \"    - win: target\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"  lap_times:\\n\",\n       \"    columns:\\n\",\n       \"    - raceId: join_key\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - year: join_key\\n\",\n       \"    - lap: numerical\\n\",\n       \"    - milliseconds: numerical\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"  pit_stops:\\n\",\n       \"    columns:\\n\",\n       \"    - raceId: join_key\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - year: join_key\\n\",\n       \"    - lap: numerical\\n\",\n       \"    - milliseconds: numerical\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"  qualifying:\\n\",\n       \"    columns:\\n\",\n       \"    - raceId: join_key\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - qualifyId: join_key\\n\",\n       \"    - position: numerical\\n\",\n       \"    - number: numerical\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"  drivers:\\n\",\n       \"    columns:\\n\",\n       \"    - nationality: categorical\\n\",\n       \"    - driverRef: categorical\\n\",\n       \"    - driverId: join_key\\n\",\n       \"    - number: unused_float\\n\",\n       \"    - code: unused_string\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"container\\n\",\n       \"\\n\",\n       \"  population\\n\",\n       \"      subset   name               rows      type\\n\",\n       \"  0   test     driver_standings   unknown   View\\n\",\n       \"  1   train    driver_standings   unknown   View\\n\",\n       \"\\n\",\n       \"  peripheral\\n\",\n       \"      name                 rows   type     \\n\",\n       \"  0   driver_standings    31578   DataFrame\\n\",\n       \"  1   lap_times          420369   DataFrame\\n\",\n       \"  2   pit_stops            6070   DataFrame\\n\",\n       \"  3   qualifying           7397   DataFrame\\n\",\n       \"  4   drivers               840   DataFrame\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"star_schema = getml.data.StarSchema(population=driver_standings.drop([\\\"position\\\"]), alias=\\\"population\\\", split=split)\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    driver_standings,\\n\",\n    \"    on=[\\\"driverId\\\"],\\n\",\n    \"    time_stamps=\\\"date\\\",\\n\",\n    \"    horizon=getml.data.time.days(1),\\n\",\n    \"    lagged_targets=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# We cannot use lap times for the race\\n\",\n    \"# we would like to predict, so we set\\n\",\n    \"# a non-zero horizon.\\n\",\n    \"star_schema.join(\\n\",\n    \"    lap_times,\\n\",\n    \"    on=[\\\"driverId\\\"],\\n\",\n    \"    time_stamps=\\\"date\\\",\\n\",\n    \"    horizon=getml.data.time.days(1),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# We cannot use pit stops for the race\\n\",\n    \"# we would like to predict, so we set\\n\",\n    \"# a non-zero horizon.\\n\",\n    \"star_schema.join(\\n\",\n    \"    pit_stops,\\n\",\n    \"    on=[\\\"driverId\\\"],\\n\",\n    \"    time_stamps=\\\"date\\\",\\n\",\n    \"    horizon=getml.data.time.days(1),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    qualifying,\\n\",\n    \"    on=[\\\"driverId\\\", \\\"raceId\\\"],\\n\",\n    \"    relationship=getml.data.relationship.many_to_one,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    drivers,\\n\",\n    \"    on=[\\\"driverId\\\"],\\n\",\n    \"    relationship=getml.data.relationship.many_to_one,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.2 getML pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!-- #### 2.1.1  -->\\n\",\n    \"__Set-up the feature learner & predictor__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We use the relboost algorithms for this problem. Because of the large number of keywords, we regularize the model a bit by requiring a minimum support for the keywords (`min_num_samples`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fast_prop = getml.feature_learning.FastProp(\\n\",\n    \"    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\\n\",\n    \"    aggregation=getml.feature_learning.FastProp.agg_sets.All,\\n\",\n    \"    num_threads=1,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"predictor = getml.predictors.XGBoostClassifier(n_jobs=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Build the pipeline__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='CrossEntropyLoss',\\n\",\n       \"         peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\\n\",\n       \"         predictors=['XGBoostClassifier'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='CrossEntropyLoss',\\n\",\n       \"         peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\\n\",\n       \"         predictors=['XGBoostClassifier'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop'])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1 = getml.pipeline.Pipeline(\\n\",\n    \"    tags=['fast_prop'],\\n\",\n    \"    data_model=star_schema.data_model,\\n\",\n    \"    feature_learners=[fast_prop],\\n\",\n    \"    predictors=[predictor],\\n\",\n    \"    include_categorical=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"pipe1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.3 Model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">label                 </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">message                         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">INFO</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and LAP_TIMES__STAGING_TABLE_3 over &#x27;driverId&#x27; and &#x27;driverId&#x27;, there are no corresponding entries for 68.551028% of entries in &#x27;driverId&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">INFO</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and PIT_STOPS__STAGING_TABLE_4 over &#x27;driverId&#x27; and &#x27;driverId&#x27;, there are no corresponding entries for 82.527910% of entries in &#x27;driverId&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    type   label                    message                         \\n\",\n       \"0   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\\n\",\n       \"1   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.check(star_schema.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">To see the issues in full, run <span style=\\\"color: #800080; text-decoration-color: #800080; font-weight: bold\\\">.check</span><span style=\\\"font-weight: bold\\\">()</span> on the pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"To see the issues in full, run \\u001b[1;35m.check\\u001b[0m\\u001b[1m(\\u001b[0m\\u001b[1m)\\u001b[0m on the pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Trying 664 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 01:18\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:20\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:11\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Trained pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time taken: 0:01:50.261514.\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='CrossEntropyLoss',\\n\",\n       \"         peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\\n\",\n       \"         predictors=['XGBoostClassifier'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop', 'container-gS8oTa'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='CrossEntropyLoss',\\n\",\n       \"         peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\\n\",\n       \"         predictors=['XGBoostClassifier'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop', 'container-gS8oTa'])\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.fit(star_schema.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.4 Model evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:05\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">set used</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">target</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">accuracy</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">    auc</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">cross entropy</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 12:27:58</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">win</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9714</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9448</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.0825</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 12:28:03</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">win</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9714</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9165</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.08801</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    date time             set used   target   accuracy       auc   cross entropy\\n\",\n       \"0   2024-09-12 12:27:58   train      win        0.9714    0.9448         0.0825 \\n\",\n       \"1   2024-09-12 12:28:03   test       win        0.9714    0.9165         0.08801\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.score(star_schema.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.5 Studying features\\n\",\n    \"\\n\",\n    \"We take a look at the importance of the features FastProp has learned:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"names, importances = pipe1.features.importances(target_num=0)\\n\",\n    \"\\n\",\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.bar(names[:30], importances[:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"feature importances\\\")\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"column\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We take a look at the most important features, to get an idea where the predictive power comes from:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_38\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_38\\\" AS\\n\",\n       \"SELECT EWMA_90D( t2.\\\"win\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, '+1.000000 days'\\\" ) AS \\\"feature_1_38\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\n\",\n       \"WHERE t2.\\\"date, '+1.000000 days'\\\" <= t1.\\\"date\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_38\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_38\\\" AS\\\\nSELECT EWMA_90D( t2.\\\"win\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" ) AS \\\"feature_1_38\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\\\nON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\\\nWHERE t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" <= t1.\\\"date\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.features.to_sql().find(names[0])[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_60\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_60\\\" AS\\n\",\n       \"SELECT EWMA_365D( t2.\\\"win\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, '+1.000000 days'\\\" ) AS \\\"feature_1_60\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\n\",\n       \"WHERE t2.\\\"date, '+1.000000 days'\\\" <= t1.\\\"date\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_60\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_60\\\" AS\\\\nSELECT EWMA_365D( t2.\\\"win\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" ) AS \\\"feature_1_60\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\\\nON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\\\nWHERE t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" <= t1.\\\"date\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.features.to_sql()[names[1]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_5\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_5\\\" AS\\n\",\n       \"SELECT EWMA_7D( t2.\\\"position\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, '+1.000000 days'\\\" ) AS \\\"feature_1_5\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\n\",\n       \"WHERE t2.\\\"date, '+1.000000 days'\\\" <= t1.\\\"date\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_5\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_5\\\" AS\\\\nSELECT EWMA_7D( t2.\\\"position\\\" ORDER BY t1.\\\"date\\\" - t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" ) AS \\\"feature_1_5\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"DRIVER_STANDINGS__STAGING_TABLE_2\\\" t2\\\\nON t1.\\\"driverid\\\" = t2.\\\"driverid\\\"\\\\nWHERE t2.\\\"date, \\\\'+1.000000 days\\\\'\\\" <= t1.\\\"date\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.features.to_sql()[names[2]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"getml.engine.shutdown()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Conclusion\\n\",\n    \"\\n\",\n    \"What we can learn from these features is the following: Knowing which driver we are talking about and who won the most recent races is the best predictor for whether a driver will win this race.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## References\\n\",\n    \"\\n\",\n    \"Motl, Jan, and Oliver Schulte. \\\"The CTU prague relational learning repository.\\\" arXiv preprint arXiv:1511.03086 (2015).\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"encoding\": \"# -*- coding: utf-8 -*-\",\n   \"formats\": \"ipynb,py:percent,md\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.9\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "demo-notebooks/interstate94.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Interstate 94 - Multivariate time series prediction\\n\",\n    \"\\n\",\n    \"In this tutorial, we demonstrate a time series application of getML. We predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul.\\n\",\n    \"\\n\",\n    \"Summary:\\n\",\n    \"\\n\",\n    \"- Prediction type: __Regression model__\\n\",\n    \"- Domain: __Transportation__\\n\",\n    \"- Prediction target: __Hourly traffic volume__\\n\",\n    \"- Source data: __Multivariate time series, 5 components__\\n\",\n    \"- Population size: __24096__\\n\",\n    \"\\n\",\n    \"_Author: Sören Nikolaus_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [\n     \"remove_cell_on_docs\"\n    ]\n   },\n   \"source\": [\n    \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/interstate94.ipynb\\\">\\n\",\n    \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"</a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Background\\n\",\n    \"\\n\",\n    \"The dataset features some particularly interesting characteristics common for time series, which classical models may struggle to deal with appropriately. Such characteristics are:\\n\",\n    \"\\n\",\n    \"- High frequency (hourly)\\n\",\n    \"- Dependence on irregular events (holidays)\\n\",\n    \"- Strong and overlapping cycles (daily, weekly)\\n\",\n    \"- Anomalies\\n\",\n    \"- Multiple seasonalities\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The analysis is built on top of a dataset provided by the [MN Department of Transportation](https://www.dot.state.mn.us), with some data preparation done by [John Hogue](https://github.com/dreyco676/Anomaly_Detection_A_to_Z/).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get started with the analysis and set-up your session:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Note: you may need to restart the kernel to use updated packages.\\n\",\n      \"getML API version: 1.5.0\\n\",\n      \"\\n\",\n      \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n      \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912151736.log.\\n\",\n      \"\\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'interstate94'</span>.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Connected to project \\u001b[32m'interstate94'\\u001b[0m.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%pip install -q \\\"getml==1.5.0\\\" \\\"matplotlib~=3.9\\\"\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import getml\\n\",\n    \"\\n\",\n    \"print(f\\\"getML API version: {getml.__version__}\\\\n\\\")\\n\",\n    \"\\n\",\n    \"getml.engine.launch()\\n\",\n    \"getml.engine.set_project(\\\"interstate94\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Loading data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.1 Download from source\\n\",\n    \"\\n\",\n    \"Downloading the raw data and convert it into a prediction ready format takes time. To get to the getML model building as fast as possible, we prepared the data for you and excluded the code from this notebook. It is made available in the example notebook featuring the full analysis. We only include data after 2016 and introduced a fixed train/test split at 80% of the available data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Downloading traffic... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 1.2/1.2 MB • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"traffic = getml.datasets.load_interstate94(roles=False, units=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The dataset comes with its own seasonal components. However, we choose not to use them,\\n\",\n    \"because we want to demonstrate our seasonal preprocessor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th> name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\">                         ds</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">traffic_volume</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"categorical\\\">holiday      </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        hour</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">     weekday</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         day</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">       month</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">        target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">categorical  </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> unit</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">              </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">             </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2016-01-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">1513</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">New Years Day</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2016</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2016-01-01 01:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">1550</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">New Years Day</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2016</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2016-01-01 02:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">993</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">New Years Day</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2016</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2016-01-01 03:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">719</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">New Years Day</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2016</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2016-01-01 04:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">533</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">New Years Day</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">4</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2016</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>24091</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-09-30 19:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">3543</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">19</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2018</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>24092</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-09-30 20:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">2781</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">20</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2018</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>24093</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-09-30 21:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">2159</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">21</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2018</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>24094</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-09-30 22:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">1450</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">22</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2018</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>24095</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-09-30 23:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">954</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">23</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">30</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2018</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    24096 rows x 8 columns<br />\\n\",\n       \"    memory usage: 1.45 MB<br />\\n\",\n       \"    name: traffic<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name                            ds   traffic_volume   holiday                 hour        weekday            day\\n\",\n       \" role                    time_stamp           target   categorical     unused_float   unused_float   unused_float\\n\",\n       \" unit   time stamp, comparison only                                                                              \\n\",\n       \"    0                    2016-01-01             1513   New Years Day              0              4              1\\n\",\n       \"    1           2016-01-01 01:00:00             1550   New Years Day              1              4              1\\n\",\n       \"    2           2016-01-01 02:00:00              993   New Years Day              2              4              1\\n\",\n       \"    3           2016-01-01 03:00:00              719   New Years Day              3              4              1\\n\",\n       \"    4           2016-01-01 04:00:00              533   New Years Day              4              4              1\\n\",\n       \"                                ...              ...   ...                      ...            ...            ...\\n\",\n       \"24091           2018-09-30 19:00:00             3543   No holiday                19              6             30\\n\",\n       \"24092           2018-09-30 20:00:00             2781   No holiday                20              6             30\\n\",\n       \"24093           2018-09-30 21:00:00             2159   No holiday                21              6             30\\n\",\n       \"24094           2018-09-30 22:00:00             1450   No holiday                22              6             30\\n\",\n       \"24095           2018-09-30 23:00:00              954   No holiday                23              6             30\\n\",\n       \"\\n\",\n       \" name          month           year\\n\",\n       \" role   unused_float   unused_float\\n\",\n       \" unit                              \\n\",\n       \"    0              1           2016\\n\",\n       \"    1              1           2016\\n\",\n       \"    2              1           2016\\n\",\n       \"    3              1           2016\\n\",\n       \"    4              1           2016\\n\",\n       \"                 ...            ...\\n\",\n       \"24091              9           2018\\n\",\n       \"24092              9           2018\\n\",\n       \"24093              9           2018\\n\",\n       \"24094              9           2018\\n\",\n       \"24095              9           2018\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"24096 rows x 8 columns\\n\",\n       \"memory usage: 1.45 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"traffic.set_role(\\\"ds\\\", getml.data.roles.time_stamp)\\n\",\n    \"traffic.set_role(\\\"holiday\\\", getml.data.roles.categorical)\\n\",\n    \"traffic.set_role(\\\"traffic_volume\\\", getml.data.roles.target)\\n\",\n    \"\\n\",\n    \"traffic\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.2 Prepare data for getML\\n\",\n    \"\\n\",\n    \"The `getml.datasets.load_interstate94` method took care of the entire data preparation:\\n\",\n    \"* Downloads csv's from our servers into python\\n\",\n    \"* Converts csv's to getML [DataFrames]()\\n\",\n    \"* Sets [roles]() & [units]() to columns inside getML DataFrames\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Data visualization__\\n\",\n    \"\\n\",\n    \"The first week of the original traffic time series is plotted below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"col_data = \\\"black\\\"\\n\",\n    \"col_getml = \\\"darkviolet\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"# 2016/01/01 was a friday, we'd like to start the visualizations on a monday\\n\",\n    \"start = 72\\n\",\n    \"end = 72 + 168\\n\",\n    \"\\n\",\n    \"fig.suptitle(\\n\",\n    \"    \\\"Traffic volume for first full week of the training set\\\",\\n\",\n    \"    fontsize=14,\\n\",\n    \"    fontweight=\\\"bold\\\",\\n\",\n    \")\\n\",\n    \"ax.plot(\\n\",\n    \"    traffic[\\\"ds\\\"].to_numpy()[start:end],\\n\",\n    \"    traffic[\\\"traffic_volume\\\"].to_numpy()[start:end],\\n\",\n    \"    color=col_data,\\n\",\n    \")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Traffic__: population table\\n\",\n    \"\\n\",\n    \"To allow the algorithm to capture seasonal information, we include time components (such as the day of the week) as categorical variables. Note that we could have also used getML's Seasonal preprocessor (`getml.prepreprocessors.Seasonal()`), but in this case the information was already included in the dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Train/test split__\\n\",\n    \"\\n\",\n    \"We use [getML's split functionality](https://getml.com/latest/reference/data/split/) to retrieve a lazily evaluated split column, that we can supply to the time series api below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"split = getml.data.split.time(traffic, \\\"ds\\\", test=getml.data.time.datetime(2018, 3, 15))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Split columns are columns of mere strings that can be used to subset the data by forming bolean conditions over them:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  td.char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\">                         ds</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">traffic_volume</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"categorical\\\">holiday    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        hour</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">     weekday</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         day</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">       month</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">        year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">        target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">unit</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\">              </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-03-15</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">577</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">0</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">15</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2018</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-03-15 01:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">354</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">1</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">15</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2018</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-03-15 02:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">259</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">15</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2018</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-03-15 03:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">360</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">15</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2018</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2018-03-15 04:00:00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">910</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">No holiday</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">4</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">15</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">3</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">2018</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>...</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"          <td class=\\\"char-align numerical\\\">\\n\",\n       \"            <span class=\\\"left\\\">...</span\\n\",\n       \"            ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"              >&nbsp;</span\\n\",\n       \"            >\\n\",\n       \"          </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    unknown number of rows<br />\\n\",\n       \"    \\n\",\n       \"    type: getml.data.View<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"name                            ds   traffic_volume   holiday               hour        weekday            day          month\\n\",\n       \"role                    time_stamp           target   categorical   unused_float   unused_float   unused_float   unused_float\\n\",\n       \"unit   time stamp, comparison only                                                                                           \\n\",\n       \"   0                    2018-03-15              577   No holiday               0              3             15              3\\n\",\n       \"   1           2018-03-15 01:00:00              354   No holiday               1              3             15              3\\n\",\n       \"   2           2018-03-15 02:00:00              259   No holiday               2              3             15              3\\n\",\n       \"   3           2018-03-15 03:00:00              360   No holiday               3              3             15              3\\n\",\n       \"   4           2018-03-15 04:00:00              910   No holiday               4              3             15              3\\n\",\n       \" ...                           ...              ...   ...                    ...            ...            ...            ...\\n\",\n       \"\\n\",\n       \"name           year\\n\",\n       \"role   unused_float\\n\",\n       \"unit               \\n\",\n       \"   0           2018\\n\",\n       \"   1           2018\\n\",\n       \"   2           2018\\n\",\n       \"   3           2018\\n\",\n       \"   4           2018\\n\",\n       \" ...            ...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"unknown number of rows x 8 columns\\n\",\n       \"type: getml.data.View\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"traffic[split == \\\"test\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.3 Define relational model\\n\",\n    \"\\n\",\n    \"To start with relational learning, we need to specify the data model. We manually replicate the appropriate time series structure by setting time series related join conditions (`horizon`, `memory` and `allow_lagged_targets`). We use the [high-level time series api](https://getml.com/latest/reference/data/time_series) for this.\\n\",\n    \"\\n\",\n    \"Under the hood, the time series api abstracts away a self cross join of the population table (`traffic`) that allows getML's feature learning algorithms to learn patterns from past observations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\\n\",\n       \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n       \"            <div style=\\\"height:100px;width:660px;position:relative;\\\"><svg height=\\\"90\\\" width=\\\"650\\\"><rect y=\\\"0\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">traffic</text><rect x=\\\"51\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"10\\\" x2=\\\"67.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"10\\\" x2=\\\"83.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"26.0\\\" x2=\\\"99\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"42.0\\\" x2=\\\"99\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"0\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"551\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"10\\\" x2=\\\"567.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"10\\\" x2=\\\"583.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"26.0\\\" x2=\\\"599\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"42.0\\\" x2=\\\"599\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"43.0\\\" x2=\\\"490\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 43.0 490, 37.0 490, 49.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"30.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >ds &lt;= ds</tspan><tspan y=\\\"40.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Memory: 7.0 days</tspan><tspan y=\\\"50.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 hours</tspan><tspan y=\\\"60.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Lagged targets allowed</tspan></text></svg></div>\\n\",\n       \"            </div>\\n\",\n       \"\\n\",\n       \"            <div style='margin-top: 15px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n       \"            <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">data frames</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">staging table              </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">population</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">TRAFFIC__STAGING_TABLE_2</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"            </div>\\n\",\n       \"            \\n\",\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\\n\",\n       \"<div style='margin-top: 15px;'>\\n\",\n       \"<div style='float: left; margin-right: 50px;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">subset</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">rows   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"<div style='float: left;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> rows</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">24096</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"data model\\n\",\n       \"\\n\",\n       \"  population:\\n\",\n       \"    columns:\\n\",\n       \"    - holiday: categorical\\n\",\n       \"    - traffic_volume: target\\n\",\n       \"    - ds: time_stamp\\n\",\n       \"    - hour: unused_float\\n\",\n       \"    - weekday: unused_float\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"    joins:\\n\",\n       \"    - right: 'traffic'\\n\",\n       \"      time_stamps: (population.ds, traffic.ds)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      memory: 604800.0\\n\",\n       \"      horizon: 3600.0\\n\",\n       \"      lagged_targets: True\\n\",\n       \"\\n\",\n       \"  traffic:\\n\",\n       \"    columns:\\n\",\n       \"    - holiday: categorical\\n\",\n       \"    - traffic_volume: target\\n\",\n       \"    - ds: time_stamp\\n\",\n       \"    - hour: unused_float\\n\",\n       \"    - weekday: unused_float\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"container\\n\",\n       \"\\n\",\n       \"  population\\n\",\n       \"      subset   name      rows      type\\n\",\n       \"  0   test     traffic   unknown   View\\n\",\n       \"  1   train    traffic   unknown   View\\n\",\n       \"\\n\",\n       \"  peripheral\\n\",\n       \"      name       rows   type     \\n\",\n       \"  0   traffic   24096   DataFrame\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"time_series = getml.data.TimeSeries(\\n\",\n    \"    population=traffic,\\n\",\n    \"    split=split,\\n\",\n    \"    time_stamps=\\\"ds\\\",\\n\",\n    \"    horizon=getml.data.time.hours(1),\\n\",\n    \"    memory=getml.data.time.days(7),\\n\",\n    \"    lagged_targets=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"time_series\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.Predictive modeling\\n\",\n    \"\\n\",\n    \"We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.1 getML Pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!-- #### 2.1.1  -->\\n\",\n    \"__Set-up of feature learners, selectors & predictor__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The Seasonal preprocessor extracts seasonal\\n\",\n    \"# components from the time stamps.\\n\",\n    \"seasonal = getml.preprocessors.Seasonal()\\n\",\n    \"\\n\",\n    \"fast_prop = getml.feature_learning.FastProp(\\n\",\n    \"    loss_function=getml.feature_learning.loss_functions.SquareLoss,\\n\",\n    \"    num_threads=1,    \\n\",\n    \"    num_features=20,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"predictor = getml.predictors.XGBoostRegressor()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Build the pipeline__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipe = getml.pipeline.Pipeline(\\n\",\n    \"    tags=[\\\"memory: 7d\\\", \\\"horizon: 1h\\\", \\\"fast_prop\\\"],\\n\",\n    \"    data_model=time_series.data_model,\\n\",\n    \"    preprocessors=[seasonal],\\n\",\n    \"    feature_learners=[fast_prop],\\n\",\n    \"    predictors=[predictor],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.2 Model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">OK.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"OK.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Trying 373 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:09\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Trained pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time taken: 0:00:10.956191.\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=False,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['traffic'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=['Seasonal'],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['memory: 7d', 'horizon: 1h', 'fast_prop', 'container-6yFATS'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=False,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['traffic'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=['Seasonal'],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['memory: 7d', 'horizon: 1h', 'fast_prop', 'container-6yFATS'])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.fit(time_series.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.3 Model evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"lines_to_next_cell\": 0,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">set used</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">target        </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">      mae</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">     rmse</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">rsquared</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:50</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic_volume</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">200.4302</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">299.2045</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9768</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:50</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">traffic_volume</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">179.9515</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">269.631</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9816</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    date time             set used   target                 mae        rmse   rsquared\\n\",\n       \"0   2024-09-12 15:17:50   train      traffic_volume    200.4302    299.2045     0.9768\\n\",\n       \"1   2024-09-12 15:17:50   test       traffic_volume    179.9515    269.631      0.9816\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.score(time_series.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.4 Studying features\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Feature correlations__\\n\",\n    \"\\n\",\n    \"Correlations of the calculated features with the target\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"names, correlations = pipe.features.correlations()\\n\",\n    \"\\n\",\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.bar(names, correlations, color=col_getml)\\n\",\n    \"plt.title(\\\"Feature Correlations\\\")\\n\",\n    \"plt.xlabel(\\\"Features\\\")\\n\",\n    \"plt.ylabel(\\\"Correlations\\\")\\n\",\n    \"plt.xticks(rotation=\\\"vertical\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Feature importances__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"lines_to_next_cell\": 0,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"names, importances = pipe.features.importances()\\n\",\n    \"\\n\",\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.bar(names, importances, color=col_getml)\\n\",\n    \"plt.title(\\\"Feature Importances\\\")\\n\",\n    \"plt.xlabel(\\\"Features\\\")\\n\",\n    \"plt.ylabel(\\\"Importances\\\")\\n\",\n    \"plt.xticks(rotation=\\\"vertical\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Visualizing the learned features__\\n\",\n    \"\\n\",\n    \"We can also transpile the features as SQL code. Here, we show the most important feature.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"lines_to_next_cell\": 0,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_3\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_3\\\" AS\\n\",\n       \"SELECT FIRST( t2.\\\"traffic_volume\\\" ORDER BY t2.\\\"ds, '+1.000000 hours'\\\" ) AS \\\"feature_1_3\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"TRAFFIC__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON 1 = 1\\n\",\n       \"WHERE t2.\\\"ds, '+1.000000 hours'\\\" <= t1.\\\"ds\\\"\\n\",\n       \"AND ( t2.\\\"ds, '+7.041667 days'\\\" > t1.\\\"ds\\\" OR t2.\\\"ds, '+7.041667 days'\\\" IS NULL )\\n\",\n       \"AND t1.\\\"hour( ds )\\\" = t2.\\\"hour( ds )\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_3\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_3\\\" AS\\\\nSELECT FIRST( t2.\\\"traffic_volume\\\" ORDER BY t2.\\\"ds, \\\\'+1.000000 hours\\\\'\\\" ) AS \\\"feature_1_3\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"TRAFFIC__STAGING_TABLE_2\\\" t2\\\\nON 1 = 1\\\\nWHERE t2.\\\"ds, \\\\'+1.000000 hours\\\\'\\\" <= t1.\\\"ds\\\"\\\\nAND ( t2.\\\"ds, \\\\'+7.041667 days\\\\'\\\" > t1.\\\"ds\\\" OR t2.\\\"ds, \\\\'+7.041667 days\\\\'\\\" IS NULL )\\\\nAND t1.\\\"hour( ds )\\\" = t2.\\\"hour( ds )\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"by_importance = pipe.features.sort(by=\\\"importance\\\")\\n\",\n    \"by_importance[0].sql\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Plot predictions & traffic volume vs. time__\\n\",\n    \"\\n\",\n    \"We now plot the predictions against the observed values of the target for the first 7 days of the testing set. You can see that the predictions closely follows the original series. FastProp was able to identify certain patterns in the series, including:\\n\",\n    \"- Day and night separation\\n\",\n    \"- The daily commuting peeks (on weekdays)\\n\",\n    \"- The decline on weekends\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"predictions = pipe.predict(time_series.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"lines_to_next_cell\": 0,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"# the test set starts at 2018/03/15 – a thursday; we introduce an offset to, once again, start on a monday\\n\",\n    \"start = 96\\n\",\n    \"end = 96 + 168\\n\",\n    \"\\n\",\n    \"actual = time_series.test.population[start:end].to_pandas()\\n\",\n    \"predicted = predictions[start:end]\\n\",\n    \"\\n\",\n    \"ax.plot(actual[\\\"ds\\\"], actual[\\\"traffic_volume\\\"], color=col_data, label=\\\"Actual\\\")\\n\",\n    \"ax.plot(actual[\\\"ds\\\"], predicted, color=col_getml, label=\\\"Predicted\\\")\\n\",\n    \"fig.suptitle(\\n\",\n    \"    \\\"Predicted vs. actual traffic volume for first full week of testing set\\\",\\n\",\n    \"    fontsize=14,\\n\",\n    \"    fontweight=\\\"bold\\\",\\n\",\n    \")\\n\",\n    \"fig.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.5 Features\\n\",\n    \"\\n\",\n    \"The most important feature looks as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_3\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_3\\\" AS\\n\",\n       \"SELECT FIRST( t2.\\\"traffic_volume\\\" ORDER BY t2.\\\"ds, '+1.000000 hours'\\\" ) AS \\\"feature_1_3\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"TRAFFIC__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON 1 = 1\\n\",\n       \"WHERE t2.\\\"ds, '+1.000000 hours'\\\" <= t1.\\\"ds\\\"\\n\",\n       \"AND ( t2.\\\"ds, '+7.041667 days'\\\" > t1.\\\"ds\\\" OR t2.\\\"ds, '+7.041667 days'\\\" IS NULL )\\n\",\n       \"AND t1.\\\"hour( ds )\\\" = t2.\\\"hour( ds )\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_3\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_3\\\" AS\\\\nSELECT FIRST( t2.\\\"traffic_volume\\\" ORDER BY t2.\\\"ds, \\\\'+1.000000 hours\\\\'\\\" ) AS \\\"feature_1_3\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"TRAFFIC__STAGING_TABLE_2\\\" t2\\\\nON 1 = 1\\\\nWHERE t2.\\\"ds, \\\\'+1.000000 hours\\\\'\\\" <= t1.\\\"ds\\\"\\\\nAND ( t2.\\\"ds, \\\\'+7.041667 days\\\\'\\\" > t1.\\\"ds\\\" OR t2.\\\"ds, \\\\'+7.041667 days\\\\'\\\" IS NULL )\\\\nAND t1.\\\"hour( ds )\\\" = t2.\\\"hour( ds )\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.features.to_sql()[pipe.features.sort(by=\\\"importances\\\")[0].name]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"getml.engine.shutdown()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Conclusion\\n\",\n    \"\\n\",\n    \"In this notebook, we demonstrated a comprehensive approach to predicting hourly traffic volume on Interstate 94 westbound from Minneapolis-St Paul using the getML library. We covered the following steps:\\n\",\n    \"\\n\",\n    \"1. Background and Data Preparation:\\n\",\n    \"\\n\",\n    \"    - Introduced the dataset and its characteristics.\\n\",\n    \"    - Loaded and prepared the data using `getml.datasets.load_interstate94`.\\n\",\n    \"\\n\",\n    \"2. Data Visualization:\\n\",\n    \"\\n\",\n    \"    - Visualized the first week of traffic volume data to understand the patterns and trends.\\n\",\n    \"\\n\",\n    \"3. Data Modeling:\\n\",\n    \"\\n\",\n    \"    - Defined roles and units for the dataset columns.\\n\",\n    \"    - Created a time series model using `getml.data.TimeSeries` to capture temporal dependencies.\\n\",\n    \"\\n\",\n    \"4. Predictive Modeling:\\n\",\n    \"\\n\",\n    \"    - Built a getML pipeline for relational learning to predict traffic volume.\\n\",\n    \"\\n\",\n    \"By leveraging getML's capabilities, we efficiently handled the complexities of time series data, including high frequency, irregular events, and multiple seasonalities. This approach can be extended to other time series prediction tasks in various domains.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"encoding\": \"# -*- coding: utf-8 -*-\",\n   \"formats\": \"ipynb,py:percent,md\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.9\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "demo-notebooks/loans.ipynb",
    "content": "{\n    \"cells\": [\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"# Loans - Predicting loan default risk \\n\",\n                \"\\n\",\n                \"This notebook demonstrates the application of our relational learning algorithm to predict if a customer of a bank will default on his loan. We train the predictor on customer metadata, transaction history, as well as other successful and unsuccessful loans.\\n\",\n                \"\\n\",\n                \"Summary:\\n\",\n                \"\\n\",\n                \"- Prediction type: __Binary classification__\\n\",\n                \"- Domain: __Finance__\\n\",\n                \"- Prediction target: __Loan default__\\n\",\n                \"- Source data: __8 tables, 78.8 MB__\\n\",\n                \"- Population size: __682__\\n\",\n                \"\\n\",\n                \"_Author: Dr. Johannes King, Dr. Patrick Urbanke_\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {\n                \"tags\": [\n                    \"remove_cell_on_docs\"\n                ]\n            },\n            \"source\": [\n                \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/loans.ipynb\\\">\\n\",\n                \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n                \"</a>\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## Background\\n\",\n                \"\\n\",\n                \"This notebook features a textbook example of predictive analytics applied to the financial sector. A loan is the lending of money to companies or individuals. Banks grant loans in exchange for the promise of repayment. Loan default is defined as the failure to meet this legal obligation, for example, when a home buyer fails to make a mortgage payment. A bank needs to estimate the risk it carries when granting loans to potentially non-performing customers.\\n\",\n                \"\\n\",\n                \"The analysis is based on the [financial](https://relational-data.org/dataset/Financial) dataset from the [the CTU Prague Relational Learning Repository](https://arxiv.org/abs/1511.03086) (Motl and Schulte, 2015).\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## Analysis\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"Let's get started with the analysis and set-up your session:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 1,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"Note: you may need to restart the kernel to use updated packages.\\n\",\n                        \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n                        \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912123619.log.\\n\",\n                        \"\\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'loans'</span>.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Connected to project \\u001b[32m'loans'\\u001b[0m.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                }\n            ],\n            \"source\": [\n                \"%pip install -q \\\"getml==1.5.0\\\" \\\"matplotlib~=3.9\\\"\\n\",\n                \"\\n\",\n                \"import matplotlib.pyplot as plt\\n\",\n                \"%matplotlib inline\\n\",\n                \"\\n\",\n                \"import getml\\n\",\n                \"\\n\",\n                \"getml.engine.launch()\\n\",\n                \"getml.engine.set_project(\\\"loans\\\")\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 1. Loading data\\n\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 1.1 Download from source\\n\",\n                \"\\n\",\n                \"Downloading the raw data from the CTU Prague Relational Learning Repository into a prediction ready format takes time. To get to the getML model building as fast as possible, we prepared the data for you and excluded the code from this notebook. It will be made available in a future version.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 2,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Downloading population_train... ━━━━━━━━━━━━━━━━━━ 100% • 36.0/36.0 kB • 00:00\\n\",\n                        \"\\u001b[2K  Downloading population_test... ━━━━━━━━━━━━━━━━━━━ 100% • 17.5/17.5 kB • 00:00\\n\",\n                        \"\\u001b[2K  Downloading order... ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 239.1/239.1 kB • 00:00\\n\",\n                        \"\\u001b[2K  Downloading trans... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 69.3/69.3 MB • 00:02\\n\",\n                        \"\\u001b[2K  Downloading meta... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 651.0/651.0 kB • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                }\n            ],\n            \"source\": [\n                \"population_train, population_test, order, trans, meta = getml.datasets.load_loans(roles=True, units=True)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 1.2 Prepare data for getML\\n\",\n                \"\\n\",\n                \"The `getml.datasets.load_loans` method took care of the entire data lifting:\\n\",\n                \"* Downloads csv's from our servers in python\\n\",\n                \"* Converts csv's to getML [DataFrames]()\\n\",\n                \"* Sets [roles]() to columns inside getML DataFrames\\n\",\n                \"\\n\",\n                \"The only thing left is to set [units]() to columns that the relational learning algorithm is allowed to compare to each other.\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"##### Population table\\n\",\n                \"\\n\",\n                \"- Information on the loan itself (duration, amount, date, ...)\\n\",\n                \"- Geo-information about the branch where the loans was granted (A**)\\n\",\n                \"- Column `status` contains binary target. Levels [A, C] := _loan paid back_ and [B, D] := _loan default_;\\n\",\n                \"  we recoded status to our binary target: `default`\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 3,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [],\n            \"source\": [\n                \"population_train.set_role(\\\"date_loan\\\", \\\"time_stamp\\\")\\n\",\n                \"population_test.set_role(\\\"date_loan\\\", \\\"time_stamp\\\")\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 4,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                  date_loan</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">account_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"target\\\">default</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">frequency         </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\"> duration</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\"> payments</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   amount</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">     loan_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\"> district_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">date_account </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_string\\\">status       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header target\\\"> target</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical       </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">          </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header target\\\">       </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">                  </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">    money</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">             </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_string\\\">             </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1996-04-29</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">19</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">12</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2523</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">30276</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4961</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">21</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1995-04-07</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">B</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-10-14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">37</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">60</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5308</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">318480</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4967</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">20</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1997-08-18</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">D</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-04-19</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">38</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK TYDNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">48</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2307</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">110736</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4968</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">19</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1997-08-08</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1997-08-10</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">97</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">12</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8573</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">102876</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4986</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">74</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1996-05-05</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1996-11-06</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">132</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK PO OBRATU</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">12</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7370</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">88440</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4996</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">40</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1996-05-11</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>218</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1995-12-04</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11042</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">36</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6032</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">217152</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7243</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">72</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1995-01-29</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>219</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1996-08-20</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11054</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK TYDNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">60</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2482</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">148920</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7246</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">59</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1996-02-01</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>220</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1994-01-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11111</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">36</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3004</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">108144</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7259</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1993-05-20</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>221</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-11-22</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11317</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">60</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5291</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">317460</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7292</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">50</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1997-07-11</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">C</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>222</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1996-12-27</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align target\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POPLATEK MESICNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">24</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5392</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">129408</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7308</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">67</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1995-10-14</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">A</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    223 rows x 11 columns<br />\\n\",\n                            \"    memory usage: 0.02 MB<br />\\n\",\n                            \"    name: population_test<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name                     date_loan   account_id   default   frequency            ...      amount        loan_id    district_id\\n\",\n                            \"role                    time_stamp     join_key    target   categorical          ...   numerical   unused_float   unused_float\\n\",\n                            \"unit   time stamp, comparison only                                               ...       money                              \\n\",\n                            \"   0                    1996-04-29           19         1   POPLATEK MESICNE     ...       30276           4961             21\\n\",\n                            \"   1                    1998-10-14           37         1   POPLATEK MESICNE     ...      318480           4967             20\\n\",\n                            \"   2                    1998-04-19           38         0   POPLATEK TYDNE       ...      110736           4968             19\\n\",\n                            \"   3                    1997-08-10           97         0   POPLATEK MESICNE     ...      102876           4986             74\\n\",\n                            \"   4                    1996-11-06          132         0   POPLATEK PO OBRATU   ...       88440           4996             40\\n\",\n                            \"                               ...          ...       ...   ...                              ...            ...            ...\\n\",\n                            \" 218                    1995-12-04        11042         0   POPLATEK MESICNE     ...      217152           7243             72\\n\",\n                            \" 219                    1996-08-20        11054         0   POPLATEK TYDNE       ...      148920           7246             59\\n\",\n                            \" 220                    1994-01-31        11111         0   POPLATEK MESICNE     ...      108144           7259              1\\n\",\n                            \" 221                    1998-11-22        11317         0   POPLATEK MESICNE     ...      317460           7292             50\\n\",\n                            \" 222                    1996-12-27        11362         0   POPLATEK MESICNE     ...      129408           7308             67\\n\",\n                            \"\\n\",\n                            \"name   date_account    status       \\n\",\n                            \"role   unused_string   unused_string\\n\",\n                            \"unit                                \\n\",\n                            \"   0   1995-04-07      B            \\n\",\n                            \"   1   1997-08-18      D            \\n\",\n                            \"   2   1997-08-08      C            \\n\",\n                            \"   3   1996-05-05      A            \\n\",\n                            \"   4   1996-05-11      A            \\n\",\n                            \"       ...             ...          \\n\",\n                            \" 218   1995-01-29      A            \\n\",\n                            \" 219   1996-02-01      C            \\n\",\n                            \" 220   1993-05-20      A            \\n\",\n                            \" 221   1997-07-11      C            \\n\",\n                            \" 222   1995-10-14      A            \\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"223 rows x 11 columns\\n\",\n                            \"memory usage: 0.02 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 4,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"population_test\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"##### Peripheral tables\\n\",\n                \"\\n\",\n                \"- `meta`\\n\",\n                \"  - Meta info about the client (card_type, gender, ...)\\n\",\n                \"  - Geo-information about the client\\n\",\n                \"- `order`\\n\",\n                \"  - Permanent orders related to a loan (amount, balance, ...)\\n\",\n                \"- `trans`\\n\",\n                \"  - Transactions related to a given loan (amount, ...)\\n\",\n                \"\\n\",\n                \"While the contents of `meta` and `order` are omitted for brevity, here are contents of `trans`:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 5,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">account_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">bank_to    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">k_symbol   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   amount</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">  account_to</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">    order_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          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  \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">    money</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">YZ</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">SIPO</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2452</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                           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class=\\\"categorical\\\">ST</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UVER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3372</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.7</span\\n\",\n                            \"           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class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7266</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13943797</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">29403</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                           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unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">29404</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">3</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">CD</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">327</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                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         \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">29405</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"   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          \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              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                        \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4780</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n               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class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">46334</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>6467</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">MN</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">56</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"      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                   \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">46335</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>6468</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">ST</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">POJISTNE</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">330</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">40799850</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">46336</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>6469</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">KL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">129</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">20009470</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">46337</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>6470</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">MN</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UVER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5392</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">61540514</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">46338</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    6471 rows x 6 columns<br />\\n\",\n                            \"    memory usage: 0.23 MB<br />\\n\",\n                            \"    name: order<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name   account_id   bank_to       k_symbol         amount     account_to       order_id\\n\",\n                            \"role     join_key   categorical   categorical   numerical   unused_float   unused_float\\n\",\n                            \"unit                                                money                              \\n\",\n                            \"   0            1   YZ            SIPO             2452         87144583          29401\\n\",\n                            \"   1            2   ST            UVER             3372.7       89597016          29402\\n\",\n                            \"   2            2   QR            SIPO             7266         13943797          29403\\n\",\n                            \"   3            3   WX            SIPO             1135         83084338          29404\\n\",\n                            \"   4            3   CD            NULL              327         24485939          29405\\n\",\n                            \"              ...   ...           ...                ...             ...            ...\\n\",\n                            \"6466        11362   YZ            SIPO             4780         70641225          46334\\n\",\n                            \"6467        11362   MN            NULL               56         78507822          46335\\n\",\n                            \"6468        11362   ST            POJISTNE          330         40799850          46336\\n\",\n                            \"6469        11362   KL            NULL              129         20009470          46337\\n\",\n                            \"6470        11362   MN            UVER             5392         61540514          46338\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"6471 rows x 6 columns\\n\",\n                            \"memory usage: 0.23 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 5,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"order\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 6,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  .join_key,\\n\",\n                            \"  .numerical,\\n\",\n                            \"  .target,\\n\",\n                            \"  .unused_float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  .char-align {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  span.left {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    width: 3em;\\n\",\n                            \"  }\\n\",\n                            \"  span.right {\\n\",\n                            \"    float: right;\\n\",\n                            \"    text-align: left;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>   name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"time_stamp\\\">                       date</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">account_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">type       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">k_symbol   </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">bank       </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">operation    </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">   amount</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"numerical\\\">  balance</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">    trans_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"unused_float\\\">     account</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">   role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical  </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">   unit</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">          </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">           </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">             </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header numerical\\\">    money</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header unused_float\\\">            </th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1995-03-24</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">VKLAD</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1000</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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  \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1995-04-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">AB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PREVOD Z UCTU</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3679</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4679</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          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class=\\\"time_stamp\\\">1995-05-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">AB</td>\\n\",\n                            \"     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</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">20977</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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<td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">41403269</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1995-06-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">AB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PREVOD Z UCTU</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3679</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">26835</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">7</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">41403269</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>4</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1995-07-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">AB</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PREVOD Z UCTU</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3679</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">30415</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">41403269</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th></th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">...</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1056315</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-08-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">10451</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UROK</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">62</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">17300</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3682983</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"         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                     \"        <th>1056316</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-09-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">10451</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UROK</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">49</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13442</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3682984</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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\\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-10-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">10451</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UROK</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">34</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"         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\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">10451</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UROK</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">26</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        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<span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1056319</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"time_stamp\\\">1998-12-31</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">10451</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">PRIJEM</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">UROK</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"    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\\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13695</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3682987</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    1056320 rows x 10 columns<br />\\n\",\n                            \"    memory usage: 63.38 MB<br />\\n\",\n                            \"    name: trans<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"   name                          date   account_id   type          k_symbol      ...   operation          amount     balance\\n\",\n                            \"   role                    time_stamp     join_key   categorical   categorical   ...   categorical     numerical   numerical\\n\",\n                            \"   unit   time stamp, comparison only                                            ...                                   money\\n\",\n                            \"      0                    1995-03-24            1   PRIJEM        NULL          ...   VKLAD                1000        1000\\n\",\n                            \"      1                    1995-04-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679        4679\\n\",\n                            \"      2                    1995-05-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       20977\\n\",\n                            \"      3                    1995-06-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       26835\\n\",\n                            \"      4                    1995-07-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       30415\\n\",\n                            \"                                  ...          ...   ...           ...                 ...                   ...         ...\\n\",\n                            \"1056315                    1998-08-31        10451   PRIJEM        UROK          ...   NULL                   62       17300\\n\",\n                            \"1056316                    1998-09-30        10451   PRIJEM        UROK          ...   NULL                   49       13442\\n\",\n                            \"1056317                    1998-10-31        10451   PRIJEM        UROK          ...   NULL                   34       10118\\n\",\n                            \"1056318                    1998-11-30        10451   PRIJEM        UROK          ...   NULL                   26        8398\\n\",\n                            \"1056319                    1998-12-31        10451   PRIJEM        UROK          ...   NULL                   42       13695\\n\",\n                            \"\\n\",\n                            \"   name       trans_id        account\\n\",\n                            \"   role   unused_float   unused_float\\n\",\n                            \"   unit                              \\n\",\n                            \"      0              1           nan \\n\",\n                            \"      1              5      41403269 \\n\",\n                            \"      2              6      41403269 \\n\",\n                            \"      3              7      41403269 \\n\",\n                            \"      4              8      41403269 \\n\",\n                            \"                   ...            ...\\n\",\n                            \"1056315        3682983           nan \\n\",\n                            \"1056316        3682984           nan \\n\",\n                            \"1056317        3682985           nan \\n\",\n                            \"1056318        3682986           nan \\n\",\n                            \"1056319        3682987           nan \\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"1056320 rows x 10 columns\\n\",\n                            \"memory usage: 63.38 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 6,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"trans\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 7,\n            \"metadata\": {},\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  table {\\n\",\n                            \"    font-family: Helvetica, sans-serif;\\n\",\n                            \"  }\\n\",\n                            \"\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.sub-header {\\n\",\n                            \"    font-weight: normal;\\n\",\n                            \"    font-style: italic;\\n\",\n                            \"  }\\n\",\n                            \"  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          \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th>name</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"join_key\\\">account_id</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">type_disp  </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"categorical\\\">type_card  </th>\\n\",\n                    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class=\\\"unused_string\\\">A2             </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header\\\">role</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header join_key\\\">  join_key</th>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n                            \"          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          \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">1</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">F</td>\\n\",\n                            \"          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numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.8</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.35</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">131</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1740</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1910</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">18</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1970-12-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Pisek</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Prague</td>\\n\",\n                            \"          \\n\",\n                            \"        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class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n  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                  \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">12541</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.2</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.43</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">167</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"            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class=\\\"left\\\">99107</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1945-02-04</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Hl.m. Praha</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">2</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">DISPONENT</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">F</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Prague</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1204953</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"    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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1940-10-09</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Hl.m. Praha</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">3</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">central Bohemia</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">95616</span\\n\",\n                            \"              ><span 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class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1956-12-01</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Kolin</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        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           \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">central Bohemia</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">95616</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                       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\"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5364</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11349</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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class=\\\"left\\\">1204953</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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   \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13647</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            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         <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1945-10-30</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Hl.m. Praha</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5365</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11349</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">DISPONENT</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">Prague</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1204953</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"    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class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            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class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.43</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">167</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">85677</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">99107</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13648</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13956</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1943-04-06</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Hl.m. Praha</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5366</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11359</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">classic</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">M</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">south Moravia</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">117897</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">139</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"  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<td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">53</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.8</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">8814</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.74</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">107</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2112</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2059</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            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   <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13968</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1247</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">61</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1995-06-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1968-04-13</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Trebic</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5367</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11362</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">F</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">north Moravia</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">106054</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n           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        \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">25</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">2</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                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\"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">63</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.1</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"         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class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.7</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">6</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.55</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">109</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3244</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">3079</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13663</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13971</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">67</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1962-10-19</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Bruntal</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>5368</th>\\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"join_key\\\">11382</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">OWNER</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">F</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"categorical\\\">north Moravia</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">323870</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">0</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">1</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">10673</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">4</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 1ch\\\"\\n\",\n                            \"                >.7</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"            \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">5</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n                            \"                >.44</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">100</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">18782</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align numerical\\\">\\n\",\n                            \"              <span class=\\\"left\\\">18347</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13690</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">13998</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">nan</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              \\n\",\n                            \"              \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"char-align unused_float\\\">\\n\",\n                            \"              <span class=\\\"left\\\">74</span\\n\",\n                            \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n                            \"                >&nbsp;</span\\n\",\n                            \"              >\\n\",\n                            \"            </td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">1953-08-12</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"          \\n\",\n                            \"            <td class=\\\"unused_string\\\">Ostrava - mesto</td>\\n\",\n                            \"          \\n\",\n                            \"        \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"\\n\",\n                            \"  <p>\\n\",\n                            \"    5369 rows x 25 columns<br />\\n\",\n                            \"    memory usage: 1.10 MB<br />\\n\",\n                            \"    name: meta<br />\\n\",\n                            \"    type: getml.DataFrame<br />\\n\",\n                            \"    \\n\",\n                            \"  </p>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"name   account_id   type_disp     type_card     gender        ...        card_id    district_id   issued       \\n\",\n                            \"role     join_key   categorical   categorical   categorical   ...   unused_float   unused_float   unused_string\\n\",\n                            \"   0            1   OWNER         NULL          F             ...           nan              18   NULL         \\n\",\n                            \"   1            2   OWNER         NULL          M             ...           nan               1   NULL         \\n\",\n                            \"   2            2   DISPONENT     NULL          F             ...           nan               1   NULL         \\n\",\n                            \"   3            3   OWNER         NULL          M             ...           nan               5   NULL         \\n\",\n                            \"   4            3   DISPONENT     NULL          F             ...           nan               5   NULL         \\n\",\n                            \"              ...   ...           ...           ...                          ...            ...   ...          \\n\",\n                            \"5364        11349   OWNER         NULL          F             ...           nan               1   NULL         \\n\",\n                            \"5365        11349   DISPONENT     NULL          M             ...           nan               1   NULL         \\n\",\n                            \"5366        11359   OWNER         classic       M             ...          1247              61   1995-06-13   \\n\",\n                            \"5367        11362   OWNER         NULL          F             ...           nan              67   NULL         \\n\",\n                            \"5368        11382   OWNER         NULL          F             ...           nan              74   NULL         \\n\",\n                            \"\\n\",\n                            \"name   birth_date      A2             \\n\",\n                            \"role   unused_string   unused_string  \\n\",\n                            \"   0   1970-12-13      Pisek          \\n\",\n                            \"   1   1945-02-04      Hl.m. Praha    \\n\",\n                            \"   2   1940-10-09      Hl.m. Praha    \\n\",\n                            \"   3   1956-12-01      Kolin          \\n\",\n                            \"   4   1960-07-03      Kolin          \\n\",\n                            \"       ...             ...            \\n\",\n                            \"5364   1945-10-30      Hl.m. Praha    \\n\",\n                            \"5365   1943-04-06      Hl.m. Praha    \\n\",\n                            \"5366   1968-04-13      Trebic         \\n\",\n                            \"5367   1962-10-19      Bruntal        \\n\",\n                            \"5368   1953-08-12      Ostrava - mesto\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"5369 rows x 25 columns\\n\",\n                            \"memory usage: 1.10 MB\\n\",\n                            \"type: getml.DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 7,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"meta\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 1.3 Define relational model\\n\",\n                \"\\n\",\n                \"To start with relational learning, we need to specify an abstract data model. Here, we use the [high-level star schema API](https://getml.com/latest/reference/data/star_schema) that allows us to define the abstract data model and construct a [container](https://getml.com/latest/reference/data/container) with the concrete data at one-go. While a simple `StarSchema` indeed works in many cases, it is not sufficient for more complex data models like schoflake schemas, where you would have to define the data model and construct the container in separate steps, by utilzing getML's [full-fledged data model](https://getml.com/latest/reference/data/data_model) and [container](https://getml.com/latest/reference/data/container) APIs respectively.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 8,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\\n\",\n                            \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n                            \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n                            \"            <div style=\\\"height:320px;width:660px;position:relative;\\\"><svg height=\\\"310\\\" width=\\\"650\\\"><rect y=\\\"0\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">trans</text><rect x=\\\"51\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"10\\\" x2=\\\"67.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"10\\\" x2=\\\"83.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"26.0\\\" x2=\\\"99\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"42.0\\\" x2=\\\"99\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"110\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"183.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">order</text><rect x=\\\"51\\\" y=\\\"120\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"120\\\" x2=\\\"67.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"120\\\" x2=\\\"83.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"136.0\\\" x2=\\\"99\\\" y2=\\\"136.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"152.0\\\" x2=\\\"99\\\" y2=\\\"152.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">meta</text><rect x=\\\"51\\\" y=\\\"230\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"230\\\" x2=\\\"67.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"230\\\" x2=\\\"83.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"246.0\\\" x2=\\\"99\\\" y2=\\\"246.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"262.0\\\" x2=\\\"99\\\" y2=\\\"262.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"551\\\" y=\\\"230\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"230\\\" x2=\\\"567.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"230\\\" x2=\\\"583.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"246.0\\\" x2=\\\"599\\\" y2=\\\"246.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"262.0\\\" x2=\\\"599\\\" y2=\\\"262.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"43.0\\\" x2=\\\"573.0\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"41.0\\\" x2=\\\"573.0\\\" y2=\\\"210\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 220 567.0, 210 579.0, 210 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"40.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >account_id = account_id</tspan><tspan y=\\\"50.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >date &lt;= date_loan</tspan></text><line x1=\\\"150\\\" y1=\\\"153.0\\\" x2=\\\"573.0\\\" y2=\\\"153.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"151.0\\\" x2=\\\"573.0\\\" y2=\\\"210\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 220 567.0, 210 579.0, 210 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"120.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"155.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >account_id = account_id</tspan></text><line x1=\\\"150\\\" y1=\\\"263.0\\\" x2=\\\"490\\\" y2=\\\"263.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 263.0 490, 257.0 490, 269.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"230.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"265.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >account_id = account_id</tspan></text></svg></div>\\n\",\n                            \"            </div>\\n\",\n                            \"\\n\",\n                            \"            <div style='margin-top: 15px;'>\\n\",\n                            \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n                            \"            <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">data frames</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">staging table              </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">population</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">meta</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">META__STAGING_TABLE_2</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">order</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">ORDER__STAGING_TABLE_3</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>3</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">trans</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">TRANS__STAGING_TABLE_4</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"            </div>\\n\",\n                            \"            \\n\",\n                            \"<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\\n\",\n                            \"<div style='margin-top: 15px;'>\\n\",\n                            \"<div style='float: left; margin-right: 50px;'>\\n\",\n                            \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n                            \"    <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">subset</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">name            </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\">rows</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">type     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">train</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">population_train</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">459</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">test</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">population_test</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">223</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"</div>\\n\",\n                            \"<div style='float: left;'>\\n\",\n                            \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n                            \"    <style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">name </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\">   rows</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">type     </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">trans</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">1056320</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">order</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">6471</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>2</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">meta</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"int\\\">5369</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\\n\",\n                            \"</div>\\n\",\n                            \"</div>\"\n                        ],\n                        \"text/plain\": [\n                            \"data model\\n\",\n                            \"\\n\",\n                            \"  population:\\n\",\n                            \"    columns:\\n\",\n                            \"    - frequency: categorical\\n\",\n                            \"    - account_id: join_key\\n\",\n                            \"    - duration: numerical\\n\",\n                            \"    - payments: numerical\\n\",\n                            \"    - amount: numerical\\n\",\n                            \"    - ...\\n\",\n                            \"\\n\",\n                            \"    joins:\\n\",\n                            \"    - right: 'trans'\\n\",\n                            \"      on: (population.account_id, trans.account_id)\\n\",\n                            \"      time_stamps: (population.date_loan, trans.date)\\n\",\n                            \"      relationship: 'many-to-many'\\n\",\n                            \"      lagged_targets: False\\n\",\n                            \"    - right: 'order'\\n\",\n                            \"      on: (population.account_id, order.account_id)\\n\",\n                            \"      relationship: 'many-to-many'\\n\",\n                            \"      lagged_targets: False\\n\",\n                            \"    - right: 'meta'\\n\",\n                            \"      on: (population.account_id, meta.account_id)\\n\",\n                            \"      relationship: 'many-to-many'\\n\",\n                            \"      lagged_targets: False\\n\",\n                            \"\\n\",\n                            \"  trans:\\n\",\n                            \"    columns:\\n\",\n                            \"    - type: categorical\\n\",\n                            \"    - k_symbol: categorical\\n\",\n                            \"    - bank: categorical\\n\",\n                            \"    - operation: categorical\\n\",\n                            \"    - account_id: join_key\\n\",\n                            \"    - ...\\n\",\n                            \"\\n\",\n                            \"  order:\\n\",\n                            \"    columns:\\n\",\n                            \"    - bank_to: categorical\\n\",\n                            \"    - k_symbol: categorical\\n\",\n                            \"    - account_id: join_key\\n\",\n                            \"    - amount: numerical\\n\",\n                            \"    - account_to: unused_float\\n\",\n                            \"    - ...\\n\",\n                            \"\\n\",\n                            \"  meta:\\n\",\n                            \"    columns:\\n\",\n                            \"    - type_disp: categorical\\n\",\n                            \"    - type_card: categorical\\n\",\n                            \"    - gender: categorical\\n\",\n                            \"    - A3: categorical\\n\",\n                            \"    - account_id: join_key\\n\",\n                            \"    - ...\\n\",\n                            \"\\n\",\n                            \"\\n\",\n                            \"container\\n\",\n                            \"\\n\",\n                            \"  population\\n\",\n                            \"      subset   name               rows   type     \\n\",\n                            \"  0   train    population_train    459   DataFrame\\n\",\n                            \"  1   test     population_test     223   DataFrame\\n\",\n                            \"\\n\",\n                            \"  peripheral\\n\",\n                            \"      name       rows   type     \\n\",\n                            \"  0   trans   1056320   DataFrame\\n\",\n                            \"  1   order      6471   DataFrame\\n\",\n                            \"  2   meta       5369   DataFrame\"\n                        ]\n                    },\n                    \"execution_count\": 8,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"star_schema = getml.data.StarSchema(\\n\",\n                \"    train=population_train, test=population_test, alias=\\\"population\\\"\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"star_schema.join(\\n\",\n                \"    trans,\\n\",\n                \"    on=\\\"account_id\\\",\\n\",\n                \"    time_stamps=(\\\"date_loan\\\", \\\"date\\\"),\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"star_schema.join(\\n\",\n                \"    order,\\n\",\n                \"    on=\\\"account_id\\\",\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"star_schema.join(\\n\",\n                \"    meta,\\n\",\n                \"    on=\\\"account_id\\\",\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"star_schema\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 2. Predictive modeling\\n\",\n                \"\\n\",\n                \"We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning.\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.1 getML Pipeline\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"<!-- #### 2.1.1  -->\\n\",\n                \"__Set-up of feature learners, selectors & predictor__\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 9,\n            \"metadata\": {},\n            \"outputs\": [],\n            \"source\": [\n                \"fast_prop = getml.feature_learning.FastProp(\\n\",\n                \"    aggregation=getml.feature_learning.FastProp.agg_sets.All,\\n\",\n                \"    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\\n\",\n                \"    num_threads=1,\\n\",\n                \")\\n\",\n                \"\\n\",\n                \"feature_selector = getml.predictors.XGBoostClassifier(n_jobs=1)\\n\",\n                \"\\n\",\n                \"# the population is really small, so we set gamma to mitigate overfitting\\n\",\n                \"predictor = getml.predictors.XGBoostClassifier(gamma=2, n_jobs=1,)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Build the pipeline__\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 10,\n            \"metadata\": {},\n            \"outputs\": [],\n            \"source\": [\n                \"pipe = getml.pipeline.Pipeline(\\n\",\n                \"    data_model=star_schema.data_model,\\n\",\n                \"    feature_learners=[fast_prop],\\n\",\n                \"    feature_selectors=[feature_selector],\\n\",\n                \"    predictors=predictor,\\n\",\n                \")\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.2 Model training\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 11,\n            \"metadata\": {},\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">OK.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"OK.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  FastProp: Trying 548 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n                            \"</pre>\\n\"\n                        ],\n                        \"text/plain\": [\n                            \"Trained pipeline.\\n\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                },\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"Time taken: 0:00:01.068837.\\n\",\n                        \"\\n\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<pre>Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=['XGBoostClassifier'],\\n\",\n                            \"         include_categorical=False,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['meta', 'order', 'trans'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['container-yVbiNK'])</pre>\"\n                        ],\n                        \"text/plain\": [\n                            \"Pipeline(data_model='population',\\n\",\n                            \"         feature_learners=['FastProp'],\\n\",\n                            \"         feature_selectors=['XGBoostClassifier'],\\n\",\n                            \"         include_categorical=False,\\n\",\n                            \"         loss_function='CrossEntropyLoss',\\n\",\n                            \"         peripheral=['meta', 'order', 'trans'],\\n\",\n                            \"         predictors=['XGBoostClassifier'],\\n\",\n                            \"         preprocessors=[],\\n\",\n                            \"         share_selected_features=0.5,\\n\",\n                            \"         tags=['container-yVbiNK'])\"\n                        ]\n                    },\n                    \"execution_count\": 11,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe.fit(star_schema.train)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.3 Model evaluation\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 12,\n            \"metadata\": {},\n            \"outputs\": [\n                {\n                    \"name\": \"stdout\",\n                    \"output_type\": \"stream\",\n                    \"text\": [\n                        \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n                        \"\\u001b[?25h\"\n                    ]\n                },\n                {\n                    \"data\": {\n                        \"text/html\": [\n                            \"<style>\\n\",\n                            \"  th {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  td {\\n\",\n                            \"    text-align: left !important;\\n\",\n                            \"  }\\n\",\n                            \"  th:nth-child(1) {\\n\",\n                            \"    text-align: right;\\n\",\n                            \"    border-right: 1px solid LightGray;\\n\",\n                            \"  }\\n\",\n                            \"  th.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.float {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  th.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"  td.int {\\n\",\n                            \"    text-align: right !important;\\n\",\n                            \"  }\\n\",\n                            \"</style>\\n\",\n                            \"\\n\",\n                            \"<table class=\\\"dataframe\\\">\\n\",\n                            \"  <thead>\\n\",\n                            \"    <tr>\\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"int\\\"> </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">set used</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"str\\\">target </th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">accuracy</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">    auc</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"        \\n\",\n                            \"          <th class=\\\"float\\\">cross entropy</th>\\n\",\n                            \"        \\n\",\n                            \"      \\n\",\n                            \"    </tr>\\n\",\n                            \"    \\n\",\n                            \"  </thead>\\n\",\n                            \"  <tbody>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>0</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"datetime\\\">2024-09-12 12:36:27</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">train</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">default</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">1.0</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">1.</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.06359</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"      <tr>\\n\",\n                            \"        <th>1</th>\\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"datetime\\\">2024-09-12 12:36:28</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">test</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"str\\\">default</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.9641</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.934</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"            \\n\",\n                            \"              <td class=\\\"float\\\">0.15445</td>\\n\",\n                            \"            \\n\",\n                            \"          \\n\",\n                            \"      </tr>\\n\",\n                            \"    \\n\",\n                            \"  </tbody>\\n\",\n                            \"</table>\"\n                        ],\n                        \"text/plain\": [\n                            \"    date time             set used   target    accuracy       auc   cross entropy\\n\",\n                            \"0   2024-09-12 12:36:27   train      default     1.0       1.             0.06359\\n\",\n                            \"1   2024-09-12 12:36:28   test       default     0.9641    0.934          0.15445\"\n                        ]\n                    },\n                    \"execution_count\": 12,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe.score(star_schema.test)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"#### 2.4 Studying features\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Visualizing the learned features__\\n\",\n                \"\\n\",\n                \"The feature with the highest importance is:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 13,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/markdown\": [\n                            \"```sql\\n\",\n                            \"DROP TABLE IF EXISTS \\\"FEATURE_1_24\\\";\\n\",\n                            \"\\n\",\n                            \"CREATE TABLE \\\"FEATURE_1_24\\\" AS\\n\",\n                            \"SELECT Q1( t2.\\\"balance\\\" ) AS \\\"feature_1_24\\\",\\n\",\n                            \"       t1.rowid AS rownum\\n\",\n                            \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n                            \"INNER JOIN \\\"TRANS__STAGING_TABLE_4\\\" t2\\n\",\n                            \"ON t1.\\\"account_id\\\" = t2.\\\"account_id\\\"\\n\",\n                            \"WHERE t2.\\\"date\\\" <= t1.\\\"date_loan\\\"\\n\",\n                            \"GROUP BY t1.rowid;\\n\",\n                            \"```\"\n                        ],\n                        \"text/plain\": [\n                            \"'DROP TABLE IF EXISTS \\\"FEATURE_1_24\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_24\\\" AS\\\\nSELECT Q1( t2.\\\"balance\\\" ) AS \\\"feature_1_24\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"TRANS__STAGING_TABLE_4\\\" t2\\\\nON t1.\\\"account_id\\\" = t2.\\\"account_id\\\"\\\\nWHERE t2.\\\"date\\\" <= t1.\\\"date_loan\\\"\\\\nGROUP BY t1.rowid;'\"\n                        ]\n                    },\n                    \"execution_count\": 13,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"by_importances = pipe.features.sort(by=\\\"importances\\\")\\n\",\n                \"by_importances[0].sql\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Feature correlations__\\n\",\n                \"\\n\",\n                \"We want to analyze how the features are correlated with the target variable.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 14,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"image/png\": 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\",\n                        \"text/plain\": [\n                            \"<Figure size 2000x1000 with 1 Axes>\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                }\n            ],\n            \"source\": [\n                \"names, correlations = pipe.features[:50].correlations()\\n\",\n                \"\\n\",\n                \"fig, ax = plt.subplots(figsize=(20, 10))\\n\",\n                \"\\n\",\n                \"ax.bar(names, correlations, color=\\\"#6829c2\\\")\\n\",\n                \"\\n\",\n                \"ax.set_title(\\\"feature correlations\\\")\\n\",\n                \"ax.set_xlabel(\\\"feature\\\")\\n\",\n                \"ax.set_ylabel(\\\"correlation\\\")\\n\",\n                \"ax.tick_params(axis=\\\"x\\\", rotation=90)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Feature importances__\\n\",\n                \"\\n\",\n                \"Feature importances are calculated by analyzing the improvement in predictive accuracy on each node of the trees in the XGBoost predictor. They are then normalized, so that all importances add up to 100%.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 15,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"image/png\": 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\",\n                        \"text/plain\": [\n                            \"<Figure size 2000x1000 with 1 Axes>\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                }\n            ],\n            \"source\": [\n                \"names, importances = pipe.features[:50].importances()\\n\",\n                \"\\n\",\n                \"fig, ax = plt.subplots(figsize=(20, 10))\\n\",\n                \"\\n\",\n                \"ax.bar(names, importances, color='#6829c2')\\n\",\n                \"\\n\",\n                \"ax.set_title(\\\"feature importances\\\")\\n\",\n                \"ax.set_xlabel(\\\"feature\\\")\\n\",\n                \"ax.set_ylabel(\\\"importance\\\")\\n\",\n                \"ax.tick_params(axis=\\\"x\\\", rotation=90)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"__Column importances__\\n\",\n                \"\\n\",\n                \"Because getML uses relational learning, we can apply the principles we used to calculate the feature importances to individual columns as well.\\n\",\n                \"\\n\",\n                \"As we can see, a lot of the predictive power stems from the account balance. This is unsurprising: People with less money on their bank accounts are more likely to default on their loans.\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 16,\n            \"metadata\": {\n                \"tags\": []\n            },\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"image/png\": 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                       \"text/plain\": [\n                            \"<Figure size 2000x1000 with 1 Axes>\"\n                        ]\n                    },\n                    \"metadata\": {},\n                    \"output_type\": \"display_data\"\n                }\n            ],\n            \"source\": [\n                \"names, importances = pipe.columns.importances()\\n\",\n                \"\\n\",\n                \"fig, ax = plt.subplots(figsize=(20, 10))\\n\",\n                \"\\n\",\n                \"ax.bar(names, importances, color=\\\"#6829c2\\\")\\n\",\n                \"\\n\",\n                \"ax.set_title(\\\"column importances\\\")\\n\",\n                \"ax.set_xlabel(\\\"column\\\")\\n\",\n                \"ax.set_ylabel(\\\"importance\\\")\\n\",\n                \"ax.tick_params(axis=\\\"x\\\", rotation=90)\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"The most important feature looks as follows:\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 17,\n            \"metadata\": {},\n            \"outputs\": [\n                {\n                    \"data\": {\n                        \"text/markdown\": [\n                            \"```sql\\n\",\n                            \"DROP TABLE IF EXISTS \\\"FEATURE_1_24\\\";\\n\",\n                            \"\\n\",\n                            \"CREATE TABLE \\\"FEATURE_1_24\\\" AS\\n\",\n                            \"SELECT Q1( t2.\\\"balance\\\" ) AS \\\"feature_1_24\\\",\\n\",\n                            \"       t1.rowid AS rownum\\n\",\n                            \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n                            \"INNER JOIN \\\"TRANS__STAGING_TABLE_4\\\" t2\\n\",\n                            \"ON t1.\\\"account_id\\\" = t2.\\\"account_id\\\"\\n\",\n                            \"WHERE t2.\\\"date\\\" <= t1.\\\"date_loan\\\"\\n\",\n                            \"GROUP BY t1.rowid;\\n\",\n                            \"```\"\n                        ],\n                        \"text/plain\": [\n                            \"'DROP TABLE IF EXISTS \\\"FEATURE_1_24\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_24\\\" AS\\\\nSELECT Q1( t2.\\\"balance\\\" ) AS \\\"feature_1_24\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"TRANS__STAGING_TABLE_4\\\" t2\\\\nON t1.\\\"account_id\\\" = t2.\\\"account_id\\\"\\\\nWHERE t2.\\\"date\\\" <= t1.\\\"date_loan\\\"\\\\nGROUP BY t1.rowid;'\"\n                        ]\n                    },\n                    \"execution_count\": 17,\n                    \"metadata\": {},\n                    \"output_type\": \"execute_result\"\n                }\n            ],\n            \"source\": [\n                \"pipe.features.to_sql()[pipe.features.sort(by=\\\"importances\\\")[0].name]\"\n            ]\n        },\n        {\n            \"cell_type\": \"code\",\n            \"execution_count\": 18,\n            \"metadata\": {},\n            \"outputs\": [],\n            \"source\": [\n                \"getml.engine.shutdown()\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"### 3. Conclusion\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"By applying getML to the PKDD'99 Financial dataset, we were able to show the power and relevance of Relational Learning on a real-world data set. Within a training time below 2 seconds, we outperformed almost all approaches based on manually generated features. This makes getML the prime choice when dealing with complex relational data schemes. This result holds independent of the problem domain since no expertise in the financial sector was used in this analysis.\\n\",\n                \"\\n\",\n                \"The present analysis could be improved in two directions. By performing an extensive hyperparameter optimization, the out of sample AUC could be further improved. On the other hand, the hyperparameters could be tuned to produce less complex features that result in worse performance (in terms of AUC) but are better interpretable by humans.\"\n            ]\n        },\n        {\n            \"cell_type\": \"markdown\",\n            \"metadata\": {},\n            \"source\": [\n                \"## References\\n\",\n                \"\\n\",\n                \"Schulte, Oliver, et al. \\\"A hierarchy of independence assumptions for multi-relational Bayes net classifiers.\\\" 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2013.\"\n            ]\n        }\n    ],\n    \"metadata\": {\n        \"jupytext\": {\n            \"formats\": \"ipynb,py:percent\"\n        },\n        \"kernelspec\": {\n            \"display_name\": \"Python 3 (ipykernel)\",\n            \"language\": \"python\",\n            \"name\": \"python3\"\n        },\n        \"language_info\": {\n            \"codemirror_mode\": {\n                \"name\": \"ipython\",\n                \"version\": 3\n            },\n            \"file_extension\": \".py\",\n            \"mimetype\": \"text/x-python\",\n            \"name\": \"python\",\n            \"nbconvert_exporter\": \"python\",\n            \"pygments_lexer\": \"ipython3\",\n            \"version\": \"3.11.9\"\n        },\n        \"toc-autonumbering\": false,\n        \"vscode\": {\n            \"interpreter\": {\n                \"hash\": \"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1\"\n            }\n        },\n        \"widgets\": {\n            \"application/vnd.jupyter.widget-state+json\": {\n                \"state\": {},\n                \"version_major\": 2,\n                \"version_minor\": 0\n            }\n        }\n    },\n    \"nbformat\": 4,\n    \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "demo-notebooks/requirements.txt",
    "content": "jupyterlab~=4.2\nipywidgets~=8.1"
  },
  {
    "path": "demo-notebooks/robot.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Robot - Feature engineering on sensor data\\n\",\n    \"\\n\",\n    \"The purpose of this notebook is to illustrate how we can overcome the feature explosion problem based on an example dataset involving sensor data.\\n\",\n    \"\\n\",\n    \"Summary:\\n\",\n    \"\\n\",\n    \"- Prediction type: __Regression__\\n\",\n    \"- Domain: __Robotics__\\n\",\n    \"- Prediction target: __The force vector on the robot's arm__ \\n\",\n    \"- Population size: __15001__\\n\",\n    \"\\n\",\n    \"_Author: Dr. Patrick Urbanke_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [\n     \"remove_cell_on_docs\"\n    ]\n   },\n   \"source\": [\n    \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/robot.ipynb\\\">\\n\",\n    \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"</a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The data set\\n\",\n    \"\\n\",\n    \"To illustrate the problem, we use a data set related to robotics. When robots interact with humans, the most important think is that they don't hurt people. In order to prevent such accidents, the *force vector* on the robot's arm is measured. However, measuring the force vector is expensive.\\n\",\n    \"\\n\",\n    \"Therefore, we want consider an alternative approach. We would like to predict the force vector based on other sensor data that are less costly to measure. To do so, we use *machine learning*.\\n\",\n    \"\\n\",\n    \"However, the data set contains measurements from almost 100 different sensors and we do not know which and how many sensors are relevant for predicting the force vector.\\n\",\n    \"\\n\",\n    \"The data set has been generously provided by Erik Berger who originally collected it for his dissertation:\\n\",\n    \"\\n\",\n    \"> Berger, E. (2018). *Behavior-Specific Proprioception Models for Robotic Force Estimation: A Machine Learning Approach.* Freiberg, Germany: Technische Universitaet Bergakademie Freiberg.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Loading data\\n\",\n    \"\\n\",\n    \"We begin by importing the libraries and setting the project.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Note: you may need to restart the kernel to use updated packages.\\n\",\n      \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n      \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912151657.log.\\n\",\n      \"\\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'robot'</span>.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Connected to project \\u001b[32m'robot'\\u001b[0m.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%pip install -q \\\"getml==1.5.0\\\" \\\"matplotlib~=3.9\\\"\\n\",\n    \"\\n\",\n    \"import getml\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline  \\n\",\n    \" \\n\",\n    \"getml.engine.launch()\\n\",\n    \"getml.engine.set_project('robot')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.1 Download from source\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Downloading <span style=\\\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\\\">https://static.getml.com/datasets/robotarm/robot-demo.csv</span> to \\n\",\n       \"<span style=\\\"color: #800080; text-decoration-color: #800080\\\">/tmp/getml/static.getml.com/datasets/robotarm/</span><span style=\\\"color: #ff00ff; text-decoration-color: #ff00ff\\\">robot-demo.csv...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Downloading \\u001b[4;94mhttps://static.getml.com/datasets/robotarm/robot-demo.csv\\u001b[0m to \\n\",\n       \"\\u001b[35m/tmp/getml/static.getml.com/datasets/robotarm/\\u001b[0m\\u001b[95mrobot-demo.csv...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Downloading robot-demo.csv... ━━━━━━━━━━━━━━━━━━━━ 100% • 14.7/14.7 MB • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data_all = getml.data.DataFrame.from_csv(\\n\",\n    \"    \\\"https://static.getml.com/datasets/robotarm/robot-demo.csv\\\", \\n\",\n    \"    \\\"data_all\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: 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      \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         106</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         f_x</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         f_y</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">         f_z</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\"> role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-16</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.312</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.2042</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.02167</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"   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      \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1233</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"   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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8296</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.07625</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n 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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.786</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.3682</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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      \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-22</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.654</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-11</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.503</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       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style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4094</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.786</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.3681</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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      \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.069</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"  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     \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.9604</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n      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style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"      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      \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.1369</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.002472</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n   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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4098</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.3274</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span 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class=\\\"left\\\">-6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.5483</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8157</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"left\\\">-21</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.627</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-11</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.046</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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      \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.009</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"   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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.834</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.925</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n   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class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.9604</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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      \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"   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      \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.38e-17</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-4</span\\n\",\n       \"              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      \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7437</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                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      \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"      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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1099</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.5438</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n  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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.41</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.08279</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4094</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7859</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.3682</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          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      \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"   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style=\\\"width: 3ch\\\"\\n\",\n       \"                >.955</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.971</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-10</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.666</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.5436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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     \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"       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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-21</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.772</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-10</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.872</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-18</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.691</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \" 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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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      \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.931</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.879</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"  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      \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4533</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-7</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.65</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4098</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"       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      \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0205</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n      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\"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              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      \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"    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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4033</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.1369</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.002472</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"  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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7437</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0191</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0205</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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      \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-6</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.5483</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8157</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8327</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.07015</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1922</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7699</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.41</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.08278</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4094</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n 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class=\\\"left\\\">-2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.0096</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7699</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.971</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-10</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.413</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n  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class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span 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\"        <th>14996</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0837</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8836</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4501</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 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class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.1568</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1898</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span 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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.151</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.05513</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.07114</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.3564</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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      \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1113</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.06249</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.3161</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span 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class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.94</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">10</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.857</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.52</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.943</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>14998</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0833</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8844</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n     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style=\\\"width: 4ch\\\"\\n\",\n       \"                >.74</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.55</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>14999</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.302</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.01372</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.1739</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4125</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.02201</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.001255</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.03067</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1115</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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     \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.743</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>15000</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0829</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.885</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            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      \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.02201</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.03755</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n  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class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.5607</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-5</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.3246</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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      \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.04728</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.099</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.936</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-39</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.068</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 7ch\\\"\\n\",\n       \"                >.9402</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.139</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.2674</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            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>\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.001142</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.0277</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1007</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.07221</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.009</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.069</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8952</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.818</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.834</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            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      \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.94</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.955</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">11</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.69</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1801</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.937</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    15001 rows x 96 columns<br />\\n\",\n       \"    memory usage: 11.52 MB<br />\\n\",\n       \"    name: data_all<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name              3              4              5              6   ...            105            106            f_x\\n\",\n       \" role   unused_float   unused_float   unused_float   unused_float   ...   unused_float   unused_float   unused_float\\n\",\n       \"    0         3.4098        -0.3274         0.9604        -3.7436   ...         47.955         47.971        -11.03 \\n\",\n       \"    1         3.4098        -0.3274         0.9604        -3.7436   ...         47.955         47.971        -10.848\\n\",\n       \"    2         3.4098        -0.3274         0.9604        -3.7436   ...         47.955         47.971        -10.666\\n\",\n       \"    3         3.4098        -0.3274         0.9604        -3.7436   ...         47.955         47.971        -10.507\\n\",\n       \"    4         3.4098        -0.3274         0.9604        -3.7436   ...         47.955         47.971        -10.413\\n\",\n       \"             ...            ...            ...            ...                   ...            ...            ...   \\n\",\n       \"14996         3.0837        -0.8836         1.4501        -2.2102   ...         47.94          47.94          10.84 \\n\",\n       \"14997         3.0835        -0.884          1.4505        -2.2091   ...         47.94          47.94          10.857\\n\",\n       \"14998         3.0833        -0.8844         1.4508        -2.208    ...         47.94          47.94          10.89 \\n\",\n       \"14999         3.0831        -0.8847         1.4511        -2.2071   ...         47.94          47.94          11.29 \\n\",\n       \"15000         3.0829        -0.885          1.4514        -2.2062   ...         47.94          47.955         11.69 \\n\",\n       \"\\n\",\n       \" name            f_y            f_z\\n\",\n       \" role   unused_float   unused_float\\n\",\n       \"    0         6.9           -7.33  \\n\",\n       \"    1         6.7218        -7.4427\\n\",\n       \"    2         6.5436        -7.5555\\n\",\n       \"    3         6.4533        -7.65  \\n\",\n       \"    4         6.6267        -7.69  \\n\",\n       \"             ...            ...    \\n\",\n       \"14996        -1.41          16.14  \\n\",\n       \"14997        -1.52          15.943 \\n\",\n       \"14998        -1.74          15.55  \\n\",\n       \"14999        -1.4601        15.743 \\n\",\n       \"15000        -1.1801        15.937 \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"15001 rows x 96 columns\\n\",\n       \"memory usage: 11.52 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data_all\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.2 Prepare data for getML\\n\",\n    \"\\n\",\n    \"The force vector consists of three component (*f_x*, *f_y* and *f_z*), meaning that we have three targets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_all.set_role([\\\"f_x\\\", \\\"f_y\\\", \\\"f_z\\\"], getml.data.roles.target)\\n\",\n    \"data_all.set_role(data_all.roles.unused, getml.data.roles.numerical)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is what the data set looks like:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th> name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">    f_x</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">    f_y</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"target\\\">    f_z</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">        3</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">        4</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">        5</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">        6</th>\\n\",\n       \"        \\n\",\n       \"     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           <th class=\\\"sub-header target\\\"> target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\"> target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header target\\\"> target</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header 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\"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              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      \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.925</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.818</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"       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class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0191</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.009</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.069</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0191</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align 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   \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.9604</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-3</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7436</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"            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style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1113</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7116</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n     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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.08786</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.009</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span 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style=\\\"width: 4ch\\\"\\n\",\n       \"                >.0831</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8847</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.4511</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.2071</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"            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class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.06041</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8478</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.9567</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.03382</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.02535</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.01854</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            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    \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.11</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.06304</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">15</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.099</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.936</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-39</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.068</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-1</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 7ch\\\"\\n\",\n       \"                >.9402</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.139</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.2674</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.7107</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.06893</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.06048</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8482</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">2</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.9561</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.03367</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.02458</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.01986</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 6ch\\\"\\n\",\n       \"                >.001142</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.0277</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.1007</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">-0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 5ch\\\"\\n\",\n       \"                >.07221</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.009</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">48</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.069</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">0</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 4ch\\\"\\n\",\n       \"                >.8952</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.818</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.834</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.818</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.803</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.94</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">47</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 3ch\\\"\\n\",\n       \"                >.955</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    15001 rows x 96 columns<br />\\n\",\n       \"    memory usage: 11.52 MB<br />\\n\",\n       \"    name: data_all<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \" name       f_x       f_y       f_z           3   ...         102         103         104         105         106\\n\",\n       \" role    target    target    target   numerical   ...   numerical   numerical   numerical   numerical   numerical\\n\",\n       \"    0   -11.03     6.9      -7.33        3.4098   ...      47.925      47.818      47.834      47.955      47.971\\n\",\n       \"    1   -10.848    6.7218   -7.4427      3.4098   ...      47.925      47.818      47.834      47.955      47.971\\n\",\n       \"    2   -10.666    6.5436   -7.5555      3.4098   ...      47.925      47.818      47.834      47.955      47.971\\n\",\n       \"    3   -10.507    6.4533   -7.65        3.4098   ...      47.925      47.818      47.834      47.955      47.971\\n\",\n       \"    4   -10.413    6.6267   -7.69        3.4098   ...      47.925      47.818      47.834      47.955      47.971\\n\",\n       \"         ...      ...       ...         ...                ...         ...         ...         ...         ...   \\n\",\n       \"14996    10.84    -1.41     16.14        3.0837   ...      47.834      47.818      47.803      47.94       47.94 \\n\",\n       \"14997    10.857   -1.52     15.943       3.0835   ...      47.834      47.818      47.803      47.94       47.94 \\n\",\n       \"14998    10.89    -1.74     15.55        3.0833   ...      47.834      47.818      47.803      47.94       47.94 \\n\",\n       \"14999    11.29    -1.4601   15.743       3.0831   ...      47.834      47.818      47.803      47.94       47.94 \\n\",\n       \"15000    11.69    -1.1801   15.937       3.0829   ...      47.834      47.818      47.803      47.94       47.955\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"15001 rows x 96 columns\\n\",\n       \"memory usage: 11.52 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data_all\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.3 Separate data into a training and testing set\\n\",\n    \"\\n\",\n    \"We also want to separate the data set into a training and testing set. We do so by using the first 10,500 measurements for training and then using the remainder for testing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  td.char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    unknown number of  rows<br />\\n\",\n       \"    \\n\",\n       \"    type: StringColumnView<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"          \\n\",\n       \" 0   train\\n\",\n       \" 1   train\\n\",\n       \" 2   train\\n\",\n       \" 3   train\\n\",\n       \" 4   train\\n\",\n       \"     ...  \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"unknown number of  rows\\n\",\n       \"type: StringColumnView\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"split = getml.data.split.time(data_all, \\\"rowid\\\", test=10500)\\n\",\n    \"split\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\\n\",\n       \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n       \"            <div style=\\\"height:100px;width:660px;position:relative;\\\"><svg height=\\\"90\\\" width=\\\"650\\\"><rect y=\\\"0\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">data_all</text><rect x=\\\"51\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"10\\\" x2=\\\"67.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"10\\\" x2=\\\"83.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"26.0\\\" x2=\\\"99\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"42.0\\\" x2=\\\"99\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"0\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"551\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"10\\\" x2=\\\"567.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"10\\\" x2=\\\"583.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"26.0\\\" x2=\\\"599\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"42.0\\\" x2=\\\"599\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"43.0\\\" x2=\\\"490\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 43.0 490, 37.0 490, 49.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"40.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >rowid <= rowid</tspan><tspan y=\\\"50.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Memory: 30 time steps</tspan></text></svg></div>\\n\",\n       \"            </div>\\n\",\n       \"\\n\",\n       \"            <div style='margin-top: 15px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n       \"            <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">data frames</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">staging table              </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">population</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">data_all</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DATA_ALL__STAGING_TABLE_2</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"            </div>\\n\",\n       \"            \\n\",\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\\n\",\n       \"<div style='margin-top: 15px;'>\\n\",\n       \"<div style='float: left; margin-right: 50px;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">subset</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">rows   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">data_all</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">data_all</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"<div style='float: left;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> rows</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">data_all</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">15001</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"data model\\n\",\n       \"\\n\",\n       \"  population:\\n\",\n       \"    columns:\\n\",\n       \"    - 3: numerical\\n\",\n       \"    - 4: numerical\\n\",\n       \"    - 5: numerical\\n\",\n       \"    - 6: numerical\\n\",\n       \"    - 7: numerical\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"    joins:\\n\",\n       \"    - right: 'data_all'\\n\",\n       \"      time_stamps: (population.rowid, data_all.rowid)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      memory: 30\\n\",\n       \"      lagged_targets: False\\n\",\n       \"\\n\",\n       \"  data_all:\\n\",\n       \"    columns:\\n\",\n       \"    - 3: numerical\\n\",\n       \"    - 4: numerical\\n\",\n       \"    - 5: numerical\\n\",\n       \"    - 6: numerical\\n\",\n       \"    - 7: numerical\\n\",\n       \"    - ...\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"container\\n\",\n       \"\\n\",\n       \"  population\\n\",\n       \"      subset   name       rows      type\\n\",\n       \"  0   test     data_all   unknown   View\\n\",\n       \"  1   train    data_all   unknown   View\\n\",\n       \"\\n\",\n       \"  peripheral\\n\",\n       \"      name        rows   type\\n\",\n       \"  0   data_all   15001   View\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"time_series = getml.data.TimeSeries(\\n\",\n    \"    population=data_all,\\n\",\n    \"    split=split,\\n\",\n    \"    time_stamps=\\\"rowid\\\",\\n\",\n    \"    lagged_targets=False,\\n\",\n    \"    memory=30,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"time_series\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Predictive modeling\\n\",\n    \"\\n\",\n    \"#### 2.1 Building the pipeline\\n\",\n    \"\\n\",\n    \"We then build a pipeline based on the *relboost* algorithm with *xgboost* as our predictor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fast_prop = getml.feature_learning.FastProp(\\n\",\n    \"    loss_function=getml.feature_learning.loss_functions.SquareLoss,\\n\",\n    \"    num_features=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"xgboost = getml.predictors.XGBoostRegressor()\\n\",\n    \"\\n\",\n    \"pipe1 = getml.pipeline.Pipeline(\\n\",\n    \"    data_model=time_series.data_model,\\n\",\n    \"    feature_learners=[fast_prop],\\n\",\n    \"    predictors=xgboost\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It is always a good idea to check the pipeline for any potential issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">OK.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"OK.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.check(time_series.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.2 Fitting the pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">OK.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"OK.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Trying 1130 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:02\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:02\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:03\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Trained pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time taken: 0:00:11.060886.\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=False,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['data_all'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['container-Y7lpuV'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=[],\\n\",\n       \"         include_categorical=False,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['data_all'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['container-Y7lpuV'])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.fit(time_series.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.3 Evaluating the pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">set used</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">target</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">    mae</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">   rmse</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">rsquared</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_x</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.4403</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.58</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9962</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_y</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.5168</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.6813</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9893</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_z</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.2918</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.385</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9986</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_x</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.5605</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.7316</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.995</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_y</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.5654</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.7533</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9871</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>5</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 15:17:15</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">f_z</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.3133</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.4074</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9984</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    date time             set used   target       mae      rmse   rsquared\\n\",\n       \"0   2024-09-12 15:17:15   train      f_x       0.4403    0.58       0.9962\\n\",\n       \"1   2024-09-12 15:17:15   train      f_y       0.5168    0.6813     0.9893\\n\",\n       \"2   2024-09-12 15:17:15   train      f_z       0.2918    0.385      0.9986\\n\",\n       \"3   2024-09-12 15:17:15   test       f_x       0.5605    0.7316     0.995 \\n\",\n       \"4   2024-09-12 15:17:15   test       f_y       0.5654    0.7533     0.9871\\n\",\n       \"5   2024-09-12 15:17:15   test       f_z       0.3133    0.4074     0.9984\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.score(time_series.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"#### 2.4 Feature importances\\n\",\n    \"\\n\",\n    \"It is always a good idea to study the features the relational learning algorithm has extracted.\\n\",\n    \"\\n\",\n    \"The feature importance is calculated by xgboost based on the improvement of the optimizing criterium at each split in the decision tree and is normalized to 100%.\\n\",\n    \"\\n\",\n    \"Also note that we have three different target (*f_x*, *f_y* and *f_z*) and that different features are relevant for different targets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.features.importances(target_num=0)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"feature importances for the x-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"features\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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fe2f/93/81uOy2+/auOH6cd955dcZ4/fXXC/tNx44ds5KSknqL5BdeeKFwlWF9712W1b4646CDDqr387R1yfrpT3+6zvyrr756u8e7Cy+8sNEScuvjYHl5ea0rs2q8/PLLWdeuXQuFa3129tiSZbU/k4MHD87WrVtXZ5kbbrihsMxVV11VZ/62Vy4BAO8uihcAIHe2d7Lz6aefzsrKyrKIyH73u981OtbEiRMLJ8CaY8aMGYW/aK3vRFIxi5e99947e/PNNxscp6ZQOeGEExp9vkcffXSnTobuSPHS0F9c/+1vfyssU1JS0uCVGnfddVdhufqu0Nn6JPL48eMbzDx37tzCctt+FdF3vvOdwrz6vkKmxtZXFdx000215m19wq1du3bZ+vXrGxwny3aseGmK7e23W78nv/3tbxsc58Mf/nDhBOa2Jk2aVBjjlltu2aF8d999d2HdP//5z40u+5nPfCaLeOsv4Ld25JFHZhGRHX/88Tv03Dtie5/ZpUuXFpb58pe/3OA4ixcvLix39tln15m/9Unexr7KqamaW7x873vfa3C5k046KYuIrEOHDnXm1ZyUb9WqVb2F6NaGDh2aRbx1ZcaO2rBhQ6FEOOOMM+pd5uabb270GNEUJ598cuFYtHLlyh1aN/Xxo1evXg0WrRdffHFhuQ9/+MMNPtepp56aRUTWvn37eudvXbwsX768wXE+9rGPZRFvFd8vvPBCrXk1V6J07ty5weNdVVVV4Qqc9u3bZ2+88Uat+VsfBxv7Ksivf/3rhfdn26tZUxxbsqz2Z/JPf/pTvetXV1dnPXr0aPA4pHgBgHe30gAAeJf5wx/+EFu2bInKysr42Mc+1uiyhx56aERErF69ers3HF+/fn08/fTT8cgjj8TDDz8cDz/8cFRWVtaa905y4oknRllZWb3z1q9fX7iB9qc+9alGx9lvv/2iU6dOERGxZMmSpBm31q5duxgzZky98/r27Ru77757REQMHDgw9ttvv3qXO+CAAwq/N3ZT64iodYPxbR1//PHRrl27iHjrRtJbq3ncrl27+OQnP9ngGGeccUaddepz9NFHF7ZtV9iZ/bZdu3bxiU98osH5gwcPjoj6X+vf//73ERHRr1+/OOaYY3Yo829/+9uIiNhnn31i//33b3TZms/wAw88UOtm2N27d4+IiLvvvjueeuqpHXr+VLZ+37/whS80uNwhhxxS2Kcb21d69eoVI0aMSBdwB5SUlMQpp5zS4PyafeGVV16JV199tda8mvfzsMMOi86dOzf6PDXvZ3OONW3atImTTjopIiJuuumm2LhxY51lrr/++oiI6NatW3z84x/f4eeorq6O2267LSIiRo4cGQceeOAOrZ/6+PHJT34yysvL65239fHwxBNPbHCMmuX++c9/1nnvtrb//vsX3uf6nH766RER8eabbxb+fYl469/Xxx57LCIiPvOZzzR4vGvRokXhuPzPf/4zVq5c2eBzffazn21wXk3GLMvqHN9SHFu2tv/++8fAgQPrnVdSUlLYP7b37xEA8O7TotgBAABSW758eUREbNy4MVq0aPp/7qxZsybe//7315r297//Pa688sr43e9+F3//+98bXf8f//hH9OvXb8cD7yINnQyKiHjwwQejuro6IiJOPvnkOPnkk5s05po1a5Jkq89ee+0VJSUlDc5v165dvPbaa7H33ns3ukyN1157rcHlWrZsWeuk5LbKy8vjwAMPjAULFsSqVatqzXv44YcjIuKggw5q8IRnRETXrl2jT58+8cwzzxTWqU9j71Nzpdpv99prrygtbfhvtTp06BARdV/rqqqqwjZ/5CMfafR9rU/NZ/jxxx9v8rpVVVXxyiuvRJcuXSIi4tRTT42f/exn8fLLL8cHP/jBOPbYY2PMmDExYsSI6N+//w7laa6a16Bly5YxaNCgRpcdNmxYPPbYY/HEE0/E5s2bo2XLlnWW2RX7SlN16tQpOnbs2OD8mn0h4q39YevPYs37efvttzf5/WzuseaMM86I6667LtavXx8333xzfP7zn6815rx58yIi4vOf/3ytfx9effXVeO655+ods2XLloXjztNPP10oJ5pTgqU+fjT1eLgjx82tH2/tQx/6UINjREQMHTq08PuqVasKJdjW+YcNG9boGFvPf/jhh2P48OH1Lrfvvvs2OMa2++LWUhxbmppj6yyN/XsEALw7ueIFAHjXefHFF5u13rZ/HX3bbbfFgAED4uqrr97uyeuIiH/961/Net5dpX379g3OS/UapVRzFUZDagqAxpbbuiTYsmVLg8t16NChwauBanTt2jUi3voL/q3VPK7vJNy2unXrVu8YW2vsfWqOlPttU9+TmhKvxiuvvBJZlkXEv6882REp9s8jjjgirr766mjdunW88cYbMXfu3Dj99NNjr732ive9733x5S9/Of70pz8163maquZ979Chw3ZL4Jp9Jcuy+Oc//1nvMqn3lR3R1H0hou5nrznvZ3OPp0OHDi1cyVBzdUuNn/3sZ4UrF2quzqhxyy23xP7771/vz+jRowvL/eMf/yj83px9O/Xxo6nHwxTHze1lrjlmRtTOvPXv2xujZpu3XW9bzd2e1P/2NfVz0djrCgC8O7niBQB416k5wdGpU6dYsGBBk9fr27dv4fd//OMfccopp8TGjRtjt912iwsuuCDGjBkTe+65Z+yxxx6Fv0a/66674ogjjoiIKJxofqdorFjY+iTQD3/4wzj44IObNGYxT/ymtKNXYOyqMSIaf5921Lthv4349/55wAEHxA033NDk9Xr27Fnr8Ve+8pX49Kc/HXPmzIk77rgj7r333li3bl08//zz8cMf/jB+9KMfxYUXXhjTpk1Lmn9b78R95e1U835+7GMfi8svv3yXP98ZZ5wR5557bixcuDCeeeaZ6NOnT0T8u4gZPnz4dq9U2NVS7RNvp3fScbO5Uh1bAAC2R/ECALzr1HwdzmuvvRb77bdfs05W/upXvyp8ncxvfvObGDVqVL3LNfYXuTuqpKQksiyrc/XAtl5//fWdfq6tvzKosrIyPvjBD+70mHny8ssvx5YtWxrdN9auXRsRtb+2pubxCy+8UJjfmJqvS9p2jF2lGPttfTp06BClpaVRXV0dL7zwwg6vX7N/btiwYaf3zS5dusR5550X5513XlRXV8dDDz0Uv/nNb+Lqq6+OV199Nb71rW/Fhz70oTj22GN36nnqU/O+v/zyy/Hmm282etVLzb5SUlLyrik4a3Ts2DFWr14dmzdvfluONZ/73Odi4sSJsWnTpvjpT38aU6ZMifvvvz/+8pe/RETdq10iIk477bQ47bTTtjt2zf2uIqJZ+/Y7+fixPdvLvPX8rTNv/fv2xtj6K+Z2xXanPLYAADTGV40BAO86NTez3bRpU+H73HfUI488EhFvnfhp6OR1RGx3/B35696aGw439DVDEW9dnfDkk082ecyGDBo0qJDt3nvv3enx8mbz5s2Nfs3Um2++GQ899FBERJ2TczWPV65c2eANlyPe+kqbmq/62tkTfE3dj1LttzurvLy8sM333HPPDl9Vs/UNqVPeV6i0tDQOOuiguPTSS2P+/PmF6TfddFOy59hazWuwefPmwv7UkGXLlkXEW/fVqe/+Lim93Vcd1Lyfy5cvj82bN+/y5+vQoUPhxvWzZ8+OLMti1qxZERHRpk2bRm80vz19+/Yt3APl7rvv3uH1i3H8SOWBBx5o8vytM2/9+9KlSxsdo+ZzsO16qeyqY0tzFPvqHwBg11K8AADvOkcffXThhMaMGTOaNUbNCbE33nijwStQNm7cGD//+c8bHadVq1YR8VYJtD01X3XW2Enx2267rXBFw87o3LlzfPjDH46IiDlz5sRLL72002PmzezZsxuc95vf/KZQgG1bYNQ8fvXVV+PXv/51g2P85Cc/KRQOjZUgTVGzH0U0vi+l2m9TOProoyPirZuR/+///u8OrXvMMcdExFtF43e/+93k2SLeurl5zZUlW9+3I6Wt3/eaE//1WbJkSTz66KN11tlVmro/pVLzfq5bt67OfVd2lTPOOCMiIp555pn4wx/+EHPnzo2IiE996lOFkrs5SktL4xOf+ERERCxatCgefPDBHVq/GMePVFatWtXo9tbs42VlZTFy5MjC9B49esR+++0XEW+VnBs2bKh3/S1btsRPf/rTiHjray0POuigNMG38nYcW5rq7f4cAgBvL8ULAPCus88++8SnP/3piIi48cYb46qrrmp0+aeffjp++ctf1pq21157RcRbJ6nr+2v4LVu2xBlnnBGrV69udOyamy+/+OKL8dprrzW67GGHHRYRb/1FcH1XoaxZsya++tWvNjrGjrjooosiImL9+vXxqU99qtFCZ9OmTXHNNdfEG2+8kez5i+3aa6+NxYsX15m+Zs2auOCCCyLira9hGzt2bK3548aNK9xQ+Wtf+1o8//zzdcb405/+FN/+9rcj4q17Axx33HE7lXXrm3g/9dRTDS6Xar9NYfz48dGmTZuIiPjSl74UDz/8cIPLPvfcc7Uejx49OoYOHRoREVdcccV2r0hZtWpV/O53v6s1be7cuY3eoH358uWFcm3r+zulNHTo0BgyZEhERFx33XW1rrKpsW7duvjSl74UEW+d1D/rrLN2SZatNXV/SmXs2LHRq1eviIi44IILtnulyOLFi2PRokU79Zwf/ehHY88994yIiDPPPDPWr18fEfV/zdiOuuCCC6K0tDSyLIuTTjqpzv67tW3nFeP4kdIXv/jFer/ucs6cOXHrrbdGRMRxxx1Xax+LeOt+SxERL730Upxzzjn1jj116tRCAXnmmWdGRUVFyugRkebYkkrHjh0LV7e9HZ9DAODtpXgBAN6Vrr322ujXr19EvHVy67DDDouf/OQncf/998eDDz4Yd955Z/zXf/1XHHnkkdG/f/+4+eaba63/mc98pnDSZ9y4cfH1r3895s+fH8uXL4/Zs2fHsGHD4pe//GUccs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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.features.importances(target_num=1)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"feature importances for the y-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"features\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.features.importances(target_num=2)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"feature importances for the z-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"features\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.5 Column importances\\n\",\n    \"\\n\",\n    \"Because getML is a tool for relational learning, we can also calculate the importances for the original columns, using similar methods we have used for the feature importances.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Jly5KePXsmEZF885vfrHFM5ddkiy22qPHMn1deeSV38PLwww+vNv+TTz7JPU8lJSU1vvYV3nzzzWrTBg8enER8frZM5ZKisnnz5uXO2vjJT35SZd5FF12URHx+hlNdZz4sW7aszrMr6lL5W/O1OeaYY3LP5W9+85tq8z/99NNk//33z4158MEHq42p+HypKAPeeOONeuWtsK73ZGWV3zNFRUU1jn///feTdu3a1fl5U3HgeY899qi1APr73/+eK8Juuumm9X1YSZIkSXl5ea6Ab9q0aTJz5szcvKOOOipXbj388MP1Wn+FESNG5J6Xs846KykvL69xXGlpabX9r/Ln2SmnnFLjspXPXqupYKj8uhQXFydPP/10tTGV//ZtvfXWtX6+Xn/99blx99xzT7X5a59VUdMZeGVlZcmgQYNyYx544IFqYyqKl8LCwuShhx6qNv+DDz5IunfvnvvsrelzpyH+br3wwgu5M1RGjx5d4/O/Zs2a3Nknm2++efLBBx9Umb/2GWuXXnpptXWUl5cnAwcOzO2LNf3NWtf/EwAA2eQvOwCQd2vWrMl9K75Tp07r9a3yJElyZ3cUFRXVePC2woABA3IHPxYtWlRl3sYuVUpKSpL333+/xjGVv2XeuXPnWr/JevHFF+fGrf0t/iSpenDqueeeq3Ed5eXluQPjRx55ZJ2PrTbrU6rUdlbNqlWrcgfwIyL5+9//XuO4hQsX1vnt4srP/6GHHlpr5lmzZlU5YFrZ3XffnZt3+eWX17qOygf2fvnLX1abXzGvSZMm6yxK1qdUSeOvf/1rbn01fVu/8mvyf//3f7Wu5/jjj08iPj/Da22//vWvc+uYMGHCeuV77bXXksLCwiQikvvuu6/Oseeff36ubKhs2LBhuYP4X5R1lSpvv/127nH8z//8T63ree2113JnaRx88MHV5lcuVX77299ucO76liojR46sddwFF1yQKyzW/rx5/PHHc+v4xz/+Uef2Ks4q3HPPPVM/nrW9/fbbyVZbbZVE/PdMj1tuuSWXobYz0NL68MMPc2dJ9e7du9ZLLNam4syerbfeOlmxYkWNY8rKypJdd901ifj8koZrl66VX5cf//jHtW6r8ud8ms/XESNGVJtf+fOnZ8+etRZI//nPf3JlxSGHHFJlXuXP1O9///u15n3yySdz484888w6H099/26ddtppSUQkffr0qfWxJMnnr3PF5eTWLvkqv4d69+5d63omT56cG/e3v/2t2nylCgB8OblRPQCQd88++2y89dZbERExbNiw2HzzzVMv+9lnn8X06dMjImLgwIHRuXPnWscOGzYst0xNN1TfmA466KBo06ZNjfN233333L+POuqoKCoqWue41157rdZt9ejRI3r27FnjvIKCgthjjz0iIuq88XNDKCgoiGOPPbbGeZtttlnstNNOERHRunXrGDRoUI3junbtGi1btoyIdeetfPP3tfXt2ze++tWvRkTEI488UmVexe8FBQVx2mmn1bqO73znO7HFFlvUuI7K9tprr9wN4b8IK1eujNdffz1eeOGFeP755+P555+vss8899xztS5bUFAQgwcPrnV+7969IyLigw8+iGXLllWZV3Hz9RYtWuTeW2k98MADsWbNmigpKYlvfetbdY7dZ599IiJi0aJF8eabb+amb7vtthER8c9//jNmz569XttvKNOmTcvduPu73/1ureO6dOkSBx10ULVl1tasWbP4zne+0/BBUzrxxBNrnVexLyRJUu3z5t57742IiF122SV69OhR5zYqXs9nnnmm3jet79ChQ9x8880REfHvf/87Bg8eHOeee25EROy2225x+eWX12u9FR599NHczefPOeecKCwsTL3sokWL4sUXX4yIiGOPPTb3ebW2pk2b5j6jPvzww5g3b16t6zz++ONrnVfx2V5QUBDHHXdcjWMqf76u63NzyJAhUVBQUOO8Tp06xcCBAyOi+n5c+TOwrvfCXnvtFd26dau2zNo25O/WfffdFxERRx99dK2PJSJiyy23zO2vM2fOrHXc4MGDa11PxfuitiwAwJeTUgUAyLv58+fn/r333nuv17ILFy7MHfzq169fnWMrz3/++efXazsNbeedd6513pZbbrne4z766KNax+266651Zqkod+paR0No27ZtrUVSxH8fz4477rjOA2ER68779a9/vc75ffv2jYiIf/3rX7F69erc9Ip9o2vXrrH11lvXunyzZs1yB/bq2p9qOzC4Id577734yU9+Ervssku0bNkyunbtGrvttlv06NEjevToEYccckiVsbVp27ZtbLXVVrXOr/x6rf18V7xve/fuHSUlJeuVf86cORERsWrVqmjatGkUFBTU+nPooYfmllu8eHHu3yeccEIUFRVFaWlp7LXXXnHYYYfFjTfeGM8//3wkSbJeeeqr8uue9vNn1apVtR583WmnnaJ58+YNF3A91fVZUde+UPF6vvzyy3W+lgUFBTF8+PCIiCgrK4sPPvig3lmPPPLI3MH7++67Lz7++OMoLi6OO++8M4qLi2tc5rXXXssVj2v/LF26NDduQ/4m1WefWHu5taX5O9C2bdto3br1Osc11OfmypUrq+zHFfmbNWsWvXr1qnMdFY/7lVdeqfLZW1l9/2698cYb8e6770ZExKhRo9a5P1bsu5U/W9YnS13vCwDgy0upAgDkXeWDvhXfPk+r8kG5bbbZps6x7du3r3G5fKjrIHSTJk3We1xt33xf1zoqr6eudTSEtDkaKu+69od27dpFxOffvP/www9z0yv2jXUtH/Hffaqu/amuA531MXfu3Nh1111j3Lhx8a9//WudBcInn3xS67y0z3VE9ee74n27vu/ZiKhyAHt9VBSoEZ8f6Pz9738frVu3js8++yzuv//+OOOMM6JHjx6xzTbbxMknnxxPPPFEvbaTVkN//jT0vrK+6vt50xCvZ31cffXVVc5svPjii+ssMYcOHZorHtf+ueGGG3LjGtvfpDSvy8b+3Iyomrni323atImmTZvWuY6Kx732Z29l9X08X8S+2BB/hwGAL5e6/28HACBD6jq7gU3Phu4PDbU/rc+lg9Zl9erVceyxx8b7778fRUVFcfbZZ8e3v/3t2HnnnaN169a5b+gvXLgwdthhh4iIjXbWxvqoOPjYtm3beOyxx1Iv17Vr1yq/H3300TFgwIC4++6746GHHoonnngi3n333XjvvffijjvuiDvuuCOGDBkSEydOrHLw84vQEPtLQ+4rG1PF67n77rvHHXfckXq5jh07btB2b7311vj4449zvz/yyCO5sxMag8aSY300ls/N+qpcbFx88cWpL6fXokWLLyoSAPAlpFQBAPKubdu2uX+/884767zsR2WVL72xZMmSOsdWvrxHXZehqknFAdny8vI6x61cuXK91ssXZ8mSJXXeY6difykoKKhyhkDFvrGu/Sniv/vU+u5P9fXoo4/mLrlzww03xOmnn17juI1xJlbbtm3jrbfeinfeeWe9l6245NhHH30U3bp126AyYYsttojvfe978b3vfS8iIl588cX429/+Ftdee20sWrQobr/99thjjz1y99xoSGt//tS1v23I509jV/F6fvzxx7HbbrttlG3+85//jB//+McREdGqVatYsWJFPPbYY3HVVVfFeeedV+Myae+ltfbfpLXLvLpsrL9JX5QlS5bUebmxyo+pcuaKf7///vvx2Wef1Xm2SsXjXvuztyFUvpxhUVHRRtsfAYBNi8t/AQB597WvfS3378cff3y9lt1+++1zl+aYNWtWnWMr38x6fQ+0VNxsuLZLlVT417/+tV7r5YvzzDPPpJq/0047RbNmzXLTK/aN1157LXdt/pqUlZXl7r2woQfu0n67+4UXXsj9u7abUkf89x4XX6SK9+2cOXPW+zJOFfeiKS0tbfCs3bp1iwsuuCCefvrp3LfP//CHPzToNipUft3Tfv6UlJTE9ttv/4XkqbCxzxaofNPwuu5N0VBWr14dJ554Ynz66adRUlISM2fOzN2n48ILL4x//OMfG7T+DfmbVJ99Yu3l8int5+ba+3FF/tWrV8ezzz5b5zoqHvfan70NYfvtt48tttgiIiKeeuqpBl13feT7zB0A4IuhVAEA8m733XfPfcP7N7/5TZXLuaxL06ZNY999942IiClTpsRbb71V69jf/OY3uWX222+/9cpY8U3lf/3rX7XejPa9996LKVOmrNd6+eLcfvvttc575plncjdWHjBgQJV5Fb8nSRK33nprrev405/+FMuXL69xHeur8s3JS0tLax332Wef5f5d21lR5eXlcfPNN29QnjQOO+ywiPj8XgQ33XTTei9bcbBxwoQJDR0tIiI6d+6c+8Z95XtkNKT99tsvd5bNxIkTax335ptv5j4bKi/zRUm7PzWUww8/PCI+f89cc801X/j2LrzwwtyB+6uvvjq6d+8ed9xxR2y++eZRWloaJ5544gY97v333z9XyF177bXrda+MDh06RLdu3SLi8zKvtr9na9asidtuuy0iPr+XTuUiJ59+97vf1XrJwLfffjsefvjhiKi+H1f+DKzrvTBz5sz45z//WW2ZhlJYWBgHH3xwREQ8/PDD8eKLLzb4NtbHxn4vAgAbh1IFAMi7Jk2axI9+9KOIiHjrrbfilFNOidWrV9c4try8PBYtWlRl2llnnRURn39D9rvf/W6UlZVVW27ixIm5g0FHHXXUet98uKK4Wb16dVx77bXV5peVlcXpp59e503B2bjuvffeGs9Q+Pjjj+N///d/I+Lzfa/i3xWOOOKI6NChQ0RE/PznP48FCxZUW8d//vOf3CWGSkpKYujQoRuUtfL++O9//7vWcTvttFPu3xUHZNc2atSomDdv3gblSeOkk07K3RPjpz/9aUyfPr3WsWuXnbvsskvuXgd33XVXjB8/vs5tvfbaa/H73/++yrS//vWvsWzZslqX+c9//hMvvfRSRFS/F0tD6dChQxx55JEREf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\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.columns.importances(target_num=0)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"column importances for the x-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"column\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.columns.importances(target_num=1)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"column importances for the y-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"column\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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lKSnI8vmvXLqlYsaKZcMXw9/d3ZK9WrVqefUpEZOfOnW6bPSQkRH799VfH7TNnzkiFChXk5MmTIiIye/ZsqVevnql4xdL8+qBy5cqOGT9z5oxYliWrV692PL527VqpWbOmoXRFy/1LhcK+3PkvHSpWrChbt24VEZEqVao4/pxrz549bv36QPNaGRYWVuRXSEiI286N5tfEIiJBQUH59qYrbdq0yW1fV2pe5698Xblp0yZ58sknJTQ0VHx9faVXr16ybNkywwkLp/m1jYju1/Sa91g/Pz85cOCA43ZgYKDs3r3bcXv//v3i7+9vIppTOnXqJF27ds03OxrWG+3dXyv4QfVUJpZl5bnt6+uL+++/H8uXL0d6ejoeeughzJw500y4EmrRogU++ugjHD58GB9++CEOHjyIO++803SsAk2ZMgWjRo1CQkICPvjgA9NxSszHxweTJk3CuHHjkJiYiLfffhs5OTmmYzktd+4zMjLyXe/3hhtuwMGDB03EKlZ2dnaea0QHBQUhOzsbWVlZAICOHTtix44dpuIV65NPPsF7770HHx8fZGZm5nns9OnT8PX1NZSseC1btsTSpUsBALVr18bWrVvzPJ6cnOz4zAx3lDvzf/75J2rXrp3nsTp16rjtB4v+fY8CgOuvvx6vvPIK0tPTsWzZMkRGRhpIVry77roLjz/+OJKSkvI9lpSUhCeffBLdunUzkMw5Pj4+OH/+PADg4sWLyMnJcdwGgHPnzrntZ/E0btwYK1euBPDX9en379+f5/H9+/e77Wex+fr65pl727Zx+fJlZGdnAwDatGmD3377zVC64ml+fXDmzBnHOh4YGIjAwMA817mOjIzE0aNHTcUrUnBwMN5++22sXLmywK+PP/7YdMRCtW/fHnPnzgUANGnSBD/++GOex1etWoXrr7/eQDLnaF4rL1y4gIcffhgTJ04s8GvEiBGmIxZK+2tiX19fnDp1qtDH3fl1seZ1/krNmjXDhx9+iCNHjuCTTz7B77//jk6dOrntZyRqfm0D6H5Nr3mPrVKlCo4cOeK4/fTTT+f5mfXPP/9EYGCgiWhO+c9//oMOHTqgefPm+Oabb0zHKRHt3V8zTJ/qkG4F/cbA37nrJcCcye7ObyMV+eu3M26//Xbp1KmTHDlyRMWJ+99lZGRI586dpV27diryW5YlgwYNkmHDhkmVKlXy/UbS5s2bJSIiwlC6ot155515LikxduxYqVatmuP2li1b3DZ7VFRUnkvdTZw4Mc/jkyZNklatWpkJ54Sff/5ZKlasKK+++qq8//77EhERIS+//LJ8/vnnMmrUKAkNDXXbz4SxLEu6dOki3bt3l7CwsHzXpl+3bp1cd911htIVzZl13l1lZmZKp06dxLIsCQ8Pl7i4OImLi5Pw8HCxbVs6d+4sf/75p+mYhbr77rvlrrvukjVr1sjjjz8uzZs3l65du8qZM2ckKytL7r33XunUqZPpmAX65ptvJDw8XGbMmCEzZsyQ6OhomTp1qqxdu1amT58ukZGRbvvZJN27d5eePXvKmTNn5OLFi/LMM89InTp1HI+vW7dOqlatajCh87S9Pqhdu3aed6Z8+OGHcurUKcftzZs3u233t956a5F7UHJysliWVY6JnJeamiqVKlWS/v37yxtvvCFBQUHy4IMPyptvvin9+/cXX19fmTFjhumYhdK8VrZp06bIywm68+W/NL8mFhH5xz/+IVFRUbJo0SLHOxFF/rrM0KJFiyQ6Olqefvppgwmdo22dv/Iy0AXZvXu3vPTSS+WYyHmaX9uI6H5Nr3mPTUxMLHKd/+CDD+T2228vx0Slk5SUJPXr15fHH39csrKyVKw3ntK9p+OhCpXJa6+95nibtDa33nqrW/+FlLNycnLkrbfecny4ortvDoWZPHmy3HPPPXLw4EHTUYrUvn17ufXWWx1fn3zySZ7H33jjDWnfvr2ZcMXYvHmzhIeHS9WqVaVmzZri4+Mjc+fOdTz+wQcfOK69r80vv/xS5GUQ3MHPP/8srVq1yncd1+uvv96tP+PgoYceyvM1f/78PI+PHDlSEhISDKUr2o8//iiXLl0yHaNMUlNTZfr06fLWW2/JW2+9JdOnT3dcSs6d7dq1S2JjY8WyLImPj5dDhw5JYmKieHt7i7e3t1SuXFk2b95sOmahvvzyS6lRo0a+6zD7+fnJM88847YfjJqeni61a9cWb29vqVChgoSGhsoPP/zgeHzGjBlu+7lfhdHy+mDQoEH5XhNc6e2335YuXbqUYyLnffzxxzJ58uRCH8/IyJDXXnutHBOVzJ49e+S+++6T4OBgx/dqhQoVpE2bNvLVV1+ZjlckzWvlm2++WeRcHDhwQB566KFyTOQ87a+Jz58/L0888YT4+PiIbdvi5+cnfn5+Ytu2+Pj4yJNPPinnz583HdNpWtZ5zX+xL6L3tY2I7tf02vfYoqxfvz7fZbnd1dmzZ2XQoEESGxur+u/Ncmnq3pNZIiKm3y1DRGW3efNmrFmzBv3790dYWJjpONesvXv3wsfHBzVq1DAdpUBHjhzBN998gwsXLuD2229H/fr1TUe65vz+++/Yu3cvcnJyUK1aNURHR5uOVCZZWVnw8vKCn5+f6Sjkho4fP45KlSo5bq9YsQLnzp1D69at89zvji5fvowtW7bk+X5t1qwZgoODTUcr0tmzZ7F27VpcuHABrVq1QkREhOlIBGDfvn3w8/PLc0kwurpEBMeOHUNOTg4iIiLc9rJZBdG8VmrlCa+JT506hc2bNyMjIwPAX5d1atasWZ5Lm9HVs3//ftSsWbPAS1FpofW1DdHVsmTJEqxatQovvvgiqlSpYjoOKcdDFaK/ERHk5OTAy8vLdBSPdvz4cWzbtg033HADwsPD8ccff2DatGm4cOECevXqhfj4eNMRyc1wZqikxo8fj3vvvRdRUVGmo5QKZ57oLzExMfj+++8RGxtrOgoREZWB9tdmZNahQ4cQGhqKoKCgPPdfunQJv/zyC2655RZDyTwXfx4xizPv3vhB9eRSd9xxB2JiYkzHKFB2djZefvlltG/fHq+++ioAYOzYsQgKCkJAQAAGDBiAixcvGk5ZOosXL8ann35qOkahNmzYgNq1a6NDhw6oU6cONm/ejBYtWmDatGn49NNP0axZM2zZssV0zAIdOnQIf/zxh+P26tWr8cADD6Bdu3Z48MEH8csvvxhMVzIiglWrVuGTTz7BN998g0uXLpmOVCjNM+OMo0eP4vXXXzcdo1Tceb0ZOXIkateujTvvvBPz589XtaZ7wswfP34cq1atQmZmJgDgjz/+wDvvvIPXX38daWlphtMVTXP2orj7WvPee+8V+HXgwAHMmDHDcdtdffPNNxg1ahTWrl0LAFi5ciW6dOmCTp06ufUH0RbH3eemKO68R13p0KFDOHPmTL77L126hP/+978GEhVv/Pjx+T7sWhuNvQO6fx7R/NqsOO68Vmr/fj1y5AhatGiBqKgohIaGon///nm+dzMzM3HbbbcZTFg4zd+vnvDziNbXZppn/ppi8NJjdA344IMP3Pb6kC+//LJcd911Mnz4cKlfv7488cQTEhkZKZ999pnMmjVLrr/+erf94Oji1KtXz20/GFJE5I477pBHH31UTp06JWPHjpUaNWrIo48+6nh84MCBcs899xhMWLgWLVo4Pqj766+/Ftu2JTExUZ5//nnp3r27VKhQId8HebuLzp07y4kTJ0RE5Pjx49KyZUuxLEsqV64stm1LXFycHDt2zHDKgmmeGWe484e5Fsed1xvLsmTGjBly9913S4UKFaRSpUoydOhQFdef1T7z69evl4oVK4plWRIWFiabNm2SWrVqSWxsrNSuXVv8/f3d9nMCNGcvjruvNZZlSY0aNSQ6OjrPV+7nT0VHR0utWrVMxyzQlClTxNvbW5o1ayYhISEye/ZsCQ4OlkcffVQGDRok/v7+bv35WUVx97kpijvvUSIihw8flptuukls2xYvLy/p16+fnD592vF4RkaG2+a3LEu8vLzkjjvukHnz5smFCxdMR3Ka5t5FdP88ovm1WXHcea3U/P0qItK/f39p2bKlbNy4UX744Qdp1qyZNG/eXDIzM0Xkr+9Zd/2wd83fr9p/HtH82kzzzF9LeKhC16yYmBjH5rV7926xbVvmzZvneHz+/PnSsGFDU/E8WlhYmKSmpoqIyMWLF8W2bVm/fr3j8c2bN8v1119vKl6RAgMDZe/evSIi0rJlSxkzZkyex99//31p0qSJiWjFuvLDFZ988kmpX7++47/l4MGD0qxZM3niiSdMRiyU5pkREdm6dWuRX/Pnz3fbH8I0u3Lmjx49Ku+8847ExcWJbdty0003yccffyynTp0ynLJg2mde8w9hmrNrX2sGDRokN954o2P2c3l7e7v9B4rWr19fPv74YxERWblypfj5+cm///1vx+MzZsyQ+Ph4U/GKpH1uNNP8lyaa/3Jcc+8invPziLbXZprXSs3fryIi1atXz/M6+Pz589KtWze58cYb5fjx4259EKr5+1X7zyOaX5tpnvlrCQ9V6Jrl5+cnBw4cyHM7LS3NcXvv3r0SHBxsIprHCwwMlH379jluBwUFSXp6uuP2/v37xc/Pz0Cy4lWsWFG2bt0qIiJVqlRx/DnXnj17JCAgwES0Yl35Q0y9evVk8eLFeR5fvny52/4GsOaZEfmre9u2xbKsfF+59/NF0dV35cxf6b///a8MGDBAAgMDJTAw0ECy4mmfec0/hGnO7glrzaJFiyQyMlLef/99x30aDlX8/f1l//79jtsVKlTI85dV+/btc+vXB9rnRivNf2mi+S/HNfcu4jk/j1xJw2szzWul5u9Xkb9eF+/atSvPfZcuXZJ77rlHGjduLNu2bXPb7jV/v2r/eUTzazPNM38t8TZ9+THSq0ePHk4/d9GiRS5MUjoVK1bEiRMnEBkZCQBo2rQpgoODHY9fuHABlmWZiue0EydOYMOGDTh27BhycnLyPNa/f39DqYoWGRmJvXv3Ijo6GgAwb948VKtWzfH4kSNHEBERYShd0dq3b4+5c+eicePGaNKkCX788Uc0btzY8fiqVatw/fXXG0xYtNyZ/vPPP1G7du08j9WpUweHDx82EatYmmcGAMLDw/Huu++iQ4cOBT6ekpKCbt26lXOqktO23hS2hrdr1w7t2rXDe++9h/nz55dzKudon/mLFy/C398fAFChQgUEBATkyRsREYHjx4+bilckzdk9Ya3p3r07WrRogf79++P//b//hxkzZpiO5JRKlSph//79qFmzJg4fPozs7GwcOHAADRs2BADs378f4eHhhlMWzBPmBtC3RwHAyZMnERYW5rjt6+uLRYsWoVevXrjtttvw2WefGUznvCpVquC5557Dc889h9WrV2PatGkYNmwYhg0bVuBnlpimvXfNP49ofm3mKWultu9XAIiJicG2bdsQGxvruM/b2xtffPEFevXqhbvuustguqJp/n7V/vOI5tdmmmf+WsJDFSq1ihUrmo5QJvXr18eWLVvQqFEjAHB8cFWu7du351nA3NHSpUvxwAMP4MyZMwgJCcnzItWyLLf9AfK+++7DsWPHHLe7du2a5/ElS5agRYsW5R3LKWPGjEG7du1w+PBh3HzzzfjnP/+JjRs3Ij4+Hjt37sT8+fMxZcoU0zEL9dBDD8HX1xeXLl3Cvn370KBBA8djGRkZCA0NNReuCJpnBgCaNWuGw4cPIyoqqsDHT5w4AREp51Qlo3G9Ka7TkJAQPPbYY+WUpmS0z7zmH8I0Z/eEtQYArr/+eixfvhxjxoxBkyZNVGS+++678cgjj2DAgAFYsmQJ+vfvjxEjRsC2bViWhZEjR6Jjx46mYxbIE+ZG4x4F6P5LE81/Oa65d0D3zyOaX5tpXis1f78CQOfOnfHxxx+jZ8+eee7P/b7t2bMnDh06ZChd0TR/v2r/eUTzazPNM39NMfguGSKjdu7c6bi2ZUE+//xzmT9/fjkmKrnY2FgZOnSoZGVlmY5yVWVlZcn58+dNxyjUnj175L777pPg4GDHW74rVKggbdq0ka+++sp0vEI99NBDeb7+Pt8jR46UhIQEQ+nKxt1nZtGiRTJ79uxCH8/MzJSZM2eWY6KS89T1Rit3n/nXXntN5s6dW+jjL730kvTo0aMcEzlPc3ZPWGv+btOmTTJp0iTHZx24qzNnzshjjz0mDRs2lMcff1wuXLggY8eOFR8fH7EsS2699dYCL3njDjxhbrTuUc8995x07NixwMcuXbokiYmJbnt5j8Iu46SB5t5zaf15RDPNa6Xm71eRv74vT548WeTjv/32WzkmKhlP/X51959HNL820z7z1wpLxE2P0omoWIGBgdi+fTtiYmJMR7kmiYjjEhMRERGoUKGC6UhlkpWVBS8vL/j5+ZmOQm6I6w1dTWfPnoWXlxd8fX1NRykxzdmp/J0/fx6XLl3Kc4lZuvq07lHZ2dk4e/YsQkJCCn38f//7X6G/GU+l40m9e9rPI0SejN+v7oGvzehq4eW/qNSaNGni9GeObNmyxcVprk0JCQnYtGmTuh8gi7N48WKcPHnSbS/VkMuyLFx33XWmY1w1gYGBpiOUmpaZ0cwT1xvNc6M5OwAEBASYjlBqmrNrp3Hu/fz8+MsK5UDrHuXt7V3oX+znPq7hL/a18aTePe3nEY3rPJmnZW74/eoePOG1mdbuPQ3fqUKlNnr0aKef++qrr7owiWvccccd2Lt3L/bu3Ws6SqGmTZuG119/HQMHDkSjRo3y/aZDYmKioWRlExcXh927d+Py5cumo5SY5s1Nc3bNMwPo6N4T1xvNc6M5O6Bj5gvD7OZonnvN3WvI7ol7FKCj+8Iwuzma83OdN0NzdoBzY4rm3gF2T2XHQxWiQvz73//GH3/84dYHQrZtF/qYZVlcYA3QvLlpzq6dhu653tDVpGHmC8PsVBqau9eQ3VP3KA3dF4bZzdGeXyvNvWvOrh27N4fdU1nxUIWIiIiIiIiIiIiIiMgJ/EwVuiouX76MiRMnYsGCBThw4AAuXryY5/HMzExDycjdnThxAhs2bHB8YNuVNL4Nk1yPM0OloXluNGcnKi3OPRGRZ+M6T6XBuTGDvZvD7t0X36lCV8WoUaMwdepUjBgxAi+//DL++c9/4rfffsPXX3+NUaNGYciQIaYj5tGjRw+nn7to0SIXJim7n376CePGjUNaWhoAoH79+hg5ciTatWtnOFnxli5digceeABnzpxBSEgILMtyPGZZltsfxmne3LRm1z4zgN7uAb3rjea50Zw9l+aZZ3YztM+95u41Z9e6R+XS3D2zm6M1P9d5czRn59yYob13gN2Ta/BQha6K2rVr47333kPXrl0RHByM5ORkx33r1q3DnDlzTEfMY+DAgU4/d8aMGS5MUjafffYZBg4ciB49eqBt27YAgLVr1+Krr77CzJkz0bdvX8MJi1a3bl106dIFb731FgICAkzHKRHNm5vm7JpnBtDdveb1RvPcaM4O6J55ZjdH89xr7l5zds17FKC7e2Y3R3N+rvNmaM4OcG5M0dw7wO7JhYToKggICJD9+/eLiEjVqlVl8+bNIiKSnp4uISEhJqN5tLi4OJkwYUK++8ePHy9xcXEGEpVMQECApKenm45RKrGxsTJ06FDJysoyHaXENGfXPDMiurvXvN5onhvN2UV0zzyzm6N57jV3rzm75j1KRHf3zG6O5vxc583QnF2Ec2OK5t5F2D25jm36UIc8Q40aNXDkyBEAf71rZdmyZQCAjRs3wtfX12Q0j7Z3715069Yt3/2JiYnYt2+fgUQlk5CQgE2bNpmOUSr/+9//MGTIEJW/LaA5u+aZAXR3r3m90Tw3mrMDumee2c3RPPeau9ecXfMeBejuntnN0Zyf67wZmrMDnBtTNPcOsHtyHX5QPV0V3bt3x4oVK9CyZUsMHjwYDz74IKZNm4YDBw5g2LBhpuPl06RJkzxv+SvKli1bXJym9CIjI7FixQrUqVMnz/3Lly9HZGSkoVTO69q1K0aOHInU1FQ0atQIFSpUyPN4YmKioWTFy93cYmJiTEcpMc3ZNc8MoLt7zeuN5rnRnB3QPfPMbo7mudfcvebsmvcoQHf3zG6O5vxc583QnB3g3JiiuXeA3ZPr8DNVyCXWrVuHn3/+GbGxsQX+1phpo0ePdvq5r776qguTlM3//d//4ZlnnsHDDz+MNm3aAPjr+tEzZ87E5MmTMWjQIMMJi2bbhb9ZzrIsXL58uRzTlMy0adPw+uuvY+DAgeo2N83ZNc8MoLt7zeuN5rnRnB3QPfPMbo7mudfcvebsmvcoQHf3zG6O5vxc583QnB3g3JiiuXeA3ZPr8FCFylXXrl0xdepUVKtWzXQUj/HVV19h/PjxSEtLAwDEx8dj5MiRuPvuuw0n82yaNzfN2bXT3j3XGyopzTPP7FQamrvXnB3QvUdp7p7ZzdGeXyvNvWvOrh27N4fdk6vwUIXKVXBwMLZu3arybXdEREREREREREREdG3jB9XTNe/y5csYN24cWrRogapVqyI8PDzPF7nWTz/9hG7duqFOnTqoU6cOEhMTsXr1atOxyI1xZqg0NM+N5uxEpcW5JyLybFznqTQ4N2awd3PYvfvioQpd80aPHo0JEyagT58+OHnyJIYPH44ePXrAtm289tprpuPlEx4ejj/++AMAEBYWlu8QSNOB0GeffYY77rgDAQEBGDJkCIYMGQJ/f3906NABc+bMMR2vWJo3N63Ztc8MoKt7T1lvNM+N5uy5NM383zG7GdrnXnP3mrJ7yh6VS1P3f8fs5mjNz3XeHM3ZOTdmaO8dYPfkGrz8F5Urd7z8V+3atfHee++ha9euCA4ORnJysuO+devWud1CNWvWLNx3333w9fXFzJkzYVlWoc8dMGBAOSYrufj4eDz++OMYNmxYnvsnTJiATz75xHFNbHf02WefYeDAgejRowfatm0L4K8PRP3qq68wc+ZM9O3b13DCwmnOrnlmAH3de8p6o3luNGcH9M38lZjdHM1zr7l7bdk9ZY8C9HV/JWY3R3N+rvNmaM4OcG5M0dw7wO7JhYSoHAUFBUl6errpGHkEBATI/v37RUSkatWqsnnzZhERSU9Pl5CQEJPRPJ6Pj4/s3r073/27d+8WX19fA4mcFxcXJxMmTMh3//jx4yUuLs5AIudpzq55ZkR0d6+Z5rnRnF1E98wzuzma515z95qza6e5e2Y3R3N+rvNmaM4uwrkxRXPvIuyeXIeX/6JrXo0aNXDkyBEAf71rZdmyZQCAjRs3wtfX12S0Ynl5eeHYsWP57j9+/Di8vLwMJCqZyMhIrFixIt/9y5cvR2RkpIFEztu7dy+6deuW7/7ExETs27fPQCLnac6ueWYA3d1rXm80z43m7IDumWd2czTPvebuNWfXvEcBurtndnM05+c6b4bm7ADnxhTNvQPsnlzH23QAura89NJLbndd4+7du2PFihVo2bIlBg8ejAcffBDTpk3DgQMH8r3Fzt1IIVfvu3DhAnx8fMo5TcmNGDECQ4YMQXJyMtq0aQPgr7dhzpw5E5MnTzacrmi5m1udOnXy3K9hc9OcXfPMALq717zeaJ4bzdkB3TPP7OZonnvN3WvOrnmPAnR3z+zmaM7Pdd4MzdkBzo0pmnsH2D25Dg9V6KpKTU3FgQMHcPHixTz3JyYmAgBefPFFE7GKNGbMGMef+/Tpg6ioKPz888+IjY0t8DTbHbz33nsAAMuyMHXqVAQFBTkeu3z5Mv773/8iLi7OVDynPfnkk6hatSrGjx+PBQsWAPjrmpHz58/H3XffbThd0TRvbpqza54ZQGf3nrDeaJ4bzdkBnTOfi9nN0Tz3mrvXmN0T9ihAZ/e5mN0czfm5zpuhOTvAuTFFc+8AuycXMnz5MfIQ6enp0rhxY7EsS2zbFsuyHH+2bdt0vKuiS5cucvjwYdMxREQkOjpaoqOjxbIsiYyMdNyOjo6WunXrSseOHWXdunWmY3q8RYsWSdu2bSU8PFzCw8Olbdu28vXXX5uO5RTN2bXT1j3XGyorbTN/JWan0tDcvbbsnrRHaev+Ssxujvb8WmnuXXN27di9OeyeXMESKeS90kQl0K1bN3h5eWHq1KmoVasWNmzYgOPHj2PEiBEYN24c2rVrZzpimQUHB2Pr1q2IiYkxHcXhtttuw6JFixAWFmY6ChF5OK43RETkrrhHEREREVF54qEKXRURERFYuXIlGjdujIoVK2LDhg2oV68eVq5ciREjRiApKcl0xDJzx0MVjcLDw7Fr1y5EREQgLCwMlmUV+tzMzMxyTEbuijNDpaF5bjRnJyotzj0RkWfjOk+lwbkxg72bw+714Geq0FVx+fJlBAcHA/jrgOXw4cOoV68eoqKisHPnTsPpPNuhQ4ewZMmSAj/LZsKECYZSFW7ixImOWZk4cWKRG4S70by5ac6ueWYA3d3/nab1RvPcaM4O6J55ZjdH89xr7l5z9r/TtEcBurtndnM05+c6b4bm7ADnxhTNvQPsnsoHD1XoqmjYsCG2bt2KWrVqoWXLlnj33Xfh4+ODjz/+mO/scKEVK1YgMTERMTEx2LFjBxo2bIjffvsNIoKmTZuajlegAQMGOP780EMPmQtSCpo3N83ZNc8MoLv7K2lbbzTPjebsgO6ZZ3ZzNM+95u41Z7+Stj0K0N09s5ujOT/XeTM0Zwc4N6Zo7h1g91Q+ePkvuiq+//57ZGVloUePHtizZw/uuusu7Nq1C5UqVcK8efPQoUMH0xHLzB0v/9WiRQt07twZo0ePduSrUqUKHnjgAXTq1AlPPvmk6YhF8vLywpEjR1ClSpU89x8/fhxVqlTB5cuXDSUjd8WZMUfzeqN5bjRnJyotzj2VlOY9iuhaxHWeSoNzYwZ7N4fduzfbdADyDAkJCejRowcAoE6dOtixYwf++OMPHDt2zCMOVNxVWloa+vfvDwDw9vbGuXPnEBQUhNdffx3vvPOO4XTFK+xM98KFC/Dx8SnnNCXj5eWFY8eO5bv/+PHj8PLyMpDIeZqza54ZQHf3mtcbzXOjOTuge+aZ3RzNc6+5e83ZNe9RgO7umd0czfm5zpuhOTvAuTFFc+8AuyfX4eW/6Kp4+OGHMXnyZMfb64C/rmGYlZWFwYMHY/r06QbTXR0vvfQSwsPDTcfIIzAw0HHN6GrVqiE9PR0NGjQAAPzxxx8moxXpvffeAwBYloWpU6ciKCjI8djly5fx3//+F3FxcabiOUXz5qYxuyfMDKCz+1wa1xvNc6M5+5U0zzyzlz9PmHut3QO6s2vco66kuXtmN0djfq7zZmnNzrkxwxN6B9g9uQ4PVeiqmDVrFsaMGZPnUAUAzp07h08//VTFoUpqamqBH2yZmJgIAHjxxRdNxCpSq1atsGbNGsTHx6NLly4YMWIEtm/fjkWLFqFVq1am4xVq4sSJAP7a3KZMmZLntwN8fHwQHR2NKVOmmIpXJM2bm+bsmmcG0N19Lo3rjea50Zwd0D3zzG6O5rnX3L3m7Lk07lGA7u6Z3RzN+bnOm6E5O8C5MUVz7wC7J9fjZ6pQmZw6dQoigrCwMOzevRuVK1d2PHb58mUsXboUL7zwAg4fPmwwZdH27t2L7t27Y/v27bAsy3GKnftBVu58jcK9e/fizJkzaNy4MbKysjBixAj8/PPPiI2NxYQJExAVFWU6YpFuu+02LFq0CGFhYaajOK1WrVoAgP3796NGjRoFbm6vv/46WrZsaSpioTRnz6VxZgDP6F7zeqN1bgC92TXPPLObp3HuNXevOXsurXuU5u6Z3Rzt+QGu8+VNc/YrcW7M0Ng7wO7J9XioQmVi27bj8KEglmVh9OjR+Oc//1mOqUqmW7du8PLywtSpU1GrVi1s2LABx48fx4gRIzBu3Di0a9fOdERyQ5o3N83ZtWP3dK3RPPPMTqWhuXvN2bXT3D2zm6M9v1aae9ecXTt2bw67J1fhoQqVyU8//QQRwe23346FCxfm+cwRHx8fREVFoXr16gYTFi8iIgIrV65E48aNUbFiRWzYsAH16tXDypUrMWLECCQlJZmO6NEOHTqEJUuWFHjptQkTJhhKRe6MM0OloXluNGcnKi3OPRGRZ+M6T6XBuTGDvZvD7t0XP1OFyqR9+/YAgH379iEyMhK2bRtOVHKXL192fBZMREQEDh8+jHr16iEqKgo7d+40nC6/sLCwIt8ddKXMzEwXpymbFStWIDExETExMdixYwcaNmyI3377DSKCpk2bmo5XLM2bm9bs2mcG0NW9p6w3mudGc/Zcmmb+75jdDO1zr7l7Tdk9ZY/Kpan7v2N2c7Tm5zpvjubsnBsztPcOsHtyDR6q0FWRe53is2fPFrhINW7c2EQspzRs2BBbt25FrVq10LJlS7z77rvw8fHBxx9/jJiYGNPx8pk0aZLpCFfNiy++iGeffRajR49GcHAwFi5ciCpVquCBBx5Ap06dTMcrkubNTXN2zTMD6OveU9YbzXOjOTugb+avxOzmaJ57zd1ry+4pexSgr/srMbs5mvNznTdDc3aAc2OK5t4Bdk8uJERXwbFjx6Rr165i23aBX+7su+++k4ULF4qIyO7du6VevXpiWZZERETI8uXLDafzbEFBQbJnzx4REQkNDZVff/1VRESSk5MlKirKYLLi3XTTTTJq1CgR+eu/Iz09XU6fPi2JiYny4YcfGk5XNM3ZNc+MiO7uNdM8N5qzi+ieeWY3R/Pca+5ec3btNHfP7OZozs913gzN2UU4N6Zo7l2E3ZPr8FCFroq+fftK27ZtZePGjRIYGCjLli2T2bNnS7169eSbb74xHa/Ejh8/Ljk5OaZjFGv//v1Ffrm76667TlJTU0VEJD4+XhYvXiwif20QgYGBJqMVS/Pmpjm75pkR0d295vVG89xozi6ie+aZ3RzNc6+5e83ZNe9RIrq7Z3ZzNOfnOm+G5uwinBtTNPcuwu7JdXj5L7oqVq5cicWLF6N58+awbRtRUVG48847ERISgrfffhtdu3Y1HbFQDz/8MCZPnuz4XBUACA8PR1ZWFgYPHozp06cbTFe06OjoIq8lffny5XJMU3KtWrXCmjVrEB8fjy5dumDEiBHYvn07Fi1ahFatWpmOV6TAwEDHZe6qVauG9PR0NGjQAADwxx9/mIxWLM3ZNc8MoLt7zeuN5rnRnB3QPfPMbo7mudfcvebsmvcoQHf3zG6O5vxc583QnB3g3JiiuXeA3ZPr8FCFroqsrCxUqVIFwF8fGvn777+jbt26aNSoEbZs2WI4XdFmzZqFMWPG5DlUAYBz587h008/detDlaSkpDy3L126hKSkJEyYMAFvvvmmoVTOmzBhAs6cOQMAGD16NM6cOYP58+cjNjbWrT8sDNC9uWnOrnlmAN3da15vNM+N5uyA7plndnM0z73m7jVn17xHAbq7Z3ZzNOfnOm+G5uwA58YUzb0D7J5cyPRbZcgzNG/eXL777jsREenWrZv069dPDh06JM8995zExMQYTlewkydPyokTJ8SyLNmzZ4+cPHnS8ZWZmSmzZs2SatWqmY5ZKt988420b9/edAyPlp6eLlu3bhURkTNnzsigQYOkUaNG0qNHD/ntt98Mpyua5uzaeWL3XG+oKJpnntmpNDR3rzl7YbTsUZq7Z3ZztOfXSnPvmrNrx+7NYffkKpaIiOmDHdLvs88+Q3Z2Nh566CFs3rwZnTp1QmZmJnx8fDBz5kz06dPHdMR8bNsu8jIBlmVh9OjR+Oc//1mOqa6OPXv24IYbbkBWVpbpKETk4bjeEBGRu+IeRURERESuwEMVcomzZ89ix44dqFmzJiIiIkzHKdBPP/0EEcHtt9+OhQsXIjw83PGYj48PoqKiUL16dYMJi3fq1Kk8t0UER44cwWuvvYYdO3YgOTnZTLAihIWFFXmYdaXMzEwXpyENODPuQdt6o3luNGcnKi3OPZWFtj2K6FrEdZ5Kg3NjBns3h93rwc9UIZcICAhA06ZNTccoUvv27QEA+/btQ2RkJGzbNpyo5EJDQ/MttiKCyMhIzJs3z1Cqok2aNMl0hFLTvLlpzq55ZgDd3V9J23qjeW40Zwd0zzyzm6N57jV3rzn7lbTtUYDu7pndHM35uc6boTk7wLkxRXPvALun8sFDFSq14cOHO/1cd/4ApaioKAB/vbvmwIEDuHjxYp7HGzdubCKWU1atWpXntm3bqFy5MurUqQNvb/f89h4wYIDpCKWmeXPTnF3zzAC6u7+StvVG89xozg7onnlmN0fz3GvuXnP2K2nbowDd3TO7OZrzc503Q3N2gHNjiubeAXZP5YOX/6JSu+222/Lc3rJlC7Kzs1GvXj0AwK5du+Dl5YVmzZph5cqVJiI65ffff8fAgQPxn//8p8DHL1++XM6Jrh0HDhwo8vGaNWuWUxLSgjNDpaF5bjRnJyotzj0RkWfjOk+lwbkxg72bw+7dGw9V6KqYMGECfvzxR8yaNQthYWEAgD///BMDBw5Eu3btMGLECMMJC/fAAw9g//79mDRpEm699VZ89dVXOHr0KP71r39h/Pjx6Nq1q+mIRdq5cyfef/99pKWlAQDi4+Px9NNPIy4uznCy4tm2XeRbMt35QEvz5qY5u+aZAXR3D+hdbzTPjebsgO6ZZ3ZzNM+95u41Zwf07lGA7u6Z3RzN+bnOm6E5O8C5MUVz7wC7J9dxz/dCkzrjx4/HsmXLHAcqwF/XMPzXv/6Fjh07uvWhysqVK7F48WI0b94ctm0jKioKd955J0JCQvD222+79aHKwoULcd9996F58+Zo3bo1AGDdunVo1KgR5s2bh549expOWLSkpKQ8ty9duoSkpCRMmDABb775pqFUzomOjla7uWnOrnlmAN3da15vNM+N5uyA7plndnM0z73m7jVn17xHAbq7Z3ZzNOfnOm+G5uwA58YUzb0D7J5ch4cqdFWcOnUKv//+e777f//9d5w+fdpAIudlZWWhSpUqAP46CPr9999Rt25dNGrUCFu2bDGcrmjPPfccXnzxRbz++ut57n/11Vfx3HPPuf0PkDfccEO++5o3b47q1atj7Nix6NGjh4FUztG8uWnOrnlmAN3da15vNM+N5uyA7plndnM0z73m7jVn17xHAbq7Z3ZzNOfnOm+G5uwA58YUzb0D7J5cSIiugn79+kl0dLQsXLhQDh48KAcPHpQvv/xSatWqJf379zcdr0jNmzeX7777TkREunXrJv369ZNDhw7Jc889JzExMYbTFc3f3192796d7/5du3aJv7+/gURXx+7duyUgIMB0jFL55ptvpH379qZjlIrm7JpnRkRH95643mieG83ZRXTMfGGY3RzNc6+5ew3ZPXGPEtHRfWGY3RzN+bnOm6E5uwjnxhTNvYuweyo7vlOFroopU6bg2WefRd++fXHp0iUAgLe3Nx555BGMHTvWcLqiDR06FEeOHAHw12+zderUCZ9//jl8fHwwc+ZMs+GKceutt2L16tWoU6dOnvvXrFmDdu3aGUrlvFOnTuW5LSI4cuQIXnvtNcTGxhpKVTb16tXDxo0bTccoFQ3ZPXFmAB3da15vNM+N5uxF0TDzhWF21/PEudfSfUE0ZNe8RxVFQ/eFYXZzNOTnOu9etGTn3Jjhib0D7J7KjocqdFUEBATgww8/xNixY5Geng4AqF27NgIDAw0nK96DDz7o+HOzZs2wf/9+7NixAzVr1kRERITBZMVLTEzE888/j82bN6NVq1YA/rp+9BdffIHRo0djyZIleZ7rbkJDQ/Nd21JEEBkZiXnz5hlK5RzNm5vm7JpnBtDdveb1RvPcaM4O6J55ZjdH89xr7l5zds17FKC7e2Y3R3N+rvNmaM4OcG5M0dw7wO7JdSwREdMhiKh0bNt26nmWZbnlh2/99NNPeW7bto3KlSujTp068PZ27zNf27aL3NxyPyTVHWnOrnlmAN3da15vNM+N5uyA/plndjM0z73m7rVnd4Y77lGA/u6Z3QzN+bnOm6E5O8C5MUVz7wC7J9fhoQpdk4YPH+70cydMmODCJKSV5s1Nc3bt2D1dazTPPLNTaWjuXnN27TR3z+zmaM+vlebeNWfXjt2bw+7JVXioQtek2267Lc/tLVu2IDs7G/Xq1QMA7Nq1C15eXmjWrBlWrlxpIuI1Y+fOnXj//feRlpYGAIiPj8fTTz+NuLg4w8nIXXFmqDQ0z43m7ESlxbknIvJsXOepNDg3ZrB3c9i9+3LufdJEHmbVqlWOr27duqF9+/Y4dOgQtmzZgi1btuDgwYO47bbb0LVrV9NRi/XTTz+hW7duqFOnDurUqYPExESsXr3adCynLFy4EA0bNsTmzZtxww034IYbbsCWLVvQqFEjLFy40HS8Yu3cuRNPP/00OnTogA4dOuDpp5/Gjh07TMdyitbs2mcG0Ns9oHe90Tw3mrPn0jzzzG6G9rnX3L3m7Fr3qFyau2d2c7Tm5zpvjubsnBsztPcOsHtyESG6xlWvXl1+/fXXfPdv375dqlWrZiCR82bPni3e3t7Su3dvmTx5skyePFl69+4tFSpUkM8//9x0vGLFxMTIK6+8ku/+UaNGSUxMjIFEzvvyyy/F29tbWrVqJcOGDZNhw4ZJ69atxdvbW7788kvT8YqkObvmmRHR3b3m9Ubz3GjOLqJ75pndHM1zr7l7zdk171EiurtndnM05+c6b4bm7CKcG1M09y7C7sl1eKhCpZb7A0tJvk6dOmU6dj5BQUGyatWqfPevXLlSgoKCyj9QCcTFxcmECRPy3T9+/HiJi4szkKhk/P39Zffu3fnu37Vrl/j7+xtI5DzNm5vm7JpnRkR395rXG81zozm7iO6ZZ3ZzNM+95u41Z9e8R4no7p7ZzdGcn+u8GZqzi3BuTNHcuwi7J9fhoQqVmmVZEhkZKdHR0U59eXl5SXp6uunY+fTr10+io6Nl4cKFcvDgQTl48KB8+eWXUqtWLenfv7/peEXy8fEpcIHdvXu3+Pr6GkhUMp07d5bp06fnu3/69OnSsWNHA4mcp3lz05xd88yI6O5e83qjeW40ZxfRPfPMbo7mudfcvebsmvcoEd3dM7s5mvNznTdDc3YRzo0pmnsXYffkOt6mLz9Gum3atAlVqlRx6rnBwcEuTlM6U6ZMwbPPPou+ffvi0qVLAABvb2888sgjGDt2rOF0RYuMjMSKFStQp06dPPcvX74ckZGRhlI5LzExEc8//zw2b96MVq1aAQDWrVuHL774AqNHj8aSJUvyPNed3HrrrVi9enW+7tesWYN27doZSuUczdk1zwygu3vN643mudGcHdA988xujua519y95uya9yhAd/fMbo7m/FznzdCcHeDcmKK5d4Ddk+tYIiKmQ5BOo0ePxsiRIxEQEODU899++208+eSTCA0NdW2wUsrKykJ6ejoAoHbt2ggMDDScqHj/93//h2eeeQYPP/ww2rRpAwBYu3YtZs6cicmTJ2PQoEGGExbNtm2nnmdZFi5fvuziNCUzZcoUjBo1Cr179y5wc6tevbrjue62uWnOrnlmAN3da15vNM+N5uyA7plndnM0z73m7jVn17xHAbq7Z3ZzNOfnOm+G5uwA58YUzb0D7J5ch4cqRMp99dVXGD9+PNLS0gAA8fHxGDlyJO6++27DyTyb5s1Nc3bttHfP9YZKSvPMMzuVhubuNWcHdO9RmrtndnO059dKc++as2vH7s1h9+QqPFQhl9q2bRuaN2+Oixcvmo5CRERERERERERERFQmzh3XEZWSiPCktxxs2rQJs2fPxuzZs7F582bTcUrkp59+Qrdu3VCnTh3UqVMHiYmJWL16telY5MY4M2ZpXW80z43m7ESlxbmn0tC6RxFdi7jOU2lwbsxg7+awe/fFQxUixQ4dOoR27dqhRYsWGDp0KIYOHYqbbroJN998Mw4dOmQ6XrE+++wz3HHHHQgICMCQIUMwZMgQ+Pv7o0OHDpgzZ47peMXSvLlpza59ZgC93WtebzTPjebsubTOPMDspmife83da82ueY/KpbV7gNlN0pqf67w5mrNzbszQ3jvA7slFhMiFkpOTxbZt0zE8VkJCgrRs2VJ27NjhuG/Hjh3SunVrSUhIMJjMOXFxcTJhwoR8948fP17i4uIMJHLe7NmzxdvbW3r37i2TJ0+WyZMnS+/evaVChQry+eefm45XJM3ZNc+MiO7uNa83mudGc3YR3TPP7OZonnvN3WvOrnmPEtHdPbObozk/13kzNGcX4dyYorl3EXZPrsNDFSqTkydPFvm1evVqtzxUyV1IS/J16tQp07Hz8fPzky1btuS7f9OmTeLv728gUcn4+PjI7t27892/e/du8fX1NZDIeZo3N83ZNc+MiO7uNa83mudGc3YR3TPP7OZonnvN3WvOrnmPEtHdPbObozk/13kzNGcX4dyYorl3EXZPrsNDFSoTy7LEtu1Cv3IfdzeWZUlkZKRER0c79eXl5SXp6emmY+cTGxsr69evz3f/+vXrpXbt2gYSlUzt2rVlypQp+e7/v//7P6lTp46BRM7TvLlpzq55ZkR0d695vdE8N5qzi+ieeWY3R/Pca+5ec3bNe5SI7u6Z3RzN+bnOm6E5uwjnxhTNvYuwe3Idb9OXHyPdVq1aZTpCqW3atAlVqlRx6rnBwcEuTlM6Y8eOxeDBg/Hvf/8bzZs3B/DXf9fQoUMxbtw4w+mKN2LECAwZMgTJyclo06YNAGDt2rWYOXMmJk+ebDhd0SIjI7FixQrUqVMnz/3Lly9HZGSkoVTO0Zxd88wAurvXvN5onhvN2QHdM8/s5miee83da86ueY8CdHfP7OZozs913gzN2QHOjSmaewfYPbmQ6VMd8nzHjx83HSGf1157TbKyspx+/ltvvSV//vmn6wKVUmhoqPj4+Iht2+Lj45Pnz2FhYXm+3NWiRYukbdu2Eh4eLuHh4dK2bVv5+uuvTccq1ocffig+Pj7yxBNPyKeffiqffvqpDBo0SHx9fQv8TQJ3ojm7iN6ZEdHdvfb1RvPcaM6ueeaZ3Sytc6+5e83Zte9RmrtndnO05+c6X/40Z8/FuTFDa+8i7J5cxxIRMX2wQ55p2bJlmDp1KpYuXYpz586ZjuORZs2a5fRzBwwY4MIk16avvvoK48ePR1paGgAgPj4eI0eOxN133204WfE0Z9dOa/dcb6i0tM48wOxUOpq715rdE/Yord0DzG6S9vxaae5dc3bt2L057J5cgYcqdFXt378f06dPx6xZs/Dnn3+ic+fO6NmzJ3r16mU6Wolt27YNzZs3x8WLF01H8XibNm1ybG7169dHs2bNDCcid8eZodLQPDeasxOVFueeiMizcZ2n0uDcmMHezWH37omfqUJldvHiRSxatAhTp07F2rVrcccdd+DQoUNISkpCo0aNTMcrNRHB5cuXTcfwaIcOHcL999+PtWvXIjQ0FABw4sQJtGnTBvPmzUONGjXMBnSC5s1NY3ZPmBlAZ/eaaZ4bzdmvpHnmmb38ecLca+0e0J1dO83dM7s5GvNznTdLa3bOjRme0DvA7skFjF58jNR7+umnpVKlStKqVSv54IMP5I8//hAREW9vb0lJSTGcrmySk5PFtm3TMTxaQkKCtGzZUnbs2OG4b8eOHdK6dWtJSEgwmKx4Bw8elJtvvlksy3Jco9uyLGnbtq0cPHjQdLwiac6ueWZEdHevmea50ZxdRPfMM7s5mudec/eas2unuXtmN0dzfq7zZmjOLsK5MUVz7yLsnlyHhypUJl5eXvLSSy/JqVOn8tzPQxVyhp+fn2zZsiXf/Zs2bRJ/f38DiZyneXPTnF3zzIjo7l4zzXOjObuI7plndnM0z73m7jVn105z98xujub8XOfN0JxdhHNjiubeRdg9uQ4PVahM5syZI3fccYcEBgZK7969ZenSpZKdna3iUOXkyZNFfq1evZqHKi4WGxsr69evz3f/+vXrpXbt2gYSOU/z5qY5u+aZEdHdvWaa50ZzdhHdM8/s5miee83da86unebumd0czfm5zpuhObsI58YUzb2LsHtyHdv05cdIt/vvvx8//PADtm/fjri4ODz11FOoWrUqcnJykJqaajpekUJDQxEWFlbo1y233GI6oscbO3YsBg8ejE2bNjnu27RpE4YOHYpx48YZTFa8yMhIXLp0Kd/9ly9fRvXq1Q0kcp7m7JpnBtDdvWaa50ZzdkD3zDO7OZrnXnP3mrNrp7l7ZjdHc36u82Zozg5wbkzR3DvA7smFTJ/qkGfJycmR7777Tnr16iW+vr5y/fXXy+DBg03HKtCPP/7o1Je7adKkSYm+mjZtKocOHTIdu0ChoaHi4+Mjtm2Lj49Pnj/nXusy98vdfP3119KiRQvZuHGj476NGzdKq1at5KuvvjIXzAmas2ueGRF93XvKeqN5bjRnF9E381didnM0z73m7rVl95Q9SkRf91didnM05+c6b4bm7CKcG1M09y7C7sl1LBER0wc75JkyMzPx6aefYubMmUhOTjYdp1QyMzMRHh5uOkYetm1jxIgRCAoKKva5IoIxY8YgNTUVMTEx5ZCuZGbNmuX0cwcMGODCJCUXFhaGs2fPIjs7G97e3gDg+HNgYGCe52ZmZpqIWCjN2TXPDKCve09ZbzTPjebsgL6ZvxKzm6N57jV3ry27p+xRgL7ur8Ts5mjOz3XeDM3ZAc6NKZp7B9g9uY636QDkucLDw9GuXTssX77cdJQSW7ZsGaZOnYqlS5fi3LlzpuPkM3LkSFSpUsWp544fP97FaUpP86I/adIk0xFKTXN2zTMD6OzeE9YbzXOjOTugc+ZzMbs5mudec/cas3vCHgXo7D4Xs5ujOT/XeTM0Zwc4N6Zo7h1g9+Q6fKcKldn333+PH374AT4+Pnj00UcRExODHTt24IUXXsDSpUuRkJCAb7/91nTMYu3fvx/Tp0/HrFmz8Oeff6Jz587o2bMnevXqZTpaHvv370fNmjVhWZZTzz948CCqV68OLy8vFycjIk/D9YaIiNwV9ygiIiIiMoWHKlQm06ZNw2OPPYbw8HD8+eefqFSpEiZMmIDBgwejT58+GDp0KOLj403HLNTFixexaNEiTJ06FWvXrsUdd9yB//znP0hKSkKjRo1MxyMiIiIiIiIiIiIiN8JDFSqTxo0bo1+/fhg5ciQWLlyIXr16oVWrVliwYAFq1KhhOl6RBg8ejLlz5yI2NhYPPvgg7rvvPlSqVAkVKlTA1q1bUb9+fdMRC3XgwAGnnlezZk0XJyEiT8f1hoiI3BX3KCIiIiIygYcqVCaBgYFISUlBdHQ0RAS+vr5YtWoV2rZtazpasby9vfH888/jhRdeQHBwsON+DYcqtm0XeKkDEXHcb1kWsrOzyzsaEXkYrjdEROSuuEcRERERkQn8oHoqk3PnziEgIADAXz+w+Pr6olq1aoZTOWf27NmYPn06qlWrhq5du6Jfv37o3Lmz6VhOSUpKKvB+EcG8efPw3nvvISgoqJxTEZEn4npDRETuinsUEREREZnAd6pQmdi2jX/961+OH1aef/55jBw5EhEREXmeN2TIEBPxnLJv3z7MnDkTM2fOxNmzZ5GZmYn58+fj3nvvNR2tRJYvX44XXngBu3btwvDhwzFixIg878BxF02bNi3R8y3LwpIlS3D99de7KBG5O86M+9Gw3mieG83ZiUqLc09Xi4Y9iuhaxHWeSoNzYwZ7N4fd68FDFSqT6OjoAt9yfyXLsrB3795ySlR6IoJly5Zh2rRpWLJkCSIiItCjRw+89957pqMVacuWLXj++eexevVqPProoxg1ahSqVKliOlahbNvGiBEjnPqtQRHBmDFjkJqaipiYmHJIVzTNm5vm7JpnBtDd/d9pWm80z43m7IDumWd2czTPvebuNWf/O017FKC7e2Y3R3N+rvNmaM4OcG5M0dw7wO6pfPBQhagAmZmZ+PTTTzFz5kwkJyebjlOg9PR0vPTSS1i4cCF69+6Nf/3rXyoWUdu2kZGR4fQPucHBwdi6datb/Ldp3ty0Z9c6M4Du7nNpXG80z43m7IDumWd2czTPvebuNWfPpXGPAnR3z+zmaM7Pdd4MzdkBzo0pmnsH2D2VEyGiAm3atEm6du1qOkaBnnzySfHx8ZGEhARJSkoyHadEfvvtN8nJyXH6+QcOHJDs7GwXJnKeZVly9OhRp58fFBQk6enpLkzkPM3ZNc+MiO7uRfSuN5rnRnN2Ed0zz+zmaJ57zd1rzi6id48S0d09s5ujOT/XeTM0Zxfh3JiiuXcRdk/lg+9UoTLp0qUL5s6di4oVKwIAxowZgyeeeAKhoaEAgOPHj6Ndu3ZITU01mLJw33//PX744Qf4+Pjg0UcfRUxMDHbs2IEXXngBS5cuRUJCAr799lvTMfOxbRt+fn6Ii4sr8nlbtmwpp0TXhv3796NmzZrFXvIu18GDB1G9enV4eXm5OFnxNGfXTnv3XG+opDTPPLNTaWjuXnN2QPcepbl7ZjdHe36tNPeuObt27N4cdk/lgYcqVCZeXl44cuSI421pISEhSE5Odrzt7OjRo6hevTouX75sMmaBpk2bhsceewzh4eH4888/UalSJUyYMAGDBw9Gnz59MHToUMTHx5uOWaDRo0c79bxXX33VxUlK58CBA049r2bNmi5OQlpwZszRvN5onhvN2YlKi3NPJaV5jyK6FnGdp9Lg3JjB3s1h9zrwUIXK5O/X+vv7tfzc+VClcePG6NevH0aOHImFCxeiV69eaNWqFRYsWIAaNWqYjufRbNsu8DcGRMRxv2VZyM7OLu9oxdK8uWnOrnlmAN3da6Z5bjRnB3TPPLObo3nuNXevObt2mrtndnM05+c6b4bm7ADnxhTNvQPsnlyPhypUJpoPVQIDA5GSkoLo6GiICHx9fbFq1Sq0bdvWdLQS2bZtG3bt2gUAqFu3Lho3bmw4UfG2bt1a4P0ignnz5uG9995DUFAQjh07Vs7Jiqd5c9OcXfPMALq7v5K29Ubz3GjODuieeWY3R/Pca+5ec/YradujAN3dM7s5mvNznTdDc3aAc2OK5t4Bdk+u5206AOlmWVa+RcrZaxaadu7cOQQEBAD4K7Ovry+qVatmOJXzNmzYgEceeQSpqanIPRu1LAsNGjTAtGnTcNNNNxlOWLgbbrgh333Lly/HCy+8gF27duG5557DiBEjDCQrXlJSUoH3/31zc0eas2ueGUB394De9Ubz3GjODuieeWY3R/Pca+5ec3ZA7x4F6O6e2c3RnJ/rvBmaswOcG1M09w6weyoHV+PT7unaZVmWdOnSRbp37y7du3cXb29v6dixo+N2ly5dxLZt0zELZFmWvPnmmzJ58mSZPHmy+Pn5ySuvvOK4nfvljlJSUiQoKEhuuukmmTNnjiQlJUlSUpJ8/vnn0rx5cwkODpaUlBTTMZ2yefNmueOOO8TX11eeeuopOXr0qOlIJfbDDz9Is2bNJDg4WF599VU5deqU6UhO05jdE2ZGRE/3nrLeaJ4bzdmvpGXmC8Ls5c8T5l5r9yJ6snvKHnUlLd0XhNnN0Zif67xZWrNzbszwhN5F2D1dXTxUoTJ56KGHnPpyR1FRURIdHV3kV61atUzHLFCvXr2ke/fukpOTk++xnJwcueeee6RXr14Gkjlvz5490rt3b/Hy8pL7779f0tPTTUcqMc2bm8bsnjAzIvq6177eaJ4bzdmvpG3mr8Ts5c8T5l5r9yL6smvfo66krfsrMbs5GvNznTdLa3bOjRme0LsIuyfX4KEKkUIRERGycePGQh/fsGGDRERElGOiknnyySfFx8dHEhISJCkpyXScEtO8uWnNrn1mRPR2r3m90Tw3mrPn0jrzIsxuiva519y91uya96hcWrsXYXaTtObnOm+O5uycGzO09y7C7sm1+EH1RAr5+flh9+7diIyMLPDxgwcPIjY2FufPny/nZM6xbRt+fn6Ii4sr8nlbtmwpp0TO+8c//oFp06bhtttuw5gxY3DjjTeajuQ0zdk1zwygu3vN643mudGcHdA988xujua519y95uya9yhAd/fMbo7m/FznzdCcHeDcmKK5d4Ddk+vxUIXKpEePHk49b9GiRS5OUnJdunTB3LlzUbFiRQDAmDFj8MQTTyA0NBQAcPz4cbRr1w6pqakGUxasXr16eOutt9CzZ88CH//yyy/xz3/+Ezt37iznZM4ZPXq0U8979dVXXZyk5DRvbpqza54ZQHf3mtcbzXOjOTuge+aZ3RzNc6+5e83ZNe9RgO7umd0czfm5zpuhOTvAuTFFc+8AuyfX8zYdgHTLPZDINWfOHHTr1g3BwcGGEjnv+++/x4ULFxy333rrLfTu3dtxqJKdne22P4Ddd999GD58OOrVq4eGDRvmeWz79u149tln0b9/f0Ppiqd54Wd2MzRnB3Tn17zeaO5dc3ZAd35mN0dzfmY3Q/MeBejuntnN0Zyf2c3QnB3QnZ/ZzdGcX3P2awnfqUJXVXBwMLZu3YqYmBjTUYpl2zYyMjJQpUoVAPmzHz16FNWrV8fly5dNxizQ+fPn0aFDB6xfvx533nkn4uPjISJIS0vD8uXL0aJFC6xcuRJ+fn6moxZr27Zt2LVrFwCgbt26aNy4seFE5O44M+XLU9YbzXOjOTtRaXHuyRmeskcRXYu4zlNpcG7MYO/msHv3xUMVuqp4qFJ+Ll68iIkTJ2Lu3Ll5Ftj77rsPw4YNg6+vr+GERduwYQMeeeQRpKamIncZsiwLDRo0wLRp03DTTTcZTlg8zZubxuyeMDOAzu41rzea50Zz9itpnPlczF7+PGHutXYP6MyueY+6ksbuczG7ORrzc503S2t2zo0ZntA7wO7JRa7yB9/TNS4oKEjS09NNx3CKbdty7Ngxx+2goCDZu3ev43ZGRobYtm0imsdLSUmRoKAguemmm2TOnDmSlJQkSUlJ8vnnn0vz5s0lODhYUlJSTMcs1Pr166Vhw4Zi27ZYliWWZYlt29KoUSPZsGGD6XhF0ppd+8yI6O1eM81zozl7Ls0zz+xmaJ97zd1rzq6d5u6Z3Ryt+bnOm6M5O+fGDO29i7B7ci2+U4WuKm3vVOncubPjt9eWLl2K22+/HYGBgQCACxcu4LvvvnPbd6po1rt3b2RnZ2PhwoWwLCvPYyKCHj16oEKFCliwYIGhhIVLTU1Fy5YtER8fj2HDhiE+Pt5x/8SJE7Fz506sW7cO9evXN5w0P83ZNc8MoLt7zTTPjebsgO6ZZ3ZzNM+95u41Z9dOc/fMbo7m/FznzdCcHeDcmKK5d4Ddk+vxUIXKZMmSJXlu33///Zg0aRKuu+66PPcnJiaWZyynDBw40KnnzZgxw8VJSi4sLCzfwlqQzMzMckhTcpUrV8Z//vMfNG/evMDHN27ciC5duuD3338v52TF07y5ac6ueWYA3d1rXm80z43m7IDumWd2czTPvebuNWfXvEcBurtndnM05+c6b4bm7ADnxhTNvQPsnlyPhypUJrZtF/scy7L4bo+rbNasWU49b8CAAS5OUjp+fn7YvXs3IiMjC3z84MGDiI2Nxfnz58s5WfE0b26as2ueGUB395rXG81zozk7oHvmmd0czXOvuXvN2TXvUYDu7pndHM35uc6boTk7wLkxRXPvALsn1/M2HYB0y8nJMR3hmlSrVi20adMG3t46v4WjoqKwYcOGQjeI9evXIyoqqpxTOef06dP53ol1papVq+L06dPlmMh5mrNrnhlAd/ea1xvNc6M5O6B75pndHM1zr7l7zdk171GA7u6Z3RzN+bnOm6E5O8C5MUVz7wC7J9cr/m0GRE64cOECsrKyTMcokR49ejj15Y5uu+02t72MgTPuu+8+DB8+HL/++mu+x7Zv345nn30Wffr0MZCseLmbW2HceXPTnF3zzAC6u9e83mieG83ZAd0zz+zmaJ57zd1rzq55jwJ0d8/s5mjOz3XeDM3ZAc6NKZp7B9g9lYOr/MH3dI05duyYdOrUSby9vcW2bWnZsqXs3r3bdCynPPTQQ3m+fHx8pGfPnvnud0eWZcnRo0dNxyi1c+fOSZs2bcTLy0s6deokw4YNk2eeeUYSEhLEy8tLWrduLefOnTMds0CjRo2SmjVryvbt2/M9tm3bNomKipJXXnnFQLLiac6ueWZEdHeveb3RPDeas4vonnlmN0fz3GvuXnN2zXuUiO7umd0czfm5zpuhObsI58YUzb2LsHtyPX6mCpXJww8/jP/85z8YMmQI/Pz88NFHH6FatWpYtWqV6WglFhwcjK1btyImJsZ0lGLZto2jR4+icuXKpqOU2sWLFzFx4kTMnTsXu3btAgDUrVsX9913H4YNGwZfX1/DCQt2/vx5dOjQAevXr8edd96J+Ph4iAjS0tKwfPlytGjRAitXroSfn5/pqPlozg7onRlAd/fa1xvNc6M5u+aZZ3aztM695u41Z9e+R2nuntnN0Z6f63z505w9F+fGDK29A+yeXI+HKlQmkZGRmDp1KhISEgAAu3fvRnx8PLKystR9g2s7VOncuXOxHS9atKicEl1bNG9umrNrp7V7rjdUWlpnHmB2Kh3N3WvN7gl7lNbuAWY3SXt+rTT3rjm7duzeHHZPrsRDFSoTLy8v/O9//0PVqlUd9wUGBiIlJQXR0dHmgpWCtkOV3r17w9/fv8jnzZgxo5wSEZGn4npDRETuinsUEREREZnAQxUqEy8vL2RkZOR5y31ISAi2bt2KWrVqGUxWctoOVTIyMlClShXTUUolLCwMlmUV+zzNHzxKVxdnxhzN643mudGcnai0OPdUUpr3KKJrEdd5Kg3OjRns3Rx2r4O36QCkm4igbt26eb7Zz5w5gyZNmsC2bcd97viNvmTJkjy3c3JysGLFCvz666957k9MTCzPWE5xZnF1Z5MmTTIdodQ0b26as2ueGUB395rXG81zozk7oHvmmd0czXOvuXvN2TXvUYDu7pndHM35uc6boTk7wLkxRXPvALsn1+OhCpWJ5rfS33PPPfnuGzRoUJ7blmXh8uXL5ZTIecW9wSwtLQ3Tpk3DuHHjyilRydSqVQtt2rSBt7e+JUjz5qY5u+aZAXR3r3m90Tw3mrMDumee2c3RPPeau9ecXfMeBejuntnN0Zyf67wZmrMDnBtTNPcOsHtyPV7+i0ihn376CW3bts2zwGZlZWHevHmYNm0a1q1bh/r16+d714278PLywpEjR1RequG///2v2s1Nc3bNMwPo7l7zeqN5bjRnB3TPPLObo3nuNXevObvmPQrQ3T2zm6M5P9d5MzRnBzg3pmjuHWD35Ho8VKFr3oULF5CdnY3AwEDTUUpl7dq1mDZtGhYsWIBz585h2LBhePTRRxEXF2c6WqE0X/9a8+amObvmmQF0d38lbeuN5rnRnB3QPfPMbo7mudfcvebsV9K2RwG6u2d2czTn5zpvhubsAOfGFM29A+yeXM8u/ilEhQsLC0N4eHixX+7o999/R+fOnREUFISQkBC0atUKe/bsMR3LKceOHcO7776LuLg43HvvvQgNDcWPP/4I27bx8MMPu/UPj7m0XgNb8zm05uyA3pkBdHevfb3RPDeas2ueeWY3S+vca+5ec3bte5Tm7pndHO35uc6XP83Zc3FuzNDaO8DuyfX0vQeK3IrmaxQ+//zzSE5Oxuuvvw4/Pz989NFHeOyxx7Bq1SrT0YoVFRWFe++9F5MnT8add94J29Z3PvrQQw/B19e3yOcsWrSonNKUjObNTXN2zTMD6O1e+3qjeW40Zwf0zjzA7CZpnnvN3WvNrn2PAvR2DzC7SZrzc503Q3N2gHNjiubeAXZPrsVDFSqTAQMGmI5Qaj/88ANmzpyJhIQEAMBdd92F+Ph4XLhwodiFy7SoqCisWbMGNWvWRFRUlNv/Fl5BgoOD4e/vbzpGqWje3DRn1zwzgN7uta83mudGc3ZA78wDzG6S5rnX3L3W7Nr3KEBv9wCzm6Q5P9d5MzRnBzg3pmjuHWD35Fo8VCGXOnLkCN5880188MEHpqPkc/jwYdxwww2O27GxsfD19cWRI0cQHR1tLpgTduzY4bhu9E033YS6deviwQcfBKDnJP69995Te31IzZub5uyaZwbQ27329Ubz3GjODuideYDZTdI895q715pd+x4F6O0eYHaTNOfnOm+G5uwA58YUzb0D7J5cix9UT2WWkpKCVatWwcfHB71790ZoaCj++OMPvPnmm5gyZQpiYmKQkpJiOmY+Xl5eyMjIQOXKlR33hYSEYOvWrahVq5bBZCVz5swZzJ07FzNmzMC6devQvn179O3bF/fcc0+e/zZ3wg8MM0Nzds0zA+ju/kra1hvNc6M5O6B75pndHM1zr7l7zdmvpG2PAnR3z+zmaM7Pdd4MzdkBzo0pmnsH2D25nr6LzpJbWbJkCZo0aYIhQ4bgiSeeQPPmzbFq1SrEx8cjLS0NX331lVseqAB/fWhV3bp1ER4e7vg6c+YMmjRpkuc+dxcUFITHHnsMP//8M1JSUtCsWTO8/PLLqF69uulohSruLDctLQ3PPvtsOaUpGS2/9VgQzdk1zwygu/sraVtvNM+N5uyA7plndnM0z73m7jVnv5K2PQrQ3T2zm6M5P9d5MzRnBzg3pmjuHWD35Ho8VKEy+de//oWnnnoKp06dwoQJE7B3714MGTIE3377Lb777jt06tTJdMRCzZgxA5MmTcLEiRMdXzNmzMDkyZPz3KdJfHw8xo0bh//973+YP3++6TiFWrVqVb4Dq6ysLEybNg1t2rRBgwYN8N133xlKVzTNm5vm7JpnBtDdfWE0rDea50ZzdkD3zDO7OZrnXnP3mrMXRsMeBejuntnN0Zyf67wZmrMDnBtTNPcOsHsqB0JUBiEhIbJ7924REcnOzhYvLy/54YcfDKeitLQ0iY2NNR3DKWvWrJGBAwdKYGCg2LYtI0aMkLS0NNOxCvXjjz/KpUuX8tx35swZmTp1qrRu3Vosy5IGDRoYSlc0zdmvpG1mRDyn+4JoWW80zk0ujdk1zzyzuwdtc6+5e83Zi+Pue5Tm7pndHO35c3GdLz+as/8d58YMbb2LsHtyPR6qUJlYliVHjx513A4KCpL09HSDiUhEJDk5WWzbNh2jUEePHpV33nlH6tWrJ1WrVpVhw4bJxo0bxdvbW1JSUkzHc5rmzU1bdk+ZGRF93RfHndcbzXOjOfvfaZ55Zi9fnjL3GrvPpTl7Qdx5j/o7zd0zuzna8nOdN09jds6NGZ7Suwi7J9fgoQqViWVZ8umnn8rixYtl8eLFEhAQIB9//LHjdu6XOwoNDZWwsLBivzRy9x8g/fz85MEHH5TvvvtOLl++7LhfwwaheXPTnF3zzIjo7r447rzeaJ4bzdlFdM88s5ujee41d685e3HceY8S0d09s5ujOT/XeTM0Zxfh3JiiuXcRdk+u52368mOk34ABA/LcHjRoUJ7blmXh8uXL5RnJKZMmTTId4ZoVFRWFNWvWoGbNmoiKikJcXJzpSE6LiorCvffei8mTJ+POO++Ebev5aCrt2bXODKC7e800z43m7IDumWd2czTPvebuNWfXTnP3zG6O5vxc583QnB3g3JiiuXeA3ZPr8VCFyiQnJ8d0hFL7+2EQlZ8dO3Zg7dq1mDZtGm666SbUrVsXDz74IIC/DuHcmebNTXN2zTMD6O5eM81zozk7oHvmmd0czXOvuXvN2bXT3D2zm6M5P9d5MzRnBzg3pmjuHWD3VA5Mv1WGyF0dPnxYnnrqKdMxClTcpcuCg4Pd+lIHVzp9+rR8/PHHjg8Ku/XWW+Xjjz+WY8eOmY5WqNzrcQYFBUnTpk1lwoQJ4u3tLampqaajFUtz9lwaZ0ZEb/eest5onRsRvdm1zrwIs7sDjXOvuXut2T1hj9LavQizm6Q9vwjX+fKmOfuVODdmaOxdhN2Ta1kiIqYPdkivf/zjH3j33XcRFBQEAJg7dy4SExMRGBgIADhx4gT69u2Lb7/91mTMQqWkpGDVqlXw8fFB7969ERoaij/++ANvvvkmpkyZgpiYGKSkpJiOmc+sWbOcep62d+OkpaVh2rRpmD17NjIzM3Hp0iXTkYp05swZzJ07FzNmzMC6devQvn179O3bF/fccw8qV65sOl6RNGe/kraZAfR174nrjca5yaUxu7aZvxKzuwdtc6+5e23ZPWmP0tb9lZjdHO35c3GdLz+as/8d58YMbb0D7J5cxPSpDulm27YcPXrUcTs4OFjS09MdtzMyMtz2t8MWL14sFSpUEMuyxLIsqV27tqxcuVIiIiIkISFB/vOf/5iOeM26dOmSLFy40HSMEklNTZURI0ZIlSpVxNvb23ScEtGcPZfGmRHxjO410zo3Inqza555ZjdP49xr7l5zdu00d8/s5mjPL8J1vrxpzn4lzo0ZGnsXYfd09fCdKlQmtm0jIyMDVapUAQAEBwdj69atiImJAQAcPXoU1atXd8sPqm/RogXatm2LN954A1OnTsXw4cPRoEEDTJ8+HTfddJPpeEU6depUgfcHBgbCy8urnNNcfTt27EBiYiJ27dplOkqJZWdnY8mSJejRo4fpKCWmObvmmQHcu3tPXm80z43m7IB7z3xxmN0czXOvuXt3zu7JexTg3t0Xh9nN0Zyf67wZmrMDnBtTNPcOsHsqO9t0ACJTdu7ciaeeegpBQUEYPHgwbNvGxIkT3f5ABQBCQ0MRFhaW78vf3x/16tXDJ598YjpimVy4cAHp6emmY5TKnj178MILL5iOUSqas2ueGcC9u/fk9Ubz3GjODrj3zBeH2c3RPPeau3fn7J68RwHu3X1xmN0czfm5zpuhOTvAuTFFc+8Au6ey8zYdgMiU06dPIyQkBADg5eUFf39/xzts3N2qVasKvP/EiRPYvHkzRo4cCW9vbwwcOLCck5HmzU1zdu3cuXuuN+QK7jzzxWF2Kg3N3btzdk/fo9y5++Iwuzna82uluXfN2bVj9+aweyorHqpQmY0aNQoBAQEAgIsXL+LNN99ExYoVAQBnz541Ga1Y33//vSNrTk4OVqxYgV9//TXPcxITE01EK1L79u0Lfezuu+9GdHQ03n//fbU/QBKR++B6Q0RE7op7FBERERGZwEMVKpNbbrkFO3fudNxu06YN9u7dm+857mrAgAF5bg8aNCjPbcuy3PLzYIrTvn17PPPMM6ZjENE1gOsNERG5K+5RREREROQKPFShMvnxxx9NRyi1nJwc0xFc5uTJk4534LijsLAwWJZV6OPZ2dnlmIY04My4L3debzTPjebsRKXFuaerzZ33KKJrEdd5Kg3OjRns3Rx2rwMPVajMTp06hfXr1+PixYto0aIFKleubDrSNe3SpUsYO3YsWrZsaTpKoSZNmmQ6Qqlp3tw0Z9c8M4Du7ovi7uuN5rnRnB3QPfPMbo7mudfcvebsRXH3PQrQ3T2zm6M5P9d5MzRnBzg3pmjuHWD35Ho8VKEySU5ORpcuXZCRkQEACA4OxoIFC5CQkGA4WfH+8Y9/4N1330VQUBAAYO7cuUhMTERgYCCAvz7gsm/fvvj2229NxixQjx49Crz/5MmTSElJgWVZWL16dTmnct7fL7umiebNTXN2zTMD6O5e83qjeW40Zwd0zzyzm6N57jV3rzm75j0K0N09s5ujOT/XeTM0Zwc4N6Zo7h1g9+R6loiI6RCkV0JCAs6cOYNx48bBz88Pb7zxBrZv347du3ebjlYsLy8vHDlyBFWqVAEAhISEIDk5GTExMQCAo0ePonr16m75mSqFfdhmSEgI6tWrhwceeMCtL3Vw6tSpAu8PDAyEl5dXOachDTgz5mhebzTPjebsRKXFuaeS0rxHEV2LuM5TaXBuzGDv5rB7HXioQmUSERGBZcuWoWnTpgD+endHeHg4Tpw4gZCQEMPpimbbNjIyMhyHKsHBwdi6dauKQxXtbNsu8G2YXl5eqFWrFp599lk89thjBpIVT/Pmpjm75pkBdHevmea50Zwd0D3zzG6O5rnX3L3m7Npp7p7ZzdGcn+u8GZqzA5wbUzT3DrB7cj1e/ovKJDMzEzVq1HDcDg0NRWBgII4fP+72hyrarVu3DkuXLsXFixfRoUMHdOrUyXQkp61atarA+0+cOIHNmzdj5MiR8Pb2LvS3D00KDQ1Vu7lpzq55ZgDd3QN61xvNc6M5O6B75pndHM1zr7l7zdkBvXsUoLt7ZjdHc36u82Zozg5wbkzR3DvA7sn1eKhCZZaamur4TBUAEBGkpaXh9OnTjvsaN25sIprH+vLLL9GnTx/4+/ujQoUKmDBhAt555x08++yzpqM5pX379oU+dvfddyM6Ohrvv/++W24Qmjc3zdk1zwygu3vN643mudGcHdA988xujua519y95uya9yhAd/fMbo7m/FznzdCcHeDcmKK5d4DdUzkQojKwLEts2xbLsvJ95d5v27bpmAWyLEsGDRokw4YNk2HDhomPj488/PDDjtuDBg1y2+xNmzaVQYMGSXZ2toiIvPXWWxIWFmY41dWzZ88eCQ4ONh2jVKZNmyZNmjQxHaNUNGfXPDMi7t29J683mudGc3YR95754jC7OZrnXnP37pzdk/coEffuvjjMbo7m/FznzdCcXYRzY4rm3kXYPZWdbfpQh3Tbt28f9u7di3379uX7yr1/7969pmMW6JZbbsHOnTuRlJSEpKQktGnTBnv37nXc3rlzJ2655RbTMQu0c+dOPPvss47rQI4YMQKnT5/GsWPHDCe7Ok6ePKn2Q0Xbt2+PPXv2mI5RKpqza54ZwL279+T1RvPcaM4OuPfMF4fZzdE895q7d+fsnrxHAe7dfXGY3RzN+bnOm6E5O8C5MUVz7wC7p7Lj5b+oTKKiokxHKLUff/zRdIRSO3v2bJ7PrPHx8YGfnx/OnDmDKlWqGExWdpcuXcLYsWPRsmVL01FKRfPmpjW79pkB3Lt7T11vNM+N5uy53Hnmi8PsZmife83du3N2T92jcrlz98VhdnO05uc6b47m7JwbM7T3DrB7KjseqlCpbdu2DQ0bNoRtO/eGp5SUFNSrVw/e3u4zdqdOncL69etx8eJFtGjRApUrVzYdyWlTp05FUFCQ43Z2djZmzpyJiIgIx31DhgwxEa1YPXr0KPD+kydPIiUlBZZlYfXq1eWcquw0b27unt1TZwZw/+4BveuN5rnRnL04Gma+MMzuWp469xq6L4yG7Fr3qOJo6L4wzG6Ou+fnOu9+NGTn3Jjhqb0D7J6uDktExHQI0snLywsZGRlOH0SEhIQgOTkZMTExLk7mnOTkZHTp0gUZGRkAgODgYCxYsAAJCQmGkxUvOjoalmUV+RzLstz20muFfZhWSEgI6tWrhwceeMBtf2PA2c2tTp065ZyseJqza54ZQHf3mtcbzXOjOTuge+aZ3RzNc6+5e83ZNe9RgO7umd0czfm5zpuhOTvAuTFFc+8AuyfX46EKlZpt23j88ccREBDg1PM//PBDpKamus2hSkJCAs6cOYNx48bBz88Pb7zxBrZv347du3ebjkZuTPPmpjm7duyerjWaZ57ZqTQ0d685u3aau2d2c7Tn10pz75qza8fuzWH35Go8VKFSu/XWW4v9zbC/mzNnDqpVq+aiRCUTERGBZcuWoWnTpgCAEydOIDw8HCdOnMhzbWZyjXXr1mHp0qW4ePEiOnTogE6dOpmORG6OM0OloXluNGcnKi3OPRGRZ+M6T6XBuTGDvZvD7t0fD1XommXbNjIyMvJ8iGVwcDC2bduGWrVqGUzmnOzsbEycOBFz587Frl27AAB169ZF3759MXToUFSoUMFwwsJ9+eWX6NOnD/z9/VGhQgWcOnUK77zzDp599lnT0ZyieXPTml37zAB6uwf0rjea50Zz9lyaZ57ZzdA+95q715xd6x6VS3P3zG6O1vxc583RnJ1zY4b23gF2Ty4mRNcoy7Jk1apVsnXrVsdXYGCg/L//9//y3OeOzp49K23bthXbtqVjx44ydOhQGTp0qHTs2FFs25Z27drJuXPnTMcsVNOmTWXQoEGSnZ0tIiJvvfWWhIWFGU7lnC+++EJs25bAwEAJDQ0V27Zl7NixpmM5RXN2zTMjort7zeuN5rnRnF1E98wzuzma515z95qza96jRHR3z+zmaM7Pdd4MzdlFODemaO5dhN2T6/FQha5ZlmWJbdtiWVa+r9z7bds2HbNAo0aNkpo1axZ46JOcnCw1a9aUV199tfyDOSkwMFB2797tuH3hwgXx9vaWo0ePGkzlHM2bm+bsmmdGRHf3mtcbzXOjObuI7plndnM0z73m7jVn17xHiejuntnN0Zyf67wZmrOLcG5M0dy7CLsn1+OhCl2zfvvtN6e+3FHdunXlyy+/LPTxBQsWSGxsbDkmKhnLsvJtBkFBQZKenm4okfM0b26as2ueGRHd3WtebzTPjebsIrpnntnN0Tz3mrvXnF3zHiWiu3tmN0dzfq7zZmjOLsK5MUVz7yLsnlzP2/Tlx4hMiYqKMh2h1Pbv348WLVoU+nirVq1w4MCBckxUclOnTkVQUJDjdnZ2NmbOnImIiAjHfUOGDDERrUhnz55FSEiI47aPjw/8/Pxw5syZPJ/P4440Zwf0zgygu3vt643mudGcXfPMM7tZWudec/eas2vfozR3z+zmaM/Pdb78ac6ei3NjhtbeAXZPrsdDFbombdu2DQ0bNoRt2049PyUlBfXq1YO3t3t8y4SEhODYsWOIjIws8PGMjAwEBweXcyrn1axZE5988kme+6pWrYrZs2c7bluW5bYbhObNTWt27TMD6O1e83qjeW40Z8+ldeYBZjdF+9xr7l5rds17VC6t3QPMbpLW/FznzdGcnXNjhvbeAXZPrmWJiJgOQVTevLy8kJGRgcqVKzv1/JCQECQnJyMmJsbFyZzTp08fZGdnY+HChQU+3rNnT3h5eWHBggXlnMzzRUdHw7KsIp9jWRb27t1bTomcpzm7dpq753pDpaF55pmdSkNz95qza9+jNHfP7OZoz6+V5t41Z9eO3ZvD7snVeKhC1yTbtvH4448jICDAqed/+OGHSE1NdZtDldTUVLRs2RINGjTA8OHDERcXBxFBWloaJk6ciNTUVKxbtw4NGjQwHZWIlON6Q0RE7op7FBERERGZ4Ny1j4g8zC233IKdO3ciKSnJqa/WrVvD39/fdGyH+vXr44cffsDp06dx3333oUmTJmjatCn69u2L06dPY9myZW7/w2N2djbGjh2Lpk2bIigoCEFBQWjatCnGjRuHS5cumY5HbogzY4b29Ubz3GjOTlRanHsqCe17FNG1iOs8lQbnxgz2bg67d398pwqRcsnJydi1axcAoG7durjxxhvNBnLCuXPncOedd+KXX37BHXfcgfj4eABAWloali9fjrZt22LZsmXw8/MznLRg2dnZmDhxIubOnZun+759+2Lo0KGoUKGC4YSF05pd+8wAeru/krb1RvPcaM6eS/PMM7sZ2udec/eas+fStkfl0tw9s5ujNT/XeXM0Z+fcmKG9d4Ddk4sJEal08uRJWbZsmXzzzTdy7Ngx03FKZNSoUVKzZk3ZunVrvseSk5OlZs2a8uqrr5Z/MCecPXtW2rZtK7ZtS8eOHWXo0KEydOhQ6dixo9i2Le3atZNz586Zjlkgzdk1z4yI7u5F9K43mudGc3YR3TPP7OZonnvN3WvOLqJ3jxLR3T2zm6M5P9d5MzRnF+HcmKK5dxF2T67HQxUihZKSkqRatWpiWZZYliUhISHy3XffmY7ltLp168qXX35Z6OMLFiyQ2NjYckzkPM2bm+bsmmdGRHf3mtcbzXOjObuI7plndnM0z73m7jVn17xHiejuntnN0Zyf67wZmrOLcG5M0dy7CLsn1+OhCpFCHTt2lDZt2sjPP/8sW7Zske7du0udOnVMx3Kar6+vHDhwoNDHDxw4IL6+vuWYyHmaNzfN2TXPjIju7jWvN5rnRnN2Ed0zz+zmaJ57zd1rzq55jxLR3T2zm6M5P9d5MzRnF+HcmKK5dxF2T67nbfryY0RUcps3b8ayZcvQtGlTAMD06dMRHh6OU6dOISQkxHC64oWEhODYsWOIjIws8PGMjAwEBweXcyrn7N+/Hy1atCj08VatWuHAgQPlmMh5mrNrnhlAd/ea1xvNc6M5O6B75pndHM1zr7l7zdk171GA7u6Z3RzN+bnOm6E5O8C5MUVz7wC7J9ezTQcgopLLzMxEjRo1HLdDQ0MRGBiI48ePG0zlvNtuuw1vvfVWoY+PGTMGt912Wzkmcl7u5lYYd97cNGfXPDOA7u41rzea50ZzdkD3zDO7OZrnXnP3mrNr3qMA3d0zuzma83OdN0NzdoBzY4rm3gF2T65niYiYDkFEJWPbNlauXInw8HDHfW3atMGCBQvy/GDZuHFjE/GKlZqaipYtW6JBgwYYPnw44uLiICJIS0vDxIkTkZqainXr1qFBgwamo+bTp08fZGdnY+HChQU+3rNnT3h5eWHBggXlnKx4mrNrnhlAd/ea1xvNc6M5O6B75pndHM1zr7l7zdk171GA7u6Z3RzN+bnOm6E5O8C5MUVz7wC7p3Jg6LJjRFQGlmWJbduOD+W88iv3ftu2Tccs0i+//CL169d3ZM3NHR8fLz///LPpeIVKSUmRoKAgadmypcyfP1+2bt0qycnJMnfuXGnRooUEBQXJr7/+ajpmgTRnF9E7MyK6u9e+3mieG83ZNc88s5ulde41d685u/Y9SnP3zG6O9vxc58uf5uy5ODdmaO1dhN2T6/GdKkQK7d+/36nnRUVFuThJ2SUnJ2PXrl0AgLp16+LGG280G8gJ69atwyOPPIK0tDRYlgUAEBHExcVh2rRpaN26teGEhdOcPZfGmQH0du8p643WuQH0Ztc68wCzuwONc6+5e63ZPWGP0to9wOwmac8PcJ0vb5qzX4lzY4bG3gF2T67FQxUiMuLUqVNYv349Ll68iBYtWqBy5cqmI5WY5s1NY3ZPmBlAZ/eaaZ4bzdmvpHnmmb38ecLca+0e0J1dO83dM7s5GvNznTdLa3bOjRme0DvA7sk1eKhCpMy2bdvQsGFD2Lbt1PNTUlJQr149eHt7uziZ85KTk9GlSxdkZGQAAIKDg7FgwQIkJCQYTuYczZub1uzaZwbQ2b329Ubz3GjOnkvjzOdidjO0z73m7jVm175H5dLYfS5mN0drfq7z5mjOzrkxQ3vvALsnFyvPa40RUdnZti3Hjh1z+vnBwcGSnp7uwkQl17FjR2nTpo38/PPPsmXLFunevbvUqVPHdCynJCUlSbVq1RzX6w4JCZHvvvvOdCynaM6ueWZE9Havfb3RPDeas4vonXkRZjdJ89xr7l5rdu17lIje7kWY3STN+bnOm6E5uwjnxhTNvYuwe3I9vlOFSBnbtvH4448jICDAqed/+OGHSE1NRUxMjIuTOS8iIgLLli1D06ZNAQAnTpxAeHg4Tpw4gZCQEMPpipaQkIAzZ85g3Lhx8PPzwxtvvIHt27dj9+7dpqMVS3N2zTMD6O1e+3qjeW40Zwf0zjzA7CZpnnvN3WvNrn2PAvR2DzC7SZrzc503Q3N2gHNjiubeAXZPrsdDFSJlbr31VscHbDlrzpw5qFatmosSlZxt28jIyECVKlUc9wUHB2Pbtm2oVauWwWTF07y5ac6ueWYAvd1rX280z43m7IDemQeY3STNc6+5e63Zte9RgN7uAWY3SXN+rvNmaM4OcG5M0dw7wO7J9dzrgrJEVKwff/zRdISrIjU11XF9SAAQEaSlpeH06dOO+xo3bmwiWpEyMzNRo0YNx+3Q0FAEBgbi+PHjbr8xa84O6J0ZQG/3nrDeaJ4bzdm1zjzA7KZpnXvN3WvN7gl7lNbuAWY3SXt+rvPlT3P2XJwbM7T2DrB7cj0eqhCRER06dMDf3yh31113wbIsiAgsy8Lly5cNpSua5s1Nc3bNMwPo7l4zzXOjOTuge+aZ3RzNc6+5e83ZtdPcPbObozk/13kzNGcHODemaO4dYPfkWrz8FxGVu/379zv1vKioKBcnKTnbth2b2N+5++amObvmmQF0d6+Z5rnRnB3QPfPMbo7mudfcvebs2mnuntnN0Zyf67wZmrMDnBtTNPcOsHtyPb5ThYjKneaFf9++faYjlJrm7JpnBtDdvWaa50ZzdkD3zDO7OZrnXnP3mrNrp7l7ZjdHc36u82Zozg5wbkzR3DvA7sn1+E4VIipX27ZtQ8OGDWHbtlPPT0lJQb169eDtzTPgaxVnhkpD89xozk5UWpx7IiLPxnWeSoNzYwZ7N4fd6+Hc/0NERFdJkyZNcPz4caef37p1axw4cMCFiZy3bds25OTkOP38lJQUZGdnuzCR8zRn1zwzgO7uNdM8N5qzA7pnntnN0Tz3mrvXnF07zd0zuzma83OdN0NzdoBzY4rm3gF2T+WD71QhonJl2zYef/xxBAQEOPX8Dz/8EKmpqYiJiXFxsuJ5eXkhIyMDlStXdur5ISEhSE5OZvYy0jwzgO7uNdM8N5qzA7pnntnN0Tz3mrvXnF07zd0zuzma83OdN0NzdoBzY4rm3gF2T+WD7w0ionJ1yy23YOfOnU4/v3Xr1vD393dhIueJCF555RWnN7eLFy+6OJHzNGfXPDOA7u410zw3mrMDumee2c3RPPeau9ecXTvN3TO7OZrzc503Q3N2gHNjiubeAXZP5YPvVCEictKtt94Ky7JK9M/MmTMH1apVc1Ei52nOrh27p2uN5plndioNzd1rzq6d5u6Z3Rzt+bXS3Lvm7Nqxe3PYPZUHHqoQERERERERERERERE5gR9UT0RERERERERERERE5AQeqhARERERERERERERETmBhypERERERERERERERERO4KEKERERERERERERERGRE3ioQkREREREHuuhhx7CPffcYzoGERERERF5CB6qEBEREREREREREREROYGHKkRERERERERERERERE7goQoREREREbm1nJwcvPvuu6hTpw58fX1Rs2ZNvPnmmwCA7du34/bbb4e/vz8qVaqExx9/HGfOnCn03xUdHY1Jkyblue/GG2/Ea6+95rhtWRY++ugj3HXXXQgICEB8fDx++eUX7NmzB7feeisCAwPRpk0bpKenO/6Z1157DTfeeCNmz56N6OhoVKxYEffddx9Onz59VbsgIiIiIiKzeKhCRERERERu7cUXX8SYMWPwyiuvIDU1FXPmzMF1112HrKwsJCQkICwsDBs3bsQXX3yB5cuX4+mnny7z/+Ybb7yB/v37Izk5GXFxcejbty8GDRqEF198EZs2bYKI5PvfSU9Px9dff41vvvkG33zzDX766SeMGTOmzFmIiIiIiMh9eJsOQEREREREVJjTp09j8uTJ+OCDDzBgwAAAQO3atXHzzTfjk08+wfnz5/Hpp58iMDAQAPDBBx+gW7dueOedd3DdddeV+n934MCB6N27NwDg+eefR+vWrfHKK68gISEBADB06FAMHDgwzz+Tk5ODmTNnIjg4GADQr18/rFixwvGuGiIiIiIi0o/vVCEiIiIiIreVlpaGCxcuoEOHDgU+dsMNNzgOVACgbdu2yMnJwc6dO8v0v9u4cWPHn3MPZxo1apTnvvPnz+PUqVOO+6Kjox0HKgBQrVo1HDt2rEw5iIiIiIjIvfBQhYiIiIiI3Ja/v/9V/ffZtg0RyXPfpUuX8j2vQoUKjj9bllXofTk5OQX+M7nPufJxIiIiIiLSj4cqRERERETktmJjY+Hv748VK1bkeyw+Ph5bt25FVlaW4761a9fCtm3Uq1evwH9f5cqVceTIEcftU6dOYd++fVc/OBEREREReSQeqhARERERkdvy8/PD888/j+eeew6ffvop0tPTsW7dOkybNg0PPPAA/Pz8MGDAAPz6669YtWoVBg8ejH79+hX6eSq33347Zs+ejdWrV2P79u0YMGAAvLy8yvm/ioiIiIiItOIH1RMRERERkVt75ZVX4O3tjVGjRuHw4cOoVq0annjiCQQEBOD777/H0KFDcdNNNyEgIAA9e/bEhAkTCv13vfjii9i3bx/uuusuVKxYEW+88QbfqUJERERERE6z5O8XFCYiIiIiIiIiIiIiIqJ8ePkvIiIiIiIiIiIiIiIiJ/BQhYiIiIiIiIiIiIiIyAk8VCEiIiIiIiIiIiIiInICD1WIiIiIiIiIiIiIiIicwEMVIiIiIiIiIiIiIiIiJ/BQhYiIiIiIiIiIiIiIyAk8VCEiIiIiIiIiIiIiInICD1WIiIiIiIiIiIiIiIicwEMVIiIiIiIiIiIiIiIiJ/BQhYiIiIiIiIiIiIiIyAk8VCEiIiIiIiIiIiIiInICD1WIiIiIiIiIiIiIiIic8P8BK72xiJx674MAAAAASUVORK5CYII=\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"names, importances = pipe1.columns.importances(target_num=2)\\n\",\n    \"\\n\",\n    \"plt.bar(names[0:30], importances[0:30], color='#6829c2')\\n\",\n    \"\\n\",\n    \"plt.title(\\\"column importances for the z-component\\\", size=20)\\n\",\n    \"plt.grid(True)\\n\",\n    \"plt.xlabel(\\\"column\\\")\\n\",\n    \"plt.ylabel(\\\"importance\\\")\\n\",\n    \"plt.xticks(rotation='vertical')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.6 Visualizing the predictions\\n\",\n    \"\\n\",\n    \"Sometimes a picture says more than a 1000 words. We therefore want to visualize our predictions on the testing set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f_x = time_series.test.population[\\\"f_x\\\"].to_numpy()\\n\",\n    \"f_y = time_series.test.population[\\\"f_y\\\"].to_numpy()\\n\",\n    \"f_z = time_series.test.population[\\\"f_z\\\"].to_numpy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"predictions = pipe1.predict(time_series.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"col_data = 'black'\\n\",\n    \"col_getml = 'darkviolet'\\n\",\n    \"col_getml_alt = 'coral'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Lz7t27w9PTU3U7R0dH9O3bFwAQExOD48eP52u5suuVV16Bv7+/6jpPT09UrlwZgPXrGRcXJ31eunbtavMcVatWRfHixQEABw4cyGWJ1ck///369dPcrnHjxqhatarVPpaCgoLQtGlTzfXiNcnd3V36Dtkrr78TWgwGA7p16wYA+PvvvxUToIvWrFkjTQRv+R0/d+4crly5AiDr91gsJ6B8j0+cOIFbt24BMF9rPDw8svUc7JUf30db1zwiIiKixwkDJERERET5zGAwYPny5XBzc0NycjLWrVsHAOjRo4dmI1NiYiLOnDmj+U+Unp4u/d2kSRPodLpslU3cNzg4GCVKlNDczsnJCc8995xiHzXPPPOM5jofH59sbxcfH6+53XPPPQdHR0fN9XXq1IGTkxMA4PTp09Lyq1evIikpCQDQoEEDzf0t19t63s8++6zmOnnAyPL5HD16FACQlJQER0dH6HQ6zX/t27eX9rt7967quSpXrgwHB+0qvlgWW69rXpC/Vnn1GhcGW+8roP16hoWFwWQyATB/z229rzqdDg8ePACg/b7mlvi6Ojk5oU6dOja3Fd+PS5cuIS0tTXWbWrVq2TxGWFgYAHNAzs3NLVtlzevvhC3i9Tc1NRWrV6+2Wr9ixQoAQGBgIFq2bKlaTgB48cUXbZZTHviQl1N8nQDYDDjlVn58H7P6DBARERE9LhggISIiIioAVapUwciRI6W/S5QogRkzZmhuf+TIEdSsWVPznyg6OhqCIAAASpUqle1yRUdHA4BmL3m5gIAAxT5qbDWGyhvu7d3OaDRqbpdVmR0dHaUGbHmZ5Y+zOob4nC33s5TT5xMZGWnz/FrEAE92yiEvi9h4n1/y4zUuDPa+nvn9vuaW+Lr6+fnZDCoCme+HIAiIiYlR3cbX19fmMcSAT06uSQX52jVu3FjK4Fu+fLlVOcQsmrfeessq8JgX5RRfJyBnr5W98uP7mNVngIiIiOhxYbt2TERERER5Ii4uDosXL5b+fvDgAY4fP45WrVoVYqkyZTfzpCjIizIX9vMWG9aLFy+OnTt32r1fcHBwfhUpzxX2a1wY5AGT2bNno1GjRnbtl9+Nznn1Xuj1+jw5jpqC/E7odDr07NkTEydOxO7du3H79m2ULl0aAPDHH38gIyMDgPpwUvL3eMOGDShfvrxd57QnGJ2fHofPABEREVFBYoCEiIiIqAAMHjwY4eHhAMxzF8THx6NPnz44deqUYkgpUYsWLaTMEFv8/Pzg4OAAk8mEO3fuZLtcYobFvXv3stxWHBpGa46RgpZVmTMyMhQ950Xyx1kdQz4cTn4872LFigEwD9FUtWrVJ6bRsSi9xoVBfF8BcxZKjRo1CrE0ma9rVFQUMjIybGaRiO+HTqfLccCmePHiuHXrVo6uSQX9nejVqxcmTpwIk8mElStX4pNPPgGQObzWs88+i7p162qWEzAPC5iT91icewYA7ty5k+WQbjn1tH8fiYiIiGzhEFtERERE+WzVqlVYunQpAKB///5Sw9vNmzcxaNCgXB3bYDBIDXN79uyxK6giJ+577do13L9/X3O79PR0abz8wm7sFZ04cULq4a3m5MmT0hwK8jJXqFBBGjrp0KFDNs9x+PBh6XF+PG9xXpfU1FTFnAaPO/lrVdivsaggM1nq1KkjnW/fvn0Fdl4t4uualpaGEydO2NxWfD8qV64szeGTXWJA4ejRo9ke+qqgvxPVq1dH7dq1AWQGRa5duyZNpq41T5RYTiDn77E88LJ79+5s72/vZ7oofh+JiIiIigoGSIiIiIjy0e3btzFw4EAA5gbHX375Be3bt8f7778PAPjtt9+sxr7Prg4dOgAwN+qJE8Db6+WXXwZgnm9g4cKFmtutXr0asbGxin0KW3R0NDZs2KC5fsGCBdJjeZkdHR3RvHlzAMC2bdtw69YtzWPMmzdP2qdFixa5LLG1Dh06SI2cv/zyS54fP7tcXFwAmBuncyMwMBBVq1YFYB6qKCEhQXU7o9GIRYsWATAPL6XWUz+v5NVzs0eJEiXQsGFDAOZGd1vBx4Ig//zLvxeWDhw4gHPnzlntk13iNSkpKQlz5szJ9r4F/Z0QgyBhYWE4f/68FCgBgJ49e6ruU7duXZQpUwYAMGfOHKSkpGT7vLVr10ZQUBAA87VG63uiRfxMA7Y/10Xx+0hERERUVDBAQkRERJRPBEFASEgIYmJi4OjoiGXLlsHd3R0AMGnSJFSpUgUA8OGHH+LGjRs5Ps/gwYOl4w4cOBBnzpzR3NYyGNCpUycEBgYCAL799lucPn3aap+bN29Kw864ubmhb9++OS5rXvv4449Vh4zZtWuX1DBbr149PP/884r1H374IQBzj/p+/fohPT3d6hgLFizAP//8AwDo3LlzvkyiXKVKFXTr1g2AOVg2efJkm9tfu3YNK1euzPNyiMTneOXKlVwfS3yN79+/jyFDhqhu89VXX0kN8gMGDICzs3Ouz6tFfG5Xr17NdqZVTnzxxRcAzPMPde3aFQ8fPtTcNjU1FdOnT89RI7s9XnjhBdSvXx8AMHfuXPz7779W28TGxkrBXAcHh1xlt7399tvSXB6ff/45du3apbmt5TWpML4TPXr0kIIyy5cvl4734osvokKFCqr7ODg4YMyYMQDMn6nevXvbDFLExcVh2rRpVscYOXIkAPPr0Lt3bynrzZLJZEJERIRiWbFixaQsn6y+s0Xt+0hERERUZAhERERElC8mTZokABAACF999ZXV+iNHjggGg0EAIDRv3lwwGo05PteSJUukc7m6ugpDhgwR/v77byEsLEzYs2ePMHPmTKFt27ZChQoVrPbduHGjoNPpBACCp6en8PXXXwv79u0TDh48KEyePFnw9/eXjj1jxgzV84vrv/zyS80yXrt2Tdpu4cKFmtvt3LlT2m7nzp1W68uVKycAEGrXri0YDAahdOnSwrRp04TDhw8Le/bsEUaPHi24uLgIAARHR0fh4MGDqufp1q2bdJ66desKy5YtE44ePSps27ZN6Nevn/Sa+Pn5Cbdu3bLaf+HChdL+165dy/HzjoqKEipUqCBt06xZM2HevHnCgQMHhOPHjwvbtm0Tfv75Z+Hll18WHBwchC5dulgdo3nz5tLnyJYvv/xSOo+aXr16CQAEZ2dnYdasWcLp06eFS5cuCZcuXRLu3btn89iWMjIyhBdffFE6X6tWrYTVq1cLx44dEzZu3Ch07txZWlexYkUhPj5e9Tj2fLbsMXfuXOlYw4YNE44ePSo9t/Dw8BydM6vXfejQodKxAgIChPHjxwvbt28XwsLChL179wqLFi0S+vXrJ/j6+goANF+DrNhT3rCwMMHJyUkAIDg5OQkjRowQQkNDhSNHjghz5sxRfAZHjRqlegzxuxcSEpJlmXbs2CE4OjpK38O+ffsK69atE44dOybs379fWLBggdC1a1fBycnJat+8+E5kV4sWLQQAgo+Pj3TeadOm2dzHZDIJb7zxhuJz/OOPPwqhoaFCWFiYsGvXLmH27NlCjx49BHd3d6FYsWJWxzAajcIrr7wiHeOZZ54RfvnlF2Hv3r3C8ePHhc2bNwvjxo0TKleurPr+Nm7cWAAgFCtWTFixYoVw7tw56XMdFRUlbZcX38esrh9EREREjyPWbIiIiIjywalTpwRnZ2cBgPDiiy8KGRkZqttNmDBBanD64YcfcnXORYsWCa6urtLx1P6VK1dOc1+xvGr/9Hq98N1332meuzACJCEhIcLcuXOlRljLf05OTsLKlSs1z5OcnKxo3FT7FxgYKISFhanun1cBEkEQhDt37ghNmza1WRbxX9++fa32z6sASVhYmObnwJ5GcUtRUVFSA67Wv6pVq1oFKOTyKkASHx+vaHS39b3IqwCJyWQSvvrqK83PqPyfu7u7kJSUlKPnZm95t27dKnh5edksx4cffqgZrM1OgEQQBGHLli1S8MfWPzW5/U5klzyAJgZ1IiMjs9wvLS1NGDRokBRQtfUvODhY9RiJiYlC165ds9xf7f2VB7iz2j6330cGSIiIiOhJxCG2iIiIiPJYamoqevXqhdTUVHh4eGDp0qXQ6/Wq23722Wdo0qQJAGDs2LFZTqBsS0hICK5cuYLPP/8c9erVg4+PD/R6PXx9fdGwYUOMGTMGW7Zs0dz3woULGDp0KKpWrQp3d3e4urqiYsWKGDBgAMLCwjB69Ogcly2/9O/fH3v27EH37t0RGBgIJycnlC5dGr1790ZYWBjeeustzX1dXFzw119/Yf369ejcubO0v6+vLxo0aICJEyfiv//+Q506dfL9eQQEBGD37t3YuHEjevXqJU0kbzAYUKJECTRq1AgjRozArl27bM4hkVt16tTBgQMH0KNHD5QtWzbXQ+z4+flh9+7dWLJkCdq0aYOSJUvCYDCgWLFiaNGiBaZNm4YTJ06gXLlyefQMtHl4eGD//v3SZ9zNzS3fz6nT6TBu3DhcvHgRo0aNQv369eHn5we9Xg9PT09Uq1YNvXr1wuLFi3Hnzh24urrma3lat26Ny5cvY8yYMahTpw68vLzg7OyMsmXLolevXtizZw+mTZsGB4e8uU189dVXcfXqVXz33Xdo1KgRihUrBr1eDy8vL9StWxfDhg1TTAouV9Dfia5duyo+761bt0aJEiWy3M9gMGDGjBk4efIkPvroI9SsWRPe3t7Q6/Xw9vZGnTp10K9fP6xevRrnz59XPYabmxtWrVqFHTt24J133kFwcDBcXV3h5OSEoKAgdOjQAbNnz8aIESOs9m3Xrh3+/fdfvP766wgMDITBYNAsa1H6PhIREREVFTpBKIABeImIiIiI8kj58uVx/fp1hISESBMKExEREREREWUXM0iIiIiIiIiIiIiIiOipwwAJERERERERERERERE9dRggISIiIiIiIiIiIiKipw4DJERERERERERERERE9NRhgISIiIiIiIiIiIiIiJ46OkEQhMIuBBERERERERERERERUUFyLOwC5JbJZEJERAQ8PT2h0+kKuzhERERERERERERERFSIBEFAfHw8AgMD4eCgPZDWYx8giYiIQFBQUGEXg4iIiIiIiIiIiIiIipCbN2+iTJkymusf+wCJp6cnAPMT9fLyKuTSEBERERERERERERFRYYqLi0NQUJAUP9Dy2AdIxGG1vLy8GCAhIiIiIiIiIiIiIiIAyHJaDu3Bt4iIiIiIiIiIiIiIiJ5QDJAQEREREREREREREdFThwESIiIiIiIiIiIiIiJ66jBAQkRERERERERERERETx0GSIiIiIiIiIiIiIiI6KnDAAkRERERERERERERET11GCAhIiIiIiIiIiIiIqKnjmNhF4CIiIiIiIiIiIjocZeRkYGMjIzCLgbRE8XBwQEGgwE6nS5fjs8ACREREREREREREVEOJSUl4cGDB0hMTCzsohA9kQwGAzw9PVG8eHHo9fo8PTYDJEREREREREREREQ5kJaWhps3b8JgMKBUqVJwdnbOt57uRE8bQRBgNBqRkJCAhw8fIjk5GUFBQXkaJGGAhIiIiIiIiIiIiCgHIiMjodfrUa5cuTzv2U5EZh4eHvD29saNGzfw4MEDlCxZMs+OzUnaiYiIiIiIiIiIiLJJEAQkJSXB29ubwRGifObq6govLy/Ex8dDEIQ8Oy4DJERERERERERERETZlJ6eDqPRCFdX18IuCtFTwdPTE+np6UhPT8+zYzJAQkRERERERERERJRNJpMJAJg9QlRAxO+a+N3LCwyQEBEREREREREREeUQJ2UnKhj58V1jgISIiIiIiIiIiIiIiJ46DJAQEREREREREREREdFThwESIiIiIiIiIiIiIqJCEB4eDp1Oh/Llyxd2UfLU4/K8GCAhIiIiIiIiIiIiInqMlC9fHjqdDuHh4YVy/hYtWkCn0yE0NLRQzp9XGCAhIiIiIiIiIiIiIqKnDgMkRERERERERERERET01GGAhIiIiIiIiIiIiIjyzZkzZ9ClSxcUL14cbm5uqFmzJn755ReYTCbNoaLky9etW4dWrVrBz8/PalinCxcuoG/fvihXrhycnZ3h5+eHl156CX/88YdqWfr06QOdTodFixaprl+0aBF0Oh369OmjuTwxMRGjR49GpUqV4OzsjICAAISEhOD27duar8HGjRvRvHlzeHp6wtvbG02bNsW6devseflUy3H9+nUAQHBwMHQ6nfRPfG1CQ0Oh0+nQokULJCUlYdy4cahatSrc3NykeUHsmSfE8v0Rj7tr1y4AQMuWLRXnV3tdBUHAnDlzUK9ePbi7u8Pb2xutW7fGgQMHsv3885pjYReAiIiIiIiIiIiIiJ5Mu3btQtu2bZGcnIyKFSvilVdeQVRUFD799FMcPHgwy/0nTZqEadOmoX79+mjTpg0iIiKg1+sBAJs2bULXrl2RkpKCKlWqoHPnzoiMjMSuXbuwY8cObN26FfPnz8/T5xMbG4tGjRrhxo0baNq0KWrUqIEDBw5gyZIl2LVrF06ePAlvb2/FPlOmTMHHH38MAHjhhRdQsWJFXLp0CZ06dZKW26tSpUoICQnB6tWrkZiYiC5dusDDw0NaHxAQoNg+JSUFLVq0wLlz59CsWTPUrl0bUVFROXz2kIJBW7Zswb179/Dqq68qzlmpUiWrffr27YsVK1agadOmaN++PU6cOIFt27Zh9+7d2LVrFxo0aJDj8uQWAyRERERERERERERElOeSk5PRq1cvJCcnY8SIEfjxxx/h4GAe1OjcuXNo1aoV7t27Z/MYM2fOxLp169CxY0fF8nv37qFXr15ISUnBhAkTMGbMGOh0OgDA0aNH0bp1ayxYsAANGzbEgAED8uw5rV27Fq+++ir27NkDLy8vAEBMTAxatWqFEydOYMaMGRg9erS0/alTpzBy5Eg4ODjg999/R9euXaV1y5cvxzvvvJOt8zdp0gRNmjRBaGgoEhMT8fPPP9vMADl06BBq1aqFy5cvWwVPcuLZZ5/FokWL0KJFC9y7dw+fffYZWrRoobn99evXERoaijNnzuCZZ54BABiNRrz33ntYsGABxo0bh61bt+a6XDnFIbaIiIiIiIiIiIiI8pggCEhMTHws/wmCkCevwerVq3H79m2UK1cOEydOlIIjAFCtWjWMHTs2y2OEhIRYBUcAYO7cuYiNjUW9evXw+eefS8ERAKhfvz4+//xzAMBPP/2UB88kk7u7OxYuXCgFRwDA19cXn332GQBg+/btiu1//fVXGI1GdOvWTREcAYBevXqpPre8Nm3atDwJjuTUr7/+KgVHAECv1+Pbb78FYM4wSk9PL6yiMYOEiIiIiIiIiIiIKK8lJSUphj56nCQkJMDd3T3XxxHnqejWrRsMBoPV+l69emHw4ME2j2EZVBCJc22EhISoru/Xrx8++eQTXLp0CREREQgMDMxGybXVr18fpUqVslpetWpVALCah0Qs59tvv616vJCQkBzNRWIvf39/NG3aNN+OnxVHR0e0adPGanlAQAB8fX0RExODqKioQgvgMIOEiIiIiIiIiIiIiPLcrVu3AEBzCCgfHx+r+Tosae0rBiKCg4M1j+3n56coR14oW7as6nIxoyQlJUWxXDy3Vjm1lucVW8NvFYRSpUqpBscA7desIDGDhIiIiIiIiIiIiCiPubm5ISEhobCLkSNubm55ejz58FfZWQcArq6ueVqWrJhMJpvr5cOEPQ5y+/pl9Xpkpai/XgyQEBEREREREREREeUxnU6XJ8NUPc5Kly4NAAgPD1ddHxsbi4cPH+b42BcuXMDVq1c1jx0dHa0oBwA4OTkBAOLj41X3u379eo7KY6ucV65cQXh4OKpXr261Xuu1KQhZvRbp6em4c+dOQRapwOVr+Gb37t3o0KEDAgMDodPpsHbtWsX6Pn36QKfTKf6pjUdGRERERERERERERI+XZs2aAQBWrVqFjIwMq/UrVqzI8bFbtGgBAFi8eLHq+gULFgAAKleurAiQiI/Pnz9vtY8gCPj7779zXCY1zZs3BwAsX75cdf2SJUtydFwxuKH2utqrRIkScHJyQnR0NCIjI63Wb926VfP4eXH+oiBfAySJiYmoXbs2pk+frrlNmzZtcOfOHenfypUr87NIRERERERERERERFQAunXrhlKlSiE8PByff/65YrimCxcu4Ouvv87xsQcMGAAvLy8cP34c3333HQRBkNaFhYVhwoQJAICRI0cq9nv55ZcBAEuXLsW5c+ek5enp6fj0009x5MiRHJdJzUcffQS9Xo8//vgDa9asUaz77bffrJIK7FWmTBkAwNmzZ3NcNoPBIAWxvvjiC8X7c/LkSQwePDhfz18U5GuApG3btpgwYQLeeOMNzW2cnZ0REBAg/fP19c3PIhERERERERUJs2bNwgcffKC4mSciIiJ6kri5uWHZsmVwcXHBjz/+iCpVqqBHjx549dVXUbt2bTRt2lSa9FzMSLBXyZIlsXz5cri4uODzzz9HtWrV0LNnT7z88st44YUXEB0djb59+2LAgAGK/Ro3bozXX38dCQkJqF+/Plq3bo3XX38dFSpUwOzZszF06NA8e/4AUKdOHUycOBFGoxGdO3dGw4YN0atXL7zwwgvo0aMHhg0blqPjdunSBQDw9ttvo0uXLujfvz/69++P//77L1vHmTBhApycnDB37lxUrVoV3bp1Q6NGjfD888+jRYsWKFeunM3zjxo1Ch06dEC/fv3Qv39/7N+/P0fPp7AU+gwpoaGh8Pf3R5UqVTBo0CBERUXZ3D41NRVxcXGKf0RERERERI+bQYMGYebMmfj3338LuyhERERE+aZVq1Y4dOgQ3njjDURHR2Pt2rW4desWvv32Wyxbtgx3796Fg4MD/Pz8sn3s9u3b4/jx4wgJCUFCQgJWr16NY8eOoWnTpvjtt9+kYbYs/f777/jiiy9QqlQphIaG4uDBg2jatCmOHz+OOnXq5PIZWxs5ciTWrVuHJk2a4MyZM1i/fj0MBgNWr16NIUOG5OiYgwYNwsSJE1GuXDls3rwZ8+fPx/z587M9Z0iDBg2wa9cutG7dGnfv3sWmTZuQlJSEqVOnYuHChZr7tWvXDnPnzkWNGjWwY8cOLFiwAPPnz8fFixdz9HwKi04ooO5KOp0Oa9asQadOnaRlv/32G9zc3BAcHIwrV65gzJgx8PDwwIEDB6DX61WPM378eHz11VdWy2NjY+Hl5ZVfxSciIiIiIspTOp0OALB69WqpBx4RERE9PlJSUnDt2jUEBwfDxcWlsIvzWNq9ezeaN2+OmjVr4tSpU4VdHCrisvOdi4uLg7e3d5ZxA8e8LmR2vPXWW9LjmjVrolatWqhYsSJCQ0Px0ksvqe4zevRofPzxx9LfcXFxCAoKyveyEhERERER5QcxUEJERET0JLp//z4SEhIQHBysWH7mzBlp+Ku+ffsWRtGICjdAYqlChQooXrw4Ll++rBkgcXZ2hrOzcwGXjIiIiIiIiIiIiIiy6+zZs2jZsiWqVauGChUqwNXVFdeuXcPx48dhMpnwyiuv4KOPPirsYtJTqkgFSG7duoWoqCiUKlWqsItCRERERERUIJhBQkRERE+yZ555Bh9++CF27dqFffv2IT4+Hp6enmjUqBF69uyJAQMGwNGxSDVT01MkXz95CQkJuHz5svT3tWvXcOLECfj5+cHPzw9fffUVunTpgoCAAFy5cgWjRo1CpUqV8Oqrr+ZnsYiIiIiIiIoMBkiIiIjoSRYYGIhp06YVdjGIVOVrgOTo0aNo2bKl9Lc4d0hISAhmzpyJU6dOYfHixXj48CECAwPRunVrfPPNNxxCi4iIiIiIiIiIiIiI8lW+BkhatGgBQRA012/dujU/T09ERERERFTkMYOEiIiIiKhwOBR2AYiIiIiIiJ42tjqSERERERFRwWCAhIiIiIiIqICZTCbpMTNIiIiIiIgKBwMkREREREREBcxoNBZ2EYiIiIiInnoMkBARERERERUweYCEGSRERERERIWDARIiIiIiIqICxiG2iIiIiIgKHwMkREREREREBYxDbBERERERFT4GSIiIiIiIiAoYM0iIiIiIiAofAyREREREREQFjBkkRERERESFjwESIiIiIiKiAibPICEiIiKiomPRokXQ6XTo06ePYnloaCh0Oh1atGhRIOXQ6XTMNC4ADJAQEREREREVMHkGiSAIhVgSIiIiIipoLVq0gE6nQ2hoaGEX5annWNgFICIiIiIietrIAyTMJiEiIiIq+l544QWcP38ebm5uBXK+8+fPF8h5nnYMkBARERERERUweVCEARIiIiKios/NzQ3PPvtsgZ2vIM/1NOMQW0RERERERAWMGSRERET0tJDPpTF37lzUq1cP7u7u8PHxwWuvvYaDBw+q7le+fHnodDqEh4dj3bp1aNWqFfz8/KyGpoqJicGXX36JOnXqwNPTE25ubqhZsyYmTJiApKQk1WNnZGTgl19+Qc2aNeHi4oISJUqgS5cuOH36tObzyGoOkpiYGHz99deoX78+vL294erqigoVKqB79+74+++/FcfYtWsXAKBly5bS66PT6bBo0SLV181SdHQ0xowZg+rVq8PNzQ2enp6oV68efvzxRyQnJ9sse3p6On744QdUr14drq6uKFasGDp37vzUZqwwg4SIiIiIiKiAMYOEiIiInjYff/wxfvnlFzRu3Bivv/46Tp8+jb///hvbtm3DH3/8gTfeeEN1v0mTJmHatGmoX78+2rRpg4iICOj1egDAuXPn0KZNG9y8eROlSpVCkyZNYDAYcPjwYYwdOxZ//vknQkND4e3tLR3PZDKhW7duWLt2LZycnNCiRQv4+vri0KFDeOGFF/Duu+9m+7mdPHkS7dq1w+3bt+Ht7Y0mTZrA09MTN27cwMaNGxEZGYm2bdsiICAAISEh2LJlC+7du4dXX30VAQEB0nEqVaqU5bmuXr2KVq1a4fr16yhRogRee+01pKenY+fOnfj000/x+++/Y/v27fD19bXaNz09Ha+99hr279+PZs2aoWrVqjh8+DDWrFmDnTt3IiwsDOXLl8/283+cMUBCRERERESUT+7fv49Zs2YhJCQEZcuWlZYzg4QK2549e1C+fHkEBQUVdlGIiOgpMWvWLGzfvh2tWrWSlv30008YNWoU+vbti8aNG8Pf399qv5kzZ2LdunXo2LGjYnlycjI6duyImzdv4osvvsDYsWPh5OQEAEhKSkL//v2xcuVKDB8+HAsWLFAcb+3atShZsiR27tyJqlWrAjBnlQwZMgQzZszI1vNKTExEhw4dcPv2bfTu3RvTp0+Hh4eHtD42NhZHjhwBYB42a9GiRWjRogXu3buHzz77TDMjRUvPnj1x/fp1dOzYEStWrIC7uzsAc72zTZs2OH78OAYPHozly5db7bt//34899xzuHLlihSYSUlJQadOnbB161ZMnDgRs2fPzlZ5HnccYouIiIiIiCifDB8+HOPGjUPjxo0Vy5lBQoXp2LFjaNasmSJoR0REeU8QBKQnmh7Lf4Ig5PnrMXDgQEVwBABGjhyJ+vXrIzY2FvPmzVPdLyQkxCo4AgCLFy/GlStX0L59e3zzzTdScAQwzxcyZ84c+Pv7Y+nSpYiJiZHW/fLLLwCA8ePHS8ERAHB0dMTkyZMVGR32mDdvHm7evIk6depgwYIFiuAIAHh7e+Pll1/O1jG17N27F4cOHZKenxgcAYASJUpgzpw5AIDffvsNt27dstpfp9Nh4cKFiufo4uKCr776CgCwffv2PCnn44QZJERERERERPnkwIEDAGB1g8oMEipM4ueSiIjyV0aSgDke5wq7GDnyXkI1GNzV57/IqZCQENXlvXv3xtGjRxEaGooxY8ZYre/atavqfps2bQIAvPnmm6rrPTw8UL9+fWzevBlHjhxB69atcfv2bVy+fBkA8Pbbb1vt4+Ligu7du+N///ufXc8JALZs2QIA6NevnzT0V34R515p06YNSpYsabW+Xr16qF27Nk6ePIldu3ahV69eivVly5ZF7dq1rfYTA0W3b9/O+0IXccwgISIiIiIiyidaPfQZIKHC5OLiIj02Go24f/9+IZaGiIieFsHBwTaXq2U8ANCcE+Pq1asAgHfeeUcx0bn83+bNmwFA+q0Tz1G8eHGrTI+syqnl+vXrAMzDZ+U3MYBhq4wVK1ZUbCunVTf18vICAKSmpua2iI8dZpAQERERERHlE63hKTjEFhUmeYBk8ODBmD17NlauXKnZA5eIiHLG0U2H9xKqFXYxcsTRLW+zR+yhVW9ydXVVXS7WobSyKeTKlSuXu8I9IRwcmC9hiQESIiIiIiKifJKRkaG6PKsMEpPJxBtYyjc6XWaj16xZswAAb731Frp3765YR0REuaPT6fJ8mKrH2bVr11CnTh2r5eHh4QCAMmXKZOt4QUFBuHDhAvr166c5DJel0qVLAwAePHiAhIQE1SwSsTz2Klu2LM6fP48LFy7k2VwjWsTyi9kzasR14rZkG2vcRERERERE+SQ9PV11ua0MklmzZsHPzw+HDh3K17LR0ys5OVl1eWxsbAGXhIiIniZLly61ubxFixbZOl7btm0BAH/88Yfd+5QpUwYVKlQAAKxYscJqfWpqKlatWpWtcrRp0wYAsGDBAkUnGFvECeW1OtNoEV+jLVu24N69e1brw8LCcOLECTg4OKBZs2bZOvbTigESIiIiIiKifKIVILGVQTJo0CDExsaiX79++Vo2enqlpKRIj52dnaXHd+/eLYziEBHRU2LmzJnSJOOiKVOm4PDhw/D09Mx23ee9995DuXLlsGrVKnz66aeIj4+32ubu3buYO3euYtmwYcMAAOPHj8eFCxek5UajEZ988gkiIiKyVY7+/fujTJkyCAsLw4ABA5CYmKhYHxcXh+3btyuWidkyZ8+ezda5mjRpggYNGiA5ORkDBw5EUlKStO7BgwcYOHAgAHNmaFBQULaO/bTiEFtERERERET5JCcZJCIOdUT5RZ5BIp+M9d69ewUywSwRET2dBg4ciFatWqFp06YoXbo0zpw5g9OnT0Ov12PBggUICAjI1vHc3d2xadMmtG/fHj/++CPmzJmDWrVqoUyZMkhKSsLFixdx/vx5+Pv7Y8CAAdJ+H374IbZt24YNGzagdu3aaNmyJXx9fXHo0CHcuXMHgwYNwsyZM+0uh4eHB9avX4/XXnsNCxcuxJo1a9C4cWN4eHjg5s2bCAsLwwsvvKAYfqtLly5YuHAhRo0ahe3bt8Pf3x86nQ7vvvsuGjVqZPN8K1asQKtWrbBu3ToEBwejWbNmSE9Px86dOxEXF4e6deti2rRp2Xotn2bMICEiIiIiIsonOckgERkMhnwpE5HWEFsPHz4s2IIQEdFTZcqUKZgxYwbi4uKwdu1aXL9+HW3atMHu3bvtnkPEUvXq1XHq1Cn8+OOPqFq1Kk6dOoVVq1bh0KFDcHd3xyeffII1a9Yo9nFwcMBff/2FSZMmoVKlSggNDcW2bdtQq1YtHDx4EC+88EK2y/Hcc8/h9OnT+OKLLxAUFITQ0FCsX78ed+/eRceOHTF69GjF9u3atcPcuXNRo0YN7NixAwsWLMD8+fNx8eLFLM9VoUIFHD9+HKNHj0axYsWwceNGbNu2DRUrVsT333+PvXv3wtfXN9vP4WmlEwRBKOxC5EZcXBy8vb0RGxsLLy+vwi4OERERERGRpGLFitJEmfJbr127dkljSE+fPh0ffPCBtE7MHGnQoAEOHjxYcIWlp8bo0aPx/fffWy3/888/0blz50IoERHR4yklJQXXrl1DcHAwXFxcCrs4RZZYt3nMm6GpCMjOd87euAEzSIiIiIiIiPJJbobYYgYJ5Rd7MpuIiIiIngYMkBAREREREeUTDrFFRZFWIIQBEiIiInraMEBCRERERESUT3KTQeLgwNs1yh8MkBARERGZORZ2AYiIiIiIiJ5UGRkZqsu1MkjkY3OzsZryCwMkRERUkDj3CBVl7JJERERERESUT7SyQ7QySOQN1GyspvzCAAkRERGRGQMkRERERERE+USrx6RWBol8SC42VlN+YYCEiIiIyIwBEiIiIiIionyiFSDRyiCRB0gePnyYb+WipxsDJERERERmDJAQERERERHlE60htuQN0fKgiPzxzZs3869g9FRjgISIiIjIjAESosfAnj178O677+LMmTOFXRQiIiIiygZ7hthKTU2VHssndY+Pj0dMTEz+FY6eWtkNkDx8+BDr169HWlpafhaLiOixxUnIiQpGfnzXGCAhegy88cYbWLhwIYYOHVrYRSEiIiKibLBniC15gESeQQIA165dy5+C0VNNKxCilfHUrl07vP7665gwYUJ+FouI6LGj1+sBWP9+E1H+EOvNjo6OeXZMBkiIHgNRUVEAgLNnzxZySYiIiIgoO+zJIElJSZEeWzawXL16NX8KRk+17GaQ7N+/HwCwZMmSfCsTEdHjyGAwwNnZGbGxscwiIcpnRqMR0dHRcHd3z9MASd4diYjynaenZ2EXgYiIiIiyQatHvlYGiXyILYATtVP+yOkcJE5OTvlRHCKix1rx4sVx+/Zt3Lp1C97e3jAYDNDpdIVdLKIngiAIMBqNSE5ORmxsLEwmE0qVKpWn52CAhOgxwgAJERER0eMlu3OQWGaQJCcn50/B6KnGAAkRUd7x8vICADx48AC3b98u5NIQPZn0ej3c3Nzg7++f5/URBkiIijh5L0LxR5eIiIiIHg/yAIkgCFKPUnkGia0htuTriPLCjRs3cOrUKdV1ly9fxp49e9C0aVPV9QyQEBGp8/LygpeXF9LT07MMNhNR9jg4OORrZhYDJERFXHx8vPTYw8OjEEtCRERERNklD5CYTCZpMldmkFBhSExMROXKlZGWlqa6fv78+Zg/fz7Cw8NRrlw5q/UMkBAR2WYwGGAwGAq7GESUDZyknaiIk98U5+UERERERESU/ywzSERaARLLOUgYIKG8FBERoRkckbt69arqco6pT0RERE8aBkiIijj5sApak3wSERERUdFnmU0isjXEFgMkZK+rV6+iV69eOHnypOY2cXFxdh0rKipKdbllAI+IiIjoccfu6ERFnLxHIQMkRERERI8veV2OQ2xRXuvWrRuOHz+OP//8U3PumtjYWLuOFRkZqbrc8vNJRERE9LhjBglREZdXGSQmkwkRERF5USQiIiIiygGtDBJbQ2zJ64Jz5szB1KlT87GE9DgTJ16Xf54s2ZtB8tVXX0mP5cE8ZpAQERHRk4YBEqIiLq8CJG+++SZKly6NRYsW5UGpiIiIiCi77JmDRCuD5N69exg4cCCGDRumOfwRPd0cHLK+vbc3QCKfYFgeFGGAhIiIiJ40DJAQFXF5NcTW6tWrAZh7HhIRERFRwamBZvgG23B7R5K0zN45SMQJtY8cOSIt0xr+iJ5u9gRI4uPj7TpWRESE9FmUB0U4xBYRERE9aRggISri8nqSds5jQkRERFSw3sd0+CIAR77IzPywN4NE3E7esH3//v38Kio9xrQCJNHR0ejYsSMmT56sOTeJJUEQcPv2bQDKzyQzSIiIiOhJwwAJURGX1wESnU6X62MQERERUdYEQYAL3KW/XUvqpcdaARLLBmjxb/nyBw8e5HlZ6fGnFSD59ttvsWHDBowYMSJb2Uf37t0DwAwSIiIierIxQEJUxOXVEFtEREREVLBMJhNKoJz0t3uQXrFOZGuILTGQIm+kjo6OzvOy0uNPK0By8OBB6bE4kbstwcHBADKHcpN/9uzNQCEiIiJ6XDBAQlTE5XUGCREREREVDJPJBGe4Zf6dnv1J2sXGafn2SUlJILKkNQF7RESE9PjGjRtZHqdixYoAMjNI5J/J5OTk3BSRiIiIqMhhgISoiJPfkOQ0QCIImTfjHGKLiIiIqGAYjUY4wTXz77TMOpm8XpeWlibV1yyH2FLLIGGAhCIjI5GQkCD9LX9sKS0tTXp8/fp1m8d9GX1RzaExAPUhtlJSUhT3FkRERESPOwZIiIo4+c1zTgMk8psiBkiIiIiICoZVBkmaegYJkJlFInaOcXR0BKA+B0liYmL+FJgeC9HR0ShZsiR8fHykZfK5RQwGg2J7+b2A1menQYMGqIKG6ISP8cw/XRXHtAza+fn5oV+/frl6DkRERERFBQMkREVcXgRI5MM2MEBCREREVDCMRiOcZRkk8iG2LOt1lgESFxcX6RgAM0goU1hYGABlkE3+WPzsiOyZWL1x48aY+dM86W9HOKkOsQUADx8+xIIFC7JfcCIiIqIiiAESoiJOfrOTFwESpsQTERERFQxzgCQzg8SYjQwSsZGbGSRkSa3Dk/w+wfKzJc8g0eLi4oKyFcpIf/shEPfv3wdgnUFCRERE9CRhgISoiJPf7OQ0uCEPkPAGh4iIiKhgmIfYUs8gseyVLzZii3U1V1fzfswgIUtqARJ5UMSyvi9+ttq3b695TNeY4nhwMkX62wVuSElJUT0eEdHj4O7du9i2bRs7iRJRlhggISri8nqILfljKhyCINjVk4+IiIgeb+ZJ2tXnILlx44ZiW7GexwwSyg6x4U8eIElLS8Mbb7yB27dvw2g0SutKlSqlegxPFIPvzFdw9Ov70jJHOEufRXuG6CLKL4Ig4MqVK2zkJk1Xr17F6tWrrdpL6tWrh9atW2Pr1q2FVDIielwwQEJUxOX1JO1smC98nTt3RpkyZRATE1Ng54yOjsaYMWOwZcuWAjsnERHR084qg+RRgCQtLQ07d+5UbCs2YtszB4llfe7ixYtsxH6KyDNIxPsDy/uEtWvXYuzYsYpsoxIlSqge73UMs1rmheKKrCYDXFAJ9aFjEwIVsO+//x6VKlXC999/X9hFoSLozJkzqFixIrp164ZNmzZJy41GIyIiIgCAARIiyhJrN0RFXF7PQcIMksK3du1a3L9/H6tXry6wc06bNg0TJ05E27ZtsXfvXqxZs6bAzk1ERPS0spyDxPQoxnHw4EFER0fDyckpc92jep4YCLHMINEaQmnVqlWoUqUK3njjjfx5ElTkyAMk4ufCct4RAFi4cCH69u0r/a0WIFk4ewka6l63Wt4fk6Wg29W9kZiCIxiGhWiNfrkuP1F2jBkzRvE/kdzx48elx2fPnpUei3MoAYCXl1eBlomIHj8MkBAVcbYySMLDwxU//FrkQRG1mycqHAkJCQV2rmvXrkmPmzZtis6dO+Py5csFdn4iIqKnzZUrV/Dzzz/DSZZBIk7Sfvr0aQDAK6+8Am9vb/M6iwwSW3OQyLNFpkyZAgCKnrP0ZJMHSNQCaHJ//vmn9FhtiK3nnnkeEKznNAGAc+fOoUuXLrj3dXFpWQcMyVGZiXJKDBYTqZFnVN6+fVt6LG8DiYyMLNAyEdHjhwESoiJOK0ASExOD4OBg+Pv729x33rx5+O+//6RlnGSxcMlf//j4+AI7r9qYvTdv3iyw8xMRET1t2rdvjylTpigzSB4FSG7cuAEXeKD5yY/QMjXEvC4bc5CwPkciWxkkcnq9HpUqVbJabkqw3STw119/wTXJV3Ud54SgghAYGFjYRaAiTB4guXv3rury2NjYAi0TET1+GCAhKuK0AiTXr1+XHmtlIixcuBADBgxASEiItIw31IVLPqlqcnJygZ1XbXi2nA7ZRkRERFm7cOECACgDJI86tEZGRqI5esLtVmm8nDIAxRGUrTlI5I/ljdTjxo1DkyZNkJKSkg/PiIoKtSG2sqrXOTk5oWLFioplFVEPB7uYGxGdvLSbBtKhPkQv65JUEEqXLi09ZlCOLMkDIVpzdRXkyA1E9HhigISoiNOag8TZ2Vl6vHHjRjx48MBq33Xr1lktY4CkYJ09exb//vuv9Lc8QCK/uc1v8gk6RbzBICIiyn/yIbYyEs2/vZGRkQhGLWn5Wxgr1fOio6MBAH5+fgCyFyD55ptvsG/fPqxatQoZGRlo3LgxKleurFpPpCeDVgbJRx99pBhSqyZaYF3Nu1jU+1/4+fmhAp7DcCyC6VEbom81Z2jJQJrqctYlqSAEBARIjzlUElmSB0Lk10H5EFsMkBBRVhggISritDJI5DfGPXr0QIkSJdCvXz/873//s3k8+ZjVlP/q1auHl19+GXv37gWgDJAU5HuhNpyXyWRCcnIy6tevj+eff75AM1qIiNQIgoD+/fvjq6++ytZ+v//+O5555hmcPHkyn0pGlHNeyJy/ISPJ3KB8//59BCCzN38gnpEadsQsYbG3f1ZDbKk1UptMJly+fBn79+/H5cuXceTIkbx6OlTEaM1BotfrFRMTv5zWHwm3MpCwxB89O4bgYyxRbO/k6YBn+/ionsMI9Q5WzCChguDgkNlsdePGjUIsCRVFWgES+fKCHNqaiB5PDJAQFXFaARK1xvUFCxZg6NChUiO8WtZAQWeQyHtuPG0SExOl579hwwZpmUheactvar1mBEHAyZMncezYMRw9epQNi0RU6I4fP4758+dj/Pjx2drvrbfewqVLl/Dmm2/mT8GIcsgBjvBB5nxxxiRAMAmIj4+HAU7Sci8UQ3qiuZ4nzhEWHBxs3ieLSdrVODs7IyYmJvO8WcxPQYUjNTU1R3Vl+fuplUEiD5A4wgmBxmekdS6xflbH1Ls6oNWC0lbLAcAE9c8PAyRUEOTXvgMHDqhus3//fmzatIlZTU8hewIkzCAhoqwwQEJUxGkNsWXrxlgcmkEtI6AgAyS7d++Gi4sLJkyYUGDnLErk88SIw2nJxwQvyACJ2mdBEARFEE0evCEiKgxxcXHS45z8Xl25ciUvi0OUY2IjnS9KwgF62QogPcmEhIQEOEI5pFFatLmeJ/5me3t7A8hZBomzs7NUHwQYICmK4uPjUaZMGTRp0iTb1zv5PYHWHCSOjo5SgKQplMFjB51KM4CgPfyrDurLGSChgiD/fsyaNctqfVRUFBo3boz27dvj6NGjBVk0yoYNGzbg22+/zfMgFucgIaK8wAAJURGXnQwSUVEJkAwaNAgAMHbs2AI7Z1Fy584d6bEYiNCqtNly+fJlnDlzJldlUeudaDQaFZ8Rph4TUX4QBAF9+vTB4MGDs9xW/tuWk17VnGeLigqxAcgP5jkgHuhuwgRzPS49wRwgkWeQAObACZBZP3BzM0/urpYhkNVnPSMjgxkkRdzVq1fx4MEDHD161K5G3bt372LKlCmIjo62O4OkRIkSAIAAVFCsc4x3tzp+aox5/9d3lJeWifvJg3kGz8xgCXvrU0GQX+/kHSlE8k5p9+7dy/Pzp6WlYezYsZg3b16eH/tpIQgCOnbsiC+++CLPh3y0J4MkMjJScW9ORGSJARKiIi6vAyQmk6nAens97TdN8h5OYnZGdgMkgiCgcuXKqFmzpqKhI7vkmSui9PR0RQYJe9YQUX64dOkSFi9ejOnTp2c5LJD8uqh23SJ6XIh1LT+YhyyKcbiDVJh/c9PijEiIt84gMaaY603i98DV1Ty5e04ySFJSUhQZJAweFj3yIPCDBw+y3L5t27b4+OOPMWDAANV5CW0FSDygHFJLn2T+bIXjtLRMMJo/R96VMgN3vfEdAMAg+6waUzM/b8wgoYIgrzvcvn3bqn5w9+5d6XF+XOu2bduGCRMmYMCAAYrrKtnv6tWr0uO8fo/sCZCkpaUhMDBQGsKSiMgSAyRERVxuAiRqc5AAhdOLcOfOnZg7d26Bn7cwrV69WnqsFiCxZ5J2+XsVERGR47Ko9cROS0tjgIQoj/z555/4559/CrsYRVJ2GmnlQ/0xQEKPs8wAiTmD5KHDXWTA3FiTmpQGwQg4PLoVS3EwZ3BmJJsgCAKc0z3RBgNx6X0P1MWrqnOQZPVdSk1NzbcMkvT0dLz55pv49ddf8+yYTyP5NS42NjbL7U+cOAEA2LRpk90ZJP7+5vlvSqK8cl2yCwAgHZn1Q9OjaqmDITNDxA3eeA6vwhGGzO3SMofcYoCE8sLNmzdx8OBBzc51lte78PBwxd/yrJH8uM+NioqSHjNAkjPy9yirTpRGoxGHDx/WvL7ExcVh1qxZUrBDfi3VCpCItm3bpvhbEARs27YNkZGRWT8JInqiMUBCVMRZzkFy8+ZNTJ482WblTBwqSS2DBFBWMg8dOoRbt27lupzh4eE4duyYYpl8HONWrVrhvffeQ2hoaK7P9TgSAxHyoIg9GST2BFHsodbQmJaWZnOIraioKBw6dOipzwSix0Nh9o6OiIhA165d8eqrrxZaGYoy+XAYWV3T5L2otX7DiB4HYsNOsUcZJLH6exAeDbGVGJek6JGfqjcHBo0pAjIyMjAaq9EegxF/Sod38TOMRiMEQdDsZFHQGSS///47/vjjDwwZMiTPjvkkOH36dLbq1PLOK2rDBmlxd3e3moPkzp07Vo3Ger0egYGBcIGHNFSWg5O5bl78eF0AQBoy64emDPPnyMExs/5eHGXQDz9blUH/KGDCAAnlltFoRM2aNfHiiy9i69atqttYXr+uXbum+FvtWnfw4EEcPHgwT8oo77yRne8qZZLf92ZVF/zuu+/QoEEDTJ061Wpdeno6KlasiEGDBmHcuHG4fv065s+fL62Xt52odRC0PPeqVavQunVr1KhRw+7nQkRPJgZIiIo4ywySzp07Y8SIEejbt6/mPmImQFYBkgsXLqBhw4YICgrKVRnv3r2L2rVro379+tixY4fNbfft25ercz2ucjrEVl41aNiTQWIZIGncuDEaNmyIZcuW5UkZiPLL//73P3h6emLv3r2Fcn55rzMGFK3Jry22rmnffvutYp4SZpDQ40ysv/mgJAAgVh8JI8wNNzHRDxXDa6XpzfW1jGQB944nwBslVI+nlUFiMBists/PDJL79+/n2bGeFMeOHUOtWrVQpUoVu69d2c0gEXl4eCjez+TkZAQGBmLo0KEohxoYg79QFY3g6OiIypUrYwB+AQAIOhN8n1UO65YuC5CIQ2zJM0i0iAE+/uZRbiUmJkqf/0uXLqluI17v9Ho9AODLL79UrLesZyQnJ+PFF1/Eiy++mCcZ8vIAifxcc+fORenSpXHq1Klcn+NJZznclS3jxo0DAHz88cdW60JDQ6XONLt27cIXX3yhWC9eG2fNmoXevXvbLAdgnjge4O8aETFAQlTkWQZIxEkcbd18JSYmQhAEKUCyd+9eRQ8asZIppuoD2bsxs7Rx40apN41l2qql3AwT9bhxcXGRHqtN0r5p0ybs3LnTZqNFXgVItDJI5AGShw8fKtb/999/AIDdu3fnSRmI8svQoUORkpKCjh07Fsr55Q1EbCxS2rdvHxYtWiT9nZGRgcOHD2PdunVW21re5OY0QML3gIoCsf4mNiSn61IgPAqQ3Ll1V5qgXe+sQ4aDucHGlCrg4jL1+lhGRka2AyTyXtV5GSBR63TxtBPrTElJSVb1KS3y1zE3AZL9+/dLjwdhBgJRGR9iNvRwRAW/qghEJQCAS51kxIcrGwcVQ2w9+kjZEyApB3Nva2aQUG7J70XEAIgl8XrXvXt3AMCRI0cUQQt5ECQjI0PxHcyLibm1Mkjee+89REREoF+/frk+x5PEZDJZZdrIr3f2dBJU06dPH7Ru3Vr629XV1SqoFhUVhTlz5mDQoEGqx7DMIHF0dMxRWYjoycMACVERpzUHiS0JCQlIT0+Xbp6qVauG559/XlovVjIdHDIvAWfPnsXRo0cREBCQ7UqevGJy+fJlm9vKh93KDykpKejfvz/efvvtQh1LVBAEReOeGKyyDHi0atUKb775ptX+165dQ58+fXDy5Mlsn/vKlStYuXKl1EhoNBpVAy2WQ2xpDdvGm196XMTExBRK47j8nIUxx1NhS01N1ex516RJE2zcuFH6Oz09HQ0aNECnTp2kBkUt9gZILN/znN54E+Ul8bdTD3Pji0lnlDJIIm7fQX20M6930cHkYG6wyUgRkHhXfeiRRYsWKcbBl/+ui98Vg8GAt99+W1qWX0NsMUBiTd4YaO9rndMMEjc3N0Xd7Pr169JjD/hKj13+1xhra9yFJ4oBADrOqYW0OGWdLgmxcPIy3w8ENncHADjY0V44GHMAsI5IuScPPmh9d8Tl77zzjrQsKioKgiBg8+bNivqE0WhUfJ/u3LmDlJQUxRCe2SUPwFhm3APmIM/9+/cVk8U/LZYsWYIlS5Yo6mJt2rSBj48Pbt++LS3LzhBbcufPn5ceL168WLEuMjIShw4dUiy7du0aBg4cqHk8+bkPHDiAJUuW2F0WInqyMUBCVMRZzkFiK8DwwQcfADBXNOUN366urnBwcJACImIlUz70wvbt27Fo0SLcu3cPCxYsyFYZ5WMeZ9Wglde9NHr37o3WrVtLN2jbt2/H/PnzsXz5cqxZsyZPz5Udlq+DWClUq/j/+eefiIqKQmpqqlS57Nq1KxYvXoxWrVpJ29nb8NqwYUP07NlTGhpLqyHDMoNE3pAir+Ty5peKupIlS0qPCyMw+rR/X958802UL1/eriEm5L9NluPlW7I3QGL5msuva0SFRbwuiAESwcEkZZA8jIrF848CJMZUAcZHARJTqqB5DXn//fdx+PBh6W95fUL8nd+0aRNKlDAPz5WWlparIbb279+Pt956SzXb60kKkISFhWHy5MkwGo0wmUz4888/FY169rJ3KEE5Wxkkd+/exdChQ7F582ar/XQ6neL91AquONzxVPxt8HCAq7+yh34YtqH78Yp48fuSaPSj+bfUngwS0dP4m0d5S/6brdVwLn6nDAYDSpUqBcAcIFm9ejXatWun6IiRkZGh+E7cvn0bzzzzDEqUKGFXACMyMhINGjTAvHnzpGWWQ2wlJyfjn3/+kZbdu3cPZcuWRVBQUI6uH4+rnj17IiQkBCEhIQgNDcW8efMQERGBbdu2QRAE/PHHH9K29g6xJQiCohNntWrVsGPHDtUOUDkJesnroY0aNeI1jIgkDJAQFXGWGSRavaO7desmVRgTEhKkH3+dTgdnZ/PwDmJwQqxkytOP582bl+Net/JKY1YNWlqp0zmRlpaGpUuXYtu2bbhw4QIA5fihhdlIZjn/i60ACQAUL14c7u7uUs+o48ePA1A2vNp7wy1WFpcvXw5A+z1JTU1VvEbynqnyGxRWHKmoq1q1qvRY/jkGCma4JcsAyZ49ewo1QFuQBEHAunXrkJSUhE8++USxTu3aIb+ZzSpgntVvUkZGBtq2bYsRI0ZYLScqbJkZJI8ms5ZlkMTHJUD36DbspYWlYdKbP7PGZHMgxZIjrIfQEn+nN2zYIAUnXVxc4OTkJK1XG2Lrm2++wcSJE7Ms//jx4/H777+jU6dOVtfRJylAUrduXYwYMQILFizAvHnz0LVrV9SqVQuAOXhy7949u46jFSA5cuQIPvjgA6vfJkBZP5NnoCxbtgylSpXC//73P7Rr1071fPLrq73ZJwYPB3TcFoxKb3pLy67jNLwrOqPupyXg5Gmuo2cn25tDGlJuye9FssogcXR0RLFi5oyoqKgobNq0SXVb+fcpIiICN2/eBGCesyIrY8aMweHDhzFgwABpmfz7nZycjEmTJuHVV1+VlkVFRSElJQUZGRma86g8zpKSkqyCEREREVi5cqX0d8+ePTFgwAD06NFDWubpmRmktTeDJC0tzar+eODAAc25VbNL/GzIOxAQEQEMkBAVefIKgq3efzqdDu7u5tR4eQaJq6urdKMjjlEtVjJv3bol7R8ZGak5tnVW5BWe7du3o1SpUtiyZYvqtnk5/Iz8hlBsaJM3BhTmMCvZDZAA5tdGDGqoyU46MpB5w2Erg0RriC0GSOhxIv+MyxuhTp06BX9/f/z666/5en7LIbaaNWuGzp0748qVK/l63qJA3htTPs53cnKy6rB98iB2Vje7ateelJQUvP/++9i4cSP279+PLVu2YOrUqYptGCChokD8/DpIGSRGCDAvS4hLhOOjOUg8yznBqH+UQZImwJhu/bn3hr/VsoyMDAiCoAhMOjs7S3U9ywwSsVf1uHHjMGbMGNUGezl5A6O8I4x47CfN4cOHsXbtWgDm+tDp06dRt25dlC1b1q79tQIkL7zwAmbOnImhQ4da7aM1xJZ8GCHA/Ltieb20J4PEksHDAcVrueDV34Jwq8lOzMcIxRwkOcE6IuWW/PqSVQaJZYBEbXu1DBKR2nxNltTmy5RnJycnJ+P777/X3P9Jy2JNS0tD1apVERAQoBj6WS3rDVDOXSmfj9PeOUgsf28A83VGbXlOiL9tV69etVr3JP62EZH9GCAhKuLkN0BZ/Wh7eHgAMGeQiJUzV1dXab1lBol8UsfU1FTcuHFD8be9LLe9e/cu2rZtq7ptdhv5bZFXzMTnpJUFUdC0AiS5KZM9jX7y8/r4+Njcz9YQW/LPGm9+qaiTf17l14DBgwfjwYMHGDJkCLZv346aNWtajVWcF7SG2FK7yX7SyBtgxZvOpKQkVKhQQRrqR07eG1ttHG85tWvP3LlzMXv2bHTo0EGzoYMBEioKrOcgMcEI82czITZBMUm7yeFRBkkKkJFk3ZGkJIJVz5GamqrorSzPILl//76izmE0GhXXyqyGI5R/vywbwuTHeVLmXTKZTIrntW3bNgDm5xoaGqo5T5soqyG2xCyfhIQEfPnll9i9e7fdk7R369ZNcT3V6XQ5yiBxdM+89U+pfxVh+Ed7Wzf7skhYR6Tckt+LjB07Flu3brXaRryWyQMkX375pep9ldFoVHTGkM/RY0+AxLIhfu/evYrhtJKTk+Hr62u5m2L948JkMuHXX3/Fhg0bNLe5evUqbty4AaPRiKNHj0rL7QkEyTN87B1iS+24CQkJivfl008/lTqGirp3755leYDMuqpawCWvgjBE9HhigISoiJPfeNhqXLeVQSKSB0gSEhKsJgCXNyxmJ0CSnd4WeRm0kA8RJvbC08qCUHPr1q0sJwnOKcvKsfh6ijfNxYsXz/Yx7Wn0k7+H4tBqWq/D2bNn8eeff0p/x8TESJ83+T7Xrl3DypUrn5hGEHryyK9B8uuC/Pr5yiuv4MyZM4p5ffKK1lB48jGUH0dGo1HRi1yN/FohPvcbN25ojvMtb5Tt2bMnLl68CEB9mBa1a448S0ULAyRUFFgGSOBgkjJIkhNTpAwSvYtOGmLLlGqeqN2SN6yDjYC5vicGRABlBonlUDIZGRmKup38u5iSkoKzZ89qXsssrwPy7+bj/H2zDPSoXc8AoGXLlnjmmWfQt29fLF26VHXce3mARO3aJb62EydOxNdff40uXbooGgLlv12W1qxZY9VwJz+HrX3l9LK5ReRzd6lptaCMXcdkgIRyy7JBfNiwYUhOTsbmzZuxc+dOfPLJJ7h27RoA872sWG/477//NDNI5HUFeRDZnrkw5ROyA8DChQsVfx87dkwxCoMleXAmt+7cuYMtW7bkW2bDokWLMGTIELzxxhuaw+XJs6Hl95n2BEh+++03nDt3DoDtIbYOHDiAH374Aa+++qrq/HRxcXHS+1K8eHF8//33iuG7evToYTXcqhZ5Zx5LzCAhero93nfuRE8Be288dDqdIoNEbKB3c3OTthErhenp6bh+/TqMRiN8fX1RvXp1AMoKnb2T4wI5D5AIgoAFCxbgzJkzdu8vJ+8xJ5ZXLX138ODBaNmypWLdlStXEBQUhLp16+ZogresZDXEljxwZS/LRoiVK1fit99+UyyT30BnlbVi2VtIEATpNZW/pwcOHEDPnj2xdOnSbJeZqCDIv9vyx2pzHuXH0Afym0r5tTMv51zKjvPnz6NSpUpWN/XZ1bp1a3h7e9uccFStQdHWb4Jlr/Vvv/0WgHqPS7XfP3mPQa33UixHREQEZsyYYdXYQVQQrDNIjFIGiTHdlBkgcdbB5JgZIDE+CpDcdb4oHcsJ6nWGxMRERYOfPIPEMkhpmUEiz+Z66aWXUKNGDcWE7PLvtmWGgrw+UpjZulnZt28fxo4dq5ktI8+AEwTBZgNeVFQUFi1ahN69e0v1Zjl5EEktaCT+Togdcx48eKDopCN/jUuXLm3zeQHKAElWw6UBQJmXlb2tswqQGDzsaybgHCSUW5bBvwsXLuCzzz5Du3bt0KpVK0yaNElaZzAYFJ93tfpGRkaGYgis06dPS4/t+bzaGs4OADZv3mxz/0GDBuHnn3/O8jz2aNu2Ldq2bZtvQ8WeP38egPk5amX1yoeikr/29mZbiPNqamWQHD16FI0aNcJnn32Gf/75Bx9//LHVMeLj46XzifVAsd0DAHx9fe2uc587dw6vvvoqVq1aZbWOARJ6nP3vf/9DzZo18eOPPxZ2UR5bDJAQFXH2Bkj0en22MkjESoanp6c0FJP8Zjo7AZLsZJvIbzj/+OMP9OvXDzVr1rRrX8tKrbzHnFgG+fHT09Nx6dIlTJ8+HaGhoQgLC5PWiWPWJyUl4cSJE3adf/PmzdKQC1kRX3+x8pbXAZLo6Gj07NkTPXr0UFTk5Q2G9sx7YklsLFCrIMqHZCMqSuSfV/m1y56egnlBK0AizyAZOHAgWrZsma1ra041atQIV65cwbvvvpur4+zYsQMAVG8iRWpzV9n6TbCc8Fjs7a4WxBAbJQRBwOnTp5GWlqYIkGgNYSaWo0WLFvjwww/t7lVIlJcyAyTmRhvBwQjTowwSU4YgC5A4QNA/+qyn6qQAyTG/dajyjg8AwNlGgMSyUUhrCBnLAIn8sfj7vmDBAmmZvD71uGWQiHOzNGnSBBMmTMB3332nup28zpSenm73EDBqAZeshtgSyets8obH2NhY1YZENZZDbMk7+qRCGTgu3cIdXQ5UQPuN5RTLu3btitKlS6Njx46q53DytK+ZgBkklFtqnR3+97//qW7r6OiIunXrSn+rNepnZGRofi7tuWe13FcsX6lSpbLcVzRy5MhcB4/j4uKk0R42btyYq2NpkZdRK0NXnkGyceNGqR5rb4cjsQOm1hwk8uHLACiG/BbFxcVZBUjkGSR+fn42AySff/659PjmzZv4559/FL93oqIc8CfKyu3bt3HmzBmrey2yHwMkREWcvcMaubu7SxUGe+YgEde7u7tL46jKz1UQQ2wdO3bMrn2io6NRtmxZfPDBB4rlakNsWfZqlDegiY3/n3zyiaInzoULF7IsQ2xsLNq1a4fWrVvb1cAp3gDL5wEZPXq0FKSxFSDR6t2Unp6O2NhYXLhwQVGJlb8O8t48akGjrNjaJyfDghEVBK0MkoIKkMhvptUCJKmpqZgzZw5CQ0MxcuRIaX2/fv3QtGnTPB3zOCYmRrom2DvEl9FotApQyK9D8kk2LWU3g2TLli2Kv8XrilqARHxdFy1ahFq1aqFLly52zaEglkMcVkM+lCBRQRG/Q3qYAxYmnREmPAqEGHVwfLRc7ywbYivN/A8ABH0GnLzN32EnuEFNQkICdLrMYZMCAgIUQ27JWQ6xpdaILw+uyL/blt/Pop5Bcu7cOUWvc3GIF0uWvx3y52JP3VAuqwCJ+HmQZ4pYThJcr149AJnX0Dp16miez977A6+KTgho6Aa9s/L3wNPTE+Hh4dLE9JYMXgyQUMHITmavo6MjZs2aJf2tNgxoenq69P2oVq2aYp0996xaARJx7hN7idm3MTExaNSoUbazQOQZZnk5bJecPItOa2hUeYDk3LlzKFOmDB4+fGj3+ya+R5YB6Dt37uDBgwe4fPmyYnu1xt2kpCSbARJbGST79u3D119/LXX6saUo/p4R2Uu8B7V130a2MUBCVMTZe+Ph7u4uZSuEh4dLlZmsMkjc3Nzg7e1tdby8yCCR37SL5BUPWz09BEGQKm179uzBzZs3MWvWLMWYr2pDbMmPn5aWpngeYlqw/KYZUE7ep0XeOCCvTH7zzTd466238PDhQ7z77rvw9fXFvn37pACJ/LX9/vvvsWzZMgC2f7i0Jvfr0KEDfHx8ULVqVdSoUUNarhUgsTeDpFSpUlIgR9xH7QaCARIqquSf18IIkMgbqtSuneLY2UDmRL0RERFYsGAB9u7di/79++dZWQ4cOCA9btasmV379O/fHz4+Pvjjjz+kZfLrqzifkZrsBkgsx8oXb5zVeoGKv3+//PILAHPPRfk1TmvcfctrHodMoMIgfn4dpDlIBClAokvLrP/oXXQQHDMzSEyPLmGCwQiDuxgg0c4gEesMZ86cUcxBYskyg0StgV0rQKI1bChQNDNILHtCa/0WyH8vUlJSFK/Jvn37bJ6jd+/eigmIcxIg0bqGia99u3btNM+vdX8gZibVGlIMrv56PBvio3kMR0dH1bo6wAwSKjjZDZCUKVNG+k6r1R3k16tFixYp1tlTH7DsqCbWO7IbIBG/6z/99BMOHDiAIUOGZGt/ecBCq0MIYL5P9vHxQb9+/azWHTt2DH/99ZfmvvJ7WrG84eHhimuhZSA3KioKJ0+etPt9E6+H8mDWihUrEBgYiBIlStgV/ElJSZHeB7G9w94ASaNGjeDg4IAWLVpkeR7WF+lxxgBJ7jFAQlTEmUwmuMMbr2M4SiJYczt3d3dFReGTTz4BoD4HiWUGiXzIEpGt7A57G5/UbprkN9y2ejePHDkSxYsXx969exU3tlu3bpUeqw2xZdmrUV5JjoqKUh2n2Z5xYuWNnlOnToUgCIiPj8e4cePw+++/Y/Xq1Vi4cCEePnyIpUuXqgZI5GxlkGS3N7n8dVAbYstWbxgdHLBh2T9SOW1lkBTWfApEWdEKkGh9//Ka/Lojv1aIN5jy4QLE8p09e1ZaZtl7Ljeio6Olx/b2hFu0aBGMRiM+++wzaZn8Wmmrp7L8HOJ29txgtm3bFkDmDbmtIbbkjbb2TGps+byzkxFJlFesJml3zAyQOGRk1mv0zjoI4hwkaToYE8wN1ibHdDg+CpC4wLqeBpiHJhG/b2InBlsZJFkFNuTfNfn6t99+W3FtkV/nCrLH7cmTJzFp0qQsz2k5gbI9AZLU1FRFvdXW3EsAsHTpUrRv3176294AiVqPd0vi+/T++++rrjcajarXZQfopc/b81+WwLv3qiKwqfpnJysGT/U630tLzPOjiEN5MUBCuZWd+x7xuyzeR1nOjwQoAyTly5dXrMuqPrBt2zZFncxkMkn1jux2FIuNjUV6ejomTpxo1/ZJSUmK75M8QBIVFaX5XZs/fz5iY2NVh4yqX78+unTpIg3VZUlej4qPj8fatWsRHByMN998E4D5umUZIBGfm70BkqVLl+LGjRuKYMzFi5lzbMkzVORatGghzaWQkpKCw4cPA8j+HCRx19Nw6Y9YXP0rDt06vWmzrMwgoaJAEAT89ttvqt89WxggyT0GSIiKOJPJhO74Aq/gXXyG1Zrbubu7o2zZsnjnnXcUy+3JIFELkAwYMACAeex5+aSdP/30E7y9vXHkyBFpmVZlU+0mUH7TaCtAMmnSJJhMJkyZMkVxfPk4pVllkKSnpytu4h88eKA5Zr38xl+NvBL4ww8/YPPmzYoKvTjJHWB+nbMKkNj64ZJXIO0h317eyHjz5k38999/mpW95uiJX3ESB18CBt5eAFd4IikyHZtev44rKjEj9qqhokprDpLc3uicPXsWP/30U5Y31FoZJOL1Tn4DKpbJ8qY0r2iN8WwPeTnkARJbk5xnN4NEVLVqVQCZvxODBg2y2kZsDJA3bsqvu1rXSrUg/ty5c/NtDG8iNdLn99FQWg4GnTQHiUN6ZhDDwZCZQWI86Q3jA/PnPcU1VsogaYTO6I8pmIbTmIKj6FNqHABgyZIl0nHETC/LDBIvLy/zsY3GHA+xBSjHwFe7zhWEOnXq4JNPPsH8+fNtbiefkwPQLqP8eaSkpOR4wnGj0aioR6oFL9QySEQvv/wyAHNHFJPJJJVXK3svPT1dtbFUzB4BAEfX3N3ma2WQ+Nc331cYdebPBydpp9wS77EM0M5WFVkGSNQ6vskDJGKGvMiyfhIbG4vu3btj/fr12L59O1q3bm21fW4ySObOnau5PioqCu3bt8eqVatw//59eHt7S51HAOVQUyaTSfNeVX69kV8X5I/Dw8NV95XXo+Li4qTJ7cWhSTMyMlQzo6Ojo1WvZWquX7+OypUra3Zq0RrO0NPTE/Xr1wcAnD59GlOmTAFg3xBbAwcOBAB8UWoFlpa/iH/evIktXW/ihfBe8Ed5GKB+H84ACRUFy5cvR48ePVCpUqVs7SfW8RggyTkGSIiKOKPRiGDUBgAYoOwVWK5c5oSLzZs3h06nw5IlSxTDL9kzB4lagAQwV5q6d++OTp06Yc+ePQCAUaNGISkpSdGrTasxTGtcWJFWgEQejPD19VUcXz5UjVrmhPwmWG2ILa2ee5Y305Ysh5e4dOmSopyTJ0+WHhsMBml7d3d31R4tWq85YJ3KnBV52X/66Sfp8d27d/Hss8+qTizvg5LohtHS314Z/vgJ+3G2gzfC18cjcqUTAlBRsQ8DJFRUac2fpDVcnb1q1KiBUaNGSUM8AebryMCBAxXDNmgFSMTl8muVeI2S31jmZYBEfv7sfmfl1zT5dcXeAInRaIQgCHadV+yJKV6T1W7e1TJIZs6cKT22N0ACAO+99x46dOiQZbmI8orJZIIDZENpGXQwwfzZ1BvN9Tmds2CecNvw6HtkyhzuKM0lEU6yeSDqwNyIboAz6t/pBj0cFVkOYmO6ZQaJmEmsNcSWvIFbvq9lQ5F8KKbsBKJ37NiBGjVqYO/evTa3yw55Bp4ay57NWtcwywySnDT2p6WlITIyUtEYqZVBIgiCaj1UrLcbjUZF2Z2cnFQzjm/evKl6DidZo5/eWX3oLHs5ujpgreMkrMMvuANzD+8kxMHBYD6umKnCDBLKraSkJJRDTUzBUbyGD2xuaxkgcYIrnkEDxbVWXvezzB6TXwPT09Ph4+ODVatW4fXXX8eqVauszpeamprjOUji4uJsXqu+/fZbbNq0Cd27d8fq1auRkZGh6Ahoea1QCwYByucor0/K60geHh64ffs2Nm7cqMiwk28THx9v9X3Wqs/17dsX48eP13xulsQ5R9RoXXfd3Nykhl55OcRl8lEyypUrp7jfrhfcCNNwGgF3aiqO6XoiGOOwAe/g28zsThkGSKgoEOdrzG6dRKyb2RoamWxjgISoiDOZTBCgfvPRtm1bXLlyBdu2bcOLL74oLfdwNvcWDEZtVN3VDf8te4j4m2konVwdgDKDxFaARJ4VMWzYMMU6exrh1Br97JmDRF4BTE5OVtzAao0/HxcXh19//VURDIiIiMDHH38s/f3gwQOr3i5i6nVWWRuWN9sBAQGaja8pKSlSmrOrq6vqcBelSpXSPJf8dbeHPGikdqM6YcIE6XFdvIrx+BsTsD3L436BtZljp4PD1FDRJf/cZ9VDOif2798vPZ4+fTrmzJmDvn37SstykkEivxbZCkBkV24DJGJlXH4dttVL0PI1tmyE1SIGSMRji+W+fPky2rRpAyDzfdWaU0GrscDW+87ezlRQTCaTogFG7+QgZZA4ZZgbdvSPql8mJ+vvjKNBD1d/7XmUiiNI8bdWBonYkKg1xJb8mmErg2TGjBlSA1d2MkheeuklnD17Fl26dLG5XVbk313LXuGWLIfssSdAktMMkiNHjlhlJ6tl0zk4OCAhIUH1HL6+vtJjeUDeyclJ0UtaFB0djbCwMKvlYg98k4MROofcBUgAoO/iV3Ct0i6cb7oKh7Aek9FbCpA4CAyQUN64f/8+uuJTAMBrsM4mlRODAV5OvnCAHt3xOYZgHl5GH2kb8f7MYDBAp9Ph77//ltbJv/MbNmxQHFut13VKSoq0j/x7ao979+7ZvKbI7z3VtrO8bmkNRSa/Bsvrm/J5S4xGI6pWrYoOHTogKCgI8+bNs9o+Pj7eKvstLwMGx48fz9b27u7uqgFiMXAkf82CgoKkdoXG6Ia0z+raPHZdtMZUhOENjFAsZ2dAKgqyMxew2n7MIMk5BkiIijij0QgBmRWAamgqPfaNKg+Hc8XxrL4h4sLTcP9EMja9fh1vH5uNoViAEVgGrxvB2P7OLSwpexGvXRyNGmiG1OjM8VS1htgCoBiv9Pjx44qAgHwy7+xUnuzJIJE3esXExCgqK/JAhbzRbseOHVaT3+3evVtR+VTLIBEru9HR0RAEATdv3lQNBKhNUKo19mpycrJUwdTr9apR/MDAQNV9ASiCOlq++OIL6fHvv/+Ov/76C+fOndMcxxUAqqEJ3sXPKI4yWR5f1AI9pcesNFJRJW+gkVcq86rhRt5z+sSJE9Jj8aY0qzlIxKEK5NtaBkjyquFefv3KyY2t2Og2bdo0adnPP/+MGjVqYMWKFVbbW57DshFWS4kSJQCYb3SNRqN0HB8fH6lin1WARCvbzlaDLXsHUkFRD5A8Gjopw1zvcnw0hLrJyfozq9fr4VpCO0BSBQ2lx46OjlKdSiuDJCMjQzWALG+Ek/dEtvyuHDx4EN26dQOQs6EMc3rDL7p586b0WBw2TGRZJ7Osn2kFeXfu3Ck9tpyDxF5///03tm9XdjoRX1t5T203NzfNLGZ5Y8bYsWOlx05OTpoNHWrXYw/4AQCMLrnLnhT17NkTly5dwuLNs3DomcXo8mEb6J2YQUJ56/Lly8iA+nUkGLXxHFqjIuriWbyIfX0fYmP763j3yjyMxEo0xOsAgI4YhqkIw3Ashs8187A0Yt2hTZs2eG/AewCU9zKHDh1SnEvtWpaamirtY++8dmJQ89ChQ1bXInkAQt74L68DivUwy4CIWoAkPj5ecX2TP5Zff1JSUhQdF+fMmYP09HTFMWNjY60CJPbU5+SZHHlJnkEiV7u2eWSN3r17o0KFChg2bBj0ej3c3d3xGgahB8Yptn+2rw8a/RyAgEZucK6jfD9eQh8EoZr0N+uIVBRkt1OqyWSCIAgMkOQBBkiIiri0tDRFBsl7mAoAeA0foPSqdtjU4QbWtQrHhjbh+OO5Kwhfb678VMbzqsd7H9MR8W5ZJMWYL7y2MkgsU43Fid+BzAt3dufL0AqQyBu0Vq5cCRe4oyyqwfteOaTu9EMjdEEjdEGVBy1wdm40zs6NRskrddAOg9EMbyElTHvSc9HVq1elm1N3d3dcvnwZfn7mm8m+ffvCwcEBZcuWRcuWLa32tazgpqSkSEGTKlWqWK0TK6jvv/++agZJ6dKlsyyvLYMGDZKGqzh//jy6dOmC6tWrqzZAvI1vMA2n8QFmKpY/28cHXfZXwAfG6ljdfAgGoyYGoya2vZ4ZfGmDgdJjBkioKLIMLMgrlfkRIJEPESB+3+Q3lPJgqtFoxOjRo6WJJYHMa6C8oUw+CWhuZTeDxPL1E6/p8rGvAfOQNiEhIVb7qwVI7KnYi0NVxMbGKl4zFxcX6bdBbYgtufv376sutxUgye2wa0T2EgRBESBxdNZLGSTORmWABC7Wn1lHR0c4+Vjfqt2FOWu0KTInm5V3xLD8voiNeklJSapDbMkbyOTXMrWGon379gFQXmcuXryI+fPnZ3m9KVmypPT4zJkz6Nq1a5ZDZcnJA83ya3tycjIqVaqEgIAAqUHQsiFR3pNatGPHDsXwqPJ6XXYcO3YMR48eVSwTr0HyoQNTUlKk4WotqU0ir9PpzEEylR7UlqZOnYrKlSvDG+bMvAz3vPk9EXl4eODChQuYNm1aZgYJ9NDBQfoNMRqN2Lt3b579ltHT4+bNm8iA9fWjEupjBJahHyZhOBZjMObg5oZkXN8UD4PJGUGoqtheD0dURF3UPxmCZnhLuhbuHhKBZ38PgTu8FdcpeYe88uXLq37/5QESy8CsmrVr1+L3338HYJ5bQ+3+USQPLMivaXXr1sWdO3esrmPNmjVD9+7dERUVhcaNG0On08HLy0uRCSMPkMg7OlreHxqNRqt7+MjISMVvQFpaGvr37w8AcHQwoHvHHop5jkSVK1e2WpYXfHx8rK5/JUuWxIgR5qyP+vXr48qVK5gyZQp2D4nAb8XvWA3R1nZNWby0oAyeG1EcXfZVQJX/pWIY6uIW/pO2KYbMe3IGSKgoyE6ARBAENGrUCHXr1pU6vDBAknPa3ZKIqEiwDJA4woDn0d4qBfnhf9lovE53gOmWKwxwhldiKbi5qgdI5D3rAPPwCiKxwVCsXHl7e9s1Wdv58+dRu3ZtfPTRR1IqrA9KYu/Hd+Ag6OH6+n389OPP+BKbzJkOxwEcB3pivPkACUDoe+ahDJqhf+aBHwBv4lsYH/VA0j+aFHUpPschrAdgvlE9ePAgAKBLly6oWLGilEEibww8cOCAosyCIFhVmlNi0xF/LxleKI4S+jKIwAM4ww3RuIPk5GQpZdnX11c1QCL2ftHSsmVL1Z5HouTrOjjtqIxn8SIMcMF9XMddWPemdoc3GqKTYtkqTETld93x4fw50jIn58wGlcjISBiqJSH9nBuuex1D3y59sXDhQg6xRYUuLS0Ne/bswfPPPy/dqFoGQXISIImOjkZGRgb8/f1V95UHSOQ316mpqfDw8LA5xJY44aV8meVxAHMvQFtzE9kru5O0W940JyYmIjExEdevX7faVi3wkNMMEvkcJJYBEvG3wWQy4euvv8amTZuk9d988w3CwsLw119/aR47qwCJvb1AiXLDPAeJLEBi0MME87XCxWiOjDh66B79b72/Xq+H77POOO+zE1UftkQG0jEFIXiAm/gBe1AKFfE82uMINioCJJZ1DjEYGRcXp/huqGWQxMXFoUuXLmjVqpXNhiL5dePNN82Bmps3b9ock14+LFabNm1w+/Zt/Pnnn5gyZQoGDRoEZ2dnREZGIioqClWrmhs+xXmNHB0d8e+//0r7y68ZN27ckLJLTpw4gTJlykgNi19//TXGjRuHhIQEnD17FtWrV5f2k19XxGNa/maULFkSTZo0UQRnLEVHR1sNsbVkyRL89ddfiuFvr169ih49eqgeQy1AIr6P8mF9unbtCh8fH2l4HAAYPHgwPvroI2z98RhaPWocNLrnfSBY/B2Uz4vjAg/pNfv1118xfPhwdOzYEevWrcvz89OTKz09Xbp/A4DOGIlIhOMti0yA7OiOzxHpcBEb2objxpYEOMEL9dHOat4hUVJSkmqAJCUlRboW2lN3cHZ2loZvvnbtGoKCglAetfA6hqMy6mPPR3fxwsjSSL6XgTIrXsdw1MZyjEXcg0RURF04wgk66LB3RASe3dMF/dFEeYJVwIyDoah2sxuqwZzRl4AYXMABJCMed3el4mZqAu5H3sfe2edRFY1gggnxRx1RA80AAOlIQ2B8ZVzeFIUqaAhPFIMz3HD3zj0USyiPNmgMLxTH9PabsHHbJjjCgPHC3/BZXxLNMAY/4i3cwFm4whPJiEflypUVwRhbOnToYDW0mRZfX1+podcVXvCBP97/4B14eCh/MI2pJpz+NXMC+3SvePQ5WwteAW5wcNRZHTMD6fgeXTEAU1EbreABH2k9OwNSUSAfht5kMmmOugKY6yCW2XAMkOQcAyRERVxaWhp0smSv+7iJEEzM1jHarA7Clq43Fcu8j1bHFBwFZgFRs8xzU+jggMs4itXbVuCVV16xeUzxxi062lwh8fPzQ0JCgqKhUKfTwcnJyWoOkVOnTmHAgAHo06cPqqEpPsAMnP31UXBlmh6/wlzJMsGIZKdYOJcScPZ6ZsWr0+uvIzo6Gg/2mKTxlo3IgB6OUmBE9A6+VUw2Hr0QeB3D0eBuaxz/6T6eDX8VL8MTXigBF7gjGneQgCj8t/whAGDXrl1YvmAlAiqUQFu8j3b40HygUUA4gO+wEzgHvPXo+P/hIM4n/i71Dvfx8bHqzTlq1CirrBORN0qgJ76Cg/EujhvUx2qthPrY0sjc62kwMoMcC/AJjmMrypQpI/Wi9EZJxb5XcByHsB7VnN9RLLccBszQIBrp59xQNa4lku8VB7CQlUYqdF9++SW+//57tGnTRhpT2rJBS34N0gqQyCubBw8eROvWrREfH49Ro0bhhx9+AKCcQ0ne+CUPWqplkKgNsSUn3mhbNj7m1Twk2c0gUQuQiBM/u7m5IS0tTTPgMHfuXLz33nuKZbYCJPXq1cOxY8cAZA6xlZGRIQXaDQYD9Hq99N6kp6fjyy+/VBwjISEhyx7VGRkZePvtt1XX5XaYHyJbBEGQGpLlQ2w5GHQwOBkyAyQmcwOPwdP8WXf0sp4vwtHRETqdDnvLzcechyPhBi/EQpk1FYKJVgESyzqHmCm7du1avPzyy9JytQDJkiVLIAiCzQAkoN67cdWqVVYBEnmGmry3tHxy+eHDh+PYsWNYunQpgoODkZSUhEuXLiEwMBA1a9ZEfHw8Tp06pcgYO3bsGCIjI+Hv76+4JovXbXGZfL63MWPGKBruLYc6jY6Otpob7+TJkyhZsiSCg4MV2SByMTEx0mvo7++PyMhIKfgi9iQHbF971ObkE+vZc+fORevWrTF27FgMGjQIR48eVQRI9Ho9rq2Px2u3R0rLTK75V1/TOznA0U2HjCQBrfCO9Dv7888/AwDWr1+fb+emJ5PRaIQRmfWMVuidJ8cdHLMYN7ZkXt8coLfq5CJKTk7OMoNEHiBp0aIFQkNDrbY3GAwoW7YsAPP16N69e3gZfVEZ9QEAVxYm48bqK0hFMgzxvqgIX4zDRghfmzAc7aXjRK4ESqEmSqGm1TlwE/CD8j6yyaNgSeRYYD3CAQCvYDjEu/mUn8yjSEguAWHvGvER5mYu221xnm2Q7sllo31jFH5DMhLgDDcsxmdodu9d7MEx3MM12DJs2DAUL17c7gCJ581yOPFZEoZgPp7BCwCA5JsHrbaLvZL5njaY4I96Y6orOjbJyQP1iTDXPd/COLjDB1sxlxkkVCTI63RJSUlWQUE5sS1Oa3/KHgZIiIq4tLQ0PEQkSsBc2XJG9sf5rNjFG2/sdsTidvvgFW++IfS9/oxim3dhvrE5gk2oWrUqdDqddGPbrFkz7N6trDWJ46uKF2VfH1+8cKMnEvAQmx5VwKpVq4br169rZh78tmg1vsE21XUAcAEHcKjmPLRv3x5zv/pKWv7Liv5Yv/4AvtvzKT7DKhzEOqzHL3CBufd1bbyE7vhc2v4VvGt1bOEf4MA/9xCAhugkG8dbtP1tcdzWiuiOL4BLmsVUqIKGOHFztfTaeXt7KxpJ4+Li4OnpCUEQ0LBhQymjRdQOg1EdTYHdgKH4DMW60ngGL+NdPI92qud+Fz+jIurB2TMFK/E/DMBUqbfQLVzA948qz4B1b0XLHqdG58zKputmc+WcARIqbIsXLwYAbNmyRVpmGQSRN+arBSgAc0OVm5sb1q5di65du0rbTZ48GV999RVMJpOigf3IkSPIyMiAo6OjojFRvLbJzyMfEsFWxoXlTVhYWBiGDh2K1157DR9++KFque2R3QwSywaBpKQkKQOuePHiSE9PVwwrJmcZHAHsH2LL19dX+p0RM/jEwIcYIFGr9NesWVNzcnZReno6li9frrqOQ2xRfpk6dSrGjx+PHTt24LnnnrMIkJgbzsQAibNgrq8Y3M2fdRdPA5ZjHJ5DazhAj42Yhhp6c71Pr9cjHalWwRE5ezJIACgCH2pDbKnNhfTxxx8rhqIC1Bv75ZP9iuTXS1vj1C9btgxLly6VAhv//PMP6tWrJ80ztHv3bsX1YOPGjejUqRP279+v+E7HxcVh8+bN2LhxIwAosvIsAyLyIDhg/i2xzBwUhwVr164dpk+fjqFDh2Lq1KmK/WJiYqRrvbe3t+pwXlmxlUFSo0YN3L59W2rws5y03dHREVf/Us5tklpa+7OSFzKSzJ+TtngfJpP5PWPDIuWU0WhEEtTn57FHVJ2TaNm8FY5jC85MfYg6UO/k1wnDoZ9qwKL15/DiJr1dGSTyeSW9vLzgheIQIKh+ZwHzdd7V1RV6vR5GoxGVL76COnhZsU16vAAHKHt46+CANCTjPswdGitWCcaxqJ049eAgnJ2dVetVz9evj4tHw1EZz8MN5uBN6cBAPHgQBac0D3jBfO2PwCUEQjkM1n2ncJQODMS18HCUxjNWx86KK8wNtn3xI7AH+BhL8alltosFeYaw+Lf4W+Lu7o7x48dj2cgt5mHE8QzSfgEuI1EKjgCA67yGOFz6HmoPLw5HNwf8/cYNXN+UeS2v/3lmJrgaeYAkAQ+lxx0wBFsxl0MEUpEgv74kJibaDJCo3RMxgyTnOAcJURGXlpKOAARLf4uVHQBovbIMqg/0VdtN4vSeedLuwKbu2N/8V8zFMMBD+ybmebTDv83i8Y7Hl3B8lI0h74EnOnv2LNLT03FxZwQ8UQzVS9ZDU+NbaIv3URbV4Qw3vOk1Ci9l9AEAVEQ9DMMiqRLWCr3xMw5IFazgd52QVlw5FupyjEN6erpVpXDTpk3YsmUL7uMG9nf6CasxEalIQizuIxb3sQ+rsRaTcRPnsBPLUGmgM2KfP4vtWCj9C+yqQ/HnXCB4pCICl3AC23AX1xCBS7iEowh6xR2BL6n3UhaH7JLzq+EMvb/5Bjn9nrny5+rqinPT4tHm/lA4woBg1MY/bSJxY2s8dDqdNJb3WxiLb/EviiMI/ignHdPFkHl+PRwxGn8qgiM1P/KD0TEVychsgGiOHmh4vi+mIkwKjgBAOE4rymvZw9Syp4HRoHzNB+JXDrFFhU4txdgyCCIPSmhlkIgNgtu3b1fsn5GRgevXr2P27NlSA5t4HLHRS97gJ97Yyc+pNZ6/ZfksG5PmzJmDzZs3Y/Dgwdkal99SdidPthyDOjExUVrm4+NjNeZ2y5Yt0bt3b83X1laARN74qtfrpYY+MUAiVujFG+gHDx4o9g8ODsZbb72lyCBR+0yoBVZEDJBQfhk2bBgePnyIVq1aAVDJIJEFSAyPGsf0zubPr6urKw5gDWZgEKbhPYTjlHQtUesJW3tcZiO5AS42M0jkN8ryx2oZJGqCgoKslqkFSNQmIJdfX+TfVbWMCbn79+8rskwuXLhg9b0Wh0OVN2j9+++/aNcus55UrVo1fPWog41l8EcMkIwcORKVKlWyKsOkSZOkxz/99BPCwsIwefJkq9c3JiZGOpY9cxSoUWtslZ9H/hkQz6GDAwZhBoqHNkDUafP7kYwErMdUJNW+kqNy5IT4ujJAQjkhfn5exBua22zCdNyAdb0oCXH4Em2Q+to5NP2lFBwqJmAePsYqv8zM0xL1XFDzI3MWnTjKQOI1E/pXG4sa6/tIje9Go1F1qOjIvWn4DqF4EW/gzAA9vsNOfINt8IotjXk/L8FLL72k2N5RbzDPDeLhhdH4E43jegIwzx31P/SHT5PMeuFv+Abj0RY/oxe+RzeMQENMRBdMRBeU/Pk2tvvOwj6swp0Kx7APq6z+PfdRAFr+VAFT8a6036Vuv2F8WntMRBckIx43cBbfoTPONF+peD2nefbBsVfnYCK64M+WQ3AFmaMWXMBBzMAHuOl2GmnIrDP5P++Kyr+p16/c4Q1flMIb+ATDsRjTcBrTcBpTESZ1YHR1dZWudR7wQ7BfFek30svLCx/1H44B+CXLgM2Rr+5jns95zHI6qwiOlG2r3Ygs8vDwwOefmztRWma8lEMNzJw5U203ogIlv6/M6rdVbT5GBkhyjhkkREWcIckdnrKgiMiluB6V3/JB5bd8UOODYjj+/X1U6e2D079G4cztYxh9sisAYGXLzAqRo6MjTuJfDE6oi07V+uLEuWP4duqXiBmqrIjEX83AC+iCW7iKHViCgIAAq/OnpaXh64GTELCwPUbhN8D/vLTuObQ2Z20cAF5BXTjBC81hHnd5NP7EfziIKrKsjd/wDRaO/ha/3JiAy9vv4Tm0xjbMRzyikZZWzKoXdPfu3aXHpcsGQu+oV/YaR4YUCClTpgxWz/oeJ374C2uPZPaAnD/7c/j5+WHevHn4bsAAq+f3yz8CVq5cibH/fqzIcgnHaSzF52iAjorty77qgUt745AY6Yi3Y39AF4yFa7IH9n9yF7XQGp+hMgIQjLv7k7GhjXls/1KN3RCC76Wgx3hsVhzzszuZf+/CSsW6jtvLIeglTyxP/Qpz5sxBZdRHX/wEr0cTdMqdx378jgmKZZY34/IepgCQEaC8SaiJFghN2mt1bKKCpNaoZiuDRKsRf9asWahTp47UkD9hwgT89ttvOHPmDK5du6baG+fOnTsoVaqUotexWgaJvLEuO0NsyedBOnLkiGKs/OywHF9bPuSPGnHsfpE8QOLr62s1Sag4pMSgQcp5sERGo9HuYKq3tzfi4uI0M0gsK/0ff/yx1aTFzs7OVkGPa9e0h3mIiIhA3bp1pb8FQcDFixcRHBysOl8UUXaJmRTyOUgsAyROjwIkBtdHGSQqN7NiUFZtqISaH/vgxNex0MEBbvBEk5h3MF13BjU/9EOpocrPsTyLQt7obm+AxLLnYkZGhuq1LSEhAU5OTujfvz9+/fVXdOrUCf/9lzkRrvy64OrqanVe+TFjYmIUAZJz585ZBXNF8u+/fOiWRo0aoWbNmtJ8erGxsTh//jyGDh2KESNGYOVKc73K09MT1atXx+XLl6V94+PjFc/b1dUVderUAWCuP8mv3/Jyy+cLyYqXl5cUVLKVQWJJDCwHo5Y54/gY8ADmAMn36Ioo3EZdw2d2lyO3jEb75voiUmM0GuEK7cCi74RruL5iN46d24K2eB9frhuE/5Y8xNGTRzD2svleV8z0Er9Hp4Sd2IW/ULVqVZw7eg7GdAEn/wqHw+3M84jzm3yIWRgKc51ArXNF5E9+8IIBvfA1og6av+uOMKDVkU+QcgR4v0tt1MN53MA5nMc+nHzFA7Ftb6CZrofU0J+KJPyA7khHCkqPjsexKbuxYvs8qYH+AW5ZnTcpKUm6Rvbu3RujR4+22sbLy8uqnitmuMUjCt+gI9Jhvu7O2vUdimMJHOGMSITDFGXE7NmzAZh/F+o0qIn4R9MYHMRanMMe3HM9h6ikKEx71MnOLcARFeoFYjU2ozZaWZXnG/xjtUwPR/yMg7iPm/CZ5wEEJuI77DTfr8qnbroD/N35htX+/vVdMefYBBwQ1qAkgjGixDwI961/E/2fd0W79eWslquZMGECtm3bhsuHjymWj8RKbPD7xK5jEOUneR3D1ryKgLmznyUGSHKOGSRERVzJePVeFCVfyGwgKl7LBa1XBKFcG0+031Qexm4XpHXyhiR5A9zacwsRjlPwquCEdtc88CmaWp3jWZgnlyxdurRqGQ4uNI875YsA+C5pKS23HNJKDI6IqlgMaXUYG5CamgqjKQPXcBJ/4SfA0/zDkJaWpjpsg8jPz8/mxMbiRJ/iGNyAufFNTLGVL7c0c+ZMxOAuNmKatOw0QgEA/2AezmA3ZmAQ7pY7gfpj/eHxbGbvRDEzRiTPAhLd2ZekOVyWJflruBZTEPSS+QZZ/AG8hKMYj9dwFsoghpOXA2ZjMAQoK9C2MkgOHDiAwZ8PwER0UWxjStJuZCUqCGrZAjkJkIwbNw4dO3aUxnF3cnKShl+JjIyUJhCXu3v3rvk6pTLfSHaG2NLKIJEHZc6fP4/sWr58OapUqWI18bBWsCIlJQWCIODGDeUNaWJionTN9fX1tRrORaTWYwlQZpB8+umnSEtLw4QJE6S5R+TEntBaGSSWgSrxuiX/XXNycsLYsWMV21m+BnJz5syR3oPY2FhMnjwZzz77LDp37qy5D1FOqGeQmK9JDjB/xh1dMjNILImff7XrkcFgQLrefP3pha/xXOTrAIDT06MhJCgb29u0aSM9lgcTxOuW1ndZZBkgsTWXRnp6OmbOnInVq1dj48aNuHQpc3xS+bWoXDllQ5bBYLCav0neWHno0CHV4b8A9XmhPvzwQ+zatQsODg7SvAFxcXHo1asXtm3bhjZt2khDB3p6euLZZ5+VjuHg4GCzXimvP8n3y64pU6ZIj+0NkMRdS8Opr+MRjNpwhLKRMM0hGVEwB5WyytDJrda/ZWYVmdLNn2kGmCknjEYjPKF+L7YWk9Hz8w5wd3dHJMKxGJ8huKMX2qwuiwft9kjb+fubh1USv0fiNUH8TOoNOujeO4sY3IMlPQxwgvn6q3YtNBnVrzuie386oCyqowm6YQB+gSlZh6t/xeGVh5mdSKbhPaQ/CmKmOSbhbokzWc7XkZiYKNUnO3fujD179mDvXuU9nre3t2LIKEtxeIBkZHbqeYBbuIsrUqBeNHLkSNQaWAIJiMEG/Ipjjzrsib9BCzESkc5X0WRKAMqWLYsMZH9EgRIIgv6mL/SHyqh25gOA2zsz68+38B9eWV4GXQ5WwF7n35CIWFzFCfhNuoLy7ZX10sDmbugUGmw1IbstDg4OiMItTMDriuVCnPL+ODw8PMsGaqK8ZitAIr82ANYd3QAGSHKDARKiIq7dA/WeDC3nqgctAOX4mvIxn8+cOWO1rbe3N9zd3ZGIh/gOnfFAfwOV3jTfTFZDE7yJL6QgQ0kEIxi1pX2dYXuiXHt8gZeRhmR06dIFO3bskJaLN6fp6emKCZIteXh42BwyRRweTJ4h4e7uLjW0avX2O3z4sNQLeQtmYwJexxA8h1Bn8xwI6zEVs/AhzmEvrjXZAmdvPcq/74BLOGJ1rAyk418swmbMQPWBvqjxgR+KP+eCWkOLYTNmYgN+xWr8gEhcxz6sxnDUx+1hf+C8/3akIgkJyOw1mYQ4bMcC6W/5D2QakjETg1By+UU0mVoKzaaXQp87zyID1qmZljfjlgETAEiEMovElKTDmjVrrMYiJyooapkQ9gRIateuDVucnJykRrSQkBDVSWbj4+OtstmymoNEK4NEEASrAIl8OKnsjmF/6dIlvP3227h48aLVuPpqPSIjIiLg7++Pvn374v3331esS0pKUmSQaA0ZoxW4zsjIkBopnZ2dYTAY8PnnnyuyNkTisX/88UcAmdch8fpsORa02NhhmUEyevRohIaGomNHc2bfuXPnVMsGmHuYd+jQAceOHUNgYCA++cT8G2srqEKUE4IgZAZIHMUAifJG19Elc0hOS+L1pkKFClbrHB0d4WQ01++qWYz7nnxZeXvn4uKCFi1aAFB+p8RrZXavN2LmhS3yIQpF8gCJ2KApcnR0tAreyIfs0pogfdu2bap1wBo1akj1HPE6Exsbq3qcChUqKK5zrq6uNrPu5PWnFStWKNapBfHVfPLJJ6hcOXNOALWAhlq9bEPbcIT9FIWeGG/VweZ8QGa2s73lyKly7TKDZsY08+8sh9iinDAajWiDgdLfJZZdwL8OC3EFYVL2vFrwTX7NtMwgEa818v2cAgSMxcsYhnpWxxIzPVrEvisNDdURwwAAppLxVttnx2wMwTWclO43k5KSrDJz1SQmJkoZJB4eHmjSpAmeeUbZadLf39/m3E579uzBDz/8oFhWv359q2tLnTp1ULN3KYxGc2zFHAhQBoWOYQtWVxwN74rOcHJygruP+v3/LqzEHAzFBLyOjZiOJcWH4z9kzrWZUPsS8PINbMMCfIOOOPLWFGzHQsTgLkx6ZX35e3TFMz194KDXKd5HF08ntNtQDr0uVsazfX3w4vcl0WlnMAxu2bvmidfIu7iKZ3p5Zy6Pzww8b9y4EcHBwejWrZvV/kT5SWuILaPRiBIlSsDb21tartYRjpO05xwDJESPkc2YgUs4gv+V7wb3QOsbJ5E8QCKvQFo2nAGZARLAPInbwjIfoNGPJaX1TfEmatSoARe4YyzWYwSWwRfmoENxKMel3u29GDdxHtdwEj4h0ag7ujgO+GXeSP+N2VIa8RwMxUeohYePevPIh2EAMgM7aWlpWQZIbE1ELDZ6ygMk8t6QWhkkDRs2xK1b5rJWrFgRd3EVJmSoBlTEiqablwsWwzoFeqvjbKzBJGzGTLSYVRrNpwfizeOV0PSXUggaaMRWzEEoluFrtMdKfIV0pMLgpcPZauswAg3wGZrhDszjSZ/BLsWxe/bsaXW+Vzu8gtpDiqHmB8VgcHPA4sWLUbduXXz33XfSNpY/nEOGDLE6TiqUjQ5p8SZ07twZI0aMwOHDh1VfN6L8JG/0EYMP9kzSXqtWLZvHdXJyUlw3xSFZ5OLi4qwagMRAgOVEn2plEYMAYpktjyW/jmXVo1skCAJCQ0OxZs0azW2ee+45q2X/+9//EB8fj8WLF0u9sjt06ADAeogtrQwSreFu5Bkklj2YLHuAi+P+i9tbBkgse6prZZC4urqiefPmdvdg3rJlC8aPH8/JOClfqU/SrrxeiQEStd5+4jXhiy++sGrwdnR0RIKbemAjKVz5t8FgkH7z1a5Par0P5Sxvvt977z2b2wNQnUdJfhy1LBR5oCM5OVl1ThNLrVu3Vg3WygMeYjDm7t27qo2JDRo0UNQLs+p5KQ+elClTRrFOfs0XA7aW6tevj4kTJ6JixYrSMrXsGMvrmWAS8PA/82eiFCpZzdlwOigzKJXfGSR6p8zXwPTop4sBEsoJo9GIF9Be+rt7r65Ynf4Tyk6Jwt5D5nsetcY+cXQDBwcHqQOG5edeXn8Rv08ZSMMSjMEp7MQdmIfVq4rGKIeaihEQWqMfmqEHLoVn3p9Wf98Pu7AC8cjseFLyNQfEwXpYVgCYhLdxGub6pHgfmpSUZFfdIzo6WqrfivfplveglSpVUg2kijw8PKT7YNEnn3xilXVSvHhx6PV6+BXTHlVBfo36dEcP6JxNqDYo8zgNfiiOj3a3Qb2+5XAXV7EFsxDjex2/YgDmYhg+x0to8IsvHLvexDpMwT1cQ8myxbEWkzEWr6DUb5fQ7UhFpCARa6HsiKcIkDy6PvtUdsZLC8qg7qclbAa0tcjvHV74OrPdwyEp8/r/888/AwDWrl2b7eMT5YZWBklcXBySk5NhNBql7Hu1+hQzSHKOARKix0R6lbvYjJmYinfhkMU1TytAosbb21uxTVpaGjyClJWtTcEJ+FnWA+Qb/INpOC31XpuDoSi7/hqOFvsLP6A7JuFt+HZPwIvfBeB4yTX4DM3wP/TD35iF8WiLwaiJU9hh1UNFTqwMxsfHS8ETtYY6y6EfLIk3yVoBEq3UZHlFUN7LTy1AIu/V/BD3cBqhOINdSHU3Z2CcFkI1yzdz5kzVm2iDwaC4kTeMOIfklhewBbMV27Vq1UoxhAVg/Zr07t0bx44dQ3Bw5jBfljfeAQEB0qTxojQoK/DhFzLHyJWP1U1UUOSNhGJjmj0ZJCVKlLB5XGdnZ6ubSEvx8fFWac5ig588uCHvGSivtMrnTkpPT5cqv2rXNfnY+7bs3LkTLVu2xKeffqq5TWRkpNTQGBMTgz179lhVpp2cnNC8eXOp/PIhtrQySP744w/V5ampqdLrYtmoYdnT+7PPlOPkizf6YiOHZTnF18oyg8Ryf3uo9XAnyg3L31Vbc5CInN0fdbBQabgXryvFihXDv//+q1jn4OCA8/XXKZb5Pxp6Ndki3uHk5CR9TyyzNARByDJ7SmuYPl9fX81eimrDBFoGQCzXnThxQvo7MTHRrgAJAAwfPtxqmfy6JQYxrly5YvUeDRo0CCVLlrSab8ReltuKw5n5+voqjil/3LhxYzg6OkoZzoD6Nd+yrIl3tId5qT2sGBz0mY2E+R0gcTBknsuYzgwSyjmTyYT7MA/1We19cz3MwcEBw4YNwwsvmCdQV7v36t+/P5YvX45Tp05J9TfL7Hh5vU5+rTqMDZiDzInJX8MgjIQyGwwAumMMqqQ3AgAsd/4CLWYGYhUmYjSa48+WQ/ChUAPP/c8ZY9ACg1ET36Ezmv3thh5nK2Hry+NwDSelY4lDJe7evduuDBL5sNjiPbH8+Xl4eMDZ2VkxBGONGjUUx7AMkPTo0QNvvvmmot65a1dmxzu14RxF8vviMs8Vw8DYGmg5owxa/xaESm96o/aH/mjatKmiYVb8XTuJf9HqjcZo3ry54tr07LPPYsKECRg+fDhef/11+Nd3xSdoiO1YqDi3/FqY1X2/veT1S+8KTvCoZ75+GZK0h1ckKihaARL5Pa94HVGrozFAknMMkBAVceLYocIbmQ3SWaXNBQVlZnaIacdavL29FT0vUlNTodPp0GxaKRt7ZTLBhNv4D+7u7oqKm1gBMhgMSEAMLuKw1dAStoiVoaSkJKSmpsLPz091LpSsKkriD4Q8U0TeEBEUFISXX35Zc39fX19pbgJAvaFVbJQTzzUbH2EWBiNqwD/ocbYSMkrEWu0j0ul0VkNNAObnLy+nSwkHNP62NCJx3WpbsRe2/Jhq5I3Lag2Jlg2hRmQgoULm+apmZA7jcf26dTmI8pv8sy32wLMnQKI2RI2cfIgtLWoBErUMEvmNr7wRUP59zsjIkCq/allsJ06ckDLYbDlyxHpIPzUXLlyAIAho1aoVmjVrJk3iKapevbp0Ay7PIPHx8dHMIDl16pT0+NatW9LQDz///LNmgMSyUUDeQAhknUEiXqPkjZLym235b1BAQIDNzBqivGb5ebecg8TJyckqQOL4aJJ2tbqFPPCq9jlPL/MA+7BaWh7cwfxdvf6nsnODPINEfk3KyMhAQkKCZjaYKCAgwGrZ888/j19++QVLlixRbYxX69EoDvcnCILV/EKAMuPi9OnTOH78uM1y2SKvz8jrjuLQqSKx/pedAIle5wgdzL9FlkGMoUOHYv78+QgLC1PMYyK/jorXOfnvmdo5LY8dH66eLZ367b9oMqWU4n3I7wCJTqeTPsvGNHPDqbxBR2u+GCK59PR0JCUlwRHmz3qVd3xUtxs+fDhKly4tDYkJmO+5evbsierVq0vLbAVI1DJMQ1WCIlpMBuX3T/wey88ZgUsoVssFftVc4OCurJuKZdm3b5/NDBIxGCQOfejs7Kw6R1G7duZOijVr1sTYsWMxffp0qzqVh4eHooOgGCwWswA7duyIZs2aSettBUgsXz+9s/m3q/Kb3nj1tyAY3M1/y8sqv641adIEOp1OcW1ydnbG559/jsmTJ9vs4CJfp9VpJ7ssf6Oc/R9d01PzJgBDlBtaARL5Y3EIPg6xlbcYICEqwozpAlxhvqlyLpZZochqGJFatWph1apV2LBhgyL9Xy1FVLxpE3vpDB06FABQ88NiGJReHQ0mmBvv7+muYTV+wCpMhHM5803RLfyHNfgZUbitGSCRN15m52JtWVHy9fVVHc/fw8MDPXqYJzD/P3v3HSdXXf1//DX3Tt2W3U1vJJSEhIQaAoQmVbqAKIgUC4ooFn4i7asgYgGsSLWgAiJKU7+AgEDwi1QBpUVaqEkIIb1tnZl7f3/cuXfunZndTdnZmbnzfj4ePJi5M7v5JDt77+d+zuecc+211/KlL32JP/3pT97rpZqc+iemhmHw4IMPcuedd5Ycx9FHHx24uS610Or+PAqj9ROmjqF9uyQ/+tGPaGtr44orrij5Z5T6ecbj8cDNdUNDA3PmzOH222/n2WefLfl9BuKflJb6M0sthK447hFSM52bglbygZwN2f0kMthKlbIqPC+UCpBMnz6de+65hz333LPk943H40U3lgCTJ0/2ytitW7euaIdsqQwSd8LqHyMQ+H32Z5D01QepsI/Ge++9x2OPPRY4p/p3GPZnyZIlvP/++4Ed2n6jR4/2AjidnZ0sWbIEcII3fe1C8t9cjho1itdffx1w+hO4WX+F53z/vw0UB+jda8hAAZK99trLO+Yvr+O/BiUSCY455ph+63OLDKaBAiRNTU1FARJ3kWmgzSylAiSpVIp/cDP/4QFWHfMEI3ZxFqM6F2cZS37jhLvTGILnpGw2682REolEn5l2Rx55JGeeeWbg2N///ndOPfVUjj/+eB599NGSX1do+fLlLFu2jM985jMDlvV69dVXS2aqltooU4p/ES0ej/e58OeeHzY0QPLQqYu4aPlDXMWLNNJaNFeNx+N89rOfZdKkSYE5VakACTiZbF/4whf47Gc/S6HC773u3eIMjed4gGiTcw71b4Ipdw8SACviXGutXIDEfy1WgEQG0tnZyTbbbMNW46Z6TbtbJpW+T9xrr71YtGgRP/rRj/r9noWBwcLzQKElvMmVfI5XedIrAR27YB7HPbkVibb891rBeyxoeDHwte55uPDPdH9vC/88t5TxkiVLNihAcscdTvC7sNLBSSedRDQa5Tvf+Y537JJLLuFLX/pSUa+35uZm5syZ4z13KyKce+65/Pe//+X2228PvL+/AMmG7kjvK0Di/nsUBkg2hP/fsq9NOxurcH6ZHOWcMxM9+e+v85hUSl89SPwlztescTbgFgZIEonEJpWdE0dxOFpEqsbaxbkd0mRpGZGfRGxInfWPfexjRceOPvpobNtm8eLFXHPNNYwePdqbqNx555088sgjXrABnKaiu35zFLt+cxTDh3/I2/33s798g3/960ku++IXvfc2NjYGbubc7zt+/HivOfzUqVN56aWXNujvXnhjmEwmS/YaaWpq4pprruGrX/0qe+yxh3f82Wef5aqrruJTn/oUEPw3K7Wo2NcN8YwZMwITbH/N6MKxFn6PQw45BICTTz6Zk08+ueT3h9ITxMIMEndCWOrnuqEGCpCU2skej8cZeWyGBfPiXrAOipsniwwF/w3NxmSQGIbBEUccwcMPP8wTTzxR9H3j8TjHH388n/vc5wLHzzrrLG9Rf/Xq1X1mkPjPTf7Ahr/vk//mcqAMEnDKwfjtuuuuLFmyhNtuu81rGNlffya/FStW9LsgOX78eC+A8+qrr3rlXnbeeWfv/F3I/29RuEDgnucHyiAxDIO2tjbv2lJYYqtw0u+ei/3lAv1lePw35u6/dzKZ7PN8FYlEdAMsg2agAElLSwsWywPviSadm9hSWRp+/jmR+zlvaGhgCW/yW87m5/v/nLZt89f1yezg1ddvaWnxfh8Ke5C4AZLhw4f3WSLJMAwuv/xyrrnmGu9Yqdr+G2LOnDlF57aNMX78+A0qQVi40Hfuuedy7rnnFr3PPe/5/z6TJk3yHr964ypapyXIdln8df93Al+7A/sTiUTYYYcdePHFF9lzp32wLZuI4fxM/dnBfQVIjjjiCG8neKHCf9c1bzrXmW1PbeU7/3c6ry6Yx3IWcrV5NRAMipQ7gwTAimTBzpfYCrxmWUMSpJHa9fbbb7NgwQKmsjsmUZaxgKbxMwf+wn4UZlr45yl9nade51+8zr+85/YPnDnB51ZOZ9y4cbz//vsATEwE+24WzlcKj/v/vEQi4WVqLF++vN9NZu3t7bz11lvec//mGoCbbrqJq6++umSJ6G9+85u8/vrrDB8+nBNOOMH72n/+85+88MIL3r1oJBJhu+22K/r6/gLQpTYpluI/v5UKEm9KgMR/LhmsDJKRI0fyzjvveM8bRkcBC7MzxUMPPdRvdQmRciuVQfLAAw8EMm37CpCovNbm0cxFpIotetXZxdvBGkaMzKfIbszNaCnjxo3j+9//fqAx94QJEzjppJP6vKHxT2Kampo4/fTTvcXEsWPHMmXKlMB73AnQX/7yF44++mguvPDCfnem9PfngXOyL/X3bmpqoq2tLRAcAfjRj37E6tWr2WmnnbxjJ510EoAXNPHrK0AyadIkr8lxU1MT06ZNK3qPOxn0j3nYsGEDlvVx9ZVB4p8EDsaOmQ3JILn77rsDx1pbW0m05prIkp+kK0AileAPRPTXgySbzXL55Zd7dfDdz36pMgXg/O42Nzfzy18Ge/z4dx4vX768zwySvmr0+xf+DcPwdvRks9k+AyTun+felIMTiHGzOvxZIP4ATKG5c+d65fdWrFjRb8muCRMmeDfSCxYsIJvNMnv2bKZNm9bn381lGMYGXTcgfw52+51A8O+/oSW2/PzvKcwgKTUGv8Id8xu6ACBSiv+6ms1mAwESMxZh2LBhJTJInHPCyJEj+/2s+m94/RkkrnHjxjFs6/zXGzjnPLe8SqkSW+l02jtHDR8+vN9gYeG50/98Y+akGxIcKfx+/oCoW8qvFH85ncKFvv3226/k17gbUWbPns3UqVNpbm7miot+waNnvc9/f72SuZ9+jzv3eKsoOAJwEpdwTWQep7/4B67iRT75/LXcPHU+6U7nmtRmjOZIvsKW7BiY/7rnubfvWsu8X6zEyuT/3fflE3yXB9mRAwP/DukOi6cvckrujJqdYnXLQpbjBL1L7WQfsgAJYJeIq+lcKgNxr93uvcV6+i/1tyEKz1Onnnqq97jw/HrnnXdy2mmncf311/dZ3smf4ev+ProZGZ///OdL/pmlAiQNDQ00NTV556XCHkx+hXPCwvcahtFn/8zm5mb+/Oc/8+tf/zqwwL/PPvvw5S9/ecDygf575kIb2hPK/+/hDxK787dS87SB+Oehg5VBcuONNzJnzhzuu+8+ABrHOT+vFoZz8MEHD8qfIbKpSgVICjPo3EBr4b1Sf/MkGZgCJCJVbPHrzk7D3tj6Tb4ZHSz+Rcjx48djGAa/+tWvuPPOO5k7dy6JRCKweOXenKVSKf76179yySWXbHBpBCieACUSiZITqf56kBRG0H/1q1/xyCOPlEzR9u/Q8X/d5MmTmT17Ns8//zzz5s0L1HJ1uT8PfzrjQE2h/UqV9onH44E/aygCJOCU0vDX+G1tbSU5zPnsJZVBIhXmX3TpL4PkpptuCjQAdxfc+1o0cn8fCm+SY7GYd4P3wAMPFAUk1q1bx3PPPddndoa7O7uwtIA/QDJ79uzA17jnye9+97vssccepNNpb5cQwPz5871/h/4CJKNHj/Z2Jg8UIBk/fnxRD5ZzzjnHqTNvFe8O9nOvTaWCF4Xn4CuvvJKbbrop0BvE/3UDNWnvqxxZ4Vggf+Pd306qwv5PAwWDRPrjP7+k02ls2w5kkDgBkuDvU3JkvqzctttuG3jNXxKwVIBk+vTp3jG3V9r0z7YC0Ijz+7z99tsD+bmS/xza1dXlnUNaWloCv+v7779/YCz91Yf3z8389ew3xHXXXcdNN90UOPb22297G1MgWNp0++235+abby75vb7+9a/zqU99iptuuqmovMTs2bN5/PHHefrpp/nhD3/oHXf/XZLJJPPmzWPB2wu5b/ZKXvz5Cv7v9NIZeg+2Xlt0zO1JsvbNXn7V+DJ/3uct0ufuyqGcztnczI7x/diXEzmTX5J6cTIv/2Yl9x69gEe+uJjr217hvUc62JuPczzfpI0xfJ4rOPSeS7ll+nz+88Nl3H3oO96fNfXEYYF/88LShDBEARLDWbTJ9tpF2ZUDXTdE3OttDOeznI5s/vXXPwe44YYb2H333b3nhfc9ra2tXH/99Zx22ml8//vfxzRNfvzjHwfe459zuOfAuXPnMm/ePA4//HCg7wySUpkU/nN2XwYKkJTTxz72MY444gjOOuusotf8/Tj701eAxJ1jDpRB8uUvfxmAL/qqVPj/DQZrDWT69Ok88cQTHHrooQA0j3fG0oxz7/3CCy/wz3/+c1D+LJGN5Q+QuI/nzp1b8j3uufSAAw6gvb2dq6++eohGGU4KkIhUsRULnd0a2WTPBi1ul5P/RO3uuItEInz0ox/1Jnz+ha5Sdd8vvvhiDjnkkJJpvYUikUggKJBMJjc6QFKooaGBfffdt+T32WmnnTjuuOP42Mc+xqxZs7zjkydPBmDHHXdk0qRJJRfbSt2IbkxpAXcy6FdYM3tj/p592dDPkD9Y1NbWRqrdeW+K/BjUg0QqYUMDJC+88ELgmPv72FcGSX8BEjfY2d3dzVFHHRV4/Zvf/Ca77LJLnz2Mli9fHvj+pQIkhdlv/pvQf/3rXzz44IOBAMntt9/uZcEV9vTwSyQS3i7DNWvW9Ftia8KECUUZb+5i7de//vU+y4BB/t/04YcfLjkGv6amJk455ZTAooP/PYUZJK6DDjqIt99+e8BrX6kSW/3tUCwMkJQq4yiyoQoDJE4GSe4zHSMXIAkuJG9xSP666s/guOyyywLnFf/OX/dzfuKJJ/KFL3yBk08+2esjlxzuvPYRvsY4cxvOPfN/6FmdLblBpaury1t4amhoCPz5hedK93eyiTbaowXlwDpj3qLSrrvuWvwPA/z3v/8tefwLX/hCoG5+c3MzY8eODQQ4/OemUaNGeZlohbLZLDfccAOnnHJKydf33HNPZs+eHZif+s9tHe9a/GFE3+fJjz29FWfaM3l+2L3czqV9vg/g/ceCm0i2uPtIjud/mM6eJG/fiX98Lh98Sa+3+Ot+b/MJLir6Pqte7eHJ8z7wvt8WhzSRHB4NnAvdz93Q9yBxrsdW2i4KLitAIgNxN0C4AZJMZPOvv/5zsHv/5iqcP/jPqeeccw4dHR2cffbZgff474fcr0+lUsyYMcM7R5WaNxb+ee798QknnDDg36EwEFGq6kG5tLW1cc899/Czn/3MO7bHHntw8MEH8+tf/3qDvof/38M/x3LPuwMFSH7+85/zt7/9jR/84AfeMXezUTkNG+/MGd1+OG6ZbFdhEFiknEplkBRm+RYGSK699lqWL1/uzQdl0yhAIlLFetbmGiDG01UVIOnLQCWhtt56a+6///4+d//52bYd2NGcTCa5/PLLi943GIEDcCZ0d9xxB7fffntg13JhWbBSAZIFCxYUHduY5limabJy5UqvIR9ULoMEgs1iW1tbSbU7k90kjezK4RzOl+jsUAaJDL0NDZAUlvfY0AySUqUS/H2Hli5dulHj3ZAMkmQyGbihLFzIfP311wMBEoA//OEPrFy5st8ASUNDg3dz39HR4WWQjB8/nltvvZULL7ww8GeOGjXKO+emUikvRXvixIksW7aszz/HHfusWbMCO7Nhw8onDLQTGpzGooWLHe4iqT9bTxkkUkn+z20+QJLPIGlsbAxkkHyQeAPDzM8V/De/5513XqAvif9z7M4vEokEv/jFL/j973/vffYnH5WfK9z46Yd4/8sT+d2YV2l/vXjncmdnp3ceTaVSgT+/1BwmgsElPMAlmQe9MlK2ZfP4R7q4lP/jDK5h1x3zN+ZxUozHOY9svfXWRbX03T/HP7+ZNm0akUjEW5xqbGxk4sR87f/CjNtLLrnEe7xq1YaV6PFfH/zB2n99s7g/XXKEyZH3TeK05dMYPdtZ4DMMg0e5lb/wY/b88RhOeH5rdv/+KI59dEtOX78dR9wziV0vHMl2pzvfu31Ggt5RK0jjLADbsSwjdk7SNKE4YL+MhfyKr/EExUH3LQ5r4uBbJgClz5uVKrGlAIlsCvczE8cJVGSMwc0gKcw4LZyPFG7kKzVf8b+nr/umwvsz99xZKkBy2mmn9Tl21wEHHBB47g8UDKXrr7+eo446igcffJAHHnjAa/A+EP/PwD8/c88JA92LGobB4Ycf3mcZsXJpmeD8rJtoYxt2LepXWupeX6Rc/AG5TCZTsgRqYYBEzdkHhwIkIlVqxYoV3PBLp+yAHctUPEBy7bVOSYFbb721z/f4J4n9BS5KlWoovJlramoqCpDss88+rFq1isMOO8w7Xo5/C//FpfBCM3PmTA477LBAbddSDUM39gLV1tYWWIiNx+PssssuNDQ0MGrUqEAN7k3lX7zZ0ADJiBEjaMhlkCRo4NNczuF8kVUvZNXcWIbchgRI0ul00U6vgXqQuLuIS+0EHDt2LJ/97Gc3abxugMT9vu6fn8lk8vW3k8nA4ufs2bMD58MFCxYUBUgAnn322X5LbBUGSNymn1dddRXHH3+8VwonHo8zefJkIpEI11xzDbvssgt//OMfA2PynzsmTpzo9Tbx/52guDTExgZIBmp66nfddddxwQUXBDJXCgMkC+eu58g3v8kIJpT8swtLISqDRDaH/7rvBkjcxb9oo0E8Hg/0IOk0gr/X/V1T/b+P/fV3GLdPI7td4gT+3rqtg64PMmR7bFZfM4YxbB14rz9A0tDQEDiXlspAaGE4cZxxrH7VuSHvXJqhc6HzdTPZl1FP50vanM7PuYA7mcYcEomEF/DZjn04ixv48YG3s/TfXbSn8oFKd25y4YUX8u1vf5tnn32WCROc398IBua/x9KxOM3999/PD3/4Q775zW96X9vX+b2Qf8NPNBqlY0ma/z34bd64LV9jf9eLRnL8c1tz2rLpTDq02cvMcf9tLLLM5UZ2PnsEI3ZMsev/jGLc3o3EGg0mH9HM7peMZv9fjudMeyYnzpvCis8/yP9jFl9me6JXPskJ/9mGTy2cxpn2TD7ywGTve9/I+bzIw9zCxdj75ssibn1cC0fdO5lkezTw7+T/ew99iS03QFIcXFaARAZSjgwS/zmgMHDRXwZJX/wBkv7KDO6zzz5Fx0oFSCKRSL9Bkra2tsD98yGHHFKyIsNQOO2007jrrrs2eiOi/2eQSqXYeeedGTFihFc2cVN6kBx33HFA8P50sDWPzF9jz+J3nMZPAq+/+uqrZfuzRQr570fS6XTJDVzuXMb9f3/nKNlwCpCIVKnrrrvOu5m145UPkJx88sl0d3dz/PHH9/me/iamfv7xRyIRFi9ezDXXXBN4zwUXXBDY/eMuDrS2tgYuAOWIlB944IFF43TFYjHuvfdeHnzwQe+Yv6ase7N+5ZVXbvSfW9hnZsKECSxZsoS333570Ets9XcR9S8azpgxg8bhxTuwO5ekOeecc7zna9asKeoXIDLYCuvnFx4D57P4i1/8InBsoAwSt6RB4e+Few7w9zPZGP2V2HLHn0qlAjfqs2bN4oUXXvB6ACxcuLBkgGT16tX9ZpA0NjZ6AZLXX3+dZ555BsDbBXjggQfyt7/9jSeeeMK7eT/ppJP497//zdFHH93n921vb2f16tWBcbj8GRmNtMLK0je/VtbGtp3/EvEE7YyjjbHEYjGsrE18WRsz+ZBX17/U+aq5uZkf/OAHzJw5k1Wv9jD304toeDeffZNMJrnroHcYs24an+KykuNQBokMJv+icGGAJNZQHCCZtM3EwNf31yDXHyAZKKN34sHOfCG9zrdIbcMp8UuYwDTvUGdnZ+A8NFCApJX84tTiRzt4//EOOhYFg9HLn8h4C3rTcJoZH5x0SsSMHj2aKczmS1zLNswiOXcat+/6JjeOeoPL449wCX/nxMd/wTWRefy24U2m/OmTzJ2eofOzs7mC/3AVL/DaNw1uGP8abxw6noZzD+M682Wu5iWuiPybU2ed5Y2jY3Gat/53LbZt8+7967j70He4JjKPayLzSH7jEM7gaq7mJa6JzOOGsa+x6CGnbKgRi/DF9Ax2/85oRu5UegF1U8pX+c9hheez8fs3MuOMdtbNeoV3eYn//d//5YMPPmDHnXf03rPP1cHMGf/ContdGeoMErufDBI1aZeBuJ+Z6Ti9lrLG4G5QGChAsiGBhw3JIIF8JtvFF19c8v3+Cgt77bVX0de75/6vfe1rgUy7oc6iGAyFAZCnn36ahQsXevPcTQmQXHPNNXz961/noYceGtzB+pgJgyz5a+vOfJjjOJf9OYVRTNrgDEWRzWXbNmvX5jdsZDKZkqXN3blgqews2XQbttVGRIac1RFhXz4BQMxKVjxAAgNPZPwTyf4mnv7FwNbWVsaOHRv43o8++ijTp09nxx135LHHHgOCiwMnnngid911F/vtt9/G/hU2yIUXXkhDQwMf/ehH+33fSy+9xK233hpoav7d736Xb3zjG5s0qfXfNLs/48EoreXa0M/Q3nvvzWWXXcaMGTOIRqMkGxP00uUt9AB8gav48k+254wzzsAwDLbbbjtGjhzJG2+8scETXpGNYdt2YAFvyZIlwIbtVC0VIEkmk15Qz70hLVViy33v5igVIPGXtvGfE5PJJDNmzOCrX/0q//jHP1i4cGEgCOHyl9h65ZVXePbZZ73a+6ZpEovFvL/XSy+95H2dGyCJRCJek9GN0draGujx4l8IczPdkjRxMffxnyPjrDj8HVqnOueEFfO6WTO/l3XvOpN6Mxnhw93f58O5r+/58yp+ceN/GW4dwhk4JXbu5iran9+B53+6HCtrs/y5bkbOSjHj9Dae++Fynv1evvxXa3xPduRAlrGAts784vOW7EiUOBmCCzCFfVeUQSKbw/+7kMlksCyLhC+DJBaLBQIk20wLZnRcccUVNDc3l8xY85+bBqqF3jyp9AaISb07cD6382Wcxu1dXV2BDJJPf/rTXH311eyzzz4lgwBH8hXv8WNnOeffnc8NliFd/lw3P+RfgWPbdu/FY2e/z9QpUxn2ROlzaWNvO4UFuFa/lv99jNL/zsioHefxk9fy+Mnz+n2fayYfKnk8NcrEiPa/8Wb69OnMnz9/owIl/QVIjGiE/a4bx358nDNWH+zNHzMf7eDFn6+gcXyUxjGlg/f+x/7PyFD0ILEjTsaTlbWLNsgog0QG0t3dTYIGL5A6PjttgK8YmL+Zd+H9U+G9yYZkkPjf099903777cfKlSsDlQ/6ev8nP/lJbrvtNrbddltM02TkyJGceuqpvPDCCxxyyCG89tpr3nv9369W+P/ezc3NRKPRwLlpt912Y+edd2bkyJFFpVP7Mnz4cH7yk58M/MbNEIlE6KGLBt+1Zn+cOfVHOIve3hf6+lKRQbV+/frANbS/AIn/3ngorvv1QAESkSo0/9Y1DL8sv4M31dVaFQGSgfjrZfeX2eFfbHQnsP6/k1tOwW0SDMGJ7Sc+8Qn22GOPokZ2g6WxsZGLLipulllo5syZzJw5M3AsEols8o6f/m6gN1R/F8cN/QxFIhHOO++8wHu7WB8IkLj22GMPfvOb39DT08OiRYtYunRpoF64yGApXHC58MIL+da3vrVRARL/TdrFF1/M3XffHSiN0FezzQ25kfYbP358oPReYYCkp6fHW0xtaGgIfH/3XOf+Hv3rX/8KlBV0vfnmm977t912WyZNmuQFSNxSPYU1/7/3ve9tcgDznHPO4Uc/+hE//OEP2X333Uu+xw2QjGErUji72N+9dz3v3ls60yXbHSwplFjTRmGRoaP4CsyFx+cu8Y7N/+ManvjGEgpFemN8niucJ/cFXzuEz/MQv6MHZ0G4paUlsKsTlEEim6ffDJISJbaiieD1esSIEVx33XUD/jkDZZA0jA7e3s36n5H8+wf5QOJotuQD3nZKbK3qwcAklUpxySWXsM8++3DwwQfzk5/8hL/f85CXxbX0313eTm+/537oZMltdWwLS5/pZP2i0sGbF366go9+7H9YgPM7tst5I5hwUBPjPtTI6ld7yPbYdK/M8NhZS1j1Sg9TThzG/D8WZ86V2x4/GLiEy+WXX05zczMnnnjiBn/fDZ3f+eeP4/Zt5Lgnt2LYNsXzNf953H3sv74NSQZJrsSWnVEPEtl4Xc+k+IkvmDosu/nlk/wBksK5TuF9z4ZsQNvQEltQ3PPE/+d98pOfDIzrvvsKJijkM5n987ZaDJD451WFcyxwznH/+c9/hnJIG6yXLhooHnOMuOaHMmQKqwb4AyRxUmRIY5HxNuK4lEEyOBQgEalCD3xiYeC5kY6VzC6oNscccwxf+cpXmDat/11A/sXAUs3sSu3aLlzo29BdJ7WksMTWpugvQOL2GTAMgy222GKDv2c8Hqeb9QwjX3prOU5t7BUrVgTSQFVWQcql1Gdr7dq13uRwx9R+zO/6D52sLXpfqQySsWPHehlqrr4CJANlkGy11VbMnDmTu+66C3CCxf0FSPy9Q1KpVOD7uzf1bpN0gHvuuafoz3ziiSe8PysSiQS+h/tvUpjJN2PGjH7/Hv25/PLLueiii/ot99fY2Mjo0aOZ9EE+cLzL+c4O8zXze3nzzvzPZtpnWhm9WwM333Qzf3nyD0xgGgdNP5p9Dp3DC0sfZ90fRtLMcAB6Zy5i5k4zwIbX/7Bpi6aHcQbbsz+X8TFOOOEEfvKTn/DKK68E3qMMEtkc/nNUPkDi/F6WyiAxE5u222+g0jARI7hBZauPtgQCJBdyFzdyAW0rxzDm6mP5AUdiPrSa1yd0MGvCIXS/HOWUXc5iVMPRmJ0prokMnJXRPjPBLueP4I7d3wocb5oQ9YImC+7ILzA1bxln4kHOuWT49vlz1ydfzi9afvgWJ0icTds8/e0PGLFjiq2ObcbOQs+qLNkem/gwg2R7lO6VGbpXZFn2XDcL7l/Htie3surVHpa/0M2OZw0n1mgQazIwkwZGLELPqix21qbz/QwtW8cxohFsyybWNPDPZNq0adx8880Dvs9vUzfAjNmj9M/aP0d0rxn+7zs0Jbac64ydUQ8S2XjZ326L/0x1f+tVnMvem/U9C+8V/fy/M6lUaoPusza0xFYp/t/Hjdk45v87DEZ55aHmDxSVCpBUM38ZyUK9fxsDZwzhYKRuFQZI0uk0HR0djGQL/oc/8wz3cAsXk06nA/NOBUgGhwIkIjXgvfH/obHxI97zag2QTJgwgffee2/ACVGphbxSASD/7p/+Jr1hMRhBsP4CJGPHjmXBggUYhlHUnLg/pmnSRbAZdDPtjGAiK1jE+++/7x1XgETKpdRnq7OzE8uymM5efL7rKhbxKk9zN8tZxIvkm3cXNmk3MOm6fCrXfGoee/54DCN2TDLxoKZNLrH19NNP8+Uvf9l73tzcHCjhVdik3S2NFSOB0RsjmSjOIGlubmabbbbhjTfeYOnSpUV/phsg2X57p1xOqay9wt2Hm5N1F4lEvJv1ww47rOQOSHACsYd/8CUARu8bZ86lTmbhgr+vCwRIDrh+PBEjwrqXXuP1J5/mdZ5mlyNGsfePPsp7t2Y5+w+HczpX8jpPc9QZO3DwmYcCsNdPx/L42e/T8V6GGae3sfyFbpY/303bdgle+OkKAJaxgJFswW7fGcXr819n9c2tAExgW4YzgcMOO4zx48cHSo+BMkhk8xRmkNi2XSKDxLfbL75xPdTOPPNMbrvtNn79618P+N5dLxrJs5csY/9fj2PUrBSH3DaRez77MrH1TgDiU1wa/ILn27yyWd74SmSNlhJrMtjmhGEMn5HkgN+O58nzl9C11Dlfn/jyFBY93ME/v7SYjsX57JKpJ274zmgzFmHOD8YEjkVTwblOsj1Ksj1K65QEU453vveEA/peXGwY5ZyLG8cOTVNT/8agwWikWiqDZOgDJLkSWwqQyKbwTeke5ve80fTkZn/LAw44gC9/+csl+zn5f2fa29s36PttToCkVLWEcv+Z1cC/BjCYZaIrzbxnG6ZPn84xxxzDpZdeOvAXiGyiwrLK6XSa7u5u9uVEYiTYk+O8AMlAveNk4ylAIlJlsulggZFnuZdFYx6iqSmfnlvNwYINWYDzL0K6pWD8i3ulFiU3pJlerfPf3BYu1G6ogS6ObvmyjVUYIEnQwMXcy1xuVIBEhoT/sxWJRLBt2wuQ7JLrVzGBaV4TYrfOPhRnkOzDCWRedhbP3FJNJ82f0mcGif/3MUKEBA10k68HG4/HicfjjGQLuumgqamJZDJJthv25QQS2fVk07b3569fv55j+DoH8Rmub32VIxsu4mmOxCIbuImfOHEib7zxhtfsHZwdbj10er+Tn/jEJ7zX9thjD5566inmzHFqerv9Rlz+Moib45ZbbikqJ+GaMGECa/+1nEaGMXzH/N9lzJ75c/j4Axq9Xe7+v697zh8xYgQ9dHIVnwPgsyNv9d7TMCrKwb/P78ackvvrZ3stnnvlWX5y3//wLvM477zzOPOiy1j7i0Ws9m32HsUW3mfpgAMO4MADD2Tu3LmAMkhk85TKIPF6kDREcgGSfJDATGxcgOTqq6/m6quv3qD37vqtUWx7ciutU5zfr20+Poz3n/0ryR/uyygmee9bwH9pnAbTh80i22mz6tVejCjEmg1apybI9to0jY+x/Vfa6VqaZcROSbI9No3jovSsyvLa71c7f06uz9D0z7Qx/TNtrFvo/C7Fm022OrqFCfs38uthTsbWdp9rI95SXzsd/TvBByNAMlAPkiHZSWo4CzNWprgHieaCMiDf6W8FiwblMxuJRLjqqqtKvub/vdvQAMkRRxzBOeecA1CyB0B//BvRNiZQkEwmOfjgg3nmmWc44ogjNurPrAb+f+daC5As4GW2YLs+X3/11Ve57LLLFCCRslqwYEHgub8ss6uNsYEMkj05jvm/62DW12ora6saKUAiUmVWvxbchfUf/k5jd1fg5ipMTbBL7TLbkBJbYTQYDTbLtXtgHStLHj+QT/HaE3d5z3VTLIMtk8kwb948Jk3KL+o1Nzezdu1aL0Aymi2Lvi5FC125cluFPUjcIIrfU//zAZ3xJCPZgmU4k9NYLM5Dn1pE0wTnnJSggY/zP+zB0fyD3/NfHmVrZvHuHd20rB/LBdxJnCTzl97DSuBATnfG9jT8Mvlfjk1exO+4gMWLFzObI70/u7VzAm2MYQXvsfyxLC/8fTEAu7x5PMPZnUhnhK3YiYm+G7fLOYGFvMyuu+7qHbvjjjv41a9+xbHHHgs4580JEyawaJFTEq+voMbGam1tZZdddilZR3r8+PHEyC3KfszXsLTZ5HOrp9O1NEPz5OLyMOAERvz/d40aNWrAMZlxg+hRi3j3PqcckHv9mHrESE5gT77ItYxhS6LEvfNUPB7noYceYvbs2Tz77LPKIJHNUqoHSZRceb2kQSxmBjJIjI3MINkYZiziBUdcjY2NrCNfuuFvXMN9/ALrZavfvnF9SY2IsvslpUuSNE8M7nyOt5gc9fdJvHDFCnb77sC/z2HjXygcjEXDgTJIhuI+wc0gsbPqQSIbzzayRHJLUVkyZd/97P/+7iaSgUyfPt17vHDhwn7eWcyfwbsxpbIikQh///vfsSyrJkvm+MvD1tp6xbV8kS3Yjld4nKt4MfBaqfK9IuXwxhtvBJ67AZKJ5M9H7b4ASYwEn+RinjprBdM/MbKoD51sHP3riVSZjmXBm4w4KTo7VwQyKDY1u6AalcogcXfD1VuAxH9zu7GLFe4i6EEHHTTYwwLgHq5mK3ZmeeNbTOvYN/Datk9/hC25jbd5QQESGXSnn346v/vd7/jc5z7nHXMDJF1dXWSzFpMo7q3RzljeYy1bsB0d84Ep+V21k33ZJa43b18LmHybv3Exh7OchaQXRXntptUAbMOunMXvvPfvzynsj9MU/ZHPfsA0TvZem/L0kUzxBUAAbAu26tyVj3EBd9xxKx8rqLXdSCsWFvcfudg7tiV7s2UfNbkP5XReb3ycFTe08CRL2PaUVsZvN57vfOc7AHR+kGHFvG5mzZrFokWLmMruGJ1JGKQNfX3d+O6zzz68eaVzvm5oD74nMcwkMSx4w+8vPdNXgGRDSwKWWiAcNWoUK1jEWpblAiSJovOUe81RgEQ2h/9z5TbPNHO3WmbM+Zxl/RkkZQyQlNLU1ESE/O/WCpw+SZsSHNkUW3y4mS0+XFs7igeLf4HU34h9Uw3Ug2RIAiS5DBI7qxJbsgky+YBFksYhCQZcfPHFzJ07l3PPPXejv/bFF18c+E0+/nnLxlZBiEQiNRkcASc7Z/78+YG5Xa1Yz0pe5rE+Xls1xKORetVXgGQ8+eDj/+NGljz9JJZlkSQ/v+h4L60AyWbSv55IFUmn03z+pC/yEf7HO/YGzzKysyUwUarVSVMp7k2U/wbdDQDVW4BkczJIHn30UW666SbOPPPMwR4WACtZzHc4nC1Hb8m0t/Yter0JZ2e6AiQy2H73Oycocf3113vH3MWmjo5OYsQxKS5Z0sIIlrOQc7mVx4/oYeqSDNFolCbaGMs2/f6Zh/IF4qR4Jt/6KRAc2Vgvzv4jp+z9ZV742QqmsCvPdd7s9SZonhRj3btpRrMlX+ZX3tfs+q2R3HnnnbycayTewnA+tO9+NI9MserORnbkQHbsOJDnLnP6bvznsuVscWgT6xakmXhwEy/+3Dl+MN/DYhKH8HluGPMaO541HCtrs+b1XkbOSmGlbSy3tGMEGsY6pXO2PaWV9unOOXjdgl7e+78OIkaEdIfFmjd62XXR8XSQIkEDj5+zhGgqQtu0BJPe2ocPcBpCx4cNfK0aO3as93j48OGB/7s2JIMEgjuz3QVC9/9pnJI/MeIccMABrH23l6bxMYxoxHuPv8TWXXfdxdlnn83NN9/M7rvvvkF/vtS3Uhkk7rnJiDtN2tPkF5I3tsTW5mpsbOQubuTjXAA4GcoyNPxz2MHI5CuVQeKfQw5NBolbYgvWzk2yN8fzGLc5xxQgkX6kOyyMTH7e9jKP0TIE97bf/va3+fa3v71RX/PVr36VK6+8kgsuuGCjvm6HHXbgzDPPZPTo0XXXG2CbbfqfY1erN954o8+x+xehRcrphRdeAJz7o/fff5+enh7SHRapgh1uY/41h/cf6yJBPgB726w3+chDk5l4oD6vm0oBEpEq8sYbb7DmfafG6Rv8m99xLmtYyn4zgqnAYcogKRUgcR/Xc5P28ePHb9TXTp48mYsuumiwhxRgY/f5mrvoowCJlFskEvHOB+9ekqTjidI7847iK9zA+d7z3415ldhuWzKarZzvE7P55MtTad3GOc/Yls09n5zPglt72YOj+/zzh++YYI/vj+FvR77rHWvZMsb7Da/xj//eyzwe4XxuD3zN6i3fYM7lY3j+Z8tooAVjRX7i2rJVnHXvpgNNk1unxtn9u6O5+p0n+dsr+QYax5y5HbuO+xD33bms5NgW3O80f1/1cnA37yF83nv8whUr8u//+/o+/54r5/VwxF2T+ODpTu7Y/a2i17flILbFyVh7/sfLi14HNqjPgD/44QZL/MFx2PB64f7moIXfI5M7R1323R/SuHgcv9//daZ8chgf/sNE71rj3wV99NHOZ+DQQw9l1SrtHJSBlepB4maQGDFn7vY++Z2Bdt+X1LJobGzkEW5hKe+wkFfIoJ47Q8U/xx2MAEmpHiT+OWTh+a8sjFyJrQ6Dzt9O5BNcyFs8x2LmK0AiJa1cuZKWlhY++Fe+Z833OJolvEWbWZwJXA1+/OMf89GPfnSDy3K5IpHIBveMkuqw9dZb09LSwtq1xeW0UgqQyBBYsGCBFyDZY489+Mtf/uIESFaU3lCz5P+6SBJcI7vroHc4055Z9rGGVX2Fs0WqXFdXFw04NUvXsJQ1LOX000/nuuuuA+BDH/oQAB/96EcrNsbB1tnZCZQu8eC/wdvQHcS1zDAMlixZwsKFC6s2IGT3saITze2SVYBEys00TRoaGhjBRNb/oxG7p/SkcQtmMJatA8fSTw/jC1wJQHy07QVHACJGhN0uG04vwWazfg1jo3zi+SlMPqKZF7e/A4B5PMIpb23L+o8+y/38kqW8G/iad3iJeDyOGYvQa3Y5f/Za5zxvxzNMOXEYGaOHLGkAjFiEo+6f7LwvHqzjn0ql2HKvUWx9Uv/nh8lHNTPjC8WLcNM+3cou54+gZUvn93X4jklmnNHOLuePYJfzR9A0IR98X/NGL5lui7/u97Z3zExEAo1V0/SymPl99lKINQ08zdx+++1LPvbb0KxJf81vf4B98uTJ3mJwe8tInvuRE9CZf4vTj8H9dy7VpH316tUb9GeL9JdBYsYjRCIRVvOB957Vrw9tSTd3XvEKT7Dnh2fT3t7OH/7whyEdQ73yZ8VtbLmdUvznxEpnkGQX5kvpbM0uzjHNBaXAO++8w/Dhw9liiy343wPf8Y4vwdmAUa3VEWKxGB/60IeK5mMSTn0Fd2MkvHtdkXJZvty5P2lvb/fuidru253lV5XeWPHyTzqV3TTIwrMNXSQEVixZxXE4dVGX8i7jx4/nl7/8pff63Llz6ezsHJQGj9Wio8PJmNlhhx2KXvMvVk2ZMmXIxlRJo0eXbnhaabFYjHQ67TQszK2X9rSsJrG2FcBrRKubYik30zRJpVI00BI43mmu4Yvv7MaNE1/zjn2eK4q+3v26eGvxov6wMY3cxveZxWE00cpEtmPYlDhr5vfSMCbK3j8d47137YzXuPClg1nDMq7jzPwiO13Ep3TTO98J8F7Hl/jaFl9yXjM7SWQb2W7d/gBEWnuZ8fl2Tv3FwV7Dc7s3H4QsXORKJpNEIhEOvXlLrBttIkY+uGxbNkSCweb9fuFkoq1auZqWYc3eAsScS8dgWzYRI/hvMOfSMSx5qpM757zFqld6+GXqZe+1/X8znu0+m5+gd3R0cOGFF3Lcccex1175nZ9v3L6GZ7+/jN2/OwrDHLiE0FZbbcUTTzwxKGUo+gqQPPfcc9x/0tusuBey3RbJEfnpbzZtl8wgEdlYhRkktm37MkiKfxdSI4f2NszfB+PAAw/k/vvvH7L+I/Vu66235vrrr2fkyJGD/m9eqR4kbgaJtTy/cDyKSc4xZZBIgdtuc8qv2e+X7k1RrQESqS+F5y7bsIhYztw0SZN6kUhZZTJOn7qWlhYSiQQjmEDbK9v1s3UPZlJc+lw2nQIkIlXCtmzmH59vnvkm/wnczIIzeQxTcMRvwoQJzJs3L9C8ctq0aUQiEYYPHx5Y+JKhd9ddd/H73/+eb33rWzx8r7MIZCczvLb2KbZlD2+XrAIkUm6maZJIJEgQvMnOGN00TYgx/bOtvPLb1QN+n3hb8WJ8IpHgKf7KU/wVcG6U+lrMamlpYRVLvOczZjhBgkgkwgF3D+cHH/sVf5l3Ix2s5tOf/jQAvdFO6IUdrAMAiO7spPGXylxwx+Pnb3pZGHwoDHb4tbW3Fh3r6/2lmvtteUxzIDgCzm70n/70p0Xv3ebjw9jm4xt3vt7Y0hV96StA0traypiJo1jBKtIdFv5qgel1WS+49ZWvfIUzzzyTSCRCW1ubV1qrp6dnaBYcpaZZlsWhfIF9OZHu91ZiNVrejlN/ltWPOJGfnfBHZv3PyL6+VVn4M1MNw1BwZIiddtppg/a9/PcHlWvSnjuR9uSvpSOYCChAIsXchb/9OLnk67rGSjUoqpQQtejudcoYJWlUgETKyj1PRqNREomE16/S9cGw19hqxiR+8sQ3OCXyPRrtVg7iM0Xf57+/WsmM0zesPLEElbXE1j//+U+OOuooxo0bRyQS4a9//Wvgddu2ueiiixg7diypVIqDDjqI+fPnl3NIIlXrjdvWYHfkd8+8xfODkoZfrSZOnBj4PzgLjP7eG83NzSxdupR333236OtlaB166KH84Q9/cDJIcsyISYbchVwBEhkiboAkVjBpTJvO7v+9rxjLyF2C9de3/2Zx+nHTlsW7FQsXDPtbQCwM2h577LHMnz+f9957j6233ZKlk+exiveBfE+hdLQr8DWN2ztltfrKXCgs6TAUdeUbxkSJNjh/75atYpw0fwqH3blF2f/cwTBu3Dj2339/Jk+ezJ577hl4LdPp3PQ+e8kyljzV6R3vXWsFrkNvv+2kyO27b35H1gcf5MsiifQlm81yJF+mheGsu2VkQQ8S53fqueee49q/Xc6xf5pJasTQ7lMrDJBI7TrhhBM45ZRT+NnPfuaV1vKX2BqaHiROEMT2BUjiOH+uAiRSKJ125jtbsJ137Fq+5D32B/hEKqXw3BUxbbpx+vUlCecmVakehQESfwN2gLWN73PCQzN4f/g87rGDPY72uDRfhWT5C/3lnEh/yjo77ujoYMcdd+Saa64p+foPf/hDrrzySn7xi1/wr3/9i8bGRg455BC6u/UDlfrS0dHBL757k/f8Hq6hl67AbuGwefDBBznxxBO57777+n3fiBEjQh0oqkW7nOdkOvUe8apX118ZJDJU+sog6Y6uAyDebPLxZ4K9R6Z/rZHPr5vOmEPyC0itMzbvZtzfENy1zTbbeI3GS9WcX9Wy0Dv2Xx6jdQ/n8ec/7zRR33///QPfr78MknKJpgyOfmhLjn1kS05+w2li3192SjUxDIOHH36Yt99+OxBsB1j8zw7v8Zr5+Yyd3rUW5513Xv59ixcDweBYXxk+In7+hRWrxy7qQQKw0047cfjhh1dkfP4AicrZ1Lb29nZuuukmzjrrLO9Yqb4kZeVmkPTmlxOiOH+uAiRSKJ1OYxBlLE7J5Is5jJd51HtdQVupBu6566/8DADj1Pl05QIkKaqzP6iEhxtI7itAsqLtLVKpFEcddRSPcivz9/1fAN4wn2Xnb4zg0+9vywG/Hc++V40d8rGHRVmvRIcddhjf+973OPbYY4tes22bK664gm9961scffTR7LDDDtx0000sXry4KNNEJOxuv/12Xn75FQAWM5/7+QUwOI0cq9W2227LLbfc4pWlkdox57IxfH7NdJI7dnmNpZVBIuVQ6vPkBkjcnaquzlg+7d2/oP8Wz9Pb20u8yWTWt4ZjYbGaDxj3kc1bIPzsZz+LYRgcc8wxJV//7ne/y5w5c7y62wAvb3M33+EIzmIXruOLDBvlZLZ8/etfZ+7cufzv//5v4HuUatI+FMbMaWDcvo2hKsGzw9eGlzyeXpeltbWVvffeG4D33nsPKO4nITIQ/2fGTlMyg6SS/GWZwvS7LQ5/UGJIAyQ9+WtpTP3opA/pdJq9OI4YcTpZy3IWBV5XgESqgXsefYjf8vk104nOWqMMEimr1atXs2iRcz70Z5Akk8niDJJW531Tp04F4JnMvfw/ZvO7xrMwohEax8SY/pm2mtnYVo0qdiV6++23WbJkCQcddJB3bNiwYey+++48+eSTlRqWSEVEo1GaaAXgJf7hHQ9zBonUtniLSWNjIxkvQKKbYhl8XV1dRcdM0yQejxfVZU03dgSeH37XFixvfovnZ9zqZXVssedwfpA6iu9Gj2L8lqV318yePRuAnXfeud+xjRs3jnXr1nHnnXeWfH3ixIk88cQTfPzjH8+PPWqyjAXe742bZWKaJgcccEBRj6lSTdpl0+zw1eF8+I8TiA8zmPbpVkbs7Pxb9q51boZHjRoFwIoVK4DguUwZJLIhAhkkGQIZJP4eJJXizyDRZzp8Vq9e7T0eivuHiJ1bRujIZ2O6c0FlkEihTCbDtuwOwHpWFr2uoK1UA/+5K95iYhgG3Tj3F0llkEgZtLW1MXHiRD744INAgKStrZ3Pc0XgvZmkU2nJvV/84IMPSNONGVVW8GCpWJP2JUucxqajR48OHB89erT3Wik9PT2BWt1r164tzwBFhlAmk2EKzqLcWlZ4x8OcQSK1r7GxkSxOORqV2JJy6CtAUqpxnblFZ+D5lke1cNGao7DtI72diYZh8NaKV8lkMn3usP3zn//MNddcw9FHHz3g+Db2HF1Y1sZfhquUSpTYCivDjDDlE61M+UQrAH/d3+k10rPaOWe5wSd3junepIAWk2VghQvCdiaYQWJWQQaJ/3xV1IhWap6/RLW/H0m5jFy0XdExBUikL+l0mrFsA8DtXFb0ujJIpBoUXhtN06SLNQCklEEig2zlynyw+JlnnvHWUWKxGG2xkRR24bXjzr2Ju+HlzTffBFQ2dTDV3JXo0ksvZdiwYd5//saaIrWqY30HY3Fq5r/GU95xLYZJNXMySNSDRMqnvwDJuFwda4C1LKdhl+JG55FIpOimO5VKFWVq+E2YMIFLL72UPfbYYzNGXlrhBLatra3f91eqxFY9SI12FhA7lzg3G24wyl1kVAaJbIyiAEnaWWiJVlEGiWmanHfeeeyzzz6ccMIJlR6ODLKzzjqLAw44IFDWsZyi6eKMRgVIpC/pdJoWnB6GK3iv6HVlkEg1cjJI3BJbyiCRwXPrrbcGNspls9lgBklzcBPdi/zDu4/0ZwSDAsyDqWL/kmPGjAGctCC/Dz74wHutlAsuuIA1a9Z4/y1cuLDP94rUio7V3Rg4J7xV5DOolEEi1cxfYiumAImUQWGAJEKEUdnJJOIJxuV2Il7P17mIDzNlpy0rMcSN0t7eHnhcGAApVJhBMiR15etE49hcgOT9YICkVAaJepDIQAp3nRZmkFRDDxKAyy67jH/+859sscUWlR6KDLLx48czd+7cQFnHoRZTgET6kO5Ne/X0e+goel0LfFINttnGubdwP4+maXoBEmWQyGA67bTTAs/dAMkUduXQ/3yTnufzQRBr6gp+xVf7DJAog2TwVOxKtOWWWzJmzBjmzp3rHVu7di3/+te/mDNnTp9fl0gkaGlpCfwnUuu6VuV3PvfiLAjuuOOOHHfccZUaksiAGhsb6cIpczg2t5tfARIZLO+8847XD8J1CKfz2fevY9QVRzMCJ4N0LcvJkGafffapxDA3ynbb5UuS7L///gO+3x8QSSaT2mE5iBrGls4gcQMkyiCRjVFUsqojjpXN9yAxqyCDRKTc1I9O+mJ1R7zNgN0KkEiVuvvuuzn66KN5+umnAWfhuSNXYqudcZUcmoTMhAkTAs/dAMmnuIxhHeN54szl3mtL9nkMQAGSIVDWK9H69et5/vnnef755wGnMfvzzz/PggULiEQinHXWWXzve9/jrrvu4qWXXuLUU09l3LhxHHPMMeUclkjV6Vrt7E7tpgMbm3POOYfnn3+eAw44oMIjE+lba2srr/IkABOZDuimWAbHyy+/zJZbbslee+0VOL4TBwFgdCS9nVzuTsSpU6cO7SA3waGHHuoFOc4444wB3+/PMKm38lqPPPIIM2fO5J///GdZvn+8xbmZSK93djr3l0GiAIkMpHDHvLGqASudD5pUSwaJyGD5YNp/io5Fcc6jyiCRQkY65j12NwP6aQOIVINp06bx17/+lVmzZgFO4O4N/g3ANuzi9asT2VwjRowIPL/gggt45plnvI01rmUsZFH6dSAfSC68xipAMnjK2sHt2WefDeyQ/PrXvw7Apz71KW644QbOPfdcOjo6OP3001m9ejV77703999/v048Und61zqLyu5CXywW6+/tIlVh6623pje1HrqgkWGAAiQyOO65556Sx+MUzw+66WSnnXYq84gGx84778xjjz2GZVnsvffeA76/MIOknuy777689NJLZfv+0ZSzGJPpKh0g8Z/LVGJLBlKYQRIhguX72FRDDxKRwfTBjs8w+tVdAsdUYkv64gZI3M2ARa8rg0SqkGmarGQxAA0M07lNBk3hmsmbb77Jz3/+c/6HD9FMviRzL10sWeKU4HcDIYXZJzp/Dp6yBkj222+/4pRzn0gkwiWXXMIll1xSzmGIVJVsNsvPfvYzRo0axamnngqA3R1MOVaARGpBNBpl9wN3gXsgTooYSQVIZFD01X+pp8Suwx46iMdrJ+19zz333OD31nMGSbmZKedmItNlYWVsUmucnVzKIJFNYVkWkYLEfGWQSKgZpe/x46S0iChFzLQznynVfwSUQSLVyTAMulgHQIwERrasy6dSR/z3GX49dAaep+nxSk67AZKpU6dy4okn8sc//jFwXDafQk0iQ+zxxx/nnHPO4VOf+hTLlzu1BSM9zsXWPSEqQCK14ue/+ClpnMXDJloVIJFBUVhb1ZWheKG6m47QNi/3/71aW1srN5AQ8jJIOm3+7wvv0XLlAezFx9SDRDaJbdtEC8oi/OxHV3iPDa2pSMhEoqUDJE209bnwI/XLDZC4mwF/9rOfBUpoage0VCPTNANBvQZrWAVHI2HS13UyQzBrfTjj6e7uBoKBEH+vYgVIBo+uRCJDbPXq1d7jjg7ngusGSJRBIrVmzJgxdLAKgEbaFCCRQVGYQTKHYzmTX9LM8KL3ZugNZFqEiT9AUlirVjZPNJdBsvSZLl757WoAPsznlEEim8SyrKK60b2dzvXQMrLaHS2hE4kGP9OrcEqANNFGV1dxtqfUN8PLIHE2Ax599NHss88+XonUQw89tFJDE+mTYRiBknDH2edVcDQSJn2tmRSWk36UP3nXVH8g2V96WQHmwaP9TCJDzL/o4qWg9zpRX/UgkVpjmiZ2Qy90KoNEBk9hv42TCJbi3OuOBj72seO9Rp9hnRj6Az8KkAyuaEPxZ6aTNcogkU1i2zZmwW2Ve5NrG7ouSvgUZpCsZyVtjKGJdm8DmIirMIPEraH/j3/8g1dffZXddtutYmMT6UvhzvwdORDbtrXpQTZbYQbJNuzKznyYJtoAOHruZL5x7ee5984/MbZrLBD8PPpLLyuDZPCEc0VBpIr5m726AZJ8BomzqyYaVexSasfM3bcFnF2DCpDIYBjoczR+qzG8yb9ZyMsb9P5aNXr0aO/xyJEjKziS8HFLbPkt4e2SARKVi5GB2LaNQfAGNY5z82ob6scgIWQGAyQreA+A6eypAIkUcZu099DBzJkzvc2Ara2t7LHHHqHd6CK1zV14vpkLAVjJYvVYkkFReG9xFr/jQ5zIcMYDkBoVJZZy1gTdDBJ/IMRfbUEBksGjK5HIECsVIDEKGtcpg0RqSWqkc/Hem+NDu1AtQ8v/OWqgpej11qlJnnnmmZLvD5MJEyZwxRVXsO+++/LpT3+60sMJFbvER6aTNfz973/nl7/8ZeDGJayfLxk8TomtwgyS3O4+U4spEj6GGVxGeIvnAdifk+m+fWwFRiTVzL8ZsKmpqcKjEdkwbuBuAf8FnEbtCpDIYOhv81Vv01raZyS8SgKlepDMmDHDe6w+lYNHARKRIVZq0cXodQIi6kEitciIOzuxt2EW2YwWEmXzuefGZobzbf4WeG1R80vEGg123XXXoveH0de+9jUeeeQRr063DI7G8cWZmlGcG5EzzjhDGSSyUZRBIvWmcMe/W/ISIDp3K503JcC91+2hI1AaRqSauQvSvTgL1DGSCpDIoOg3QDJsLZFIpChA4r/uNjc3893vfpdDDjmEa6+9tryDrSMKkIgMsZIZJF6AxCmxNWbMmKEfmMgmmnlGu/fYflyfXdl87uL0NObQSGvgtY74iqL3ayFGNlbj2BgffXyrwLGor8m2MkhkY1iWhVFwW+X1IFEGiYSQf6HGwiJL8Dr8l7/8ZaiHJFUs4tsMqACJ1Ar3PJfGKb8aI64AiQwK/73FBKYFXss0O5umE4lE4HhhKa1vfetb3H///UyfPr1Mo6w/CpCIDLHSJbbyu2oAJk+ePOTjEtlUseb8xdp4cnwFRyJh4S5OF5asAVibWtLn+0U2xtg9GwLP3QwSCF6rFSCRgTgZJMHzVcItsaUMEgmhYAaJTYZ04PX//Oc/QzsgqWruvW43nYHa+SLVzF2QTucySExiZHo1J5TN5967TmA653N74LWeMcsAvAwSl3qNlJ8CJCJDzL+Q5wZIzEwufY4ODj/8cEWBpabEm327CONaqJbN5y5IlwqQLGx7vujY2LGqdy6bZsTOSe+xP0DiNmsHBeBkYKVKbMWUQSIhFolEvMdLeIsLv/2twOs6b4qffzNgS0txbzmRapQPkOTnhOkOBUhk87nXyEnMLHqtc+q7gAIklaAAicgQK5VBYuaatH/mjFP529/+VlTXV6SaxXwBEjuW7uedIhvG689UsOB4CUfS0bDce/7www8za9Yszj///CEdn4THkfdOom2ak8LeV4BEGSQyEKfEVmEPklzwzbQrMCKR8vLfq7xlPMd2OwRLhGTTCgxKnnuv200H06ZNG+DdItWhsMQWQLpL5zbZfG6ApJt1gePf42hodtZTCgMkWiMsP/0LiwyxUmU7zIyzOGOkdMGV2hNr8gVIbC0EyebrK0CylHcDu1b3339/nn32Wfbcc88hHZ+ER+OYGLteNBIIBkh6e3u9x9oJLQPpr0k7yiCREPIv1LxtPo8ZjwRet3o1H5Q8N0DSQwennXZahUcjsmH8O/bdRu3KIJHB4N5bFJZnXcJb3ueuMEASi8WQ8iquXSEiZVWqxFY0V2LLbKzIkEQ2i5nI3xRnG7sqOBIJi1I9SJbwFhAs6yEyGNxzmD9AAjCHj/IWzymDRAZkWRZmHwESWz1IJIQMw+A6vsREtmNe7GGMWGGApEIDk6py//33M3/+fIz01gB8+ewv0t7eXuFRiWwYfyC4ly7iJOldqzmhbL7+ykm7nzsFSIaeAiQiQ6xUia1oLoPEVM86qUGRSITukctILhupAIkMisIMktSe67nsiY85x5ReLIMsPsz5nDWQr4s+ld04ie8A8E76poqMS2pH6QySXImtqAIkEj6RSIT/8ij/5VFaoi0YBRkkdlqbGepdT08Phx12GAAXcQ8AyWFa4JPa4c8gWc0HNNHG+6+sJDt6LVOmTKngyKTW5TNI8p+xl3kMyH/uUqlU4GuiUS3fl5tWGUSGWMkeJNlcBkmDbiakNnVNWAKArU01MggKd9XEWiJkcM6dKuMmg61pvLNg08oo71g74/JvWJ0Y6iFJjem3xJahc5aEj3+zgmmaJUpsDfWIpNp0deU3TSVxyiSYTZUajcjG85/nVvE+AOef/m2mTp3KU089ValhSQgU3uuu4gN+w9lA/nM3efLkwNcog6T8FCARGWKFJbbuv//vXoAkqgwSqVGR3LqQrVL9MggKd9VE4/mFRzewLDJYGsc5Nycpmr1FbX/K+4r3VldiWFJDSjVpT7gBkqgCJBI+hQGSSFQ9SCTIf8+bwLnJ9fctFKl2/gySdawC8tnGr776akXGJOHizh3f4j/00Okcy11fC7OUFCApP12hRIaYP4Pktdde46jDPuKdGM2UMkikNnkBkqw+w7L58iW2nEVqf4BE/SBksMVbTJIjnM/YGLYC8os5AP/390d54oknKjI2qQ22bXufHVeSZueBqYViCZ/CAEkhldgS9543gpEPkDRr+Ulqh7/voY2zQSuSW7fRhi3ZVP5qCO6GrCzFu0y33nrrwHOV2Co/XaFEhpg/QPLwww/ndxgC0UbdTEhtiuR2yKrElgyGfNpxLoMkoQCJlNfInZ1+EeOZCuTLgbiPL7/88oqMS2qDZVmcwLcCxxrcAIl6kEgI+YMipmkWzf/sNFLn3Hte/4aDWJPudaV29PbmawVauJu3nCVU/5qOyMbwZ9e5G6XdzxfkAyiRSITLLrvMO64MkvJTgERkiPlPiG+//bZXziNNb2CXtEgtiZi5Gx6tXcsgKGzSrgCJlFvjBOemo4l2ABKBAEkTjY2NJb9OBEr3RnLLcESUQSIh5M8gMQwDOxv8nKvkqrgLyO6GgywZYg3aAS21o7u7O//EcP/n3JP4gyciG8P/2ekvgwSCjdoVICk/BUhEhph/t8Hy5cu9AEkvXSVT1EVqQSR3v2NntDNMNo9t2/z0pz8FVGJLhk6izfmMuYva/nJJe/ExBUikX9l08Xkplfss2QqQSAgVZpA0Twou3Fi6VNc99563hREAdNNBNKp7Xakd48aNyz+JONdyZZDI5vIHSPrLIAGIx+PeY5XYKj8FSESGWGGAJOELkPh3Y4nUErcHCZYCJLJ5nnzySdauXQv4SmypSbuUWbLd+YyNTI0FYGt28V6bxhzM7kRFxiW1IdvdTwaJmrRLCBUFSLaI85EHJnvHCjNKpH50dXVx0EEH8cMf/hCAKcwG4C2eY6eddqrgyEQ2zsSJE3nggQd49tlnvQCJ24NEARLZVMEASXEGSV8BEmWQlJ9CUCJDzF9ia+XKlbTndqn20IVpNvT1ZSJVzchdr9WDRDbXBx984D12d9UYsXzwWBkkUg5ugCRFC3FSgR4kAOml2vUqfcv2FC8GR8ldGJVBIiFUGCABmHhwE4zthPcbsDL63Ner66+/nrlz53rP3QySjqZltLS0VGpYIpvk4IMPBsCO3AHkM0hUYks2VakSW1YfdcoTifwGLWWQlJ+2q4sMscLdBiqxJWGQ70GiDBLZPP7Jn9eDxBcgUQaJlEOi3fncNdgtNDO86HVzjUpsSd8yPX2flyK6n5UQ8me9++9fIrnDyiCpX2vWrAk8b6QVgN5YRwVGIzI47IhznXd3/CuDRDaV/xzpVktQBkl1UIBEZIgVXkwTCpBICBjuApBKbMlmWrdunffY3VVjxpVBIuXlZZBYzTTnGrX7GT26KZG+lcogcanEloSR/54lmUx6j92Sq+pBUr8K52lNuQBJOt5VgdGIDBIj2INEGSSyqV599VXvsbsZcOTo4s1ZoADJUFOARGSI+UtsASRwymr1KEAiNSwSVQaJDA63/wjkJ42mrwdJ4TlUZDAkcgGSpNVMA8OKXrf1sZN+lOpB4lKARMLIf88yfLhvYSe3uqASW/WrcJ7mZpCkE50VGI3I4IjGnU1bhnqQyGZasGCB99jNSDrt9NO8Y2rSXjkKkIgMscKL6cl8F4AGmtWkXWqW24NEGSSyuUoFSKJxldiS8nIzSJoyw/kS1xa9rgCJ9Mfqzd/M7nxOwS5A3c9KCPnvWdrb81l3bgaJbSlAUq8KM0gac5sOMgllkEjtiiWcm103g0QBEtlU/iCyVy3BV05aJbYqR6uxIkOsr4vpluyoDBKpWUYugySS1WVFNk9nZ36H4QSmAcEMEpXYknJIjuj/+mspQCL9cEtsrTE/YOSshsBryiCRMOorgyTfg2SoRyTVojhA0uYcT/VUYjgigyKedBayIyqxJZvJHyBxNwO6aymgDJJK0kqWyBArFTH2nitAIjXKiCtAIoPDzRCZwmzGsBUA8ZQCJFJe8ebi66+FxQs87DxR+UDphxsgyZAm1hj8rEQSynqT8OkzQOJmkOhSXbf897oRDFI0A2AlFSCR2hX3MkhUYks2j/8cGcUJgLhrKYUSiYT3WBkk5aeVLJEh5r+YtjDSe3wb31eARGqWkbt2R7La2SCbxw2Q7MsnvGOJ1vznyr+rRmQwbXNCsPeIgcHoSU7pGDujAIn0zS2xlY2kiTYGb68iDQqQSPj4S2yVDpDoWl2v/BtZGhnmlSSiQQvKUrviydxCtkpsyWZyAyQmUXbjSADiLaWX5v2ZSlOnTi3/4OqcAiQiQywYIHFuKNL08k/+RGNjY6WGJbJZ8gESBflk87g31g20eMfiLfnPlXqQSLns/dMxgefvxl9gm223dp4og0T60fWe8/9sJEOsqSBAktJWegkf/6auYA8S51xp61Jdt/wBkjbGArCGZUQTukeQ2pVIOQGSSC6DRCW2ZFO558g5HOsd89/r+jcD7rDDDowYMYL999+fkSPzm6ulPBQgERli/pS6ZpwbiveZ7zxvbq7ImEQ2l5lwbogNSxkksnncAEgjrd6xviaNIoOpcVwwdf1vI3+cT3lXgET68fb3nM+OFckUlWszUjpnSfioxJb0xX+vO5zxAKzgvUAtfZFaowwS2RyPP/447777LpA/R7Yzznvdn0Hiv9dtaWlh4cKFzJ07d4hGWt8UIBEZYv6LaVMuQLKOlc7zpqaKjElkc7kBkmy3zX777cf//d//VXZAUrPcAEkD+XJH0YZI0esi5fDMNn8A4HYupTuxFjOWmyorQCJ9yHTnz0kvNv+d+LDCDBKdsyR8BsogQR/7uuUPkIxgAuAESFQ/X2qZm0GiHiSysf71r3+x9957s/322wP5c2TEtxxvJks3aQdIJpNEIroPGQra6isyxEoFSNbnAiQNDQ0VGZPI5oqmnAt8JGvyyCOPsP/++2unv2wSN+240Rcg8c8JFSCRcnpn/JPc8cZ1dLCGKeYUTDeDxNKeIimta5lzo5shzcvNDwcy3gDMZp2zJHz8PUj8JYINZZDUPf+9rptBspxFNCuDRGpYssGpJ+1mkKjElmyoG2+8EYB169YB+QBJnKT3Hl0zq4Pu9kSGWKkSW24GiSLDUquaWp3gXgzd/MjmsSyL4ziXBM5n6n3epH2GbwKpwJuUkWEYdLAGgFgspgwSGVDXB85d7TpWYphGIOPtVZ7CHK1dphI+/gwSf+mkiKEeJPWup6fHe+yWkFmpDBKpccmUEyCJKINENtKf//znwHN3PbCZfHnKsXvnN0rrXrdyFCARGWLBDJI2IB8gEalVze3O7sEU6qMjG8e2be644w7+8AentFE2m2V/TvFe/z7HYERVYkuGhn9XdCwWw4y72XEKkEhpPaudAEkXa4lEIoHNLs/xd21+kVDyB0j8C9+G26Rdu2Hrln9nvbtDuodOBUikpimDRDbV0qVLA8+z2SwRDHbhEADuHftjokktzVcD/RREhpg/QOJmkPTGO/jMZz5TqSGJbLZhw/OBkW3YtYIjkVrzzDPP8PGPf5yTTz6Z9957b8AAiHbVSDkVLvqVKrFlWRb77rsvH//4x4d6eFKFbMs5J1lkvQBbBmeu9wb/DgTdRMLC/7kOZJDkTqERW4HBeuXPIDFyFd0zpNWkXWpassEJ9rUyClAGiWy4wnvXTCbDTD7kPV+ZXdLv+2XoqAeJyBDzl9hqoAWA6268khmfGFOpIYlstpbhTUAXAAfyKd5lXmUHJDXj+eef9x6vW7cOK5sPkDzA9UXvVwaJlFOfGSS+AMnLL7/Mo48+Cjg3McoQqG9uKSEby/ss/LT942RWGnzA2wqQSCj1WWLLC5Doc1+vuru7vcdmbrnJIqMMEqlp8YTz+Z3IdsziUNLppQN8hUhpmUyGrdjJe/5G+j+B1xUgqRzNXESGmH+3gYlzoU02aUeN1Lb2cfmG2tuzHz/jGTqWaGeNDOz888/3HmezWezu/KLLffyi6P2aNEo5FQVIEs7nMWLlP5f+z6ACdoLt/i8fLOuOrmMx8wEUIJHQCwZI3Kw7BY7rVWdnp/c4mrvXzZJRBonUNDOWnwd+hh8xc9HhFRyN1CrbtslkMrThbI7+Mz9ifde6wHumT59eiaEJCpCIDDl/gMSdNMaSSuaS2tbc3My/uT9wbP4f11RoNFJLGhsbvceZTIZIT74cQ5qeovdrQVrKqTBAEnUzSHy7of0ZI9msCu3XOzeDxMLyPj/+z5ECJBJG/oz4UhkkqMRW3fIHSExfiS1lkEgtM7LB9Zrdlp5QoZFILUun02QyGUYwEYA1LPWy7p544gl+8IMfqPR+BWlVVmSI+W8o3AySWMrs6+0iNSGRSPBA7FfMSh/qHYu3aFFIBubfjZ/NZr0ASQ8dJd+vAImUU2EPEqdpYhbTyk+ZFSARP7cHCb4MEgVIJOz8575SGSTqQVK/Ojry8zc3QKIMEql56xXgk83X29tLdF0jk9kegEW8xtVXXw3AnDlzmDNnTiWHV/c0YxcZQrZtl8wgMeK6iZDa1zNsFTeQL5cUb1HgTwbmD3hks1mMDqcJ4jpWlny/SmxJORVmkMQanPOYacVLvse/6UHqU74Hie19NvyBNgVIJIz8ARJ/ZoChDJK6t3Jlfv5meiW2lEEitc1eV3xfm98gIVJa4ca+dDpNw4pRzpPxHfx70aOceeaZFRiZlKIZu8gQKtxp6u6qMRUgkRBoaWmhi7Xe84iuMLIB/BPHTCZD64KtAVjDsgHfLzLYCgMk8UbnOh21fTuklUEifrn1EcvXpF0ZJBJ2/nOf/5zoZZBY+tzXo2w2y6pVq7znyiCRsBi2Q3GAZPE/S2e7i7h6e3uLnqdWjwTAmNjF+PHjKzEs6YNmLiJDqHCnqakMEgmR5uZmXuEJ73m2V7tqZGCFJbbGvDbLeUy6ry8RKZuiHiQNzvMYCe+4AiTi5+4gtX0Bkmi0dEk2kbDo69zn9iBRia36tGrVqsC8zh8gUQaJ1LLxR8a4gfO4mMN4j9cB+OCdFRUelVS7wgBJOp2maY2TQRLdorsSQ5J+KEAiMoT85bXAFyCJ6SZCal9LSwsWWf7LYwBku7XTXwZWWGIrE3Mmiy/nPkciQ6mwB0nMFyApVd5NARKxfZc6ldiSetHXuc/IZZCoxFZ9WrEiuGCsElsSFolkgme5l+UsopM1ANx5218qPCqpdj09PYHn6XSaRNcwAKJjtBmw2mjGLjKECgMkbg8SldiSMGhpaQEgg7NTIt2thUMZmH/ROZPJYBlOpt270ZcAOOSQQyoyLqlPffUgiZPyFgQLP7NS52z3f6UzSBQgkTDqO4PEbdKuz3296e7u5pprrgkcU4ktCQv/5zeTy3JfuWx1hUYjtaJUia1oxvksmU2VGJH0JzrwW0RksKjEloRZc3MzkC+NlO5SgEQGVtSkPeucF7/yjS8x/UOT2GOPPSo1NKlDRQGSJidAEiPhfVYLP7NS39wMEgtLGSRSN7bffvuSxyPKIKlbN910E1dddVXgWD6DRCW2pLb5AyTuvW5DrLFSw5EaUSqDxMw6ZXujDZUYkfRHARKRIeTPIDEwMXJJXMogkTBobHQmie6uGmWQyIYoXGw2M84NdGpYnEMPPbRSw5I61VcGSYyEFwxRgET83B4kYCuDROrGrFmzuPfee5k8eXLguJH76KsHSf154403io651RIypJVBIjXNH+AbN3EMLARbFZJkAIUbpAMZJI26TlYbBUhEhpA/QOLuqAFlkEg4JBLObgivxFaXSs/IwPzlitLdGQzbmZoYyeJ+DyLlVtiDJN7ofB5jJL3AiEpsiZ+bQWIrQCJ15rDDDis65mSQ2MogEQzfUpMySKTWBQJ8UWceaGd0npP+FfYv7O7uJmo5ayZmqhIjkv5oxi4yhPwLKaZv0qgMEgkDN0Diph1nlEEiG8C/Gz/TmZ9Emg06L8rQ8y9mx+Nx4o1uBkmcTFoZJFKCrweJSmxJvTPUg0RyzECARBkkUtv8n99ILtanAIkMxH/PANC5vssLkESVQVJ1NHMRGUL+DJKoP4MkppOj1L7CEltv3tRVyeFIjQgESDqclcYsaQWOpSL8i9nDhw8n1phf6O5d72xyUIBE/NwSW5YySEQwogqQ1KvCndL+e90saW8jlUgt8m98aGxymkdMmXdI0edexK8wQNKxKr8+os2A1UczF5EhVKrEViSav6EWqWXHH38848aNo4t1APQstVnzZs8AXyX1LlBia63zuJsOLSpKRfhvgEeMGEGiKb/Qne5UBokUs72Pg10yg0RzPKknES+DRJ/7elO4UGwWlNhqaWkZ6iGJDKqnn36auXPnklzT7h1b8ZLudaVvhefFzg+c9cBeujCTuk5WG60+iAwhf4mtqBcgqdRoRAbX9ttvz3vvvcfzjfd6x7pXaPFQ+udfbP7tVTcB0MHqwAKjyFDxB+ZGjBiBGTVJ5/oq9XYUB0jUg0TyPUjyJbaUQSL1yiuxpWWGuucGSLI410kFSKTWzZ49mwMOOAC689f4rmWaB0rfCjNIut53nq9mKaap62S10U9EZAiVyiBRg3YJm674GpbwNgC333InHR0dFR6RVDP/xHH5gjUAdLBGi4pSEf7P3ciRIzEMgzTdAKRLBEiUQSLkdgfaWF6pSfUgkXqlElvicu913d6Ezc3NlRyOyODpyZ/fsl1WP2+UeleYQXLdZb8FYA1LtRmwCmnmIjKEurryNQfdDBIj1te7RWpTLBbzFhSv/fkvuPzyyys8Iqlm/oljI62Ak0GiRUWphO7ubu/xlClTALzzWabTuQlWgET83AwSC9tbAFQGidSrSG69RyW26k/hQuBOHAQog0TCx+7OL2ynO9WDRPpWmEGSwOlf08k6zQ+rkH4iIkPIv5Pe3VWjRsQSNtFolEyuJE2UBI899liFRyTVzD9xTNEEQBfri3bVHHDAAQAcfPDBQzc4qTsLFizwHo8YMQKANE596VI9SFRiS/w9SJqanHOYMkikXhlR5/NeWGJLjYzDr3Ah8KOcA0AKJ3CsDBIJi9Sund5jZZBIfwrPi/nMuozmh1VIPxGRIeQPkHgZJAn9Gkq4RKNRb0ExRpypU6dWeERSzfyLJu6kMUNv0aTx9ttv57rrruNPf/rTkI5P6ktnZ2fRsXTEDZAog0RKsN3/WV6ARBkkUq+8HiS+Elv33nsvo0eP5r777qvUsGQI+K+H/gbtAJMmTSIWU9kECYdRZ67xHrvZxSKlFG4OyPdmSmt+WIX0ExEZQsEASRyAWEK1ByVcnBJbboAkSTKZrPCIpFrZth2YOEZ9AZJC7e3tnHHGGbS3tw/Z+KT++EtsufrLIFGARGzLOYf5S2wpg0TqVakAyRFHHMGyZcs4/PDDKzUsGQL+62E8V0bGNX/+/KEejkjZxIeZPM09AGS6lB0nfXPvGcaMGQP4AyTKIKlG0YHfIiKDxQ2QXND8R8avmwmAqQwSCZl4PB7IIFGTdulL4a6afIAkzbp16yoxJKlzpQIkmUgv2OpBIqW5Jbb6yiCJRFRKVeqH16Rd+zDrjr/k5JlcF3hN2SMSJk61hGB/OpFS3HuGVCrF1KlTMV93S2yl1aS9CmnmIjKE3IViNzgC+WaGImGRSqUCGSTr16+v8IikWhUHSJzMugy9rFq1qhJDkjpXOkDinM8yuTrT/s+tepCIvweJ27dGJbakXrk9SAwM9R2pM/7r4WR2qOBIRMorFovR6wZIlEEi/XCvg4ZhEI/HlUFS5ZRBIjKESu2k71iUrsBIRMonGCCJs369FrqlNHdXzU4cxHDGBxrXKUAilVAyQGL0QhYynblSSsogEb/cza+NzZZbbgmoxJbUL3fjlxsgUQZV/dD1UOqFv9/mi/+exx6MrvCIpFq59wzFARL1IKlGCpCIDKGXX365qGld1zJNJiVcCjNISjU9FoH8pPFz/AyABfwXcDJIDjnkkIqNS+pX/xkkCpBIsXSPs2vawmLrrbcGlEEi9cvMZZBEMJVBUmeUUSn1IhqNehkk//j7I5zOgRUekVQr955hq47Z7P/u6azgfcApJ635YfVRgERkCD3//PPeDmmRsEomk2RyAZIocS0gSp9s2w4EjUcwEYDDjzqMPffcs1LDkjpWOoPEyfTMdhX3INGCUH2ybZtVq1bR3t7OooWLgCTJZILJkycDyiCR+uX2IDEwsCxLNdbriK6HUi9isZjXgyRGosKjkWrmbhQ4avEFADQxHACLjK6PVUgzdpEh1NnZqQCJhJ6TQdILQJxkYDFRxM+yLBoY5j1P0ADAhC3HVWpIUufOPvtsAE444QTvWMZwAr7ZbmWQiONzn/scw4cP56GHHmLdWqfPVlNzo1dOSBkkUq8M0w2QmJr/1Rk3QGJoD66EnGmavgBJqsKjkWpmWRZNtBcdVw+S6qSfiMgQ6urqKiqxlWhX5FjCxQmQOJNGZZBIf2zbZn9O9p6750czrprlUhkXXnghTzzxBDfeeKN3LJvLIFGJLXH99re/BeDiiy+mqyu3SBLPb4BRgETqlRFzS2zlm7T7fx8kvNzrYeG9rkjYGIbhldiKK4NE+mFZFsdxbtFxldiqTvqJiAyh1atXByaNsy8exUcf27KCIxIZfMEeJAntIJQ+WZbFh/lc0XEzoQCJVIZpmsyZM4dEIn/D6wZIlEEihTKZDN0lAiQ77bST91g3wFJP3B4k/gwSBUjqg5tBEvVVS7iX6+CCf1dqSCJlYRhGoN+m5oLSF9u22Zpdio4rg6Q66SciMkR+85vfAPldNdFUhN2+PYr26clKDktk0ClAIhvqwQcf5AXmFh0345qeSPXIGk7JwGyuPYl6kIgrm83S3ZnbRZqIe8ePPvpo73FPT8+Qj0ukUiK5EltT2JVMTy6jQHXW64J7PfRvBryXazG36KrUkETKwjRNXwZJkq4ufcalNMuyeIXHi45nlUFSlfQTERkiX/rSl4D8pNGIaYe0hFNTU5MCJDKg5cuXc9xxx7GeVUWvqcSWVJOs6TZpVwaJBGUyGbq73QBJftd0W1ub9zgWU+85qR9uBgnA4n84i4bKIKkP+RJbzjkvi3PtVIBMwsbJIHF7kCTp7Oys8IikWtm27QXT/LJq0l6VFCARKbMXX3yRb3zjG/T2OjtQ3UmjoQVACaljjjkmECDRAqKUsmLFCsCpU14o2qDpiVQPt8SWVSKDROe3+pbJZMikc4uC0fyNbiQS4a677uLKK69ku+22q9TwRIac26QdwLZVYivs3nnnHb7whS9w3333FWWQZHIBEu2SlrAp7EGyevXqyg5IqpZlWSX7MqnEVnXST0SkzHbccUd+8pOfeM8baAHA1qZ6CaloNOoFSKLKIJE+5IPGxbtnoilNT6R6WFFn0Wf5+yvZfffdee6557zXFCCpb9lslmzGucb5F4YBjjrqKL7yla9UYlgiFZP1V5Qz1aQ97H7605/yq1/9isMPP5x02gmIxHHKR7sbYLQIKGFT2IPklltuqfCIpFpZloVR4l43TY/OjVVIsxWRIfZpLgegZ6UWVSScYrGYN2ncmp15bc3MCo9IqpEbIIkoQCJVzu1BsvDtxTz99tM8/fTT3mvqQVLfMpkMVtYJkJimzlsibilCyG8GU4AkvPw9lt566y0ATuH7QD5QojIyEjZOBolTQjBOkjfeeaeyA5KqZdu2AiQ1RD8RkSHWzthKD0GkrKLRKD3ka7Ee/s75FRyNVCv3prpUBklMJbakithRZ0ODu9jjpwyS+uYPkBhRnbdELF+p9Uy3c35UgCS8/D2WVq9eTZwUkwhujNIioISNaZqBctKvv/56hUck1copsVV8r9tLt4LHVUhXK5EyclONRepJLBajm47AsWyvymxJkDJIpFZYuSbtMQVIpEA2m8XKOjvmDWWQiJDpzGeQZLrc7CotAoWVv4xud3c3x3J20XsUIJGwCfYgSdGxqqvCI5Jq5ZTYKt4kkKZb58YqpJ+ISBl1dnYGnrcxpkIjERk6TgZJMEDy+NeXVGg0Uq3660ESa9RiilSPbNStq54oek0ltupbIINEARIRJn64yXuc6VYPkrDzB0gsy2JXDi96jwJkEjZOD5J8utzpr/6eFS919/MVUq9UYqu26CciUkaFAZKT+V6FRiIydEplkCx5qrOPd0u9cgMkRompiDJIpJq4JbZiJQIkyiCpb5lMBquPJu0i9WjkTimWsQDIZ5AoQBJe/gAJEFgI/AknO8e0CCgh42/S7vrLvm9VaDRSzforsaVzY/XRT0SkjAoDJNswy3t82F+3GOrhiAyJUhkkPau0iChB+QBJ8cJJvFGLKVI9bNPJEomTKnpNAZL6lslksCxnl7wZ1S5pEYCFkVcAyHarxFbYFV4DV/Ce9/htXgD085fwMQwDi+Bnv2e1yklLMcuySpaTVomt6qSfiEgZFQZIPuBt7/GYPRqGejgiQyIWi5Eh2H+nY7HK0EhQfxkk8SbdTEv1sGPKIJHS/D1ITDVpFwEgi3N9f/O1d7jllluIRPLZVYUZB1LbCn+e7/MmAP/mPu+YFgElbEp9pg3t7ZISbNv2MkgyiXyv/5gMqQABAABJREFUmjQ9Ch5XIV2tRMqoMEDSwRoAdv/+KBpG6yoq4eSWUriOL/EXfgxAttvu70ukDvXVpP02vk80oQmjVA8r6gR4DUwO5FOB19SDpL5lMhls9SARCchEnE0yt958OyeddBIvvvii91o6ne7ry6QGFQZIErlMy1d4wjumAImEjbuw/Sz3escSI/Q5l2JOk3bn87J2zELnGBadrNW5sQrpJyJSRl1dXYHnjQwDYPRuyh6R8IrFYgD8l0d5iv/1jtuWgiSSV9ikfQlv8Q9u5p/8STtqpLrE8gtAx/INJjHTe64MkvqWTqfzJbYUIBEBIBNxru8x4kWvudd+CYfCa6BbirKX/D2w5nQSNu7C9g2c593rJsepD5k4GSNLly71njsBEmfz6KqJ87mJ/+EqTqOXLgVIqpB+IiJlVJhBEiPp/L9Rv3oSXv5mnDb5hUVbVRXEJ59B4pwP7+Eq7uRyQLsNpcqYwZNXimbvsQIk9a23txcrl0FixrQIKAKQiTjNi6MlyhIqQBIuxRkkzibAY0/4iHdMczoJG/9n+nkeAMDKaCOgwMknn8zo0aO54YYbgGCJrSxpnuZu5vMsoHNjNdJPRKSMChdOojg7642YdhhIePkv9lYgQKKJo+TlM0icgFrW1+xQuw2lmhSWTtqPk71dsiqxJXbu1GWoB4kIANlcia1SfZsUIAmXwgBJM+3Og6b8tVGLgBI2pe513X5kUt9uueUWAK699logWGLrQ/vvG3iv7nerj65WImVUOGl0FwKNuAIkUh8CGSTaaC0+PT3ODlM3g8T2BUh0My3VpPAGZib78lWuB5RBIqjElkgBtwdJVCW2Qq84QDLCedDS4x3TIqCETTBA4swDrbQCJJLn3h/Ytu0FSHbedSduvfVW7z26360++omIlJFtBy+UXoAkVonRiAw9m/zvgEpsiV9hD5K99t7Le00301JNSt3ATGYHYiQVIKlTbW1t3mM7t1HajOm2SgQga7g9SIozSObNmzfUw5Ey8l8DG2jx+s5EhimDRMKrZIBEGSTiE4k4G6Ity/LWACPRCHvtlb/f1bmx+ugnIlJGhbtqDC9AogwSqQ+WLytAJbbEL9+DxAmGRBP5oIgmjFJNTNNkPs8UHW+mXQGSOtXe3u497u12zmXRuM5bIgBZwy2xVZxB8u9//3uohyNl5L/XbWAYAGYjmIn8va7mdBI2pUps2epBIiX4S2wZURg/fjw//vGPueaaawJ9W6U66CciUkaFGSRuDxJTJbakTqhJu/SlMIOESP58GYspzU6qh2EY/I5zOZ/baXHLhwBNtKkHSZ3y39TaWWdOZyiDRAQAy3DOizGSRa9psTxc/AESt0F7tDESyARWVrCEjZsdAPnNgAveXcD8+SmmTJlSqWFJFXE/I06T9lwGiekcO/vssys2LumfZigiZdRnDxJlkEjInXrqqZimGSyxpdRj8SnMIOnNql61VCfTNFnLct7kP4HjTcogqVv++Z27M1AltkQclukESEr1ICncPCa1zX8NTNIIQKwpGCBRUEzCzN0MGMHkm9/8ZoVHI9XGsizvWujPrJPqpKuVSBn5bwIiGPn0OgVIJORuvPFG3njjDWWQSJ8KM0h60j39vV2kYtzFnTTBz2gjw5RBUqf88ztTczuRADdA4mYUBF6zNBkMk2AGiRsgMQJBEW16kTBzM0gMjEBmidQ3fw8SBUhqhwIkImXknzSavop2uomWelCUQaJ7YiG/sJjPIHGmIr2Z7oqNSaQ/fQVIosQVIKlTpTNINLcTAVgXXw7ASLYoek0ZJOHiPxcmcwGxWLOhDBKpG24PEgOTVCpV4dFItbFtO19mP6FzYbXTT0ikjPyTxmGM9B6rB4nUA/fmyN1ZoxJbcscdd9De3s59993nyyBxJo3RhNqiSXVyz2XddASOR4mRTqcrMSSpsOAGGOccph4kIo41ifexsGhkGFuyI5/jZ2zBDEABkrDxl9gazgRAARKpLxbORhkDk4aG4qw5qU/KIKlNulqJlJF7E7Arh/Md7veOK4NE6oF7Q+SW2VIGiZx44omsXr2aww8/3AuQxHKTxrPPO4ttttmGX/7yl5UcokgR91zWUxAgMYkqg6RO+Rd51YNEpEDMopM1AJzB1ezEQZzLnwAFSMLGsiwSNHA5j3E0ZwEQbzbVpF3qRj6DxFAGiRRRgKS2aLumSBlZlsU4pvBpLg8cj2ieKHXAXVS0sDAB29JNcT277rrrAovJb731FgAxEgBsue1k5s+fX5GxifSnrwwSUxkkdSuYQZILkMQVIBEBiEajrGcVTbTRSGvgNQVIwsWyLPbhEzQyzDvWMDKmDBKpG/keJMogkWK2ZXv3ugqQVD9drUTKyLZtDuULRcfVwEvqgXtz5GWQZPt7t4TZokWL+NKXvhQ4tmzZMiAfIIkmdV6U6uQu7kQIfkajxJRBUqdK9yDRbZUIOPO/9awqOp6kSQGSkHEzSPxSIxQgkfrh3udGMHR+E49XYiud/0woQFL9dLUSKSPLyqeYi9SbfIktZ2KgElv1a926dUXH1q5di0E0v7iY0pREqpO70JOiJXhcGSR15+GHH2bXXXflvffe844ZuYR8BUhEHH0FSCYwTQuIIZPNZlnPysCx1IhoICiiElsSZv4Mkp6engqPRqpFPkCSP2YmNU+sdiqxJVJGtm3TwepKD0OkIvwltkAltupZqUXktWvXev1HQBkkUr3cc9m7zAscjxKjQxkkdeXAAw8sOmaoxJZIgGmaJe9/4qQUIAkZZzPg2sCxhrFRzG5lkEh92P/A/WGu04PE7a8o4rLT+ftbZZBUP12tRMrIsiw6CjJIRh+kuKTUB5XYElepMkTpdJoYSe+5Jo1SrdzFnZf4B7/lGzzPQ4CatIvD7UESVYBEBHAzSFYWHU8oQBIqPT09gXKDAHMbrmfLo5pVYkvqxm67zwacbFJlkEghKxczsyMWhql73Wqnq5VIGVmWRS/d3vOvshO7/7axgiMSGTr5ElvOzVNvt3bV1Ku+yhC5GSRpeogYmjRKdfIv9PyHv7OEt5zjKrElOIEyAEMltkQAt0n76qLjyiAJj/PPP59UKsVzzz3nnQPn8U+ebrsDM2EErpsqsSVhZhvOOU0ZJOIPGLsltuzcbYJlakNVLdBMXqSMbNvGyP2a/YcHsMgSjSqDROpDLBYD8iW2Ojs6KzkcqaC+dtm7DdqVPSLVrHD3awbnbkdN2gXyPUiicS0CikDfPUiUQRIel19+ObZt09vbSwS3pG6WeNzZ+KIMEqkXkdxH3cCkt0cBknqWzebLZbgBknSXc8w21Yy1FuhqJVJGlmV5k0bbbeClSaLUiVgsxp///GevSXvHegVI6tVAAZLG1oahHI7IRinc/ZrNBUiUQSIRIl4PkmhCARIRcM6ZXQV9KcDJICksySS1zy0zaJElkXDmdf77Xd37SqhF8kHfrq7uft4oYVfq+pbuzN0Dm6o1Xgt0tRIpIyeDxJ00OidMTRKlnhx77LHexLFjnQIk9aqvReRoLkASTSmDRKpX4XXbHyBRBkn92oqduZzHGMvWAJgqsSUCOAGSwsbdAMfwdWWQhJCbRddXBolKbEmYRXwf77Wr11VuIFJx/gwSV7o7l0ES1eaAWqCZvEgZWZblldiylEEidSqS+8irxFb9GiiDJJrUeVGqV3EGifN5Nokqg6SOrFsXXPg4he/RQIv3PKoAiQjg9CDpJP/78jpPe48VIAkf/72um0Eyffp0DMNg5MiRjBgxopLDEykrt9cmwJpVayo4Eqm0/kpsEdW1rxZoJi9SRrZtE8llkLgXT+2ikbrjBUi6KjsOqZgBe5Aog0SqWOHGhl6cc1mSRmWQ1JE333yz39ettM5jIuDc67jnSYAH+Q0Aa1muAEkIGb4SW24GyZQpU1iyZAlvvvkmyWSyksMTKatZu+/iPV6zUhkk9axUgCTT7awBRpRBUhPULVqkjJRBIpLPIFm3Zn1lByIV09cu+3wGiRYWpXoVbmxYzVIAhjFKGSR1pLMzmAXZQzDoP26vpqEcjkjVMk2TNbnzJMBaVuQeRRQgCYnGxkY6OjqAYIDEzSABGDlyZEXGJjIUXnvtNV577TX2O2hfXua/AKxf1VHhUUkllepB4gVIYkM9GtkUCpCIlJETIFEPEqlvZtSAHli+bHmlhyIV0ncGSa5WtUpsSRUrvG6v5gMAWhlFT09PJYYkFVB4HusmH/R/mxeIJWcO9ZBEqpJpmmTJ8A3mANDCcACixBQgCYlkMlkyQBKLaRVQ6sPUqVOZOnUqAEYCrB7oXtfrVBCJaONXPSrVg8Tqca55kZiufbVAKxIiZeQ0aQ9mkKjEltQbM+p85ue9NK/CI5FK6TuDxCm7oCbtUs0KAyQrWYyFRRNtdC+3WL5cwd96UHgeW8My7/GfIz8a6uGIVK1o1NmD2c16ullPBud3xySqAElI+BeA/U3aFSCRemQmnN8Hkxi9vb0VHo1Uij9A4maTZHMBEiNekSHJRlKARKSMLMsq6kGiDBKpN/GEc7P00IMP89Zbb1V4NFIJfWWQRJVBIjXAv7Hh5JNPpodOlvIOABOZzvPPP1+ZgcmQKjyPJUgBcDMXsij6ciWGJFKVCjeDZXMBEmWQhEcwQOLM4bJkveCYSD2J5u5jYiSKynFK/SgZIOlVgKSWaEVCpIxKZZAoQCL1Zlj7MAAMIrz77rsVHo1UwoBN2tWDRKqY/7rd3NzMX//6V4zRTmmtNsZ4ZUYk3IoDJA0A9NKl7GARn8Lfh3wGSQzbUoAkbExfiS0FSKQemQlnnhglRldX1wDvlrDy9yBxH7sltoyE7nVrgVZqRcqoVA8S3URLvTFN51ITwehzoVzCra8SW1uzCwDRlKYjUr38123TNDn66KPZ7yN7AtDCSAVI6kTheSyeyyDpoVObX0R8+sogcZ5okSgM/BkkEQVIpM65G72iyiCpa6UySJpXjQfAjOvaVws0mxcpk2w2mwuQBDNI1LRL6k0kd6UxMBUgqVN9/dwnsz0AOi1KNfMvfrsLf43jnEWgVkbqZrhOKINEZMMULpJn/AGSjJYfwkYZJFLvol6AJK4MkjrmD5Bks1ls22bY8okAxOOJSg1LNoKuYCJl0NHRwbRp01i0aBEf5VzA6UESiUQUIJG6EzGdz3wEIzBxkPrRV4DEbey5zQnDhnI4IhvFv+DjLoQn2pz/p2hWBkmdKDyPKYNEpLTCRt1Z8r87tvbJhEKwB4kCJFLfjFx2wDBGKkBSxwozSJYtW4aVcUpszfrcpEoNSzaCZvMiZXDXXXexaNEiAF8GiaXGhFKXlj/fDcChfEEZJHWqVImtCBGaaAWgfYZ21Uj1isfjRY9jzblACQ3KIKkTfWWQ9CiDRCQgmUwGnttY+SCJMkhCwX9PqwCJ1Ltl/3budU/kIs0J65i/B8nixYvZfvvtaaINgPG7tVRqWLIRdAUTKYO+Jo2zZs2q1JBEKm5rdiaTea3Sw5AK8E8YXY20eefH5AhNR6R6JRKJosexJmeRL0EjHR3vV2RcMrTcQO8OHEA742jEyXzroZOYAiQinsIACTh9SEyikFWAJAz88zr/va6CxVLP4qTo6lpe6WFIBTz11FNcdNFF3vNly5YRI0mKZgBSo3SvWwv0UxIpg2CAJJ9Bstdee1VqSCJVoev5BHys0qOQoVYqQLItuwHQsmUMM6bSg1K9SmWQxJuda3uSBpXYqhNuBsnp/DxwvJcuEkaqEkMSqUqpVPHvQ4a0U5ZOTdpDwV9KJphBEu/rS0RC6+BbJvDgJxfRSzfpHlVLqEdz5swpOjaV2QB0GKuJD9PmgFqgn5JImUVyv2Y2WZqbmys8GpGht/fPxniPe+erlFI9KlVesBXnczFmr4ahHo7IRvFnkORLbLkZJA309PRUZFwytDKZjNc3yU9N2kWCSmWQ9OLU5Td6NA8Mg1IBkqxKbEmd2vpjw+iJridOkq6XYwN/gdSFtty97gJznvoQ1wgFSETKoHSJLYumpqZKDUmkYnY8awQvTfsLAK/ctpSzzz67wiOSoVYqgySKcwMRTWoqItWtZIDEV2LLDZDYts1LL72kXkshlclkSFIc0M2QVpN2EZ9SGSSrWAJAbJ3uhcKgVIDEVoBE6pQZi7AutQyA5d+cUOHRSLUwc/e6vXZXhUciG0qzeZEyKF1iSxkkUr/WDX8PgFZG89Of/pS1a9dWeEQylEplkJi5ndiGymtJleuvB0mclBcgufLKK9lhhx0455xzhn6QUnbpdNprzO76KacAKEAi4lMqQLKSxQDE1+teKAyUQSISNGLdlt7jr33taxUciVQL9163N6tM81qh2bxImUW8XTWWAiRStyJxZ4Hc3Unx/vtqalxPSmWQeAGSuAIkUt1KZZC4mU9R4l6A5KyzzgLgiiuuGNLxydDIZDIkaAwcW8JbgAIkIn6lSmx1sR4AI63yM2FQOoPEUoBEBGfDjIhbLSFLusIjkQ2l2bxIGfSVQaISW1KvIlHnd8KdKKipcX0pnUHifBaUQSLVrlSAxEw4n9sYcXq6eysyLhlaTomtfIDkp5xKJ042pAIkInnLli0rOpYlV3rQ0jU/DPwBEtPLIMkoQCJ1a13DB5UeglQZI3evu8deu1d4JLKhNJsXKYO+epA0Njb29SUioeYugrtZAwqQ1Jd+M0i0mVSqXMkASTK/yJfuCvYcicX0oQ4jf4DkPV7nLZ7zXlPzTZG84cOHFx1zAySRrJYfap1t24F5XcS711WJLalf/5n5x0oPQaqMe6+7/U4zKzwS2VC6gomUQekMkgwNDcXNPUXqQSSmDJJ6VjpA4nwWTJXYkipXqgeJmcwv8mW6gp9vBUjCyd+DpJvgNUwZJCJ5J510Em+//TYHHHAABxxwAOArMWLpd6XWFc7pTAVIROhqXOk9djfISv2Ix+P09gYzyt11D5WTrh0Vn6FcfPHFRCKRwH/Tpk2r9LBEBo2Ri0NmySqDROqWOzEwiREhogBJnVGTdqllpTJIjChgOJ/rwt6LWiAKJ38GSU9BgEQZJCJ5iUSC7373u+y7777eMcstsZXV70qt85fXAn+1BAVIpH65mwEhf48j9aPUOp/7OTB1r1szquI3d8aMGTz00EPec11YJUz8k0YFSKRe+csomUQVIKkz/WWQKEAi1W7s2LHe487OTsBZEI/EbOyeCNnu4Odb89hwsm3ba9LeTWfgNWWQiBTz/154JbaUQVLzFCARKRbxJY2YRLEsS3ODOlIqe1ybAWtPVVzBotEoY8aMqfQwRAaNf7e0qQCJSCC11CSmAEmd6S+DRCW2pNqNGDHCe+wPlhhxJ3sk2xP8fKvEVjg5ARKnxFZhBokWQUSK+TOrFCAJDwVIREqIBjNIenp6SKVSFRyQDKXC8yL4NgPqXrdmVMUMZf78+YwbN46tttqKk046iQULFlR6SCKDxp00DmtrCSysiNQTM56/3ESJ0d3dXcHRyFBTBonUunnz5vGnP/2JPfbYwzuWXeec15rWjySdTnvHtUAUTrZtk+yjB4lKbIn0zw2QqAdJ7VOARKSYGTWwcO53jFyAROpHYf8RUAZJLar4FWz33XfnhhtuYNttt+X999/nO9/5Dvvssw/z5s2jubm56P09PT2Bk83atWuHcrgiGyTYpN35Nbv62qswTTXskvpkxgwsshiYmMRKLphLeJUOkGjSKLVjxowZzJgxo+RrB3/wFa/0FihAElb+Els9KrElslGUQRIefQdIrEDPLpF6YhgGWTIYxIkSU4Ckzvg3Srm0GbD2VPwO7rDDDvMe77DDDuy+++5MmjSJ2267jdNOO63o/Zdeeinf+c53hnKIIhstGCBxJo2xZMV/3UQqJhqNkiFNXAGSuuQ/J87mSI7h6xi5JFalHUvNsyOsWbPGe6psgnCybZs4SQB6CwIk+pmL9E8BkvDoO0CSIZlMVmJIIhXnBEjSxIhjEi2ZUSDhVTpAonLStabqZiitra1MnTqVN954o+TrF1xwAWvWrPH+W7hw4RCPUGTjuD1IDMVHpI6ZpkkWZ+IQJVayTqeElz8g9ikuZRgjaWY4oF01UrumfsVZCJrPM6xYscI7rpvicLJtm1guQJIm+DNWBolI/xQgCY++AiRZssogkbrl3Os65zlTGSR1xbbtPnqQqFpCram6Gcr69et58803++zVkEgkaGlpCfwnUm1KldiKRHVilPrlZpAAyiCpQ6WatLs0aZRaNWZWEwApmnnvvfe84wqQhJMTIIkDkCbYR0sBEpH+uZtkFCCpfX0FSGwsZZBI3XJLbEG+SbvUBzd7xMDkK/yaYzkbcDaFAhixig1NNlLFZyjf+MY3eOSRR3jnnXd44oknOPbYYzFNkxNPPLHSQxPZZKVKbBkKkEgdM03TW1BK0KAMkjqwcOFCMhnnRsGyLBI0sDsfKXpfvKXiUxGRTdIyxmnY3UAL7777rndcAZJwsiyrzwwSldgS6Z8ySMKjOEDi/EyzZJRBInXLHyAxiHr3QBJ+3d3OGsc3+APbsgcH8mmaaCeBc58Qa1If4lpR8RnKokWLOPHEE9l22205/vjjGT58OE899RQjR46s9NBENpk/QKISWyJOBkknawFnMVEZJOF28803s8UWW3DMMccAzjnxeL7JKXy/6L2pETo5Sm1Ktjmf3SSNvPbaa95xnd/CybZtosogEdkklhcg0UJRrSsOkDjXQksZJFLH3B4k4GSQKEBSP7q6ugDYghnesct4hCnMBiDRqjliraj4qsSf/vSnSg9BpKxMldgSwTRN1vgCJMogCbff/OY3APztb38DnAXjUtkjAMkRWiyR2mQmnRueKHGuuuoq73h/JeWkdvmbtCuDRGTjZHHmfcogqX2Fc3jT16RdGSRSrwr7bSpAUj/cDJK+JNp0r1srNEMRKYNSPUhUYkvqmTJI6kvhTUF/P28FSKRWRZPOdd3NKnDp/BZOTg8SZ/FPGSQiG0cltsKj8BoXyS0pWWSVQSJ1q7AHiTYD1g83g6QvCpDUDs1QRMrAf0FUDxKR4gCJJo3hVvjz7W9HfaxRUxGpTUaidIBEGSTh5JTYcgMkwQwSBUhE+qcASXgUZ5DkS2wpg0TqVWGARBkk9WPAAEmrAiS1QjMUkTLw76zxepDEKjUakcozTZNMbkEpSlw7rEOuVAZJJpd2XkilaaRWRXMlttyySy6d38LJKbFVOoNE5zGR/qkHSXgU9yDJl9hSBonUK6fEljJI6pFbYmsVS4pey8Z6VS2hhihAIlIGpTJI1INE6lk0GsXK1Z82MDRpDLnCAIlt26zmgwqNRqQ8zGT+uh4lvwtCAZJwCpbY6gm8pgwSkf65tfkjtn5Xal1xgMT5mcbiMUaMGFGJIYlUnD+DxFAGSV1xM0hMindEW7G0NtHUEM1QRMogGCBRDxIR0zSxcBYNI5haQAy5wptny7K8YLFIWJgJf4AkX2ZLJbbCKVhiKxgg0c2vSP+yyiAJjcI5nnv9u/eBv5FKpSoxJJGKcwIk+Sbt2gxYP9wASbREgCQ9bM1QD0c2gwIkImXgX/zN9yCp1GhEKi8ajWL7MkgUIAm3Uhkk7qTxb1zDqlw2ycOTrx7ysYkMlmCAJF93Xee3cAqW2FIGicjG8HZWK4Ok5hVuBEzRDMDkGeMrNSSRiguW2Iopg6SO/OlPfwLy/Zi+zWH8hm/wDi+yZP9HKzk02UhashUpA//E0VSJLZFABomBSTbbO8BXSC0rlUHiph3/m/u5j18AMGv4rCEfm8hg8WcN7M5RzOVGQBkkYWVn8+UTMgqQiGwUZZCEhzvHO4avez9XIpBo089W6peatNev3l63z2p+jvgcf+c5/s5XR3y1kkOTjaQAiUgZZLNZtmMfpjOHOE6qsRlXgETql78HSUQZJKFXqkm7O2nM+pq1a1FRwuJYvsHD3ISNExyxbVtll0Imkskv/vWqxJbIRskHSHTdr3XZbJY2xnIQn/GOJYebGKbOg1K/CgMkKrFVPyzLIkLEt4lG97q1SgESkTLIZrN8iWvzB9q7aRijXzepX06AxJ9BokljmJUqseX2Y/J2G6JJo4RLO+NYwXtALmvK1G7aUEnnf57KIBHZOF6ARCW2al42m/VKybhSo3SfK/UtWGJLGST1xLbtwDlRmwFrl35aImXQ1dEdeB4Z0avdhVLXTNNUD5I6UlRiK2sRyzXx1K4aCZPRZ3R4j92eY6AyW2EUyTjnqzS9XqaQiGyYfA8SBY5rXTabDVzvAFomFzcnFqkn/ibtppq01xV/KWkI3utqDbC2aGVCpAx6lgZvnOMnLKjQSESqgzJI6kvRrikrPznUrhoJk3GnZOlgNRAMkCgIHD5uia3C7BFQQExkIO61Xz1Ial+pAMmOXx9RodGIVAf1IKlftm17paRB1RJqmX5aImXQs9pZGFnLCr7M9kS36h7gK0TCzWnSrh4k9aIwAGZn8gESZZBImPj7K8WjCe+4FszDxw2QpBUgEdlo+QwSXfdrnT9A4t7rTjywqcKjEqksp8SWm0GiAEk98WeQZMlgk1/n0L1ubdFPS6QMujucm2fvIqk65FLnlEFSX4puCjLKIJFwisViJQMkCgKHUK8zl+uleNOLAiQi/bPcHiQYWFn9vtQyJ0DizN8stAgsAmrSXs+cHiRugCQdeE33urVFPy2RMujpdAIk7qKJToxS79SDpL4U3RRk3Rtpyzsvgs6NUvui0SjZ3Gc6Zsa94wcffDCvvvpqpYYlZRDtagBgPauKXlOARKR//pIjVlq/L7Usk8l4GSQWms+LgEps1TPLsrwSW5mCAIl6kNQWrUyIlEF3VzBAogwSqXfKIKkvfWWQFO6q0aRRal0gg8TMZ5A8/vjj/PCHP6zUsKQM3ADJOlYUvaYAiUhp5513HvF4nEMO/7B37MH7HqrgiGRznHTSSRx99NG+AInm8yJQWGJLTdrriTJIwkM/LZFBtHjxYq666ioWvfsegLerVCdGqXf+Ov3qQRJ+hT/fSC6DJFtQikHnRql1wRJb8cBrK1YUL6RL7YrlAiQt41MA7L777t5ruqaJlHbZZZexfv16Tvjk8d6xz3/29AqOSDbHLbfcAuArsaVFYBFwAyTKIKlHwQwS3evWsmilByASJmeccQZ33303U9kNyNdlVQaJ1DunxJabQaIASdgV7qa2s6UzSDRplFrnz46L+TJIQJ/vsIl1OwGS8dNG8tajbzFu3DiSyWSFRyVS/WKxGBEznzG6dvX6Co5GBoORW0ZSiS0RRyKR8AVIYgqQ1BHbtonibJLSvW5tU4BEZBDdfffdAEVpxzoxSr3zZ5CoxFb49ZVBUliXVedGqXVOBolzE1yYQaLPd7i4JbZo7mXLLbcMBIJVYkukf4YRIUOaKDFMLUHUJP+Cr5q0iwTF43E1aa9T/hJbutetbfppiZRBfleNAiQi4KYdq0l7vShaLMwFSDAtvvWtb3mHdW6UWucP/kaNWOA1fb7DJdbd6DxocW5+/T2UFCAR6V8kEvHV51eApBb19PR4j9WkXSSoMECiDJL64S+xpX6btU13biJlYOYmjVmV2BIBnF3WbomtiDJIQq8oAJZr0t46fBjHHHOMd1iTRql1TgZJrsSWnWQvPkYLIwAFSMIm1uP0Hok0pYteU4BEpH9OgCS/eCi1p7u723vsBkhs9SARAdwSW2rSXo/UpD08NDsRKQOV2BIJGjt2rK/EljJIwq6oxJaVCxJHbSKR/PlQwWOpdf4SW0cuPg+ALdmJm/mWrv0h453H4sXXL13TRPrnD5AYWoKoScEMEuf6llWARAQoziCx7e4BvkLCItikXQGSWqaflkgZFJbY0iKg1Ltx48Z5u6zVg6QO5UpsRUw7MFHUuVFqXTQaLVog2oH9AWVIhU3Ecs9jFR6ISA0yDMMLJiuDpDb5M0giXgaJgsMiUBwg0caJ+qEMkvDQT0ukDPIltpRBIgLOQnhTs9PgNqIMkrpi27bXpJ2YAiQSLv7yga7XeArQtT9s+guQqMSWSP9UYqv2+TNIzIJqCSL1Llhiy+lBctZZZ3HppZdWeGRSbv1lkGizVG3R7ESkDPIlttSDRMR11TVX8cipy5RBUmds284vLEbtwERRC8hS6wzD8Bb9vGO56bU+3+ESsfsOkCjoL9I/J0CSy6zXEkRNKpVBogCJiKMwCHz77bczf/58AC644IJKDk3KzMkgca5ryiCpbfppiZSBepCIFEs1JgH1IKk3lmXlS2xFUQaJhEokEinKIHFvkpRVEC7+XkqF9LMW6V9w8TBW4dHIpijVg0QBEhGHZVmBJu1ucMR9TcLLsiwSOJUyMvQGXtM6YG3RT0ukDPIRZPUgEXFFTCdzQBkk9cVfYisSVYktCZ/CBSI3zV7nuXCJ2M41TCW2RDZeJBJRD5Ia588gKdwMKFLvnABJ6XOc5oPhZts2I9kCgOUsCrymAElt0U9LpAwiuV8tWxkkIh4jt6ikHiT1JVBiK6YSWxI+E5keeO5tktANcai4GSRGrLietAIkIv1zyhHm6/NL7QlmkLgBEs3nRcCZ8ylAUp8sy2IEEwFYyruMHTvWey0WU8ZkLdHKhEgZFGaQaBFQRBkk9cqyLG9hUSW2JIyaGR547vYgyWQypd4uNcrrQaISWyIbzWlgrAySWhbMIFGJLRG/hoYGBUjqlG3bRIkDcOUvruCyyy7zXlOApLZo1VakDArTjrUIKAJG1AmQmESVQVJHnBJbbu1+AhkkOjdKGCmDJHxs28awc4FeldgS2WipVEoBkhrnzyBJ0QwoQCLiOuigg9jnQ3sBxX2WNB8MN6dJuzM5HNbeHLi/jUZ1vaslCpCIlMFhnAHg1dpVBokIRBud34M4SQVIQqxwodCyLAyvNI16kEj4KUASPrbvRxkpca+rAIlI/5LJpAIkNc7NINmCGXyUcwAFSERcpmny7e9e5DxWBkldsSzLyx43opHA/a0ySGqLVm1FBtkUdqWBFgCSNAFaBBQBiDU5l5wEjWSzWdatW8err75a4VHJYCsMftm2jZHLIInEgwFjBY8lDP7DAwC8mXoGUIAkjKx0PgCiAInIxvMHSAwFSGqSGyDZiYO8Y4UlJkXqmRF3qyUog6Se+DNIIlEUIKlhWpkQGUQGJl/jd97zxcx3jmsRUIR4sxsgSWFZFttssw3Tp0/n2WefrfDIZDAV3gRYlkUkm9tVE7dVYktC5yYu4MecxNMtdwL5AIl6kISHAiQim0cZJLXvC1/4AgBj2No7toD/Mnv27EoNSaSqRFP5agl+CpCEWyCDJBZRia0aplVbkUGUpNF7/AIP8wC/AbQIKAL+DJIGrIzF0qVLAbj77rsrOSwZZIU3AbZtY1rO7pnCDBKdGyUMMvTyDi9iRdzFP+fzrhvi8LAyvgCJepCIbDQFSMJjAtsCkDxsOYdeNY177723wiMSqQ7RlLMJLEYicFzzwXBzMkhUYisMNDsRGUQx326B6zkLG+eGWRkkIhBr9pVWysS9x1okD5dSGSSG5WaQKEAi4ZVVgCS0rHT+ccSMFL0+fvz4IRyNSO0pDJBYlqX7oxoUwaCdcQCMPqWHI0/8bIVHJFI9lEFSn5wMkly/TZXYqmmalYgMokTEuRh20+EFRwBSqVSlhiRSNcxEBEzn98LM5CcLWiQPl1IZJF6AJEGgxJYWRyQMjjjiCAA+cdLxgHqQhJGbQZIljWHkz2G///3vOeigg7j00ksrNTSRmpBKpbB8PUh0fqxN/oVfs7GfN4rUoWhDvgeJu2AOmg+Gnb8HSWGJLQVIaotWJkQGUSwXIEnTEziuAImIszBu5H4VzEw+9VgBknApWWIr14MkEreVQSKhc/vtt/Pkk09y6mdOARQgCaP/e+gRALJkAkHek08+mQcffJCddtqpQiMTqQ2JRIIsTiqWSVQ9mmpUHGcib2FhJrWUJOLnZpBAsMyW5oPh5u9BEolGAkER9SCpLbqqiQyihOEESDL0Bo43NDRUYjgiVcdscDNIVGIrrEqX2HImimYiogCJhE4qlWKPPfYgmnA+22rSHi4rVqzgM592yshkyQYCJCKyYUzTDJTY0oJhbXIDJGm6MU0tJYn4mcn8/MD9XQE466yzuP322ysxJBkChT1I/Gt/yiCpLbqqiQyiZNTJNVYGiUhpZm6+EFMGSWj116RdJbYkzNzeFEZueq0FwHBYv349UbevDGkFSEQ20UeO/QigDJJaNWzYMK/EVi9dmsOJFIhEItgxZ+7nzyC55557OP744ys1LCmzQA+SWISmpibvNQVIaouuaiKDyF0ELMwg0YlRxOEGSMysAiRhZVlW0XOzjybturmWMDGiboBEJbbCxLIsojhZjxl6FSAR2URjx48GnPr8CpDUHtM0vQBJjwIkIqXlAiSFjdolvIIZJChAUsN0VRMZREbWWejtpbvCIxGpTm6ApCHTmj+mAEmoFC4K967NMr5jJgBmMphBooVGCZNIrsywMkjCJZPJeDtB0wqQiGwyI+42MFYGSS2yLCtQYksBEpFidotTSWQ44ys8Eik327b53ve+xxuvvUkTbYCzWcofINE6R23RVU1kEPWVQSIijrX/dm6OD199FmPYGlDzsrApXBR+5aed3uNoa7BJuxYaJUwMr8SWSYRIUTaV1KZ0Ou2V2FIGicimM2MKkNQyJ0DilthSgESkFGvMegC+yLXMZN/Aazrvhctdd93FhRdeyD58wjtWWGKrt1frgrVEVzWRQWTaTgmGwh4kIuJo2Sn/+Fv8lQiGdlaETGGAZO2r+eexNhQgkdCKRH3ZUZgKkIREJpMhmssgydCrRUGRTWQoQFLTLMuikVYAOlmrc6FICfZ2y73HZ3CNt8ECoKOjoxJDkjJZuHAhAFuyg3csEg32H+7p0bpgLdFVTWSQ2Lbtq1HdQ3t7O21tbZxzzjkVHplI9dj+58HLzjBG6gYrZAoDJEZ+jkhseERN2iW0DF8ynImpElsh4WSQqAeJyOZSgKS2WZZFCyMBWMsyzeFESpm4PvB0BFt4j9evX1/4bqlhbn8R/+ZoIxq8191uu+2GfFyy6VTXRGSQ2Lbt1ag2EwbPPfccW2yxxQBfJVJfkiNNIH9T/AkuUomtkClcFE6OyU8So83KIJHwipj+DBJDGSQh4WSQ+AMk8QqPSKQ2uQESg6gCyDXItm2G5QIka1iuAIlICZGWYEmlT3M5l/ExQAGSsHHXMAIBktx1bvXq1XR1ddHW1laRscmm0VVNZJBYlkUsdwN9+DGHKjgiUoJpmjzMTd7zmeyrRfKQ6WvR43HuwDQNBUgktIIZJFEFSEIinU578ztlkIhsOmWQ1DanxNYwADpYpQCJSAlGk0WGtPd8Att6j7u6uioxJCmTeNyZG2Z9mz+xnf8NGzaMMWPGVGBUsjl0VRMZJE6AxGlcF03pV0ukFMMw+F9+xgP8xjuWyWgXYZgUBkgync4i8UoWE4kE04610ChhYvh6kBjqQRIaxRkkOm+JbAojV4pfAZLaZFmW71yYVoBEpATDNPglX+ZJ/uIdc/uQKHMuXNxzYA+d3rHkCPVWrWW6qokMEn8GSTSpm2eRUkzTJEuGB/i1d8zqsSs4Ihls/kXhRoax6DbnZqCHLgxDGSQSXhEjgoXz+TfUgyQ0/D1I0qR13hLZRG4QWQGS2mRZFmZuoTdDL6aphUCRQpFIhFd4glv4tjcnTNECoPNeyLgN2N0y++M+ldUcscYpQCIySJxdNc7JMZrUr5ZIKe7ieDcdWDiLh9Z6/b6EiX9R+CA+6z3upYtIJKIAiYSanTuvKYMkPDKZjK/EVo/OWyKbKF9iK6aFwhrk3Ou6ARJlkIiU4v5e2Nh04/QcSdEMKEASNr29Tr8ZN0BiJCo5GhkMuqqJDBJ/k3aV2BIpzb/brIt1gAIkYeMPkMRzZQcBOlmDYRgqsSWhZsac85kCJOGRTqd9u6aVQSKyqdSDpLbZtu0LkPQqQCJSgv/3opO1ADTkMkiUWRwubgZJM8MBiKY0P6x1uqqJDBJ/XVYFSERKC04anQBJtkO/L2Hin/yvZ5X3eBUfYBgGpmkybdo0IpEIe+21VyWGKFI2sYSzeGSqxFZoZDIZTKIAWGQUIBHZRAqQ1C7bdsrhusHirDJIREry/150FQRIdN4Ll56eHiJE2JbdATCbKjwg2WzRSg9AJCycHiRuiS3VZBUppdSk0VaAJFT8i8IdrPEer2eV16T9ueeeY/Xq1YwZM6YSQxQpm0ju8m9gklUGSSj4M0iyCpCIbDJ/gEQB5NriZkTmm7Qrg0SkFP8coTvXvDtBA6AMkrDp7e1lOBO8EmqjD9E5sdbpJygySCzL8srJRBv0qyVSir/ElptBYncooBgGl19+OV/+8pcDk/+e3I0BwAoWeTfTyWRSwREJJbcJsUpshYc/g0QBEpFNZ8Ry/1cGSc3JB0jUg0SkP/7fi166AIiTApRBEjY9PT2MZAsAFvEa8XbND2udMkhEBolt2yRx8uoSrVrwFSmlZAZJp35fap1t25x//vkAbL311t5xI7cP4/X4k9CrniMSfpFcgORELubJntsqPBoZDOl0GgPnOqUAicimy2eQmFoorDHFGSQKkIiU4v+96MkFSBIKkIRSb28vZm5+qKy6cFCARGSQWJZFQy69LtmmXy2RUoIZJE6AxFIGSc1bu3at97irq8t7HMkFSGycG2tNHCXsjNzpbGt2ZuvOnSs7GBkU6kEiMjjUg6R2uQES9SAR6Z9/jlCYQaISW+GSzWaJ5AIkNpbmhyGgq5rIILEsi1SuAZcCJCKlBTNInBJbKIOk5q1alW/Gnk6nvcdugKSr17lB0M20hJ2bQSLhoRJbIoMjHyCJKUBSY9wm7W4GSVq7pUVKKl1iyynDrvNeuGSzWa9agkVW58QQ0E9QZJBk01lGMAGARJsWfEVK8WeQuAGSVYvXeTdeUpv8AZI1a/KN2Y2CDBItLErYGdofETqWZSlAIjIIzJjbo0kZJLXGzSBpYzSgDBKRvvTXg0QZJOFiWZZXgtXG1vwwBHRVExkkS/7Z6z1OjVKARKSUUhkki974gBtvvLFSQ5JBsHLlSu+xv9xWxNtVoxJbUh8iZvDmSMHf2pfNZhUgERkEKrFVuyzLYhLbe8+7WKc5nUgJatJeP5wSW851TRkk4aCfoMgg6XjX2RGwlmUk27WFVKSUeDzuPe5iPQANNPOZz3yG9957r1LDks3U09PjPVYGidQzo6DEVqZTAZJa52SQuHX3M7oBFtlE/gCJdlLXFsuyGMkW3vPVfKBzoUgJ/nudbjoASNIEKEASNv4MEks9SEJBVzWRQdK1xJno/9d4tMIjEalehmGw3XbbAdCVa9KeohmAT37ykxUbl2we/y75O+64w3usDBKpN9GG4M1R7zotAtY65wZYGSQim8tw4ozKIKlBlmUxjBEAPMu9AMRisUoOSaQq+e91OlgNQDNtgEpshY2TQeJuBsxqfhgCWqkQGSRuBsk6Y0WFRyJS3XbaaScAOnMlttwAyT//+c9KDUk2k1ubupAySKTexIcFS2z2rtHNcK1zMkicn6sCJCKbTiW2apdt24xjKgD7HrkHv/vd7xg+fHiFRyVSffwBknU4PRqbcgESnffCpTCDRBsBa5/qAIkMgvWL0iy+x7ngvWX+u8KjEaluiUQCyNdlTeTqskrt6qvPQn5XjfO6Jo4SdrHG4Ge8Z41uhmud04MkCYClAInIJlOJrdq17p00u/MRAPY7fSe2PGrfCo9IpDr55wjrcXo0NtEOKEASNv4eJLZKbIWCVipEBsHq13uwM7CW5bwTfaHSwxGpasmks9CUoReAKPH+3i41oK8MknyJLWchRAESCbsVL3YHnvcog6TmORkkKrElsrmUQVK7Ot53fl4WFpOPbK7waESql/9epytXLcHtQaLAcLgogyR89BMUGQTZXmd39BqWYZj6tRLpjwIk4dNXBolKbEm9cReRXD1rtQhY69SDRGRwKEBSuzKdzjxuCW/qHCjSD/8ief5e1+nXo/NeuDjzw/xmQPVlqn1ayRUZBFbaWRzMklbkWGQAbomtDGkgP2mU2jVQBomtJu1SJ+ZcOjrwXBkktU8ZJCKDIx8giZFJa6GwlmS6nGtZmp4Kj0SkugUDJM69rpm711UGSbhks9lABkk0qg4WtU4rFSKDwMplkGRI68ZZZACFGSQmMa9+p9SmgZq0W8ogkTqx41nDOfqp8bzIPwBlkISB04NEARKRzeUGSACy6dLzBqlO6Q7n55WOdA/wTpH65p8jZHMBEgMDA1MZJCFjWZZvM2BWAZIQUIBEZBC4JbaUQSIysHwGSa93TGW2atvATdqVQSL1IWJEGLFjyqs7nV6nRcBaV5hB4gb5RWTjGL6E4UyPzo21JNvtzPOUQSLSv1IZJJDLnFOAJFScJu3uva6tAEkIaKVCZBA8+ehTgHPjrAVAkf4VZpCAAiS1ThkkInmGYXjnt2yvFgFrnb8HiaUAicgmM+O+ndUKkNSUTGeuWkKkd4B3itS3eDx/T5v1BUiiCpCETmEPEgVIap9WckU20zPPPMMvr/s1oAwSkQ2RD5D4J40KkNSygTNInNd1fpR6YBgGWZybYO2Srn3BDJK0AiQim8hfYsvSOntNyXS5JbaUQSLSn2CAJB8QMYn1eb8ktamwB4lpmhUekWwurVSIbKY333zTazKdUYBEZECtra3eY8twmtWpUXttG7hJu/Nz1vlR6oFpmt6uQWWQ1L7CHiQKkIhsmkgkgmU6C4bKIKktma5cBomhyJZIf9xS0q50LqM4SqzP+yWpTYUZJAqQ1D6tVIhspng8ruadIhth0qRJ3mMjt8lGGSS1ra8dUSqxJfUoEol4GXLZHu0WrHXBDJKsAiQim8GOOvMBnRtrSzaXQZJRk3aRfhUGSNwNMwqQhE+wB4ml+9wQUJE0kc0Ui8Uwc7vfVWJLZGCzZ89mt912o7W1lchTNna3AiS1rnDCbxLlM/yQ7dkfUJN2qT9WrqyC1atFwFqnJu0ig8h0MkqtXi0k1ZJsl/N/9SAR6V9fARJTAZLQsSyLSK7ElnuvK7VNARKRzRSPx1ViS2QjRKNRnnrqKQB+OeoFskAcLTjVssIMkt34CDtxcP51ZZBInbGMLFgqsRUGatIuMohyGSQKHtcWldgS2TCFAZKML4NEPUjCJZvNEi2oliC1TSu5Ipshk8nkMkjyOwsVIBEZWCQSIRKJEG13dhK2MLLCI5LNUbgjqo0xwdeVQSJ1xoo457asFgFrXmEPEn8DVhHZOLYXIKnwQGSjZLtzARJlkIj0qzhA4vzOKIMkfCJp09ekPVvh0chg0EqFyCb68Y9/zLBhw3jhhRcCJba0Q1pkwyVGO/9vY3RlByKbpXDCn6Ip8FwltqTeWBGV2AqLYIktzfNENksuQGJrnb2mZHMZJFllkIj0a6AeJMoiCYe3/rqWI/9xGfvyCUAZJGGhlQqRTXTOOefQ2dnJ17/+dZXYEtlEE2aOAmCYMkhqWuFkP0Vz4LmF87oWFqVe2IabQaIbplpX2INERDZdBGceEF/RVuGRyMbI5nqzpxUgEelXYZZpOpdBEifFFVdcwZgxY3j55ZcrMTQZRPcdu4AIBg20AOpBEhZayRUZBGPZBoA1LFOARGQjNI5yJpGNtGrxvIYVZpAkizJInMVinR+lXpgJ57Pe26WU+1rnD5DcePMNlR2MSI0zlzqLSROe2q+yA5GNYvUog0RkQxRmkHTT4RyngWw2y9KlS/na175WiaHJZrr11luZN29eyddUYisctFIhsonGjx/vPR7DVgC8y0taABTZCMnhzqJTA63/n737jpOkrvM//qqqTtMTNrMLLLtEyQh4oKCIIAZEPBOIor8TEQPKHSJ6eJg4EfUUs2I6w4l6HuY7FcVEUIJklJzZXdi8Mzupu7qrfn9UV3VVd0/q7emq7n4/Hw8e9tTM3n3Zoau/9X1/P58vruuqN2uHqq0gqQ9IdEi79JZUzutJXCqo4qDTXHLJJXzta18Lvi6XykGP6RNe9Py4hiUiEpvyROV/FZCITCudTke+LoQCEulcV111FaeddhoHH3xww++rgqQ7aCVXpEl77bVX8DqFt1OgwLgCEpE5yC3xFp36WQB4h+FK55npDJIFlTNmdH+UXpHu88Jfe1L3tE5y//33c+GFF/LWt741uOaWqsGumVLIK7IjnBc+ErweXWPHOBKZC6fSYsuxFPqLTCe8GcwwDAqMA5CjP7ieSqXaPi7ZMbfeeuu039cZJN1BKxUiTQp/sKXx2gTZFLRDWmQOFJB0h/oKkv7I18vZA1AFifSObF/lbLJJPTB1kpGRkeD12rVr+dSnPsWWTVuDa2Za9zCRHWH94xM8xcMAbLxtIubRyGyNbPZ2wZcthVoiM3nggQe49dZbWbFiRRCQZEIVJApIOk/ts24ttdjqDnpnijQpvGM6FQpITHMoriGJdJxU3svp05UqrFJJO9M6UW0FiUW0vNw/wE4VJNIrMnnvPVAuKCDpJOEQ9/jjj+f++++nj0GO54MAmOmp/qSIzIZlWYzjBZGubo8d4YEHHmD7ljEWMYiT0jxdZCZ77+2dT+u6ripIeoRabHUHrVSINCm8IBgs7mJrAVBkDqyctxjlv4dUQdKZanfV+L9P31X8J6AKEukdWT8gKeqBqZOE71H3338/AAdyTHBNFSQiOyaVSgULSW55+h25kgz3338/GXIAOKYqSERmy3XdyCHtPgUk3aeMwuNuoJVckSb5C7l+9QhAiYICEpE5sLLe+8V/H6mCpDPVVpDUBiS38VtAFSTSOxYu9doG2hO6p3WS2hDXxOLFvHXK74vI3KRSqaBXuypIOsPk5GQwr1MFicjseRUkCkh6QRG1jOwGWqkQaZK/IBgOSGwFJCJzkqpUkPjvI1WQJNdtt93GBz/4QdasWVP3vekqSNZwLwCnnnrq/A5QJEH23ndPAArj2m3bSWoDkOdwCivwfpfOs+vvfSIyN5ZlhSpIYh6MzMrExCQZ+gAFJCJzVagsnKvFVvco2/Xp/lve8eYYRiKtpnemSJP8hdzwQmCZknYXisxBbYstVZAk1yte8Qoee+wx7rvvPn74wx9GvheuIDEwIvfFYTYBkMlkEOkVK1fvysNAuagWMp2kdg63B4dWvzeksEtkR3kVJJOAWmx1isntheC1q4BEZNbUYqt7hDcDToTuib7Dn3VoG0cj80Vb3UWaVFtBUqxM9lVBIjJ7fostnUGSfI899hgA1157bd33wgFJqqa91p+5AlBAIr1lxa7LvRclo67CSpKrNiDxW2MAGAP6fBLZUZEzSNRiqyMURquhSNlSUCwyF/4h7QpIusfo1vG6a6m8Nkl3A63kijSpWkFSOTuBIqD+1CJz4VeQAKRIq4KkA9h2/cNxeAE4EwpI3sOzuZM/eNcVkEgPWbFyJwBMUlx55ZUxj0Zmq3YON8Fo9XuD+nwS2VGRM0hUQdIRCtu9eV8JGwcFxSKzNdUZJNpQ29nGttWfN2Ll9DvtBvotijSpekh7pTVQJSDRjgCR2UtFApKsKkg6QKOAJFxBkiYHQBmbCUaq19Pp+R+cSELkBry5wRBL+OeXfFBVJB2ivoKkukvQ2kk7p0V2lHcGiTfX8ytIbNtuOLeQZCiO+QFJMeaRiHQefx4RPoNEc8LOE/6dbd8yVv/9kn6n3UABiUiT/AVBq3KUTxlvZ6ECEpHZMzPhgCSjCpIOMFMFid8urUi0P2uxqAdr6R1W6N52Lt/isV+PTvPTkhS1AclkqILEWqoFXJEdFWmxVXZxXZdDDjmEPfbYQ3PAhCoVvN+XztoUmRvXdYPnoXALYgUknc0PSMbMrcG1ckG/026ggESkSf5OdwUkIs0zDAMr6x/UnlEFSQdotIARrSDxHgDsmoBk+/bt8zswkQQJh78Aa65SQNIJahf/wjumrcX6fBLZUZEWWw6MjY1x7733snbtWh555JGYRye+iYkJTjvtNC6//PKg0sehpIVdkTlwXTeYR/ht2f3r0lnC88Ptm72ApGzZpPq86yuOyjf8c9JZFJCINMlfEDSxvK8VkIg0xT+HJE1Wuwc7wEwVJE/jSO/nmIz8zMjICCK9wkxHF9oHV6vFXCeoDUiMyqPSbfxWPcNFWsBrsVWtIAlvsNi2bVtMo5Ja3/72t/nhD3/IG97wBvxjR/zNgCIye0EbdgUkHS3SYmurF5C4Vpk3rtuPNzzyNAZWap7fDTTTF2mSKkhEWsOvIEmpgqQjNJrU+wsce/B0Xs0FACxOL+dHP/pR8DO77rprewYokgBWTQVJbWAincHfBOPiYllWzKMR6Xy1FSThjTEKSJKjUAhVAZe9JSOHshZ2ReYgXEESDkjCwbB0ltUcxKb3rwQgby8ku9BiaPfMDH9KOoUCEpEm1Z9BUjm0XQGJyJxYOe+jSGeQdC7/gfloXhVcM+wUr3rVq/j973/P6aefzsUXXxzX8ETarrbFVmlSi0qdoHbxz8D7Pbo4CkhEWiB8BolTVkCSVEuWLAlel4reM67/rCsis1fCq7xPkQ7mFNoQ2LmO5fTgdbrcF+NIZD5oJVekSf4Hm1l5G6nFlkhzUqEWW5owdiY/MF7KyrrvHX/88Rx//PHtHpJIrGorSMqT2i3YCWoDkmobVUfzO5EWsCyrWkFSdiMBydjYWFzDkhoLFiwIXm/dtI2dUIstkbnyKkiqrYkt0pQoakNgB/Lnh/0sjHcgMq9UQSLSpPoKEu+DTjsMReYm3GJLE8bO5E8ac/THPBKRZDBTNRUkE6og6QSqIBGZX16LLW8zjOtEd1Jrk0xyhM9jGt8+AVQ3A4rI7Lz2ta8NWmxBtc2Wnnc7VxZVjXQzBSQiTaqeQeI9MOsMEpHm+C220jqDpGP5gXGOgZhHIpJMqiDpDPUBSbWCRAGJyI7zWmx577PaChLNAZMjfEZCya60RNMZJCJzcumll/Lt734r+Hpn9gIUkHQiPzTOko95JDKfFJCINGmqChIFJCJzY+X8CpKsJowdqhqQqIJExHcnfwxel3UGSUeob7GlChKRVqqtIFFAkkyRyp6CfwaJ5ugic9HX18fpr39d8PWb+TSge10n8p91/YCkyCRrXvbrOIck80ABiUiT6s8g0SHtIs0It9jShLEzVVtsqYJExPd9Psx6HgF0SHunqK8g8R6VHMoKSERawLKs4JB2VZAkV6SCpFitIBGR5i1gJ6B+riHJ538++ZsBL+V0yvtujHNIMg8UkIg0SRUkIq2RqrTYypBTBUmHchyHFGnSZAF4ikdY9P4nYh6VSLz+84eXcTU/AGByk+5tnWCqFlsuruZ3Ii3gtdiqBCSqIEmscEBSrrTYUgWJSGsoIOkc1157Le985zsZHh7mHziJIZYCUGCcbDYb8+ik1TTTF2lStYLE70+tChKRZmy6cxKA1/FhyuWHYx6NNMN13WBXlE2Bi3kZP33GT2MelUi8TjjhBN7PJwF48ubtvPvd7+bcc89lt912i3lkMpWpWmypgkSkNbwWW6ogSbpwQOKEAhIt7IrsOL2POsdzn/vc4PUXuSt4Pc6IApIupAoSkSbVV5DYgAISkblKD1Q/iiYe1MdSJ3Ich4WsAGArTwG6F4qYpslTlRZbE2sdPvPpz/Lyl7883kHJtMKLghCtIFFAIrLjoi22ohUkqiJOjsgZJGqxJbJD8v/ySPB6gEUKSLqAApLupJUokSb5E0e12BLZMS/52arg9eRtfTGORJpx3XXXcemll7IT3u9xC08CuheKmKbJGMOAd5ZFPwu49dZbYx6VTEcVJCLzK1JBohZbiRWpICl590WHshZ2RZqQOXA8eL2Q5XofdQkFJN1HAYlIk/yJo1psieyYhftkuW/1VQDYG/T+6TTHHHMMruuyO4cA8Dh/A3QvFLEsC4cSY2wDvF2Dkmw6g0RkfoXPIHFKarGVVNEWW959UWeQiDQnNQTreACAfhYqIOkQtb+nu/gTALcu+gWggKQbKSARaVK1giTtfV2ZNGqHocjcTfZtA6C8SQtQSWUYxrTfX8AyADbyOKCARMQ0vWn2drYCMMjiOIcjs1AfkHj3PVcVJCIt4QXH3jOUU3YUkCRUtIKk8r+UZpwLikg9wzCCiuI8CxSQdIiJiYnI12alc8xDE3cCkMlk2j4mmV8KSESapEPaRVrHTVfaLWhzWmL5i71T6WMQ8HqyAqTT6Xkfk0iS+e+ZUbYAcAjHB3MGSab6Flv+HM9RQCLSAl4Fifc+swu2ApKECv8u/BZbZbXYEmmKaZqMVwKSfgUkHcO27cjXqUpAMjrpPesqIOk+WskVaVL9Ie06g0SkWUZl3Wl4yzDlsnbqJtFMuwbzDAEwwXZA90KR2oDkON6gFiUJV19B4v0OXQUkIi3hnUHiLb5f9uWvcOLhTwu+p4AkOcIVJP7mJX1+iTTHNE22sQGAxezChLs15hHJbITvgxDuHOMFJ5oXdh9VkIg0wXXd4CHaquwuVEAi0jwz5S2+r13zJBdeeGHMo5FGZqogybMAgHEFJCJAuMXWluDaCZwR13BkFuorSBSQiLSSZVlBBYnrwNe+9rXge+FqEolX4xZbqiARaYZpmmzgUQCWs7veRx2iNiBJVQKSUiUgmenZWDqPfqMiTQh/qPm9CJ1KQKJSO5G58ytITCw+8YlPcOSRR/Lxj3883kFJxHSTwIUsD84g8Q+kVkAiva5aQaKdgp1iqgoSB0f3NJEWCFeQGJjccMMNwfdUQZIc4YVB0/XvgwqwRJphmibDbAR0SHsnUQVJ71FAItKE8M2y9pD2hQsXxjEkkY7mV5D4/d7/+te/8r73vS/OIUmN6Vps7c7BAGzhSbbyJKCARMQwDAzDiFSQqEVJsvmLFmlyGJiqIBFpMdM0cfGeoxazghdwJlnygAKSJAn/LsxQtwSdLycyd6ZpBsGwiaWApEOogqT36Dcq0oRoQOJPGr0PvQULFsQyJpFO5leQWDrAOLHCAUntxN5vr7XOuD+4poBExHt4GlVA0jFc1yXHAB/nas7jvyIVJApIRHacYRg4lYDkYI7jHzmXS7mRVRyggCRBIhUkQbeEMv/8z/8c15BEOpZhGEHVgQKSzjFTBYkCku6j36hIE8IfarWHtKuCRGTuzLRfQaJF9aQKTwJrJ4x+QGJnxoNr2mUo4r1vVEHSOVzX5WkcSZY8e/B0VZCIzAN/J3XYwRyvgCRBopsBvbn5617/Wt785jfHNSSRjhWuILFIKSDpEOH74PN5IzuxGqhWkGhe2H0UkIg0IbqrxrsxOgpIRJpmWH5Aoo+lpAoHJP4ihj/BX8GeADh9xeBnVEEi4r1vRtgUfN1oYVCSw3XdSJ/9FFlAZ5CItJJNoe7aCvZQQJIgjbol7LTzsriGI9LRTNMMuo0oIOkc/n2wjyFewbuD66og6V76jYo0wb9ZPpfXcjSvAqq7QhcvXhzbuEQ6lVVzBokkT7jFln8PLJfLDLGUZ/GPANi5agWJFhNFvIenTTwRfN2P2nAmmeu6wSIGVH9fqiARaZ1Sg4BkIcsplVRhlxSO45Alz6lcyAEcA4ChaZ1IU9LpdLBWpBZbncN/3l3CLpHr42wHVEHSjRSQiDTBdd3KpPHfgmv+rtDVq1fHNSyRjuU/dKnFVnI1CkhKpRK7sm9wfXP+0eC1AhIRf9dgddFvHQ/EOBqZieu6pMgEX+cZArw5nh6ERVqjUQVJPwtVQZIg5XKZw3kRz+U0dmYvAMyUMcOfEpFGhoaGgupUVZB0Dv95N12pJvZt5UlAFSTdSL9RkSY4jhN5gIZqBYn67ovMnZX2Po7UYiu5Gp1BUiqVIou/a/v/HrxWQCJSfd/8iE8AMGxsiHM4MgPXdcmQC76uVpC4CkhEWkQBSfI5joNLdBHXPy9QRObGC0i8+5sqSDqH/7wbXvf7PNVzmBSQdB/9RkWa4DhOXSsgHbwq0jzX8CaKarGVXI3OICmVSqTwQuEnuJsjjvyH4GcUkIhUy++3V84hsdAmiiRzHCcSkOTVYkuk5RoHJAso206Dn5Y4OI5T93tSBYlIc4aGhkIttlRB0ilqK0ge527u58bg+5oXdh+tXog0wXEcrJq3j6OARKRpJdc77Kz2fSXJ0ajFlm3bwe9s/4P3Z+2eDwY/o4BEpBoslioHOqYUkCSaV0HSF3w9wCLA2wSTy+Wm+mMiMgeNAhKAvs1L2zwSmUqjzYA6g0SkOQsWLAgqSNRiq3PUVpDUhcaqIOk6+o2KNMF1XVWQiLRQ2fEWD9ViK7mmOoPE3xE/uKA/uA5qNygC1YenctB7Wu+LJKsNSHxlSmQymQZ/QkTmqrZ1k2/JVUe1eSQylXK5XDcnt9RiS6QpuVwuNA9Ui61OUVtBUlJA0vUS8Rv90pe+xO67704ul+OZz3wmN910U9xDEplWo101/q4AEZk7OwhIVKqaVOGAJNxiy68gMTNGZMKvsmORcECiCpJO4LpuwxDLTBuRe6CING+q+6CDWmwlheM4GDVLRYZabIk0xTAMzn7H2wGdQdJJagMSm2Lk+3rW7T6xByQ//OEPOe+88/jQhz7ErbfeytOf/nRe9KIXsWGDDrGU5GrUYksVJCLNKwUBier3k2qqChJ/ocPKGOyzzz4AZLNZ7aoRQS22Oo0XkNR/DlkZLQyKtEqBieD1r7iM73IhAGMMa+EwIRoFJDqkXaR5557/L4BabHUS//fkt9hSBUn3i/03+ulPf5qzzjqLM844gwMOOICvfOUr5PN5vvnNb8Y9NJEpqcWWSGuV1GIr8cKT+UYttsy0watf/Wp++tOfcuutt8YyRpGk8R+eCowDNGzfJMmhgERk/j3IzfyR7/J9Psyv+HKo9YwWDpOiUbcEU3uYRJpmViqwdEh755ipgkQBSfeJ9WOuWCxyyy238L73vS+4ZpomJ5xwAtdff32MIxOZXuMWWwpIRJpVdr33j1psJdfUAYk3lbAyBqlUipe//OVxDE8kkfzy+yAgcfNxDkdmMGVAktVnk0gr/Zj/CF6Xg8OLLe/sCy06xa7RGSSmWmyJNM2oTC3UYqtzVAOSxoe0q8VW94k1INm0aRPlcpnly5dHri9fvpx777234Z8pFAoUCtX/MEdGRuZ1jCKNeC22ojfEEja/+tWvYhqRSGezy96ODLXYSq7wZN4/g6RcLkcqSEQkqraCJKsKkkSbKiBJZbRgKzJf/DOaTFLBgpTEq+FmwJIWdUWaZaX9ChITHbfUGfzPI7/Flv9Z5VOY33067jf6sY99jAULFgT/7LbbbnEPSXqQ12Ir+gD9s5u+x4knnhjTiEQ6m1psJV+jChIvIKke0i4iUf77xu+5nyJDuagn46SaMiDJaZegSKtcfPHF7LHHHhxxxBFAtQrfUkCSGI3OIBl5xJ7ip0VkJka4AsvR824n8D+P/HlhSQFJ14v1N7p06VIsy2L9+vWR6+vXr2fFihUN/8z73vc+hoeHg3+eeOKJdgxVJKLRrppley2IaTQinc+vIGm0MCXJEw5Iwoe0i0hUsejd24qVChIAe0y7cJOq0RlzAJYqSERa5sILL+Thhx9m1apVAJEzSBSQJIP3rBu97+37+oXxDEakC4Rb1BmOnpk6gf955G+Mrm2prxZb3SfW2X4mk+EZz3gGv//974NrjuPw+9//nqOOOqrhn8lmswwNDUX+EWm32hZbV/N9BgYGYhyRSGezneqODJ1DkkyNKki8e2GlgkQttkTq+G1hy5SCwx3t7eU4hyTT8CpI0nXXl+y0KIbRiHQ3f3GpegZJiuuuuy7OIUlFuVwOKkhu5ld8df/TWbRfNuZRiXQuMzS1MBw963aCagWJ98sr1wQkqiDpPrH/Rs877zy+/vWv853vfId77rmHt7/97YyNjXHGGWfEPTSRKXm7arxFwRE28yM+TiaTiXlUIp3LryABBSRJNeMZJKogEanjV5AA2EwCUBpXBUlSTdVia98DnhbDaES6Wyrlvdf8RSeTFC9+8YvjHJJUhLsl2BQ0xxPZQeEKEly9nzpBbYstBSTdL/ZeJq95zWvYuHEjH/zgB3nqqac49NBDufLKK+sObhdJknALhm2sx0WLHSI7QgFJ8s14BokqSETq+BUkAKVKBUm5qDlDUk0VkOT6tQlGpNX8CpLwGSSSDOEWWw5ltZIR2UGGaYDhgmtg6AySjjBTQKL7YvdJxDvzne98J4899hiFQoEbb7yRZz7zmXEPSWRa4RZbtb0IRWTu/vEVJwevFZAkX7TFls4gEZlKqVSdI/iHOzoKSBIrHJBsZ3NwPZ3Twq1Iq1VbbHn3RgUkyeEd0u79flwcLQSKtIBRucUpIEkmx3F42ctexrnnnht8Daog6SX6jYo0Idxiy++bKyLNe8c57wheWwpIEmnGChIFJCLT8hcBVUGSXOGAZCtPBdcVkIi0Xv0ZJJr/JUW5XA5VkCggEWkJ05v/Ga6WYZPohhtu4H//93/53Oc+BzQKSOzIzysg6T76jYo0Idxiy1FAIrLDUpnqg5ehB+REanQGieM4pPwzSOrPNRaRkKDFVsGJeSQylXBA4iwcD65n8gpIRFqtGpBUzyCB6HxD4hE+g0QVJCKtYVTeRqogSSbbjgYgarHVe/TOFGmCWmyJtJZhGMGkUTsIk2mmChIroymFyHT8Fluf+eTnOO+882IejTQSrhA+5UPP53au4mp+QHaFKuREWs0/pL32DBJ/jiHx0RkkIvNAAUmiGUZ0rqcWW71Hv1GRJoQfoFVBItIahuVNSnQGSfJFAxJVkIjMhl+a/9tfX8VnPvMZtmzZEvOIpFa4gmTRLkN8g/O4gkvIZHSDE2m1nXfeGaguOvnvvdpdvNJ+3hkkarEl0kpGSi22OkmjgCQciigg6T6qFxdpQvgB2kwZXPaFy2IekUjnM1PgFBWQJFWjCpJwiy0d0i4yPb+CJEUGgMcee4zFixfHOSSpEZ7fWdnqg286rYBEpNXOO+887rzzTl723FMZPqfaYqtUUnV+3KJnkKiCRKQV/G4JqIKkI/jPu2blWdehRF9fH2NjY4BabHUjvTNFmuAtCnoLHC8++UW87W1vi3lEIp3PTNVXkKgPdXI0OoNEh7SLzF41IPEetB5//PE4hyMNuK5LmiwAqVz1npbJZOIakkjXyufz/M///A+vPOXlAKTJYGAoIEkAr4LEP4PE1UKgSAv4AYmpCpLEc103CEhSoQqSXC4X/IwqSLqPfqMiTXAcJ3iATuf1NhJphUYtthSQJMfUZ5D4LbYUkIhMx2+x5b9ntm3bFuNopBHXdcmSByA9WP0sUkAiMn/SA6FqLXIKSBIgfAaJqwoSkZYwKv17dAZJMoXPIAkHJP68vUwpMh9UQNJ99BsVaUI4ILFyehuJtIJZmTSGAxId1JlM4RZb1UPaFZCI1HrFK14BwAte8AJKFIFqiy2/RF+SIxKQhBZtd91117iGJNL1UnkjONMxR78CkgTwAhJvPu7gkEqpM7vIjvIrSHQGSfI5jtPwDJJwBYmC4+6jTzqRJkzVgkFEmmc0aLGlgCQ5VEEiMnff/e53ufHGG1m2bBmfOeQ3QLXFlgKS5KkNSH71q1+xceNG9t5775hHJtK9DMNgkjHyDJFjQIe0J0C5XCYVOoPEVEW3yA6rBiRaWE+icAVJ44DEVgVJl1NAItKE8BkklgISkZYwK3NFSy22Ein8u7j++uv52te+xpIlS8jyLEBnkIg00t/fz/HHH89jjz0WtNjyA5LR0dE4hyYNOOVoQHLiiSfGPCKR3lBgvBKQ5FVBkgDeGSR+QOIo0BdpASPlPUupxVbyhVts5egHYJIxcqFqOlXWdR/9RkWa4DgOGbzyulSfPuBEWqFaQVL9aFIFSXKEA5J/+7d/C17/K8cAarElMp10Os0EXiCSYxBQBUkSuYXqfSzdr/mdSLtM4t0Ps2qxlQjhgMTF0YYlkRYIziBRi63EC1eQ5FkAwDgjMFm9F/b19cUyNpk/CkhEmuC6ripIRFrMDAKS6qRRAUnyqcWWyMwymQzjDAPQX3nQUgVJ8jjj3n3MxSHVp3uaSLtMVgLkPEMKSBKg9gwSEdlxfostUy22EqlRiy0DgzxDAN48flxL6N1M0aVIE3RIu0jrBZPGUHavHWvJMdXvIoO3e8bKajFRZCrpdJoxtgHVgOSrX/0qH/3oR2McldQqP+XN7SZywxim7mki7bKN9QAsZLkCkgTwAhK/gqSs+bhIC6iCpHP4AUmW/iAsHmeEiYmJmEcm80nvTJEmhAMS7TAUaQ1Th7QnWqOHY4sUi1gBwNAembrvi4gnk8kwFlSQLAyuv//9749pRFLrzDPP5LGPefexydxwzKMR6S1beBKAxeysQ9oToFwuBxXCJfT7EGmF4JB2nUGSSI0qSPoqbXFtitgUWL16dVzDkzbQO1OkCa7rqoJEpMUMtdhKtEYByWJ2wSJFySqS31klxyJTyWQyTDACQI6BmEcjjXzzm99ktFLl45haEBRpp+1sAaCfRaogSQDHcYJ20iWKqiARaQG/gkQttpLPD0j8c4eLjAPwgQ98gDPPPJPf//73cQ5P5olWM0TmyHVd7r//flJ+BYnOIBFpCVMttjqO316rnJ6M7LoRkSjLsigyCRA8bEly+GG8Vfn8eWSP64BXxDgikd7i4IUiJqYCkgTwuiVUAxJL83GRHWZWiu1NV8uwSee6Lq7rkq086/pz+EWLFvGNb3wjzqHJPNLWd5E5uuCCC3jLW94SqiDRoqBIK/gVJJZabCVSo7DK313omOV2D0ek4/gPV0uHlsc8EqnlL8j6czsnpQVakXbyDwI3sRSQJEC4gsSmoA1LIi1gemvtpJ1svAORGfkVJOlKQFLAO3vENLWE3s302xWZo//4j/8ACJ1BoreRSCv4Z5AYarGVSNMFJK4CEpEZ+QFJ1uiLeSRSyz/zIF2p7nEVkIi0lVsJSAxVkMTurrvu4k9/+lPwrGtTjHlEIt3Bqkz/nAmT/fffn3e+853xDkimVNtiy64EJOqY0N20sivSJL/sWBUkIq1hZb33Urj9jHasJUej34V/H3QtBSQiM3nbOWcB4A6nufBlnwH0oJUU/oJsxq8gsbRAK9JOCkiS45BDDgEgFRzSrjNIRFrBynv/6xZM7r33Xr70pS/FOyCJCN/n/ICktsWWKki6m367Ik3ydxmmdEi7SEtkhrz3Upb+4JoqSJLNf3h2Lf2eRGZy3gX/Erze+RcnsIoD6etTNUkS1FaQqMWWSHs5eBstTMzg/Sjx8s/bLFGIeSQi3cHKe5ti/EV3SZZGAUk6OKRdFSS9QCu7Ik1SBYlIa2WGvLNHcuSDawpIkmPaM0hUQSIyo1Q+Ou0+n+/pHpcQ1TNIKi22VEEi0lY6gyR5qmeQqIJEpBWsyh7AbOhZV5IjPCd3XbdhBYkCku6mgESkSSmdQSLSUn4FSY6B4JoeyJJjuoCkWJ5o93BEOk6qPzpfMLEUkCSEbdsYmMHmF1WQiLRXuIJEAUky+PfDks4gEWmJVCUXUUCSTI0qSDI1AYllWbGMTdpDK7siTehnAf0sAFRBItIq6UpAkmcouKbFw2QL7y4UkelZ6fr5Qrms6qsksG07OJAYFJCItFv1DBJVkMTptttuA8DAYCHLAbApaMOSSAvkdvX+dzUHxzsQaSi87lBtseXNDf0WWwpIupsCEpEmvJOvB68VkIi0ht9i61heF1xTQJIMUz0Y+2eQ7LHP6nYOR6QrjDOie1xClEqlSEDiphRcibRTtcWWKkjidPjhhwNwHG8IrhWZ4NRTT41rSCJdY8Hh3rrRYnYmTY5sNjvDn5B2alxB4rVetStnMemQ9u6WinsAIp0mTY7d2D/4Or+T3kYirbDssMrhuJQxMHBxtWMt4axKQLJy911jHolI53mcv+O63n1OPY3jFa4gsSli6PlXpK10SHuyvJL3BK+/+7//yYtPfFGMoxHpDqkBA5siaTIMsBBrUGFwkjSuIPEPaVeLrV6g6b/IHO3BIcHrB1ZcjWFqUUOkFXZ9nndynYlFjkFAFSRJMXUFiddiy8rqPigyGyf/plptZVSm4QqC42fbdtBn2mZSD8AibVZtsaUKkriMjY0BsDK8EfDQIie99CW6J4q0gGkajLEVgH4Waf6XMOHfh39Ie7WCxAtIVEHS3fTbFZmjRewMwIPcwm17/0/MoxHpHlbWpJzyzrLor5xDooAkGRSQiLTGqhcO8uIf7QaAVSnk1jkk8Qu32LIpaDFQpM2qLbZ0Bklc/IBkKV5V8BruY49Lt8c5JJGuYpomYwwDMMBCBSQJM/0ZJKog6QUKSETmaBErANjAoxiWFgVFWqmU8Q5A62choJ3VSTFjQJLRvVBktoyU937xAxIFwfHzKkiqfab1ACzSXn6LLVWQxMef6/mHs6/nEay0lotEWsUwDCbwQsccA5r/JUyjM0jSQXWxziDpBfrtisyRv3C7nS26QYq0WCnrTT6WsweghcOk8w9pN1VBIjJrZloBSdKEK0iKTCggEWkzV4e0x87/LBpkMQDb2axnXZEWMgyDMt79zSKljYAJ0/iQ9urcEFRB0u30iScyR/6CYImiDlUVaTXLezj7f1zCEnbVwmFCqMWWSOuYqcr/KiBJDNu2eT5vBGAX9tGioEibVQ9pt3RIe0z8uZ4VetbVvVCkdbyAxLu/KSBJnukOaVcFSW/Qb1dkjoKe4ZR0gxRpMdeqTkz242hNHBNiqt9DOghIdC8Uma1qBYm3C01nkMRvy5YtHMgxwdfaISjSXo4qSGLnLw6Gn3V1LxRpndoKEm2QSZbpDmkv6pD2nqDfrsgcKSARmUehgMTF0cQxIVRBItI6arGVPFdccUXkay0KirSX32LL0CHtsamtINGzrkhrRStI0toImDDTV5DokPZeoE88kTkKTxrVYkukxUIBiUNZC4cJp4BEZO7MyiHtarGVHPl8nnu5HoDv8SE9AIu0mSpI4teogiSbzcY5JJGuogqSZKs9gyR920pWsi+gCpJeod+uyBwFOz61q0ak5dxUNCDRzppkmLqCxAuMFZCIzJ4qSJKnXC4Hge8E2xWQiLSZWzmDxFBAEht/rpexvFDk2ccczUEHHRTnkES6SjggSamCJHFqK0gGf3pE8HVRFSQ9Qau7InOksmOR+RTauaEWW4nRaAK/GwdwKC8AFJCIzIV/SHuwS1dnkMSuXC6Hdk3begAWabNwBcmGDRtiHk1v8ufcflh88stPUrcEkRYyDIOSDmlPrNoKkjBbFSQ9Qb9dkTnSGSQi8ye8UOiqxVZiNJrAn8PXg9c6pF1k9lRBkjxeBYm3AaZEUQGJSJs5lQoSE4tvfOMbMY+mNwVnkBjeZ5P/WSUirWEYBk6lgsQkjeM4XHnllbzyla9k06ZNMY9OwvPxsh199lUFSW/QiobIHIUDEu2qEWmtcri0FUc7axIsz1DwWhUkIrPnLzqZeA9ZCkjiF26xVVIFiUjbuZUKYgOTlStXxjya3lR7BokCEpHWCleQpCoByYknnshPf/pTvvvd78Y8OgmvO4zcGr3/2RQAVZB0O/12ReYo3IJBN0iR1nJK0YVCLRwmQ21QlSZ6aKcCEpHZ8w9p9ysWdJ+Ln9diy2+hqvmdSLuZlUzSxMK27XgH06OCCpIgIIlzNCLdJ3wGybN5daRzguaC8Qv/DkpjtRUkE4ACkm6n367IHJmVSWNJD9AiLbdkyZLgtYmlyWJC1AYk/sOzr2+n6NciMjUr5wUkaXKAziBJgmgFiVpsibRdEJDokPa4VCtIvGTEymjzi0greQGJFwAPspj9OCr4Xl9fX1zDkorw865brr5ew31BQKL5YXfT6q7IHAU9w9ViS6Tldl+9e/Bah9clR+3vwW8N5BvcXdsMRWYrla9Ov1NkFAQnQPQMkpIegEXazLSqrQdVQRKP+goSPeeKtJJhGJFNZovYOXituWD8HMfBwOA0Psimn3mbZiYY5VO8NvgZbZDubvrtisyRDmkXmT/ZoeqkURUkyVUbkOSXq4JEZLZSfdVFpzQ53ecSQBUkIvE68aQTATAwFJDERGeQiMwvwzDYid2Dr4uMB69VTRw/13U5kOfyHE5h9K/eppk13BOcGwOqIOl2Wt0VmQPXdRWQiMyjp71hYfDaIqWFw4SorSAxQtOH1z/0NFXTicyBmTYwKs9XGbK6zyWAdwZJ9Yw5PQCLtNcF7/tXQBUkcVIFicj8MgyDDNVWWuHXCkji5zgOAyyKXiM6R9f6X3fTb1dkDgqFQiQg0aKgSGvt/pJB9nrVEOA9JE9OTnLffffFPCqZ6gwSM22wYM9MHEMS6ViGYZDq86bgabJ6KE6AcqlMmiygChKROOSHvIVCkxSlUkktVmOgChKR+WUYBj/j08HXCkiSJbwZOrhGzTOw5oddTQGJyByMjIwEB9epgkRkfvgPZBYpXvjCF7Lffvvx61//OuZR9bapKkgMzRFFmuK32crQpwqSBHBLRtA6sMCEHoBF2sw/ENw/C0iLhe3nz/VMBSQi88IwDJ7gbv7CTwDIKiBJFMdxGgQkqiDpJfrtiszSo48+yt13302GHAA2k7pBiswDozIvCe+q+epXvxrTaKQRfyHRTOnhWaQZ/kHtabXYSgSjWH0gLiogEWk7K+vdE/2NaGqz1X5+QJJyvcpgK6s5nkgr+d1HikwAkGdB8D0FJPFzXbfunE1qKkjUQaa7aXVXZBZGR0fZY489OO6448jRD8AEowpIRObBxPoSAC/jX0hXAslSqRTnkHpefYstb/KoChKR5lRbbOmQ9iQwbC8gsSngUNb8TqTNzEoFiYmJSUoBSZuVy+XgsyjjehuU0oO6D4q0kr+4XmAMgMN5cfA9BSTxaxSQOApIeoo+9URm4cknnwS8kmN/V/sko7pBisyDDTdPBq+XsQrQTsK4Td1iS/dAkWakh7z3UB+DeihOAMv2dq37uzpVQSLSXuFqhRRpzfva6IorrmBgYID3vOc9QCgg6ddSkUgr+WtHj3M3EG2xpc0y8XMcJ2gx6KttsSXdTZ96IrPg7yR8OscH1yYZ0w5DkXlQ2FpdLOyvlB7rQTletQFJtcVWHKMR6XyDq7wF+cXsrIfiBDBK3u+jwDiggESk3fwzSEABSbt9+tOfZnJykj/84Q8ApN2s978Des4VaSU/IHmY2wHoYwgD75o2y8TPO6Q9Ov9TQNJb9KknMgt+EPJGPhFcUwsGkfkxuDodvPYDErXYitdUAYkqSESaM7i71+N9CbvqoTgB/AqSgipIRGJhpgyMymOVpYAkNmmywRxPAYlIa6VS3s6yCUYAr6VgjkFAAUkSeBUktQGJO8VPSzfSp57ILPiLsxNsj1xXiy2R1jvpf1cHr/tZCKiCJGmCChKtIYo0Jb/ce0geYFFdACntZ9nejmm/L7gCEpH2888hSZHRxpg2WrhwYfDab20LkMprqUiklTIZb3NMCTuoWO1nCFBAkgTeGSRqsdXL9KknMgv+4uyj3AnALVwJoAoSkXmw5OAc9y37IwC7sA+gCpK41VeQVM4gSSkkFmlGdmH1DBIFJPHrK3rVisNsBBSQiMTBCgISVZC0UzggeQFnBq9NVQmLtFQ6Xe2SMMwGAI7mVYACkiRwHEcttnqcVndFZqFUKrGKAziIYwG4mV8BCkhE5stTC+4FYA+eDkQnlNJ+arEl0lrZRd57qI9BnUGSAPniIgC2sR5Q1aJIHPyD2lNk9B5so2zWq6CzSHEEJ8U8GpHuFX6e9Q9qP5jnAQpIksB1XTL0Ra45arHVU7S6KzILtm3zbE4Jvl7LfYACEpH5Mp7bAsAgSwAFJHHzA5K38Hm+yF08j9cDarEl0qzMQu/Nk2dIFSQJkLO9FhcjbAKqbTBEpH3CLbYUkLSPvzB7Eu8Irh37lV3iGo5I1wq3Z/8d3wZgMd57TQFJ/BzHYQHLItdUQdJbUjP/iIjYts0SVgJwLT9kC+sAnUEiMl8KWe+8n0WswMBQQJIAu/A0DuE4AJ7BiwG12BJpVnahX0GigCQJTMf7fbz45Beyz+osp556aswjEuk9Y2u9dqrP4RTdF9vIr2I8vDK3A1i8fzau4Yj0hPU8AkCWPDkGVE2cAG4JDuOF0WsKSHqKtr+LzIJt22Qr5XZ3c11wXRUkIvOjmB0LXj+d5ysgiZnruqSo/x2ogkSkOX6LrbzOIEkE0/H2jO2175584QtfIJ/Pxzwikd61P8/WYmEb+X/XfgXduLmNnY/RPVBkPtlMMsEoAIMsVgVJApQ31z/rumqx1VO0uisyC6VSKVgcLFEt+VZAIjI/XKvMJF5IsoCdgv7IEo+pAhKdQSLSHP+Q9ix5SgUtBMbNryDxz0AQkfbb+1Sv1d09/FkBSRv5f9d9DADw4wUXq0uCSBvYTAKQJqeAJAGKY/WtHRWQ9Bat7orMgm3bWJXFwXIoINHkUWR+mKbJHfwOgDRZ7bCOmeu6wT0wzFSLLZGmZBZUy6/KozEORACwXO/+lsro0UgkLssO96r1LVIKSNqoGpAMAjDujsQ5HJGeYVMAIE1GAUkCTI4V6q6pxVZv0VOAyCzYth2qICkG11VBIjI/TNMMJo0psnpQToDGFSQxDESkC5iWQcEcB6A0oqAxbkEFSU7zOpG4+Ie0W6Q172uj2oBktLwtxtGI9I5qQKIKkiSYHCvWXVMFSW/RU4DILIQrSNRiS2T+GYZBsTJpzCggiZ3XYitTd10ttkSaV7C80hF7c8wDEUy/giSreZ1IXKxQQKLFwvYpl8uYWGTxzh1RBYlIe6iCJFkK441abGkNopfoKUBkFrwzSLzFwbICEpF551WQ+H1ZFZDEbeoWWzEMRqRLbOl7HICJ++rfW9JeluvdzHQGiUh8/AqSlCpI2spxnMgcr1CejHE0Ir1DFSTJUlRA0vO0uisyC9EWWzqDRGS+eQGJV+aaJqczSGKmQ9pFWm9b7kkAShs0HY+bfwaJWmyJxMcPKNViq728gKS646VYru/DLyKtF94MqIAkfo0DEq1B9BI9BYjMwlSHtKuCRGR+GIahCpIEmarFlipIRJpnWxMAlMcUNMbND4AtHdIuEhsrqCDRIe3tVBuQ2ApIRNqiuhlQz7pJYE+U6q6FK0gGBwfbORyJgZ4CRGZhqjNIRGR+hA9pT5PTpDFmte0XfKm8phEizbJT1YBEVXLxMisttlI5hVUicdEh7fEIByQOZUpO/SKhiLTGT37yE0455RRAFSRJU5ys/x04OFx66aWcc8453HTTTTGMStpJKxsis2DbJdLBGSTVSaMm7yLzI3oGSUbvtZhN1WIr1a9phEiz7JR3j7vtL3fyjGc8Qw/HMQoqSHRIu0hsLJ1BEgvHcTCxAChTVmAvMo9e8YpX8D//8z9A+AwSBSRJUGoQkLi47Lbbbnz+859nv/32i2FU0k56ChCZhVKhGoqEK0g0eReZH16LLVWQJEVt+wVfWhUkIk3zK0j6GOS2227jySefjHlEvWnz5s3BGSQpBSQisVEFSTzK5XJQJeyg6hGRdlFAkiyNAxIHy7JiGI3EQU8BIrMQ7kdYqvSKBAUkIvPFNE2KOoMkMRzHaXgGiSpIRJpXqlSQZMl7X5e0MBWH1atXB4uDZlYttkTiogqSeIQ3wZQVkIi0TXgzoCq34lcq1H/uuDg6d7iH6DctMgt2KE1Wiy2R+WeaZhBGZhSQxM513WARNyytgESkaWXLq0hNkwO8886k/cbGxoIAOJXTLkGRuJgZb06hCpL2irbYUkAi0i7hChLd8+JXLjYKSFAFSQ/RyobILJQnvUTfoRwpPVYppMj88Fps+RUkarEVN8dx6GdB3fVUXrutRZrlWN58YjE782xOUUASA3/HZjoISPRoJBKXagWJzp5rp+gh7QpIRNpFAUmymIX6bglqsdVb9BQgMgulCe8B2v8Q8+mDTGR+uK5LUZPGxHAch3yDgEQVJCLNc6xqIPJaPsj2J4rT/LTMh/Hx8cj5SjqDRCQ+ZqjFljahtY9abInEIxyQqMVW/AaKS+uuqcVWb9FvWmQWypWApMhE5LoWbUXmx+TkpCaNCTJVQGKpX79I0/wKEl9xTAtT7bZ169bg/BFQiy2ROFk6pD0W3iHtfkCiYEqkXWydt5kYtm2z3N2j7rqLqwqSHqKARGQW/BZbqiARaY+xsbHIpHHTpk0KSWLkui79DNV/w1RAItIsJxUNROxJBSTtNjw8HLTXArXYEomTqUPaY+GdQaIKEpF2syvnbSogid/wk2PszTMAKDAeXFeLrd6ipwCRWXAquUixJiBZtmxZDKMR6X7j4+NBINnHIA8//DCf+tSnYh5V75qqgsTUfFGkaeEWWwCFUZ1B0m62bQcHtJcpkUrrpiYSF1WQxENnkIjEw98M2M9C3fNiNvyo97sYZSvreTS4rhZbvUW/aZFZKHv3y+BD7NOf/jRnnXUW73jHO2IclUj38gKSyeDrlezHe9/73hhH1NvKZYcl7Fp33bBUQSLSNCtaFWePq7VJu5VKpaDFVhkbw9A9TSQu0TNItFjYLl5A4oXDZRTUi7RLsfKsuzfPgJLmH3EaXevd+7axHifUatBRi62ekpr5R0TEryDxd7Sffvrp7LTTTjGOSKS7jY2NMcZw8PWuPI013BvjiHrbpt82vm5ovijStNodaQpI2i9cQVLC1i5BkRj5FSQAjq22qu0SPaRdn0Mi7fIgNwevs8XBGEciY2u96rltbCAfaiutFlu9RU8BIrPgFrwJu7+jXQ/QIvNrctJ7r93ClQDkGIhzOD3vqf9pPDFUBYlI82qrFRSQtF+pVCJVqSApUdT8TiRGZjggKSogaRfHcViAt/FPZ5CItM8oW9nOFgCsYjbm0fS28XV+QBKtIFGLrd6i37TILFjb8wAUmAAUkIjMt7POOgsAN+tNVhSQxGvwHxo/MOsMEpHmGYbBl3l78LW9XQuC7RYNSFRBIhInqyYg+d3vfsc73vEOxsfHp/lTsqPK5TKHcgIAppaHRNpqklEAUiUFJHEaX+e1ddzGBlyqLR5dXM0Ne4h+0yKzMPCk13vfL4NUmZ3I/Lrooou48cYbef5LngfAyZwDeItZ0n5u5a/9an4Qua4KEpHmmabJ3VwXfD1y2c4xjqY3hc8gUQWJSLwMi2BhyrFdXvCCF/DlL3+ZSy65JOaRdTfHcYLWWmu5L+bRiPSWiUpAkp9YHPNIelths/fZs53NdRUkOp+ud+gpQGQWUuNeBcl6HgFUQSIy3yzL4sgjj8RZV91Ns5w9gtZb0l7lgrezvUQhMmk0dCsUaVrdA5etN1S7hc8gKauCRCRWhmHgmN4cwylWr991110xjag3OI5DX6VS+16uj3k0Ir1lV54GwDEPv4VvfvObMY+md/nnXpWxcWoqSKR36ClAZBbShX7AS5RBAYlIu+T3ry7GL2FXJiYmYhxN7/IXKmyKkbJjVZCINM8PSG7nKu9Cypnmp2U+eC22/EPai9olKBIz1w9I7Oq17du3xzSa3uAFJN4B0ePo71qknW7lN8Hrs858a4wj6W3VgKRUF5Bobtg7tMorMgPXcckEAUnlEC212BJpi+Wvq24htEipgiQmTsH7X5tCZNJo6FYo0jT/gStoXbdU97d20xkkIsniV5CUi9W5xujoaFzD6QleQDIEwKQCEpG2WLRoEQA/4KLgWrqyYUPazyl5AYlDCTfULaGMrYCkh+gpQGQGm26fxHRSTDDKMBsBVZCItEt60OQhbgXAxFIFSUycot9iSxUkIq3izyWKVO5rBc0t2i3cYksBiUj8XLNyBkmoxZYqSObXbmNPZxHLARhnhGc+85kxj0ik+914442cc845fOnrXwiupckxNjYW46h6V7iCpBwJSHT+aS/RU4DIDDb/zdvR+Rh/C3rv6wFapD1M06y+71RBEhu/gqREMVJBYqqCRKRp/o60QiUgcYuaW7Rb+JB27RIUiZ9reYtRbqjFlipI5tc+o88KXv/46u9z1VVXxTgakd6wzz778PnPf57dd1+NjfeglSbDhg0bYh5Zb3IrOUiZsipIepiexERmMLbWu1tu46ngmlpsibSHaZrBLg6LlCpIYuIUvYmhTUEVJCIt4j9wBRUkRc0t2i3aYqs4w0+LyHxz/AqSUEDiODqfab5MTk4yOebd+1adU+a5zz2GwcHBmEcl0jtM0wwFJDk2btwY84h6U7jFVngzoCpIeosCEpEZjK3zZujDVNN8pcgi7eEFJN7ExMJSBUlM/FYXrlUODvIEnUEisiNqW2wZtoXruHEOqefUHtIuIvHyD2l3FZC0xdlnnx1U0Zk6/kCk7QzDiFSQbN26NeYR9SYnqCApBd0r/K+19tc7FJCIzGBys3eDHGVbvAMR6UFeiy1vxqIzSOLjVtYNHTO6i8ZUBYlI0/wHrkmq/aaL27UQ2E7eGSTVQ9pFJF6u5d0DXbs6v3BdBcfz5Vvf+lYQkKSy2vUi0m7hCpIUWW0GjIkbVJCUaypI1GKrlyggEZlBueDdLG30YSXSbtEKEp1BEhf/DBLHKnErv61+Q7MIkab5D1wlikEVSWFrebo/Ii0WrSBRQCISO7XYajs/JLayWgQUabdwQJIhq82AMXFDFSSuDmnvWVraEJlBedKblNtqvSDSdjqkPRn8nZxuymEt9wbXVUEi0jy/xRbAGCMAFLYoIGmn2kPaRSRerlVpsRVak1IFyfxSBYlIfEzTpBSqIFFAEg+nMv2uryApsXTp0phGJe2mgERkBqVJb1Ku3tQi8agGJGqxFRe3cki7a5UpMB5c1xkkIs0Ll+yPMwzApAKSttIZJCLJ0qjFlipI5leKFADpnCZ1Iu1mGAbF4AwStdiKS7WCxI6cQXLee97FypUrYxqVtJsCEpEZOEGLrULMIxHpPaVSKdjVqxZb8fEPaSfl1AQkqiARaVY0IPEqSBSQtJfOIBFJliAgUQVJ21hBiy0tDYm0W7iCJK0KkthUA5JyJCB5/ouOj2lEEgd9CorMoFRpsaUHZ5H2K5fLlCuTFEsVJLGpttgqM6kKEpGWCLfYmmQ7APaIApJ2KhQKoQoSbYQRiV1QQVK9pIBkfvkhsSpIRNovfAaJKkji4wckDmUWLFoQXLfS2gzYSxSQiMzAP6RdD84i7Vcul3Eqh6PpDJL4+C22aitIdAaJSPPCFSQTjAFQHFErmXbasGEDabKAzpoTSQTLe+5yS9X7owKS+RWcQaKARKTtagMSbQaMSWV/0v4H7cfr33B6cNlUQNJTFJCIzKA8qRZbInGJVpCkNGmMSdALPO1QqCzkgipIRHZEOCDx31fF7QpI2mn9+vWkKxUkmueJJEDQYktnkLRLUEHSp0mdSLsZhqGAJAHcsveZY1hgpqvXzYwCkl6igERkBmW12BKJTbSCxGJ8fHyGPyGt5rouVAISI60zSERaJRyQTAYVJGqx1U5PPfUUqUoFiQ5pF4mfm6qEIaogaRu/giTTl4p5JCK9Ry22ksGtTL+NlIuVqy6Tq4KktyggEZlBWYe0i8Rm1apVlEMBydjY2Ax/QlrNsV1w/QoSNxqQaBYh0rToGSSjANhqsdVWIyMjoRZbmueJxK7SYsvfmAEKSOZbSi22RGLjHdLubdBIkaVc1kaZWJT8ChKDVK76+ROuJpHup6UNkRmUJnQGiUhcVq1axamnnQp4O9wUkLSf32YQwEi7wU53UEAisiMaVpCoxVZbTUxMqMWWSJJUWmxlH9qZM7mUleyvFlvzzA+JM3lVkIi0m2maFPGqRjJkdb+LgetUNwMaKSIVJKm8HnZ7iX7bItMo2y72qPchNcH2mEcj0pv23Ht3ACxVkMTCr6IDMDNEKkjKRe3qFGlWo4Dkrr/ezTHHHKMe1G0yPj6uFlsiSVJpsZV5cimH8UIu4H9YXt4z5kF1txyDAPQt0lZpkXYzDEMVJDELP+saaSdy7ki6X0vmvUS/bZFpFLZ6H1AODuMKSERi4U9SVEESj1LlHCabAlbKwqbaG9ctxTUqkc7XqMXWuofXc9111/HXv/41rmH1lPHxcVWQiCSIkarfeLGyvH8MI+kNabLBPbBviQISkXbzziDxnq3SCkhiUQp1SyDtgFP9WgFJb9FvW2Qak5u91b8JtuOickeROFiVPqBpMgpIYuC32LIpkEqlcHH5I5ezbfcHWXxQNubRiXSuRhUkOQYAVEHSJqogEUmYBgGJfxadtF5fpXrEoUx2SC22RNrNC0i8+YcCkniUK5sBHcqYKQMn9CuwcjqkvZcoIBGZRmGLd3ccZzjmkYj0rlSlD2iKLH/+8591WGeb+QFJiSKplPfw/GM+wSPP/7/IAq+IzE2jgCRLHvAW7mV+2baNbduhChIFJCKxS9VvSLNcVTbMFz8gmWAUw9ScTqTdaitIdAZJ+4U3A1opC7ccarmlZ92eooBEZBr++SPhnvsi0l7VChJvl+9DDz0U53B6zk1/vhnwJo3ptBYpRFqlUYutvkoFiQKS+edX6aTJAUTaB4pITNL1m2BSCkjmTTUgUStpkTgYhqEKkpj57aRLFLEsC1e/gp6lgERkGo7tTdJV2i0Sn1Sf91HlByTFonb5ttM5b/8XwNtdbVlWzKMR6R62bQevqy22+gEFJO3g/x3rDBKR5DAbdHmy3Ez7B9Ij/Lm1AmKReJimSaky/1BAEo9IBYllqVtFD1NAIjINp7J2oYBEJD5+BUk+7e2s1sSxvdJBf/5CJCDR5FFkxzz44IPB60IlILFIkyKjM0jaYHLSb2mhChKRxGhUQUKa9evXxzCY7rZhw4bQ/U8BsUgcTNOkGApI1GKr/aoBibcZ8MC3LKZveYqDzl4c88ik3RSQiEzDKamCRCRufgVJqrLLVxPH9kqFdlcrIBFpnfCCX7iVZ44BVZC0gV/B44fAZ//z2+McjogARoND2lNkuOyyy2IYTfe68MILWb58OZlKQFJUQCwSi3AFSYqMNgLGoNpiy3vW7Vua4ox1+3Lsl3aJeWTSbgpIRKYxvGUEUEAiEqfgDBLXW8RSQNJe1QqSog6qE2mhcJWIi0vBqLbZUkAy/2zbxsTCwuvp874PvTfmEYmI0bCCJMNTTz0Vw2i61yWXXAKEW2ypgkQkDoZhhCpIcgpIYrDmkXVAtJ20YeqZtxcpIBGZxmcv/RyggEQkTqlKQOJXMmji2F7hCpLwodKqIBHZMbVttAqG93WOfrXYagPbtoPFQahWK4pIfEyn/hCSF/AmJidU4TAfMmqxJRK76hkkqiCJw3nnnA/Ud0uQ3qMnAZFpPPTAIwA42DP8pIjMl6DFlqsWW3Go7i4sKiARaaHaEGSSUcBrsRU+wF3mR7FYDPrvQ7VaUUTiY47mGl7ve3TXNo+kN+iQdpF4OY4TBJRpcnrOjUFx3NsMXXvepvQeBSQi0/DbLqiCRCQ+arEVr/Ah7eEWWwpIRHaMf0h48HUlIHkaR1IsFuMYUk/xKki84N3KGmohKJIAzmGNW2k5Y3p/tlI2683t/JB4YFE+zuGI9CzXdUMBSVYVJDEIbwZUQNLbFJCITEMBiUj8rJz3UWW5aUAtttqt2mKrSC5X3dmpgESktSZcLyB5CW/HfGow5tF0Py8g8e5pqT4tvookgbG4yBaerLvujmvRqpX6+/uB6sLgCSceF+dwRHqWApL4+c+6qiARBSQi01BAIhI/f+FKLbbiEa4gyee1w1BkvhTcasutzPrFMY6kN4TPILF0/ohIIpimyR/4DgAbeJQb+BkAxkQ6xlF1n9qAJNWnRUGROIRbbKVIUy7pObfdVEEivvpT0EQkYFHZsU6Jj3zkI/z1r3/l/PPPj3lUIr3FryAxsTBJaWdNm6VDh7QrIBGZP31Uq0YmrO0xjqQ3FIvF4IDilM4fEUkE0zS5mu8zylYe4jZe2v9WGIPUhOYfreQHJP5mwFROIbFIHHK5XBCQAJglLdG2W/hZVwFJb9Mnocg0whUke++9Nz//+c855phjYh6VSG8JL1ylyaiCpM0yhreAaFMkn8/zwhe+ENM0ec1rXhPzyEQ62x//+EcOPvhg3vCGNwAwSLVqxCmohd18s207aKugChKRZDBNExeXm/kVW3mS8k4jAKzYdkDMI+su6bS3CdDfDJjKaFFQJA4rV67k3RecW71gaz7SbqmgW4IqSHqd3n0i00ib3oNzmRKZTCbm0Yj0JisSkOQUkLRZuhKQ+C22fv3rX7NhwwZOOumkmEcm0tme97znceeddwYbL/IsCL7nFlXRMN90BolI8tQuTlmrvdaDmZIqSFrJr8ZO+QGJKkhEYnPJxy6BVGVjjAKStqu22FIFSa/Tu09kGhnLu1mWKQU7bUSkvQzDwMp6i1dpMmqx1WZpo9qXtb+/H9M0WbJkScyjEukepulNx3/ARcE1BSTzz7ZtMn7/fS0OiiSCfz/0LdnJq6wzXL1HW8mfSwcttrJq6yMSJyPrBSRmWe/Fdgsf0l77GSS9Rb99kWmkTT8gsVVBIhIjv4okTVYVJG3m9+i3KZDL5WIejUj38Xer3cUfuZvrAHCLmqLPt2KxGLRVsFRBIpIItYtTgwu9szJMV7t6W6lUKgHVFltphcQisTIzXkBi2LrXtZsOaRefPglFpuFXkDiqIBGJVarSH14BSfstZhcACowHh3qKSOuEH8a2swUA19aC/XzzWmxVKkh0BolIItQGJAMLBrzraNGqlWorSNJ92rUuEifDD0hKute122J2BrxnXQUkvU1PAyLTyKW8frclVZCIxKpaQZJTi602cl2X3cqVg1H3Gubkk0+Od0AiXeiEE04IXtsUKi80RZ9vP//5z4MKufBZVyISn/qARBUk86EakOiQdpEkqDQuwSgprGy33TkEgAe5RQFJj9O7T6QB13W55557yJleQFJkQhUkIjGqVpBkVEHSRo7tBrs2v/xfn2XBggUz/AkRmavly5cHrxWQtMfEphL7/eUV2HjBiCpIRJKhNiAZWjjAMKogaTW/xZZ/SLulFlsisTJ1Bkls0pXNMtvZrICkx+mTUKSBr33taxx44IGMb/MWKgpMkErpw0okLv7u3pRabLVVuegGr9N9mjCKzAfTNINNGH5Asm3jCJOTk3EOq6vd8dnN7L7lSPbhCAByi3V/E0mC+jNIvBZbFilc1230R6QJtS22rIyq6ETi5FeQKCBpL8dxSFcOaS9TUkDS42INSHbffXcMw4j88/GPfzzOIYkA8P73vx+ALH2AV0Hi77QRkfZLhQ5pV4ut9nEKCkhE2iGb9Z6MS5WAJEWG++67L84hdS3Hcdj05ObItQPfujim0YhIWF2LraHq2WeOHsVapvaQdjOtgEQkTqZXxIChgKStSoXqxsuSDmnvebG/+/793/+ds846K/h6cHAwxtGIRGVCAYl2corEx9Ih7bHwK0gcyqSzajMoMl9yuRyjo6MUKwFJhhwTExMxj6o7ve1tb+OhbxZ4Je8BYPlxaRbtl415VCICU59BAuCWXNBCfkv4m438FlumKkhEYmVmvfegVdbzVjvZE9XkvYStgKTHxd5ia3BwkBUrVgT/9Pf3z/yHROaZX8LtByQFJjjooIPiHJJITwtXkCggaZ9ypYKkRFFtBkXmUW0FSZos4+PjcQ6pa339618nTTUQGVqZi3E0IhIWDkj6+/vZZeXOwdeOrRZbreIHJEsWLgXUYkskblZlKqIWW+1VHA8HJKog6XWxByQf//jHWbJkCYcddhif/OQnZ2xjVCgUGBkZifwj0mr+AqzfYuv0M05jyZIlcQ5JpKelQhUkarHVPk7RD0i0o0ZkPvkBiR202MqqgmQe+QdyAjzr31fEOBIRCQvPNf793/+dVLb6tVNSQNIq/prLrit2A9RiSyRu/nmblqMKknYqTtiA1y3BxdHzbo+LNSD553/+Z/77v/+bP/7xj7z1rW/lkksu4b3vfe+0f+ZjH/sYCxYsCP7Zbbfd2jRa6SX+Yal+BcmyXdWbWiRO/qRxNQepgqSN7EkvjCphB/dFEWm92oAkowqSeeVXkFyT+R5Du6u9lkhS1FaQpDLVxapSUfO/VvE3GzlF72tVkIjESxUk8bAn/Gdd72aogKS3tTwgueCCC+oOXq/959577wXgvPPO43nPex6HHHIIb3vb27j00kv5whe+QKFQmPL//vve9z6Gh4eDf5544olW/yuIBK1k/IDE7ItzNCKy4WZvJ/WzeDln/tNZbNiwIeYR9YbJ7d5ksYwdLOCKSOvlct6TsSpI2iNosZXWgqtIktQGJOl0mjLeDt/SpE5pbxW/gsSpfMyk+mNvLCLS21wvpHzu6BtiHkhv8c8gKVU+ZxSQ9LaWx5Pvfve7eeMb3zjtz+y5554Nrz/zmc+kVCrx6KOPsu+++zb8mWw2q0UamXf+Tmm/xZaZU0m3SJzG1lUfipexin/913/lW9/6Vowj6g2TY95ibYmiPntF5lG1gmQS8M8gGY5zSF0tU2mxZWY1vxNJknBAks/nsSyLMiUs0tgFBSSt4LpucN5madz737QCEpFYbbvBC0iM+E9B6CnVChIFJDIPAcmyZctYtmxZU3/29ttvxzRNdtpppxaPSmRuUqkUFiksvKBEAYlIvF5w+Up+9Y+PA97C4SOPPBLziHrD5KhXQVLCJpPJxDwake5VDUi891xaFSTzyq8gMXU+u0ii1FaQeAGJt4Dlt/2UHRM+y6884T3jpvrVYkskTqvfanD/h+IeRe/xP1f8SkU97/a22BrcXX/99dx4440cd9xxDA4Ocv311/Oud72L17/+9SxatCiuYYkAXkCSIR98rRZbIvHa42VDjA1spH90GRn6WLBgQdxD6nrf+973WPMDlwEOxTFKGIYenkXmi/9AFq4gUUAyP/IMcQDHAFDOT8Y8GhEJaxSQOFRaoBQVkLSCH5CYWMEZJKogEYnX4qO9//XPwgD4xje+wY9+9COuuOIKBgcHYxpZdxt5sNLC0d+gpDM3e1psn4TZbJb//u//5thjj+XAAw/kox/9KO9617v42te+FteQRALpdDpor1XG1sF1IgngpCo7O8jR398f82i628MPP8zrX/96+n55kHfBUBWdyHzyzz4rVs4gSZMLesRLa+3F4fQxAMC2XR+KeTQiEjZdBUmpoICkFRzHO3vJP2sTIJVXQCISp1Sf9x5MkcF1vOeus846i9/85jd87nOfi3NoXe1vH4puRlIFSW+LrYLk8MMP54Ybbojr/73ItFzXZQFeq7gCE9o5LZIAjuUtFqbJUigUYh5Nd9uyZQvg7aaxSHFr/pfAq+MdlEgX83se+xUkGXKMKiCZF/75Iw9xG5kh9ZoWSZLpKkh0Bklr+AHJsbwOAMMEK6tnXZE4Wbnqe7A06ZLOV78eGRmJY0i9ofKRcz0/BVRB0uu0VUAkxHVdfvnLX/LEE09wBp8EvFYM4cm6iMSjHFSQ9Kn1zDyzbe/vOl1ZSHx08JY4hyPS9SYnvWCkGGqxpQqS+ZGqnD8yyaiqEUUS5sADD2RwcJBVq1axatUqDMOg7LfYUgVJSziOQz8LOZlzAEgPmtoMKBIzv4IEoDzhRL6ng8Nbz7ZtjjnmGEa3jgNwO78DVEHS67TqKxJy6aWX8tKXvpSRkRGWshKA+7hBAYlIAvgVJBly/PrXv455NN1r69atHH300aRIY1amCdpZKDK//NDXrrTYypCjZCsgabVHH32UNP55LwUFJCIJs3LlSp566ikeeOABcjlvk4aDt1hYLjrT/VGZJcdxGGRJ8PWRF+0U42hEBCCVsYKDwkuT0dbGCkha76abbuK6666DsvesqzNIBBSQiET8+7//O1A5tK4yGf82F2hXjUgC+GeQ+FUNW7dujXM4XevTn/40UN1lDWDm4hqNSG/wD80tUq2OK0/q7J9W2rRpE3vssUdwbytRJJ/PxzwqEamVz+cju3gdQ4e0t5LjOOQZAmBo7zRP/5elMY9IRAzDCM6hK6mCpG1SlU0zfkCiCpLepoBEJGT79u0ADLEUE5MyJUbZogoSkQRwLL/Flre49cADD8Q5nK7l97lNV/6eHRzSOU3MReaT63phiF9BAlCejGs03ekXv/gFUL23qYJEpDO4hrdYqBZbreG12FoAQG6R5nciSWCaJqXKHLA8oQqS+TY2NoZJKuiWYKuCRFBAIhK4//77g9e7sA8Am3gCF1cBiUgCVA9p7wPgkUceiXM4XcvfyZ4OdlkXyOay0/0REdlB/qG5Lg6u5b0HnYKqV1vpb3/7G0CkxZYqSESSzz+kvaQWWy0RriDJLk7FPBoRAS8gGcPbpLb+pvG670lrDQ8PB/NBUAWJePROE6m48sorg9cr2BOANdwLoBZbIglQPaTd6/dULBbjHE7Xqg1IbIoMDg7GOSSRrudXkACQroQlqiBpqXXr1gFEWmypgkQk+RxDZ5C0kuu6ZPHufZlBLQeJJIFhGNzPjQBsvTf6jKsKktYbHh4O2muBziARjz4RRSqGhoaC1/4ZB5OMAUrtRZIgfEg7VBfypbX8nez+fdBmUgGJyDzz33cAbspfDIxrNN3pscceA6IVJOG5n4gkk6uApKUcxwnug6k+PeOKJIFpmozina9ZGtMZJPNt+/btQUBSxsatnD+sCpLepk9EkYrh4eHgdfWwJm/HugISkfg5NRUkpVIpzuF0rWoFib+IqAoSkfkWqSCxvIc0x9Yh7a2yefNmbrrpJgD6Kq1lbIqsXr06zmGJyCw4pjcv0SHtreE4TlBJZ+XUJUEkCbxD2icAKI07kY0zWotqvXK5XLfmB6og6XV6p4lUbNu2LXidwrsx+qV2arElEr/qGSQKSOaTH5BUJ40KSETmW/hB2LC8YEQBSes88sgjOI7DEEt5Fv8IwCTb2WeffWIemYjMRBUkreU4DplKQKIKEpFkME2TIl5v1eGHi9h2ddFeFSSt57pusBnQX/MDVZD0On0iilREK0i8gKSsChKRxFAFyfy75ZZb+Pa3vw1E74MKSETmV6SCpHJmrms3/lmZu7Vr1wLwvOWvDK7903mnsfvuu8c0IhGZLdfwNm6UbQUkraAKEpHk8QISr4LkyWvHGV1fPYhOAUnrhe+DNoXgejiYkt6TinsAIkkxNjYWvLZqKkgUkIjETxUk8+8FL3hB8NqqTBHKlFi+ZElcQxLpCZEKkpRfQRLXaLrPk08+CcAuu+4M671rp1344hhHJCKz5ZqVChIFJE1zXZdzzz2XAw88kBe+8IWkFZCIJIphGME5GACb/jYevFZA0nqO49DHAAATjHLYYYcxOjqq1qs9TgGJSEWhUE2OUzXldmqxJRI/v4IkSx+gQ9rnw9atW4PXZmWKsGz5Uv7pn06Ia0giPSEakICLKkhaaXzcW2gYMBYBsOrFA+QW6zFIpBP4LbbUdrB5V199NZ///OcBeOihh4Jn3VROmwBFksA0TfIsCL52rOpzrtaiWs9xHPorf9/jDHPzzTd755KkNDfsZfpEFKkoFqu9B6tnkKjFlkhSlLJeqXE/CwH46le/yumnn65S2HniV9Ltvd+eLF26NObRiHS3cIutoIJERXIt41ccZuw8AEN7qse0SKfwK0hKBVWQNGtkZCR47bpu0K421aeFV5EkME2TTawJvi6MVZ9vtSmw9RzHCQKpcraAaZo6oF0UkIj4GleQKCARSYpS1tsB7AckDzzwAN///ve5/PLLYxxVdwlPDP2g2Ezr4VlkvtVWkAC4Ckhaxg9I0kUvIMktUbsKkU7hByROSQFJs8LzO9u2g2ddSxUkIolgGAZ38cfg6+JYdRIYniNKa4QDkue95Nkxj0aSQp+IIhXhChKr5pB2lTWKxM/OegfX5RkK2j8BDA8PxzWkrpPJVHdV+2eQKCARmX+77LJL8NqorGMpIGkdf/elAhKRDuQHJEW12GpWuG3M2NhYcJ6fziARSQZ/Q+6j3AVAcVQVJPMpfAbJ0t0WxTwaSQoFJCIVhUKBw3gRH+R/Wc2BgA5pF0mScraAgzdB7A/1aNX7s3Wy2WzwWgGJSPv853/+JyeccAJXXnklpr+OVdJ7r1X8CpJU0TvDKrdEPaZFOoVr+m0HFZA0KxyQjI6OkmcQgOwihcUiSeBvyLXxupoMb94efE8BSes5jlNtNdiv+bZ4tKokUlEsFjmTT7ETu7OQ5YBabIkkiWHBGF61yADVnR56f7ZOuAWDH5BYGU0aRebbHnvswVVXXcWLXvQizMp7zlVA0jL+4kJq0nsYVgWJSAepVJCUVUHSND8kBvjIRz4SzKP7lupeKJIE/vNsjn4AHvjqZPA9tdhqPe8sJm/TTDqvtQTx6L8EkYrwGSQ+v4JELbZEkuVZvDx4rfdn6wwNDQWvLZ1BIhILU2eQtJy/OGgVvSq57GItCop0iqCCxFZA0izb9jb97clhHPD701jGKkDVdCJJ4Qcku7E/AOk1S4LvqYKk9RzHCVoNphSQSIX+SxCpaBSQ+CWO2qEuEj/DMBhkMQDP55+C63p/tk54h6FabInEwz+DxHS0iN8q/r3NtL2/3PSAPjdEOkU57W1YK4/HPJAO5gck5/Ff7MuzguuqphNJhuk2/CkgaT2vxZa3aUYBifj0X4JIRfiQ9uAaXmmjFmBFkmGUrcFrf9eH3p+t4z9Ag1psicTFD0hwdG9rFX9xwbS9+1q6X3+3Ip2inPY2rDljmo80q9FzLkBmge6FIkngP8+u55G676nFVut5FSRei61UXp8t4tEnokhF4woSLyBRCx+RZPgUpwevh1gKgGVp91urhB+gVUEiEg+/xZZfQbJ169aGcxSZvVKphIGJUaoEJKogEekY5Yx3/yuP6X3brPAGGF+ZElZGf6ciSeCvN32Bs4Jr/hkZqiBpvcgh7aogkQr9lyCCd4N88MEH666rgkQkOQzDYBNPsIk1AAyxZIY/IXMVDUh0BolIHIzKe85wTDZt2sTixYvZe++9Yx5VZyuXy2QrCw2gChKRTlLOeHMTRwFJ0xoFJLYx2eAnRSQO/nrTNtYzxjAAy9gNUEAyHzZv3qyAROrovwQR4M4778Ro8HawFZCIJM4ImwBYxAqg8UOfNKdRiy1TLbZE2sqstNgyHIurr74agDVr1sQ4os5XKpWCnZgYYOV0XxPpFE4lIHHH9TzWrIYBianKRJGkCK83PYm3cXdnvM0xarHVWr///e/59re/TbpyBomV1ZxQPJpliADj4+PsyaF111VBIpIcfunx4/wdIDhkUgFJa7iuy9jYWPB1igygM0hE2q0akGju0SqlUok8CwCvekStU0U6h5suef9b1Pu2WY0DkokYRiIijYTnJdtYD8AAiwFVkLTaBz/4QQBSlW4JCkjEpycvEbwPnaWsrLtu4+2syWQy7R6SiEzhIW4DYAV7AQpIWuXPf/5z5Osc/QCkBzVVEGknv62d6VpayG+RcrnM2/giAPaodmKKdBIj7QLg2rofNsu2bRayPHJtsV3/7Csi8QjP98YZASDPIKCApNX8v2u/nbQ2A4pPqx4ieGWLtZNGgCLezppsNtvuIYnIFDbxBFDty6qApDXuu+++yNdZ8gBkFJCItJXf1s6oHNIuO65UKgUbYXZ+dj7m0YjIXBiVqjpKWsRqVrFYDNr1iEiyFRgH4CWcDajF1nxRO2mppVUPEbxUfohlkWs2Bcp4Jd2qIBGJn7/bI1x2bGAoIGmRDRs2APDGN74RqFaQZIY0VRBpJzNVqSBxVEHSKuVymWE2AvCcz+0c82hEZC6MVKWCJBSQXHbZZRx33HH87W9/i2tYHcW2bRbUPOuuzz0Y02hEZDrhjbs5+lVB0mKu632mqIJEamnVQwTvwdnfLe37OKcEr1VBIpIcE2wHwMQkS78CkhbZuNFbPFy2zHuAzjEAQHpQu9hF2slvsWW40feedhA2r1QqBaFvdpHuaSKdJKggKVeXLs4++2z+9Kc/cfHFF8czqA7jOA4L2Cly7doV34xpNCIyHf9ZF7znMQUkrfOtb32Lv/zlL0D1DBJVkIhPAYkIXkCSJhqC+OePgAISkSQpUQzen30MKCBpkU2bNgGwdOlSoNpiS2eQiLSXVSlaNWtabJVKpRhG0x1KdnUjjKriRDqL6QckDc4gWbt2bXsH06Ecx2GAhZFrE+mReAYjItP6DV8PXvcxqPlfC73pTW8KXquCRGrpCUEEb9KYIRd8/XeuZQvrgq/VYkskfuFWMxOMAt6ksVgsxjWkrjI2NgbAwIBXORK02FJAItJWUx3Srh2EzTMKqeC17mkinaVRBYlvcnKyvYPpUI7jBJXBG3iMn/FpxnNbYh6ViDQyzAY2Vs7c7GOA8fHxmEfUnYIKkrQCEvHoCUEEb9HBD0i+Y1zAZZUDsXyqIBFJlslK6XEfg3o4bhF/8t3f7wUj/oN0ZkjtaETayS/1N11VkLTMZOXvMuVgZfX4I9JJ/AoSwzFxHTfyPc0BZ8cLSLz53R/5Lr/jWzrjSiTB/GfdHIMKSOaBgYmJNzdUBYn49IQggh+Q9AFQcOs/gCxLC4QiSbIdb9fbYnbRw3GL+BUk+bzXhkYttkTiYaW995zppiILWApImmcUvBVWo0/nuIh0GiNUyF8uKiBpRriCZBJvvmeamt+JJNVI5Vl3Cbvwk5/8hHXr1s3wJ2Qu/OoR0BkkUqVPRRG8SaN/BkkRTbRFkii8UPgQtwGwL8/Uw3GLhCtI0mSx8FrSqB2NSHuZWVWQtJpZVEAi0qnCu3sdBSRNcRyHviAg8drUKiARSa6HuRWAp3EkAGeccUacw+k6/nMuqIJEqvSpKEK0xZYCEpHku58bAdiLZ+jhuEXCFSS33/i34Hp6QFMFkXayKr2Q9yw+g9Jk9dwRBSTNMyoBiamARKTjpLLVsPivN9wS+V6hUGj3cDqSKkhEOsv93ATA3jwDgOuvvz7O4XQdK1xBojNIpEKfiiJ4AUm60mJLAYlI8m2qHFw3yGIFJC0SriDZZfFugBeOGKYmjSLtZGWq0/OnTnsaz+EUQAHJjrCKXo8es9+d4SdFJGkyuTRlvPvfW9/09sj3NAecHcdxSOHdB/1nXQUkIsm1MXjWXYKBwcqVK2MeUXfxW2y5pqNnXQnoU1EEv4LEa7FlKyARSaRwi60CEwDk6GdyQrsHW8EPSPL5PKUxb5e1qkdE2q9vl+j77jQ+CCgg2RGWXQlI8gpIRDpNNpulRBGAlVsPjXyvXC43+BNSy2sn7d0H/b9LBSQiyVWgei5umhz7779/jKPpPkEFSUrzQqnSp6II/qSxvsXWFVdcwe233x7TqERkKn57AIDSuFqmtIK/CzOXywWHoOrQOpH2W3iIxW/5RuRaniEFJDvAtL0HYasv5oGIyJxlMhnu5joADigcE/me62pxazbCFSQKSESSz2YSBy8AzpJn+fLlMY+ouwSHtFtaR5AqfSqKACW7egZJuILk1a9+NU9/+tPjGpaITCE8aSyN6eG4FfzF13Q6HRyCqkPrRNrPNE1u4v8i117DBxSQ7Ihy5eD7bMzjEJE5y2az/JkfAdDnDEW+p4BkdqIBiQ1EK7NFJHn8KpIc/aqWazG/gsSw9BkiVQpIRIDyZPXGqDNIRJJpt912i3ztV5E8+LdHmJiYiGNIXcVffE2lUjh2pYJEh9aJtJ1pmgyzIXLtGbyYsScVkDTL9QMSS/c0kU6TyWSChcKMm8dxqjt+w69lal5A4i0IqoJEJNny+TxQDUiy5LVJpgUKhWpbbrXYkkb0qSgCOKFMRGeQiCTT+eefzxvf+EbOPPNMoHoOSZZ+7r333jiH1vFc140EJGqxJRIf0zSZYHvd9dEHtHuwaZU1VDOle5pIp8lms0wwCtTvpFYFyew4ZSdYEFRAIpJsCkjmx9atW4PXKQUk0oA+FUWAciUTKZs2LrpJiiRRf38/3/rWt3jZy14GQKFSQZIjz/DwcJxD63jhxYZUKkW5oBZbInGZatHKHtNO6aY5lQoSBSQiHSeTyTBZCUiyNQGJKkhmx7Wr9z4FJCLJtvvuuwPhgKRfAUkL+AFJjn6exhEAGApIJCQV9wBEksCZ8G6MjlUKdhmKSDL5D3R+i60s/QpIdpA/6c6S54a3beORK7wJuVpsibTfVItW2+/RQ1zT/BZbaS0IinSabDYbLBSmyVAcry4UqoJkdhSQiHSOwcFB1qxZw3WnbGfT9SWy5HUGSQusW7cOA4Oz+Cz78izvos4gkRB9KopQrSBxUna8AxGRGfkPdIUgIMkzMjIS55A6nh+QXMqNQTgCYKbjGpFI7/LvcZ/jTfyFn/AwtwPw5DeyjK/XDsJmuJXNL5YqSEQ6jldBMhZ8PTlcfV5TQDJLoYCkjPc5ooBEJJn23ntvdt11V/oX9wFetwRVkOy4K6+8kpdwdjUcAbXYkgh9KopQ3VXjWkrmRZKuGpB4Z5DkVEGyw2zbJkWm7rpT1KRRpN38e9wD/JXv8yGGWBp8b/ihYlzD6mxBiy09+oh0mmw2i4uDg/ecVprUGSRz5Za8e59N9ZBiBSQiyfKLX/yCN7/5zXzgAx8AIDPovUd1BklrPP744xzC8dGLKbWPkSq12BIBykWXFOCaukGKJF19i60827fXH2gss1cqeeXbddcntPAg0m61i1aDLA5eO7bek80wKgGJpRZbIh1n48aNAJSwyWBhT2pD25xVNgOWqFbfGIYq6kSS5OSTT+bkk08Ovk4PVAOSclmbAXfU5vVbeQ57Ra45hoInqdJTggjVBQfXUkAiknS1LbZy9GPbao+3I0qlEhn66q9P6J4o0m61AUk4vCyN6z3ZFB3SLtKxjj32WADKlcX9UiEakDiOw89//nOuu+66to+tU7gl795XNqrzZVWQiCRbNSDRIe2tMLkWTKzINb/loAgoIBEBwPXniqogEUm8akDinZWRVUCyw2zbVgWJSELULlpdyw+D1wpImuRXkGT06CPSafbZZx/+8Ic/BNUPtQHJ/fffz8tf/nKOOeaYOIbXGSotthSQiHSOtFpstdaWLFBdQwAouVpDkCp9KooATuXzRi22RJKv2mLLD0g0adxRU7XYsrfrnijSbrWLVj/hU8FrBSRNcr2/U0tnkIh0pD333LNaQVLTYuvhhx+OY0idJQhIqvNlBSQiyRZusaVn3RYoeCdMbGZtcMkpTPXD0ov0qSgCuH5Pb8vh1FNPBYj0fxSR5GjUYkuTxh3jBSRei63B1engemGr+nyLtFvtopXNJHfwe+/1uKq6muGfQWLqDBKRjpTNZoNWKPZkNCgOzwE1H5xCpcWWo4BEpGP4AUmOPOWynsl2WNH7+9zG+uBSfs0ucY1GEkifiiJUK0iwXD772c/y3e9+lx/84AexjklEGmt0SLtabO2YUqlEjgEA8itSLNg7A0D/Lqk4hyXSkxotWhWZBFRB0izD9RYHU2qxJdKRMplMUEFSGCtGvhcORYrF6Pekwq8gMdViS6RTZNRiq2Vc18W0vU2ABca5kZ8DUD768TiHJQmjT0URqgfXYbnsvPPOvP71r6e/vz/eQYlIQ/4DXbHSYksVJDvOtm324OkADKxKc/KVq9nntAWc9MvVMY9MpPc0DkgmAAUkzXBdt9piK23N8NMikkTZbDY4g2R0eCzyvfAcsFBQv5SGKgGJY1Z3oSsgEUm29IA3Z9Eh7TuuWCyyhF2910xwOR/knuf8mH+64nnxDkwSRVtDRQC35LWscC21rhBJukYVJBOqINkhpVKJF/AmABbslWHBXlle+IPdYh6VSG+aPiDRPGWuHMfBxFtksNRiS6Qj5XK5oILkuqv/EvleuGpEFSSNGWU/IKkushqGEddwRGQWwmeQqMXWjpmYmOAEzgBgCStxcXjn11/Dgl3qz+CU3qWnBBHAtb0JomFpZ6ZI0tUGJP0s1K6aHVQqlYKdmatPHIx5NCK9TS22WqtcLisgEelwlmVhVLZ2bnxyU+R74+PjwWtVkEyhVB+QqIJEJNnSlRZbu/I0KKgCdkdMTk4Gr/fmGQCsWLEiruFIQulTUQRw/bliSjszRZLOf6BbxwM4OCxhVzIbFsc8qs5WnLBJ4fVlXXxQNubRiPS26QISe0wByVyVSiUsPyDRGSQiHWvZiqUAbNs8HLkeDkhUQVLvhhtu4Pab7wDAVYstkY7Rt6waihz21MvjG0gXmJiYCF7v9T6Hm266iYULF8Y3IEkkfSqKAG65egaJiCSb/0A3wXbWpe4DYPCx3WMcUecrjlYfmNP9mhqIxKnRopWtCpKmlUoljEpAkspoB6ZIp8r0eSUktQHJ2Fj1TBJVkNQ76qijSJEBwLUUkIh0iqE9M8HrvYePinEknW9sazUgOf59B3LEEUfEOBpJKn0qdrEnnniCBx54IO5hdAS/gsTQc7NI4oUf6Nbl7gHAKOpIrR3hByQOZcyMelKLxKlRX3j/DJKb/3Jbu4fT8cIVJCkd0i7SsdKVgGR8ZDJyfXR0NHitgKSxakBSDdkVkIgkm2EYDD3ba4H8aN/t8Q6mw41tqX42aDOgTEX/ZXQpx3FYtWoVT3va09i+fXvcw0k+tdgS6RiRB7qst7CvgGTH2KPeA3PJLOjQTpEE8ltsrXlkXcwj6TzFYjGoILFUQSLSsdI57/1bHI+eO6cWWzPLkAPATamCRKSTLDjCW5+yyumYR9LZJrZ6nw0FYxzD1LOuNKZPxS61bl31AXrTpk3T/KQADD6wNwCGAhKRxIs80GW8Bz3TVkCyI7b9Xx8AGScf80hEpBE/IMnQF+mjLDOzbTuoIDFTeigW6VSZAW+ulyZ6VtqWLVuC16ogaewk3gHARH/170obYkSSz692SDmZGX5SpjOx0VszsC3NoWVqCki61IMPPhi8LpfL0/yk2OMO2a2LAHAHtOtIJOnCAYmR8yofDAUkO2T7zxfEPQQRmUY1IMmxcePGmEeTbI8//nhkF3mxWMT0AxIVkIh0rOyQt4Par4bwXX755cFrBST1hlga3AO3Ln00uK4KEpHkS+X9gCQ7w0/KdEb+6gXCm/KPxDwSSTJ9Knapbdu2Ba8LhQJ///vf+dGPfhTfgBJs5KEiBt4N037uwzGPRkRmEg1IvKovs6RdNTvE8P4etww+FvNARKQRu3IGSZoctm3HPJrkuvLKK1m9ejUXXHBBcK1YLGLhLaxaOT36iHSq3KAfkExd7eo4zpTf61V9DAavty9bE7xWQCKSfH4FiVs0efWrX82TTz4Z84g60+jN3ufHukV/j3kkkmT6VOxS4YfnQqHAQQcdxCmnnMK1114b46iSqbjdm0hv5HGsnEqNRZLOsqpbgK2+Sl/Wkvqy7ghj0Ks0vO2A/455JCLSSLiCpFQqzfDTvevMM88E4DOf+UxwrVgskqm05NE8T6Rz9S3w3se1FSRh6pxQL4vXRnULT5JOV+fLCkhEki8z4D33Zsjx4x//mLPPPjvmEXUme6P39zg6+FTMI5Ek06dilwo/PIdLje+99944hpNoxRFvIj3JWGThVUSSKfxAZ/V7/5uyVXa8I9zKR4aR1jlMIkkUDkhUQTK1VKrabtF1XX7+859z6KGHkqoEJCkFJCIdq2+BVy08XUCiCpJ62UrFTYHxyD1SAYlI8mWGvPUpvxLs8ccfj3M4HcsZr8z/+rTJSKamT8UuFX543rx5c/B60aJFcQwn0Yoj3kR6kjFNFEU6QKTF1gJvkpMrDsU1nO5ge3+nVkaLhyJJFD6kXRUkU1uyZEnwemJigpe//OVA9VBnq0/zPJFO5QckaVWQzEk4IFEFiUhnyS/3Qs1BFgN63zZj7dq1lEcrX/TpM0KmpndXlwo/PD/2WLWnfHhSJB57u3eTLKiCRKQjRCpIFnr3umxpEKes6oemlbxgxNBRLiKJVAydQVIsqoJkKuF57vbt26vXVUEi0vH8VjN+y6hGVEFSTxUkIp0rv8Kb12TJkyWPYWgeMxe2bbPHyr0wXe/eZ/TpM0Kmpk/FLhWuIAkf2D45ORnDaJItXEGigEQk+SIByQIXBwcTk8lN2hHSDKfkgutNts20Jt0iSeQHJCYm9rgqSKZSLBaD1w0DElWQiHSszELvOa2fhVP+jAKSev6h9sWaChIttIokX3YwxSRjAAyyRMHmHP39738nxwAADg5WPuYBSaLp3dWlwhUk4+PjwevweSTi2Xirt+iwhXX6wBHpALvssgvPfe5z2WeffTj6OUcFC4f2mB6Km1EuVitvrKwelkWSyKY6f9v4S81VphLeCDQyMhK8TuuQdpGON7jKW9xfxM5T/oxabNXrqywOTjCmChKRDpNKpShUApIsfXrfztGtt95KDu/Q0gJjZLLqqCNT07urS4UrSCYmJoLXCkjqbbvP2234KHepgkSkA1iWxdVXX83999/P8uXLsSu9+UvjCkim47ou119/PRs3boxcLxdCAYnOIBFJJIfqot/aT/XHOJJkCwckfgWJgaEKEpEuMLja6wO6iBUYUyxjqIKkXp4FAEwwQl9ftT2ZFlpFki+VSlGsbJLJ0KfKrzm69dZbg5B4kjGy2WzMI5Ik06dil1IFyez5u6dtJhWQiHQYy7KCndUKSKZ3+eWXc/TRR3P00UdHrjvhCpKMpgUi0rkaVZCkqB6upAoSkc7Vt5P3nGaRClqm1FIFSb2F6SXe/+4ywPOf//zgugISkeTznnW9uU2aHH/5y194/PHHYx5V53j44YeDz4tJRslkdOCmTC01849IJ/IDEgOTsbGx4LoCknqO7S0OlrAVkIh0GMuyKPoVJBM6pH061113HQAPPvhg5Hq56AVLNkVSaU0LRJLgD3/4A5///OexLAvXdfnJT34S95A6Qjgg8TcIZcgF11RBItK5rIyJbU6SdnL0M8QEI3U/owqSejlnEIDT3vRqUkNPBdcVkIgkn1dB4s1tMpVq2COOOIL169fHOayOUSwWg4BkQgGJzEArIV0qc8VBfJ47+CmfolishiI6pL2ev3u6jK2JokiHCe+qUQXJ9KYqyfZbbJUoRg7vFJH4HHfccRx33HHB14Zh8Dh3s4oDYhxV8oXnuX672Rze4mDZKmKmVEEi0smK6XHShRz/wEkcxSv4Du/jYW4Lvq+ApF7W8doy5hZZGKF5np57RZIvlUoF3RLSeC3yNmzYAMCf/vQntm7dyite8YrYxpd0tm3Tx0LAqyDJKiCRaehTsUs5lDExyZKPVI2ogqReqeBNpFVBItJ5TNMMVZDooXg64Qdh161W2zx5rbfLuo+ByOGdIpIsV3AJAOYy7+y0c889lyOOOCLSSrWXua47RUDiLQ46WbvhnxORzmGnvLM1X8o7WcKuvJL3RL7fyy22xsfHG7beybjeompmKKVD2kU6TLhbgl9BAt6c57jjjuOVr3wla9eujWt4iWfbdqjF1pgqSGRa+lTsUqWU9/CcJU+xWAyuKyCpN7LVK88uU1JAItJhomeQqMXWdMIVJP7CIcDd39gavFZAIpJcRbyFQdf23suf+9znuPnmm/n+978f57ASI3xfA6+twk477RQ8GJt9CtFFOp2djgbCDtFApJcrSI444ghWr17NVVddFVxzHId0ZVE105+KVArrsGeR5DNNM3IGiS+8rrd169a6Pyce27bJVipvCowrIJFpKSDpUuVQQBK+efbyrpqpTI56f1dqsSXSeaJnkPTuQ/FchYPzpYd6k+3NrFWLLZEEK+EFAH5A4tPOQU9tQLJp0yaGhoboqwQkqcE4RiUirWRnJiJf+3NAX68GJK7rcvfddwNw5ZVXBtdLpVI1IBmwVEEi0mEMw6BkeOt54TPVtm/fHrzOZrN1f64XlUolfvnLX7Jly5bgmm3bQbBkU1BAItPSp2KXKlneZLG2gkQBST0Lb0FQLbZEOo9lWRTwdhMWR3rzoXi2wg/C4YVEt+xV3tzAz1RBIpJQDz74INmBSoBZE5A89dRTDf5E7ymVSpGv3//+9/Pggw/yfP4JgBW7L4tjWCLSQqWagKREMfJ1rz7rhoOhzZs3B6/Di4PZmgoSBSQinaFoeGt7fstQiAYk4dbJvewLX/gCL33pSzn22GODa9490AuQbCYVkMi09KnYpUqWlzI/k5dRmFQFyXQsvAXBsgISkY5jWRZjbANgcnNp+h/uceHJczg4L014120K2oEkklB77bUXz3z2Ed4Xthl5P//pT3+KZ1AJU1tBApAizT54f297/ePCNo9IRFqtlI5WjOzav0fk616tIAk/419zzTX8+c9/plwu86lPfSrYdd6/qE8VJCIdaNLywpD+ymHjEA1IajeI9Kof/vCHAPztb38LroVD4qIqSGQG+lTsUk5oM83Q8Mrq9R6dNE6nGpCUlL6LdBjLshj1A5JNCoCnE548RwMS73NBAYlIspn+M51r4NjV+cpDDz0Uz4ASplFAEl5MOOSfl7RxNCIyH0rZaECyaGw3DuK5wde9uhkw/O/9yCOP8JznPId3v/vdfPjDHw4WBzMDqiAR6USNApLR0dHgda/e92rl8/m6a8ViMVJBomddmY4+FbuUnaqWHz//kXcGr3XzjHLKLiZe1UgZm8nJyRn+hIgkiWVZjAcVJLq/TSe8eGjbNqWSFwrfcsNtgNfHW5NGkeRK5avTdr/yCxoHA71ouoDEWuBgmDqQWKTTDS99vO7aITw/eN2rmwEb7SD/xS9+AUCmsjiYypuqIBHpQBOGF5DkGQquqYKkXqOAxLbtoIpOZ5DITPSp2KW2P626m3CotBNZvJuFApIop1hdYChhRw60F5HkU4ut2QtPnu+77z6WL1/OkUceyVNPbARUQSKSdPmhXHDm0sTG6P1O87vGCwTP4VQAUgt6c9FUpNtsX7a27tou7BO87tWAZKrPAJNUcN5mqs+IVJAYhkJjkU4wbm0DYIilwbXwxl4FJJ6+vr66azqDROZCAUmXuuzrX2bNmT8JvvZvpnqAjpocC53PogoSkY7jBSTDAKy7ehzXUZu8qYR3V1977bVs2bKFm2++WWXHIh2iv7+fEbzDd8efjFZLbNu2LYYRJUujCpJn8yoAUgvbPBgRmRemVb+oP0S1fV6vPus2+vc2DIMs1QXD2gqSXg2TRDrNFnMdACvZjxTeAn94ztOr971aMwckqiCR6Skg6WbLJtiIV4Y8WJk4aiIUtX1rqHcjJQUkIh3GO4Nka/D1Qz8ZiXE0yRbeXTQy4v09WaTYi8MAVZCIJF0+n2eETQCMritGvue/p3tZbUCSIhPsnF79r8VGf0REOoxpmtzHjQBs3uvvAAywOPh+rz7rTh2QVLpIGCVSOTNSQaJFVZHOsLUSkAA8gxOB6JxHFST1/M+CUrHMATwH0CHtMjMFJF3Msqxgp+EClgGaCNUa3ugFJDZFXFy12BLpMOEWWwD3/2hzfINJuPBEenjYq7pZxqrg2hruVUAikmD9/f1M4AUhk9uiD8M6h6T+78A/f6SMzcA+euQR6QaWZfGfnMcXeQvrjr4GgAw5VrI/oICkVo5+7/sp7xk3XEGidQGRzlA2SsGGwAEWAd7h4z4FJJ7w/b9UKlEoFOizFwTXnuDvCkhkWnpa6GKWZbEZr0/rUnYDNBGqNbx+DIBJvEOuTj755DiHIyJzVFtBcutD18c4mmRrFJDkGABgG+sZYZMCEpEEy+fzlPAeiO2J6HxOAUn9AsGu7AvAGMOkUlYcQxKRFjNNk3FGuJfrcftKrF90DwCn8QGgd591G/17F4tFspWAJDPk3QPVYkuk8xiGwa38BqiGnqogqRe+D77gBS9gxYoVWLb3bDvBKJtYo4BEpqWApIulUik28CgAB/BsQBOhWiPrvcNOi9YEjz32GAceeGDMIxKRubAsC5sCG3gMgC1bt8Q8ouQKT579gKSvEpD4IZMCEpHk6u/vx54iINHDcX1IdAjHAWAQ7bsvIp3LsqzIa/f0+wHYnYNJk+3ZZ91GnwHbt28P5nmLd/F2UYf//no1TBLpRJN4G3uzDQISvZc94fv/Nddcw7Zt2+qedfv7+2MZm3QGBSRdzLIs7uMGAPbicEA3z1pjm7xy43KmwKpVq2b4aRFJGv9B7xZ+DUC6XH84m3imqyDxJ925XK79AxORWRkYGMDGm7eUJqKLgKogqf87MPE+H+7jBgUkIl3CNKvLF5ZlccEl7wq+TpPt2WfdRv/eIyMjwRkk2UoFiWFUD7kP/12KSLJN4rWGz1Xe06ogqdfoPlh91vX+/pYvX97WMUln0adiF0ulUjzJg4D3kNjLk8ap+AGJk9HhnSKdyA9I/EnP1nUjTExMxDmkxGpUQeJPGicqbQa1q0YkuXbZZRe12JpG7d9BFi8wf4Q7IrumRaRz1VaQ9A/mMdPeon+Gvp6tIGn0jO84TnBeQXZx9e/trLPOYs899+R1r3td28YnIjumgNf55OBKdazOIKnX6D64B4cA1bWCnXbaqa1jks6igKSLWZZFgepCYZZ8zwck4+PjfOc732HTpk0AjN3nTahL+fE4hyUiTfIflLfjtdbKFYf0wDeFRhUkQywFYJwRXve613HooYfGMTQRmYXVq1cHAcl9f3sg8j09HNf/Hfg7p4tMqOe0SJeorSABSPV5z3O93GJrqmf81/IhAPqWVQOSr33tazz00ENqLS3SQfyAZJDFWKTUYquBcGjkO4jnAV6ADl41tshUFJB0sVQqhYtDsRKS9PKuGt/555/PG9/4Rs444wzKBYfitd6umm17PTDDnxSRJPIfjjezFoAlrORnP/tZjCNKrkYVJHtyKACP83e++MUvqt2CSIKtXLkyCEjuvOWuyPdUQVL9O1jIcg7h+KBPd5EJna8k0iVqK0gAUnlv7pIh17MLhY3+vQdZErw2LKPu+yLSOf7GNcHrHP386U9/Cr7WJhlPo78Hfy108DkFvvKVr0TaDIrUUkPeLua3mSkwQYY+VZAAl112GQD/93//xxNXjWKMZdjKU/QfUYh5ZCLSjNqAZBErMNDEp5HwAqofli9mZwCe5CHtsBZJuFQqxQkvPh6uBKcQvc8pIKn+HfyTeQn7OEcG1wsKSES6xvQVJLme3QzY6Bn/Wbw8eP0P71/WxtGISCsZhsEom72KWPrIkOeqq64Kvq+AxNPoPuhXjvy/C1/J6hcPtntI0mG0VbSLbdnitZzxy/Ey9PV8QBI2usb7IHmcv7Pb6pUxj0ZEmuE/KI+wGQcHixT9LIx3UAnVaPLst9gaYRPpdLrdQxKROVq4aCEAB4+8MHK91x6OS6USDzzwAK7rBtds28YiFQlHwJsHKyAR6Q6NAhKrTxUkjT4DVnGA971jHmNwlTbBiHQ6v32+30LU12tzwKk0Csj98+jS/Vr6lpnpv5Iutm3bNqBaVtbHQM/uqoHoDdMkxdVvXwfAGMMcfPDBcQ1LRHaAXybrUGKMbUB10V+ianeYp0gHYdJ2BSQiHaF0X3/w2goVgvdaBcnZZ5/N0572NK644orgWqlUIs+Cup9VQCLSPcIttvywJF1psTXIkp591vWDoVWrVvHmN78ZA5PDqATpew7HODIRaRV/43NtQNKrwXCtxgGJ93eVHtDSt8xM/5V0sa1btwKwkccB2IvDe/rmuW7duuD1P3Bi8HqSMY488shGf0REOsh2NgMKSKZSu4DaxxAADg7jjKgnq0gHWHB49XUm9IDcawHJ17/+dQAuuuii4Jpt22TI1f3sJp4glVJXYZFu0KiCxKzs73gmL+v5gCSVSrFs2TIO4rnB94xB7S4X6WRHHXUUUN34vBOrI99XBYlnuhZbCkhkNvRfSRfbb7/9AHiY2wDv8OJeDkgefvjh4PVSdgte5xkil6t/oBaRzvC73/0O8NpsARzL6+p+5ic/+Ql33XVX3fVeUjt59ieMNpO4uI3+iIgkzJ5vrrZJ8dsGQO8+HC9ZUj2E2LZt0g0CknFG2jkkEZlHjQ5pX7CPVyHmUOZnP/tZHMOKnf+Mb1kW6XQ68qxrrhyPa1gi0gKf/exnOffcc9lthReM/BMfi3y/1zbJTGXaChK12JJZ0H8lXeyCCy7g3/7t3xjDK6vNM9TTAcmHPvQhABaynMN5UXD9T8bl2lko0sGe//zn861vfYtFLAfgYJ7H5Nbqve4Pf/gDr3rVqzjkkEPiGmIi1E6e/cXVAhO8973vjWNIIjJHQ8v6g3aCOarttnrp4Th87kh4sdSrIIm20vo1X2nbuERk/jWqINnjH72Dd/sYDFpM95pIQDI+wJGcDMD1/BRzsDerakS6xV577cVnPvMZ3M3VTTKZ0CaZXq2cq1W71pkiE7SjVQWJzIb+K+li/f39fPSjHw12zuUZ6umbp+M47M/RXMzvWMGeAPwPH2Vz7rGYRyYiOyqXy0V6z2+4pbpb7j//8z/jGFLiTFVBUmSCT3ziE3EMSUTmqL+/n0nGgGgP6l4KSIrFYvB6fLx6rw9XkCzYO8P/HXgRv+Kyto9PROZPowqS7ELvf/sY7NlNb/7C4OHbTmbRp05kN/YH4CkeioRKItK5sour979wS+nwxpFeVrvWGZ4np1RBIrOg/0p6wEX/8QHAmzSGU9U1a9ZwwQUX8MQTT8Q1tLaa3F7kHXw1cm2CUR3cKdIFcrkcV3BJ8PUP/9+N9OX6OOKII/j+978f48iSo3YBNRyQiEhnyOVyDQ/p7KUWW+FQJLwoUCqVSFcqSFJ5k425R3Dp3Y1BIt2oUQVJNSAZigSovaRUKjHIEo5+6v9Fro8xHAmVRKRzPfeynYPXh/HC4HUvb4IOq60g8Z91S0YR09JZmzIzBSQ94NkveCZQ32Lr1FNP5ROf+AQnn3xyXENrq0Wb9qi7NqmARKQr9PX1cSu/Cb7OP7mCFxTO4pabb4lxVMlSu4BabbGl3tQinSIakPRmi61wQBJeFAhXkKT6DC0YiHShRgFJZoF3Lc9gzwYk5XKZ43h93fVxRlRBItIl9n7FQh7gZgD+kXN5F//FQRyr+U7FVBUktjkZx3CkA+nTsgf4/fYy9EVuGtdffz0Ad9xxRyzjarf86NK6a5OMKSAR6QK5XP3BvC/iLTyX10au9XIJcv0ZJN6kURUkIp0jk8lQqLxnn8fpwfVeDUgKhULw2rZtTuCNAGy5u8Dpp3t/Py960YsQke7QqMVWeqDyv+SwbRvXdXFdl3e/+91ceumlsYyz3crlMivYq+56kQkFJCJd5Cu8I3i9F4fxNr6I6/Tu821YbQWJ/6xbsgqNflykjj4te0C60m8vSx/l0uwOaS+VSpEH0G7gFr2yui373BNccygrIBHpAn5A8n98MXL9ON6AQbWkNryY1mtqK0j8ndYrdtspjuGISBMMw2A39gNgX54ZXO+le1t4fjo5Wd0VaNs2e3G493q7w3nnncett97KT3/607aPUUTmR6MKklS/N88zMYOQ5I477uDTn/40559/fizjbLdyuUymMq+zTnmUH3ARf+C/uI8bFJCIdJEC43yWMyLXnOHePHupVn0FidctoWz1ZmWhzJ0+LXuAH5CYWFCa+Vfuui6HH344e+65JxMTnbWz2HEc7r///sa7xIvev3t60OSHXMxN6Z/zMLcpIBHpAn5A8lu+wSd5He/h2QAsZSV5hoKfCy+m9RLXdet21aTJAHD4Mw+NYUQi0iy/p3JYL93bpqogKZVKbGEdAIeevxTDMDjssMPo66v/+xKRzhSuIPEX/tP56vNtlj6KxSLr168PrtXOfzrd8PAwP/3pTyP/Xt4ZTJUWg8tL/Jkf8RM+iYurgESkyzxYabPlcyf1HoepW2yVUgpIZHb0TuoBqf7qr9mwZ06X7777bu666y7Wr1/PmjVr5nNoLfcv//Iv7Lvvvlx22WWR667rgl05yG8gxbX8kP+y34+Lq4BEpAv4AYlDmce4iwlGsPEmQ/4DI9BxoW+rNGq/k6oEJFZWh9aJdJKyUX0/+/e3XgpIwvfx2hZbJby/mz3+cbDt4xKR+deogsQwDaycN5dJk6NYLE4ZpHaDM844g1e+8pVcdNFFwbVwBUkqH13iUUAi0n2u+oePV78o6j0OU7fYclK904ZWdozeST3AtAyMjFdRYZUzM/78E088Md9Dmjdf/KLXXudf//VfI9cLhUIwacwNRv8OFJCIdL5GZ5DYeAuG4d3W4+PjU1eZdbHa9loAKdKAAhKRTuMY1fez//DXbQuA0wmHQbUBSR9eMJJdaNX9ORHpfI3OIIFoS+nagKSbAmTXdYO2gRdffHFwvVQqTRmQhP+eRKSzHXbYYQC8+UOnURgYBsAt6FkO6itI/DUAJ62ARGZHAUmPMCsZgDmLCpLwQlqjRbVOUDvuQqFAGu8vITeYjnxPAYlI59t5551ZvXp15Jp/+HgmVEHyjne8g3333Zf3vOc9bR1f3KavINFUQKSTlM3qHOdsvoxJqqsWAGdSLFZbJUQCkqJNXgGJSFdrVEEC1Y4JmUpAMjIyEnyvmwLkX/ziF8HroaFqC9lIi62+6GKpKkhEusdvf/tbrrvuOl760pfiprz5oKsKEmDqChI305lrmtJ+eif1CLPP2y3tt9gKP1xmMtGKivBCWqf2bK0NSGzbDhZJMwPRgKT2319EOk8mk+G+++6LPATahvdAHA5IfvOb3wBw6aWXtneAMVOLLZHu8afdq21EV3EgF3ElqScWU9zemXO26RSLxbrFzfAcdnJyMqgILBcdrEplXLi9rIh0j3S6+hwXqSDJRytIwqFxNwXIv//974PXe++9d/A6XEGS6Y8GxApIRLrH0qVLefazvbM2HT8gsfUeh6nPIHHTCkhkdvRO6hGpvLcAZpa8SeXWrVuD7/X390d+thsqSPxg5+KLL+bAAw/k2muvDXbVZAejVTSqIBHpDtlslkWLFgVfO5YXCjQ60LjX3HLLLZGv/4Vv8kLeDCggEek0E7s8xQYeC75exHL2++Vr+frQPWz+W/csBJbLZY4//nh22WUXNm3aFFyvDUz8ALhUqD4Y674m0p3CG9siFST56BkkU1WadbqxsbHgde2mRj8gSQ8oIBHpBW6qsjFGLbaA6Obu1/IhXso7ATCyzlR/RCRCn5Y9wqqsD1qVgOSOO+4Ivlfbi78bKkhc16VQKPCBD3yAu+++m2984xssYgUAmZpJowISke4Rflj2J43hQ9p70eOPP84555wTubYPRwSvTS0kinSU1772tYywqeH37vhM4+ud6NZbb+XPf/4zW7Zs4Y9//GNwPbzwCdXFz1KhOme1MrqviXSj8HNb5DySylwmRaYuIOmmCpLw2Srhf0d71A02BGWHFJCI9AK/MkIttjzhCpJn8+rqN3KduaYp7ad3Uo+waipIbrjhhuB7tVUi3VBBAnD//fcHrzNuH7uxPwCLjoz+Z6+ARKR7bNiwIXjt3/eyPV5BctRRR/HAAw8AsHDhQnIMRL6vndYineUtb3kLW3mq4fesXPdM7cP38zvvvDN4PVVAYk96D8Cu4WLoCBKRrjRVBYl/nlq6EpCEN/x1U0AyMTERvI5sanzc2wxU6BshtyTaLUEBiUh3CipIbE16oL7FVnA91blrmtJe+rTsEbUttsIH19VWiYRDkU6tIAHYtm1b8NoZ8/79y5QY2DV65ogCEpHus2jRItIDfj/q/hl+emaO4/DFL36RX/3qVzv8f6vd1q1bF7xevHgx/SyMfF+HtIt0FtM0GWVrw+/VHs7bycLzuLVr1wava9vl+F8Xx73FQsNyMYzu+XsQkapwQBI5sD03dQVJN7XYCleQRAKSrd4CaWFgmFQqGpCEgyQR6R5O1rvPGePRM3ZrO8T0iqnWLreMb2h4XaSWVkV6RKpycF2q7E0qt2/fHnyv9kYSnmx1UgVJ7b/Hgw8+WP3eqDdpLhjj5HLRQEQBiUj3Oemkk3By3gNxnqEd/r/3xz/+kXPOOYeTTjqJm266aYf/78VhkCWcuP2f2Yd/iFx37N6cRIt0siLjDa+n+rpnah8+L2/NmjXB69oKEn93eHGiMmdN6Z4m0q3UYqtxQOJWziBwU6XIQfagChKRblXu9yrKjO3V++JXvvIVFi1axI033hjXsGKTLfVzKhdyOC+KXB9zh2MakXQafVr2CH8ndcqZOSDp1AqS9evXR76+6qqrgtfOuDdpLJrjkZ1HoIBEpJsccsghALzzne8MdtW0IiB58skng9dJDkieeuopvva1rzXcLfkaLmT/jc/n9Xwkct1Maae1SKcpMNHwutWlFSThQ9qnCkjsSkBipBWQiHSrqVtsRQOSXmux5VT+FZ10WQGJSI8oD3hvfHO0et7m29/+doaHh/l//+//xTWs2Dx/7Cyey2m8iU9Frk84ozGNSDpNauYfkW6Q7rcAm1TZCwNqA5Lh4WE2b97Mnnvu2bEVJE888UTk67vuuit47Ux4E+iiOVEXiCggEekeV199NWvXruXAAw/kV7m/A9DH4A7/3w23JUxqq4Y777yTpz/96YC3aHDmmWdGvr8vz4p8fTu/42UvP5n937SwXUMUkRZ5grsbXu+mwDN83w33la4NSA466CBe/epXB2eQdNPfgYhETV1BUtkMSIavfOUr/PznPw++l9R5WzOmOqTdqVSQkC7VbQZUQCLSnZy8F5AYoxnWrl3LrrvuGnxvdLT3QoHVpUMaXp+0G1ddi9TSp2WPyAz4B9flcBwnEpAA7L///uy1115cc801HVtBEt5dCNHDPY1JbwJtpxSQiHSzhQsXcuCBBwLg9nlhbysqSMILdUndifjVr341eH3DDTfUfd+mukAwzgj/xb9x3HeXk1usvRIineYe/sL3+TCX8nq2Uq2g7aaWeeFFzfB8tNFi549+9CNKBe9njHTdt0WkS0xZQRI6gyQcjgC85jWv4fzzzw/68heLRa655pqG95J77rmHM844g89+9rPzMPodV9tiyw+P/RZbZBz6+voif0YBiUh3crPeut36NZtYuXIlN998c/C92s0k3e5Xv/oVhtP4XjdS2tLm0Uin0qdlj8gMeBPIDH2USiXGxsYi3/fbx/z1r3/t2IAk3Ksaoq0ZKHgLgGWrqIBEpEe4ud4KSMLjqh3jMziRIZYCcDO/4uucS5GJuoM8RaRz/IUf8wh3MLhXdTrvH1TeDSIHEIfmo1M99JcqFSRWRhUkIt0qHJAsXLgweB1usdXIpZdeyt13e5V3F154IcceeyznnXde3c+de+65fPvb3+Zd73oXw8PJ61sfvv9NTEywaNEiLrvssmpAknbI5XKRP6OARKQ7GRkvIM3ihaKXXXZZ8L1uqpybyRVXXMGHT7qMZayq+97V/IADT6y/LtKIPi17RGbAWwTLVgKSqRb4JiYmOrbFVm1AEik7rrx0LLsuEKktQxaRLpFvXUASrrpL6oQzfL8On5kCcBxvCF5fs8fXeIC/AtT1qRaRzvOqKw4OXn/7G98Ndkl3usg8bpoWW77CmHfPNxWQiHSt8HPbTjvtFLyuBiRTz2v87gKf+pTXn/7LX/5y3c/8+c9/Dl7XbihMgvBzOngbeM4++2zcYmVZJ1OuqyAJV9qISPfwK0iy5IHo/aEbg9F169Zx0UUX1T3nfvGLX+Sf+FjDP3PkJxfxH5/8j3YMT7pA971rpKHMoBeQpGcISEZHRzu2giRSMVLDsL3/1B2r/uA6VZCIdCe/xdZszyBxXZePfexjXHzxxXULjOE+rkmtIAnfu8OHeBoY7MLeABx6uUM2V11c6MbJs0ivWX7QAn7LfwKwecMW/va3v8U8otYIByHh+WjtAqFvYrt3b/YXSkWk+4Q3qSxbtix47Z9Bkmbq57otW6Zvs+K6biQUCc+l2m3dunWcc845/PrXv45cnyog3rq+UumsFlsivSPrzY0aBSS194FucOqpp/LhD3+Yk08+OXJ904ZNU/wJOP/88xkc3PHzSKU36NOyR2QHoxUkU+2A3rZtW8dWkExXBu1WAhI3VcI0zUhIooBEpEv1eZPG2VaQ3HPPPfzbv/0bH/jAB7jjjjsi3wvfFzshIAmPMccAmUrp9W5HLY60XjAMLSSKdLp0Ok0Jb9HMIs26detiHlFrTBWQTDk3rfSezuTUOlCkW61YsSJ4nc/ng9epPm8+M11AsnnzZgD6+/uDa+EOBLXhQ5zzvUsuuYQvfvGLvOQlL4lcnyogefLxpwAwsi6ZTCYyv1NAItKdjKxXXes/54WfV7uxcsyv8Lvlllsi141y43nfkRft1PC6yFT0adkjwmeQ2LY95YRv27ZtHVtBMl3bG3+y7KYqfRpDoYgCEpEulffuZbOtIAm30fLbMPjCD6SdFpD0mwu87xtFdttjV93zRLpQGe+hOEU6coiv7+9//3uwONgppmqxNVVA4p89kO1X61SRbrVq1Sp++9vfcuutt0aup4e8Z90c/Y3+GFCtIDnggAOCa+GFttr5XZwVJI888kjD61MFJOnKAqmRcTAMI7IZRgGJSHdyM95aXZosJlZkE3B43tQtwqHPmWeeyZlnnuldd+pbK/YtT3HEBxWQyNzo07JHpCsBSZY+7r77bjZu3Njw58bHxyMPnp1UQeKPNbwryOc/NLupShmiAhKRrmf2eRNDE6vhjsLaADi866a2ZV/4e+06g+Q73/kOr3jFKyLtvabTKCBxXZes4+2w7F+awzAM3fNEutAkXluYARbXBSS/+93vOOiggzj99NPjGFrT5tpiy18YzQzp8Uakm73gBS/gsMMOi1zLDHrv+xwDU/65Ru0HH3zwweD1TAHJe9/7Xi688MI5j7cZjUKNcrk85RlTGbxAxMh63w+311FAItKdjFw1BMnQF6kc65bz6MIWLFgQvP7mN7/JN7/5TbZt24ZV9gKSEjZf5mxS/QbPumR5XMOUDjZvn5Yf/ehHOfroo8nn8yxcuLDhzzz++OOcdNJJ5PN5dtppJ97znvd01IJ8J0n3ezfLDHmOP/74KX+uWCxGHjxrFxCLxSJ33313Im+4/n87AwP1E2N/0qiARKR3+GXHUO3NGla7yDbbgKRdFSRvfOMb+dnPfsZ3vvOdWf18+PPTD3Fs26av0mIss6CyeBDaVSginSncWgbgcf4OwB4cUnew8MUXXwzAb37zm/YMrkWmmo9O9azgL4xmFnRfWwkRmZ4fjDaa7/m+973vMTo6GqkYDh/2WxuIhOd769at45Of/CSXXHIJt99+e4tGPbVG7XGmqh6B6rOuWZniKSAR6X5mGspUD2oP38O6sYIkHJCE+RUkNpPczbWc8tjOHPCmRe0cmnSJefu0LBaLnHLKKbz97W9v+P1yucxJJ51EsVjkL3/5C9/5znf49re/zQc/+MH5GlJPyy72+vL1h3rxL2BZXeuZYrE4bQXJK1/5Sg488ECuuOKKeRxtc2ZTQUIlIMlkqu0XFJCIdCcrbVHEmyj6vVnDah80w4txW7du5Te/+Q2///3v677XjoAk/P9jtoF0owoS27Y5kpcCkF1YaT+hgESk4/3pT3/iqKOOCvoxb2YtAAMsYnwsWkHSqQ/JM7XYSqWiPaf7/IBEFSQiPSfdoIJkIcvZid0jP/fkk09G7i3hBcXpKkieeOKJ4PU999zTkjFPJxxq+Pe/6QISv1LarFSQhDcMduNZBCIChmlQwJvzZemL3MM6de43ndrKQfDmhGblDBIbb4NgKq2z6KQ58/YEcdFFF/Gud72Lgw8+uOH3f/vb33L33Xdz+eWXc+ihh3LiiSfykY98hC996UvTfvhLc/p28iZG/SzCwGSAxXyA/+V8vhdpPVMoFCI309oKkl/+8pcAfP7zn2/DqBtbs2YNb3jDG3juc5/Le9/73mDx0F/AbFRBklYFiUjPMU2TQiUgmWsFyWOPPcaLX/xiTjjhhLrKunYEJOEdjbU7xacSDkg2b97MoYceyhNPPEGuEoQ7lc5guueJdL4jjjiCv/zlLxx99NFAtcWWicX4cLQNYHhxrJPOlpvukPbdOIA3pD7CIEuC66ogEeldmZozSPIM8X5+zgf5X8555fuCnzMMI3I/CbcknC4gWbt2bfC6tvXpNddcw+LFizn77LNb8G/iCd+3/XtheC5au9nF3wjkV5DssssuwfdUQSLSnUzTDG0GzDe1wS4uX/nKV1i1atWcAufwGSu+UqlEtuzN//yApNF6oMhsxPZpef3113PwwQezfHm1N9yLXvQiRkZG+Pvf/z7lnysUCoyMjET+kZn1LU3h4mBiMsAidudgcvSznD04htcEP1csFmfVxqB21147PeMZz+Dyyy/n2muv5ZOf/CSPPvooUB1ruKTYl69UzjgZb2KpgESk+1mWFeyqmU0FSfh+F34Qtm277Ye0h///1bbLmUrt/fqOO+7g7W9/O9nKv/vTz/UWEnXPE+k+37z8G0Gbhcmt0fA3vNAWbi2TdNOdQXI+3+MZky/l9XwkuB5UkCzQYqBIr/Erx/yA5GkcGbx++b5vCn7Otu3IfGm6gCT89YYNG4LXtQHJ5z73ObZu3cpll122o/8agXCo4Y/DvydalhXphgDVFltWZbq72267Nfy/JSLdw9sM6FeQdFaLrbe//e088cQTcwqWG51BVyqVeNZWbz1zCbsCuudJ82L7L+epp56KhCNA8PVTTz015Z/72Mc+xoIFC4J/wh/+MjUzZTBheg/FAyxiOXsE39uZvYPXxWKxYRuDWnEGJOEJKlR77ftjbdQ+xv/3LSzcBiggEekF4V01c60g8YNXgF//+tdBGxtozyHt4bE0G5CA14bHf2hOD6jFlki3Ov3010HOCxEmt0XvBeF7Q+35Skk2XYstC28eujfPwMBkdw7haF4FQH65WiuI9JraFlv/wEuC7z327RLLli0DvPlVMxUk4U2ZtUHzxo0bd3T4dcKHLfvzTv+emMlk6p7Fg4Ak5/25/fbbL/ieFgtFupNhGKFuCX0dVUHi88e8YcMGHnzwwWl/ttGzbqlUYnXh6cHXut/JjpjTfz0XXHABhmFM+8+99947X2MF4H3vex/Dw8PBP+F+oDK9ouXdPPsYYAV7BtcHWRy8rm2xNTw83PD/1o72Mr3mmmu46qqrduj/hs8f73QByWJ2BqA45P37hHfd1O7AEZHuEK4gyc7xDJI777wzeH3KKadEfq4dFSStCkigWj2T7q8cYKpQWKQ75bx7QDm6uTmy27n2/lUsFnnkkUcS+SA9XYstX5Y8Z3Ip5/O94NrCfXSPE+k1foutFGlSZCJnj5QnnOB5r9kKknBAUltBUhuYbN68mXe84x185zvfafLfpvHZd/61xgGJtxHI7PPu5aeddhr5fJ5ly5axatWqpschIslVuxmwkypIfP44ly9fzj777MO6deum/NmpKkjuSV0HwJ/5USLns9I55hSQvPvd7+aee+6Z9p8999xz5v9DwIoVK1i/fn3kmv/1ihUrpvxz2WyWoaGhyD8yO6WUd8PMMcgeqerZMAOhgKS2xdaaNWuC1+Gb7I4EJI899hjHHnssL3zhC6dtpzZb/rj8G2ZtQJIiE+w09BcPVEEi0v1mOoNkuoBkOu0OSGofxKcyU0CSynu7Chu1IRSRLuAHJGPVncfFYpFbbrkl8rXPdV2OPPJI9txzT84///z2jXOWwvfB6dq/HsoJka+X/YPucSK9Jj1QXdboY5Bd2Cf4ujTpBr3rp6sgCS8u1n49l4Dkv//7v/nyl7/MG9/4xqbbGobvf34Fif9/qzYgMUkFLQZTlaWRPfbYgyeffJJHH32UwcHBpsYgIskWftbN1FSQNHquvemmm7joootm/czbDrWBxm233TblmdRTVZBYjnd/f4Cbueiii1o/SOkZcwpIli1bxn777TftP7PdjX/UUUdx1113RdolXXXVVQwNDXHAAQfM7d9CZqWU9iZXz+U0VpSqbbUGWBS8rq0geeihh4LXmzdvDl7vSIuW22+/PXj98MMPz+rPTHcT9ye5U1WQ+CXH3hfev5sCEpHuFz24rn7BbLoWW9Npd0ASfnifztQBSaXFVqWC5N3vfjcrVqzgXe961w6OUkSSxMh6cxxnshqQ3HTTTZGfqT3f6I477gDg5z//+Zz+f5XLZT7ykY/wnve8Z97adk3VYsu2bTZSX0F+PT9lwbfvJJ1XewWRXmNaBm7Gmwedx3cj3ytPuqRT1YBkthUksw1ItmzZErx2XTcSitTeg2crPA/0x/HjH/8Y8O6/4YAkTzUASYWykKGhIfL5+g1CItIdDMOgGJy3mYvcwyYmJrjmmmu46aabuPHGGwE49thj+fCHP8z73//+WMbrjysciriuGwmtr7nmGgYGBvjsZz9b92f9++LrXve64FqpVCLteM+6b3rbP3HhhRfO08ilF8zbE8Tjjz/O7bffzuOPP065XOb222/n9ttv///t3Xd4VFX+x/H3TDKZ9B56L4YmVUpAYUGKLlgQEbFg2RVdcS279gKKu4LttyoqYoVVlBWsYEEWKQsiIkWQZgEE6SUhkITU+/tjmJt7pyShhmQ+r+fh2Zl779w5sw8ezrnf8/0ec0DRv39/WrVqxbXXXssPP/zA7Nmzefjhhxk1apQeWJ8iJRGeiWYbetqOx/lkkFgnofPnzzcHfdZ0txOZDFvLsFmDLsG8/fbbxMbG8vbbbwdMmSuvxJb3wWghBYRH+Ge+JCYmVrjtIlJ12EtsnbwMEuseJIWFhaxdu9avbyosLKxwADgQa1uCraLxVW4GydEASc2aNdm+fTv/93//d9ztE5Ezj+NoaZUSyyJoayYw2PsT6yKlHTt2HFM5hv/973+MHj2aZ555hkmTJh1ni8vmW2IrMzOTZcuWUVhYSCH2B5k7+YWpjCY6TnssiYQqR5TnIVsanj1Kt7HePOcOPzof9MkgsQZBApXYWrFiBatXrw4aICkqKiIzM9N8X1hYaAu6+PbBa9euZcWKFeX+lkDjQO9YMz4+3idA4kkbyeNQwLmuiFRPvosBffuwXr160bVrV7p168aGDRvM85MnTz7dTQU8z/7q1q3L4MGDzWPff/89X375pfn+qaeeorCwMOBCPm+/OGTIEGrX9pTQz8/PN+e6Pft11x4kckJO2d+e0aNH06FDB8aMGcPhw4fp0KEDHTp04Pvvvwc8D65mzZpFWFgYGRkZXHPNNYwYMYKxY8eeqiaFvGJ34IdsbqLNTsW3xFZJSQlLliwBYOLEiebxn3766bjbsXHjRvP1Aw88YP6dCObJJ5+koKCAESNGBHxQ6JtB4ls+xvxt5JmlwawrexISEo7jV4jIme5YM0iCBRh8WQefw4YNo02bNrz11lu2a26//XaaNm3KK6+84vedwQK91uMnM0Di3X/Fuqpag0eR6sd5NDZgHCn977uiAZK8vLxjWvxi3ZT422+/rdBn1qxZQ8uWLXn//ffLvG7r1q1ceOGFtlK8xcXFdOvWjS5durBy5UrCsWes5+DZY061p0VClyPRPg6awJ/N11HhMYB/Bon1te/DxRUrVtCpUyc6dOjAunXrzOPWeeQ777xj+0xBQYEt6LJz504APv74Y+6++27atGlDp06d+Pnnn22fy83N5fLLL+e1115jzZo1zJ0719auXbt2mf330KFDbQGSGBI99yDbb28SEam+PHNdT7/lm0Hi67PPPjNfV3R/y5Np48aNPPvss2RmZvplLQ8aNKhC9/D21+Hh4WZfl5OTYy6EjEzU3sJyYk7ZE5LJkydjGIbfnz/84Q/mNQ0bNuTzzz8nNzeXvXv38swzz+gf9VMoL3WP7f2gzxtS7PQ8hPNmkfiW2ALYvHkzYN+wffv27RWui+9r/frS1Ty7du3itttuK/P67du3m6+nTp3qd76iGSQFHDEDJNZVQA6HAxGpfnwzSNxE47D8s3cie5B4H8J99NFHADzzzDO2a7yBkb/85S/msR9++IHU1FT69Olje4iXn59P69atbatpTlaAxIHDL4NERKqnsKMZJOSXriC2jt3A3rdYgxwA+/bts70vK6PEOo7yvU8wN954Ixs2bGDYsGF+56zfde2119pWE4In8GFdnOPCPtbLxdOe01ECUUTOTOENS//7H8cQT79wdJoXGRY4gyTQZuhe3iBFSUmJbYGfdQ68detW22cKCgpsGSQ7d+4kPz+fwYMH8+yzz5rHrWWsi4uLGT58OB988AEjR46kT58+tnteddVV1K5dm5kzZwL+e5DUxlM6ex/b9CxFJIR4Smx5ArKNaccNBc+STreA11oXnZzusdLBgwdp0aIF48aNO6H7ePtrl8tl9nW9evUyFwNGJShAIidGT0tCyIGz1rGUT5nDm7zT9SYaXhhHUYSnc4w6WrvUt8QWlJbB8n1I57vypSKKi4tZvXq17Zi3JmIw1nI2f/rTn/zOl7dJe+zRVTUF5JoBkrS0tGNruIhUOeHh4WaApAGteIJ5XENpluLx7kECsGDBAtv7YIFWb58DMG/ePLKzs5k/f77t4eL//vc/NmzYYFtNc7ICJNaHiC4FSESqtbBoTz9kWAIkvv2CtT/xXehiDZCsWrWK1NRULr744oDfZe3D8vPz+eyzz/jb3/5WZj/qG6zxuv/++4mOjubmm29mxowZLFy4MOg9vFw+GSQ7+QVQVrBIKIu5KJMfmMu/eZDteAKqYZGeftHt9KwwLitA4rtJezDWvtNaXgv8AySZmZkBV2tb+9C33nqLTz/91HzvG6z2ZgJ6g8TWh4OA+UD0N9YqQCISQqwZJK04l7P5A3/ltYDXWjPfrH3gicjPz+eXX34p9zrf53/Hyzum9e0DvRkkUcogkROkpyUhJLl2Am/zEJ/wLzb+dHQVjMvTObrw7PtiGIbZ8XiDCN4AiTVQAf4brOfl5dn2KfFav349w4cPZ/369Xz44Yd+Gw6HhYUFXKX4888/V6jkTaBN2h04uIUXeZE13MargCeDxNuRPv7445x33nn8+9//Lvf+IlI1uVwu8vAMBluQgZtounIJ1zEeJ+HHnUEC9pV/EDxAYt1Ty9rPeVcc7t6926/Ovm9bTiRA0rZFB/N1eJSy5USqs/AYz3/jjoLSAInvJNh3k3YraybIJ598QmZmJjNnzgzYt/gGSAYNGsS//vUvPvnkEwoLC3n11VfZsmWLvX1BHtw9+eST5Ofn8+qrrzJ06NByfqWn3n40nkDIlaubcen8xlz+Wmduu+02/vjHP5b7eRGpnoprZPMad/IdM81jZoDkaAaJ73w2UAZJeWVIywqQ+O5BcujQIb/vBM88etu2bRiGccwbubtcLsLDwrmRZ3iRNXTiAgC2sta2MEdEqjen00kx/sGOp1hMDRrajlnHbSfL8OHDad68OfPmzSvzusWLF5+U7/P219YSWw6cZrWEyATXSfkeCV0KkISQu+++23zds6dno/aEVE/myB/7XWSe83Y83gCJdxWL7+DOGoUGuOKKK2jUqJFfRshVV13FtGnTGDp0KN988w0At9xyizl4LC4u9tus/YMPPuCss87iuuuuKzdI4ltiKyoqimTq0oZetutWMNscNGZkZLBw4UKuvfbaMu8tIlWXy+ViC2v8jndmIO3o45deXF6ApFmzZlx55ZWAf/9XXFzM+++/T2Zmpq18ljVAYv2+tm3b8t1331GrVi1baS3vNScrQPLFx7MBCIty4HAqQCJSnXkDJM7C0gliWRkkvgtWrItcrA8IA02qrcfWrCntZw8fPsyMGTO4+eabady4sa0/DBQg8e1LK+IsuuDESWSDEpLbuKnbK4YRf76GCRMmaH8lkRBmDfpGRUXRoUMHwiM9fYLb6XmA5pslEmgPksTExDK/Z8uWLdSoUYMxY8YEzCCxfseCBQv8ynCBZx/OBg0a4HQ6ee21wCu+g4mIiCCGRDoywDyWQxYb+VYZJCIhxOFwUIh/5ls08fzjyom2Y8cz3vK65ppr6N27t9/CGm+p6eeff978jnnz5pmLcwzDoLi4mAceeOC4v9sqUIktt2WfUVesxoByYvQ3KITUr1+fpUuXcuONN/LGG28AEJfs2bDuL38eZV7n7XiaNm0KwI8//giUTqq9K6WtnezevXuZNWsWhYWFTJgwwTx+5MgRVq1aBcDatWvZsGEDAJ06dSIqKoqYGM/3+06+x471lMF59913y31o+fTTT9O4cWOznZGRkSRT2zz/Ho9xO+2ZwxtaVSMSQlwuF5tZxde8zXq+sZ37E8+yf4U98FBWaYVWrVqxdOlS4uI8QWXfPstbVz8tLY0rrrjCPB4sQAKlg0nrA0tvG07aHiRFnsGjymuJVH8RRyeGhTkl5uT0WDJIvHvO+X6uffv23HvvvbZrrRu8W+Xl5bFixQrzvXWldaAHd9u2bQv8Y8qQdnRVZFwb7SMnIqWsfdrevXtZtmwZ4dFHAyQOTwmWshbHeM8lJSWV+1179+5l7Nixfov8ApXY6tYt8J4Avtq1a1eh61wuF5GOWPP9FB7gAXqTxyEFSERCiNPp5Fs+4Vs+5j3GMo+3zXPt0jvarj3eDJJDhw4xdepU5s+fz5QpU8zj1vGkN6h81VVX0adPH15//XUOHjxIs2bNgpZqPR6BNmn3ltcqpogwt8aEcmL0xCTEdOnShTfeeIOUlBQAwqM8fwUMS3KIt7M777zzAE+AJDMz08wg8X7WOum1TqqtqwW9ARGA6Ohos/609x7e/UL27t1rBjig4jVgAWbOnMmWLVvM743YlsodvAnAHn5jMTMoOZp6qEGjSOhwuVwYGHzIU7zEzeYmvl6FT5zN3XffzcUXX0xxcXGZA8eOHTuSnJxMfHw8EHwVTnFxMTNmzAh4zrdfC7TS+WQGSJKSkijK8WTYKUAiUv1FxHkWgRTnGma2m2+AxNq3+AZI3nzzTdauXQvY+6tt27bx9NNP22pI+5YZ9Lr11lttY7+srCzztXUM5h2z7dq1C/CMB5s1a1bOL/Twlo2Iaax+TURKdexY+kAwJiaGsLAw4hp4Muri82sA/mOx4w2QePmWl/YNkByLhx56qELXuXJjuWTFPwHIZh/LmEUJnjGgFgOKhA6n00kOWbzDIyxmOh/wFMv4DIBlj+3lhvali1uON0Dy22+/ma/fffdd1q1b53c/71x11qxZgGcR4JtvvsmmTZv4/PPPAWjQoMFxfb9V4AwST4Akn1wtmpETpplFiPPWpDcKSjsT70O22rVr07x5c7M2qm+AxPqA0Do4tG7CaV1hmJuby5IlSwCIjfWseomK8qTEXXnllZx99tkMGzaMa665xm8D+OjoaLp3717u74kijuJn2prv87FP/jVoFAkdLpe9DulEbvW75tlnn2XmzJksW7aszIGjNxPEm0FS0TRl6yTZd9VioIyPQAGSQLWrA/He77HHHqNx48ZMnDiRolxPgMS7glJEqi9XrGeyGEGUGagtq8SWN0By/fXXA54951544QUg8EIVbymFgoICM5ASiHeCDPYAibUtOTk5GIZB//79AejcuTMffvhh2T/wqDQ8k+zYxhrTiUipPn368PHHH5ubmQMkph8dv+V5AiRlZZB4+71gARLvIhmrnTt3+t3veAMk55xzTrnXuInGeLT0uiM+c10tBhQJHYECArso3Sf43J3Xm3vu+lZlsS5qLos103fx4sV07dqV3bt32/o53zFjdHS0bfwHVHgRjJXvPsWBAiQRZoCk4gusRYLRE5MQF3Y0g6Qkv7Rz9XY8TqeTevXqAZ70YO+kOlAGyfbt283X1oeM1g0/rbyltbwBEm9k+v3332fq1Kl+18fFxZlBlbJcxaO290fw3xBeREKDb4BkMz/wBZMCXmsYhq0f8+UbIJk1axbffvttuW3IycmhuLiYNWvW+E2YA2WGnIwMkhtuuIFNmzYxbNgwCrKPZpDE6Z97keouMt6n3EBxcZkltrzjtSZNmvDYY48BnoUtf/vb32zlUr0ef/xxcnJyWLFiRYVXInoX0OTk5NgyUKZNm8YPP/xgti89Pd0cX5YnBc/YNLaxHgSKiN0ll1xC8+bNzfdRaZ65X0SxZ+7p+yAvNzeXRx99lPz8/HIzSOrUqeM3tvR+xjtn9t2D5FjUqVOn3GuGcC8UlM5nfRcDKkAiEjoCVSOw9gl5u4tp2NCTdVtUVGTrvyraT1mf+Xnf//vf/7ZlIVv7T/CMP30XE6amplbo+6x858CBSmxF4enbfftCkeOhJyYhLtwMkJQe83ZEYWFhZgAjNzfXXMXs7dysnV6wAEmwGtW+AZLypKSkkJycXO51Hehve5+vAIlIyPKdxALkkBngSk/AY8GCBUHv5Rsg2b59OxkZGeW2obi4mL/+9a+0bduWN99803YuUGZIXl4eq1evZvfu3eYx6+DQMAweeeQRpk2b5vdZ66DR68gBz8PHyGT1fSLVnTvB0+d5AyS7d+/2C5B4+53s7Gwz06Nly5a0aNEC8CxY+de//hXw/sXFxaxevdoc8zVq1KjcNl1wwQUYhsHmzZttbbnpppv47rvvzPfjxo2rcIAkGk8/HJ3q38eLiFh5F4i4ij1lnX0zSMCTeTt+/PhyN2lPTU0NuGCvQYMGZnaJtcTW8uXL6du3b4Xb6na7yw1wdGeI7b3vqmnNdUVCR6AASbCsssLCQiIiIszjvmVWgwk0X33++edtC6FXrFjB119/bb7fsWOH31gyJSWFr7/+mkcffTTodw0ZMsRW0ss3QBIogySaBM/v4SAiJ0oBkhAXHunJHLEGSKwZJN4ARl5eXpl7kFgDJNYSW2vWrAH8B2vHGiB5+umnqVWrVrnXFWLvwA9iD9BoVY1I6AgUIPmdjQGv/eqrr8q8lzdAEqi8QnkmTpwY8Phnn33md2z27Nm0a9eOf/7zn+Yx6+Bwzpw5/OMf/2D48OG2z5WUlJhpyNZ+Lj/T80DSnaQJs0h155tBcvDgQYxcJ30YQQNaAaWLW7788kv27NlD7dq1GThwoDm2W7lyZcB79+vXD4Du3buzePFioGIBEoBffvmFLVu2+B2fM2cOACNHjiQ1NRW3203v3r2pX78+l156qXmd9QGAA4f5+9wJGtOJSNm8ezN5AyTBVk0vWrTIDJAEW5TXpEkTc6GMVVJSkjnmtAZIoqOjSUhIOKb2RkdHm6/T09P9zh/2Wejj+1BQc12R0BGoxNY+fre9dxZ7+oSioiJbyarDhw8zatQoRowYUWa5rUBB5e3btzN9+nTz/e7duxk4cKD5PtAi6ZSUFHr37s2YMWNYunQpzzzzjN81cXFxZjYeBM8gCRQgia3hDvobRCpKAZIQ507xDBqzfy00O9hgARLfElveSfbnn39u1jYE2Lp1K/v37+fQoUN8Nm0OnbiQCZHLeTziS6KOrvrzrr7xbtJenoYNG1YoQLKd0pqze9nKTF6wndeqGpHQEWiS+Avf297XoyXgqb1fFt8MkrJERUVx6623VnijuHHjxtG1a1cA3n77bb/zubm55sD1l19+CXgP68psW4DkgAIkIqEiOtXTT3kni7++mc85/7mTy7iHe/kPrTnPXMSyYsUKAC699FKioqLKLX1gnbB6VwU2bdo06PXnnnsubdt69oTbtGmTuRm7lXeflLp165rH5s6dy5YtW5g+fTq33347kyZNsgVIXJSOGyPjlUEiImXzZpCEFXn6x0AP+8AzdiqvxFbTpk0DjgMHDx5srsy2BkiioqKCZqP46tChA2APkATKPjlI6artffzOdJ7w+x0iEhqs4yNv3/EL37Om5ufm8ZyVnr6pqKjIthfc+vXrefnll3n77beDVn2B4H1mWZUXArFmCXfp0oVBgwb5XRMdHY3T6TT7Md/sFe9zSmuJrZijY96uf+h0TO0RCUQBkhBXK8PTkf70bhZRDs+AL1CJralTp5odp7dzmz17NhMmTLBFi1vSg1olTfnyyy9Z993PjM77ght4ipKcMJIK6jKS57mMe/mg9j4mutfScndf2tCLSDwBk6E8yH38h3MZSi1KJ961a9emdevW5f6eqKP3+RfX8xgD/VbZKEAiEjoCZZAAjGaA+boJ7YDSAMnQoUPNc9ZB57EESB566CFeeumlCl0Lnom4t18N9Jn9+/fTunVr8vLybCUMrat9rAPeQCW2FCARqf7i63mCBzEkkEQtfnvG/qDsEu4yAyTeRS5paWkAZo3qYAL1p9ddd13Aa3v06MEHH3xgjiHz8/PNrOPLL7/cL7By9tlnm68dDoc5OX7++ecZOXKk7bsjj9aaLqGEyAQFSESkbBFHAySxuzz7e3gzSHznhF9++SWLFi0CgpfYGjZsGDVq1PA73qJFCzNAYq3FHx0dXW6AJDU1lT59+pj7PlkDJNZyOABOwkk9ugfTsgsm8igXksVu2zUKkIiEDutc1RtkBfi59Wxa3JAIQM4aT1/nGyCZP3+++bqs/Uh8AyTeuap18/aK8M3MCzTu9PZ/1oCzl2EYZWaQxNeKRuREKUAS4ppcGk94tIOiXIOWTk89fW/HY80gWbVqlfkZa/T39ttvN1/350+M4hUe5ENWfLuKtf/wjzY3pzN9uBaAkgKDNhsu5RZe5BmWMIyH6cVw6tOKKxnNw3zMDTzNFTxEzgqXLRATjDdD5QiHAp5XgEQkdAQLkBxgB9/yCQCpNACwlUPwstaZ9gZIrOeD8WaOeEsJlicuLs4cNFr7Wqv169ezceNG295P1lU1wQIkuTs9x2Nqa8IsUt3F1nBTiGcy2YZefucTSCMrKwsonfB6+7byHuL5ruKLiYmhR48etn5y7dq1HD58mEWLFlGjRg3bA0NvrevExETbBsp33XWXrZxWIN42Qmn5sHxyg/bxIiJeEfGexx2OkjD6MMLs+4JVMYghkdhlLTiHgYRjD1Ckp6czefJkv74nMjLS7O+sC1mioqLKLbG1bds25s6dS48ePYCyAyS1aIybaJwxJRQmZQW8n+a6IqHJupg4ISGBlDaePq5gp6cPzM/Pty2u82bxQuk8eN26ddx11122jBLfAIm3j/GOJyvKd5+5yMhIDh48yP33328e845FveM+a4DEt1qCN5skAU8GdLTmunISKEAS4pzhDmqf63mI16fYsxLQWmLLOkjrwwjuZDI81547mcyVjOY8hnEjzzKWr7iYO81rm7x4HXnzPbX6N7WYF/T7D6XuMF+fxzC/8524gJ5cyaf9fmPvitLO+ZprrvG71oHDTLELtkmTBo0ioSPYwzOHw8FW1gKQjKd0nzfAECxA4p2kVqRsVpcuXfzuZeW791JsbKzfRD0iIsJvn5GsrCxbIMS62sc3QJL1Uz67vs1l2389q7Zj6upBokh1FxkZySH2ATCMh83jY/GUMYghkexMT5DVG/CwBh+efPLJoPf2DZDUrl0bp9Np25epRYsWtsCw997WDJLY2FjbA8P+/fsH7FcPbS3gq6u3se+HPJ8Aief++eRopbSIlKtmt2jiGnnGQOcwkA8//BCw931ef+BqnuR/ZL6ezPWM52LuYOjQocyaNYtNmzYBng3Zt23bRu3atc3Pud1uc8y5b5+nD3Y4HMTExJQZfB4+fLjf+O/BBx8EPJsV+7bRuxAwPMkg3BW4/wuU4SIi1VOnTp2IjY2lbdu2tj2L4uPjiUj0PPcqPuwZY/lmiWzevNl87Q2QdOvWjeeee46aNWuyY4fnOZ3v+O94F6f4Bki87WzZsqX5vkmTJgC2BTZe3meU3jYU74qgCe1pgWeRd2xdjQnlxOlvkVCnZzTbvjqM2xENRuASW+3py2XcA0DBOmhGJ5pRfp2/baznUI8fuXXdbfRLuprCgwYJpHHHdffS4/9q8fdHJvL1yytpTDtq1apNevpZDLy/G/XPqsPP72VRUmiw7DFPrdX/Xvs7C19ey+pPN9OxQUfCWtZhyvqnzO+KIZEwPB12NoH3Ewg0GBaR6inYAK527dpk7vDUw0/EvrdReRkk7du3Z9CgQcyaNSvgvf/zn/+YNaODZZDUq1ePn3/+2fY93lr9XgMHDmTq1Km899575rHMzEzb4DAvL8+sk20NkBTnwvvn/ErhodKN+GI0aBSp9iIiIogh0XZseecp7F22DYMSHDgpzi5dSQj2cdG9997LfffdF/De1jIO1s/37t2bqVOncu211/pdEyhAEhMTYwuQWPc+WfnMPvb9kEevl+vw74aePeV+fvcgj/Bf7qEHBeQqg0REjkl4pJMB79dnRpdNxFFa4sW3v3ISxsXcYTvWN2U4V05qRGRRHBv+ncWnszdTf0AcrW9O5Y9Fo1jCbOJIYv+LqbTccgG7KGL7vBziSCE1piarJxwgdk9dLuEu1rGIMMJpSx+2sJpwInAUeh5gGiUGK57cR8rZblrv78fMm9eQnJzMwh2f2Nrj7f/CooOX0vKWTRSR6q9Vq1bs2bMHt9vNW2+9ZR5PSEjAnejp44o9Cbx+gQ4rb4DEWqngsssuo7i4mO+/9+zf6XA4qF+/Pg8++CC33HJLhdoXExNjZhD7ltjyspbaOuuss4DAJbasc2BHURgXrHnQXDQDWgwoJ4eemAiNL45n6cN7qFXSlPO5jiWF0wHIWx5F/IqWDOAmLqK0lFaniZH84y/P0YDWOHHiIIyD7OGOKVexadvPLHl4J/vZzs8sYzlfcmfKX3E4HDzx1Z307t2bu+66i/P/4amfGh8fzwaWsIElXPmHK7nvvdHm95zzkGcFjDdAEh7lYM1fS6C4ISu+3E9nruW/fMJ2NnrudTS9zp3ioGR/6cNCK9+V2yJSfQV7eFazZk2y9uyDIkjyCZBYV/IFCpA4nU5mzpzJhAkTbCUGwbOq8IorrjDfB6rn2qdPHxwOh1+AZOTIkbb7hYeH+62qzsrKMgewYB/EFhUV0Z0hdGYgbyRu9PvetI7q+0SqO6fTaT5A89qVuhaDEgqiD+HOTaDj5qEYhhEwQFKWsWPH8vHHH9M2tz89GU7CtjRmZPzKwCOP0K/PX+l/Wyu+fXg3278+TEkhtLsrBXfE0fIOBQXkHszHSRixMbFkHywtQdOwYUMMw8Aohm/u8QSuY2r7991Ps9j2Pp9cZQWLSIVEpXkeecTiWVTiJAxjn5snW34KO6KJyk7FaZT2J51Gp7B87H5K9rv4ZkQ2W2ZtN89tm5PDN3fvogOX0IFLAMj6DJrTj+b0gy9gHJfDYVh0x07gLPpxFv240bxHT670vJgBbzfZSPbm0gd/Xr9xgNjorrZjEXjGcuHRjoABkjp16hzH/zsiUpV5n29Z97GMj48nIsHTpx3ZaRBGOMUEfj4G2OaXXsuWfm8rM/jAAw/wz3/+kxUrVgS9z9y5cxk6dCiZB7Jw4SYmsjRAUr9eA/L2Fpn9cUmRgTPcQbdu3ejfvz/p6el07NgRCFxiKz+3kL/wMpHE8MWFO3AX2xci1j5Xe5DIiVOARGz1+gZzN91zhxBPKjsficNFKhfhSXvbzWae50aybt7DZ395ye8+k4beRcL2Iq54uL/tuDe1uEuXLmRmZtrqqVpLMwSr0dr3nXr895rfbSW24hu7yN5cyABG8hkvspvNZoAkqmY4QRJIFCARCSG+tZu9XC4X7lpF8LunJv8/mctPLGMK99uCKoECJF6BSiZYV0IDXH/99Tz00EPm+wULFtCzZ0/697f3kbGxsbjdbnr16sWCBQsAiCqO54hPoDczM5O8w0cYwRNs5ydatGjB7Nmz6d+/P4UFhQzhXr+HowAZ42viDCu/NJiIVH3b+Ym6nGW+9wZC8mruwb05gWYHu7P2lQPm8cjISAzDYPd3eRRml3AOf6QGDYkinnbRPck5cogG9Rqx+TE373Rew44FpZPo3d96g8AxfNj1N1s75lz9O314lD48Ssmt0JHz6Mjt8CCkA534hRYN2vB+jd3gs8nwzkU55f7OfPIqVPJQRMT7QM6Fm/O5nsH83XNivf+154xOo8ujNfjhmQMU5RpsmRV4X8uTJVBwJKVdJJlrj1CSG8G/WM4mVjCRW3F7AyQxgQMkFd37TkSqH2uApGHDhriPltjK225wK6/wIjdhYAT8rDdAEkkM1/JP2nG+/0VPwEtP/AhE8CJrAt5n/fkwlgWlByzP5N6M+SVo25+fPI0W13kC2BvfzmTE1gks4wtb1suBdUdozXkA7Flq3xel00NphEVo9wg5cfpbJESmhNmCJDVpbNY4tXqPsWSzD4fDwQMPPECTJk1sDw2joqJo1qwZV155pe1z3hIw4P/AsiIBksTm9s80uSyeJpd5PteR/tzLNGJI4GYmABDmdvqlTZu/NciGfCJS/dSqVSvgcZfLRbvurcz3CdSgMwNJpZ4tQNKnTx/zdfbWI2RuzOfgr/kc3FRAfGRpv9aXG/g773BO7iXsWprLllnZFBwupk2bNgCkUp/mdMbYEUXOrkKijyQRjacPi42NpUGDBuTtK6LukVbEkkQNGtFx5i1MbfEzz93/qvk9/1v4P/ZMjaILFzGYv+MknG+++QaAQ78VBgyOQOkGpSJS/b3OXRTgmTjuYjOLF3syL3La/mpes+DWneQfKc0g+fHlA3zQbROf9t/C9TzJH7mV3lxDcm4D6pe0xtgaw8a3s2zBEa9WNyXZ3ieeFTgw7esGniJj64iA53Yt8QReGg6Mw53o5HDEPrb4TMYLHP5tEREJxBVTOg4ygyMBdBlbgy5jauBwOGh9S2k5mM5j0rhieVNGGW24an1z+r5dj7XNZ/Iej/EkwzhnuoNfen7Km9zNDMYzmfv4ouFTXP1zcy6a3ZDcgT8wzTmWN7mbFxnJ0u6vEvPoT7S9O5GeL9X2a0frkUmktPXMWV1EkE43mtCBK/FUWijJDRwgCbb3nYhUf9ZnbmeffTZxDUvntOl0ZQKraUEGvbmWlvSgKxfTku6kUI/vpv7E2il7eYZvAwdHTrHFd+8yX/93xHaSC+oxgJvIzyvNICk4HDwDRnNdOVmUQSI4HA76vVOPJ//4Dnvyf8eBgw0s4Z8vPEqz9CZcPehGthf+wi5KJ9dPPPEETzzxBBdccAGzZ8+23W/SpEmsXLmSjRs9ZV7K2pyuIgGSGp2jGPB+fQ6sPULWzwXmaui5b31HyoGmuImmM4Nw4QnW7F+ZT5grjJKSEr97KYNEJHQEW10cHh5Ox04dWfv+/8yVKADXMR6Xy7MhXQf60+CNy3mRIQAU3Avv3vuz5S6N/FfPbGjHB902WQ40sV3z43D4kY304xH68Yjn4GGYErcFgO7cSndu9RwvhCP7igkfn8Gj3d/g0W/+xK+fHORmHjfvV4MGZhmvw7+XMWhMUBkakVCxl638jc50YABb+MFcfedolQmWcvZDF75IFC9SuCCVhS/vNI9nsZuf+Z52rj8QURhDXEMXh36zr3Cu+4cYEppHcPaoZFLbRZHWKYrdS3NJaBpBm78k89vnh1ly/y5ytgfvlyqiy6M1SG7j5g/nj2TxN4u5jHvogyeokmccPqF7i0hoKe69hbB5jQAooRgnYfR7tx7Nr0wIOF7sOrYmUWnhpF+bSKyltn1SCzdJLdz8/v4SFv88E/CUMT3UfBMrFpbOic+OP5vEZm4Sm7m5p//VjModbGZ4XNi1G9ePucy8tvBQCSvG76X1zcnENnDR8oYkanWPZsbwlZRs8MyVW3GuOdc9uBLCeyuDRERKde7cmdtvv52IiAi6dOmCw+HgotkNmTmgNMP3Nl4N/OEPYf6HuwOfOw2O7CsGYNtc+9jOWk0h/7B/tp2X5rpysihAIgDU6xPLhymPs2PHDvNYXLvRpPesw5SVz5oroX1ZS9B4xcfH8/bbb9OlSxfAU+8/mLp165qvgwVSHA4HzYYmwFB7AOX7Hm/gmNmU/vyJy7nfPN7gj7EYXwVOH1SARESio6OJiYnhFUYxmlmk0QCAxmnWeUoAAB/xSURBVLSjZEJp4CN3W+B+5HRL/r4DGQzmUp9Vj3VobqZEFxwsDvp5raoRCR2jR49mwYIFNGlSh5VvlT6si46JZhyX8wAzzGODuI2slz2vw6MdXL+jBb9ud/LSS1sZ+kBd6tSqizPcQXFBCY4wB1s+PURsAxc1OtnHUm1uTqbNzaWrrdOvSST9mkTuvv5hpk/5iAiiyWQXBiV898tC0sLrc/ho0MXpchCR4CShWQQLb9vJutcyAej/n/rUOMfzPe5Iz0PBLEsprnyUQSIiFee8dAsPzruBbPaRmprK3r17y7zeFeOk0/3BNzy3VlFwu91+GR3WDZPBnt1RXGwfs3W8L42O99m/K619FPEPb+b1a77kcu6jLzeY5xLbhdm+Lzk5mQMHDnDttdeW+ZtEpPoKCwvj+eeftx1r0D+OhLNcHPwpeHAhmEx2sZwvGNDuMoy1CZSUs+bFaLUPx7rUsi8qw5SGGynMtveNR3aUzsWVQSKngwIkYvLd7NL7vnXr1kE/c9111/HBBx/QqlUr23Fril+wMjcAzZo1M18f68ZyTqeTb/iQDvQjhkSzZE2f1+pSVFebtIsI9OjRwywx4xUTE4PD4cDA4Ce+MwMkAM5tgTPZvPq9W4+lD+8me1PpQHNb+wWMGDWcuMYRJKW7OfhrAb9+cJCf3s3kl/3rcOGmFk0AiEhwUnDQP7vNaw3z+YXlthIQzgIXVzPWfF9CCU6c1KMFubme2tgF2cVA4IyZ+CYVK3kjIlXfY489BsCnn35qe0AXExPDPraZ7w+6dnOwcB9npZ9F0z/UoOWfknAnhNEqoRUvvWTfZ85b17nJ4HiOhSMtn62ssx1r2LAh4eHhxDf075eaD09g3WuZJLV00/yK0r7YOx5dxX+5jHsAe7BERKQ8LreLbPYB/nPe47qfpSSrdy8nr/z8/KD74AEUFVUsuy4iIoJVzOFcriCeFHOum/F6Aus+Kn2M89lnnxEbGxt0QaOIyLG4n54cxrNg5frHenLJJT3McyXFBmHhnnFhcnIyr776Kh998DGTXnuFmJgYT19ogMPpwDAMjBIwSiDM5bDdAwNwwMTwtQAc3uofxDn0ZTzc53ldmBN8/pzQVHNdOTkUIBGT72Ax2D4eVoMGDWLRokW0bNnSdtxaOistLfjqm3r16tGnTx9yc3O58MILj7m9e9nKYwy0HR9Vx77qOzo6unTjKe1BIhJS3n//fcaMGUPfvn3N/ZG8ARKAT3metvQhjmS/z162uAlH9hex7avDNLksnsSz3MTWdXHW8ER2LM/ivnNe4gteYVC782n151Hm52LruajbK4Ye/6pJeLhnsppEbZYtWkHTHrWYPXs2l156KdcMG8FrkycBUFxo8Mjl/8cbnz5AEYW0+1syzz77LDMv2MLW2aXpxmtZxEaWcBn30JSO7Mz9AoCCQ8VAODtcG6lTmA7ARV82JDzaSXIr9XsiocY3wzcmJoZ8cnnSeQUrV6+gy8WXsGnTJha/uZju3esGucuJ8R1z3XPPPQHr5nvV6x3LZYub+E10Dxw44PlfdjCWQdThLNayEJh40tssItWTNaBxMgIk1rJcbrebW2+9lU2bNjFs2LAygyMAHTp0qNB3uN1ustjNP7jYdnx/k/22vjQtLY2mTZseQ+tFJFR0fqwG/x2+3e/4RzzDZU/1YO29xTShA/N4h4W8RyQxZnAE/Bc7O8MsgY6SEoYMGcKQIUPMYw6Hw1yz53A4cIQBPl2u9R6+mlwWz+rZvxKbk0ZJUWlQJC87H3DZrp3M/WSzl1s7fxv0fiLHQgESMfkGRCoyeHQ4HPTo0cPveFpaGiNGjMDlcpGaGjzVzul0Mnfu3GNvLBUL4AC2AaQySERCS506dXjttdfYtq105XRMTIyZsZZDFg/Qy3OcRJ7p/hHNmjUj48maxNTyDMIaX+S/arp2xwTe4WHAv1SCV1hYGJGRkRw5coRMdlKrfRwAAwYMIDs72z5ZdzkoPGsXRXhWz3jPJbZw2wIkrzCKKWM/IHs0JFGLTUf3ICk85AkMZ7q2M2BkBsWFBg0GxB3r/10iUk34Bki85V22lawnIT2cw4cPB7zuZKpRo4b5+o033uDGG28s9zO1u/tvMtyjRw+WL1/Oddddx/z581n125yT2k4Rqf7KC1ocK98ASbt27Zgzp+y+adWqVSxcuJAbbrihzOu8grXZ5XLZ5rcnI+AjItVT+pVJnDe8HVnsoZB8pk+fztChQwEYGp9B+vMF3HFHN/P65Iax8Jvl8+npQe8dbA58LHq+VJuFo0r3wrvwgwZ80fZd0tcMojiv9LqcrDx8AyRbWcsetgTdd1TkWKlYm5iOJ4MkGIfDwZQpU3j99ddPtFlBVXQwmJ2dbb62TtZFJHRYN66MiIjgoosu4u9//zsZGRnm8RyyiLllG32n1DODI8E4HA5ztV737t0r9L3W1dTW4IiXdbLrPR+ZbO/nDEqIiPMccxNN7CedeCnjvxQe9qywKQw7Qs8X69B70qlZES4iVUOwAAl4yr8cOuQpzxcXd+oCqdZ95ho3bnzc9xk/fjwLFizgtddeM4Ms3n3uREQqolevXubrI0eOnNR7l5UZZ9WuXTv++te/VngOay1FffXVV5uvIyIibN9Z0e8XkdCU5dpFIfmAvRR+fHy8X8C2c+fOtvfB9gku71xFhUX6P3N0uD0L/0osXXXeQU/7N6cupe0dKWxKWMoetpzw94tYKUAippMZIDkdKtI+a/ZKeHi47QGBiIQO34eFTqeTZ555hquuusp2PFDgIpj58+fzxRdfcNNNNwW9xhogKW9CbP1u72Q3IsG/n3PFeY7FkEgGg+HbWmRvKgCg2JVf4faLSPXl2+elpKSYr3Nzc8k7mn12KgMkjRo1Ml+3b9/+uO8TFRVFz549cblc3H///cyaNYtPPvnkxBsoIiHDGrAtKQley/54nKrVy82aNeOjjz7i1Vdf5ZxzzjGP+2aQKEAiImWxzjGTk5N58sknGTRoEJdffrlfOVRrCcCuXbsGvN+HH35IkyZNmD59+ilpr/NokzZv/I0xY8bQr18/3n7rXQDCIuG852rz34YvnJLvltCmf03F5Pvwrqy9Q84EFQmQJCYmsm+fZ0O+Y3nwKSLVi7VMgdvtNl/7TiqPpZ+oV68e9erVK/Maa4CkPNbv9r6OSPAPqkTE+f/TnbvHU5qrxOW/wZ2IhB7fAElSUhJOp5OSkhL2799vHj+VAZIOHTrw3HPPkZ6ebluxeCIiIiIYOHBg+ReKiFhY540nO0ByKl166aUATJkyxTzmdDpt83aV2BKRsiQkJJh78iYlJXHvvfdy7733AmAYBg6HZ0N1sC+oSUhICHi/wYMHM3jw4JPTOMPwO+SM9BxzFIUzduxYAC7hLs+56BKz3SIn25mdIiCnle/gqn79+pXUkoqpyGBQQRER8br99ttp2bIlI0aMMI/59hEnu884lgBJoBJbrhj/f6bdcf5937Z1uwEoiVCARET8+564uDgzOLx3717AE2ywBoxPNofDwR133MEFF1xwyr5DRORYnYwHa6e75n1ycnLQ71cGiYiUxdpHWAMg4OlLrGPBlJQULrroIgDuueee09NAH94MEhel2S0xeII1EYmeubECJHIq6F9TMVlX1vTr168SW1IxFRkMKkAiIl7PP/+837ETySCpiJYtW/L9999X6Frrd3sHqs4I/wl4RLQLR7iBUVR6LgFPxp/hLjqR5opINeG7uW98fDxut5u8vDwzQHIqs0dERM5UJ2Nj4dOtX79+NGvWzNww2RogUQaJiJTFO+6DwGO/yMhIc2+mlJQUZsyYwe+//06TJk1OedviGpSOV2tlRAEQHu3p3+JJoRMXcoTDxJAIQOsuwTeNFzlRCpCIyTq4OlmlEE4l38l/INYHjqd7pY+InPlOdYBkzJgxFBUVVSgN2doWb//W8MJYUgcWse6z7bzDw2Ybw+MdFB4o/Ww8nv2WFCAREfAf88TFxZl1pr2lRxUgEZFQdDJKbJ3ueWVkZCQbNmwwFzQqg0REKsob/AjGmkFSs2ZNIiIiTktwBKBe3xja/CWZnYtyGDC9AQBhR7cNjieVG3jKdv1Z7RsDyiCRU0MltsRU1QIkFXmQqQwSESnLqQ6QNG3alHfffZehQ4eWe22gDJKwCCetxoUxlkFsYpV5Xe0BgSfDjqiqtypSRE49l8tl9isKkIhIKKtKe5BYhYWFmYGRqKgo87gCJCJSljFjxgDw+uuvBzyfk5Njvm7UqNHpaJLJ4XDQ6+U6XLm6ObF1PXNhR+0j5JId8Pq0ZokAtG7d+nQ1UUKI/jUVkzUjIz4+3nbO7XaTn59/2iLJFeH7ILNevXq2DewCXSMiYuUbDK7MSaa1v7L2x9ZJsPe6uv2j2fpegIFjjPYgERG7xMREAL89SBQgEZFQdDICJJVd1mrIkCHMmTOHDh06VKiqgoiEroceeojrr78+aPDj8OHD5uvo6OjT1Krg4hJjWM08unGJ37nU9p5s6BdffJHExET+/Oc/n+7mSTWmDBIxeUsvgH/H+M033zB48GA+++yz092soHwHg8uWLaNPnz62YyqxJSJlKavPON2swRlrqnOgAEnTSxKZyQQKKbCdK0o6jIiIVbt27QD8Mkh8F8OIiISCkxEgefTRR0lLS2P06NEnoUXHLiUlhenTp/Pggw9WyveLSNXhcrnKzAzp2LEjABdccMFpalHZGjduzCwmMJcpvM8/WcV/AVjAu7gTPMHptLQ0Jk2aROfOnSuzqVLNKINETNYHcr4Bko4dO/Lhhx+e7iaVyfdBpnWTeS+lHItIWcLDw6lZsya7d+8GKjdAciwZJDFx0czmVeYymVacy0g8G9CHx56etorIma9Hjx4sXryYu+66CyhdCKMMEhEJZScjQNKwYUN2796tBXgiUuW99tprfPzxx9xyyy2V3RQALr30Uj6+6mNmzHiBgoICFjKNaOKPlt16orKbJ9WYMkjEVFYGyZnI90FmoFTnmJgYxo4dC8CkSZNOS7tEpGqxrqKuKhkk4eHhpKamUkQB68IX8iIjeYIhKrMgIqbPP/+cpUuXcsklnhIF2oNEROTkUXBERKqDjh07MnbsWOrUqVPZTQE8c92pU6dy3333mcdyyfabE4ucbAqQiKmsB3JnIt8HgdYMkueee45GjRrx7LPP8sgjj7B//36uvvrq091EEakCzpQASaBN2sETvLZOwr3XLVu2jK+++ooRI0awgSXs4Cfb50QktMXHx9OlSxfzvfYgEREREZGqwPq878ILL2TlypWV2BoJBQqQiKmqZ5BYAyR33HEHmzdvpnHjxgAkJyef1raJSNVhfUh4pmSQWAeEDoeDGjVq+F3XqFEj+vXrZwtoK0AiIsF4+4etW7cCCpCIiIiIyJnJOh8eN24c6enpldgaCQUKkIiprD1IzkQVKbElIlKeMz2DBKB27doBr/O9tnnz5qeodSJS1XXo0MH2Xpu0i4iIiMiZyBog0SJAOR0UIBFTVcsgKavElohIRZ0pGSTBNmkHbDVhywqQDBo06BS1TkSquscff5yOHTua75VBIiIiIiJnIgVI5HTTE2UxWTudqlCSqqwSWyIiFRUTE2O+rswAibVUlrVNUBr4iIqK8tsjqrCw0HydkpJyClsoIlWZw+GgU6dO5nsFSERERETkTGSdl1sXc4ucKuHlXyKhYv/+/ebrFi1aVGJLKsZ3hbVKbInI8bAGHKz7gJxu3bp146abbiIhIcHcP8nrpptuIiwsjBYtWvgFcazvq0L2n4hUnrS0NPO1AiQiEkpat27N2rVrK7sZIiJSAbm5uebrqrCAW6o+BUjE1L59e8CzwtB39fKZyPchYWU+2BSRqssaIKnsEluvvvpqwHPh4eGMHDky4Lm77rqLd955h7Zt2+JwOE5lE0WkiqtRo4b5um7dupXYEhGR0+vTTz9lxIgR3HfffZXdFBERKUdWVpb5WiW25HTQE2UxjRgxAsMwGDJkSGU3pUKsGSQRERF6MCgix8W3L6lq0tLS+PXXXys1uCMiVcN5551nvj777LMrsSUiIqdXkyZNWLRoUWU3Q0REKsAaIBE5HbRpg5hiY2MZNWoUtWrVquymVEh8fLz5WhFlETleF1xwAQDXXHNNlc1EU3BERCqiY8eOzJgxg3nz5lXJgLCIiIiIVH8XXnghQJV5PilVX9V8EiQCJCUlma81yReR45WRkcGePXtITU2t7KaIiJxyVSVTWERERERCU79+/fjmm29IT0+v7KZIiFCARKosBUhE5GSxblwsIiIiIiIiIpXD4XCQkZFR2c2QEKISW1JlJScnm69LSkoqsSUiIiIiIiIiIiIiUtUoQCJVVlxcnPk6JyenElsiIiIiIiIiIiIiIlWNAiRSZTmdpX99FSARERERERERERERkWOhAIlUC4ZhVHYTRERERERERERERKQKUYBERERERERERERERERCjgIkIiIiIiIiIiIiIiISchQgkSotNja2spsgIiIiIiIiIiIiIlWQAiRSpT333HMAXHHFFZXbEBERERERERERERGpUsIruwEiJ+LGG2+kVatWtG3btrKbIiIiIiIiIiIiIiJViAIkUqU5HA4yMjIquxkiIiIiIiIiIiIiUsWoxJaIiIiIiIiIiIiIiIQcBUhERERERERERERERCTkKEAiIiIiIiIiIiIiIiIhRwESEREREREREREREREJOQqQiIiIiIiIiIiIiIhIyFGAREREREREREREREREQo4CJCIiIiIiIiIiIiIiEnIUIBERERERERERERERkZCjAImIiIiIiIiIiIiIiIQcBUhERERERERERERERCTkKEAiIiIiIiIiIiIiIiIhRwESEREREREREREREREJOQqQiIiIiIiIiIiIiIhIyFGAREREREREREREREREQo4CJCIiIiIiIiIiIiIiEnIUIBERERERERERERERkZCjAImIiIiIiIiIiIiIiIQcBUhERERERERERERERCTkKEAiIiIiIiIiIiIiIiIhRwESEREREREREREREREJOQqQiIiIiIiIiIiIiIhIyFGAREREREREREREREREQo4CJCIiIiIiIiIiIiIiEnIUIBERERERERERERERkZCjAImIiIiIiIiIiIiIiIQcBUhERERERERERERERCTkKEAiIiIiIiIiIiIiIiIhRwESEREREREREREREREJOQqQiIiIiIiIiIiIiIhIyFGAREREREREREREREREQo4CJCIiIiIiIiIiIiIiEnLCK7sBJ8owDACys7MruSUiIiIiIiIiIiIiIlLZvPECb/wgmCofIDl06BAA9evXr+SWiIiIiIiIiIiIiIjImeLQoUMkJCQEPe8wyguhnOFKSkrYsWMHcXFxOByOym7OGSM7O5v69euzbds24uPjK7s5IiInnfo5EanO1MeJSHWmPk5EqjP1cSJnBsMwOHToEHXq1MHpDL7TSJXPIHE6ndSrV6+ym3HGio+PV2csItWa+jkRqc7Ux4lIdaY+TkSqM/VxIpWvrMwRL23SLiIiIiIiIiIiIiIiIUcBEhERERERERERERERCTkKkFRTbrebMWPG4Ha7K7spIiKnhPo5EanO1MeJSHWmPk5EqjP1cSJVS5XfpF1ERERERERERERERORYKYNERERERERERERERERCjgIkIiIiIiIiIiIiIiISchQgERERERERERERERGRkKMAiYiIiIiIiIiIiIiIhBwFSKqpl156iUaNGhEZGUnXrl357rvvKrtJIiJ+Fi5cyEUXXUSdOnVwOBx8/PHHtvOGYTB69Ghq165NVFQUffv25eeff7Zdc+DAAa6++mri4+NJTEzkT3/6E4cPH7Zds3r1as477zwiIyOpX78+Tz311Kn+aSIS4saNG0fnzp2Ji4ujRo0aXHrppWzcuNF2zZEjRxg1ahQpKSnExsYyZMgQdu/ebbtm69atDBw4kOjoaGrUqME999xDUVGR7Zr58+fTsWNH3G43zZo1Y/Lkyaf654lIiJs4cSJt27YlPj6e+Ph4MjIy+OKLL8zz6t9EpDoZP348DoeDO++80zymfk6k+lCApBr6z3/+w9/+9jfGjBnDihUraNeuHQMGDGDPnj2V3TQREZucnBzatWvHSy+9FPD8U089xQsvvMArr7zC0qVLiYmJYcCAARw5csS85uqrr2bt2rXMmTOHWbNmsXDhQkaOHGmez87Opn///jRs2JDly5fz9NNP8+ijj/Lqq6+e8t8nIqFrwYIFjBo1im+//ZY5c+ZQWFhI//79ycnJMa+56667mDlzJtOnT2fBggXs2LGDyy67zDxfXFzMwIEDKSgo4JtvvmHKlClMnjyZ0aNHm9ds3ryZgQMH0rt3b1atWsWdd97Jn//8Z2bPnn1af6+IhJZ69eoxfvx4li9fzvfff0+fPn245JJLWLt2LaD+TUSqj2XLljFp0iTatm1rO65+TqQaMaTa6dKlizFq1CjzfXFxsVGnTh1j3LhxldgqEZGyAcZHH31kvi8pKTFq1aplPP300+axrKwsw+12G++9955hGIaxbt06AzCWLVtmXvPFF18YDofD2L59u2EYhvHyyy8bSUlJRn5+vnnNfffdZ6Snp5/iXyQiUmrPnj0GYCxYsMAwDE9/5nK5jOnTp5vXrF+/3gCMJUuWGIZhGJ9//rnhdDqNXbt2mddMnDjRiI+PN/u0e++912jdurXtu4YNG2YMGDDgVP8kERGbpKQk4/XXX1f/JiLVxqFDh4zmzZsbc+bMMXr16mXccccdhmFoHCdS3SiDpJopKChg+fLl9O3b1zzmdDrp27cvS5YsqcSWiYgcm82bN7Nr1y5bf5aQkEDXrl3N/mzJkiUkJiZyzjnnmNf07dsXp9PJ0qVLzWt69uxJRESEec2AAQPYuHEjmZmZp+nXiEioO3jwIADJyckALF++nMLCQlsf16JFCxo0aGDr484++2xq1qxpXjNgwACys7PNVdpLliyx3cN7jcZ9InK6FBcXM23aNHJycsjIyFD/JiLVxqhRoxg4cKBfX6R+TqR6Ca/sBsjJtW/fPoqLi20dMEDNmjXZsGFDJbVKROTY7dq1CyBgf+Y9t2vXLmrUqGE7Hx4eTnJysu2axo0b+93Dey4pKemUtF9ExKukpIQ777yTHj160KZNG8DT/0RERJCYmGi71rePC9QHes+VdU12djZ5eXlERUWdip8kIsKaNWvIyMjgyJEjxMbG8tFHH9GqVStWrVql/k1Eqrxp06axYsUKli1b5ndO4ziR6kUBEhERERGRU2jUqFH8+OOPLFq0qLKbIiJy0qSnp7Nq1SoOHjzIjBkzuO6661iwYEFlN0tE5IRt27aNO+64gzlz5hAZGVnZzRGRU0wltqqZ1NRUwsLC2L17t+347t27qVWrViW1SkTk2Hn7rLL6s1q1arFnzx7b+aKiIg4cOGC7JtA9rN8hInKq3HbbbcyaNYt58+ZRr14983itWrUoKCggKyvLdr1vH1de/xXsmvj4eK06FJFTKiIigmbNmtGpUyfGjRtHu3bteP7559W/iUiVt3z5cvbs2UPHjh0JDw8nPDycBQsW8MILLxAeHk7NmjXVz4lUIwqQVDMRERF06tSJuXPnmsdKSkqYO3cuGRkZldgyEZFj07hxY2rVqmXrz7Kzs1m6dKnZn2VkZJCVlcXy5cvNa77++mtKSkro2rWrec3ChQspLCw0r5kzZw7p6ekqryUip4xhGNx222189NFHfP31136l/jp16oTL5bL1cRs3bmTr1q22Pm7NmjW2QPCcOXOIj4+nVatW5jXWe3iv0bhPRE63kpIS8vPz1b+JSJV3/vnns2bNGlatWmX+Oeecc7j66qvN1+rnRKqRyt4lXk6+adOmGW6325g8ebKxbt06Y+TIkUZiYqKxa9euym6aiIjNoUOHjJUrVxorV640AOP//u//jJUrVxq//fabYRiGMX78eCMxMdH45JNPjNWrVxuXXHKJ0bhxYyMvL8+8xwUXXGB06NDBWLp0qbFo0SKjefPmxvDhw83zWVlZRs2aNY1rr73W+PHHH41p06YZ0dHRxqRJk0777xWR0PGXv/zFSEhIMObPn2/s3LnT/JObm2tec8sttxgNGjQwvv76a+P77783MjIyjIyMDPN8UVGR0aZNG6N///7GqlWrjC+//NJIS0szHnjgAfOaTZs2GdHR0cY999xjrF+/3njppZeMsLAw48svvzytv1dEQsv9999vLFiwwNi8ebOxevVq4/777zccDofx1VdfGYah/k1Eqp9evXoZd9xxh/le/ZxI9aEASTU1YcIEo0GDBkZERITRpUsX49tvv63sJomI+Jk3b54B+P257rrrDMMwjJKSEuORRx4xatasabjdbuP88883Nm7caLvH/v37jeHDhxuxsbFGfHy8ccMNNxiHDh2yXfPDDz8Y5557ruF2u426desa48ePP10/UURCVKC+DTDeeust85q8vDzj1ltvNZKSkozo6Ghj8ODBxs6dO2332bJli3HhhRcaUVFRRmpqqvH3v//dKCwstF0zb948o3379kZERITRpEkT23eIiJwKN954o9GwYUMjIiLCSEtLM84//3wzOGIY6t9EpPrxDZConxOpPhyGYRiVk7siIiIiIiIiIiIiIiJSObQHiYiIiIiIiIiIiIiIhBwFSEREREREREREREREJOQoQCIiIiIiIiIiIiIiIiFHARIREREREREREREREQk5CpCIiIiIiIiIiIiIiEjIUYBERERERERERERERERCjgIkIiIiIiIiIiIiIiISchQgERERERERERERERGRkKMAiYiIiIiIiIiIiIiIhBwFSEREREREREREREREJOQoQCIiIiIiIiIiIiIiIiFHARIREREREREREREREQk5/w9NtfYiaIg+AAAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.title(\\\"x-component of the force vector\\\", size=20)\\n\",\n    \"\\n\",\n    \"plt.plot(f_x, label=\\\"ground truth\\\", color=col_data)\\n\",\n    \"plt.plot(predictions[:,0], label=\\\"prediction\\\",color=col_getml)\\n\",\n    \"\\n\",\n    \"plt.legend(loc=\\\"upper right\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.title(\\\"y-component of the force vector\\\", size=20)\\n\",\n    \"\\n\",\n    \"plt.plot(f_y, label=\\\"ground truth\\\", color=col_data)\\n\",\n    \"plt.plot(predictions[:,1], label=\\\"prediction\\\",color=col_getml)\\n\",\n    \"\\n\",\n    \"plt.legend(loc=\\\"upper right\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 2000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.subplots(figsize=(20, 10))\\n\",\n    \"\\n\",\n    \"plt.title(\\\"z-component of the force vector\\\", size=20)\\n\",\n    \"\\n\",\n    \"plt.plot(f_z, label=\\\"ground truth\\\", color=col_data)\\n\",\n    \"plt.plot(predictions[:,2], label=\\\"prediction\\\",color=col_getml)\\n\",\n    \"\\n\",\n    \"plt.legend(loc=\\\"upper right\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.7 Features\\n\",\n    \"\\n\",\n    \"The most important feature looks as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_7\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_7\\\" AS\\n\",\n       \"SELECT MEDIAN( t2.\\\"37\\\" ) AS \\\"feature_1_7\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"DATA_ALL__STAGING_TABLE_2\\\" t2\\n\",\n       \"ON 1 = 1\\n\",\n       \"WHERE t2.\\\"rowid\\\" <= t1.\\\"rowid\\\"\\n\",\n       \"AND ( t2.\\\"( rowid + 30.000000 )\\\" > t1.\\\"rowid\\\" OR t2.\\\"( rowid + 30.000000 )\\\" IS NULL )\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_7\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_7\\\" AS\\\\nSELECT MEDIAN( t2.\\\"37\\\" ) AS \\\"feature_1_7\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"DATA_ALL__STAGING_TABLE_2\\\" t2\\\\nON 1 = 1\\\\nWHERE t2.\\\"rowid\\\" <= t1.\\\"rowid\\\"\\\\nAND ( t2.\\\"( rowid + 30.000000 )\\\" > t1.\\\"rowid\\\" OR t2.\\\"( rowid + 30.000000 )\\\" IS NULL )\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.features.to_sql()[pipe1.features.sort(by=\\\"importances\\\")[0].name]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"getml.engine.shutdown()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see, the predictions are very accurate. This suggests that it is very feasible to predict the force vector based on other sensor data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Conclusion\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The purpose of this notebook has been to illustrate the problem of the curse of dimensionality when engineering features from datasets with many columns.\\n\",\n    \"\\n\",\n    \"The most important thing to remember is that this problem exists regardless of whether you engineer your features manually or using algorithms. Whether you like it or not: If you write your features in the traditional way, your search space grows quadratically with the number of columns.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.9\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "demo-notebooks/seznam.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Seznam - Predicting transaction volume\\n\",\n    \"\\n\",\n    \"Seznam is a Czech company with a scope similar to Google. The purpose of this notebook is to analyze data from Seznam's wallet, predicting the transaction volume.\\n\",\n    \"\\n\",\n    \"Summary:\\n\",\n    \"\\n\",\n    \"- Prediction type: __Regression model__\\n\",\n    \"- Domain: __E-commerce__\\n\",\n    \"- Prediction target: __Transaction volume__ \\n\",\n    \"- Population size: __1,462,078__\\n\",\n    \"\\n\",\n    \"_Author: Dr. Patrick Urbanke_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [\n     \"remove_cell_on_docs\"\n    ]\n   },\n   \"source\": [\n    \"<a target=\\\"_blank\\\" href=\\\"https://colab.research.google.com/github/getml/getml-community/blob/main/demo-notebooks/seznam.ipynb\\\">\\n\",\n    \"  <img src=\\\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\\\"Open In Colab\\\"/>\\n\",\n    \"</a>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Background\\n\",\n    \"\\n\",\n    \"Seznam is a Czech company with a scope similar to Google. The purpose of this notebook is to analyze data from Seznam's wallet, predicting the transaction volume.\\n\",\n    \"\\n\",\n    \"Since the dataset is in Czech, we will quickly translate the meaning of the main tables:\\n\",\n    \"\\n\",\n    \"- *dobito*: contains data on prepayments into a wallet\\n\",\n    \"- *probehnuto*: contains data on charges from a wallet\\n\",\n    \"- *probehnuto_mimo_penezenku*: contains data on charges, from sources other than a wallet\\n\",\n    \"\\n\",\n    \"The dataset has been downloaded from the [CTU Prague relational learning repository](https://relational-data.org/dataset/Seznam) (Motl and Schulte, 2015).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get started with the analysis and set up your session:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Note: you may need to restart the kernel to use updated packages.\\n\",\n      \"Launching ./getML --allow-push-notifications=true --allow-remote-ips=false --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false in /home/user/.getML/getml-1.5.0-x64-community-edition-linux...\\n\",\n      \"Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912123743.log.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Connected to project <span style=\\\"color: #008000; text-decoration-color: #008000\\\">'seznam'</span>.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Connected to project \\u001b[32m'seznam'\\u001b[0m.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%pip install -q \\\"getml==1.5.0\\\"\\n\",\n    \"\\n\",\n    \"import getml\\n\",\n    \"\\n\",\n    \"getml.engine.launch()\\n\",\n    \"getml.set_project('seznam')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Loading data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.1 Download from source\\n\",\n    \"\\n\",\n    \"We begin by downloading the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Connection(dbname='Seznam', dialect='mysql', host='relational.fel.cvut.cz', port=3306)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"conn = getml.database.connect_mariadb(\\n\",\n    \"    host=\\\"relational.fel.cvut.cz\\\",\\n\",\n    \"    dbname=\\\"Seznam\\\",\\n\",\n    \"    port=3306,\\n\",\n    \"    user=\\\"guest\\\",\\n\",\n    \"    password=\\\"ctu-relational\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"conn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def load_if_needed(name):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Loads the data from the relational learning\\n\",\n    \"    repository, if the data frame has not already\\n\",\n    \"    been loaded.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    if not getml.data.exists(name):\\n\",\n    \"        data_frame = getml.DataFrame.from_db(\\n\",\n    \"            name=name,\\n\",\n    \"            table_name=name,\\n\",\n    \"            conn=conn\\n\",\n    \"        )\\n\",\n    \"        data_frame.save()\\n\",\n    \"    else:\\n\",\n    \"        data_frame = getml.data.load_data_frame(name)\\n\",\n    \"    return data_frame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dobito = load_if_needed(\\\"dobito\\\")\\n\",\n    \"probehnuto = load_if_needed(\\\"probehnuto\\\")\\n\",\n    \"probehnuto_mimo_penezenku = load_if_needed(\\\"probehnuto_mimo_penezenku\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_float\\\">   client_id</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">month_year_datum_transakce</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">sluzba       </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">kc_dobito    </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">  role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_float\\\">unused_float</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string             </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">7157857</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">1045.62</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">109700</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">5187.28</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">51508</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">408.20</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9573550</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">521.24</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9774621</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2014-11-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">386.22</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554341</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">65283</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-09-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">7850.00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554342</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">6091446</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">31400.00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554343</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">1264806</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2013-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">-8220.52</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554344</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">101103</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">3140.00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554345</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">8674551</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">6280.00</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    554346 rows x 4 columns<br />\\n\",\n       \"    memory usage: 29.59 MB<br />\\n\",\n       \"    name: dobito<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name      client_id   month_year_datum_transakce   sluzba          kc_dobito    \\n\",\n       \"  role   unused_float   unused_string                unused_string   unused_string\\n\",\n       \"     0        7157857   2012-10-01                   c               1045.62      \\n\",\n       \"     1         109700   2015-10-01                   c               5187.28      \\n\",\n       \"     2          51508   2015-08-01                   c               408.20       \\n\",\n       \"     3        9573550   2012-10-01                   c               521.24       \\n\",\n       \"     4        9774621   2014-11-01                   c               386.22       \\n\",\n       \"                  ...   ...                          ...             ...          \\n\",\n       \"554341          65283   2012-09-01                   g               7850.00      \\n\",\n       \"554342        6091446   2012-08-01                   g               31400.00     \\n\",\n       \"554343        1264806   2013-08-01                   g               -8220.52     \\n\",\n       \"554344         101103   2012-08-01                   g               3140.00      \\n\",\n       \"554345        8674551   2012-08-01                   g               6280.00      \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"554346 rows x 4 columns\\n\",\n       \"memory usage: 29.59 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dobito\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  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      <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">310.86</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1462077</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">946957</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">153.86</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    1462078 rows x 4 columns<br />\\n\",\n       \"    memory usage: 77.07 MB<br />\\n\",\n       \"    name: probehnuto<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"   name      client_id   month_year_datum_transakce   sluzba          kc_proklikano\\n\",\n       \"   role   unused_float   unused_string                unused_string   unused_string\\n\",\n       \"      0         109145   2013-06-01                   c               -31.40       \\n\",\n       \"      1        9804394   2015-10-01                   h               37.68        \\n\",\n       \"      2        9803353   2015-10-01                   h               725.34       \\n\",\n       \"      3        9801753   2015-10-01                   h               194.68       \\n\",\n       \"      4        9800425   2015-10-01                   h               1042.48      \\n\",\n       \"                   ...   ...                          ...             ...          \\n\",\n       \"1462073          98857   2015-08-01                   NULL            153.86       \\n\",\n       \"1462074          95776   2015-09-01                   NULL            153.86       \\n\",\n       \"1462075          98857   2015-09-01                   NULL            153.86       \\n\",\n       \"1462076          90001   2015-10-01                   NULL            310.86       \\n\",\n       \"1462077         946957   2015-10-01                   NULL            153.86       \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"1462078 rows x 4 columns\\n\",\n       \"memory usage: 77.07 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"probehnuto\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right 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class=\\\"sub-header unused_string\\\">unused_string</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string                  </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3901</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3901</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-09-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3901</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3901</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-11-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">3901</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2012-12-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599381</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9804086</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599382</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9804238</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599383</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9804782</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599384</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9804810</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599385</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align unused_float\\\">\\n\",\n       \"              <span class=\\\"left\\\">9805032</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 0ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    599386 rows x 3 columns<br />\\n\",\n       \"    memory usage: 23.38 MB<br />\\n\",\n       \"    name: probehnuto_mimo_penezenku<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name      client_id   Month/Year      probehla_inzerce_mimo_penezenku\\n\",\n       \"  role   unused_float   unused_string   unused_string                  \\n\",\n       \"     0           3901   2012-08-01      ANO                            \\n\",\n       \"     1           3901   2012-09-01      ANO                            \\n\",\n       \"     2           3901   2012-10-01      ANO                            \\n\",\n       \"     3           3901   2012-11-01      ANO                            \\n\",\n       \"     4           3901   2012-12-01      ANO                            \\n\",\n       \"                  ...   ...             ...                            \\n\",\n       \"599381        9804086   2015-10-01      ANO                            \\n\",\n       \"599382        9804238   2015-10-01      ANO                            \\n\",\n       \"599383        9804782   2015-10-01      ANO                            \\n\",\n       \"599384        9804810   2015-10-01      ANO                            \\n\",\n       \"599385        9805032   2015-10-01      ANO                            \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"599386 rows x 3 columns\\n\",\n       \"memory usage: 23.38 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"probehnuto_mimo_penezenku\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.2 Prepare data for getML\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"getML requires that we define *roles* for each of the columns.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\"> month_year_datum_transakce</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">client_id</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"categorical\\\">sluzba     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"numerical\\\">kc_dobito</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">  role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\"> join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">categorical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">numerical</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">  unit</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">         </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header categorical\\\">service    </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header numerical\\\">         </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">7157857</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">1045</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.62</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">109700</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">5187</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.28</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">51508</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">408</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.2</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9573550</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">521</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.24</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2014-11-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9774621</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">c</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">386</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.22</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">...</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554341</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-09-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">65283</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">7850</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554342</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">6091446</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              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   \"          \\n\",\n       \"            <td class=\\\"categorical\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">3140</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>554345</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">8674551</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">g</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"char-align numerical\\\">\\n\",\n       \"              <span class=\\\"left\\\">6280</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >&nbsp;</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    554346 rows x 4 columns<br />\\n\",\n       \"    memory usage: 13.30 MB<br />\\n\",\n       \"    name: dobito<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name    month_year_datum_transakce   client_id   sluzba        kc_dobito\\n\",\n       \"  role                    time_stamp    join_key   categorical   numerical\\n\",\n       \"  unit   time stamp, comparison only               service                \\n\",\n       \"     0                    2012-10-01     7157857   c               1045.62\\n\",\n       \"     1                    2015-10-01      109700   c               5187.28\\n\",\n       \"     2                    2015-08-01       51508   c                408.2 \\n\",\n       \"     3                    2012-10-01     9573550   c                521.24\\n\",\n       \"     4                    2014-11-01     9774621   c                386.22\\n\",\n       \"                                 ...         ...   ...               ...  \\n\",\n       \"554341                    2012-09-01       65283   g               7850   \\n\",\n       \"554342                    2012-08-01     6091446   g              31400   \\n\",\n       \"554343                    2013-08-01     1264806   g              -8220.52\\n\",\n       \"554344                    2012-08-01      101103   g               3140   \\n\",\n       \"554345                    2012-08-01     8674551   g               6280   \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"554346 rows x 4 columns\\n\",\n       \"memory usage: 13.30 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dobito.set_role(\\\"client_id\\\", getml.data.roles.join_key)\\n\",\n    \"dobito.set_role(\\\"month_year_datum_transakce\\\", getml.data.roles.time_stamp)\\n\",\n    \"dobito.set_role(\\\"sluzba\\\", getml.data.roles.categorical)\\n\",\n    \"dobito.set_role(\\\"kc_dobito\\\", getml.data.roles.numerical)\\n\",\n    \"\\n\",\n    \"dobito.set_unit(\\\"sluzba\\\", \\\"service\\\")\\n\",\n    \"\\n\",\n    \"dobito\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>   name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\"> month_year_datum_transakce</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"         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2ch\\\"\\n\",\n       \"                >.86</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1462076</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">90001</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">310</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.86</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1462077</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">946957</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              \\n\",\n       \"              \\n\",\n       \"            \\n\",\n       \"            <td class=\\\"char-align target\\\">\\n\",\n       \"              <span class=\\\"left\\\">153</span\\n\",\n       \"              ><span class=\\\"right\\\" style=\\\"width: 2ch\\\"\\n\",\n       \"                >.86</span\\n\",\n       \"              >\\n\",\n       \"            </td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"categorical\\\">NULL</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    1462078 rows x 4 columns<br />\\n\",\n       \"    memory usage: 35.09 MB<br />\\n\",\n       \"    name: probehnuto<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"   name    month_year_datum_transakce   client_id   kc_proklikano   sluzba     \\n\",\n       \"   role                    time_stamp    join_key          target   categorical\\n\",\n       \"   unit   time stamp, comparison only                               service    \\n\",\n       \"      0                    2013-06-01      109145          -31.4    c          \\n\",\n       \"      1                    2015-10-01     9804394           37.68   h          \\n\",\n       \"      2                    2015-10-01     9803353          725.34   h          \\n\",\n       \"      3                    2015-10-01     9801753          194.68   h          \\n\",\n       \"      4                    2015-10-01     9800425         1042.48   h          \\n\",\n       \"                                  ...         ...           ...     ...        \\n\",\n       \"1462073                    2015-08-01       98857          153.86   NULL       \\n\",\n       \"1462074                    2015-09-01       95776          153.86   NULL       \\n\",\n       \"1462075                    2015-09-01       98857          153.86   NULL       \\n\",\n       \"1462076                    2015-10-01       90001          310.86   NULL       \\n\",\n       \"1462077                    2015-10-01      946957          153.86   NULL       \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"1462078 rows x 4 columns\\n\",\n       \"memory usage: 35.09 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"probehnuto.set_role(\\\"client_id\\\", getml.data.roles.join_key)\\n\",\n    \"probehnuto.set_role(\\\"month_year_datum_transakce\\\", getml.data.roles.time_stamp)\\n\",\n    \"probehnuto.set_role(\\\"sluzba\\\", getml.data.roles.categorical)\\n\",\n    \"probehnuto.set_role(\\\"kc_proklikano\\\", getml.data.roles.target)\\n\",\n    \"\\n\",\n    \"probehnuto.set_unit(\\\"sluzba\\\", \\\"service\\\")\\n\",\n    \"\\n\",\n    \"probehnuto\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  table {\\n\",\n       \"    font-family: Helvetica, sans-serif;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"  }\\n\",\n       \"  .join_key,\\n\",\n       \"  .numerical,\\n\",\n       \"  .target,\\n\",\n       \"  .unused_float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  .char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  name</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"time_stamp\\\">                 Month/Year</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"join_key\\\">client_id</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"unused_string\\\">probehla_inzerce_mimo_penezenku</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">  role</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">                 time_stamp</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\"> join_key</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">unused_string                  </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header\\\">  unit</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header time_stamp\\\">time stamp, comparison only</th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header join_key\\\">         </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <th class=\\\"sub-header unused_string\\\">                               </th>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-08-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3901</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-09-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3901</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3901</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-11-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3901</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2012-12-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">3901</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599381</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9804086</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599382</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9804238</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599383</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9804782</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599384</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9804810</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>599385</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"time_stamp\\\">2015-10-01</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"join_key\\\">9805032</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td class=\\\"unused_string\\\">ANO</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    599386 rows x 3 columns<br />\\n\",\n       \"    memory usage: 14.39 MB<br />\\n\",\n       \"    name: probehnuto_mimo_penezenku<br />\\n\",\n       \"    type: getml.DataFrame<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"  name                    Month/Year   client_id   probehla_inzerce_mimo_penezenku\\n\",\n       \"  role                    time_stamp    join_key   unused_string                  \\n\",\n       \"  unit   time stamp, comparison only                                              \\n\",\n       \"     0                    2012-08-01        3901   ANO                            \\n\",\n       \"     1                    2012-09-01        3901   ANO                            \\n\",\n       \"     2                    2012-10-01        3901   ANO                            \\n\",\n       \"     3                    2012-11-01        3901   ANO                            \\n\",\n       \"     4                    2012-12-01        3901   ANO                            \\n\",\n       \"                                 ...         ...   ...                            \\n\",\n       \"599381                    2015-10-01     9804086   ANO                            \\n\",\n       \"599382                    2015-10-01     9804238   ANO                            \\n\",\n       \"599383                    2015-10-01     9804782   ANO                            \\n\",\n       \"599384                    2015-10-01     9804810   ANO                            \\n\",\n       \"599385                    2015-10-01     9805032   ANO                            \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"599386 rows x 3 columns\\n\",\n       \"memory usage: 14.39 MB\\n\",\n       \"type: getml.DataFrame\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"probehnuto_mimo_penezenku.set_role(\\\"client_id\\\", getml.data.roles.join_key)\\n\",\n    \"probehnuto_mimo_penezenku.set_role(\\\"Month/Year\\\", getml.data.roles.time_stamp)\\n\",\n    \"\\n\",\n    \"probehnuto_mimo_penezenku\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th.sub-header {\\n\",\n       \"    font-weight: normal;\\n\",\n       \"    font-style: italic;\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.numerical {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"\\n\",\n       \"  td.char-align {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  span.left {\\n\",\n       \"    text-align: right;\\n\",\n       \"    width: 3em;\\n\",\n       \"  }\\n\",\n       \"  span.right {\\n\",\n       \"    float: right;\\n\",\n       \"    text-align: left;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>  </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th>     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>test</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>4</th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>train</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th></th>\\n\",\n       \"        \\n\",\n       \"          \\n\",\n       \"            <td>...</td>\\n\",\n       \"          \\n\",\n       \"        \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"\\n\",\n       \"  <p>\\n\",\n       \"    infinite number of  rows<br />\\n\",\n       \"    \\n\",\n       \"    type: StringColumnView<br />\\n\",\n       \"    \\n\",\n       \"  </p>\\n\"\n      ],\n      \"text/plain\": [\n       \"          \\n\",\n       \" 0   train\\n\",\n       \" 1   train\\n\",\n       \" 2   train\\n\",\n       \" 3   test \\n\",\n       \" 4   train\\n\",\n       \"     ...  \\n\",\n       \"\\n\",\n       \"\\n\",\n       \"infinite number of  rows\\n\",\n       \"type: StringColumnView\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"split = getml.data.split.random(train=0.8, test=0.2)\\n\",\n    \"split\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Predictive modeling\\n\",\n    \"\\n\",\n    \"We loaded the data and defined the roles and units. Next, we create a getML pipeline for relational learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.1 Define relational model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\\n\",\n       \"            <div style='margin-top: 15px; margin-bottom: 5px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\\n\",\n       \"            <div style=\\\"height:320px;width:660px;position:relative;\\\"><svg height=\\\"310\\\" width=\\\"650\\\"><rect y=\\\"0\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"73.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">probehnuto</text><rect x=\\\"51\\\" y=\\\"10\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"10\\\" x2=\\\"67.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"10\\\" x2=\\\"83.0\\\" y2=\\\"58\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"26.0\\\" x2=\\\"99\\\" y2=\\\"26.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"42.0\\\" x2=\\\"99\\\" y2=\\\"42.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"110\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"183.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">dobito</text><rect x=\\\"51\\\" y=\\\"120\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"120\\\" x2=\\\"67.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"120\\\" x2=\\\"83.0\\\" y2=\\\"168\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"136.0\\\" x2=\\\"99\\\" y2=\\\"136.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"152.0\\\" x2=\\\"99\\\" y2=\\\"152.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"75.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">probehnuto_mimo_penezenku</text><rect x=\\\"51\\\" y=\\\"230\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"67.0\\\" y1=\\\"230\\\" x2=\\\"67.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"83.0\\\" y1=\\\"230\\\" x2=\\\"83.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"246.0\\\" x2=\\\"99\\\" y2=\\\"246.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"51\\\" y1=\\\"262.0\\\" x2=\\\"99\\\" y2=\\\"262.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><rect y=\\\"220\\\" x=\\\"500\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"90\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text y=\\\"293.8\\\" x=\\\"575.0\\\" dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\">population</text><rect x=\\\"551\\\" y=\\\"230\\\" rx=\\\"4\\\" ry=\\\"4\\\" width=\\\"48\\\" height=\\\"48\\\" style=\\\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\\\" /><line x1=\\\"567.0\\\" y1=\\\"230\\\" x2=\\\"567.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"583.0\\\" y1=\\\"230\\\" x2=\\\"583.0\\\" y2=\\\"278\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"246.0\\\" x2=\\\"599\\\" y2=\\\"246.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"551\\\" y1=\\\"262.0\\\" x2=\\\"599\\\" y2=\\\"262.0\\\" style=\\\"stroke:white;stroke-width:3\\\" /><line x1=\\\"150\\\" y1=\\\"43.0\\\" x2=\\\"573.0\\\" y2=\\\"43.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"41.0\\\" x2=\\\"573.0\\\" y2=\\\"210\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 220 567.0, 210 579.0, 210 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"10.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"30.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >client_id = client_id</tspan><tspan y=\\\"40.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >month_year_datum_transakce &lt;= month_year_datum_transakce</tspan><tspan y=\\\"50.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Horizon: 1.0 days</tspan><tspan y=\\\"60.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Lagged targets allowed</tspan></text><line x1=\\\"150\\\" y1=\\\"153.0\\\" x2=\\\"573.0\\\" y2=\\\"153.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><line x1=\\\"573.0\\\" y1=\\\"151.0\\\" x2=\\\"573.0\\\" y2=\\\"210\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"573.0, 220 567.0, 210 579.0, 210 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"120.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"150.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >client_id = client_id</tspan><tspan y=\\\"160.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >month_year_datum_transakce &lt;= month_year_datum_transakce</tspan></text><line x1=\\\"150\\\" y1=\\\"263.0\\\" x2=\\\"490\\\" y2=\\\"263.0\\\" style=\\\"stroke:#808080;;stroke-width:4\\\" /><polygon points=\\\"500, 263.0 490, 257.0 490, 269.0 \\\" style=\\\"fill:#808080;;stroke-width:0;\\\" /><rect y=\\\"230.0\\\" x=\\\"249.0\\\" rx=\\\"10\\\" ry=\\\"10\\\" width=\\\"150\\\" height=\\\"70\\\" style=\\\"fill:#6829c2;stroke-width:0;\\\" /><text dominant-baseline=\\\"middle\\\" text-anchor=\\\"middle\\\" fill=\\\"white\\\"><tspan y=\\\"260.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >client_id = client_id</tspan><tspan y=\\\"270.0\\\" x=\\\"324.0\\\" font-size=\\\"7pt\\\" >Month/Year &lt;= month_year_datum_transakce</tspan></text></svg></div>\\n\",\n       \"            </div>\\n\",\n       \"\\n\",\n       \"            <div style='margin-top: 15px;'>\\n\",\n       \"            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\\n\",\n       \"            <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">data frames              </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">staging table                   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">population</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">POPULATION__STAGING_TABLE_1</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">dobito</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DOBITO__STAGING_TABLE_2</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">PROBEHNUTO__STAGING_TABLE_3</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>3</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto_mimo_penezenku</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">PROBEHNUTO_MIMO_PENEZENKU__STAGING_TABLE_4</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"            </div>\\n\",\n       \"            \\n\",\n       \"<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\\n\",\n       \"<div style='margin-top: 15px;'>\\n\",\n       \"<div style='float: left; margin-right: 50px;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">subset</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name      </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">rows   </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">unknown</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">View</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"<div style='float: left;'>\\n\",\n       \"<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\\n\",\n       \"    <style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">name                     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\">   rows</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type     </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">1462078</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">dobito</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">554346</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>2</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">probehnuto_mimo_penezenku</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"int\\\">599386</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">DataFrame</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"data model\\n\",\n       \"\\n\",\n       \"  population:\\n\",\n       \"    columns:\\n\",\n       \"    - sluzba: categorical\\n\",\n       \"    - client_id: join_key\\n\",\n       \"    - kc_proklikano: target\\n\",\n       \"    - month_year_datum_transakce: time_stamp\\n\",\n       \"\\n\",\n       \"    joins:\\n\",\n       \"    - right: 'probehnuto'\\n\",\n       \"      on: (population.client_id, probehnuto.client_id)\\n\",\n       \"      time_stamps: (population.month_year_datum_transakce, probehnuto.month_year_datum_transakce)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      horizon: 86400.0\\n\",\n       \"      lagged_targets: True\\n\",\n       \"    - right: 'dobito'\\n\",\n       \"      on: (population.client_id, dobito.client_id)\\n\",\n       \"      time_stamps: (population.month_year_datum_transakce, dobito.month_year_datum_transakce)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      lagged_targets: False\\n\",\n       \"    - right: 'probehnuto_mimo_penezenku'\\n\",\n       \"      on: (population.client_id, probehnuto_mimo_penezenku.client_id)\\n\",\n       \"      time_stamps: (population.month_year_datum_transakce, probehnuto_mimo_penezenku.Month/Year)\\n\",\n       \"      relationship: 'many-to-many'\\n\",\n       \"      lagged_targets: False\\n\",\n       \"\\n\",\n       \"  probehnuto:\\n\",\n       \"    columns:\\n\",\n       \"    - sluzba: categorical\\n\",\n       \"    - client_id: join_key\\n\",\n       \"    - kc_proklikano: target\\n\",\n       \"    - month_year_datum_transakce: time_stamp\\n\",\n       \"\\n\",\n       \"  dobito:\\n\",\n       \"    columns:\\n\",\n       \"    - sluzba: categorical\\n\",\n       \"    - client_id: join_key\\n\",\n       \"    - kc_dobito: numerical\\n\",\n       \"    - month_year_datum_transakce: time_stamp\\n\",\n       \"\\n\",\n       \"  probehnuto_mimo_penezenku:\\n\",\n       \"    columns:\\n\",\n       \"    - client_id: join_key\\n\",\n       \"    - Month/Year: time_stamp\\n\",\n       \"    - probehla_inzerce_mimo_penezenku: unused_string\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"container\\n\",\n       \"\\n\",\n       \"  population\\n\",\n       \"      subset   name         rows      type\\n\",\n       \"  0   test     probehnuto   unknown   View\\n\",\n       \"  1   train    probehnuto   unknown   View\\n\",\n       \"\\n\",\n       \"  peripheral\\n\",\n       \"      name                           rows   type     \\n\",\n       \"  0   probehnuto                  1462078   DataFrame\\n\",\n       \"  1   dobito                       554346   DataFrame\\n\",\n       \"  2   probehnuto_mimo_penezenku    599386   DataFrame\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"star_schema = getml.data.StarSchema(population=probehnuto, alias=\\\"population\\\", split=split)\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    probehnuto,\\n\",\n    \"    on=\\\"client_id\\\",\\n\",\n    \"    time_stamps=\\\"month_year_datum_transakce\\\",\\n\",\n    \"    lagged_targets=True,\\n\",\n    \"    horizon=getml.data.time.days(1),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    dobito,\\n\",\n    \"    on=\\\"client_id\\\",\\n\",\n    \"    time_stamps=\\\"month_year_datum_transakce\\\",\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema.join(\\n\",\n    \"    probehnuto_mimo_penezenku,\\n\",\n    \"    on=\\\"client_id\\\", \\n\",\n    \"    time_stamps=(\\\"month_year_datum_transakce\\\",  \\\"Month/Year\\\"),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"star_schema\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.2 getML pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<!-- #### 2.1.1  -->\\n\",\n    \"__Set-up the feature learner & predictor__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fast_prop = getml.feature_learning.FastProp(\\n\",\n    \"    aggregation=getml.feature_learning.FastProp.agg_sets.All,\\n\",\n    \"    loss_function=getml.feature_learning.loss_functions.SquareLoss,\\n\",\n    \"    num_threads=1,    \\n\",\n    \"    sampling_factor=0.1,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"feature_selector = getml.predictors.XGBoostRegressor(n_jobs=1, external_memory=True)\\n\",\n    \"\\n\",\n    \"predictor = getml.predictors.XGBoostRegressor(n_jobs=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Build the pipeline__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=['XGBoostRegressor'],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=['XGBoostRegressor'],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop'])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1 = getml.Pipeline(\\n\",\n    \"    tags=['fast_prop'],\\n\",\n    \"    data_model=star_schema.data_model,\\n\",\n    \"    feature_learners=[fast_prop],\\n\",\n    \"    feature_selectors=[feature_selector],\\n\",\n    \"    predictors=[predictor],\\n\",\n    \"    include_categorical=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"pipe1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.3 Model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">type</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">label                 </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">message                         </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">INFO</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and DOBITO__STAGING_TABLE_2 over &#x27;client_id&#x27; and &#x27;client_id&#x27;, there are no corresponding entries for 2.228789% of entries in &#x27;client_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">INFO</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">FOREIGN KEYS NOT FOUND</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">When joining POPULATION__STAGING_TABLE_1 and PROBEHNUTO_MIMO_PENEZENKU__STAGING_TABLE_4 over &#x27;client_id&#x27; and &#x27;client_id&#x27;, there are no corresponding entries for 26.543966% of entries in &#x27;client_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    type   label                    message                         \\n\",\n       \"0   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\\n\",\n       \"1   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.check(star_schema.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Checking data model<span style=\\\"color: #808000; text-decoration-color: #808000\\\">...</span>\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Checking data model\\u001b[33m...\\u001b[0m\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">The pipeline check generated <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">2</span> issues labeled INFO and <span style=\\\"color: #008080; text-decoration-color: #008080; font-weight: bold\\\">0</span> issues labeled WARNING.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"The pipeline check generated \\u001b[1;36m2\\u001b[0m issues labeled INFO and \\u001b[1;36m0\\u001b[0m issues labeled WARNING.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">To see the issues in full, run <span style=\\\"color: #800080; text-decoration-color: #800080; font-weight: bold\\\">.check</span><span style=\\\"font-weight: bold\\\">()</span> on the pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"To see the issues in full, run \\u001b[1;35m.check\\u001b[0m\\u001b[1m(\\u001b[0m\\u001b[1m)\\u001b[0m on the pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Trying 721 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:39\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 01:28\\n\",\n      \"\\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 16:04\\n\",\n      \"\\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 09:06\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">Trained pipeline.\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"Trained pipeline.\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time taken: 0:27:20.028950.\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre>Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=['XGBoostRegressor'],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop', 'container-mlm7a1'])</pre>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(data_model='population',\\n\",\n       \"         feature_learners=['FastProp'],\\n\",\n       \"         feature_selectors=['XGBoostRegressor'],\\n\",\n       \"         include_categorical=True,\\n\",\n       \"         loss_function='SquareLoss',\\n\",\n       \"         peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\\n\",\n       \"         predictors=['XGBoostRegressor'],\\n\",\n       \"         preprocessors=[],\\n\",\n       \"         share_selected_features=0.5,\\n\",\n       \"         tags=['fast_prop', 'container-mlm7a1'])\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.fit(star_schema.train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.4 Model evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\\n\",\n      \"\\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:20\\n\",\n      \"\\u001b[?25h\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>\\n\",\n       \"  th {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  td {\\n\",\n       \"    text-align: left !important;\\n\",\n       \"  }\\n\",\n       \"  th:nth-child(1) {\\n\",\n       \"    text-align: right;\\n\",\n       \"    border-right: 1px solid LightGray;\\n\",\n       \"  }\\n\",\n       \"  th.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.float {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  th.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"  td.int {\\n\",\n       \"    text-align: right !important;\\n\",\n       \"  }\\n\",\n       \"</style>\\n\",\n       \"\\n\",\n       \"<table class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"int\\\"> </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"datetime\\\">date time          </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">set used</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"str\\\">target       </th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">       mae</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">       rmse</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"        \\n\",\n       \"          <th class=\\\"float\\\">rsquared</th>\\n\",\n       \"        \\n\",\n       \"      \\n\",\n       \"    </tr>\\n\",\n       \"    \\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>0</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 13:05:20</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">train</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">kc_proklikano</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">2922.93</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">14339.2964</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.9427</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"      <tr>\\n\",\n       \"        <th>1</th>\\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"datetime\\\">2024-09-12 13:05:41</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">test</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"str\\\">kc_proklikano</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">2981.8108</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">18545.8816</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"            \\n\",\n       \"              <td class=\\\"float\\\">0.8766</td>\\n\",\n       \"            \\n\",\n       \"          \\n\",\n       \"      </tr>\\n\",\n       \"    \\n\",\n       \"  </tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"    date time             set used   target                 mae          rmse   rsquared\\n\",\n       \"0   2024-09-12 13:05:20   train      kc_proklikano    2922.93      14339.2964     0.9427\\n\",\n       \"1   2024-09-12 13:05:41   test       kc_proklikano    2981.8108    18545.8816     0.8766\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.score(star_schema.test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.5 Features\\n\",\n    \"\\n\",\n    \"The most important feature looks as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/markdown\": [\n       \"```sql\\n\",\n       \"DROP TABLE IF EXISTS \\\"FEATURE_1_38\\\";\\n\",\n       \"\\n\",\n       \"CREATE TABLE \\\"FEATURE_1_38\\\" AS\\n\",\n       \"SELECT EWMA_TREND_1H( t2.\\\"kc_proklikano\\\" ORDER BY t1.\\\"month_year_datum_transakce\\\" - t2.\\\"month_year_datum_transakce, '+1.000000 days'\\\" ) AS \\\"feature_1_38\\\",\\n\",\n       \"       t1.rowid AS rownum\\n\",\n       \"FROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\n\",\n       \"INNER JOIN \\\"PROBEHNUTO__STAGING_TABLE_3\\\" t2\\n\",\n       \"ON t1.\\\"client_id\\\" = t2.\\\"client_id\\\"\\n\",\n       \"WHERE t2.\\\"month_year_datum_transakce, '+1.000000 days'\\\" <= t1.\\\"month_year_datum_transakce\\\"\\n\",\n       \"AND t1.\\\"sluzba\\\" = t2.\\\"sluzba\\\"\\n\",\n       \"GROUP BY t1.rowid;\\n\",\n       \"```\"\n      ],\n      \"text/plain\": [\n       \"'DROP TABLE IF EXISTS \\\"FEATURE_1_38\\\";\\\\n\\\\nCREATE TABLE \\\"FEATURE_1_38\\\" AS\\\\nSELECT EWMA_TREND_1H( t2.\\\"kc_proklikano\\\" ORDER BY t1.\\\"month_year_datum_transakce\\\" - t2.\\\"month_year_datum_transakce, \\\\'+1.000000 days\\\\'\\\" ) AS \\\"feature_1_38\\\",\\\\n       t1.rowid AS rownum\\\\nFROM \\\"POPULATION__STAGING_TABLE_1\\\" t1\\\\nINNER JOIN \\\"PROBEHNUTO__STAGING_TABLE_3\\\" t2\\\\nON t1.\\\"client_id\\\" = t2.\\\"client_id\\\"\\\\nWHERE t2.\\\"month_year_datum_transakce, \\\\'+1.000000 days\\\\'\\\" <= t1.\\\"month_year_datum_transakce\\\"\\\\nAND t1.\\\"sluzba\\\" = t2.\\\"sluzba\\\"\\\\nGROUP BY t1.rowid;'\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe1.features.to_sql()[pipe1.features.sort(by=\\\"importances\\\")[0].name]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"getml.engine.shutdown()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Conclusion\\n\",\n    \"\\n\",\n    \"In this notebook, we successfully demonstrated the process of predicting transaction volume for Seznam, a Czech company similar to Google, using the getML library. The key steps we covered include:\\n\",\n    \"\\n\",\n    \"1. Background and Data Preparation:\\n\",\n    \"\\n\",\n    \"    - Introduced Seznam and the purpose of the analysis.\\n\",\n    \"    - Loaded and prepared the data from Seznam's wallet, including prepayments and charges.\\n\",\n    \"\\n\",\n    \"2. Data Visualization and Preparation:\\n\",\n    \"\\n\",\n    \"    - Translated and understood the main tables in the dataset.\\n\",\n    \"    - Defined roles and units for the dataset columns to prepare them for modeling.\\n\",\n    \"\\n\",\n    \"3. Predictive Modeling:\\n\",\n    \"\\n\",\n    \"    - Created a relational model using getML's `StarSchema` to capture the relationships between different tables.\\n\",\n    \"    - Built a getML pipeline using `FastProp` for feature learning and `XGBoostRegressor` for prediction.\\n\",\n    \"\\n\",\n    \"4. Model Training and Evaluation:\\n\",\n    \"\\n\",\n    \"    - Trained the model on the prepared data.\\n\",\n    \"    - Evaluated the model's performance, achieving an R-squared of 87.01%, RMSE of 14,783, and MAE of 2,974.\\n\",\n    \"\\n\",\n    \"By leveraging getML's capabilities, we efficiently handled the complexities of relational data and built a robust regression model to predict transaction volume. This approach can be extended to other e-commerce datasets and prediction tasks, providing valuable insights and accurate forecasts.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## References\\n\",\n    \"\\n\",\n    \"Motl, Jan, and Oliver Schulte. \\\"The CTU prague relational learning repository.\\\" arXiv preprint arXiv:1511.03086 (2015).\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"encoding\": \"# -*- coding: utf-8 -*-\",\n   \"formats\": \"ipynb,py:percent,md\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.9\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "docker-bake.hcl",
    "content": "variable \"GETML_VERSION\" {\n  default = \"0.0.0\"\n}\n\nvariable \"GETML_BUILD_OUTPUT_DIR\" {\n  default = \"build\"\n}\n\nvariable \"GETML_CMAKE_FRESH_PRESET\" {\n  default = \"false\"\n}\n\ntarget \"cli\" {\n  args = {\n    VERSION=\"${GETML_VERSION}\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"cli\"\n}\n\ntarget \"engine\" {\n  args = {\n    CMAKE_FRESH_PRESET=\"${GETML_CMAKE_FRESH_PRESET}\"\n    ENGINE_REPO_SOURCE=\"src/engine\",\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  dockerfile = \"src/engine/Dockerfile\"\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"package\"\n}\n\ntarget \"package\" {\n  args = {\n    CMAKE_FRESH_PRESET=\"${GETML_CMAKE_FRESH_PRESET}\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  # for naming build contexts, we use the following convention:\n  # <dockerfile-default-context>-<default-foreign-target-stage>\n  # i.e. the context 'engine-package' by default points to the 'package' target\n  # inside the engine dockerfile, the context 'monorepo-export' by default\n  # points to the 'export' target inside the monorepo (root) dockerfile\n  contexts = {\n    engine-package = \"target:engine\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"export\"\n}\n\ntarget \"docker\" {\n  args = {\n    BUILD_OR_COPY_ARTIFACTS=\"build\"\n    CMAKE_FRESH_PRESET=\"${GETML_CMAKE_FRESH_PRESET}\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  contexts = {\n    monorepo-export = \"target:package\"\n  }\n  dockerfile = \"runtime/Dockerfile\"\n  output = [\"type=docker\"]\n  tags = [\"getml/getml:${GETML_VERSION}\", \"getml/getml:latest\"]\n}\n\ntarget \"docker-copy-artifacts\" {\n  args = {\n    BUILD_OR_COPY_ARTIFACTS=\"copy\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  dockerfile = \"runtime/Dockerfile\"\n  output = [\"type=docker\"]\n  tags = [\"getml/getml:${GETML_VERSION}\", \"getml/getml:latest\"]\n}\n\ntarget \"python\" {\n  args = {\n    BUILD_OR_COPY_ARTIFACTS=\"build\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  contexts = {\n    engine-package = \"target:engine\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"python\"\n}\n\ntarget \"python-copy-artifacts\" {\n  args = {\n    BUILD_OR_COPY_ARTIFACTS=\"copy\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"python\"\n}\n\ntarget \"archive-copy-artifacts\" {\n  args = {\n    BUILD_OR_COPY_ARTIFACTS=\"copy\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"archive\"\n}\n\ntarget \"archive\" {\n  args = {\n    CMAKE_FRESH_PRESET=\"${GETML_CMAKE_FRESH_PRESET}\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  contexts = {\n    engine-package = \"target:engine\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"archive\"\n}\n\ntarget \"all\" {\n  args = {\n    CMAKE_FRESH_PRESET=\"${GETML_CMAKE_FRESH_PRESET}\"\n    OUTPUT_DIR=\"${GETML_BUILD_OUTPUT_DIR}\"\n    VERSION=\"${GETML_VERSION}\"\n  }\n  contexts = {\n    engine-package = \"target:engine\"\n  }\n  output = [\"type=local,dest=${GETML_BUILD_OUTPUT_DIR},platform-split=false\"]\n  target = \"all\"\n}\n"
  },
  {
    "path": "mise.toml",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n[env]\n_.path = [\n    \"scripts\"\n]\n_.python.venv = \"src/python-api/.venv\"\n\nUV_PROJECT = \"{{ config_root }}/src/python-api\"\n\n[tools]\n\"pipx:hatch\" = \"latest\"\nuv = \"latest\"\n"
  },
  {
    "path": "runtime/Dockerfile",
    "content": "ARG VERSION=\"0.0.0\"\nARG PACKAGE_NAME=\"getml-community-$VERSION-$TARGETARCH-linux\"\nARG OUTPUT_DIR=\"build\"\nARG BUILD_OR_COPY_ARTIFACTS=\"build\"\n\nFROM debian:bookworm-slim AS runtime-base\nARG PACKAGE_NAME\nARG OUTPUT_DIR\nARG PORT_MIN=1707\nARG PORT_MAX=1733\nENV PORT_MIN=$PORT_MIN\nENV PORT_MAX=$PORT_MAX\n\nRUN apt-get update && apt-get install -y socat\n\nWORKDIR /usr/local/getML\n\nFROM runtime-base AS runtime-build-artifacts\nCOPY --from=monorepo-export /$PACKAGE_NAME ./package\n\nFROM runtime-base AS runtime-copy-artifacts\nCOPY $OUTPUT_DIR/$PACKAGE_NAME ./package\n\nFROM runtime-${BUILD_OR_COPY_ARTIFACTS}-artifacts AS runtime-install\nRUN ./package/getML install\n\nCOPY runtime/entrypoint.sh .\n\nWORKDIR /home/getml\n\nENV GETML_ALLOW_PUSH_NOTIFICATIONS=false\nENV GETML_ALLOW_REMOTE_IPS=false\nENV GETML_HOME_DIRECTORY=/home/getml\nENV GETML_HTTP_PORT=1711\nENV GETML_IN_MEMORY=true\nENV GETML_INSTALL=false\nENV GETML_LAUNCH_BROWSER=false\nENV GETML_LOG=false\nENV GETML_PROJECT_DIRECTORY=/home/getml/projects\n\nRUN useradd getml\nRUN chown -R getml:getml /home/getml\nRUN chown -R getml:getml /usr/local/getML\n\nUSER getml\n\nENTRYPOINT [\"/usr/local/getML/entrypoint.sh\"]\n"
  },
  {
    "path": "runtime/README.md",
    "content": "# getML docker runtime\n## Multiplatform builds\n> [!IMPORTANT]\n> Until we have native cross compilation for the engine, it is too costly to  build for the foreign platform through emulation. Therefore, we have introduced `...copy-artifacts` variations for `python`, `docker` and `archive` targets which effectively just look for [build artifacts](https://www.notion.so/5cfaed1347af454d83261e4ea772b303?pvs=21) inside `GETML_BUILD_OUTPUT_DIR` (default `build`) and copy them over. So you have to ensure that an up-to-date version of all build artifacts is present inside `build` . Kindly ask someone on the team to provide artifacts for the foreign platform.\n> \n> If everything is present, `build`  should have roughly the following structure:\n> \n> ```bash\n> build/\n> ├── getml-community-<version>-amd64-linux/\n> │   ├── bin/\n> │   ├── lib/\n> │   └── config.json\n> └── getml-community-<version>-arm64-linux/\n>     ├── bin/\n>     ├── lib/\n>     └── config.json\n> ```\n> \n> You can call `ls -lah build` to check the timestamps of artifacts.\n> If you want to build for a single platform, you can override the target's `platform` setting: `./scripts/getml build runtime -p <linux/arm64 linux/amd64>`\n\nAs the docker runtime is multiplatform. You need [a docker engine with multiplatform build support enabled](https://docs.docker.com/build/building/multi-platform/).\n\nThe recommended way is [to use the `containerd` image store](https://docs.docker.com/build/building/multi-platform/#enable-the-containerd-image-store), which supports multiplatform images out of the box.\n\nTo enable the `containerd` image store, add the following to your `/etc/docker/daemon.json`:\n```json\n{\n  \"features\": {\n    \"containerd-snapshotter\": true\n  }\n}\n```\n\n\nAfter changing the configuration, restart the docker daemon:\n```bash\nsudo systemctl restart docker\n```\n\nIf you are using the Docker Engine (not Docker Desktop), also make sure that you have [setup QEMU](https://docs.docker.com/build/building/multi-platform/#qemu) for emulating the foreign platform.\n\n## Building image\n```bash\n../scripts/getml build docker-copy-artifacts -p linux/arm64,linux/amd64\n```\n\n## Running image\n```bash\ndocker run -it getml/getml -p 1708-1733:11708-11733\n```\n\n## Publishing (pushing) the image\n```bash\n../scripts/getml publish docker\n```\n"
  },
  {
    "path": "runtime/docker-compose.yml",
    "content": "services:\n  getml:\n    image: getml/getml\n    environment:\n      GETML_ALLOW_PUSH_NOTIFICATIONS: false\n      GETML_ALLOW_REMOTE_IPS: false\n      GETML_HOME_DIRECTORY: /home/getml\n      GETML_HTTP_PORT: 1711\n      GETML_IN_MEMORY: true\n      GETML_INSTALL: false\n      GETML_LAUNCH_BROWSER: false\n      GETML_LOG: false\n      GETML_PROJECT_DIRECTORY: /home/getml/projects\n      GETML_PROXY_URL: \"\"\n      GETML_TOKEN: \"\"\n    ports:\n      - \"1708-1733:11708-11733\"\n    volumes:\n      - getml:/home/getml/\nvolumes:\n  getml:\n    external: false\n"
  },
  {
    "path": "runtime/entrypoint.sh",
    "content": "#!/usr/bin/env bash\n\nfor port in $(seq \"$PORT_MIN\" \"$PORT_MAX\"); do\n    socat tcp-l:$((port + 10000)),fork,reuseaddr tcp:127.0.0.1:$port &\ndone\n\nif [ -z \"$1\" ]; then\n    /usr/local/bin/getML \\\n        -launch-browser=${GETML_LAUNCH_BROWSER} \\\n        -home-directory=${GETML_HOME_DIRECTORY} \\\n        -http-port=${GETML_HTTP_PORT} \\\n        -in-memory=${GETML_IN_MEMORY} \\\n        -install=${GETML_INSTALL} \\\n        -log=${GETML_LOG} \\\n        -project-directory=${GETML_PROJECT_DIRECTORY} \\\n        -proxy-url=${GETML_PROXY_URL} \\\n        -token=${GETML_TOKEN}\nelse\n    exec \"$@\"\nfi\n"
  },
  {
    "path": "scripts/build",
    "content": "#!/usr/bin/env bash\n\nSCRIPTS_DIR=$(dirname -- \"$(realpath -- \"$0\")\")\nPLATFORM=${GETML_BUILD_PLATFORM}\n\nparse_opt_multiple_args() {\n  while [[ ${OPTIND} -le $# && ${!OPTIND:0:1} != \"-\" ]]; do\n    OPTARG[i]=${!OPTIND}\n    ((OPTIND++))\n    ((i++))\n  done\n}\n\nprocess_build_args() {\n  processed=\"\"\n  for arg in \"$@\"; do\n    processed+=\" --set $target.args.${arg}\"\n  done\n  build_args=$processed\n}\n\nshow_help() {\n  cat <<-EOF\n\t\tBuild utilities\n\n\t\tUsage:\n\t\t  build <subcommand> [options]\n\n\t\tSubcommands:\n\t\t  [a]ll       Build all (whole package, [p]ackage + [py]thon API + tar+gz [ar]chive)\n\t\t  [ar]chive   Create tar.gz archive of [p]ackage\n\t\t  [c]li       Build cli\n\t\t  [d]docker   Build Docker runtime\n\t\t  [e]ngine    Build Engine\n\t\t  [p]ackage   Export runable [e]ngine + [c]li package\n\t\t  [py]thon    Package Python API\n\n\t\tOptions:\n\t\t  -b <args>   Specify build args (-b KEY=VALUE); passed to docker build\n\t\t  -h          Show help (this message)\n\t\t  -o <path>   Set output path (default: build); passed to docker build\n\tEOF\n}\n\nprocess_opts() {\n  while getopts \":b:ho:p:\" opt; do\n    case ${opt} in\n    b)\n      parse_opt_multiple_args \"$@\"\n      process_build_args \"${OPTARG[@]}\"\n      ;;\n    h)\n      show_help\n      exit 0\n      ;;\n    o)\n      output=\"$OPTARG\"\n      ;;\n    p)\n      PLATFORM=\"$OPTARG\"\n      ;;\n    \\?)\n      echo \"Invalid option: -$OPTARG\" >&2\n      exit 1\n      ;;\n    esac\n  done\n}\n\nif [[ -z $1 || $1 = \"-h\" ]]; then\n  show_help\n  exit 0\nfi\n\nwhile [[ \"$#\" -gt 0 ]]; do\n  case \"$1\" in\n  a | all)\n    target=\"all\"\n    shift\n    ;;\n  ar | archive)\n    target=\"archive\"\n    shift\n    ;;\n  archive-copy-artifacts)\n    target=\"archive-copy-artifacts\"\n    shift\n    ;;\n  c | cli)\n    target=\"cli\"\n    shift\n    ;;\n  d | docker)\n    target=\"docker\"\n    shift\n    ;;\n  docker-copy-artifacts)\n    target=\"docker-copy-artifacts\"\n    shift\n    ;;\n  e | engine)\n    target=\"engine\"\n    shift\n    ;;\n  p | package)\n    target=\"package\"\n    shift\n    ;;\n  py | python)\n    target=\"python\"\n    shift\n    ;;\n  python-copy-artifacts)\n    target=\"python-copy-artifacts\"\n    shift\n    ;;\n  -*)\n    process_opts \"$@\"\n    if [[ $? -eq 0 ]]; then\n      shift $((OPTIND - 1))\n      break\n    else\n      exit 1\n    fi\n    ;;\n  *)\n    echo \"Unrecognized subcommmand: $1\" >&2\n    exit 1\n    ;;\n  esac\ndone\n\n(\n  export GETML_VERSION=${GETML_VERSION:-$(<\"$SCRIPTS_DIR/../VERSION\")}\n  export GETML_CMAKE_FRESH_PRESET=${GETML_CMAKE_FRESH_PRESET:-false}\n\n  if [[ -n $PLATFORM ]]; then\n    build_args+=\" --set $target.platform=$PLATFORM\"\n  fi\n\n  cd \"${SCRIPTS_DIR}/..\"\n  docker buildx bake $target \\\n    $build_args \\\n    \"${@}\"\n)\n"
  },
  {
    "path": "src/getml-app/README.md",
    "content": "# getml-app\n\nThe getML App is used by the Windows and Linux version as the main entry point.\n\n(The macOS Version uses a Swift implementation instead.)\n"
  },
  {
    "path": "src/getml-app/format.sh",
    "content": "#!/bin/bash\ngo fmt getML config install\n"
  },
  {
    "path": "src/getml-app/setup.sh",
    "content": "#!/bin/bash\n\ncd src \n\ngo build\n"
  },
  {
    "path": "src/getml-app/src/changeToResourceDir.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nimport (\n\t\"getML/install\"\n\t\"os\"\n\t\"path\"\n)\n\n// Changes to the path containing the resources,\n// such as config.json\nfunc changeToResourceDir(packageName string) {\n\n\texecPath, err := os.Executable()\n\n\tif err != nil {\n\t\treturn\n\t}\n\n\texecDir := path.Dir(execPath)\n\n\tif execDir == install.UsrLocalBin {\n\t\tos.Chdir(install.GetMainDir(install.UsrLocal, packageName))\n\t\treturn\n\t}\n\n\tos.Chdir(execDir)\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/Parse.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage commands\n\nimport (\n\t\"getML/config\"\n\t\"getML/install\"\n\t\"os\"\n\t\"path/filepath\"\n)\n\n// Parse parses the command line arguments\nfunc Parse(packageName string) (*config.CommandLine, config.Config) {\n\n\t// TODO: ParseHomeDir in DefaultCommandLine\n\tcommandLine := config.DefaultCommandLine()\n\n\tcommandLine.ForceInstall = !install.FileExists(\n\t\tinstall.GetConfigPath(commandLine.HomeDir, packageName))\n\n\tconf := loadConfig(commandLine.ForceInstall, commandLine.HomeDir, packageName)\n\n\tif !filepath.IsAbs(conf.ProjectDirectory) {\n\t\tconf.ProjectDirectory = filepath.Join(\n\t\t\tinstall.GetBinDir(commandLine.HomeDir, packageName), \"..\", conf.ProjectDirectory)\n\t}\n\n\trunCmd := makeRunCommand(packageName, &conf, commandLine)\n\n\tinstallCmd := makeInstallCommand(commandLine)\n\n\tstopCmd := makeStopCommand(&conf)\n\n\tuninstallCmd := makeUninstallCommand(commandLine)\n\n\tif len(os.Args) == 1 {\n\t\trunCmd.Parse(os.Args[1:])\n\t\treturn commandLine, conf\n\t}\n\n\tswitch os.Args[1] {\n\n\tcase \"install\":\n\t\tcommandLine.ForceInstall = true\n\t\tcommandLine.InstallOnly = true\n\t\tinstallCmd.Parse(os.Args[2:])\n\n\tcase \"run\":\n\t\trunCmd.Parse(os.Args[2:])\n\n\tcase \"stop\":\n\t\tcommandLine.Stop = true\n\t\tstopCmd.Parse(os.Args[2:])\n\n\tcase \"uninstall\":\n\t\tcommandLine.Uninstall = true\n\t\tuninstallCmd.Parse(os.Args[2:])\n\n\tcase \"version\":\n\t\tcommandLine.ShowVersionOnly = true\n\n\tdefault:\n\t\tisFlag := printHelpMenu(packageName, runCmd)\n\n\t\tif isFlag {\n\t\t\treturn commandLine, conf\n\t\t}\n\n\t\treturn nil, conf\n\t}\n\n\treturn commandLine, conf\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/addHomeDirFlag.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"getML/config\"\n\t\"getML/install\"\n)\n\nfunc addHomeDirFlag(cmd *flag.FlagSet, commandLine *config.CommandLine) {\n\tcmd.StringVar(\n\t\t&commandLine.HomeDir,\n\t\t\"home-directory\",\n\t\tinstall.GetHomeDir(\"\"),\n\t\t\"The directory which should be treated as the home directory by getML. \"+\n\t\t\t\"getML will create a hidden folder named '.getML' in said directory. \"+\n\t\t\t\"This is where the binaries are installed, if \"+\n\t\t\t\"the install process does not have root rights.\")\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/loadConfig.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage commands\n\nimport (\n\t\"getML/config\"\n\t\"getML/install\"\n)\n\nfunc loadConfig(forceInstall bool, homeDir string, packageName string) config.Config {\n\n\tfname := \"./config.json\"\n\n\tif !forceInstall {\n\t\tfname = install.GetConfigPath(homeDir, packageName)\n\t}\n\n\treturn config.Load(fname)\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/makeInstallCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"getML/config\"\n)\n\nfunc makeInstallCommand(commandLine *config.CommandLine) *flag.FlagSet {\n\n\tcmd := flag.NewFlagSet(\"install\", flag.ExitOnError)\n\n\taddHomeDirFlag(cmd, commandLine)\n\n\treturn cmd\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/makeRunCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"getML/config\"\n)\n\nfunc makeRunCommand(\n\tversion string,\n\tconf *config.Config,\n\tcommandLine *config.CommandLine) *flag.FlagSet {\n\n\t// ------------------------------------------------------------------------\n\n\tcmd := flag.NewFlagSet(\"run\", flag.ExitOnError)\n\n\t// ------------------------------------------------------------------------\n\n\taddHomeDirFlag(cmd, commandLine)\n\n\tcmd.StringVar(\n\t\t&conf.ProjectDirectory,\n\t\t\"project-directory\",\n\t\tconf.ProjectDirectory,\n\t\t\"The directory in which to store all of your projects.\")\n\n\tcmd.BoolVar(\n\t\t&conf.InMemory,\n\t\t\"in-memory\",\n\t\tconf.InMemory,\n\t\t\"Whether you want the Engine to process everything in memory.\")\n\n\tcmd.BoolVar(\n\t\t&conf.Monitor.AllowRemoteIPs,\n\t\t\"allow-remote-ips\",\n\t\tconf.Monitor.AllowRemoteIPs,\n\t\t\"Whether you want to allow remote IPs to access the\"+\n\t\t\t\" http-port.\")\n\n\tcmd.IntVar(\n\t\t&conf.Monitor.HTTPPort,\n\t\t\"http-port\",\n\t\tconf.Monitor.HTTPPort,\n\t\t\"The local port of the getML Monitor.\"+\n\t\t\t\" This port can only be accessed from your local computer,\"+\n\t\t\t\" unless you set allow-remote-ips=True.\")\n\n\tcmd.BoolVar(\n\t\t&conf.Monitor.Log,\n\t\t\"log\",\n\t\tconf.Monitor.Log,\n\t\t\"Whether you want the Engine log to appear in the command line.\"+\n\t\t\t\" The Engine log also appears in the 'Log' page of the Monitor.\")\n\n\tcmd.IntVar(\n\t\t&conf.Monitor.TCPPort,\n\t\t\"tcp-port\",\n\t\tconf.Monitor.TCPPort,\n\t\t\"Local TCP port which serves as the communication point for the engine.\"+\n\t\t\t\" This port can only be accessed from your local computer.\")\n\n\tcmd.StringVar(\n\t\t&conf.Monitor.ProxyURL,\n\t\t\"proxy-url\",\n\t\tconf.Monitor.ProxyURL,\n\t\t\"The URL of any proxy server that that redirects to the getML Monitor.\")\n\n\tcmd.StringVar(\n\t\t&conf.Monitor.Token,\n\t\t\"token\",\n\t\t\"\",\n\t\t\"The token used for authentication. Authentication is required when remote IPs are \"+\n\t\t\t\"allowed to access the Monitor. If authentication is required and no token is passed, \"+\n\t\t\t\"a random hexcode will be generated as the token.\")\n\n\tcmd.BoolVar(\n\t\t&conf.Monitor.AllowPushNotifications,\n\t\t\"allow-push-notifications\",\n\t\tconf.Monitor.AllowPushNotifications,\n\t\t\"Whether you want to allow the getML Monitor to send push notifications to your desktop.\")\n\n\tcmd.BoolVar(\n\t\t&conf.Monitor.LaunchBrowser,\n\t\t\"launch-browser\",\n\t\tconf.Monitor.LaunchBrowser,\n\t\t\"Whether you want to automatically launch your browser.\")\n\n\tcmd.BoolVar(\n\t\t&commandLine.ForceInstall,\n\t\t\"install\",\n\t\tcommandLine.ForceInstall,\n\t\t\"Installs \"+version+\", even if it is already installed.\")\n\n\treturn cmd\n\n\t// ------------------------------------------------------------------------\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/makeStopCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"getML/config\"\n)\n\nfunc makeStopCommand(conf *config.Config) *flag.FlagSet {\n\n\tcmd := flag.NewFlagSet(\"stop\", flag.ExitOnError)\n\n\tcmd.IntVar(\n\t\t&conf.Monitor.TCPPort,\n\t\t\"tcp-port\",\n\t\tconf.Monitor.TCPPort,\n\t\t\"The TCP port of the getML instance you would like to stop.\")\n\n\treturn cmd\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/makeUninstallCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"getML/config\"\n)\n\nfunc makeUninstallCommand(commandLine *config.CommandLine) *flag.FlagSet {\n\n\tcmd := flag.NewFlagSet(\"uninstall\", flag.ExitOnError)\n\n\taddHomeDirFlag(cmd, commandLine)\n\n\treturn cmd\n\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/parseHomeDirFlag.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage commands\n\nimport (\n\t\"os\"\n\t\"strings\"\n)\n\nfunc parseHomeDirFlag() string {\n\n\tisProperDirectory := func(homeDir string) bool {\n\n\t\tinfo, err := os.Stat(homeDir)\n\n\t\tif os.IsNotExist(err) {\n\t\t\treturn false\n\t\t}\n\n\t\tif !info.IsDir() {\n\t\t\treturn false\n\t\t}\n\n\t\treturn true\n\n\t}\n\n\tfor i := 0; i < len(os.Args); i++ {\n\n\t\targ := os.Args[i]\n\n\t\tif !strings.Contains(arg, \"-home-directory\") {\n\t\t\tcontinue\n\t\t}\n\n\t\tif strings.Contains(arg, \"-home-directory=\") {\n\t\t\thomeDir := strings.Split(arg, \"-home-directory=\")[1]\n\t\t\tif isProperDirectory(homeDir) {\n\t\t\t\treturn homeDir\n\t\t\t}\n\t\t\treturn \"\"\n\t\t}\n\n\t\tif i+1 == len(os.Args) {\n\t\t\treturn \"\"\n\t\t}\n\n\t\thomeDir := os.Args[i+1]\n\n\t\tif isProperDirectory(homeDir) {\n\t\t\treturn homeDir\n\t\t}\n\n\t\treturn \"\"\n\t}\n\n\treturn \"\"\n}\n"
  },
  {
    "path": "src/getml-app/src/commands/printHelpMenu.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage commands\n\nimport (\n\t\"flag\"\n\t\"fmt\"\n\t\"os\"\n)\n\nfunc printHelpMenu(packageName string, runCmd *flag.FlagSet) bool {\n\n\tisFlag := (len(os.Args[1]) > 0 && os.Args[1][0] == '-')\n\n\tif !isFlag {\n\t\tfmt.Println(\"usage: ./getML <command> [<args>] or ./getML [<args>].\")\n\t\tfmt.Println(\"\")\n\t\tfmt.Println(\"Possible commands are: \")\n\t\tfmt.Println(` run        Runs getML. Type \"./getML -h\" or \"./getML run -h\" to display the arguments. \"run\" is executed by default.`)\n\t\tfmt.Println(` install    Installs getML.`)\n\t\tfmt.Println(` stop       Stops a running instance of getML. Type \"./getML stop -h\" to display the arguments.`)\n\t\tfmt.Println(` uninstall  Uninstalls ` + packageName + `.`)\n\t\tfmt.Println(` version    Prints the version (` + packageName + `).`)\n\t\tfmt.Println(\"\")\n\t}\n\n\trunCmd.Parse(os.Args[1:])\n\n\treturn isFlag\n}\n"
  },
  {
    "path": "src/getml-app/src/config/CommandLine.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage config\n\n// CommandLine contains all the configurations\n// that can only be passed via the command line,\n// but not via config.json.\ntype CommandLine struct {\n\tForceInstall bool\n\n\tHomeDir string\n\n\tInstallOnly bool\n\n\tShowVersionOnly bool\n\n\tStop bool\n\n\tUninstall bool\n}\n\n// DefaultCommandLine is a constructor\nfunc DefaultCommandLine() *CommandLine {\n\treturn &CommandLine{\n\t\tForceInstall:    false,\n\t\tHomeDir:         parseHomeDirFlag(),\n\t\tInstallOnly:     false,\n\t\tShowVersionOnly: false,\n\t\tStop:            false,\n\t\tUninstall:       false,\n\t}\n\n}\n"
  },
  {
    "path": "src/getml-app/src/config/Config.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage config\n\nimport (\n\t\"encoding/json\"\n\t\"errors\"\n\t\"io/ioutil\"\n\t\"os\"\n\t\"strconv\"\n\t\"strings\"\n)\n\n// --------------------------------------------------------------------\n\n// Config contains all variables that CAN be altered by the user.\ntype Config struct {\n\tFname string `json:\"-\"`\n\n\tInMemory bool `json:\"inMemory\"`\n\n\tMonitor MonitorConfig `json:\"monitor\"`\n\n\tProjectDirectory string `json:\"projectDirectory\"`\n}\n\n// --------------------------------------------------------------------\n\n// GetFromFile reads the Config struct from a file.\nfunc (c *Config) GetFromFile(fname string) {\n\n\tconfigStr, err := ioutil.ReadFile(fname)\n\n\tc.Fname = fname\n\n\tif err != nil {\n\t\tconfigStr, err = ioutil.ReadFile(\"../config.json\")\n\t\tc.Fname = \"../config.json\"\n\t}\n\n\tif err != nil {\n\t\tpanic(errors.New(\"config.json not found\"))\n\t}\n\n\tdecoder := json.NewDecoder(strings.NewReader(string(configStr)))\n\n\terr = decoder.Decode(c)\n\n\tif err != nil {\n\t\tpanic(err)\n\t}\n\n}\n\n// --------------------------------------------------------------------\n\n// ToFlags expresses the configurations as command line flags\n// that can be passed to subprocesses.\nfunc (c *Config) ToFlags() []string {\n\n\tflags := []string{}\n\n\tflags = append(flags, \"-in-memory=\"+strconv.FormatBool(c.InMemory))\n\n\tflags = append(flags, \"-project-directory=\"+c.ProjectDirectory)\n\n\tflags = append(flags, \"-allow-remote-ips=\"+strconv.FormatBool(c.Monitor.AllowRemoteIPs))\n\n\tflags = append(flags, \"-http-port=\"+strconv.Itoa(c.Monitor.HTTPPort))\n\n\tflags = append(flags, \"-log=\"+strconv.FormatBool(c.Monitor.Log))\n\n\tflags = append(flags, \"-tcp-port=\"+strconv.Itoa(c.Monitor.TCPPort))\n\n\tflags = append(flags, \"-proxy-url=\"+c.Monitor.ProxyURL)\n\n\tflags = append(flags, \"-allow-push-notifications=\"+strconv.FormatBool(c.Monitor.AllowPushNotifications))\n\n\tflags = append(flags, \"-launch-browser=\"+strconv.FormatBool(c.Monitor.LaunchBrowser))\n\n\tif c.Monitor.Token != \"\" {\n\t\tflags = append(flags, \"-token=\"+c.Monitor.Token)\n\t}\n\n\treturn flags\n}\n\n// --------------------------------------------------------------------\n\n// WriteToFile writes the Config struct to config.json\nfunc (c *Config) WriteToFile() error {\n\n\tdata, err := json.MarshalIndent(*c, \"\", \"    \")\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tf, err := os.OpenFile(c.Fname, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, 0644)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\t// Opening the function did work. Therefore we have to close it\n\t// again.\n\tdefer f.Close()\n\n\t_, err = f.WriteString(string(data))\n\n\treturn err\n\n}\n\n// --------------------------------------------------------------------\n"
  },
  {
    "path": "src/getml-app/src/config/Load.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage config\n\nimport (\n\t\"path/filepath\"\n)\n\n// Load loads the config.json.\nfunc Load(fname string) Config {\n\n\tconf := Config{}\n\n\tfname, err := filepath.Abs(fname)\n\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\n\tconf.GetFromFile(fname)\n\n\treturn conf\n\n}\n"
  },
  {
    "path": "src/getml-app/src/config/MonitorConfig.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage config\n\n// MonitorConfig contains all variables related to the Monitor\n// that CAN be changed by the user.\ntype MonitorConfig struct {\n\tAllowPushNotifications bool `json:\"allowPushNotifications\"`\n\n\tAllowRemoteIPs bool `json:\"allowRemoteIPs\"`\n\n\tLaunchBrowser bool `json:\"launchBrowser\"`\n\n\tHTTPPort int `json:\"httpPort\"`\n\n\t// TODO: Uncomment once we have a proper login.\n\t// HTTPSPort int `json:\"httpsPort\"`\n\n\tLog bool `json:\"log\"`\n\n\tProxyURL string `json:\"proxyUrl\"`\n\n\tTCPPort int `json:\"tcpPort\"`\n\n\tToken string `json:\"-\"`\n}\n"
  },
  {
    "path": "src/getml-app/src/config/parseHomeDirFlag.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage config\n\nimport (\n\t\"os\"\n\t\"strings\"\n)\n\nfunc parseHomeDirFlag() string {\n\n\tisProperDirectory := func(homeDir string) bool {\n\n\t\tinfo, err := os.Stat(homeDir)\n\n\t\tif os.IsNotExist(err) {\n\t\t\treturn false\n\t\t}\n\n\t\tif !info.IsDir() {\n\t\t\treturn false\n\t\t}\n\n\t\treturn true\n\n\t}\n\n\tfor i := 0; i < len(os.Args); i++ {\n\n\t\targ := os.Args[i]\n\n\t\tif !strings.Contains(arg, \"-home-directory\") {\n\t\t\tcontinue\n\t\t}\n\n\t\tif strings.Contains(arg, \"-home-directory=\") {\n\t\t\thomeDir := strings.Split(arg, \"-home-directory=\")[1]\n\t\t\tif isProperDirectory(homeDir) {\n\t\t\t\treturn homeDir\n\t\t\t}\n\t\t\treturn \"\"\n\t\t}\n\n\t\tif i+1 == len(os.Args) {\n\t\t\treturn \"\"\n\t\t}\n\n\t\thomeDir := os.Args[i+1]\n\n\t\tif isProperDirectory(homeDir) {\n\t\t\treturn homeDir\n\t\t}\n\n\t\treturn \"\"\n\t}\n\n\treturn \"\"\n}\n"
  },
  {
    "path": "src/getml-app/src/createConnectionToEngine.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nimport (\n\t\"net\"\n\t\"strconv\"\n\t\"time\"\n)\n\nfunc createConnectionToEngine(tcpPort int) (*net.TCPConn, error) {\n\n\ttcpAddr, err := net.ResolveTCPAddr(\"tcp4\", \"localhost:\"+strconv.Itoa(tcpPort))\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tfor i := 0; i < 5; i++ {\n\n\t\tconn, err := net.DialTCP(\"tcp\", nil, tcpAddr)\n\n\t\tif err == nil {\n\t\t\treturn conn, nil\n\t\t} else if i == 4 {\n\t\t\treturn nil, err\n\t\t}\n\n\t\ttime.Sleep(time.Second)\n\n\t}\n\n\treturn nil, err\n\n}\n"
  },
  {
    "path": "src/getml-app/src/data/Projects.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage data\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"getML/config\"\n\t\"log\"\n\t\"math/rand\"\n\t\"os\"\n\t\"os/exec\"\n\t\"path/filepath\"\n\t\"sync\"\n\t\"syscall\"\n\t\"time\"\n)\n\n// ----------------------------------------------------\n\n// Projects is a handler for all projects.\ntype Projects struct {\n\tConfig *config.Config\n\n\t// Random number drawn at login and used to identify the usage of\n\t// the software by the user.\n\tLicenseSeedDynamic string\n\n\tLock *sync.RWMutex\n\n\tstdVarsMap *map[string]StandardVariables\n\n\tVersionNumber string\n}\n\n// ----------------------------------------------------\n\n// NewProjects is a constructor.\nfunc NewProjects(\n\tconfig *config.Config,\n\tversionNumber string) Projects {\n\n\trand.Seed(time.Now().Unix())\n\n\tlicenseSeedDynamic := fmt.Sprintf(\"%.10f\", rand.Float64())\n\n\tstdVarsMap := make(map[string]StandardVariables)\n\n\treturn Projects{\n\t\tConfig:             config,\n\t\tLicenseSeedDynamic: licenseSeedDynamic,\n\t\tLock:               &sync.RWMutex{},\n\t\tstdVarsMap:         &stdVarsMap,\n\t\tVersionNumber:      versionNumber,\n\t}\n\n}\n\n// --------------------------------------------------------\n\n// Add adds a new project.\nfunc (p *Projects) Add(project string, cmd *exec.Cmd, port int) {\n\n\tp.Lock.Lock()\n\n\tdefer p.Lock.Unlock()\n\n\t(*p.stdVarsMap)[project] = NewStandardVariables(\n\t\tcmd,\n\t\tproject,\n\t\tp.Config,\n\t\tp.LicenseSeedDynamic,\n\t\tport)\n\n\tlog.Print(\"Launched project '\"+project+\"'. Port: \", port, \", PID: \", cmd.Process.Pid, \".\")\n\n}\n\n// --------------------------------------------------------\n\n// GetStandardVars returns the standard variables related to the\n// project.\nfunc (p Projects) GetStandardVars(project string) (*StandardVariables, error) {\n\tp.Lock.RLock()\n\n\tdefer p.Lock.RUnlock()\n\n\tstdVars, found := (*p.stdVarsMap)[project]\n\n\tif !found {\n\t\treturn nil, errors.New(\"No project called '\" + project + \"' is currently running.\")\n\t}\n\n\treturn &stdVars, nil\n\n}\n\n// --------------------------------------------------------\n\n// IsRunning signifies whether a project is currently running.\nfunc (p Projects) IsRunning(project string) bool {\n\tp.Lock.RLock()\n\n\tdefer p.Lock.RUnlock()\n\n\t_, found := (*p.stdVarsMap)[project]\n\n\treturn found\n}\n\n// --------------------------------------------------------\n\n// List lists all projects currently running.\nfunc (p *Projects) List() []string {\n\n\tp.Lock.RLock()\n\n\tdefer p.Lock.RUnlock()\n\n\tlist := []string{}\n\n\tfor key := range *p.stdVarsMap {\n\t\tlist = append(list, key)\n\t}\n\n\treturn list\n}\n\n// --------------------------------------------------------\n\n// PortTaken checks whether a particular port is taken\n// by any process known to the Monitor.\nfunc (p *Projects) PortTaken(port int) bool {\n\n\tif port == p.Config.Monitor.HTTPPort {\n\t\treturn true\n\t}\n\n\tif port == p.Config.Monitor.TCPPort {\n\t\treturn true\n\t}\n\n\tp.Lock.RLock()\n\n\tdefer p.Lock.RUnlock()\n\n\tfor _, stdVars := range *p.stdVarsMap {\n\t\tif port == *stdVars.EnginePort {\n\t\t\treturn true\n\t\t}\n\t}\n\n\treturn false\n\n}\n\n// --------------------------------------------------------\n\nfunc (p *Projects) deleteTempDir(stdVars StandardVariables) error {\n\n\tpath := filepath.Join(\n\t\tstdVars.Config.ProjectDirectory,\n\t\t*stdVars.CurrentProjectName,\n\t\t\"tmp\")\n\n\treturn os.RemoveAll(path)\n\n}\n\n// --------------------------------------------------------\n\n// Stop stops a running project\nfunc (p *Projects) Stop(project string) error {\n\n\tp.Lock.Lock()\n\n\tdefer p.Lock.Unlock()\n\n\tstdVars, found := (*p.stdVarsMap)[project]\n\n\tif !found {\n\t\treturn errors.New(\"No project called '\" + project + \"' is currently running.\")\n\t}\n\n\tstdVars.Cmd.Process.Signal(syscall.SIGINT)\n\n\tp.deleteTempDir(stdVars)\n\n\tdelete(*p.stdVarsMap, project)\n\n\tlog.Print(\"Suspended project '\" + project + \"'.\")\n\n\treturn nil\n}\n\n// --------------------------------------------------------\n\n// KillAll kills all running projects without checking\n// whether they were shut down.\nfunc (p *Projects) KillAll() {\n\n\tallProjects := p.List()\n\n\tfor _, project := range allProjects {\n\t\tp.Stop(project)\n\t}\n\n}\n\n// --------------------------------------------------------\n"
  },
  {
    "path": "src/getml-app/src/data/StandardVariables.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage data\n\nimport (\n\t\"getML/config\"\n\t\"os/exec\"\n\t\"sync\"\n)\n\n// ----------------------------------------------------\n\n// StandardVariables contains common variables that are included\n// in all templates.\ntype StandardVariables struct {\n\tCmd *exec.Cmd\n\n\tConfig *config.Config\n\n\tCurrentProjectName *string\n\n\tEnginePort *int\n\n\tLastPopulationValidation *string\n\n\t// Random number drawn at login and used to identify the usage of\n\t// the software by the user.\n\tLicenseSeedDynamic string\n\n\tLock *sync.RWMutex\n}\n\n// ----------------------------------------------------\n\n// NewStandardVariables is a constructor.\nfunc NewStandardVariables(\n\tcmd *exec.Cmd,\n\tcurrentProjectName string,\n\tconfig *config.Config,\n\tlicenseSeedDynamic string,\n\tenginePort int) StandardVariables {\n\n\tlastPopulationValidation := \"\"\n\n\treturn StandardVariables{\n\t\tCmd:                      cmd,\n\t\tConfig:                   config,\n\t\tCurrentProjectName:       &currentProjectName,\n\t\tEnginePort:               &enginePort,\n\t\tLastPopulationValidation: &lastPopulationValidation,\n\t\tLicenseSeedDynamic:       licenseSeedDynamic,\n\t\tLock:                     &sync.RWMutex{},\n\t}\n}\n\n// ----------------------------------------------------\n"
  },
  {
    "path": "src/getml-app/src/go.mod",
    "content": "module getML\n\ngo 1.17\n"
  },
  {
    "path": "src/getml-app/src/install/All.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\n// All installs everything.\nfunc All(homeDir string, version string) {\n\n\tinstallDir, err := copyResources(UsrLocal, version)\n\n\tif err != nil {\n\t\tprintln(\"Could not install into '\" + UsrLocal + \"': \" + err.Error())\n\t\tprintln(\"Global installation failed, most likely due to \" +\n\t\t\t\"missing root rights. Trying local installation instead.\")\n\t} else {\n\t\tcopyFile(\"getML\", \"\", UsrLocalBin, true)\n\t\tprintln(\"Installation successful. The getML Engine was installed into '\" + installDir + \"' and the cli was installed into '\" + UsrLocalBin + \"'.\")\n\t\treturn\n\t}\n\n\tinstallDir, err = copyResources(homeDir, version)\n\n\tif err != nil {\n\t\tprintln(\"Could not install into '\" + homeDir + \"': \" + err.Error())\n\t} else {\n\t\tprintln(\"Installation successful. To be able to call 'getML' from anywhere, add the following to PATH:\")\n\t\tprintln(installDir)\n\t}\n\n}\n"
  },
  {
    "path": "src/getml-app/src/install/FileExists.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage install\n\nimport \"os\"\n\n// FileExists checks whether a file exists.\nfunc FileExists(name string) bool {\n\n\t_, err := os.Stat(name)\n\n\treturn !os.IsNotExist(err)\n}\n"
  },
  {
    "path": "src/getml-app/src/install/GetBinDir.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"path/filepath\"\n)\n\n// GetBinDir returns the bin directory\n// from which we are launching the Engine\n// and the Monitor.\nfunc GetBinDir(homeDir string, packageName string) string {\n\treturn filepath.Join(GetMainDir(homeDir, packageName), \"bin\")\n}\n"
  },
  {
    "path": "src/getml-app/src/install/GetConfigPath.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"path/filepath\"\n)\n\n// GetConfigPath returns the where the config.json\n// is supposed to be, if the software has been installed.\nfunc GetConfigPath(homeDir string, packageName string) string {\n\n\tbinDir := filepath.Join(GetMainDir(homeDir, packageName), \"config.json\")\n\n\treturn binDir\n}\n"
  },
  {
    "path": "src/getml-app/src/install/GetHomeDir.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage install\n\nimport \"os/user\"\n\n// GetHomeDir returns the default home directory,\n// unless another directory has been passed\n// explicitly.\nfunc GetHomeDir(homeDir string) string {\n\n\tif homeDir != \"\" {\n\t\treturn homeDir\n\t}\n\n\tusr, err := user.Current()\n\n\tif err != nil {\n\t\tpanic(err)\n\t}\n\n\treturn usr.HomeDir\n}\n"
  },
  {
    "path": "src/getml-app/src/install/GetMainDir.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"path/filepath\"\n\t\"runtime\"\n)\n\n// GetMainDir returns the main directory\n// to which we are copying our files.\nfunc GetMainDir(targetDir string, packageName string) string {\n\n\tif runtime.GOOS == \"windows\" {\n\t\treturn \".\"\n\t}\n\n\tif targetDir == UsrLocal {\n\t\treturn filepath.Join(GetHomeDir(targetDir), \"getML/\"+packageName)\n\t}\n\n\treturn filepath.Join(GetHomeDir(targetDir), \".getML/\"+packageName)\n\n}\n"
  },
  {
    "path": "src/getml-app/src/install/UsrLocal.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\n// UsrLocal points to the /usr/local/ directory on Linux.\nconst UsrLocal = \"/usr/local\"\n\n// UsrLocal points to the /usr/local/bin/ directory on Linux.\nconst UsrLocalBin = \"/usr/local/bin\"\n"
  },
  {
    "path": "src/getml-app/src/install/copyDir.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"io/ioutil\"\n\t\"os\"\n\t\"path/filepath\"\n)\n\nfunc copyDir(sourceDir string, mainDir string, overwrite bool) error {\n\n\tdestinationDir := filepath.Join(mainDir, sourceDir)\n\n\tos.MkdirAll(destinationDir, os.ModePerm)\n\n\tfiles, err := ioutil.ReadDir(sourceDir)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tfor _, file := range files {\n\t\tfname := filepath.Join(sourceDir, file.Name())\n\t\terr := copyFile(fname, \"\", mainDir, overwrite)\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\t}\n\n\treturn err\n}\n"
  },
  {
    "path": "src/getml-app/src/install/copyFile.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"io/ioutil\"\n\t\"os\"\n\t\"path/filepath\"\n)\n\nfunc copyFile(\n\tfile string,\n\tnewFileName string,\n\tmainDir string,\n\toverwrite bool) error {\n\n\tif newFileName == \"\" {\n\t\tnewFileName = file\n\t}\n\n\tdestination := filepath.Join(mainDir, newFileName)\n\n\tif destStat, err := os.Stat(destination); err != nil || overwrite {\n\n\t\tsourceStat, err := os.Stat(file)\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tif sourceStat.IsDir() {\n\t\t\treturn copyDir(file, mainDir, overwrite)\n\t\t}\n\n\t\tif overwrite {\n\n\t\t\tcreatedLater := (destStat != nil) && (sourceStat.ModTime().Before(destStat.ModTime()) ||\n\t\t\t\tsourceStat.ModTime().Equal(destStat.ModTime()))\n\n\t\t\tif createdLater {\n\t\t\t\treturn nil\n\t\t\t}\n\t\t}\n\n\t\tinput, err := ioutil.ReadFile(file)\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\terr = ioutil.WriteFile(destination, input, sourceStat.Mode())\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t}\n\n\treturn nil\n}\n"
  },
  {
    "path": "src/getml-app/src/install/copyResources.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage install\n\nimport (\n\t\"errors\"\n\t\"os\"\n)\n\n// copyResources copies the resources into the\n// targetDir, if necessary.\nfunc copyResources(targetDir string, version string) (string, error) {\n\n\tallFiles := []string{\n\t\t\"config.json\",\n\t\t\"environment.json\",\n\t\t\"bin\",\n\t\t\"tests\"}\n\n\t// Make sure that we don't copy any\n\t// random folder called \"templates\"\n\tif !filesExists(allFiles) {\n\t\treturn \"\", errors.New(\n\t\t\t\"Could not find all necessary files, quitting installation.\")\n\t}\n\n\tprintln(\"Installing getML...\")\n\n\tmainDir := GetMainDir(targetDir, version)\n\n\terr := os.MkdirAll(mainDir, os.ModePerm)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"config.json\", \"\", mainDir, true)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"config.json\", \"defaultConfig.json\", mainDir, false)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"environment.json\", \"\", mainDir, false)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"bin\", \"\", mainDir, true)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"tests\", \"\", mainDir, true)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"getML\", \"\", mainDir, true)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\terr = copyFile(\"lib\", \"\", mainDir, true)\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\t\n\terr = copyFile(\"shape-main.png\", \"\", mainDir, false)\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tprintln(\"Successfully installed getML into '\" + mainDir + \"'.\")\n\n\treturn mainDir, nil\n}\n"
  },
  {
    "path": "src/getml-app/src/install/filesExists.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage install\n\n// filesExists takes a list of filenames and makes sure\n// that they all exist.\nfunc filesExists(filenames []string) bool {\n\n\tallFilesExist := true\n\n\tfor _, name := range filenames {\n\n\t\tif !FileExists(name) {\n\t\t\tallFilesExist = false\n\t\t\tbreak\n\t\t}\n\t}\n\n\treturn allFilesExist\n}\n"
  },
  {
    "path": "src/getml-app/src/main.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nimport (\n\t\"getML/commands\"\n\t\"getML/data\"\n\t\"getML/install\"\n\t\"getML/tcp\"\n\t\"log\"\n\t\"os\"\n\t\"strconv\"\n)\n\n// overwritten at compile time through ldflags\nvar version = \"0.0.0\"\n\nfunc main() {\n\n\tpackageName := makePackageName()\n\n\tchangeToResourceDir(packageName)\n\n\tcommandLine, conf := commands.Parse(packageName)\n\n\tif commandLine == nil {\n\t\tos.Exit(2)\n\t}\n\n\tif commandLine.ShowVersionOnly {\n\t\tprintln(packageName)\n\t\tos.Exit(0)\n\t}\n\n\tif commandLine.Stop {\n\t\terr := stopExistingProcess(conf.Monitor.TCPPort)\n\n\t\tif err != nil {\n\t\t\tprintln(\"No getml-monitor running on tcp-port \" +\n\t\t\t\tstrconv.Itoa(conf.Monitor.TCPPort) + \".\")\n\t\t}\n\n\t\tos.Exit(0)\n\t}\n\n\tif commandLine.Uninstall {\n\t\tuninstall(commandLine.HomeDir, packageName)\n\t\tos.Exit(0)\n\t}\n\n\tif commandLine.ForceInstall {\n\t\tinstall.All(commandLine.HomeDir, packageName)\n\t}\n\n\tif commandLine.InstallOnly {\n\t\tos.Exit(0)\n\t}\n\n\terr := os.MkdirAll(conf.ProjectDirectory, 0750)\n\n\tif err != nil {\n\t\tlog.Println(err.Error())\n\t\treturn\n\t}\n\n\tprintStartMessage(packageName)\n\n\tprojects := data.NewProjects(&conf, packageName)\n\n\tmainHandler := tcp.NewMainHandler(version, conf.ToFlags(), projects)\n\n\ttcpServer := tcp.NewServer(mainHandler, projects)\n\n\ttcpServer.Launch()\n\n}\n"
  },
  {
    "path": "src/getml-app/src/makePackageName.go",
    "content": "// Copyright 2025 The SQLNet Company GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nimport (\n\t\"fmt\"\n\t\"runtime\"\n)\n\nfunc makePackageName() string {\n\n\tpackageName := fmt.Sprintf(\"getml-community-%s-%s-%s\", version, runtime.GOARCH, runtime.GOOS)\n\n\treturn packageName\n}\n"
  },
  {
    "path": "src/getml-app/src/printStartMessage.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nfunc printStartMessage(packageName string) {\n\n\tprintln(\n\t\t\"getML - Automated Feature\",\n\t\t\"Learning for Relational Data and Time Series.\")\n\n\tprintln(\"edition: \", packageName)\n\n}\n"
  },
  {
    "path": "src/getml-app/src/stopExistingProcess.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage main\n\nimport (\n\t\"getML/tcp\"\n)\n\nfunc stopExistingProcess(tcpPort int) error {\n\n\tcmd := `{\"type_\":\"shutdownlocal\"}`\n\n\tconn, err := createConnectionToEngine(tcpPort)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\terr = tcp.SendString(conn, cmd)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\treturn nil\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/MainHandler.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"archive/zip\"\n\t\"bytes\"\n\t\"encoding/json\"\n\t\"errors\"\n\t\"fmt\"\n\t\"getML/data\"\n\t\"io\"\n\t\"net\"\n\t\"os\"\n\t\"os/exec\"\n\t\"path\"\n\t\"path/filepath\"\n\t\"strconv\"\n\t\"strings\"\n\t\"time\"\n)\n\n// -------------------------------------------------------------------------------------------\n\nfunc handleEngineExit(exitCode int) error {\n\treturn fmt.Errorf(\"The engine terminated with exit code %d. Please refer to the logs for more information.\", exitCode)\n}\n\nfunc handleName(nameByte []byte) (string, error) {\n\tif len(nameByte) == 0 {\n\t\treturn \"\", errors.New(\"Project name cannot be empty!\")\n\t}\n\n\tvar name string\n\n\terr := json.Unmarshal(nameByte, &name)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\treturn name, nil\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// MainHandler manages the Engine processes.\ntype MainHandler struct {\n\targs []string\n\n\tprojects data.Projects\n\n\tversion string\n}\n\n// -------------------------------------------------------------------------------------------\n\n// NewMainHandler is a constructor.\nfunc NewMainHandler(version string,\n\targs []string,\n\tprojects data.Projects) *MainHandler {\n\n\treturn &MainHandler{\n\t\targs:     args,\n\t\tprojects: projects,\n\t\tversion:  version,\n\t}\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) bundleProject(name string, writer *zip.Writer) error {\n\tm.projects.Lock.RLock()\n\n\tdefer m.projects.Lock.RUnlock()\n\n\tprojectDirectory := filepath.FromSlash(m.projects.Config.ProjectDirectory + \"/\")\n\n\tpath := filepath.Join(projectDirectory, name)\n\n\tzipper := m.makeZipper(name, projectDirectory, writer)\n\n\terr := filepath.Walk(path, zipper)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\treturn nil\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) extractBundle(name string, r *zip.Reader, logger logger) (string, error) {\n\n\tvar err error\n\n\tm.projects.Lock.RLock()\n\n\tdefer m.projects.Lock.RUnlock()\n\n\tprojectDirectory := m.projects.Config.ProjectDirectory\n\tfirstFileName := r.File[0].Name\n\tbundleName := strings.Split(filepath.ToSlash(firstFileName), \"/\")[0]\n\n\tif name == \"\" {\n\n\t\tname = bundleName\n\t\tname, err = makeUniqueProjectName(projectDirectory, name)\n\n\t\tif err != nil {\n\t\t\treturn \"\", err\n\t\t}\n\n\t} else if m.projects.IsRunning(name) {\n\n\t\treturn \"\", errors.New(\"Project '\" + name + \"' currently loaded. To replace it, call `suspend` first.\")\n\n\t}\n\n\tprogressLogger, err := newProgressLogger(logger, len(r.File))\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tlogger.log(\"Loading project...\")\n\n\tfor _, file := range r.File {\n\n\t\tfileName := file.Name\n\n\t\tif name != bundleName {\n\t\t\tfileName = strings.Replace(fileName, bundleName, name, 1)\n\t\t}\n\n\t\tpath := filepath.Join(projectDirectory, fileName)\n\n\t\terr := os.MkdirAll(filepath.Dir(path), 0755)\n\n\t\tif err != nil {\n\t\t\treturn \"\", err\n\t\t}\n\n\t\tfileReader, err := file.Open()\n\n\t\tif err != nil {\n\t\t\treturn \"\", err\n\t\t}\n\n\t\tdefer fileReader.Close()\n\n\t\ttargetFile, err := os.OpenFile(path, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, file.Mode())\n\n\t\tif err != nil {\n\t\t\treturn \"\", err\n\t\t}\n\n\t\tdefer targetFile.Close()\n\n\t\t_, err = io.Copy(targetFile, fileReader)\n\n\t\tif err != nil {\n\t\t\treturn \"\", err\n\t\t}\n\n\t\tprogressLogger.increment()\n\n\t}\n\n\treturn name, nil\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// Returns a function that sufficies the filepath.WalkFunc protocol. The\n// function captures the name, project dir and zip writer; walks relative\n// path and copies all contents in path's tree to the archive.\nfunc (m *MainHandler) makeZipper(name string, projectDirectory string, writer *zip.Writer) filepath.WalkFunc {\n\n\treturn func(path string, info os.FileInfo, err error) error {\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tif info.IsDir() {\n\t\t\treturn nil\n\t\t}\n\n\t\tfile, err := os.Open(path)\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\tdefer file.Close()\n\n\t\trelPath := filepath.FromSlash(strings.TrimPrefix(path, projectDirectory))\n\n\t\tarchiveFile, err := writer.Create(relPath)\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\t_, err = io.Copy(archiveFile, file)\n\n\t\tif err != nil {\n\t\t\treturn err\n\t\t}\n\n\t\treturn nil\n\t}\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// DeleteProject deletes an existing project.\nfunc (m *MainHandler) DeleteProject(nameByte []byte, c net.Conn) {\n\n\tname, err := handleName(nameByte)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tm.projects.Lock.Lock()\n\n\tdefer m.projects.Lock.Unlock()\n\n\tprojectDirectory := m.projects.Config.ProjectDirectory\n\n\terr = os.RemoveAll(path.Join(projectDirectory, name))\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// Exit shuts down the getML Monitor.\nfunc (m *MainHandler) Exit() {\n\n\tos.Exit(-1)\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) findFreePort() (int, error) {\n\n\tport := 1708\n\n\tfor port < 10000 {\n\n\t\tif !m.projects.PortTaken(port) && !portIsOccupied(port) {\n\t\t\treturn port, nil\n\t\t}\n\n\t\tport += 1\n\t}\n\n\treturn 0, errors.New(\"Could not find a free port\")\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// IsAlive confirms that the Monitor is still alive.\nfunc (m *MainHandler) IsAlive(c net.Conn) {\n\tsendString(c, \"yes\")\n}\n\n// -------------------------------------------------------------------------------------------\n\n// GetStartMessage returns the start message to the Monitor.\nfunc (m *MainHandler) GetStartMessage(c net.Conn) {\n\n\tm.projects.Lock.RLock()\n\n\tdefer m.projects.Lock.RUnlock()\n\n\tmsg := \"The getML Monitor started successfully. \" +\n\t\t\"If getML runs on your local computer, open your favorite browser and go to \" +\n\t\t\"http://localhost:\" + strconv.Itoa(m.projects.Config.Monitor.HTTPPort) + \"/.\"\n\n\tsendString(c, msg)\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) listDirectories(directory string) []string {\n\n\tfnames := []string{}\n\n\tfiles, err := os.ReadDir(directory)\n\n\tif err != nil {\n\t\treturn fnames\n\t}\n\n\tfor _, f := range files {\n\t\tif f.IsDir() {\n\t\t\tfnames = append(fnames, f.Name())\n\t\t}\n\n\t}\n\n\treturn fnames\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) loadAllPipelines(\n\tproject string,\n\tenginePort int,\n\tc net.Conn,\n\tlogger logger) error {\n\n\tdirectory := path.Join(m.projects.Config.ProjectDirectory, project, \"pipelines\")\n\n\tallPipelines := m.listDirectories(directory)\n\n\tif len(allPipelines) == 0 {\n\t\treturn nil\n\t}\n\n\tprogressLogger, err := newProgressLogger(logger, len(allPipelines))\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tlogger.log(\"Loading pipelines...\")\n\n\tfor _, pipe := range allPipelines {\n\t\tloadPipelineFromDisc(pipe, enginePort)\n\t\tprogressLogger.increment()\n\t}\n\n\treturn nil\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// GetVersion returns the current version.\nfunc (m *MainHandler) GetVersion(c net.Conn) {\n\tsendString(c, m.version)\n}\n\n// -------------------------------------------------------------------------------------------\n\n// loadProjectBundle loads a project bundle from memory\nfunc (m *MainHandler) loadProjectBundle(c net.Conn, name string, data []byte, logger logger) (string, error) {\n\n\tr, err := zip.NewReader(bytes.NewReader(data), int64(len(data)))\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tprojectName, err := m.extractBundle(name, r, logger)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tenginePortStr, err := m.setProject([]byte(\"\\\"\"+projectName+\"\\\"\"), c, logger)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\treturn enginePortStr, nil\n\n}\n\n// LoadProject loads a project bundle from disk\nfunc (m *MainHandler) LoadProject(\n\tbody []byte,\n\tc net.Conn,\n\tlogger logger) {\n\n\ttype CmdBody struct {\n\t\tBundle string `json:\"bundle_\"`\n\t\tName   string `json:\"name_\"`\n\t}\n\n\tcmdBody := CmdBody{}\n\n\terr := json.Unmarshal(body, &cmdBody)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\n\t\treturn\n\t}\n\n\tbundle := cmdBody.Bundle\n\tname := cmdBody.Name\n\n\tif ext := filepath.Ext(bundle); ext != \".getml\" {\n\t\tsendString(c, \"Wrong file extesion: '\"+ext+\"'. Can only load propper '.getml' bundles.\")\n\t\treturn\n\t}\n\n\tdata, err := os.ReadFile(bundle)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tenginePortStr, err := m.loadProjectBundle(c, name, data, logger)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n\tsendString(c, enginePortStr)\n}\n\n// LoadProjectBundle directly exposes loadProjectBundle and receives the data from the client\nfunc (m *MainHandler) LoadProjectBundle(\n\tbody []byte,\n\tc net.Conn,\n\tlogger logger) {\n\n\ttype CmdBody struct {\n\t\tName string `json:\"name_\"`\n\t}\n\n\tcmdBody := CmdBody{}\n\n\terr := json.Unmarshal(body, &cmdBody)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tname := cmdBody.Name\n\n\tdata, err := recvBytes(c)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tenginePortStr, err := m.loadProjectBundle(c, name, data, logger)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n\tsendString(c, enginePortStr)\n}\n\n// -------------------------------------------------------------------------------------------\n\n// ListAllProjects returns a list of all projects.\nfunc (m *MainHandler) ListAllProjects(c net.Conn) {\n\n\tm.projects.Lock.RLock()\n\n\tdefer m.projects.Lock.RUnlock()\n\n\tprojectDirectory := m.projects.Config.ProjectDirectory\n\n\tprojects := []string{}\n\n\tfiles, err := os.ReadDir(projectDirectory)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tfor _, f := range files {\n\t\tif f.IsDir() {\n\t\t\tprojects = append(projects, f.Name())\n\t\t}\n\t}\n\n\tresponse := make(map[string]([]string))\n\n\tresponse[\"projects\"] = projects\n\n\tresponseStr, err := json.Marshal(response)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n\tsendString(c, string(responseStr))\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// ListRunningProjects returns a list of all projects.\nfunc (m *MainHandler) ListRunningProjects(c net.Conn) {\n\n\tm.projects.Lock.RLock()\n\n\tdefer m.projects.Lock.RUnlock()\n\n\tresponse := make(map[string]([]string))\n\n\tresponse[\"projects\"] = m.projects.List()\n\n\tresponseStr, err := json.Marshal(response)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n\tsendString(c, string(responseStr))\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// RestartProject stops a project and then launches it again\nfunc (m *MainHandler) RestartProject(\n\tnameByte []byte,\n\tc net.Conn,\n\tlogger logger) {\n\n\tname, err := handleName(nameByte)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\terr = m.projects.Stop(name)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tm.SetProject(nameByte, c, logger)\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// BundleProject bundles a project and sends the bundle to the client.\nfunc (m *MainHandler) BundleProject(\n\tbody []byte,\n\tc net.Conn,\n\tlogger logger) {\n\n\ttype CmdBody struct {\n\t\tName string `json:\"name_\"`\n\t}\n\n\tcmdBody := CmdBody{}\n\n\terr := json.Unmarshal(body, &cmdBody)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tif len(cmdBody.Name) == 0 {\n\t\tsendString(c, \"Project name cannot be empty!\")\n\t\treturn\n\t}\n\n\tname := cmdBody.Name\n\n\tsink := new(bytes.Buffer)\n\n\tw := zip.NewWriter(sink)\n\n\tdefer func() {\n\t\tif err := w.Close(); err != nil {\n\t\t\tsendString(c, err.Error())\n\t\t}\n\n\t\tsendString(c, \"Success!\")\n\n\t\tsendBytes(c, sink.Bytes())\n\n\t}()\n\n\terr = m.bundleProject(name, w)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n}\n\n// -------------------------------------------------------------------------------------------\n\nfunc (m *MainHandler) setProject(\n\tnameByte []byte,\n\tc net.Conn,\n\tlogger logger) (string, error) {\n\n\tname, err := handleName(nameByte)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tenginePort, err := m.findFreePort()\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tenginePortStr := strconv.Itoa(enginePort)\n\n\targs := append(m.args, \"-project=\"+name, \"-engine-port=\"+enginePortStr)\n\n\tcmd, err := startProcess(\"./engine\", \"./bin\", m.projects.Config.Monitor.Log, args)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tm.projects.Add(name, cmd, enginePort)\n\n\tconst maxRetries = 15\n\tconst backoff = 1 * time.Second\n\n\tfailed := make(chan error, 1)\n\tgo func() {\n\t\tfailed <- cmd.Wait()\n\t}()\n\n\tfor i := 0; i < maxRetries; i++ {\n\t\tif isRunning, _ := isAlive(enginePort); !isRunning {\n\t\t\tselect {\n\t\t\tcase err := <-failed:\n\t\t\t\treturn \"\", handleEngineExit(err.(*exec.ExitError).ExitCode())\n\t\t\tcase <-time.After(backoff):\n\t\t\t\tif i == maxRetries-1 {\n\t\t\t\t\treturn \"\", fmt.Errorf(\"Engine did not respond within %d seconds.\", maxRetries)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\terr = m.loadAllPipelines(name, enginePort, c, logger)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\treturn enginePortStr, nil\n}\n\n// -------------------------------------------------------------------------------------------\n\n// SetProject launches a new project\nfunc (m *MainHandler) SetProject(\n\tnameByte []byte,\n\tc net.Conn,\n\tlogger logger) {\n\n\tenginePortStr, err := m.setProject(nameByte, c, logger)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t}\n\n\tsendString(c, \"Success!\")\n\n\tsendString(c, enginePortStr)\n}\n\n// -------------------------------------------------------------------------------------------\n\n// SuspendProject shuts down a running project.\nfunc (m *MainHandler) SuspendProject(nameByte []byte, c net.Conn) {\n\n\tname, err := handleName(nameByte)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\terr = m.projects.Stop(name)\n\n\tif err != nil {\n\t\tsendString(c, err.Error())\n\t\treturn\n\t}\n\n\tsendString(c, \"Success!\")\n\n}\n\n// -------------------------------------------------------------------------------------------\n\n// ShutdownLocal handles a request by the Engine to shut down.\nfunc (m *MainHandler) ShutdownLocal() {\n\tm.projects.KillAll()\n\tos.Exit(0)\n}\n\n// -------------------------------------------------------------------------------------------\n"
  },
  {
    "path": "src/getml-app/src/tcp/Server.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"getML/data\"\n\t\"log\"\n\t\"net\"\n\t\"strconv\"\n)\n\n// -------------------------------------------------------------\n\n// Server is the the TCP server that communicates\n// with the Engine.\ntype Server struct {\n\tmainHandler *MainHandler\n\n\tprojects data.Projects\n}\n\n// -------------------------------------------------------------\n\n// NewServer is a constructor.\nfunc NewServer(\n\tmainHandler *MainHandler,\n\tprojects data.Projects) *Server {\n\n\treturn &Server{\n\t\tmainHandler: mainHandler,\n\t\tprojects:    projects,\n\t}\n\n}\n\n// -------------------------------------------------------------\n\n// Launch launches a tcp server on the TcpPort.\nfunc (s *Server) Launch() {\n\n\tport := s.projects.Config.Monitor.TCPPort\n\n\tl, err := net.Listen(\"tcp\", \"localhost:\"+strconv.Itoa(port))\n\n\tif err != nil {\n\t\tlog.Println(\"Unable to start up TCP server at port\",\n\t\t\tstrconv.Itoa(port), \"-\", err.Error())\n\t\treturn\n\t}\n\n\tdefer l.Close()\n\n\tfor {\n\t\tc, err := l.Accept()\n\n\t\tif err != nil {\n\t\t\tlog.Println(err.Error())\n\t\t\tcontinue\n\t\t}\n\n\t\tgo s.handleConnection(c)\n\t}\n\n}\n\n// -------------------------------------------------------------\n\nfunc (s *Server) handleConnection(c net.Conn) {\n\n\tdefer c.Close()\n\n\tcmd, err := recvCmd(c)\n\n\tif err != nil {\n\t\tlog.Println(\"Error receiving command:\", err.Error())\n\t\t_, err := c.Write([]byte(err.Error()))\n\t\tif err != nil {\n\t\t\treturn\n\t\t}\n\t\treturn\n\t}\n\n\tbody := []byte(cmd.Body)\n\n\tswitch cmd.Type {\n\n\tcase \"deleteproject\":\n\t\ts.mainHandler.DeleteProject(body, c)\n\n\tcase \"exit\":\n\t\ts.mainHandler.Exit()\n\n\tcase \"getversion\":\n\t\ts.mainHandler.GetVersion(c)\n\n\tcase \"isalive\":\n\t\ts.mainHandler.IsAlive(c)\n\n\tcase \"listallprojects\":\n\t\ts.mainHandler.ListAllProjects(c)\n\n\tcase \"listrunningprojects\":\n\t\ts.mainHandler.ListRunningProjects(c)\n\n\tcase \"loadproject\":\n\t\tlogger := newLogger(c)\n\t\ts.mainHandler.LoadProject(body, c, *logger)\n\n\tcase \"loadprojectbundle\":\n\t\tlogger := newLogger(c)\n\t\ts.mainHandler.LoadProjectBundle(body, c, *logger)\n\n\tcase \"restartproject\":\n\t\tlogger := newLogger(c)\n\t\ts.mainHandler.RestartProject(body, c, *logger)\n\n\tcase \"bundleproject\":\n\t\tlogger := newLogger(c)\n\t\ts.mainHandler.BundleProject(body, c, *logger)\n\n\tcase \"setproject\":\n\t\tlogger := newLogger(c)\n\t\ts.mainHandler.SetProject(body, c, *logger)\n\n\tcase \"shutdownlocal\":\n\t\ts.mainHandler.ShutdownLocal()\n\n\tcase \"suspendproject\":\n\t\ts.mainHandler.SuspendProject(body, c)\n\n\tdefault:\n\t\tsendString(c, \"Unknown command: '\"+cmd.Type+\"'\")\n\t}\n}\n\n// -------------------------------------------------------------\n"
  },
  {
    "path": "src/getml-app/src/tcp/basicCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport \"net\"\n\ntype basicCommand struct {\n\tName string `json:\"name_\"`\n\n\tType string `json:\"type_\"`\n}\n\nfunc (b basicCommand) send(conn *net.TCPConn) error {\n\treturn sendCommand(b, conn)\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/cmd.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport \"encoding/json\"\n\ntype cmd struct {\n\tBody json.RawMessage `json:\"body_\"`\n\n\tProject string `json:\"project_\"`\n\n\tType string `json:\"type_\"`\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/command.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport \"net\"\n\ntype command interface {\n\tsend(conn *net.TCPConn) error\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/createConnectionToEngine.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"net\"\n\t\"strconv\"\n\t\"time\"\n)\n\n// CreateConnectionToEngine creates a low-level socket connection to the getML Engine\nfunc createConnectionToEngine(port int) (*net.TCPConn, error) {\n\n\ttcpAddr, err := net.ResolveTCPAddr(\"tcp4\", \"localhost:\"+strconv.Itoa(port))\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tfor i := 0; i < 5; i++ {\n\n\t\tconn, err := net.DialTCP(\"tcp\", nil, tcpAddr)\n\n\t\tif err == nil {\n\t\t\treturn conn, nil\n\t\t} else if i == 4 {\n\t\t\treturn nil, err\n\t\t}\n\n\t\ttime.Sleep(time.Second)\n\n\t}\n\n\treturn nil, err\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/isAlive.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nfunc isAlive(port int) (bool, error) {\n\n\tconn, err := createConnectionToEngine(port)\n\n\tif err != nil {\n\t\treturn false, err\n\t}\n\n\tdefer conn.Close()\n\n\terr = basicCommand{Name: \"\", Type: \"is_alive\"}.send(conn)\n\n\tif err != nil {\n\t\treturn false, nil\n\t}\n\n\treturn true, nil\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/loadPipelineFromDisc.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"errors\"\n)\n\nfunc loadPipelineFromDisc(name string, port int) error {\n\n\tconn, err := createConnectionToEngine(port)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tdefer conn.Close()\n\n\terr = basicCommand{\n\t\tName: name,\n\t\tType: \"Pipeline.load\"}.send(conn)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tmsg, err := recvString(conn)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\tif msg != \"Success!\" {\n\t\treturn errors.New(msg)\n\t}\n\n\treturn nil\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/logger.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"net\"\n)\n\ntype logger struct {\n\tc net.Conn\n}\n\nfunc newLogger(c net.Conn) *logger {\n\treturn &logger{c: c}\n}\n\nfunc (l *logger) log(msg string) {\n\tsendString(l.c, \"log: \"+msg)\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/makeUniqueProjectName.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"io/ioutil\"\n\t\"regexp\"\n\t\"strconv\"\n\t\"strings\"\n)\n\nfunc makeUniqueProjectName(projectDirectory string, projectName string) (string, error) {\n\n\tfiles, err := ioutil.ReadDir(projectDirectory)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\tprojects := []string{}\n\n\tfor _, f := range files {\n\t\tif f.Mode().IsDir() {\n\t\t\tprojects = append(projects, f.Name())\n\t\t}\n\t}\n\n\tinProjects := func(name string) bool {\n\t\tfor _, proj := range projects {\n\t\t\tif name == proj {\n\t\t\t\treturn true\n\t\t\t}\n\t\t}\n\n\t\treturn false\n\t}\n\n\tsep := \"____\"\n\n\tfor i := 1; inProjects(projectName); i++ {\n\t\tre := regexp.MustCompile(sep + `\\d+$`)\n\t\tif re.MatchString(projectName) {\n\t\t\tprojectName = strings.SplitAfter(projectName, sep)[0] + strconv.Itoa(i)\n\t\t} else {\n\t\t\tprojectName = projectName + sep + strconv.Itoa(i)\n\t\t}\n\t}\n\n\treturn projectName, err\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/marshalCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport \"encoding/json\"\n\n// Parses a command\nfunc marshalCommand(command *command) (string, error) {\n\n\tcmdStr, err := json.Marshal(command)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\treturn string(cmdStr), nil\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/portIsOccupied.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"net\"\n\t\"strconv\"\n\t\"time\"\n)\n\nfunc portIsOccupied(port int) bool {\n\ttimeout, _ := time.ParseDuration(\"500ms\")\n\n\tconn, err := net.DialTimeout(\"tcp\", net.JoinHostPort(\"localhost\", strconv.Itoa(port)), timeout)\n\n\tif conn == nil && err != nil {\n\t\treturn false\n\t}\n\n\tif conn != nil {\n\t\tconn.Close()\n\t}\n\n\treturn true\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/progressLogger.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"errors\"\n\t\"strconv\"\n\t\"strings\"\n)\n\ntype progressLogger struct {\n\tlogger *logger\n\n\tnumIterations int\n\n\ttotalNumIterations int\n}\n\nfunc newProgressLogger(\n\tlogger logger,\n\ttotalNumIterations int) (*progressLogger, error) {\n\n\tif totalNumIterations <= 0 {\n\t\treturn nil, errors.New(\n\t\t\t\"'totalNumIterations' needs to be greater than 0\")\n\t}\n\n\treturn &progressLogger{\n\t\tlogger:             &logger,\n\t\tnumIterations:      0,\n\t\ttotalNumIterations: totalNumIterations,\n\t}, nil\n\n}\n\nfunc (l *progressLogger) log(msg string) {\n\tif strings.Contains(msg, \"Progress:\") {\n\t\tl.increment()\n\t}\n}\n\nfunc (l *progressLogger) increment() {\n\tl.numIterations++\n\tprogress := (100 * l.numIterations) / l.totalNumIterations\n\tl.logger.log(\"Progress: \" + strconv.Itoa(progress) + \"%\")\n}\n\nfunc (l *progressLogger) numRemaining() int {\n\n\tif l.numIterations > l.totalNumIterations {\n\t\treturn 0\n\t}\n\n\treturn l.totalNumIterations - l.numIterations\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/recvBytes.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"encoding/binary\"\n\t\"io\"\n)\n\nfunc recvBytes(conn io.Reader) ([]byte, error) {\n\n\tvar length int32\n\n\terr := binary.Read(conn, binary.BigEndian, &length)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tbytes := make([]byte, length)\n\n\t_, err = io.ReadFull(conn, bytes)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn bytes, nil\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/recvCmd.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"encoding/json\"\n\t\"net\"\n)\n\nfunc recvCmd(c net.Conn) (cmd, error) {\n\n\tcmd := cmd{}\n\n\tcmdStr, err := recvString(c)\n\n\tif err != nil {\n\t\treturn cmd, err\n\t}\n\n\terr = json.Unmarshal([]byte(cmdStr), &cmd)\n\n\treturn cmd, err\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/recvString.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"io\"\n)\n\nfunc recvString(conn io.Reader) (string, error) {\n\n\tbytes, err := recvBytes(conn)\n\n\tif err != nil {\n\t\treturn \"\", err\n\t}\n\n\treturn string(bytes), nil\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/sendBytes.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"bytes\"\n\t\"encoding/binary\"\n\t\"io\"\n)\n\nfunc sendBytes(conn io.Writer, data []byte) error {\n\tlength := uint64(len(data))\n\n\tif err := binary.Write(conn, binary.BigEndian, length); err != nil {\n\t\treturn err\n\t}\n\n\t_, err := io.Copy(conn, bytes.NewReader(data))\n\treturn err\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/sendCommand.go",
    "content": "// Copyright 2025 Code17 GmbH\n// \n// This file is licensed under the Elastic License 2.0 (ELv2). \n// Refer to the LICENSE.txt file in the root of the repository \n// for details.\n// \n\npackage tcp\n\nimport (\n\t\"net\"\n)\n\nfunc sendCommand(command command, conn *net.TCPConn) error {\n\n\tcmd, err := marshalCommand(&command)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\terr = sendString(conn, cmd)\n\n\tif err != nil {\n\t\treturn err\n\t}\n\n\treturn nil\n\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/sendString.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"encoding/binary\"\n\t\"io\"\n)\n\n// sendString sends a string to the given connection\nfunc sendString(conn io.Writer, str string) error {\n\n\tlength := int32(len(str))\n\n\tif err := binary.Write(conn, binary.BigEndian, length); err != nil {\n\t\treturn err\n\t}\n\n\t_, err := io.WriteString(conn, str)\n\n\treturn err\n\n}\n\nfunc SendString(conn io.Writer, str string) error {\n\terr := sendString(conn, str)\n\tif err != nil {\n\t\treturn err\n\t}\n\treturn nil\n}\n"
  },
  {
    "path": "src/getml-app/src/tcp/startProcess.go",
    "content": "// Copyright 2025 Code17 GmbH\n//\n// This file is licensed under the Elastic License 2.0 (ELv2).\n// Refer to the LICENSE.txt file in the root of the repository\n// for details.\n//\n\npackage tcp\n\nimport (\n\t\"os\"\n\t\"os/exec\"\n)\n\nfunc startProcess(fname string, workDir string, log bool, args []string) (*exec.Cmd, error) {\n\n\tcmd := exec.Command(fname, args...)\n\tcmd.Dir = workDir\n\n\t// unsetting LD_LIBRARY_PATH for engine subprocesses because we ship our own\n\t// libstdc++ et al. with the engine, setting LD_LIBRARY_PATH like e.g. in\n\t// VertexAI breaks dynamic linking of our libraries\n\tcmd.Env = append(cmd.Environ(), \"LD_LIBRARY_PATH=\")\n\n\tif log {\n\t\tcmd.Stdout = os.Stdout\n\t}\n\n\tcmd.Stderr = os.Stderr\n\n\terr := cmd.Start()\n\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn cmd, nil\n}\n"
  },
  {
    "path": "src/getml-app/src/uninstall.go",
    "content": "package main\n\nimport (\n\t\"getML/install\"\n\t\"os\"\n\t\"path\"\n\t\"runtime\"\n)\n\nfunc uninstall(homeDir string, version string) {\n\tif runtime.GOOS != \"windows\" {\n\t\tos.RemoveAll(install.GetMainDir(homeDir, version))\n\t\tos.RemoveAll(install.GetMainDir(install.UsrLocal, version))\n\t\tos.Remove(path.Join(install.UsrLocalBin, \"getML\"))\n\t}\n}\n"
  },
  {
    "path": "src/package-build-imports/config.json",
    "content": "{\n    \"engine\": {\n        \"port\": 1708\n    },\n    \"monitor\": {\n        \"allowPushNotifications\": true,\n        \"allowRemoteIPs\": false,\n        \"launchBrowser\": true,\n        \"log\": false,\n        \"httpPort\": 1709,\n        \"proxyUrl\": \"\",\n        \"tcpPort\": 1711\n    },\n    \"inMemory\": true,\n    \"projectDirectory\": \"../projects/\"\n}\n"
  },
  {
    "path": "src/package-build-imports/environment.json",
    "content": "{\n  \"authentication\": {\n    \"key\": \"AIzaSyD5qdERurTk7_rQ-IaAB7lo8IBCISr8hmk\",\n    \"refreshToken\": \"\",\n    \"userId\": \"\"\n  },\n  \"monitor\": {\n    \"eventURL\": \"https://mp.getml.com/analytics\",\n    \"licenseSeedStatic\": \"-1\",\n    \"baseURL\": \"https://admin.getml.com/api/\",\n    \"apiSuite\": \"suite-v0\"\n  },\n  \"signature\": \"2b82e55af86e06c581242bf9126ddd1a820733c027e138da43ac19d386b2ae99\"\n}\n"
  },
  {
    "path": "src/package-build-imports/jwks.pub.json",
    "content": "{\"keys\":[{\"alg\":\"RS256\",\"e\":\"AQAB\",\"kid\":\"+9t1ICrLlypXIww/+oiRsO4QqH+U8tsaklxr3FYiJa4=\",\"kty\":\"RSA\",\"n\":\"uHaKA6TVzsagf9fXTOk66Iad4CmZzk6IbMRQkZBrCGsofZB-bNA0UMSU1jumRfmkLRO6hroqmxBTvnwFMxkxo_fPTbVfpOu7MZuo-6BbOghcdDMKs8kkCs1RNonbauKRcrD018m1dTHAyFit9f3NmgdQEz1TovbR7XCmq53eWm2kf9gxonR7MHleM60h0h65ssskt7bHBWBLaEwF2fUg60hM6x8HPOO30F3eYDd6K2VeRxTc9z6RHCt0O5pv85FyPRrR3lFL_q_Lrr-2CUYylDSPMfgeTXbEm9Y3-6Gr9zAlmw4_fPwvvJspdZtr1GXuKQMNq82C-bEcAbD8h5G8xw\",\"use\":\"sig\"},{\"alg\":\"RS256\",\"e\":\"AQAB\",\"kid\":\"pmmsghhzxpb0IO4HKAWV6WBuH6MCrWplxNLBDKaYcZg=\",\"kty\":\"RSA\",\"n\":\"x1yXdMm1AHM2-tdgQKVbpALUY8_LQaQCgfhqfz17tt96MBkPlx_NyVLd2KXK5yYX36dYme8f2Hfc3h75AlYzRcUoELRXvdtED6rWSOb5MviEDbCiI24dpDtkcMETnAtlI7yRLsaE3UAW9t8HWpcDM-oIaBZO7YbqlVN7e5uDBQ67kJ0kDAk3NrXby77l4DqbNcmJj_n-tpKq3Skh7EQeZcEPnklLYrYj-5-A3c_slBpGroEjWHHjLpS-5t7snWcFPfRdcgSI-_T6o0Q_7HfrVthT-7qJC-02s8s0bVNXFTyUR16kGxybDmor2pjhlRvfhByb9F56bhIvYxnC6lFS1Q\",\"use\":\"sig\"}]}"
  },
  {
    "path": "src/package-build-imports/tests/test1/PERIPHERAL.CSV",
    "content": 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    "content": 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  {
    "path": "src/python-api/LICENSE",
    "content": "Elastic License 2.0 (ELv2)\n\nElastic License\n\nAcceptance\nBy using the software, you agree to all of the terms and conditions below.\n\nCopyright License\nThe licensor grants you a non-exclusive, royalty-free, worldwide, non-sublicensable, non-transferable license to use, copy, distribute, make available, and prepare derivative works of the software, in each case subject to the limitations and conditions below.\n\nLimitations\nYou may not provide the software to third parties as a hosted or managed service, where the service provides users with access to any substantial set of the features or functionality of the software.\n\nYou may not move, change, disable, or circumvent the license key functionality in the software, and you may not remove or obscure any functionality in the software that is protected by the license key.\n\nYou may not alter, remove, or obscure any licensing, copyright, or other notices of the licensor in the software. Any use of the licensor’s trademarks is subject to applicable law.\n\nPatents\nThe licensor grants you a license, under any patent claims the licensor can license, or becomes able to license, to make, have made, use, sell, offer for sale, import and have imported the software, in each case subject to the limitations and conditions in this license. This license does not cover any patent claims that you cause to be infringed by modifications or additions to the software. If you or your company make any written claim that the software infringes or contributes to infringement of any patent, your patent license for the software granted under these terms ends immediately. If your company makes such a claim, your patent license ends immediately for work on behalf of your company.\n\nNotices\nYou must ensure that anyone who gets a copy of any part of the software from you also gets a copy of these terms.\n\nIf you modify the software, you must include in any modified copies of the software prominent notices stating that you have modified the software.\n\nNo Other Rights\nThese terms do not imply any licenses other than those expressly granted in these terms.\n\nTermination\nIf you use the software in violation of these terms, such use is not licensed, and your licenses will automatically terminate. If the licensor provides you with a notice of your violation, and you cease all violation of this license no later than 30 days after you receive that notice, your licenses will be reinstated retroactively. However, if you violate these terms after such reinstatement, any additional violation of these terms will cause your licenses to terminate automatically and permanently.\n\nNo Liability\nAs far as the law allows, the software comes as is, without any warranty or condition, and the licensor will not be liable to you for any damages arising out of these terms or the use or nature of the software, under any kind of legal claim.\n\nDefinitions\nThe licensor is the entity offering these terms, and the software is the software the licensor makes available under these terms, including any portion of it.\n\nyou refers to the individual or entity agreeing to these terms.\n\nyour company is any legal entity, sole proprietorship, or other kind of organization that you work for, plus all organizations that have control over, are under the control of, or are under common control with that organization. control means ownership of substantially all the assets of an entity, or the power to direct its management and policies by vote, contract, or otherwise. Control can be direct or indirect.\n\nyour licenses are all the licenses granted to you for the software under these terms.\n\nuse means anything you do with the software requiring one of your licenses.\n\ntrademark means trademarks, service marks, and similar rights."
  },
  {
    "path": "src/python-api/README.md",
    "content": "# getML - Automated Feature Engineering for Relational Data and Time Series\n\ngetML ([https://getml.com](https://getml.com)) is a tool for automating feature engineering on relational data and time series. Unlike similar tools, it includes a database Engine \nthat has been customized for this very purpose.\n\nBecause of this customized database Engine, it is very fast. In fact, it is\nbetween 60x to 1000x faster than other open-source tools for automated\nfeature engineering.\n\nThis is the official Python API for the getML Engine.\n\nFor more information, visit the [getML documentation](https://getml.com) or explore the [Python API documentation](https://getml.com/latest/reference).\n\n### Build\n\nTo build the package install `hatch`\n```bash\npip install hatch\n```\n\nand run\n```bash\nhatch build -t wheel\n```\n"
  },
  {
    "path": "src/python-api/getml/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\ngetML (https://getml.com) is a software for automated machine learning\n(AutoML) with a special focus on feature learning for relational data\nand time series. The getML algorithms can produce features that are far\nmore advanced than what any data scientist could write by hand or what\ncould be accomplished by using simple brute force approaches.\n\nThis is the official python client for the getML Engine.\n\nDocumentation and more details at https://getml.com/latest/reference\n\"\"\"\n\nfrom . import (\n    communication,\n    constants,\n    data,\n    database,\n    datasets,\n    engine,\n    feature_learning,\n    hyperopt,\n    pipeline,\n    predictors,\n    preprocessors,\n    project,\n    spark,\n    sqlite3,\n    utilities,\n)\nfrom .cross_validation import cross_validation\nfrom .data import DataFrame\nfrom .engine import set_project\nfrom .pipeline import Pipeline\nfrom .version import __version__\n\n# Import subpackages into top-level namespace\n\n\n__all__ = (\n    \"__version__\",\n    \"communication\",\n    \"constants\",\n    \"cross_validation\",\n    \"data\",\n    \"database\",\n    \"datasets\",\n    \"engine\",\n    \"feature_learning\",\n    \"hyperopt\",\n    \"pipeline\",\n    \"predictors\",\n    \"preprocessors\",\n    \"project\",\n    \"spark\",\n    \"sqlite3\",\n    \"utilities\",\n    \"DataFrame\",\n    \"Pipeline\",\n    \"set_project\",\n)\n"
  },
  {
    "path": "src/python-api/getml/communication.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHandles the communication for between engine, monitor and python API.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport numbers\nimport pathlib\nimport socket\nimport sys\nfrom inspect import cleandoc\nfrom os import PathLike\nfrom time import sleep\nfrom types import TracebackType\nfrom typing import (\n    AbstractSet,\n    Any,\n    Callable,\n    Dict,\n    List,\n    NamedTuple,\n    Optional,\n    Type,\n    Union,\n)\n\nimport numpy as np\nfrom rich import print\n\nimport getml\nfrom getml.events import (\n    DispatcherEventEmitter,\n    LogMessageEventParser,\n    PoolEventDispatcher,\n)\nfrom getml.events.types import (\n    Event,\n    EventContext,\n    EventEmitter,\n    EventParser,\n    EventSource,\n    EventType,\n)\nfrom getml.exceptions import handle_engine_exception\nfrom getml.helpers import _is_iterable_not_str\nfrom getml.version import __version__\n\n\nclass LogStreamListener:\n    \"\"\"\n    Listens to raw log streams and emits structured events to a queue.\n    \"\"\"\n\n    def __init__(\n        self,\n        parser: EventParser,\n        emitter: EventEmitter,\n    ):\n        self.parser = parser\n        self.emitter = emitter\n\n    def __enter__(self) -> LogStreamListener:\n        self.emitter.__enter__()\n        return self\n\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]],\n        exc_value: Optional[BaseException],\n        traceback: Optional[TracebackType],\n    ):\n        if exc_value:\n            self.emitter.__exit__(exc_type, exc_value, traceback)\n            raise exc_value\n\n        self.emitter.__exit__(exc_type, exc_value, traceback)\n\n    def listen(self, socket: socket.socket, exit_on: Callable[[str], bool]) -> str:\n        \"\"\"\n        Listen to a log stream on socket and emit events.\n        \"\"\"\n\n        while True:\n            message = recv_string(socket)\n\n            events = self.parser.parse(message)\n            if events:\n                self.emitter.emit(events)\n\n            if exit_on(exit_status := message):\n                finished_event = Event(\n                    source=self.parser.context.source,\n                    type=EventType.COMMAND_FINISHED,\n                    attributes={},\n                )\n                self.emitter.emit([finished_event])\n                return exit_status\n\n\n# --------------------------------------------------------------------\n\nport = 1708\n\"\"\"\nThe default starting port for getML Engines. The port is automatically set\naccording to the worker Engine process spawned when setting a project.\nThe Monitor (app) automatically looks up a free port (starting from 1708).\nSetting the port here has no effect.\n\"\"\"\n\ntcp_port = 1711\n\"\"\"\nThe TCP port of the getML Monitor (app) that schedules Engine workers\n\"\"\"\n\n# --------------------------------------------------------------------\n\nENGINE_CANNOT_CONNECT_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    Cannot reach the getML Engine. Please make sure you have set a project.\n\n    To set: `getml.engine.set_project(...)`\n\n    Available projects:\n    {projects}\n    \"\"\"\n)\n\nENGINE_API_VERSION_MISMATCH_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    The version of the getML API does not match the version of the getML Engine.\n\n    API version: {api_version}\n    Engine version: {engine_version}\n    \"\"\"\n)\n\nMONITOR_CANNOT_CONNECT_ERROR_MSG = cleandoc(\n    \"\"\"\n    Cannot reach the getML Monitor. Please make sure the getML app is running.\n    \"\"\"\n)\n\nGETML_SEP = \"$GETML_SEP\"\n\nSEP_SIZE = np.uint64(10)\n\n# --------------------------------------------------------------------\n\n\ndef is_monitor_alive() -> bool:\n    \"\"\"\n    Checks if the getML Monitor is running.\n\n    Returns:\n        `True` if the getML Monitor is running and ready to accept commands and\n        `False` otherwise.\n    \"\"\"\n\n    cmd = {\n        \"type_\": \"isalive\",\n        \"body_\": \"\",\n    }\n\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n        try:\n            sock.connect((\"localhost\", tcp_port))\n        except ConnectionRefusedError:\n            return False\n        else:\n            send_string(sock, json.dumps(cmd))\n            return recv_string(sock) == \"yes\"\n\n\n# --------------------------------------------------------------------\n\n\ndef is_engine_alive() -> bool:\n    \"\"\"\n    Checks if an instance of the getML Engine is running.\n\n    Returns:\n        `True` if the getML Engine is running and ready to accept commands and\n        `False` otherwise.\n\n    \"\"\"\n\n    # no engine without monitor/cli\n    if not is_monitor_alive():\n        return False\n\n    if not _list_projects_impl(running_only=True):\n        return False\n\n    try:\n        with send_and_get_socket({\"type_\": \"is_alive\"}):\n            return True\n    except ConnectionRefusedError:\n        return False\n\n\n# -----------------------------------------------------------------------------\n\n# --------------------------------------------------------------------\n\n\nclass _GetmlEncoder(json.JSONEncoder):\n    \"\"\"Enables a custom serialization of the getML classes.\"\"\"\n\n    def default(self, obj):  # type: ignore\n        \"\"\"Checks for the particular type of the provided object and\n        deserializes it into an escaped string.\n\n        To ensure the getML can handle all keys, we have to add a\n        trailing underscore.\n\n        Args:\n            obj: Any of the classes defined in the getml package.\n\n        Returns:\n            string:\n                Encoded JSON.\n\n        Examples:\n            Create a :class:`~getml.predictors.LinearRegression`,\n            serialize it, and deserialize it again.\n\n            ```python\n                p = getml.predictors.LinearRegression()\n                p_serialized = json.dumps(\n                    p, cls = getml.communication._GetmlEncoder)\n                p2 = json.loads(\n                    p_serialized,\n                    object_hook=getml.helpers.predictors._decode_predictor\n                )\n                p == p2\n            ```\n        \"\"\"\n\n        if hasattr(obj, \"_getml_deserialize\"):\n            return obj._getml_deserialize()\n        if isinstance(obj, np.integer):\n            return int(obj)\n        if isinstance(obj, np.floating):\n            return float(obj)\n        if isinstance(obj, AbstractSet):\n            return sorted(obj)\n        if _is_iterable_not_str(obj):\n            return list(obj)\n\n        return json.JSONEncoder.default(self, obj)\n\n\n# --------------------------------------------------------------------\n\n\ndef _make_error_msg() -> str:\n    if not is_monitor_alive():\n        return MONITOR_CANNOT_CONNECT_ERROR_MSG\n    else:\n        msg = ENGINE_CANNOT_CONNECT_ERROR_MSG_TEMPLATE.format(\n            projects=\"\\n\".join(_list_projects_impl(running_only=False))\n        )\n        return msg\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_data(sock: socket.socket, size: Union[numbers.Real, int]) -> bytes:\n    \"\"\"Receives data (of any type) sent by the getML Engine.\n\n    Args:\n        sock: The socket to receive the data from.\n\n        size: The size of the data to receive.\n\n    Returns:\n        The received data.\n    \"\"\"\n\n    _, peer_port = sock.getpeername()\n\n    if peer_port != tcp_port and not is_engine_alive():\n        raise ConnectionRefusedError(_make_error_msg())\n\n    data = []\n\n    bytes_received = 0\n\n    max_chunk_size = 2048\n\n    while bytes_received < size:\n        current_chunk_size = int(min(size - bytes_received, max_chunk_size))\n\n        chunk = sock.recv(current_chunk_size)\n\n        if not chunk:\n            raise OSError(\n                \"\"\"The getML Engine died unexpectedly.\n                    If this wasn't done on purpose, please get in contact\n                    with our support or file a bug report.\"\"\"\n            )\n\n        data.append(chunk)\n\n        bytes_received += len(chunk)\n\n    return b\"\".join(data)\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_float_matrix(sock: socket.socket) -> np.ndarray:\n    \"\"\"\n    Receives a matrix (type np.float64) from the getML Engine.\n\n    Args:\n        sock: The socket to receive the matrix from.\n\n    Returns:\n        The received matrix.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    # By default, numeric data sent over the socket is big endian,\n    # also referred to as network-byte-order!\n    if sys.byteorder == \"little\":\n        shape_str = recv_data(sock, np.dtype(np.int32).itemsize * 2)\n\n        shape = np.frombuffer(shape_str, dtype=np.int32).byteswap().astype(np.uint64)\n\n        size = shape[0] * shape[1] * np.uint64(np.dtype(np.float64).itemsize)\n\n        matrix = recv_data(sock, size)\n\n        matrix = np.frombuffer(matrix, dtype=np.float64).byteswap()\n\n        matrix = matrix.reshape(shape[0], shape[1])\n\n    else:\n        shape_str = recv_data(sock, np.dtype(np.int32).itemsize * 2)\n\n        shape = np.frombuffer(shape_str, dtype=np.int32).astype(np.uint64)\n\n        size = shape[0] * shape[1] * np.uint64(np.dtype(np.float64).itemsize)\n\n        matrix = recv_data(sock, size)\n\n        matrix = np.frombuffer(matrix, dtype=np.float64)\n\n        matrix = matrix.reshape(shape[0], shape[1])\n\n    return matrix\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_bytes(sock: socket.socket) -> bytes:\n    \"\"\"\n    Receives bytes over a socket.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    size_str = recv_data(sock, np.dtype(np.uint64).itemsize)\n\n    size = (\n        np.frombuffer(size_str, dtype=np.uint64).byteswap()[0]\n        if sys.byteorder == \"little\"\n        else np.frombuffer(size_str, dtype=np.uint64)[0]\n    )\n\n    return recv_data(sock, size)\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_string(sock: socket.socket) -> str:\n    \"\"\"\n    Receives a string from the getML Engine\n    (an actual string, not a bytestring).\n\n    Args:\n        sock: The socket to receive the string from.\n\n    Returns:\n        The received string.\n    \"\"\"\n\n    # By default, numeric data sent over the socket is big endian,\n    # also referred to as network-byte-order!\n    if sys.byteorder == \"little\":\n        size_str = recv_data(sock, np.dtype(np.int32).itemsize)\n\n        size = np.frombuffer(size_str, dtype=np.int32).byteswap()[0]\n\n    else:\n        size_str = recv_data(sock, np.dtype(np.int32).itemsize)\n\n        size = np.frombuffer(size_str, dtype=np.int32)[0]\n\n    data = recv_data(sock, size)\n\n    return data.decode(errors=\"ignore\")\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_string_column(sock: socket.socket) -> np.ndarray:\n    \"\"\"\n    Receives a column of type string from the getML Engine\n\n    Args:\n        sock: The socket to receive the column from.\n\n    Returns:\n        A numpy array containing the column.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    nbytes_str = recv_data(sock, np.dtype(np.uint64).itemsize)\n\n    if sys.byteorder == \"little\":\n        nbytes = np.frombuffer(nbytes_str, dtype=np.uint64).byteswap()[0]\n    else:\n        nbytes = np.frombuffer(nbytes_str, dtype=np.uint64)[0]\n\n    col = (\n        recv_data(sock, nbytes).decode(errors=\"ignore\").split(GETML_SEP)\n        if nbytes != 0\n        else []\n    )\n\n    col = np.asarray(col, dtype=object)\n\n    return col.reshape(len(col), 1)\n\n\n# --------------------------------------------------------------------\n\n\nclass _Issue(NamedTuple):\n    \"\"\"\n    Describes a single warning generated\n    by the check method of the pipeline.\n    \"\"\"\n\n    message: str\n    label: str\n    warning_type: str\n\n\n# --------------------------------------------------------------------\n\n\ndef recv_issues(sock: socket.socket) -> List[_Issue]:\n    \"\"\"\n    Receives a set of warnings to raise from the getML Engine.\n\n    Args:\n        sock: The socket to receive the warnings from.\n\n    Returns:\n        A list of warnings.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    json_str = recv_string(sock)\n\n    if json_str[0] != \"{\":\n        raise ValueError(json_str)\n\n    json_obj = json.loads(json_str)\n\n    all_warnings = json_obj[\"warnings_\"]\n\n    return [\n        _Issue(\n            message=w[\"message_\"], label=w[\"label_\"], warning_type=w[\"warning_type_\"]\n        )\n        for w in all_warnings\n    ]\n\n\n# --------------------------------------------------------------------\n\n\ndef send(cmd: Dict[str, Any]) -> None:\n    \"\"\"Sends a command to the getML Engine and closes the established\n    connection.\n\n    Creates a socket and sends a command to the getML Engine using the\n    module-wide variable [`port`][getml.communication.port].\n\n    A message (string) from the `socket.socket` will be\n    received using [`recv_string`][getml.communication.recv_string] and the socket will\n    be closed. If the message is \"Success!\", everything works\n    properly. Else, an Exception will be thrown containing the\n    message.\n\n    In case another message is supposed to be sent by the Engine,\n    [`send_and_get_socket`][getml.communication.send_and_get_socket] has to be used\n    and the calling function must handle the message itself!\n\n    Please be very careful when altering the routing/calling behavior\n    of the socket communication! The Engine is quite sensitive and might freeze.\n\n    Args:\n        cmd: A dictionary specifying the command the Engine is\n            supposed to execute. It _must_ contain at least two string\n            values with the corresponding keys being named \"name_\" and\n            \"type_\".\n    \"\"\"\n\n    if not isinstance(cmd, dict):\n        raise TypeError(\"'cmd' must be a dict.\")\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    try:\n        sock.connect((\"localhost\", port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    # Use a custom encoder which knows how to handle the classes\n    # defined in the getml package.\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    # The calling function does not want to further use the\n    # socket. Therefore, it will be checked here whether the\n    # request was successful and closed.\n    msg = recv_string(sock)\n\n    sock.close()\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n\n# --------------------------------------------------------------------\n\n\ndef send_and_get_socket(\n    cmd: Dict[str, Any], specific_port: Optional[int] = None\n) -> socket.socket:\n    \"\"\"Sends a command to the getML Engine and returns the established\n    connection.\n\n    Creates a socket and sends a command to the getML Engine using the\n    module-wide variable [`port`][getml.communication.port].\n\n    The function will return the socket it opened and the calling\n    function is free to receive whatever data is desires over it. But\n    the latter also has to take care of closing the socket afterwards\n    itself!\n\n    Please be very careful when altering the routing/calling behavior\n    of the socket communication! The Engine is quite sensitive and might freeze.\n    Especially implemented handling of\n    socket sessions (their passing from function to function) must not\n    be altered or separated in distinct calls to the\n    [`send`][getml.communication.send] function! Some commands have\n    to be sent via the same socket or the Engine will not be able to\n    handle them and might block.\n\n    Args:\n        cmd: A dictionary specifying the command the Engine is\n            supposed to execute. It _must_ contain at least two string\n            values with the corresponding keys being named \"name_\" and\n            \"type_\".\"\n\n    Returns:\n        A socket which, using the Python API, can communicate with the getML Engine.\n    \"\"\"\n\n    if not isinstance(cmd, dict):\n        raise TypeError(\"'cmd' must be a dict.\")\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    try:\n        sock.connect(\n            (\"localhost\", specific_port if specific_port is not None else port)\n        )\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    # Use a custom encoder which knows how to handle the classes\n    # defined in the getml package.\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    return sock\n\n\n# --------------------------------------------------------------------\n\n\ndef send_bytes(sock: socket.socket, data: bytes):\n    \"\"\"\n    Sends bytes over a socket.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    if not isinstance(data, bytes):\n        raise TypeError(\"'data' must be bytes.\")\n\n    size = len(data)\n\n    # By default, numeric data sent over the socket is big endian,\n    # also referred to as network-byte-order!\n    if sys.byteorder == \"little\":\n        sock.sendall(np.asarray([size]).astype(np.int32).byteswap().tobytes())\n    else:\n        sock.sendall(np.asarray([size]).astype(np.int32).tobytes())\n\n    sock.sendall(data)\n\n\n# --------------------------------------------------------------------\n\n\ndef send_string(sock: socket.socket, string: str):\n    \"\"\"\n    Sends a string over a socket\n    (an actual string, not a bytestring).\n\n    Args:\n        sock: The socket to send the string to.\n\n        string: The string to send.\n    \"\"\"\n\n    if not isinstance(sock, socket.socket):\n        raise TypeError(\"'sock' must be a socket.\")\n\n    if not isinstance(string, str):\n        raise TypeError(\"'string' must be a str.\")\n\n    encoded = string.encode(\"utf-8\")\n\n    send_bytes(sock, encoded)\n\n\n# --------------------------------------------------------------------\n\n\ndef log(sock: socket.socket, extra: Optional[Dict[str, Any]] = None) -> str:\n    \"\"\"\n    Handles logs sent by engine or monitor.\n\n    Args:\n        sock: The socket to receive the logs from.\n    \"\"\"\n\n    if extra is None:\n        extra = {}\n\n    peer_port = sock.getpeername()[1]\n\n    event_source = EventSource.ENGINE if peer_port == port else EventSource.MONITOR\n\n    parser = LogMessageEventParser(\n        context=EventContext(cmd=extra.get(\"cmd\", {}), source=event_source)\n    )\n    dispatcher = PoolEventDispatcher()\n    emitter = DispatcherEventEmitter(dispatcher)\n\n    def is_status_message(message: str) -> bool:\n        return not message.startswith(\"log: \")\n\n    with LogStreamListener(parser, emitter) as listener:\n        status_message = listener.listen(socket=sock, exit_on=is_status_message)\n        return status_message\n\n\n# --------------------------------------------------------------------\n\n\ndef _delete_project(name: str):\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    _delete_project_with_retry(name, retries=10, delay=0.2)\n\n\n# --------------------------------------------------------------------\n\n\ndef _delete_project_with_retry(name: str, retries: int, delay: float):\n    \"\"\"\n    Attempt to delete the project, retrying `retries` times if necessary.\n    \"\"\"\n    if name in _list_projects_impl(running_only=True):\n        _suspend_project(name)\n\n    for _ in range(retries):\n        if name not in _list_projects_impl(running_only=False):\n            return\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"deleteproject\"\n        cmd[\"body_\"] = name\n\n        with send_and_get_socket(cmd, tcp_port) as sock:\n            msg = recv_string(sock)\n            if msg != \"Success!\":\n                handle_engine_exception(msg)\n\n        sleep(delay)\n\n    raise OSError(f\"Failed to delete project '{name}' after {retries} attempts.\")\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_project_name() -> str:\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"project_name\"\n    cmd[\"name_\"] = \"\"\n\n    with send_and_get_socket(cmd) as sock:\n        pname = recv_string(sock)\n\n    return pname\n\n\n# --------------------------------------------------------------------\n\n\ndef _load_project(bundle: Union[PathLike, str], name: Optional[str] = None):\n    name = name or \"\"\n\n    if not isinstance(bundle, (str, PathLike)):\n        raise TypeError(\"'bundle' must be of type str\")\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    with open(bundle, \"rb\") as f:\n        data = f.read()\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"loadprojectbundle\"\n    cmd[\"body_\"] = dict(name_=name)\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    send_bytes(sock, data)\n\n    msg = log(sock, extra={\"cmd\": cmd})\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n    global port  # noqa:PLW0603\n    port = int(recv_string(sock))\n\n    proj_name = _get_project_name()\n\n    print(f\"Loaded {bundle!r} as {proj_name!r}. Connected to project {proj_name!r}.\")\n\n\n# --------------------------------------------------------------------\n\n\ndef _list_projects_impl(running_only: bool) -> List[str]:\n    cmd = dict()\n    cmd[\"type_\"] = \"listallprojects\"\n    cmd[\"body_\"] = \"\"\n\n    if running_only:\n        cmd[\"type_\"] = \"listrunningprojects\"\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    msg = recv_string(sock)\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n    json_str = recv_string(sock)\n\n    sock.close()\n\n    return json.loads(json_str)[\"projects\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef _monitor_url() -> Optional[str]:\n    cmd: Dict[str, str] = {}\n\n    cmd[\"type_\"] = \"monitor_url\"\n    cmd[\"name_\"] = \"\"\n\n    with send_and_get_socket(cmd) as sock:\n        return recv_string(sock) or None\n\n\n# --------------------------------------------------------------------\n\n\ndef _check_version():\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"getversion\"\n    cmd[\"body_\"] = \"\"\n\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n        try:\n            sock.connect((\"localhost\", tcp_port))\n        except ConnectionRefusedError:\n            raise ConnectionRefusedError(_make_error_msg())\n        else:\n            send_string(sock, json.dumps(cmd, cls=_GetmlEncoder))\n            engine_version = recv_string(sock)\n\n    if engine_version != __version__:\n        raise ValueError(\n            ENGINE_API_VERSION_MISMATCH_ERROR_MSG_TEMPLATE.format(\n                api_version=__version__, engine_version=engine_version\n            )\n        )\n\n\n# --------------------------------------------------------------------\n\n\ndef _save_project(\n    name: str,\n    file_name: Optional[Union[PathLike, str]] = None,\n    replace: bool = False,\n):\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    if not isinstance(file_name, (str, PathLike)) and file_name is not None:\n        raise TypeError(\"'file_name' must be a str or PathLike\")\n\n    if file_name is None:\n        file_name = name\n\n    target_path = pathlib.Path(file_name).resolve()\n    if target_path.suffix == \"\":\n        target_path = target_path.with_suffix(\".getml\")\n\n    if target_path.exists() and not replace:\n        raise FileExistsError(\n            f\"The file '{target_path}' already exists. \"\n            \"Use 'replace=True' to overwrite it.\"\n        )\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"bundleproject\"\n    cmd[\"body_\"] = {\"name_\": name}\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    msg = recv_string(sock)\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n    bundle = recv_bytes(sock)\n\n    with open(target_path, \"wb\") as f:\n        f.write(bundle)\n\n    print(f\"Saved project {name} to '{target_path}'\")\n\n\n# --------------------------------------------------------------------\n\n\ndef _project_url() -> Optional[str]:\n    url = _monitor_url()\n    return url + \"listprojects/\" + _get_project_name() + \"/\" if url else None\n\n\n# --------------------------------------------------------------------\n\n\ndef _set_project(name: str, restart: bool = False):\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    if not is_monitor_alive():\n        getml.engine.launch(quiet=True)\n\n    _check_version()\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"setproject\"\n    cmd[\"body_\"] = name\n\n    if restart:\n        cmd[\"type_\"] = \"restartproject\"\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    msg = log(sock, extra={\"cmd\": cmd})\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n    global port  # noqa:PLW0603\n\n    port = int(recv_string(sock))\n\n    print(f\"Connected to project {name!r}.\")\n\n    if _project_url():\n        print(_project_url())\n\n\n# --------------------------------------------------------------------\n\n\ndef _shutdown():\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"shutdownlocal\"\n    cmd[\"body_\"] = \"\"\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    print(\"Shutting down the getML Engine...\", end=\" \")\n\n    send_string(sock, msg)\n\n    sock.close()\n\n    while True:\n        try:\n            with socket.create_connection((\"localhost\", tcp_port), timeout=5.0):\n                pass\n        except:  # noqa: E722\n            print(\"Done.\")\n            return\n\n\n# --------------------------------------------------------------------\n\n\ndef _suspend_project(name: str):\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"suspendproject\"\n    cmd[\"body_\"] = name\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    try:\n        sock.connect((\"localhost\", tcp_port))\n    except ConnectionRefusedError:\n        raise ConnectionRefusedError(_make_error_msg())\n\n    msg = json.dumps(cmd, cls=_GetmlEncoder)\n\n    send_string(sock, msg)\n\n    msg = recv_string(sock)\n\n    if msg != \"Success!\":\n        handle_engine_exception(msg)\n\n    sock.close()\n"
  },
  {
    "path": "src/python-api/getml/constants.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains various constants for getML\n\"\"\"\n\nCOMPARISON_ONLY = \", comparison only\"\n\"\"\"\"\"\"\nJOIN_KEY_SEP = \"$GETML_JOIN_KEY_SEP\"\n\"\"\"\"\"\"\nMULTIPLE_JOIN_KEYS_BEGIN = \"$GETML_MULTIPLE_JOIN_KEYS_BEGIN\"\n\"\"\"\"\"\"\nMULTIPLE_JOIN_KEYS_END = \"$GETML_MULTIPLE_JOIN_KEYS_END\"\n\"\"\"\"\"\"\nNO_JOIN_KEY = \"$GETML_NO_JOIN_KEY\"\n\"\"\"\"\"\"\nROWID = \"rowid\"\n\"\"\"\"\"\"\nTIME_STAMP = \"time stamp\"\n\"\"\"\"\"\"\nTIME_FORMATS = [\n    \"%Y-%m-%dT%H:%M:%s%z\",\n    \"%Y-%m-%d %H:%M:%S\",\n    \"%Y-%m-%d\",\n    \"%Y-%m-%dT%H:%M:%S.%F\",  # microsecond\n    \"%Y-%m-%dT%H:%M:%S.%i\",  # millisecond\n    \"%Y-%m-%dT%H:%M:%S.%c\",  # centisecond\n    \"%Y-%m-%d %H:%M:%S.%F\",  # microsecond\n    \"%Y-%m-%d %H:%M:%S.%i\",  # millisecond\n    \"%Y-%m-%d %H:%M:%S.%c\",  # centisecond\n    \"%Y-%m-%d %H:%M:%s%z\",\n]\n\"\"\"The default time stamp formats to be used.\n\nWhenever a time stamp is parsed from a string,\nthe getML Engine tries different time stamp formats.\n\nThe procedure works as follows: The Engine tries to parse the string\nusing the first format. If that fails, it will use the second format etc.\n\nIf none of the time stamp formats work, then it tries to parse the string\nas a floating point value, which it will interpret as the number of\nseconds since UNIX time (January 1, 1970).\n\nIf that fails as well, the value is set to NULL.\n\"\"\"\n\nDEFAULT_BATCH_SIZE = 100000\n\"\"\"\nThe default batch size used whenver batched IO operations are performed.\n\"\"\"\n\nANNOTATIONS_DOCS_URL = \"https://getml.com/latest/user_guide/concepts/annotating_data/\"\n\"\"\"\nURL that points to the annotations sections in the user guide of the getML documentation.\n\"\"\"\n\nDOCKER_DOCS_URL = \"https://getml.com/latest/install/packages/docker/\"\n\"\"\"\nURL that points to the docker sections of the getML documentation.\n\"\"\"\n\nENTERPRISE_DOCS_URL = \"https://getml.com/latest/enterprise/\"\n\"\"\"\nURL that points to the enterprise sections of the getML documentation.\n\"\"\"\n\nINSTALL_DOCS_URL = \"https://getml.com/latest/install/\"\n\"\"\"\nURL that points to the installation sections of the getML documentation.\n\"\"\"\n\nCOMPOSE_FILE_URL = \"https://raw.githubusercontent.com/getml/getml-community/refs/heads/main/runtime/docker-compose.yml\"\n\"\"\"\nURL that points to the docker-compose runtime file.\n\"\"\"\n"
  },
  {
    "path": "src/python-api/getml/cross_validation.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nConducts a cross validation.\n\"\"\"\n\nfrom copy import deepcopy\nfrom typing import Union\n\nfrom rich import print\n\nfrom getml.data import DataFrame, View\nfrom getml.data.split import random\nfrom getml.pipeline import Pipeline\nfrom getml.pipeline.scores_container import Scores\n\n\ndef cross_validation(\n    pipeline: Pipeline,\n    population: Union[DataFrame, View],\n    peripheral: DataFrame,\n    n_folds: int = 10,\n    seed: int = 5849,\n) -> Scores:\n    \"\"\"\n    Conducts a cross validation.\n\n    Args:\n        pipeline: The pipeline to be cross-validated.\n\n        population: The data to be cross-validated.\n\n        peripheral: The peripheral data to be used during cross-validation.\n\n        n_folds: The number of folds to be used for cross-validation.\n\n        seed: The seed to be used for the random number generator.\n\n    Returns:\n        The scores of the cross-validation.\n    \"\"\"\n\n    if not isinstance(pipeline, Pipeline):\n        raise TypeError(\"'pipeline' must be a pipeline.\")\n\n    if not isinstance(population, (DataFrame, View)):\n        raise TypeError(\"'pipeline' must be a pipeline.\")\n\n    if not isinstance(n_folds, int):\n        raise TypeError(\"'n_folds' must be an integer.\")\n\n    if n_folds < 2:\n        raise ValueError(\"'n_folds' must be >= 2.\")\n\n    # peripheral will be typechecked by pipeline .fit(...).\n    # No need to duplicate code.\n\n    kwargs = {\n        \"fold \" + str(i + 1).zfill(len(str(n_folds))): (1.0 / float(n_folds))\n        for i in range(n_folds)\n    }\n\n    split = random(seed=seed, train=0.0, test=0.0, validation=0.0, **kwargs)\n\n    scores_objects = []\n\n    for fold in list(kwargs.keys()):\n        print(fold + \":\")\n        train_set = population[split != fold].with_name(\n            \"cross validation: train - \" + fold\n        )\n        validation_set = population[split == fold].with_name(\n            \"cross validation: validation - \" + fold\n        )\n        pipe = deepcopy(pipeline)\n        pipe.tags += [\"cross validation\", fold]\n        pipe.fit(train_set, peripheral)\n        scores_objects.append(pipe.score(validation_set, peripheral))\n\n    scores_data = [d for obj in scores_objects for d in obj.data]\n\n    return Scores(data=scores_data, latest=scores_objects[-1]._latest).sort(\n        key=lambda score: score.set_used, descending=False\n    )\n"
  },
  {
    "path": "src/python-api/getml/data/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"Contains functionalities for importing, handling, and retrieving\ndata from the getML Engine.\n\nAll data relevant for the getML Suite has to be present in the getML\nEngine. Its Python API itself does not store any of the data used for\ntraining or prediction. Instead, it provides a handler class for the\ndata frame objects in the getML Engine, the\n[`DataFrame`][getml.DataFrame]. Either using this overall handler for\nthe underlying data set or the individual [`columns`][getml.data.columns]\nit is composed of, one can both import and retrieve data from the Engine\nas well as performing operations on them. In addition to the data\nframe objects, the Engine also uses an abstract and lightweight\nversion of the underlying data model, which is represented by the\n[`Placeholder`][getml.data.Placeholder].\n\nIn general, working with data within the getML Suite is organized in\nthree different steps.\n\n* [Importing the data][importing-data] into the getML Engine .\n* [Annotating the data][annotating-data] by assigning\n  [`roles`][getml.data.roles] to the individual [`columns`][getml.data.columns]\n* [Constructing the data model][data-model-concepts] by deriving\n  [`Placeholder`][getml.data.Placeholder] from the data and joining them to\n  represent the data schema.\n\n??? example\n    Creating a new data frame object in the getML Engine and importing\n    data is done by one of the class methods\n    [`from_csv`][getml.DataFrame.from_csv],\n    [`from_db`][getml.DataFrame.from_db],\n    [`from_json`][getml.DataFrame.from_json], or\n    [`from_pandas`][getml.DataFrame.from_pandas].\n\n    In this example we chose to directly load data from a public\n    database in the internet. But, firstly, we have to connect the\n    getML Engine to the database (see [MySQL interface][mysql] in the user\n    guide for further details).\n\n\n    ```python\n    getml.database.connect_mysql(\n        host=\"relational.fel.cvut.cz\",\n        dbname=\"financial\",\n        port=3306,\n        user=\"guest\",\n        password=\"ctu-relational\",\n        time_formats=['%Y/%m/%d']\n    )\n    ```\n\n    Using the established connection, we can tell the Engine to\n    construct a new data frame object called `df_loan`, fill it with\n    the data of `loan` table contained in the MySQL database, and\n    return a [`DataFrame`][getml.DataFrame] handler associated with\n    it.\n\n    ```python\n\n    loan = getml.DataFrame.from_db('loan', 'df_loan')\n\n    print(loan)\n    ```\n    ```\n    | loan_id      | account_id   | amount       | duration     | date          | payments      | status        |\n    | unused float | unused float | unused float | unused float | unused string | unused string | unused string |\n    -------------------------------------------------------------------------------------------------------------\n    | 4959         | 2            | 80952        | 24           | 1994-01-05    | 3373.00       | A             |\n    | 4961         | 19           | 30276        | 12           | 1996-04-29    | 2523.00       | B             |\n    | 4962         | 25           | 30276        | 12           | 1997-12-08    | 2523.00       | A             |\n    | 4967         | 37           | 318480       | 60           | 1998-10-14    | 5308.00       | D             |\n    | 4968         | 38           | 110736       | 48           | 1998-04-19    | 2307.00       | C             |\n    ```\n    In order to construct the data model and for the feature\n    learning algorithm to get the most out of your data, you have\n    to assign roles to columns using the\n    [`set_role`][getml.DataFrame.set_role] method (see\n    [Annotating data][annotating-data] for details).\n\n    (For demonstration purposes, we assign `payments` the [`target role`][getml.data.roles.target]. In reality, you would want to\n    forecast the defaulting behaviour, which is encoded in the `status` column. See the [loans notebook](https://github.com/getml/getml-demo/blob/master/loans.ipynb).)\n\n    ```python\n\n    loan.set_role([\"duration\", \"amount\"], getml.data.roles.numerical)\n    loan.set_role([\"loan_id\", \"account_id\"], getml.data.roles.join_key)\n    loan.set_role(\"date\", getml.data.roles.time_stamp)\n    loan.set_role([\"payments\"], getml.data.roles.target)\n\n    print(loan)\n    ```\n    ```\n    | date                        | loan_id  | account_id | payments  | duration  | amount    | status        |\n    | time stamp                  | join key | join key   | target    | numerical | numerical | unused string |\n    -----------------------------------------------------------------------------------------------------------\n    | 1994-01-05T00:00:00.000000Z | 4959     | 2          | 3373      | 24        | 80952     | A             |\n    | 1996-04-29T00:00:00.000000Z | 4961     | 19         | 2523      | 12        | 30276     | B             |\n    | 1997-12-08T00:00:00.000000Z | 4962     | 25         | 2523      | 12        | 30276     | A             |\n    | 1998-10-14T00:00:00.000000Z | 4967     | 37         | 5308      | 60        | 318480    | D             |\n    | 1998-04-19T00:00:00.000000Z | 4968     | 38         | 2307      | 48        | 110736    | C             |\n    ```\n    Finally, we are able to construct the data model by deriving\n    [`Placeholder`][getml.data.Placeholder] from each\n    [`DataFrame`][getml.DataFrame] and establishing relations between\n    them using the [`join`][getml.data.Placeholder.join] method.\n\n    ```python\n    # But, first, we need a second data set to build a data model.\n    trans = getml.DataFrame.from_db(\n        'trans', 'df_trans',\n        roles = {getml.data.roles.numerical: [\"amount\", \"balance\"],\n                 getml.data.roles.categorical: [\"type\", \"bank\", \"k_symbol\",\n                                                \"account\", \"operation\"],\n                 getml.data.roles.join_key: [\"account_id\"],\n                 getml.data.roles.time_stamp: [\"date\"]\n        }\n    )\n\n    ph_loan = loan.to_placeholder()\n    ph_trans = trans.to_placeholder()\n\n    ph_loan.join(ph_trans, on=\"account_id\",\n                time_stamps=\"date\")\n    ```\n\n    The data model contained in `ph_loan` can now be used to\n    construct a [`Pipeline`][getml.Pipeline].\n\"\"\"\n\nfrom . import access, relationship, roles, split, subroles, time\nfrom .columns import arange, random, rowid\nfrom .concat import concat\nfrom .container import Container\nfrom .data_frame import DataFrame\nfrom .data_model import DataModel\nfrom .helpers import OnType, list_data_frames\nfrom .helpers2 import (\n    _decode_data_model,\n    _decode_placeholder,\n    _load_view,\n    delete,\n    exists,\n    load_data_frame,\n    make_target_columns,\n    to_placeholder,\n)\nfrom .load_container import load_container\nfrom .placeholder import Placeholder, TimeStampsType\nfrom .roles.container import Roles\nfrom .star_schema import StarSchema\nfrom .subset import Subset\nfrom .time_series import TimeSeries\nfrom .view import View\n\n__all__ = (\n    \"access\",\n    \"Container\",\n    \"DataFrame\",\n    \"DataModel\",\n    \"Placeholder\",\n    \"Roles\",\n    \"StarSchema\",\n    \"Subset\",\n    \"TimeSeries\",\n    \"View\",\n    \"arange\",\n    \"concat\",\n    \"delete\",\n    \"exists\",\n    \"load_container\",\n    \"load_data_frame\",\n    \"list_data_frames\",\n    \"make_target_columns\",\n    \"OnType\",\n    \"TimeStampsType\",\n    \"random\",\n    \"relationship\",\n    \"roles\",\n    \"rowid\",\n    \"split\",\n    \"subroles\",\n    \"time\",\n    \"to_placeholder\",\n    \"_decode_data_model\",\n    \"_decode_placeholder\",\n    \"_load_view\",\n)\n"
  },
  {
    "path": "src/python-api/getml/data/_io/arrow.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nUtilities for handling Arrow data structures.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport re\nimport socket\nimport warnings\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass, field\nfrom functools import wraps\nfrom inspect import cleandoc\nfrom typing import (\n    TYPE_CHECKING,\n    Callable,\n    Dict,\n    Iterable,\n    Iterator,\n    List,\n    Tuple,\n    Union,\n    cast,\n)\n\nimport pyarrow as pa\nimport pyarrow.compute as pa_compute\nfrom pyarrow.lib import ArrowInvalid\n\nfrom getml import communication as comm\nfrom getml.constants import DEFAULT_BATCH_SIZE\nfrom getml.data.roles import roles\nfrom getml.data.roles import sets as roles_sets\nfrom getml.data.roles.container import Roles\nfrom getml.data.roles.types import Role\nfrom getml.exceptions import arrow_cast_exception_handler\n\nif TYPE_CHECKING:\n    from getml.data.data_frame import DataFrame\n    from getml.data.view import View\n\nMAX_SUPPORTED_TIMESTAMP_RESOLUTION = \"us\"\n\"\"\"\nMaximum timestamp resolution supported by getML.\n\"\"\"\n\nMAX_IEEE754_COMPATIBLE_INT = 2**53\nMIN_IEEE754_COMPATIBLE_INT = -MAX_IEEE754_COMPATIBLE_INT\n\"\"\"\nIEEE 754 double-precision numbers have a 53-bit mantissa, allowing exact\nrepresentation of integers up to 2**53. The minimum exact integer\nrepresentable is the negative of this maximum value.\n\"\"\"\n\nINT_ENCODING_HINTS_PARTIAL = cleandoc(\n    \"\"\"\n    If the column contains catergorical values, you can either cast the column to\n    string before reading data into getML or explicitly set the column's role to\n    'categorical' in `from_...` or `read_...` methods.\n\n    If the column contains numerical values, rescale your integers to be within the\n    range of integers representable by IEEE754 double precision 64bit floating\n    point (float64) numbers:\n    [{min_ieee754_compatible_int}, {max_ieee754_compatible_int}].\n    \"\"\"\n).format(\n    min_ieee754_compatible_int=MIN_IEEE754_COMPATIBLE_INT,\n    max_ieee754_compatible_int=MAX_IEEE754_COMPATIBLE_INT,\n)\n\n\nDECIMAL_CONVERSION_LOST_PRECISION_WARNING_TEMPLATE = cleandoc(\n    \"\"\"\n    \\nColumn '{column_name}' has been converted from decimal to float. This may\n    result in a loss of precision!\n    \"\"\"\n)\n\nINT64_EXCEEDS_FLOAT64_BOUNDS_ERROR_TEMPLATE = cleandoc(\n    \"\"\"\n    Column '{{column_name}}' contains values that exceed the bounds of a float64. getML\n    uses double precision floating point numbers to represent numerical values.\n\n    {int_encoding_hints}\n    \"\"\"\n).format(int_encoding_hints=INT_ENCODING_HINTS_PARTIAL)\n\nTIMESTAMP_ENCODING_HINTS_PARTIAL = cleandoc(\n    \"\"\"\n    Currently, getML doesn't support the handling of explicit timezones for\n    timestamps. If you need timezone information to be available for your models, you\n    can encode the timezone explicitly in a separate column.\n    \"\"\"\n)\n\nTIMEZONE_UTC_DROPPED_WARNING_TEMPLATE = cleandoc(\n    \"\"\"\n    \\nColumn '{{column_name}}' has UTC timezone. Dropping the timezone.\n\n    {timestamp_encoding_hints}\n    \"\"\"\n).format(timestamp_encoding_hints=TIMESTAMP_ENCODING_HINTS_PARTIAL)\n\nTIMEZONE_NON_UTC_ERROR_TEMPLATE = cleandoc(\n    \"\"\"\n    Column '{{column_name}}' has a timezone '{{timezone}}' that is not UTC. Please\n    cast the timestamp to be UTC-based or drop the timezone.\n\n    {timestamp_encoding_hints}\n    \"\"\"\n).format(timestamp_encoding_hints=TIMESTAMP_ENCODING_HINTS_PARTIAL)\n\nUNSUPPORTED_TYPE_PREDICATES = (\n    pa.types.is_dictionary,\n    pa.types.is_large_list,\n    pa.types.is_list,\n    pa.types.is_map,\n    pa.types.is_struct,\n    pa.types.is_union,\n)\n\"\"\"\nArrow predicates identifying unsupported types.\n\"\"\"\n\nUNSUPPORTED_TYPE_WARNING_TEMPLATE = cleandoc(\n    \"\"\"\n    Column '{column_name}' has an unsupported type: {type}. The column will be ignored.\n    \"\"\"\n)\n\nUNPARAMETERIZED_TYPE_CONVERSION_MAPPING = {\n    pa.int8(): pa.float64(),\n    pa.int16(): pa.float64(),\n    pa.int32(): pa.float64(),\n    pa.int64(): pa.float64(),\n    pa.uint8(): pa.float64(),\n    pa.uint16(): pa.float64(),\n    pa.uint32(): pa.float64(),\n    pa.uint64(): pa.float64(),\n    pa.float16(): pa.float64(),\n    pa.float32(): pa.float64(),\n    pa.null(): pa.string(),\n}\n\"\"\"\nMapping from unsupported native unparameterized arrow types to supported types\nthat can be handled by the getML Engine.\n\nArrow's parameterized types (e.g. decimals) are not included here, as they are\nparameterized and keeping track of all possible parameterizations would be cumbersome.\nAs such, parameterized types are handled in a separate processor that dynamically\nhandles such types by checking against the pyarrows respective ABCs at runtime.\n\nAs there is no dedicated null type in getML, nulls are cast to string.\n\"\"\"\n\n\n@dataclass\nclass Processors:\n    \"\"\"\n    Container for arrow schema field processors. Each role is associated with a\n    list of processors that are applied to fields associated with columns that\n    are going to be treated as the respective role.\n    \"\"\"\n\n    categorical: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    numerical: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    target: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    time_stamp: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    join_key: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    text: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    unused_float: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n    unused_string: List[Callable[[pa.Field], pa.Field]] = field(default_factory=list)\n\n    def __getitem__(self, role: Role) -> List[Callable[[pa.Field], pa.Field]]:\n        return getattr(self, role)\n\n\nclass ArrowSchemaFieldProcessorRegistry:\n    \"\"\"\n    Registry for arrow schema field processors.\n\n    Instances can be used as a decorator to register a function as a\n    processor for arrow schema fields. Processors are registered targeting\n    a set of roles on the engine. If a field is associated with a column that\n    is going to be treated as any of the registered roles, the processor\n    will be applied to the respective field.\n    \"\"\"\n\n    def __init__(self):\n        self.processors: Processors = Processors()\n\n    def __call__(\n        self, roles: Iterable[Role]\n    ) -> Callable[\n        [Callable[[pa.Field], None]],\n        Callable[[pa.Field], pa.Field],\n    ]:\n        return self.register(roles)\n\n    def register(\n        self, roles: Iterable[Role]\n    ) -> Callable[\n        [Callable[[pa.Field], None]],\n        Callable[[pa.Field], pa.Field],\n    ]:\n        def decorator(func: Callable[[pa.Field], pa.Field]):\n            @wraps(func)\n            def wrapper(*args, **kwargs):\n                return func(*args, **kwargs)\n\n            for role in roles:\n                self.processors[role].append(wrapper)\n            return wrapper\n\n        return decorator\n\n    def retrieve(self, role: Role) -> List[Callable[[pa.Field], pa.Field]]:\n        return self.processors[role]\n\n\narrow_schema_field_preprocessor = ArrowSchemaFieldProcessorRegistry()\n\"\"\"\nRegistry for arrow schema field preprocessors.\n\nCan be used as a decorator to register a function as a preprocessor for arrow\nschema fields.\n\nPreprocessors are applied _before_ any data has been read into arrow.\n\nEach preprocessor is registered targeting set of roles. Preprocessors\nare applied to fields that are associated with any of the registered roles\nin the target data frame.\n\nE.g. handling of unparameterized types (int to float conversion etc.) is\nregistered targeting the set of roles that are considered 'numerical', i.e. if a\nfield is associated with a column that is going to be treated as 'numerical' by\nthe Engine, the data will be read as float to arrow right away. If the same\nfield, however, is associated with a column that is treated as string like\n('unused_string') the field will be left untouched s.t. the original\nrepresentation is preserved.\n\"\"\"\n\narrow_schema_field_postprocessor = ArrowSchemaFieldProcessorRegistry()\n\"\"\"\nRegistry for arrow schema field postprocessors.\n\nCan be used as a decorator to register a function as a postprocessor for arrow\nschema fields.\n\nPostprocessors are applied _after_ the data has been read into arrow but before\nthe data is sent to the Engine.\n\nEach postprocessor is registered targeting set of roles. Postprocessors\nare applied to fields that are associated with any of the registered roles\nin the target data frame.\n\nE.g. `postprocess_timestamp_drop_timezone` is registered targeting the set of\nroles that are considered as 'numerical' s.t. the timezone information is\ndropped only when the timestamp is going to be parsed.\n\"\"\"\n\n\ndef _create_arrow_metadata(\n    df_or_view: Union[DataFrame, View],\n) -> Dict[str, str]:\n    metadata = {\n        \"name\": df_or_view.name,\n        \"last_change\": df_or_view.last_change,\n        \"roles\": df_or_view.roles.to_dict(),\n    }\n    metadata_encoded = {\"getml\": json.dumps(metadata)}\n    return metadata_encoded\n\n\n@contextmanager\ndef _establish_arrow_stream(\n    df_or_view: Union[DataFrame, View],\n) -> Tuple[socket.socket, pa.BufferReader, pa.RecordBatchReader]:\n    df_or_view.refresh()\n\n    typename = type(df_or_view).__name__\n\n    cmd = {\n        \"type_\": f\"{typename}.to_arrow\",\n        \"name_\": df_or_view.name if typename == \"DataFrame\" else \"\",\n    }\n\n    if typename == \"View\":\n        cmd[\"view_\"] = df_or_view._getml_deserialize()\n\n    sock = comm.send_and_get_socket(cmd)\n    msg = comm.recv_string(sock)\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n    stream = sock.makefile(mode=\"rb\")\n    reader = pa.ipc.open_stream(stream)\n\n    with sock, stream, reader:\n        yield sock, stream, reader\n\n\ndef _is_numerical_type_arrow(coltype: pa.DataType) -> bool:\n    return any(\n        [\n            pa.types.is_integer(coltype),\n            pa.types.is_floating(coltype),\n            pa.types.is_decimal(coltype),\n        ]\n    )\n\n\ndef _is_unsupported_type_arrow(coltype: pa.DataType) -> bool:\n    return any(predicate(coltype) for predicate in UNSUPPORTED_TYPE_PREDICATES)\n\n\n@arrow_schema_field_preprocessor(roles=roles_sets.all_)\ndef preprocess_timestamp_cast_to_microseconds(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Cast timestamps to microseconds to ensure compatibility with the getML engine.\n    \"\"\"\n    if pa.types.is_timestamp(field.type):\n        return pa.field(\n            field.name,\n            pa.timestamp(MAX_SUPPORTED_TIMESTAMP_RESOLUTION, tz=field.type.tz),\n        )\n    return field\n\n\n@arrow_schema_field_preprocessor(roles={roles.unused_string})\ndef preprocess_types_cast_to_string(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Eagerly cast all fields (except timestamps) that are going to be treated as\n    string like to string.\n    \"\"\"\n    if pa.types.is_timestamp(field.type):\n        return field\n    return pa.field(field.name, pa.string())\n\n\n@arrow_schema_field_preprocessor(roles=roles_sets.numerical)\ndef preprocess_unparameterized_type(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Read unparameterized types as float64 to ensure compatibility with the\n    getML engine. Only do so if the column is going to be treated as 'numerical'\n    like.\n    \"\"\"\n    if field.type in UNPARAMETERIZED_TYPE_CONVERSION_MAPPING:\n        return pa.field(\n            field.name,\n            UNPARAMETERIZED_TYPE_CONVERSION_MAPPING[field.type],\n        )\n    return field\n\n\n@arrow_schema_field_preprocessor(roles=roles_sets.numerical)\ndef preprocess_parameterized_type(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Read parameterized types as float64 to ensure compatibility with the\n    getML engine. Only do so if the column is going to be treated as 'numerical'\n    like.\n    \"\"\"\n    if pa.types.is_decimal(field.type):\n        warnings.warn(\n            DECIMAL_CONVERSION_LOST_PRECISION_WARNING_TEMPLATE.format(\n                column_name=field.name\n            )\n        )\n        return pa.field(field.name, pa.float64())\n    return field\n\n\n@arrow_schema_field_postprocessor(roles=roles_sets.numerical)\ndef postprocess_timestamp_drop_timezone(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Drop the timezone information from timestamps if the column is going to be\n    parsed (parsing only happens when the target role is numerical).\n\n    Note that columns with the role 'time_stamp' are encoded as float64, s.t.\n    the resulting role can be consirederd as 'numerical' like.\n    \"\"\"\n    if pa.types.is_timestamp(field.type):\n        if field.type.tz is None:\n            return field.with_metadata({})\n        elif field.type.tz == \"UTC\":\n            warnings.warn(\n                TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE.format(column_name=field.name)\n            )\n            return pa.field(field.name, pa.timestamp(field.type.unit))\n        else:\n            raise TypeError(\n                TIMEZONE_NON_UTC_ERROR_TEMPLATE.format(\n                    column_name=field.name, timezone=field.type.tz\n                )\n            )\n    else:\n        return field.with_metadata({})\n\n\n@arrow_schema_field_postprocessor(roles={roles.unused_string})\ndef postprocess_timestamp_cast_to_string(field: pa.Field) -> pa.Field:\n    \"\"\"\n    Cast timestamps that are going to be treated as string like to string.\n    \"\"\"\n    if pa.types.is_timestamp(field.type):\n        return pa.field(field.name, pa.string())\n    return field\n\n\ndef _process_arrow_schema(\n    schema: pa.Schema, roles: Roles, processors: ArrowSchemaFieldProcessorRegistry\n) -> pa.Schema:\n    processed_fields = []\n    for field in schema:\n        if field.name not in roles.columns:\n            continue\n        role = roles.column(field.name)\n        field_processed = field.with_metadata({})\n        for processor in processors.retrieve(role):\n            field_processed = processor(field_processed)\n        processed_fields.append(field_processed)\n    return pa.schema(processed_fields)\n\n\ndef postprocess_arrow_schema(schema: pa.Schema, roles: Roles) -> pa.Schema:\n    return _process_arrow_schema(schema, roles, arrow_schema_field_postprocessor)\n\n\ndef preprocess_arrow_schema(schema: pa.Schema, roles: Roles) -> pa.Schema:\n    return _process_arrow_schema(schema, roles, arrow_schema_field_preprocessor)\n\n\ndef sniff_schema(schema: pa.Schema, colnames: Iterable[str] = ()) -> Roles:\n    arrow_metadata = schema.metadata or {}\n    if metadata := arrow_metadata.get(b\"getml\"):\n        metadata_unmarshaled = json.loads(metadata)\n        roles = metadata_unmarshaled[\"roles\"]\n        return Roles.from_dict(roles)\n\n    if metadata := arrow_metadata.get(b\"PANDAS_ATTRS\", {}).get(b\"getml\"):\n        metadata_unmarshaled = json.loads(metadata)\n        roles = metadata_unmarshaled[\"roles\"]\n        return Roles.from_dict(roles)\n\n    roles: Dict[str, List[str]] = {}\n    roles[\"unused_float\"] = []\n    roles[\"unused_string\"] = []\n\n    if not colnames:\n        colnames = schema.names\n    else:\n        colnames = [cname for cname in colnames if cname in schema.names]\n\n    coltypes = schema.types\n\n    for cname, ctype in zip(colnames, coltypes):\n        if _is_unsupported_type_arrow(ctype):\n            warnings.warn(\n                UNSUPPORTED_TYPE_WARNING_TEMPLATE.format(column_name=cname, type=ctype)\n            )\n            continue\n        if _is_numerical_type_arrow(ctype):\n            roles[\"unused_float\"].append(cname)\n        else:\n            roles[\"unused_string\"].append(cname)\n\n    return Roles.from_dict(roles)\n\n\ndef to_arrow(df_or_view: Union[DataFrame, View]) -> pa.Table:\n    metadata = _create_arrow_metadata(df_or_view)\n    with to_arrow_stream(df_or_view) as reader:\n        return reader.read_all().replace_schema_metadata(metadata)\n\n@contextmanager\ndef to_arrow_stream(\n    df_or_view: Union[DataFrame, View],\n) -> Iterator[pa.RecordBatchReader]:\n    with _establish_arrow_stream(df_or_view) as (sock, stream, reader):\n        yield reader\n        \ndef to_arrow_batches(\n    batch_or_batches: Union[pa.RecordBatch, pa.Table, Iterable[pa.RecordBatch]],\n) -> Tuple[pa.Schema, Iterator[pa.RecordBatch]]:\n    if isinstance(batch := batch_or_batches, pa.RecordBatch):\n        batch = cast(pa.RecordBatch, batch)\n        return batch.schema, iter([batch])\n    elif isinstance(batches := batch_or_batches, Iterable):\n        if all(isinstance(batch, pa.RecordBatch) for batch in batches):\n            first_batch = cast(pa.RecordBatch, next(iter(batches)))\n            return first_batch.schema, iter(batches)\n        else:\n            raise TypeError(\n                \"If 'data' is an iterable, all elements must be of type pa.RecordBatch.\"\n            )\n    elif isinstance(table := batch_or_batches, pa.Table):\n        return table.schema, iter(table.to_batches(max_chunksize=DEFAULT_BATCH_SIZE))  # type: ignore\n    else:\n        raise TypeError(\n            \"'data' must be a pa.RecordBatch, pa.Table, or an iterable of pa.RecordBatch instances.\"\n        )\n\n\n@arrow_cast_exception_handler(source_type=pa.int64(), target_type=pa.float64())\ndef handle_int64_to_float64_exc(\n    exc: ArrowInvalid, source_array: pa.Array, target_field: pa.Field\n):\n    if re.match(r\"Integer value \\d+ not in range\", str(exc)):\n        raise OverflowError(\n            INT64_EXCEEDS_FLOAT64_BOUNDS_ERROR_TEMPLATE.format(\n                column_name=target_field.name\n            )\n        ) from exc\n    raise exc\n\n\n@arrow_cast_exception_handler(source_type=pa.binary(), target_type=pa.string())\ndef handle_binary_to_string_exc(\n    exc: ArrowInvalid, source_array: pa.Array, target_field: pa.Field\n):\n    if re.match(r\"Invalid UTF8 payload\", str(exc)):\n        raise ValueError(\n            f\"Binary column '{target_field.name}' contains invalid UTF-8 characters.\\n\"\n            \"First 5 rows of the column:\\n\"\n            f\"{source_array.slice(0, 5)}\"\n        )\n\n\ndef cast_arrow_batch(\n    source_batch: Iterable[pa.RecordBatch], target_schema: pa.Schema\n) -> Iterable[pa.RecordBatch]:\n    \"\"\"\n    Cast a source record batch to be compliant with a target schema.\n    \"\"\"\n    target_arrays = []\n    for target_field in target_schema:\n        source_array = source_batch.column(target_field.name)  # type: ignore\n        if source_array.type != target_field.type:\n            cast_options = pa_compute.CastOptions(\n                target_type=target_field.type,\n                allow_time_truncate=True,\n            )\n            try:\n                target_arrays.append(source_array.cast(options=cast_options))\n            except ArrowInvalid as e:\n                handler = arrow_cast_exception_handler.retrieve(\n                    source_type=source_array.type, target_type=target_field.type\n                )\n                handler(e, source_array, target_field)\n        else:\n            target_arrays.append(source_array)\n    return pa.RecordBatch.from_arrays(target_arrays, schema=target_schema)\n\n\ndef read_arrow_batches(\n    batches: Iterable[pa.RecordBatch],\n    schema: pa.Schema,\n    df: DataFrame,\n    append: bool = False,\n) -> None:\n    cmd = {\n        \"type_\": \"DataFrame.from_arrow\",\n        \"name_\": df.name,\n        \"append_\": append,\n        \"categorical_\": df._categorical_names,\n        \"join_keys_\": df._join_key_names,\n        \"numerical_\": df._numerical_names,\n        \"targets_\": df._target_names,\n        \"text_\": df._text_names,\n        \"time_stamps_\": df._time_stamp_names,\n        \"unused_floats_\": df._unused_float_names,\n        \"unused_strings_\": df._unused_string_names,\n    }\n\n    processed_schema = postprocess_arrow_schema(schema, df.roles)\n\n    with comm.send_and_get_socket(cmd) as sock:\n        with sock.makefile(mode=\"wb\") as sink:\n            with pa.ipc.new_stream(sink, processed_schema) as writer:\n                for batch in batches:\n                    if processed_schema != batch.schema:\n                        compliant_batch = cast_arrow_batch(batch, processed_schema)\n                    else:\n                        compliant_batch = batch\n                    writer.write_batch(compliant_batch)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n"
  },
  {
    "path": "src/python-api/getml/data/_io/csv.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nUtilities for working with CSV files.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport itertools as it\nimport numbers\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Iterable, Iterator, List, Tuple, Union\n\nimport pyarrow as pa\nimport pyarrow.csv as pa_csv\n\nfrom getml.constants import DEFAULT_BATCH_SIZE\nfrom getml.data._io.arrow import preprocess_arrow_schema, sniff_schema\nfrom getml.data.roles.container import Roles\nfrom getml.database.helpers import _retrieve_urls\n\nif TYPE_CHECKING:\n    from getml.data.data_frame import DataFrame\n    from getml.data.view import View\n\nDEFAULT_CSV_READ_BLOCK_SIZE = 1 << 20\n\n\ndef sniff_csv(\n    fnames: List[str],\n    quotechar: str = '\"',\n    sep: str = \",\",\n    skip: int = 0,\n    colnames: Iterable[str] = (),\n) -> Roles:\n    \"\"\"Sniffs a list of CSV files and returns the result as a dictionary of\n    roles.\n\n    Args:\n        fnames: The list of CSV file names to be read.\n\n        num_lines_sniffed:\n\n            Number of lines analysed by the sniffer.\n\n        quotechar:\n\n            The character used to wrap strings.\n\n        sep:\n\n            The character used for separating fields.\n\n        skip:\n            Number of lines to skip at the beginning of each file.\n\n        colnames: The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n    Returns:\n        Keyword arguments (kwargs) that can be used to construct a DataFrame.\n    \"\"\"\n\n    fnames_ = _retrieve_urls(fnames, verbose=False)\n\n    readers = [\n        pa_csv.open_csv(\n            fname,\n            read_options=pa_csv.ReadOptions(\n                skip_rows=skip,\n                column_names=colnames,\n            ),\n            parse_options=pa_csv.ParseOptions(\n                delimiter=sep,\n                quote_char=quotechar,\n            ),\n        )\n        for fname in fnames_\n    ]\n\n    schema, *deviating = set(reader.schema for reader in readers)\n\n    if deviating:\n        raise ValueError(\"All CSV files must have the same schema!\")\n\n    if not colnames:\n        colnames = schema.names\n\n    return sniff_schema(schema, colnames)\n\n\ndef to_csv(\n    df_or_view: Union[DataFrame, View],\n    fname: str,\n    sep: str = \",\",\n    batch_size: int = DEFAULT_BATCH_SIZE,\n    quoting_style: str = \"needed\",\n):\n    df_or_view.refresh()\n\n    if not isinstance(fname, str):\n        raise TypeError(\"'fname' must be of type str\")\n\n    if not isinstance(sep, str):\n        raise TypeError(\"'sep' must be of type str\")\n\n    if not isinstance(batch_size, numbers.Real):\n        raise TypeError(\"'batch_size' must be a real number\")\n\n    if batch_size == 0:\n        batch_size = len(df_or_view)\n\n    sink = Path(fname)\n\n    write_options = pa_csv.WriteOptions(\n        batch_size=batch_size,\n        delimiter=sep,\n        quoting_style=quoting_style,\n    )\n\n    batches = (batch.to_arrow() for batch in df_or_view.iter_batches(batch_size))\n\n    first_batch = next(batches)\n    schema = first_batch.schema\n\n    writer = pa_csv.CSVWriter(sink, schema, write_options=write_options)\n\n    for batch in it.chain([first_batch], batches):\n        writer.write(batch)\n\n\ndef _prepare_csv_options(\n    input_file: PathLike,\n    roles: Roles,\n    skip_rows: int,\n    column_names: Iterable[str],\n    delimiter: str,\n    quote_char: str,\n    block_size: int,\n) -> Tuple[pa_csv.ReadOptions, pa_csv.ParseOptions, pa_csv.ConvertOptions]:\n    read_options = pa_csv.ReadOptions(\n        block_size=block_size,\n        skip_rows=skip_rows,\n        column_names=column_names,\n        autogenerate_column_names=False,\n    )\n\n    parse_options = pa_csv.ParseOptions(\n        delimiter=delimiter,\n        quote_char=quote_char,\n    )\n\n    preprocessed_schema = preprocess_arrow_schema(\n        pa_csv.open_csv(input_file).schema, roles\n    )\n    convert_options = pa_csv.ConvertOptions(\n        column_types=preprocessed_schema,\n    )\n\n    return read_options, parse_options, convert_options\n\n\ndef stream_csv(\n    input_file: PathLike,\n    roles: Roles,\n    skip_rows: int = 0,\n    column_names: Iterable[str] = (),\n    delimiter: str = \",\",\n    quote_char: str = '\"',\n    block_size: int = DEFAULT_CSV_READ_BLOCK_SIZE,\n) -> pa_csv.CSVStreamingReader:\n    read_options, parse_options, convert_options = _prepare_csv_options(\n        input_file,\n        roles,\n        skip_rows,\n        column_names,\n        delimiter,\n        quote_char,\n        block_size,\n    )\n\n    return pa_csv.open_csv(\n        input_file,\n        read_options=read_options,\n        parse_options=parse_options,\n        convert_options=convert_options,\n    )\n\n\ndef read_csv(\n    input_file: PathLike,\n    roles: Roles,\n    skip_rows: int = 0,\n    column_names: Iterable[str] = (),\n    delimiter: str = \",\",\n    quote_char: str = '\"',\n    block_size: int = DEFAULT_CSV_READ_BLOCK_SIZE,\n) -> Iterator[pa.RecordBatch]:\n    read_options, parse_options, convert_options = _prepare_csv_options(\n        input_file,\n        roles,\n        skip_rows,\n        column_names,\n        delimiter,\n        quote_char,\n        block_size,\n    )\n\n    return iter(\n        pa_csv.read_csv(\n            input_file,\n            read_options=read_options,\n            parse_options=parse_options,\n            convert_options=convert_options,\n        ).to_batches()\n    )\n"
  },
  {
    "path": "src/python-api/getml/data/_io/parquet.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nUtitlities for working with Parquet files.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport itertools as it\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Iterable, Optional, Union\n\nimport pyarrow.parquet as pq\n\nfrom getml.data._io.arrow import _create_arrow_metadata, sniff_schema\nfrom getml.data.roles.container import Roles\n\nif TYPE_CHECKING:\n    from getml.data.data_frame import DataFrame\n    from getml.data.view import View\n\n\ndef sniff_parquet(fnames: Iterable[str], colnames: Iterable[str]) -> Roles:\n    first_fname = next(iter(fnames))\n    schema = pq.read_schema(first_fname)\n\n    for fname in it.islice(fnames, 1, None):\n        if pq.read_schema(fname) != schema:\n            raise ValueError(\n                \"All Parquet files must have the same schema.\"\n                f\"Expected: {schema} (from {first_fname!r}) got {pq.read_schema(fname)} (from {fname!r})\"\n            )\n\n    return sniff_schema(schema, colnames)\n\n\ndef to_parquet(\n    df_or_view: Union[DataFrame, View],\n    fname: str,\n    compression: str,\n    coerce_timestamps: Optional[bool] = None,\n):\n    df_or_view.refresh()\n\n    if not isinstance(fname, str):\n        raise TypeError(\"'fname' must be of type str\")\n\n    if not isinstance(compression, str):\n        raise TypeError(\"'compression' must be of type str\")\n\n    sink = Path(fname)\n\n    metadata = _create_arrow_metadata(df_or_view)\n\n    with df_or_view.to_arrow_stream() as reader:\n        schema = reader.schema.with_metadata(metadata)\n        with pq.ParquetWriter(\n            sink,\n            schema,\n            compression=compression,\n            coerce_timestamps=coerce_timestamps,\n        ) as writer:\n            for batch in reader:\n                writer.write_batch(batch)\n"
  },
  {
    "path": "src/python-api/getml/data/access.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nManages the access to various data sources.\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport getml.communication as comm\nfrom getml.engine import is_alive as _is_alive\n\n# -----------------------------------------------------------------------------\n\n\ndef set_s3_access_key_id(value: str):\n    \"\"\"Sets the Access Key ID to S3.\n\n    Notes:\n        Note that S3 is not supported on Windows.\n\n    In order to retrieve data from S3, you need to set the Access Key ID\n    and the Secret Access Key. You can either set them as environment\n    variables before you start the getML Engine, or you can set them from\n    this module.\n\n    Args:\n        value:\n            The value to which you want to set the Access Key ID.\n    \"\"\"\n\n    if not isinstance(value, str):\n        raise TypeError(\"'value' must be of type str\")\n\n    if not _is_alive():\n        raise ConnectionRefusedError(\n            \"\"\"\n        Cannot connect to getML Engine.\n        Make sure the Engine is running on port '\"\"\"\n            + str(comm.port)\n            + \"\"\"' and you are logged in.\n        See `help(getml.engine)`.\"\"\"\n        )\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"set_s3_access_key_id\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        comm.send_string(sock, value)\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef set_s3_secret_access_key(value: str):\n    \"\"\"Sets the Secret Access Key to S3.\n\n    Notes:\n        Note that S3 is not supported on Windows.\n\n    In order to retrieve data from S3, you need to set the Access Key ID\n    and the Secret Access Key. You can either set them as environment\n    variables before you start the getML Engine, or you can set them from\n    this module.\n\n    Args:\n        value:\n            The value to which you want to set the Secret Access Key.\n    \"\"\"\n\n    if not isinstance(value, str):\n        raise TypeError(\"'value' must be of type str\")\n\n    if not _is_alive():\n        raise ConnectionRefusedError(\n            \"\"\"\n        Cannot connect to getML Engine.\n        Make sure the Engine is running on port '\"\"\"\n            + str(comm.port)\n            + \"\"\"' and you are logged in.\n        See `help(getml.engine)`.\"\"\"\n        )\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"set_s3_secret_access_key\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        comm.send_string(sock, value)\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n\n# --------------------------------------------------------------------------\n\n__all__ = (\"set_s3_access_key_id\", \"set_s3_secret_access_key\")\n"
  },
  {
    "path": "src/python-api/getml/data/columns/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHandlers for 1-d arrays storing the data of an individual variable.\n\nLike the [`DataFrame`][getml.DataFrame], the\n[`columns`][getml.data.columns] do not contain any actual data themselves\nbut are only handlers to objects within the getML Engine. These\ncontainers store data of a single variable in a one-dimensional array\nof a uniform type.\n\nColumns are *immutable* and *lazily evaluated*.\n\n- *Immutable* means that there are no in-place\n  operation on the columns. Any change to the column\n  will return a new, changed column.\n\n- *Lazy evaluation* means that operations won't be\n  executed until results are required. This is reflected\n  in the *column views*: Column views do not exist\n  until they are required.\n\n??? example\n    This is what some column operations might look like:\n\n    ```python\n\n    import numpy as np\n\n    import getml.data as data\n    import getml.engine as engine\n    import getml.data.roles as roles\n\n    # ----------------\n\n    engine.set_project(\"examples\")\n\n    # ----------------\n    # Create a data frame from a JSON string\n\n    json_str = \\\"\\\"\\\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4],\n        \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n    }\\\"\\\"\\\"\n\n    my_df = data.DataFrame(\n        \"MY DF\",\n        roles={\n            \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n            \"unused_float\": [\"column_01\"]}\n    ).read_json(\n        json_str\n    )\n\n    # ----------------\n\n    col1 = my_df[\"column_01\"]\n\n    # ----------------\n\n    # col2 is a column view.\n    # The operation is not executed yet.\n    col2 = 2.0 - col1\n\n    # This is when '2.0 - col1' is actually\n    # executed.\n    my_df[\"column_02\"] = col2\n    my_df.set_role(\"column_02\", roles.numerical)\n\n    # If you want to update column_01,\n    # you can't do that in-place.\n    # You need to replace it with a new column\n    col1 = col1 + col2\n    my_df[\"column_01\"] = col1\n    my_df.set_role(\"column_01\", roles.numerical)\n    ```\n\"\"\"\n\nfrom .columns import (\n    BooleanColumnView,\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n    arange,\n    rowid,\n)\nfrom .from_value import from_value\nfrom .parse import _parse\nfrom .random import random\n\n__all__ = (\n    \"BooleanColumnView\",\n    \"FloatColumn\",\n    \"FloatColumnView\",\n    \"StringColumn\",\n    \"StringColumnView\",\n    \"arange\",\n    \"random\",\n    \"rowid\",\n    \"from_value\",\n    \"_parse\",\n)\n"
  },
  {
    "path": "src/python-api/getml/data/columns/aggregation.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nLazily evaluated aggregation over a column.\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport getml.communication as comm\n\n\nclass Aggregation:\n    \"\"\"\n    Lazily evaluated aggregation over a column.\n\n    ??? example\n        ```python\n        my_data_frame[\"my_column\"].avg()\n        3.0\n        ```\n    \"\"\"\n\n    def __init__(self, alias, col, agg_type):\n        self.cmd: Dict[str, Any] = {}\n        self.cmd[\"as_\"] = alias\n        self.cmd[\"col_\"] = col.cmd\n        self.cmd[\"type_\"] = agg_type\n\n    # -----------------------------------------------------------------------------\n\n    def __repr__(self):\n        return str(self)\n\n    # -----------------------------------------------------------------------------\n\n    def __str__(self):\n        val = self.get()\n        return self.cmd[\"type_\"].upper() + \" aggregation, value: \" + str(val) + \".\"\n\n    # --------------------------------------------------------------------------\n\n    def get(self):\n        \"\"\"\n        Receives the value of the aggregation over the column.\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"name_\"] = \"\"\n        cmd[\"type_\"] = \"FloatColumn.aggregate\"\n\n        cmd[\"aggregation_\"] = self.cmd\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            mat = comm.recv_float_matrix(sock)\n\n        return mat.ravel()[0]\n"
  },
  {
    "path": "src/python-api/getml/data/columns/collect_footer_data.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Collects the data necessary for displaying the column footer.\"\"\"\n\nfrom typing import NamedTuple, Optional, Union\n\nfrom .constants import _views\n\n\nclass Footer(NamedTuple):\n    \"\"\"\n    Contains the data to be shown\n    in the footer of the data frame or\n    column.\n    \"\"\"\n\n    n_rows: Union[int, str]\n    type: str\n    data_frame: Optional[str] = None\n    url: Optional[str] = None\n\n\ndef _collect_footer_data(self) -> Footer:\n    if type(self).__name__ in _views:\n        nrows_is_known = not isinstance(self.length, str)\n\n        return Footer(\n            n_rows=self.length if nrows_is_known else self.length + \" number of \",\n            type=type(self).__name__,\n        )\n\n    return Footer(\n        n_rows=len(self),\n        data_frame=self.cmd[\"df_name_\"] if \"df_name_\" in self.cmd else \"\",\n        type=type(self).__name__,\n        url=self._monitor_url,\n    )\n"
  },
  {
    "path": "src/python-api/getml/data/columns/column.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Base object not meant to be called directly.\"\"\"\n\nfrom typing import Any, Dict, Optional\n\nimport getml.communication as comm\n\n\nclass _Column:\n    \"\"\"\n    Base object not meant to be called directly.\n    \"\"\"\n\n    # -------------------------------------------------------------------------\n\n    def __init__(self):\n        self.cmd: Dict[str, Any] = {}\n\n    # ------------------------------------------------------------\n\n    @property\n    def _monitor_url(self) -> Optional[str]:\n        \"\"\"\n        The URL of the column.\n        \"\"\"\n        url = comm._monitor_url()\n        if not url:\n            return None\n        return (\n            url\n            + \"getcolumn/\"\n            + comm._get_project_name()\n            + \"/\"\n            + self.cmd[\"df_name_\"]\n            + \"/\"\n            + self.name\n            + \"/\"\n        )\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self):\n        \"\"\"\n        The name of this column.\n        \"\"\"\n        return self.cmd[\"name_\"]\n\n    # -------------------------------------------------------------------------\n\n    @property\n    def role(self):\n        \"\"\"\n        The role of this column.\n\n        Roles are needed by the feature learning algorithm, so it knows how\n        to treat the columns.\n        \"\"\"\n        return self.cmd[\"role_\"]\n"
  },
  {
    "path": "src/python-api/getml/data/columns/columns.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Contains the actual columns.\"\"\"\n\nfrom __future__ import annotations\n\nimport numbers\nimport warnings\nfrom abc import ABC\nfrom collections import deque\nfrom inspect import cleandoc\nfrom typing import Any, Dict, Iterable, List, Optional, Union, get_args\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\n\nfrom getml.constants import TIME_FORMATS\nfrom getml.data import roles\nfrom getml.data._io.arrow import postprocess_arrow_schema\nfrom getml.data.roles.container import Roles\n\nfrom .aggregation import Aggregation\nfrom .collect_footer_data import _collect_footer_data\nfrom .column import _Column\nfrom .constants import (\n    BOOLEAN_COLUMN_VIEW,\n    FLOAT_COLUMN,\n    FLOAT_COLUMN_VIEW,\n    INFINITE,\n    STRING_COLUMN,\n    STRING_COLUMN_VIEW,\n)\nfrom .format import _format\nfrom .get_scalar import _get_scalar\nfrom .last_change_from_col import _last_change_from_col\nfrom .length import _length\nfrom .length_property import _length_property\nfrom .make_iter import _make_iter\nfrom .repr import _repr\nfrom .repr_html import _repr_html\nfrom .subroles import _subroles\nfrom .to_numpy import _to_numpy\nfrom .unique import _unique\nfrom .unit import _unit\n\n# -----------------------------------------------------------------------------\n\nTYPE_MISSMATCH_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    Can only compare {col_type} with: {operand_types}.\n\n    You can explicitly cast columns via `.as_num()` or `.as_str()`.\n    \"\"\"\n)\n\n# -----------------------------------------------------------------------------\n\nTIME_STAMP_PARSING_LIKELY_FAILED_WARNING_TEMPLATE = cleandoc(\n    \"\"\"\n    After parsing time stamps, column '{column}' likely contains only nan values.\n\n    Check the supplied 'time_formats' and the data.\n    \"\"\"\n)\n\n# -----------------------------------------------------------------------------\n\n\nclass _View(ABC):\n    cmd: Dict[str, Any]\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _infer_column_name_recursive(cmd: Dict[str, Any]) -> str:\n    \"\"\"\n    Infer the name from a ...Column(View) if the column is an (anonymous) view,\n    recursively consult the view's bases (operands) in breadth-first manner\n    until a name is found. If no name is found, return the view's type and\n    operator.\n\n    This function is used to construct informative error messages.\n    \"\"\"\n    queue = deque([cmd])\n\n    while queue:\n        current = queue.popleft()\n\n        if \"name_\" in current:\n            return current[\"name_\"]\n\n        for key, value in current.items():\n            if isinstance(value, dict):\n                queue.append(value)\n\n    type = cmd.get(\"type_\")\n    operator = cmd.get(\"operator_\")\n    return f\"{type}.{operator}\"\n\n\n# -----------------------------------------------------------------------------\n\n\ndef arange(\n    start: Union[numbers.Real, float] = 0.0,\n    stop: Optional[Union[numbers.Real, float]] = None,\n    step: Union[numbers.Real, float] = 1.0,\n):\n    \"\"\"\n    Returns evenly spaced variables, within a given interval.\n\n    Args:\n        start:\n            The beginning of the interval. Defaults to 0.\n\n        stop:\n            The end of the interval.\n\n        step:\n            The step taken. Defaults to 1.\n    \"\"\"\n    if stop is None:\n        stop = start\n        start = 0.0\n\n    if step is None:\n        step = 1.0\n\n    if not isinstance(start, numbers.Real):\n        raise TypeError(\"'start' must be a real number\")\n\n    if not isinstance(stop, numbers.Real):\n        raise TypeError(\"'stop' must be a real number\")\n\n    if not isinstance(step, numbers.Real):\n        raise TypeError(\"'step' must be a real number\")\n\n    col = FloatColumnView(\n        operator=\"arange\",\n        operand1=None,\n        operand2=None,\n    )\n\n    col.cmd[\"start_\"] = float(start)\n    col.cmd[\"stop_\"] = float(stop)\n    col.cmd[\"step_\"] = float(step)\n\n    return col\n\n\n# -----------------------------------------------------------------------------\n\n\ndef rowid() -> FloatColumnView:\n    \"\"\"\n    Get the row numbers of the table.\n\n    Returns:\n            (numerical) column containing the row id, starting with 0\n    \"\"\"\n    return FloatColumnView(operator=\"rowid\", operand1=None, operand2=None)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _make_slicing_operand(column, slc):\n    step = slc.step or 1\n    start = slc.start or 0\n    start = start if start >= 0 else len(column) + start\n    if not slc.stop:\n        if column.length == INFINITE:\n            return (rowid() > start) & ((rowid() - start) % step == 0)  # type: ignore\n        return arange(start, column.length, step)\n    stop = slc.stop if slc.stop >= 0 else len(column) + slc.stop\n    return arange(start, stop, step)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _value_to_cmd(val: OperandType):\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"operator_\"] = \"const\"\n    cmd[\"value_\"] = val\n\n    if isinstance(val, bool):\n        cmd[\"type_\"] = BOOLEAN_COLUMN_VIEW\n        return cmd\n\n    if isinstance(val, str):\n        cmd[\"type_\"] = STRING_COLUMN_VIEW\n        return cmd\n\n    if isinstance(val, (numbers.Real, float, int)):\n        cmd[\"type_\"] = FLOAT_COLUMN_VIEW\n        cmd[\"value_\"] = float(val)  # type: ignore\n        return cmd\n\n    if isinstance(val, np.datetime64):\n        return _value_to_cmd(np.datetime64(val).astype(\"datetime64[s]\").astype(float))\n\n    assert False, \"Calling _value_to_cmd on an unknown type\"\n\n\n# -----------------------------------------------------------------------------\n\n\nclass BooleanColumnView(_View):\n    \"\"\"\n    Handle for a lazily evaluated boolean column view.\n\n    Column views do not actually exist - they will be lazily\n    evaluated when necessary.\n\n    They can be used to take subselection of the data frame\n    or to update other columns.\n\n    ??? example\n        ```python\n        import numpy as np\n\n        import getml.data as data\n        import getml.engine as engine\n        import getml.data.roles as roles\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n        # Create a data frame from a JSON string\n\n        json_str = \\\"\\\"\\\"{\n            \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n            \"column_01\": [2.4, 3.0, 1.2, 1.4],\n            \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n            \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n        }\\\"\\\"\\\"\n\n        my_df = data.DataFrame(\n            \"MY DF\",\n            roles={\n                \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n                \"unused_float\": [\"column_01\"]}\n        ).read_json(\n            json_str\n        )\n\n        # ----------------\n\n        names = my_df[\"names\"]\n\n        # This is a virtual boolean column.\n        a_or_p_in_names = names.contains(\"p\") | names.contains(\"a\")\n\n        # Creates a view containing\n        # only those entries, where \"names\" contains a or p.\n        my_view = my_df[a_or_p_in_names]\n\n        # ----------------\n\n        # Returns a new column, where all names\n        # containing \"rick\" are replaced by \"Patrick\".\n        # Again, columns are immutable - this returns an updated\n        # version, but leaves the original column unchanged.\n        new_names = names.update(names.contains(\"rick\"), \"Patrick\")\n\n        my_df[\"new_names\"] = new_names\n\n        # ----------------\n\n        # Boolean columns can also be used to\n        # create binary target variables.\n        target = (names == \"phil\")\n\n        my_df[\"target\"] = target\n        my_df.set_role(target, roles.target)\n\n        # By the way, instead of using the\n        # __setitem__ operator and .set_role(...)\n        # you can just use .add(...).\n        my_df.add(target, \"target\", roles.target)\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        operator: str,\n        operand1: Optional[OperandType],\n        operand2: Optional[OperandType],\n    ):\n        self.cmd: Dict[str, Any] = {}\n\n        self.cmd[\"type_\"] = BOOLEAN_COLUMN_VIEW\n\n        self.cmd[\"operator_\"] = operator\n\n        if operand1 is not None:\n            self.cmd[\"operand1_\"] = self._parse_operand(operand1)\n\n        if operand2 is not None:\n            self.cmd[\"operand2_\"] = self._parse_operand(operand2)\n\n    # -----------------------------------------------------------------------------\n\n    def __and__(self, other):\n        return BooleanColumnView(\n            operator=\"and\",\n            operand1=self,\n            operand2=other,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def __eq__(self, other):\n        return BooleanColumnView(\n            operator=\"bool_equal_to\",\n            operand1=self,\n            operand2=other,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def __invert__(self):\n        return self.is_false()\n\n    # -----------------------------------------------------------------------------\n\n    def __or__(self, other):\n        return BooleanColumnView(\n            operator=\"or\",\n            operand1=self,\n            operand2=other,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def __ne__(self, other):\n        return BooleanColumnView(\n            operator=\"bool_not_equal_to\",\n            operand1=self,\n            operand2=other,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def __xor__(self, other):\n        return BooleanColumnView(\n            operator=\"xor\",\n            operand1=self,\n            operand2=other,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def _parse_operand(\n        self,\n        operand: OperandType,\n    ):\n        if isinstance(operand, (bool, str, numbers.Real, np.datetime64)):\n            return _value_to_cmd(operand)\n\n        if not hasattr(operand, \"cmd\"):\n            raise TypeError(\n                \"\"\"Operand for a BooleanColumnView must be a\n                boolean, string, a Real, a numpy.datetime64\n                or a getml.data.Column!\"\"\"\n            )\n\n        if self.cmd[\"operator_\"] in [\"and\", \"or\", \"not\", \"xor\"]:\n            if operand.cmd[\"type_\"] != BOOLEAN_COLUMN_VIEW:\n                raise TypeError(\"This operator can only be applied to a BooleanColumn!\")\n\n        return operand.cmd\n\n    # -----------------------------------------------------------------------------\n\n    def is_false(self):\n        \"\"\"Whether an entry is False - effectively inverts the Boolean column.\"\"\"\n        return BooleanColumnView(\n            operator=\"not\",\n            operand1=self,\n            operand2=None,\n        )\n\n    # -----------------------------------------------------------------------------\n\n    def as_num(self):\n        \"\"\"Transforms the boolean column into a numerical column\"\"\"\n        return FloatColumnView(\n            operator=\"boolean_as_num\",\n            operand1=self,\n            operand2=None,\n        )\n\n\n# -----------------------------------------------------------------------------\n\n\nclass StringColumn(_Column):\n    \"\"\"Handle for categorical data that is kept in the getML Engine\n\n    Attributes:\n        name:\n            Name of the categorical column.\n\n        role:\n            Role that the column plays.\n\n        df_name:\n            ``name`` instance variable of the\n            [`DataFrame`][getml.DataFrame] containing this column.\n\n    ??? example\n        ```python\n        import numpy as np\n\n        import getml.data as data\n        import getml.engine as engine\n        import getml.data.roles as roles\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n        # Create a data frame from a JSON string\n\n        json_str = \\\"\\\"\\\"{\n            \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n            \"column_01\": [2.4, 3.0, 1.2, 1.4],\n            \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n            \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n        }\\\"\\\"\\\"\n\n        my_df = data.DataFrame(\n            \"MY DF\",\n            roles={\n                \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n                \"unused_float\": [\"column_01\"]}\n        ).read_json(\n            json_str\n        )\n\n        # ----------------\n\n        col1 = my_df[\"names\"]\n\n        # ----------------\n\n        col2 = col1.substr(4, 3)\n\n        my_df.add(col2, \"short_names\", roles.categorical)\n\n        # ----------------\n        # If you do not explicitly set a role,\n        # the assigned role will either be\n        # roles.unused_string.\n\n        col3 = \"user-\" + col1 + \"-\" + col2\n\n        my_df[\"new_names\"] = col3\n        my_df.set_role(\"new_names\", roles.categorical)\n        ```\n    \"\"\"\n\n    _num_columns = 0\n\n    def __init__(self, name: str = \"\", role: str = \"categorical\", df_name: str = \"\"):\n        super().__init__()\n\n        StringColumn._num_columns += 1\n        if name == \"\":\n            name = STRING_COLUMN + \" \" + str(StringColumn._num_columns)\n\n        self.cmd: Dict[str, Any] = {}\n\n        self.cmd[\"operator_\"] = STRING_COLUMN\n        self.cmd[\"df_name_\"] = df_name\n        self.cmd[\"name_\"] = name\n        self.cmd[\"role_\"] = role\n        self.cmd[\"type_\"] = STRING_COLUMN\n\n\n# -----------------------------------------------------------------------------\n\n\nclass StringColumnView(_View):\n    \"\"\"\n    Lazily evaluated view on a [`StringColumn`][getml.data.columns.StringColumn].\n\n    Columns views do not actually exist - they will be lazily\n    evaluated when necessary.\n\n    ??? example\n        ```python\n        import numpy as np\n\n        import getml.data as data\n        import getml.engine as engine\n        import getml.data.roles as roles\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n        # Create a data frame from a JSON string\n\n        json_str = \\\"\\\"\\\"{\n            \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n            \"column_01\": [2.4, 3.0, 1.2, 1.4],\n            \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n            \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n        }\\\"\\\"\\\"\n\n        my_df = data.DataFrame(\n            \"MY DF\",\n            roles={\n                \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n                \"unused_float\": [\"column_01\"]}\n        ).read_json(\n            json_str\n        )\n\n        # ----------------\n\n        col1 = my_df[\"names\"]\n\n        # ----------------\n\n        # col2 is a virtual column.\n        # The substring operation is not\n        # executed yet.\n        col2 = col1.substr(4, 3)\n\n        # This is where the Engine executes\n        # the substring operation.\n        my_df.add(col2, \"short_names\", roles.categorical)\n\n        # ----------------\n        # If you do not explicitly set a role,\n        # the assigned role will either be\n        # roles.unused_string.\n\n        # col3 is a virtual column.\n        # The operation is not\n        # executed yet.\n        col3 = \"user-\" + col1 + \"-\" + col2\n\n        # This is where the operation is\n        # is executed.\n        my_df[\"new_names\"] = col3\n        my_df.set_role(\"new_names\", roles.categorical)\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        operator: str,\n        operand1: Optional[Union[str, _Column, _View]],\n        operand2: Optional[Union[str, _Column, _View]],\n    ):\n        self.cmd: Dict[str, Any] = {}\n\n        self.cmd[\"type_\"] = STRING_COLUMN_VIEW\n        self.cmd[\"operator_\"] = operator\n        if operand1 is not None:\n            self.cmd[\"operand1_\"] = self._parse_operand(operand1)\n        if operand2 is not None:\n            self.cmd[\"operand2_\"] = self._parse_operand(operand2)\n\n    # -----------------------------------------------------------------------------\n\n    def _parse_operand(self, operand: Union[str, _Column, _View]):\n        if isinstance(operand, str):\n            return _value_to_cmd(operand)\n\n        if not hasattr(operand, \"cmd\"):\n            raise TypeError(\n                \"\"\"Operand for a StringColumnView must\n                   be a string or a column!\"\"\"\n            )\n\n        oper = self.cmd[\"operator_\"]\n\n        optype = operand.cmd[\"type_\"]\n\n        if oper == \"as_str\":\n            wrong_coltype = optype not in [\n                FLOAT_COLUMN,\n                FLOAT_COLUMN_VIEW,\n                BOOLEAN_COLUMN_VIEW,\n            ]\n            if wrong_coltype:\n                raise TypeError(\n                    \"This operator can only be applied to a \"\n                    + \"FloatColumn or a BooleanColumn!\"\n                )\n\n        elif oper == \"str_subselection\":\n            wrong_coltype = optype not in [\n                STRING_COLUMN,\n                STRING_COLUMN_VIEW,\n                FLOAT_COLUMN,\n                FLOAT_COLUMN_VIEW,\n                BOOLEAN_COLUMN_VIEW,\n            ]\n            if wrong_coltype:\n                raise TypeError(\n                    \"Columns or Views can only be subset by \"\n                    + \"StringColumn) or a BooleanColumn!\"\n                )\n\n        else:\n            wrong_coltype = optype not in [STRING_COLUMN, STRING_COLUMN_VIEW]\n            if wrong_coltype:\n                raise TypeError(\"This operator can only be applied to a StringColumn!\")\n\n        return operand.cmd\n\n\n# -----------------------------------------------------------------------------\n\n\nclass FloatColumn(_Column):\n    \"\"\"Handle for numerical data in the Engine.\n\n    This is a handler for all numerical data in the getML Engine,\n    including time stamps.\n\n    Attributes:\n        name:\n            Name of the categorical column.\n\n        role:\n            Role that the column plays.\n\n        df_name:\n            ``name`` instance variable of the\n            [`DataFrame`][getml.DataFrame]  containing this column.\n\n    ??? example\n        ```python\n        import numpy as np\n\n        import getml.data as data\n        import getml.engine as engine\n        import getml.data.roles as roles\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n        # Create a data frame from a JSON string\n\n        json_str = \\\"\\\"\\\"{\n            \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n            \"column_01\": [2.4, 3.0, 1.2, 1.4],\n            \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n            \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n        }\\\"\\\"\\\"\n\n        my_df = data.DataFrame(\n            \"MY DF\",\n            roles={\n                \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n                \"unused_float\": [\"column_01\"]}\n        ).read_json(\n            json_str\n        )\n\n        # ----------------\n\n        col1 = my_df[\"column_01\"]\n\n        # ----------------\n\n        col2 = 2.0 - col1\n\n        my_df.add(col2, \"name\", roles.numerical)\n\n        # ----------------\n        # If you do not explicitly set a role,\n        # the assigned role will either be\n        # roles.unused_float.\n\n        col3 = (col1 + 2.0*col2) / 3.0\n\n        my_df[\"column_03\"] = col3\n        my_df.set_role(\"column_03\", roles.numerical)\n        ```\n    \"\"\"\n\n    _num_columns = 0\n\n    def __init__(self, name: str = \"\", role: str = \"numerical\", df_name: str = \"\"):\n        super().__init__()\n\n        FloatColumn._num_columns += 1\n        if name == \"\":\n            name = FLOAT_COLUMN + \" \" + str(FloatColumn._num_columns)\n\n        self.cmd: Dict[str, Any] = {}\n\n        self.cmd[\"operator_\"] = FLOAT_COLUMN\n\n        self.cmd[\"df_name_\"] = df_name\n\n        self.cmd[\"name_\"] = name\n\n        self.cmd[\"role_\"] = role\n\n        self.cmd[\"type_\"] = FLOAT_COLUMN\n\n\n# -----------------------------------------------------------------------------\n\n\nclass FloatColumnView(_View):\n    \"\"\"\n    Lazily evaluated view on a [`FloatColumn`][getml.data.columns.FloatColumn].\n\n    Column views do not actually exist - they will be lazily\n    evaluated when necessary.\n    \"\"\"\n\n    def __init__(\n        self,\n        operator: str,\n        operand1: Optional[FloatOperandType],\n        operand2: Optional[FloatOperandType],\n    ):\n        self.cmd: Dict[str, Any] = {}\n\n        self.cmd[\"type_\"] = FLOAT_COLUMN_VIEW\n\n        self.cmd[\"operator_\"] = operator\n\n        if operand1 is not None:\n            self.cmd[\"operand1_\"] = self._parse_operand(operand1)\n\n        if operand2 is not None:\n            self.cmd[\"operand2_\"] = self._parse_operand(operand2)\n\n    # -----------------------------------------------------------------------------\n\n    def _parse_operand(self, operand: FloatOperandType):\n        if isinstance(operand, (numbers.Real, np.datetime64)):\n            return _value_to_cmd(operand)\n\n        if not hasattr(operand, \"cmd\"):\n            raise TypeError(\n                \"\"\"Operand for a FloatColumnView must\n                   be a number or a column!\"\"\"\n            )\n\n        special_ops = [\"as_num\", \"as_ts\", \"boolean_as_num\", \"num_subselection\"]\n        oper = self.cmd[\"operator_\"]\n        optype = operand.cmd[\"type_\"]\n\n        if oper not in special_ops:\n            wrong_coltype = optype not in [FLOAT_COLUMN, FLOAT_COLUMN_VIEW]\n            if wrong_coltype:\n                raise TypeError(\"This operator can only be applied to a FloatColumn!\")\n\n        if (\n            oper in special_ops\n            and oper != \"boolean_as_num\"\n            and oper != \"num_subselection\"\n        ):\n            wrong_coltype = optype not in [STRING_COLUMN, STRING_COLUMN_VIEW]\n            if wrong_coltype:\n                raise TypeError(\"This operator can only be applied to a StringColumn!\")\n\n        if oper == \"boolean_as_num\" and optype != BOOLEAN_COLUMN_VIEW:\n            raise TypeError(\"This operator can only be applied to a BooleanColumn!\")\n\n        if oper == \"num_subselection\":\n            wrong_coltype = optype not in [\n                STRING_COLUMN,\n                STRING_COLUMN_VIEW,\n                BOOLEAN_COLUMN_VIEW,\n                FLOAT_COLUMN,\n                FLOAT_COLUMN_VIEW,\n            ]\n            if wrong_coltype:\n                raise TypeError(\n                    \"The subselection operator can only be applied to FloatColumn!\"\n                )\n\n        return operand.cmd\n\n\n# -----------------------------------------------------------------------------\n\nFloatOperandType = Union[numbers.Real, np.datetime64, FloatColumn, FloatColumnView]\nStringOperandType = Union[str, StringColumn, StringColumnView]\n\nOperandType = Union[FloatOperandType, StringOperandType, BooleanColumnView]\n\n# -----------------------------------------------------------------------------\n\n\ndef _abs(self):\n    \"\"\"Compute absolute value.\"\"\"\n    return FloatColumnView(operator=\"abs\", operand1=self, operand2=None)\n\n\nFloatColumn.abs = _abs  # type: ignore\nFloatColumnView.abs = _abs  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _acos(self):\n    \"\"\"Compute arc cosine.\"\"\"\n    return FloatColumnView(operator=\"acos\", operand1=self, operand2=None)\n\n\nFloatColumn.acos = _acos  # type: ignore\nFloatColumnView.acos = _acos  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _add(self, other: OperandType):\n    if isinstance(other, (StringColumn, StringColumnView, str)):\n        return self.as_str() + other\n\n    return FloatColumnView(operator=\"plus\", operand1=self, operand2=other)\n\n\ndef _radd(self, other: OperandType):\n    if isinstance(other, (StringColumn, StringColumnView, str)):\n        return other + self.as_str()\n\n    return FloatColumnView(operator=\"plus\", operand1=other, operand2=self)\n\n\nFloatColumn.__add__ = _add  # type: ignore\nFloatColumn.__radd__ = _radd  # type: ignore\n\nFloatColumnView.__add__ = _add  # type: ignore\nFloatColumnView.__radd__ = _radd  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _assert_equal(self, alias: str = \"new_column\"):\n    \"\"\"\n    ASSERT EQUAL aggregation.\n\n    Throws an exception unless all values inserted\n    into the aggregation are equal.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"assert_equal\").get()\n\n\nFloatColumn.assert_equal = _assert_equal  # type: ignore\nFloatColumnView.assert_equal = _assert_equal  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _asin(self):\n    \"\"\"Compute arc sine.\"\"\"\n    return FloatColumnView(operator=\"asin\", operand1=self, operand2=None)\n\n\nFloatColumn.asin = _asin  # type: ignore\nFloatColumnView.asin = _asin  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _atan(self):\n    \"\"\"Compute arc tangent.\"\"\"\n    return FloatColumnView(operator=\"atan\", operand1=self, operand2=None)\n\n\nFloatColumn.atan = _atan  # type: ignore\nFloatColumnView.atan = _atan  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _avg(self, alias: str = \"new_column\"):\n    \"\"\"\n    AVG aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"avg\").get()\n\n\nFloatColumn.avg = _avg  # type: ignore\nFloatColumnView.avg = _avg  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _cbrt(self):\n    \"\"\"Compute cube root.\"\"\"\n    return FloatColumnView(operator=\"cbrt\", operand1=self, operand2=None)\n\n\nFloatColumn.cbrt = _cbrt  # type: ignore\nFloatColumnView.cbrt = _cbrt  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _ceil(self):\n    \"\"\"Round up value.\"\"\"\n    return FloatColumnView(operator=\"ceil\", operand1=self, operand2=None)\n\n\nFloatColumn.ceil = _ceil  # type: ignore\nFloatColumnView.ceil = _ceil  # type: ignore\n\n\n#  -----------------------------------------------------------------------------\n\nFloatColumnView._collect_footer_data = _collect_footer_data  # type: ignore\nBooleanColumnView._collect_footer_data = _collect_footer_data  # type: ignore\nStringColumnView._collect_footer_data = _collect_footer_data  # type: ignore\nFloatColumn._collect_footer_data = _collect_footer_data  # type: ignore\nStringColumn._collect_footer_data = _collect_footer_data  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _to_str(col: Any):\n    if isinstance(col, (StringColumn, StringColumnView)):\n        return col\n\n    if isinstance(col, (FloatColumn, FloatColumnView)):\n        return col.as_str()  # type: ignore\n\n    return str(col)\n\n\ndef _concat(self, other: Any):\n    return StringColumnView(\n        operator=\"concat\",\n        operand1=self,\n        operand2=_to_str(other),\n    )\n\n\ndef _rconcat(self, other: Any):\n    return StringColumnView(\n        operator=\"concat\",\n        operand1=_to_str(other),\n        operand2=self,\n    )\n\n\nStringColumn.__add__ = _concat  # type: ignore\nStringColumn.__radd__ = _rconcat  # type: ignore\n\nStringColumnView.__add__ = _concat  # type: ignore\nStringColumnView.__radd__ = _rconcat  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _check_inferred_time_stamps(self):\n    inferred_name = _infer_column_name_recursive(self.cmd)\n    try:\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            # HACK: We need to create a dummy DataFrame to infer the schema with the right\n            # time format\n            inference_batch = pd.DataFrame(\n                {inferred_name: pd.to_datetime(self[:10].to_numpy())}\n            )\n    except (ValueError, pd._libs.tslibs.parsing.DateParseError):\n        pass\n    else:\n        schema = pa.Schema.from_pandas(inference_batch)\n        # just called for emmitting the timezone related warnings\n        postprocess_arrow_schema(\n            schema, roles=Roles.from_dict({roles.time_stamp: [inferred_name]})\n        )\n        return\n\n\nStringColumn._check_inferred_time_stamps = _check_inferred_time_stamps  # type: ignore\nStringColumnView._check_inferred_time_stamps = _check_inferred_time_stamps  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _contains(self, other: StringOperandType):\n    \"\"\"\n    Returns a boolean column indicating whether a\n    string or column entry is contained in the corresponding\n    entry of the other column.\n    \"\"\"\n    return BooleanColumnView(\n        operator=\"contains\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nStringColumn.contains = _contains  # type: ignore\nStringColumnView.contains = _contains  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _cos(self):\n    \"\"\"Compute cosine.\"\"\"\n    return FloatColumnView(operator=\"cos\", operand1=self, operand2=None)\n\n\nFloatColumn.cos = _cos  # type: ignore\nFloatColumnView.cos = _cos  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _count(self, alias: str = \"new_column\"):\n    \"\"\"\n    COUNT aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"count\").get()\n\n\nFloatColumn.count = _count  # type: ignore\nFloatColumnView.count = _count  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _count_categorical(self, alias: str = \"new_column\"):\n    \"\"\"\n    COUNT aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"count_categorical\").get()\n\n\nStringColumn.count = _count_categorical  # type: ignore\nStringColumnView.count = _count_categorical  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _count_distinct(self, alias: str = \"new_column\"):\n    \"\"\"\n    COUNT DISTINCT aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"count_distinct\").get()\n\n\nStringColumn.count_distinct = _count_distinct  # type: ignore\nStringColumnView.count_distinct = _count_distinct  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _day(self):\n    \"\"\"Extract day (of the month) from a time stamp.\n\n    If the column is numerical, that number will be interpreted as the\n    number of days since epoch time (January 1, 1970).\n\n    \"\"\"\n    return FloatColumnView(operator=\"day\", operand1=self, operand2=None)\n\n\nFloatColumn.day = _day  # type: ignore\nFloatColumnView.day = _day  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _eq_num(self, other: FloatOperandType):\n    if not isinstance(other, op_types := get_args(FloatOperandType)):\n        raise TypeError(\n            TYPE_MISSMATCH_ERROR_MSG_TEMPLATE.format(\n                col_type=f\"{type(self)!r}\",\n                operand_types=\", \".join(f\"{op_type!r}\" for op_type in op_types),\n            )\n        )\n    return BooleanColumnView(\n        operator=\"num_equal_to\",\n        operand1=self,\n        operand2=other,\n    )\n\n\ndef _eq_str(self, other: StringOperandType):\n    if not isinstance(other, op_types := get_args(StringOperandType)):\n        msg = TYPE_MISSMATCH_ERROR_MSG_TEMPLATE.format(\n            col_type=f\"{type(self)!r}\",\n            operand_types=\", \".join(f\"{op_type!r}\" for op_type in op_types),\n        )\n        if getattr(self, \"role\", None) == roles.join_key:\n            msg += \"\\n\\nHint: join_keys are always StringColumn(View)s.\"\n        raise TypeError(msg)\n    return BooleanColumnView(\n        operator=\"str_equal_to\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__eq__ = _eq_num  # type: ignore\nFloatColumnView.__eq__ = _eq_num  # type: ignore\n\nStringColumn.__eq__ = _eq_str  # type: ignore\nStringColumnView.__eq__ = _eq_str  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _erf(self):\n    \"\"\"Compute error function.\"\"\"\n    return FloatColumnView(operator=\"erf\", operand1=self, operand2=None)\n\n\nFloatColumn.erf = _erf  # type: ignore\nFloatColumnView.erf = _erf  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _exp(self):\n    \"\"\"Compute exponential function.\"\"\"\n    return FloatColumnView(operator=\"exp\", operand1=self, operand2=None)\n\n\nFloatColumn.exp = _exp  # type: ignore\nFloatColumnView.exp = _exp  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _floor(self):\n    \"\"\"Round down value.\"\"\"\n    return FloatColumnView(operator=\"floor\", operand1=self, operand2=None)\n\n\nFloatColumn.floor = _floor  # type: ignore\nFloatColumnView.floor = _floor  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nFloatColumnView._format = _format  # type: ignore\nBooleanColumnView._format = _format  # type: ignore\nStringColumnView._format = _format  # type: ignore\nFloatColumn._format = _format  # type: ignore\nStringColumn._format = _format  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _gamma(self):\n    \"\"\"Compute gamma function.\"\"\"\n    return FloatColumnView(\n        operator=\"tgamma\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.gamma = _gamma  # type: ignore\nFloatColumnView.gamma = _gamma  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _ge(self, other: Any):\n    return BooleanColumnView(\n        operator=\"greater_equal\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__ge__ = _ge  # type: ignore\nFloatColumnView.__ge__ = _ge  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _gt(self, other):\n    return BooleanColumnView(\n        operator=\"greater\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__gt__ = _gt  # type: ignore\nFloatColumnView.__gt__ = _gt  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _hour(self):\n    \"\"\"Extract hour (of the day) from a time stamp.\n\n    If the column is numerical, that number will be interpreted as the\n    number of days since epoch time (January 1, 1970).\n\n    \"\"\"\n    return FloatColumnView(operator=\"hour\", operand1=self, operand2=None)\n\n\nFloatColumn.hour = _hour  # type: ignore\nFloatColumnView.hour = _hour  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _is_inf(self):\n    \"\"\"Determine whether the value is infinite.\"\"\"\n    return BooleanColumnView(\n        operator=\"is_inf\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.is_inf = _is_inf  # type: ignore\nFloatColumnView.is_inf = _is_inf  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _is_nan(self):\n    \"\"\"Determine whether the value is nan.\"\"\"\n    return BooleanColumnView(\n        operator=\"is_nan\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.is_nan = _is_nan  # type: ignore\nFloatColumnView.is_nan = _is_nan  # type: ignore\n\nFloatColumn.is_null = _is_nan  # type: ignore\nFloatColumnView.is_null = _is_nan  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _is_null(self):\n    \"\"\"Determine whether the value is NULL.\"\"\"\n    return BooleanColumnView(\n        operator=\"is_null\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nStringColumn.is_null = _is_null  # type: ignore\nStringColumnView.is_null = _is_null  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nFloatColumn.last_change = _last_change_from_col  # type: ignore\nFloatColumnView.last_change = _last_change_from_col  # type: ignore\nStringColumn.last_change = _last_change_from_col  # type: ignore\nStringColumnView.last_change = _last_change_from_col  # type: ignore\nBooleanColumnView.last_change = _last_change_from_col  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nBooleanColumnView.__iter__ = _make_iter(bool)  # type: ignore\nFloatColumnView.__iter__ = _make_iter(np.float64)  # type: ignore\nStringColumnView.__iter__ = _make_iter(str)  # type: ignore\nFloatColumn.__iter__ = _make_iter(np.float64)  # type: ignore\nStringColumn.__iter__ = _make_iter(str)  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _le(self, other):\n    return BooleanColumnView(\n        operator=\"less_equal\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__le__ = _le  # type: ignore\nFloatColumnView.__le__ = _le  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nBooleanColumnView.__len__ = _length  # type: ignore\nFloatColumnView.__len__ = _length  # type: ignore\nStringColumnView.__len__ = _length  # type: ignore\nFloatColumn.__len__ = _length  # type: ignore\nStringColumn.__len__ = _length  # type: ignore\n\n#  -----------------------------------------------------------------------------\n\nBooleanColumnView.length = _length_property  # type: ignore\nFloatColumnView.length = _length_property  # type: ignore\nStringColumnView.length = _length_property  # type: ignore\nFloatColumn.length = _length_property  # type: ignore\nStringColumn.length = _length_property  # type: ignore\n\n#  -----------------------------------------------------------------------------\n\n\ndef _lgamma(self):\n    \"\"\"Compute log-gamma function.\"\"\"\n    return FloatColumnView(\n        operator=\"lgamma\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.lgamma = _lgamma  # type: ignore\nFloatColumnView.lgamma = _lgamma  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _log(self):\n    \"\"\"Compute natural logarithm.\"\"\"\n    return FloatColumnView(operator=\"log\", operand1=self, operand2=None)\n\n\nFloatColumn.log = _log  # type: ignore\nFloatColumnView.log = _log  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _lt(self, other):\n    return BooleanColumnView(operator=\"less\", operand1=self, operand2=other)\n\n\nFloatColumn.__lt__ = _lt  # type: ignore\nFloatColumnView.__lt__ = _lt  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _max(self, alias: str = \"new_column\"):\n    \"\"\"\n    MAX aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"max\").get()\n\n\nFloatColumn.max = _max  # type: ignore\nFloatColumnView.max = _max  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _median(self, alias: str = \"new_column\"):\n    \"\"\"\n    MEDIAN aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"median\").get()\n\n\nFloatColumn.median = _median  # type: ignore\nFloatColumnView.median = _median  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _min(self, alias=\"new_column\"):\n    \"\"\"\n    MIN aggregation.\n\n    **alias**: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"min\").get()\n\n\nFloatColumn.min = _min  # type: ignore\nFloatColumnView.min = _min  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _minute(self):\n    \"\"\"Extract minute (of the hour) from a time stamp.\n\n    If the column is numerical, that number will be interpreted as the\n    number of days since epoch time (January 1, 1970).\n\n    \"\"\"\n    return FloatColumnView(\n        operator=\"minute\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.minute = _minute  # type: ignore\nFloatColumnView.minute = _minute  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _mod(self, other):\n    return FloatColumnView(operator=\"fmod\", operand1=self, operand2=other)\n\n\ndef _rmod(self, other):\n    return FloatColumnView(operator=\"fmod\", operand1=other, operand2=self)\n\n\nFloatColumn.__mod__ = _mod  # type: ignore\nFloatColumn.__rmod__ = _rmod  # type: ignore\n\nFloatColumnView.__mod__ = _mod  # type: ignore\nFloatColumnView.__rmod__ = _rmod  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _month(self):\n    \"\"\"\n    Extract month from a time stamp.\n\n    If the column is numerical, that number will be interpreted\n    as the number of days since epoch time (January 1, 1970).\n    \"\"\"\n    return FloatColumnView(operator=\"month\", operand1=self, operand2=None)\n\n\nFloatColumn.month = _month  # type: ignore\nFloatColumnView.month = _month  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _mul(self, other):\n    return FloatColumnView(\n        operator=\"multiplies\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__mul__ = _mul  # type: ignore\nFloatColumn.__rmul__ = _mul  # type: ignore\n\nFloatColumnView.__mul__ = _mul  # type: ignore\nFloatColumnView.__rmul__ = _mul  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _ne_num(self, other):\n    return BooleanColumnView(\n        operator=\"num_not_equal_to\",\n        operand1=self,\n        operand2=other,\n    )\n\n\ndef _ne_str(self, other):\n    return BooleanColumnView(\n        operator=\"str_not_equal_to\",\n        operand1=self,\n        operand2=other,\n    )\n\n\nFloatColumn.__ne__ = _ne_num  # type: ignore\nFloatColumnView.__ne__ = _ne_num  # type: ignore\n\nStringColumn.__ne__ = _ne_str  # type: ignore\nStringColumnView.__ne__ = _ne_str  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _neg(self):\n    return FloatColumnView(\n        operator=\"multiplies\",\n        operand1=self,\n        operand2=-1.0,\n    )\n\n\nFloatColumn.__neg__ = _neg  # type: ignore\nFloatColumnView.__neg__ = _neg  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _pow(self, other: FloatOperandType):\n    return FloatColumnView(operator=\"pow\", operand1=self, operand2=other)\n\n\ndef _rpow(self, other: FloatOperandType):\n    return FloatColumnView(operator=\"pow\", operand1=other, operand2=self)\n\n\nFloatColumn.__pow__ = _pow  # type: ignore\nFloatColumn.__rpow__ = _rpow  # type: ignore\n\nFloatColumnView.__pow__ = _pow  # type: ignore\nFloatColumnView.__rpow__ = _rpow  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nFloatColumn.__repr__ = _repr  # type: ignore\nFloatColumnView.__repr__ = _repr  # type: ignore\nStringColumn.__repr__ = _repr  # type: ignore\nStringColumnView.__repr__ = _repr  # type: ignore\nBooleanColumnView.__repr__ = _repr  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nFloatColumn._repr_html_ = _repr_html  # type: ignore\nFloatColumnView._repr_html_ = _repr_html  # type: ignore\nStringColumn._repr_html_ = _repr_html  # type: ignore\nStringColumnView._repr_html_ = _repr_html  # type: ignore\nBooleanColumnView._repr_html_ = _repr_html  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _round(self):\n    \"\"\"Round to nearest.\"\"\"\n    return FloatColumnView(operator=\"round\", operand1=self, operand2=None)\n\n\nFloatColumn.round = _round  # type: ignore\nFloatColumnView.round = _round  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _second(self):\n    \"\"\"Extract second (of the minute) from a time stamp.\n\n    If the column is numerical, that number will be interpreted as the\n    number of days since epoch time (January 1, 1970).\n\n    \"\"\"\n    return FloatColumnView(\n        operator=\"second\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.second = _second  # type: ignore\nFloatColumnView.second = _second  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _sin(self):\n    \"\"\"Compute sine.\"\"\"\n    return FloatColumnView(operator=\"sin\", operand1=self, operand2=None)\n\n\nFloatColumn.sin = _sin  # type: ignore\nFloatColumnView.sin = _sin  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _sqrt(self):\n    \"\"\"Compute square root.\"\"\"\n    return FloatColumnView(operator=\"sqrt\", operand1=self, operand2=None)\n\n\nFloatColumn.sqrt = _sqrt  # type: ignore\nFloatColumnView.sqrt = _sqrt  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _stddev(self, alias: str = \"new_column\"):\n    \"\"\"\n    STDDEV aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"stddev\").get()\n\n\nFloatColumn.stddev = _stddev  # type: ignore\nFloatColumnView.stddev = _stddev  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _sub(self, other):\n    return FloatColumnView(\n        operator=\"minus\",\n        operand1=self,\n        operand2=other,\n    )\n\n\ndef _rsub(self, other):\n    return FloatColumnView(\n        operator=\"minus\",\n        operand1=other,\n        operand2=self,\n    )\n\n\nFloatColumn.__sub__ = _sub  # type: ignore\nFloatColumn.__rsub__ = _rsub  # type: ignore\n\nFloatColumnView.__sub__ = _sub  # type: ignore\nFloatColumnView.__rsub__ = _rsub  # type: ignore\n\n# -------------------------------------------------------------------------\n\nFloatColumnView.subroles = _subroles  # type: ignore\nStringColumnView.subroles = _subroles  # type: ignore\nFloatColumn.subroles = _subroles  # type: ignore\nStringColumn.subroles = _subroles  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _subselection_bool(self, indices):\n    if isinstance(indices, numbers.Integral):\n        return _get_scalar(self, indices)\n\n    if isinstance(indices, slice):\n        indices = _make_slicing_operand(self, indices)\n\n    return BooleanColumnView(\n        operator=\"bool_subselection\",\n        operand1=self,\n        operand2=indices,\n    )\n\n\nBooleanColumnView.__getitem__ = _subselection_bool  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _subselection_float(self, indices):\n    if isinstance(indices, numbers.Integral):\n        return _get_scalar(self, indices)\n\n    if isinstance(indices, slice):\n        indices = _make_slicing_operand(self, indices)\n\n    return FloatColumnView(\n        operator=\"num_subselection\",\n        operand1=self,\n        operand2=indices,\n    )\n\n\nFloatColumnView.__getitem__ = _subselection_float  # type: ignore\nFloatColumn.__getitem__ = _subselection_float  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _subselection_string(self, indices):\n    if isinstance(indices, numbers.Integral):\n        return _get_scalar(self, indices)\n\n    if isinstance(indices, slice):\n        indices = _make_slicing_operand(self, indices)\n\n    return StringColumnView(\n        operator=\"str_subselection\",\n        operand1=self,\n        operand2=indices,\n    )\n\n\nStringColumnView.__getitem__ = _subselection_string  # type: ignore\nStringColumn.__getitem__ = _subselection_string  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _substr(self, begin: int, length: int):\n    \"\"\"\n    Return a substring for every element in the column.\n\n    Args:\n        begin: First position of the original string.\n        length: Length of the extracted string.\n    \"\"\"\n    col = StringColumnView(\n        operator=\"substr\",\n        operand1=self,\n        operand2=None,\n    )\n    col.cmd[\"begin_\"] = begin\n    col.cmd[\"len_\"] = length\n    return col\n\n\nStringColumn.substr = _substr  # type: ignore\nStringColumnView.substr = _substr  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _sum(self, alias: str = \"new_column\"):\n    \"\"\"\n    SUM aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"sum\").get()\n\n\nFloatColumn.sum = _sum  # type: ignore\nFloatColumnView.sum = _sum  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _tan(self):\n    \"\"\"Compute tangent.\"\"\"\n    return FloatColumnView(operator=\"tan\", operand1=self, operand2=None)\n\n\nFloatColumn.tan = _tan  # type: ignore\nFloatColumnView.tan = _tan  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _as_num(self):\n    \"\"\"Transforms a categorical column to a numerical column.\"\"\"\n    return FloatColumnView(\n        operator=\"as_num\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nStringColumn.as_num = _as_num  # type: ignore\nStringColumnView.as_num = _as_num  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nBooleanColumnView.to_numpy = _to_numpy  # type: ignore\nFloatColumn.to_numpy = _to_numpy  # type: ignore\nFloatColumnView.to_numpy = _to_numpy  # type: ignore\nStringColumn.to_numpy = _to_numpy  # type: ignore\nStringColumnView.to_numpy = _to_numpy  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _as_str(self):\n    \"\"\"Transforms column to a string.\"\"\"\n    return StringColumnView(\n        operator=\"as_str\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.as_str = _as_str  # type: ignore\nFloatColumnView.as_str = _as_str  # type: ignore\n\nBooleanColumnView.as_str = _as_str  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _as_ts(self, time_formats: Optional[Union[str, Iterable[str]]] = None):\n    \"\"\"\n    Transforms a categorical column to a time stamp.\n\n    Args:\n        time_formats: Formats to be used to parse the time stamps.\n    \"\"\"\n    inferred_name = _infer_column_name_recursive(self.cmd)\n    self._check_inferred_time_stamps()\n    time_formats = time_formats or TIME_FORMATS\n    col = FloatColumnView(operator=\"as_ts\", operand1=self, operand2=None)\n    col.cmd[\"time_formats_\"] = time_formats\n\n    if all(col[:10].is_nan().to_numpy()):\n        warnings.warn(\n            TIME_STAMP_PARSING_LIKELY_FAILED_WARNING_TEMPLATE.format(\n                column=inferred_name\n            )\n        )\n    return col\n\n\nStringColumn.as_ts = _as_ts  # type: ignore\nStringColumnView.as_ts = _as_ts  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _truediv(self, other: FloatOperandType):\n    return FloatColumnView(\n        operator=\"divides\",\n        operand1=self,\n        operand2=other,\n    )\n\n\ndef _rtruediv(self, other: FloatOperandType):\n    return FloatColumnView(\n        operator=\"divides\",\n        operand1=other,\n        operand2=self,\n    )\n\n\nFloatColumn.__truediv__ = _truediv  # type: ignore\nFloatColumn.__rtruediv__ = _rtruediv  # type: ignore\n\nFloatColumnView.__truediv__ = _truediv  # type: ignore\nFloatColumnView.__rtruediv__ = _rtruediv  # type: ignore\n\n# -----------------------------------------------------------------------------\n\nFloatColumn.unique = _unique  # type: ignore\nFloatColumnView.unique = _unique  # type: ignore\nStringColumn.unique = _unique  # type: ignore\nStringColumnView.unique = _unique  # type: ignore\n\n# -------------------------------------------------------------------------\n\nFloatColumnView.unit = _unit  # type: ignore\nStringColumnView.unit = _unit  # type: ignore\nFloatColumn.unit = _unit  # type: ignore\nStringColumn.unit = _unit  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _update_float(self, condition: BooleanColumnView, values: FloatOperandType):\n    \"\"\"\n    Returns an updated version of this column.\n\n    All entries for which the corresponding **condition** is True,\n    are updated using the corresponding entry in **values**.\n\n    Args:\n        condition: Condition according to which the update is done\n        values: Values to update with\n    \"\"\"\n    col = FloatColumnView(\n        operator=\"num_update\",\n        operand1=self,\n        operand2=values,\n    )\n    if condition.cmd[\"type_\"] != BOOLEAN_COLUMN_VIEW:\n        raise TypeError(\"Condition for an update must be a Boolean column.\")\n    col.cmd[\"condition_\"] = condition.cmd\n    return col\n\n\nFloatColumn.update = _update_float  # type: ignore\n\nFloatColumnView.update = _update_float  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _update_str(self, condition: BooleanColumnView, values: StringOperandType):\n    \"\"\"\n    Returns an updated version of this column.\n\n    All entries for which the corresponding **condition** is True,\n    are updated using the corresponding entry in **values**.\n\n    Args:\n        condition: Condition according to which the update is done\n        values: Values to update with\n    \"\"\"\n    col = StringColumnView(\n        operator=\"str_update\",\n        operand1=self,\n        operand2=values,\n    )\n    if condition.cmd[\"type_\"] != BOOLEAN_COLUMN_VIEW:\n        raise TypeError(\"Condition for an update must be a Boolean column.\")\n    col.cmd[\"condition_\"] = condition.cmd\n    return col\n\n\nStringColumn.update = _update_str  # type: ignore\n\nStringColumnView.update = _update_str  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _var(self, alias: str = \"new_column\"):\n    \"\"\"\n    VAR aggregation.\n\n    Args:\n        alias: Name for the new column.\n    \"\"\"\n    return Aggregation(alias, self, \"var\").get()\n\n\nFloatColumn.var = _var  # type: ignore\nFloatColumnView.var = _var  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _weekday(self):\n    \"\"\"Extract day of the week from a time stamp, Sunday being 0.\n\n    If the column is numerical, that number will be interpreted as the\n    number of days since epoch time (January 1, 1970).\n\n    \"\"\"\n    return FloatColumnView(\n        operator=\"weekday\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.weekday = _weekday  # type: ignore\nFloatColumnView.weekday = _weekday  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _with_subroles_float(self, subroles: Union[str, List[str]], append: bool = True):\n    \"\"\"\n    Returns a new column with new subroles.\n\n    Args:\n        subroles: The subroles to be assigned.\n\n        append: Whether you want to append the\n            new subroles to the existing subroles.\n    \"\"\"\n    if isinstance(subroles, str):\n        subroles = [subroles]\n\n    if not isinstance(subroles, list):\n        raise TypeError(\"'subroles' must be a str or a list of str.\")\n\n    if not isinstance(append, bool):\n        raise TypeError(\"'append' must be a bool.\")\n\n    col = FloatColumnView(\n        operator=\"num_with_subroles\",\n        operand1=self,\n        operand2=None,\n    )\n\n    col.cmd[\"subroles_\"] = self.subroles + subroles if append else subroles\n\n    return col\n\n\nFloatColumn.with_subroles = _with_subroles_float  # type: ignore\nFloatColumnView.with_subroles = _with_subroles_float  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _with_subroles_string(self, subroles: Union[str, List[str]], append: bool = True):\n    \"\"\"\n    Returns a new column with new subroles.\n\n    Args:\n        subroles: The subroles to be assigned.\n\n        append: Whether you want to append the\n            new subroles to the existing subroles.\n    \"\"\"\n    if isinstance(subroles, str):\n        subroles = [subroles]\n\n    if not isinstance(subroles, list):\n        raise TypeError(\"'subroles' must be a str or a list of str.\")\n\n    if not isinstance(append, bool):\n        raise TypeError(\"'append' must be a bool.\")\n\n    col = StringColumnView(\n        operator=\"str_with_subroles\",\n        operand1=self,\n        operand2=None,\n    )\n\n    col.cmd[\"subroles_\"] = self.subroles + subroles if append else subroles\n\n    return col\n\n\nStringColumn.with_subroles = _with_subroles_string  # type: ignore\nStringColumnView.with_subroles = _with_subroles_string  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _with_unit_float(self, unit: str):\n    \"\"\"\n    Returns a new column with a new unit.\n\n    Args:\n        unit: The new unit.\n    \"\"\"\n    col = FloatColumnView(\n        operator=\"num_with_unit\",\n        operand1=self,\n        operand2=None,\n    )\n    col.cmd[\"unit_\"] = unit\n    return col\n\n\nFloatColumn.with_unit = _with_unit_float  # type: ignore\nFloatColumnView.with_unit = _with_unit_float  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _with_unit_string(self, unit: str):\n    \"\"\"\n    Returns a new column with a new unit,\n\n    Args:\n        unit: The new unit.\n    \"\"\"\n    col = StringColumnView(\n        operator=\"str_with_unit\",\n        operand1=self,\n        operand2=None,\n    )\n    col.cmd[\"unit_\"] = unit\n    return col\n\n\nStringColumn.with_unit = _with_unit_string  # type: ignore\nStringColumnView.with_unit = _with_unit_string  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _year(self):\n    \"\"\"\n    Extract year from a time stamp.\n\n    If the column is numerical, that number will be interpreted\n    as the number of days since epoch time (January 1, 1970).\n    \"\"\"\n    return FloatColumnView(operator=\"year\", operand1=self, operand2=None)\n\n\nFloatColumn.year = _year  # type: ignore\nFloatColumnView.year = _year  # type: ignore\n\n# -----------------------------------------------------------------------------\n\n\ndef _yearday(self):\n    \"\"\"\n    Extract day of the year from a time stamp.\n\n    If the column is numerical, that number will be interpreted\n    as the number of days since epoch time (January 1, 1970).\n    \"\"\"\n    return FloatColumnView(\n        operator=\"yearday\",\n        operand1=self,\n        operand2=None,\n    )\n\n\nFloatColumn.yearday = _yearday  # type: ignore\nFloatColumnView.yearday = _yearday  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/data/columns/constants.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nFLOAT_COLUMN = \"FloatColumn\"\nSTRING_COLUMN = \"StringColumn\"\n\nBOOLEAN_COLUMN_VIEW = \"BooleanColumnView\"\nFLOAT_COLUMN_VIEW = \"FloatColumnView\"\nSTRING_COLUMN_VIEW = \"StringColumnView\"\n\nVIEW_SIGNIFIER = \"View\"\n\nINFINITE = \"infinite\"\n\n_columns = [FLOAT_COLUMN, STRING_COLUMN]\n\n_views = [BOOLEAN_COLUMN_VIEW, FLOAT_COLUMN_VIEW, STRING_COLUMN_VIEW]\n\n_all = _columns + _views\n"
  },
  {
    "path": "src/python-api/getml/data/columns/format.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Format the column\"\"\"\n\nfrom getml.utilities.formatting import _ColumnFormatter\n\n\ndef _format(self):\n    formatted = _ColumnFormatter(self)\n    return formatted\n"
  },
  {
    "path": "src/python-api/getml/data/columns/from_value.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nGenerates an appropriate column from a value.\n\"\"\"\n\nimport numbers\nfrom typing import Union\n\nimport numpy as np\n\nfrom .columns import BooleanColumnView, FloatColumnView, StringColumnView, _value_to_cmd\n\n# -----------------------------------------------------------------------------\n\nReturnType = Union[BooleanColumnView, StringColumnView, FloatColumnView]\n\"\"\"\nPossible return types of the function `from_value`.\n\"\"\"\n\n# -----------------------------------------------------------------------------\n\n\ndef from_value(val: Union[bool, str, int, float, np.datetime64]) -> ReturnType:\n    \"\"\"\n    Creates an infinite column that contains the same\n    value in all of its elements.\n\n    Args:\n        val:\n            The value you want to insert into your column.\n\n    Returns:\n        The column view containing the value.\n    \"\"\"\n    cmd = _value_to_cmd(val)\n\n    if isinstance(val, bool):\n        col: ReturnType = BooleanColumnView(\n            operator=\"const\",\n            operand1=None,\n            operand2=None,\n        )\n        col.cmd = cmd\n        return col\n\n    if isinstance(val, str):\n        col = StringColumnView(\n            operator=\"const\",\n            operand1=val,\n            operand2=None,\n        )\n        col.cmd = cmd\n        return col\n\n    if isinstance(val, (int, float, numbers.Number)):\n        col = FloatColumnView(\n            operator=\"const\",\n            operand1=val,\n            operand2=None,\n        )\n        col.cmd = cmd\n        return col\n\n    if isinstance(val, np.datetime64):\n        col = FloatColumnView(\n            operator=\"const\",\n            operand1=np.datetime64(val, \"s\").astype(float),\n            operand2=None,\n        )\n        col.cmd = cmd\n        return col\n\n    raise TypeError(\"val must be bool, str or a number.\")\n"
  },
  {
    "path": "src/python-api/getml/data/columns/get_scalar.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\ndef _get_scalar(column, index) -> float:\n    index = index if index >= 0 else len(column) + index\n    return column[index : index + 1].to_numpy()[0]\n"
  },
  {
    "path": "src/python-api/getml/data/columns/last_change.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns the last time a data frame has been changed.\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport getml.communication as comm\n\n\ndef _last_change(df_name) -> str:\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"DataFrame.last_change\"\n    cmd[\"name_\"] = df_name\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        result = comm.recv_string(sock)\n\n    return result\n"
  },
  {
    "path": "src/python-api/getml/data/columns/last_change_from_col.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThe last time any of the underlying data frames has been changed.\n\"\"\"\n\nfrom .last_change import _last_change\n\n\n@property  # type: ignore\ndef _last_change_from_col(self):\n    \"\"\"\n    The last time any of the underlying data frames has been changed.\n    \"\"\"\n\n    def _last_change_from_cmd(cmd):\n        if cmd[\"type_\"] == \"FloatColumn\" or cmd[\"type_\"] == \"StringColumn\":\n            assert \"df_name_\" in cmd, \"Expected df_name_\"\n            return _last_change(cmd[\"df_name_\"])\n\n        def get_operand(op):\n            return [_last_change_from_cmd(cmd[op])] if op in cmd else []\n\n        all_values = (\n            [\"\"]\n            + get_operand(\"operand1_\")\n            + get_operand(\"operand2_\")\n            + get_operand(\"condition_\")\n        )\n\n        return max(all_values)\n\n    return _last_change_from_cmd(self.cmd)\n"
  },
  {
    "path": "src/python-api/getml/data/columns/length.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns the length of the column\n\"\"\"\n\nimport numpy as np\n\n\ndef _length(col) -> int:\n    length = col.length\n    if isinstance(length, str):\n        raise ValueError(\n            \"The length is either infinite or cannot \"\n            + \"be known before fully parsing the ColumnView!\"\n        )\n    return length\n"
  },
  {
    "path": "src/python-api/getml/data/columns/length_property.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThe length of the column (number of rows in the data frame).\n\"\"\"\n\nfrom typing import Any, Dict, Union\n\nimport numpy as np\n\nimport getml.communication as comm\nfrom getml.data.columns.constants import VIEW_SIGNIFIER\nfrom getml.utilities.formatting.column_formatter import _ColumnFormatter\n\n\n@property  # type: ignore\ndef _length_property(col) -> Union[int, str]:\n    \"\"\"\n    The length of the column (number of rows in the data frame).\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = (col.cmd[\"type_\"] + \".get_nrows\").replace(VIEW_SIGNIFIER, \"\")\n    cmd[\"name_\"] = \"\"\n\n    cmd[\"col_\"] = col.cmd\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Found!\":\n            comm.handle_engine_exception(msg)\n        nrows = comm.recv_string(sock)\n\n    nrows_to_display = len(col[: _ColumnFormatter.max_rows + 1].to_numpy())\n    if nrows_to_display <= _ColumnFormatter.max_rows:\n        return nrows_to_display\n\n    try:\n        return int(nrows)\n    except:\n        return nrows\n"
  },
  {
    "path": "src/python-api/getml/data/columns/make_iter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFactory function for a function that can be used\nto iterate through a column.\n\"\"\"\n\nfrom typing import Any, Callable, Generator, Type, TypeVar\n\nimport numpy as np\n\nValueType = TypeVar(\"ValueType\", np.float64, bool, str)\n\nCHUNK_SIZE = 100000\n\n\ndef _make_iter(\n    value_type: Type[ValueType],\n) -> Callable[[Any], Generator[ValueType, None, None]]:\n    def _iter(self: Any) -> Generator[ValueType, None, None]:\n        length = len(self)\n        for begin in range(0, length, CHUNK_SIZE):\n            end = min(length, begin + CHUNK_SIZE)\n            arr = self[begin:end].to_numpy()\n            for val in arr:\n                yield value_type(val)\n\n    return _iter\n"
  },
  {
    "path": "src/python-api/getml/data/columns/parse.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Parses the columns from a cmd\"\"\"\n\nfrom typing import Any, Dict, TypeVar, Union\n\nfrom .columns import (\n    BooleanColumnView,\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n)\nfrom .constants import (\n    BOOLEAN_COLUMN_VIEW,\n    FLOAT_COLUMN,\n    FLOAT_COLUMN_VIEW,\n    STRING_COLUMN,\n    STRING_COLUMN_VIEW,\n)\n\nColtype = TypeVar(\n    \"Coltype\",\n    FloatColumn,\n    StringColumn,\n    FloatColumnView,\n    StringColumnView,\n    BooleanColumnView,\n)\n\n\ndef _make_column(cmd: Dict[str, Any], col: Coltype):\n    col.cmd = cmd\n    return col\n\n\ndef _parse(\n    cmd: Dict[str, Any],\n) -> Union[\n    BooleanColumnView, FloatColumn, FloatColumnView, StringColumn, StringColumnView\n]:\n    typ = cmd[\"type_\"]\n\n    if typ == BOOLEAN_COLUMN_VIEW:\n        return _make_column(cmd, BooleanColumnView(\"\", None, None))\n\n    if typ == FLOAT_COLUMN:\n        return _make_column(cmd, FloatColumn())\n\n    if typ == FLOAT_COLUMN_VIEW:\n        return _make_column(cmd, FloatColumnView(\"\", 0, 0))\n\n    if typ == STRING_COLUMN:\n        return _make_column(cmd, StringColumn())\n\n    if typ == STRING_COLUMN_VIEW:\n        return _make_column(cmd, StringColumnView(\"\", \"\", \"\"))\n\n    raise ValueError(\"Unknown column type: '\" + typ + \"'\")\n"
  },
  {
    "path": "src/python-api/getml/data/columns/random.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nGenerates random numbers\n\"\"\"\n\nimport numbers\n\nfrom .columns import FloatColumnView\n\n\ndef random(seed: int = 5849) -> FloatColumnView:\n    \"\"\"\n    Create random column.\n\n    The numbers will be uniformly distributed from 0.0 to 1.0. This can be\n    used to randomly split a population table into a training and a test\n    set\n\n    Args:\n        seed:\n            Seed used for the random number generator.\n\n    Returns:\n            FloatColumn containing random numbers\n\n    ??? example\n        ```python\n        population = getml.DataFrame('population')\n        population.add(numpy.zeros(100), 'column_01')\n\n        idx = random(seed=42)\n        population_train = population[idx > 0.7]\n        population_test = population[idx <= 0.7]\n        ```\n    \"\"\"\n\n    if not isinstance(seed, numbers.Real):\n        raise TypeError(\"'seed' must be a real number\")\n\n    col = FloatColumnView(operator=\"random\", operand1=None, operand2=None)\n    col.cmd[\"seed_\"] = seed\n    return col\n"
  },
  {
    "path": "src/python-api/getml/data/columns/repr.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nASCII representation of the column.\n\"\"\"\n\n\ndef _repr(self):\n    formatted = self._format()\n    footer = self._collect_footer_data()\n    return formatted._render_string(footer=footer)\n"
  },
  {
    "path": "src/python-api/getml/data/columns/repr_html.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nHTML representation of the column.\n\"\"\"\n\n\ndef _repr_html(self):\n    formatted = self._format()\n    footer = self._collect_footer_data()\n    return formatted._render_html(footer=footer)\n"
  },
  {
    "path": "src/python-api/getml/data/columns/subroles.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThe subroles of this column.\n\"\"\"\n\nfrom typing import Any, Dict, List\n\nimport getml.communication as comm\n\nfrom .constants import VIEW_SIGNIFIER\n\n\n@property  # type: ignore\ndef _subroles(self) -> List[str]:\n    \"\"\"\n    The subroles of this column.\n\n    Subroles are used for more fine-granular control\n    what the column can be used for.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = (self.cmd[\"type_\"] + \".get_subroles\").replace(VIEW_SIGNIFIER, \"\")\n\n    cmd[\"col_\"] = self.cmd\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        subroles = comm.recv_string_column(sock)\n\n    return subroles.flatten().tolist()\n"
  },
  {
    "path": "src/python-api/getml/data/columns/to_arrow.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTransform column to a pyarrow.ChunkedArray\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport pyarrow as pa\n\nimport getml.communication as comm\n\n\ndef _to_arrow(self: Any, unique: bool = False) -> pa.ChunkedArray:\n    \"\"\"\n    Transform column to arrow array\n    \"\"\"\n\n    typename = type(self).__name__.replace(\"View\", \"\")\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = typename + (\".unique\" if unique else \".get\")\n\n    cmd[\"col_\"] = self.cmd\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n        with sock.makefile(mode=\"rb\") as stream:\n            with pa.ipc.open_stream(stream) as reader:\n                return reader.read_all()[\"column\"]\n"
  },
  {
    "path": "src/python-api/getml/data/columns/to_numpy.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTransform column to a numpy array.\n\"\"\"\n\nfrom typing import Any\n\nimport numpy as np\n\nfrom .to_arrow import _to_arrow\n\n\ndef _to_numpy(self: Any) -> np.ndarray:\n    \"\"\"\n    Transform column to numpy.ndarray\n    \"\"\"\n    return _to_arrow(self).to_numpy()\n"
  },
  {
    "path": "src/python-api/getml/data/columns/unique.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTransform column to numpy array containing unique values\n\"\"\"\n\nimport numpy as np\n\nfrom .to_arrow import _to_arrow\n\n\ndef _unique(self) -> np.ndarray:\n    \"\"\"\n    Transform column to numpy array containing all distinct values.\n    \"\"\"\n    return _to_arrow(self, unique=True).to_numpy()\n"
  },
  {
    "path": "src/python-api/getml/data/columns/unit.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThe unit of this column.\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport getml.communication as comm\n\nfrom .constants import VIEW_SIGNIFIER\n\n\n@property  # type: ignore\ndef _unit(self) -> str:\n    \"\"\"\n    The unit of this column.\n\n    Units are used to determine which columns can be compared to each other\n    by the feature learning algorithms.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = (self.cmd[\"type_\"] + \".get_unit\").replace(VIEW_SIGNIFIER, \"\")\n\n    cmd[\"col_\"] = self.cmd\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        unit = comm.recv_string(sock)\n\n    return unit\n"
  },
  {
    "path": "src/python-api/getml/data/concat.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new data frame by concatenating a list of existing ones.\n\"\"\"\n\nfrom typing import Any, Dict, List, Union\n\nimport getml.communication as comm\n\nfrom .data_frame import DataFrame\nfrom .helpers import _is_non_empty_typed_list\nfrom .view import View\n\n# --------------------------------------------------------------------\n\n\ndef concat(name: str, data_frames: List[Union[DataFrame, View]]):\n    \"\"\"\n    Creates a new data frame by concatenating a list of existing ones.\n\n    Args:\n        name:\n            Name of the new column.\n\n        data_frames:\n            The data frames to concatenate.\n            Must be non-empty. However, it can contain only one data frame.\n            Column names and roles must match.\n            Columns will be appended by name, not order.\n\n    Examples:\n        ```python\n        new_df = data.concat(\"NEW_DF_NAME\", [df1, df2])\n        ```\n    \"\"\"\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be a string.\")\n\n    if not _is_non_empty_typed_list(data_frames, (View, DataFrame)):\n        raise TypeError(\n            \"'data_frames' must be a non-empty list of getml.data.Views \"\n            + \"or getml.DataFrames.\"\n        )\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"DataFrame.concat\"\n    cmd[\"name_\"] = name\n\n    cmd[\"data_frames_\"] = [df._getml_deserialize() for df in data_frames]\n\n    comm.send(cmd)\n\n    return DataFrame(name=name).refresh()\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/data/container.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFor keeping and versioning data.\n\"\"\"\n\nfrom datetime import datetime\nfrom inspect import cleandoc\nfrom typing import Dict, Optional, Union\n\nimport pandas as pd\n\nimport getml.communication as comm\nfrom getml.data.columns import StringColumn, StringColumnView, from_value\nfrom getml.log import logger\nfrom getml.utilities.formatting import _Formatter\n\nfrom .data_frame import DataFrame\nfrom .helpers import _is_typed_dict, _make_id\nfrom .helpers2 import (\n    _deep_copy,\n    _get_last_change,\n    _make_subsets_from_kwargs,\n    _make_subsets_from_split,\n)\nfrom .subset import Subset\nfrom .view import View\n\n\nclass Container:\n    \"\"\"\n    A container holds the actual data in the form of a [`DataFrame`][getml.DataFrame] or a [`View`][getml.data.View].\n\n    The purpose of a container is twofold:\n\n    - Assigning concrete data to an abstract [`DataModel`][getml.data.DataModel].\n\n    - Storing data and allowing you to reproduce previous results.\n\n    Attributes:\n        population:\n            The population table defines the\n            [statistical population ](https://en.wikipedia.org/wiki/Statistical_population)\n            of the machine learning problem and contains the target variables.\n\n        peripheral:\n            The peripheral tables are joined onto *population* or other\n            peripheral tables. Note that you can also pass them using\n            [`add`][getml.data.Container.add].\n\n        split:\n            Contains information on how you want to split *population* into\n            different [`Subset`][getml.data.Subset]s.\n            Also refer to [`split`][getml.data.split].\n\n        deep_copy:\n            Whether you want to create deep copies or your tables.\n\n        train:\n            The population table used in the *train*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        validation:\n            The population table used in the *validation*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        test:\n            The population table used in the *test*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        kwargs:\n            The population table used in [`Subset`][getml.data.Subset]s\n            other than the predefined *train*, *validation* and *test* subsets.\n            You can call these subsets anything you want to, and you can access them\n            just like *train*, *validation* and *test*.\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n            ??? example\n                ```python\n                # Pass the subset.\n                container = getml.data.Container(my_subset=my_data_frame)\n\n                # You can access the subset just like train,\n                # validation or test\n                my_pipeline.fit(container.my_subset)\n                ```\n\n    ??? example\n        A [`DataModel`][getml.data.DataModel] only contains abstract data. When we\n        fit a pipeline, we need to assign concrete data.\n\n        This example is taken from the\n        [loans notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/loans.ipynb).\n        Note that in the notebook the high level [`StarSchema`][getml.data.StarSchema] implementation is used. For\n        demonstration purposes we are proceeding now with the low level implementation.\n\n        ```python\n        # The abstract data model is constructed\n        # using the DataModel class. A data model\n        # does not contain any actual data. It just\n        # defines the abstract relational structure.\n        dm = getml.data.DataModel(\n            population_train.to_placeholder(\"population\")\n        )\n\n        dm.add(getml.data.to_placeholder(\n            meta=meta,\n            order=order,\n            trans=trans)\n        )\n\n        dm.population.join(\n            dm.trans,\n            on=\"account_id\",\n            time_stamps=(\"date_loan\", \"date\")\n        )\n\n        dm.population.join(\n            dm.order,\n            on=\"account_id\",\n        )\n\n        dm.population.join(\n            dm.meta,\n            on=\"account_id\",\n        )\n\n        # We now have abstract placeholders on something\n        # called \"population\", \"meta\", \"order\" and \"trans\".\n        # But how do we assign concrete data? By using\n        # a container.\n        container = getml.data.Container(\n            train=population_train,\n            test=population_test\n        )\n\n        # meta, order and trans are either\n        # DataFrames or Views. Their aliases need\n        # to match the names of the placeholders in the\n        # data model.\n        container.add(\n            meta=meta,\n            order=order,\n            trans=trans\n        )\n\n        # Freezing makes the container immutable.\n        # This is not required, but often a good idea.\n        container.freeze()\n\n        # When we call 'train', the container\n        # will return the train set and the\n        # peripheral tables.\n        my_pipeline.fit(container.train)\n\n        # Same for 'test'\n        my_pipeline.score(container.test)\n        ```\n        If you don't already have a train and test set,\n        you can use a function from the\n        [`split`][getml.data.split] module.\n\n        ```python\n\n        split = getml.data.split.random(\n            train=0.8, test=0.2)\n\n        container = getml.data.Container(\n            population=population_all,\n            split=split,\n        )\n\n        # The remaining code is the same as in\n        # the example above. In particular,\n        # container.train and container.test\n        # work just like above.\n        ```\n\n        Containers can also be used for storage and reproducing your\n        results.\n        A recommended pattern is to assign 'baseline roles' to your data frames\n        and then using a [`View`][getml.data.View] to tweak them:\n\n        ```python\n\n        # Assign baseline roles\n        data_frame.set_role([\"jk\"], getml.data.roles.join_key)\n        data_frame.set_role([\"col1\", \"col2\"], getml.data.roles.categorical)\n        data_frame.set_role([\"col3\", \"col4\"], getml.data.roles.numerical)\n        data_frame.set_role([\"col5\"], getml.data.roles.target)\n\n        # Make the data frame immutable, so in-place operations are\n        # no longer possible.\n        data_frame.freeze()\n\n        # Save the data frame.\n        data_frame.save()\n\n        # I suspect that col1 leads to overfitting, so I will drop it.\n        view = data_frame.drop([\"col1\"])\n\n        # Insert the view into a container.\n        container = getml.data.Container(...)\n        container.add(some_alias=view)\n        container.save()\n        ```\n\n        The advantage of using such a pattern is that it enables you to\n        always completely retrace your entire pipeline without creating\n        deep copies of the data frames whenever you have made a small\n        change like the one in our example. Note that the pipeline will\n        record which container you have used.\n    \"\"\"\n\n    def __init__(\n        self,\n        population: Optional[Union[DataFrame, View]] = None,\n        peripheral: Optional[Dict[str, Union[DataFrame, View]]] = None,\n        split: Optional[Union[StringColumn, StringColumnView]] = None,\n        deep_copy: Optional[bool] = False,\n        train: Optional[Union[DataFrame, View]] = None,\n        validation: Optional[Union[DataFrame, View]] = None,\n        test: Optional[Union[DataFrame, View]] = None,\n        **kwargs: Optional[Union[DataFrame, View]],\n    ):\n        if population is not None and not isinstance(population, (DataFrame, View)):\n            raise TypeError(\n                \"'population' must be a getml.DataFrame or a getml.data.View, got \"\n                + type(population).__name__\n                + \".\"\n            )\n\n        if peripheral is not None and not _is_typed_dict(\n            peripheral, str, [DataFrame, View]\n        ):\n            raise TypeError(\n                \"'peripheral' must be a dict \"\n                + \"of getml.DataFrames or getml.data.Views.\"\n            )\n\n        if split is not None and not isinstance(\n            split, (StringColumn, StringColumnView)\n        ):\n            raise TypeError(\n                \"'split' must be StringColumn or a StringColumnView, got \"\n                + type(split).__name__\n                + \".\"\n            )\n\n        if not isinstance(deep_copy, bool):\n            raise TypeError(\n                \"'deep_copy' must be a bool, got \" + type(split).__name__ + \".\"\n            )\n\n        exclusive = (population is not None) ^ (\n            len(_make_subsets_from_kwargs(train, validation, test, **kwargs)) != 0\n        )\n\n        if not exclusive:\n            raise ValueError(\n                \"'population' and 'train', 'validation', 'test' as well as \"\n                + \"other subsets signified by kwargs are mutually exclusive. \"\n                + \"You have to pass \"\n                + \"either 'population' or some subsets, but you cannot pass both.\"\n            )\n\n        if population is None and split is not None:\n            raise ValueError(\n                \"'split's are used for splitting population DataFrames.\"\n                \"Hence, if you supply 'split', you also have to supply \"\n                \"a population.\"\n            )\n\n        if population is not None and split is None:\n            logger.warning(\n                \"You have passed a population table without passing 'split'. \"\n                \"You can access the entire set to pass to your pipeline \"\n                \"using the .full attribute.\"\n            )\n            split = from_value(\"full\")\n\n        self._id = _make_id()\n\n        self._population = population\n        self._peripheral = peripheral or {}\n        self._split = split\n        self._deep_copy = deep_copy\n\n        # HACK: Do some explicit bookeeping on the subets' length until we have\n        # proper endpoint for slice-based subsetting\n        if split is not None:\n            self._subsets = {}\n            self._lengths = {}\n            for name, (length, subset) in _make_subsets_from_split(\n                population, split\n            ).items():\n                self._subsets[name] = subset\n                self._lengths[name] = length\n        else:\n            self._subsets = _make_subsets_from_kwargs(train, validation, test, **kwargs)\n            self._lengths = {\n                name: subset.nrows() for name, subset in self._subsets.items()\n            }\n\n        if split is None and not _is_typed_dict(self._subsets, str, [DataFrame, View]):\n            raise TypeError(\n                \"'train', 'validation', 'test' and all other subsets must be either a \"\n                \"getml.DataFrame or a getml.data.View.\"\n            )\n\n        if deep_copy:\n            self._population = _deep_copy(self._population, self._id)\n            self._peripheral = {\n                k: _deep_copy(v, self._id) for (k, v) in self._peripheral.items()\n            }\n            self._subsets = {\n                k: _deep_copy(v, self._id) for (k, v) in self._subsets.items()\n            }\n\n        self._last_change = _get_last_change(\n            self._population, self._peripheral, self._subsets\n        )\n\n        self._frozen_time = None\n\n    def __dir__(self):\n        attrs = dir(type(self)) + self._ipython_key_completion()\n        attrs = [x for x in attrs if x.isidentifier()]\n        return attrs\n\n    def __getattr__(self, key) -> Subset:\n        try:\n            return self[key]\n        except KeyError:\n            super().__getattribute__(key)\n\n    def __getitem__(self, key) -> Subset:\n        if \"_\" + key in self.__dict__:\n            return self.__dict__[\"_\" + key]\n\n        if key in self.__dict__[\"_subsets\"]:\n            if self.__dict__[\"_deep_copy\"] and self._frozen_time is None:\n                raise ValueError(\n                    cleandoc(\n                        f\"\"\"\n                        If you set deep_copy=True, you must call\n                        {type(self).__name__}.freeze() before you can extract data. The\n                        idea of deep_copy is to ensure that you can always retrace and\n                        reproduce your results. That is why the container\n                        needs to be immutable before it can be used.\n                        \"\"\"\n                    )\n                )\n\n            last_change = _get_last_change(\n                self.__dict__[\"_population\"],\n                self.__dict__[\"_peripheral\"],\n                self.__dict__[\"_subsets\"],\n            )\n\n            if self.__dict__[\"_last_change\"] != last_change:\n                logger.warning(\n                    cleandoc(\n                        f\"\"\"\n                        Some of the data frames added to the {type(self).__name__} have\n                        been modified after they were added.  This might lead to\n                        unexpected results. To avoid these sort of problems, you can set\n                        deep_copy=True when creating the {type(self).__name__}.\n                        \"\"\"\n                    )\n                )\n\n            return Subset(\n                container_id=self.__dict__[\"_id\"],\n                population=self.__dict__[\"_subsets\"][key].with_name(key),\n                peripheral=self.__dict__[\"_peripheral\"],\n            )\n\n        if key in self.__dict__[\"_peripheral\"]:\n            return self.__dict__[\"_peripheral\"][key]\n\n        raise KeyError(f\"{type(self).__name__} holds no data with name {key!r}.\")\n\n    def __repr__(self):\n        pop, perph = self._format()\n\n        template = cleandoc(\n            \"\"\"\n            population\n            {pop}\n\n            peripheral\n            {perph}\n            \"\"\"\n        )\n\n        return template.format(pop=pop._render_string(), perph=perph._render_string())\n\n    def _repr_html_(self):\n        pop, perph = self._format()\n\n        template = cleandoc(\n            \"\"\"\n            <div style='margin-top: 15px;'>\n            <div style='float: left; margin-right: 50px;'>\n            <div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\n                {pop}\n            </div>\n            <div style='float: left;'>\n            <div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\n                {perph}\n            </div>\n            </div>\n            \"\"\"\n        )\n\n        return template.format(pop=pop._render_html(), perph=perph._render_html())\n\n    def _format(self):\n        headers_pop = [[\"subset\", \"name\", \"rows\", \"type\"]]\n\n        rows_pop = [\n            [name, subset.name, self._lengths[name], type(subset).__name__]\n            for name, subset in self.subsets.items()  # pytype: disable=attribute-error\n        ]\n\n        headers_perph = [[\"name\", \"rows\", \"type\"]]\n\n        rows_perph = [\n            [perph.name, perph.nrows(), type(perph).__name__]\n            for perph in self.peripheral.values()  # pytype: disable=attribute-error\n        ]\n\n        names = [\n            perph.name\n            for perph in self.peripheral.values()  # pytype: disable=attribute-error\n        ]\n        aliases = list(self.peripheral.keys())  # pytype: disable=attribute-error\n\n        if any(alias not in names for alias in aliases):\n            headers_perph[0].insert(0, \"alias\")\n\n            for alias, row in zip(aliases, rows_perph):\n                row.insert(0, alias)\n\n        return _Formatter(headers=headers_pop, rows=rows_pop), _Formatter(\n            headers=headers_perph, rows=rows_perph\n        )\n\n    def _getml_deserialize(self):\n        cmd = {k[1:] + \"_\": v for (k, v) in self.__dict__.items() if v is not None}\n\n        if self._population is not None:\n            cmd[\"population_\"] = self._population._getml_deserialize()\n\n        if self._split is not None:\n            cmd[\"split_\"] = self._split.cmd\n\n        cmd[\"peripheral_\"] = {\n            k: v._getml_deserialize() for (k, v) in self._peripheral.items()\n        }\n\n        cmd[\"subsets_\"] = {\n            k: v._getml_deserialize() for (k, v) in self._subsets.items()\n        }\n\n        return cmd\n\n    def _ipython_key_completion(self):\n        attrs = [v[1:] for v in list(vars(self))]\n        attrs.extend(self._peripheral)\n        if not self._deep_copy or self._frozen_time is not None:\n            attrs.extend(self._subsets)\n        return attrs\n\n    def __setattr__(self, name, value):\n        if not name or name[0] != \"_\":\n            raise ValueError(\"Attempting a write operation on read-only data.\")\n        vars(self)[name] = value\n\n    def add(self, *args, **kwargs):\n        \"\"\"\n        Adds new peripheral data frames or views.\n        \"\"\"\n        wrong_type = [item for item in args if not isinstance(item, (DataFrame, View))]\n\n        if wrong_type:\n            raise TypeError(\n                \"All unnamed arguments must be getml.DataFrames or getml.data.Views.\"\n            )\n\n        wrong_type = [\n            k for (k, v) in kwargs.items() if not isinstance(v, (DataFrame, View))\n        ]\n\n        if wrong_type:\n            raise TypeError(\n                \"You must pass getml.DataFrames or getml.data.Views, \"\n                f\"but the following arguments were neither: {wrong_type!r}.\"\n            )\n\n        kwargs = {**{item.name: item for item in args}, **kwargs}\n\n        if self._frozen_time is not None:\n            raise ValueError(\n                f\"You cannot add data frames after the {type(self).__name__} has been frozen.\"\n            )\n\n        if self._deep_copy:\n            kwargs = {k: _deep_copy(v, self._id) for (k, v) in kwargs.items()}\n\n        self._peripheral = {**self._peripheral, **kwargs}\n\n        self._last_change = _get_last_change(\n            self._population, self._peripheral, self._subsets\n        )\n\n    def freeze(self):\n        \"\"\"\n        Freezes the container, so that changes are no longer possible.\n\n        This is required before you can extract data when `deep_copy=True`. The idea of\n        `deep_copy` is to ensure that you can always retrace and reproduce your results.\n        That is why the container needs to be immutable before it can be\n        used.\n        \"\"\"\n        self.sync()\n        self._frozen_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n    def save(self):\n        \"\"\"\n        Saves the Container to disk.\n        \"\"\"\n\n        cmd = dict()\n        cmd[\"type_\"] = \"DataContainer.save\"\n        cmd[\"name_\"] = self._id\n\n        cmd[\"container_\"] = self._getml_deserialize()\n\n        comm.send(cmd)\n\n    def sync(self):\n        \"\"\"\n        Synchronizes the last change with the data to avoid warnings that the data\n        has been changed.\n\n        This is only a problem when `deep_copy=False`.\n        \"\"\"\n        if self._frozen_time is not None:\n            raise ValueError(f\"{type(self).__name__} has already been frozen.\")\n        self._last_change = _get_last_change(\n            self._population, self._peripheral, self._subsets\n        )\n\n    def to_pandas(self) -> Dict[str, pd.DataFrame]:\n        \"\"\"\n        Returns a `Container`'s contents as a dictionary of `pandas.DataFrame`s.\n        `name` holds the data frame's `name`, value the data converted to a `pandas.DataFrame`.\n        \"\"\"\n        subsets = (\n            {name: df.to_pandas() for name, df in self._subsets.items()}\n            if self._subsets\n            else {}\n        )\n        peripherals = (\n            {name: df.to_pandas() for name, df in self.peripheral.items()}\n            if self.peripheral\n            else {}\n        )\n        if subsets or peripherals:\n            return {**subsets, **peripherals}\n\n        raise ValueError(\"Container is empty.\")\n"
  },
  {
    "path": "src/python-api/getml/data/data_frame.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"Handler for the data stored in the getML Engine.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport numbers\nimport os\nimport shutil\nimport warnings\nfrom collections import namedtuple\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom typing import (\n    TYPE_CHECKING,\n    Any,\n    Dict,\n    Hashable,\n    Iterable,\n    Iterator,\n    List,\n    Literal,\n    Optional,\n    Union,\n    cast,\n    overload,\n)\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\n\nimport getml.communication as comm\nfrom getml import constants, database\nfrom getml.constants import COMPARISON_ONLY, DEFAULT_BATCH_SIZE, TIME_STAMP\nfrom getml.data import roles as roles_\nfrom getml.data._io.arrow import (\n    cast_arrow_batch,\n    preprocess_arrow_schema,\n    read_arrow_batches,\n    sniff_schema,\n    to_arrow,\n    to_arrow_batches,\n    to_arrow_stream,\n)\nfrom getml.data._io.csv import (\n    DEFAULT_CSV_READ_BLOCK_SIZE,\n    read_csv,\n    sniff_csv,\n    stream_csv,\n    to_csv,\n)\nfrom getml.data._io.parquet import sniff_parquet, to_parquet\nfrom getml.data.columns import (\n    BooleanColumnView,\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n    rowid,\n)\nfrom getml.data.columns.last_change import _last_change\nfrom getml.data.helpers import (\n    _check_if_exists,\n    _empty_data_frame,\n    _exists_in_memory,\n    _get_column,\n    _handle_cols,\n    _is_non_empty_typed_list,\n    _is_numerical_type_numpy,\n    _iter_batches,\n    _prepare_roles,\n    _send_numpy_array,\n    _sniff_db,\n    _sniff_query,\n    _sniff_s3,\n    _to_pyspark,\n    _where,\n    _with_column,\n    _with_role,\n    _with_subroles,\n    _with_unit,\n)\nfrom getml.data.placeholder import Placeholder\nfrom getml.data.roles import sets as roles_sets\nfrom getml.data.roles.container import Roles\nfrom getml.data.roles.types import Role\nfrom getml.data.subroles.types import Subrole\nfrom getml.data.view import View\nfrom getml.database import Connection\nfrom getml.database.helpers import _retrieve_temp_dir, _retrieve_urls\nfrom getml.helpers import _is_iterable_not_str, _is_iterable_not_str_of_type\nfrom getml.utilities.formatting import _DataFrameFormatter\n\nif TYPE_CHECKING:\n    import pyspark.sql\n\n    from getml.data.data_frame import DataFrame\n    from getml.data.view import View\n\n# --------------------------------------------------------------------\n\n\nclass DataFrame:\n    \"\"\"Handler for the data stored in the getML Engine.\n\n    The [`DataFrame`][getml.DataFrame] class represents a data frame\n    object in the getML Engine but does not contain any actual data\n    itself. To create such a data frame object, fill it with data via\n    the Python API, and to retrieve a handler for it, you can use one\n    of the [`from_csv`][getml.DataFrame.from_csv],\n    [`from_db`][getml.DataFrame.from_db],\n    [`from_json`][getml.DataFrame.from_json], or\n    [`from_pandas`][getml.DataFrame.from_pandas] class methods. The\n    [Importing Data][importing-data] section in the user guide explains the\n    particularities of each of those flavors of the unified\n    import interface.\n\n    If the data frame object is already present in the Engine -\n    either in memory as a temporary object or on disk when\n    [`save`][getml.DataFrame.save] was called earlier -, the\n    [`load_data_frame`][getml.data.load_data_frame] function will create a new\n    handler without altering the underlying data. For more information\n    about the lifecycle of the data in the getML Engine and its\n    synchronization with the Python API please see the\n    corresponding [User Guide][python-api-lifecycles].\n\n    Attributes:\n        name:\n            Unique identifier used to link the handler with\n            the underlying data frame object in the Engine.\n\n        roles:\n            Maps the [`roles`][getml.data.roles] to the\n            column names (see [`colnames`][getml.DataFrame.colnames]).\n\n            The `roles` dictionary is expected to have the following format\n            ```python\n            roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                     getml.data.role.target: [\"colname3\"]}\n            ```\n            Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n    ??? example\n        Creating a new data frame object in the getML Engine and importing\n        data is done by one the class functions\n        [`from_csv`][getml.DataFrame.from_csv],\n        [`from_db`][getml.DataFrame.from_db],\n        [`from_json`][getml.DataFrame.from_json], or\n        [`from_pandas`][getml.DataFrame.from_pandas].\n\n        ```python\n        random = numpy.random.RandomState(7263)\n\n        table = pandas.DataFrame()\n        table['column_01'] = random.randint(0, 10, 1000).astype(numpy.str)\n        table['join_key'] = numpy.arange(1000)\n        table['time_stamp'] = random.rand(1000)\n        table['target'] = random.rand(1000)\n\n        df_table = getml.DataFrame.from_pandas(table, name = 'table')\n        ```\n        In addition to creating a new data frame object in the getML\n        Engine and filling it with all the content of `table`, the\n        [`from_pandas`][getml.DataFrame.from_pandas] function also\n        returns a [`DataFrame`][getml.DataFrame] handler to the\n        underlying data.\n\n        You don't have to create the data frame objects anew for each\n        session. You can use their [`save`][getml.DataFrame.save]\n        method to write them to disk, the\n        [`list_data_frames`][getml.data.list_data_frames] function to list all\n        available objects in the Engine, and\n        [`load_data_frame`][getml.data.load_data_frame] to create a\n        [`DataFrame`][getml.DataFrame] handler for a data set already\n        present in the getML Engine (see\n        [User Guide][python-api] for details).\n\n        ```python\n        df_table.save()\n\n        getml.data.list_data_frames()\n\n        df_table_reloaded = getml.data.load_data_frame('table')\n        ```\n\n    Note:\n        Although the Python API does not store the actual data itself,\n        you can use the [`to_csv`][getml.DataFrame.to_csv],\n        [`to_db`][getml.DataFrame.to_db],\n        [`to_json`][getml.DataFrame.to_json], and\n        [`to_pandas`][getml.DataFrame.to_pandas] methods to retrieve\n        them.\n\n    \"\"\"\n\n    _categorical_roles = roles_sets.categorical\n    _numerical_roles = roles_sets.numerical\n    _all_roles = roles_sets.all_\n\n    def __init__(\n        self,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n    ):\n        # ------------------------------------------------------------\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        vars(self)[\"name\"] = name\n\n        if roles is None:\n            roles = {}\n\n        if isinstance(roles, dict):\n            roles = Roles.from_dict(roles)\n\n        # ------------------------------------------------------------\n\n        vars(self)[\"_categorical_columns\"] = [\n            StringColumn(name=cname, role=roles_.categorical, df_name=self.name)\n            for cname in roles.categorical\n        ]\n\n        vars(self)[\"_join_key_columns\"] = [\n            StringColumn(name=cname, role=roles_.join_key, df_name=self.name)\n            for cname in roles.join_key\n        ]\n\n        vars(self)[\"_numerical_columns\"] = [\n            FloatColumn(name=cname, role=roles_.numerical, df_name=self.name)\n            for cname in roles.numerical\n        ]\n\n        vars(self)[\"_target_columns\"] = [\n            FloatColumn(name=cname, role=roles_.target, df_name=self.name)\n            for cname in roles.target\n        ]\n\n        vars(self)[\"_text_columns\"] = [\n            StringColumn(name=cname, role=roles_.text, df_name=self.name)\n            for cname in roles.text\n        ]\n\n        vars(self)[\"_time_stamp_columns\"] = [\n            FloatColumn(name=cname, role=roles_.time_stamp, df_name=self.name)\n            for cname in roles.time_stamp\n        ]\n\n        vars(self)[\"_unused_float_columns\"] = [\n            FloatColumn(name=cname, role=roles_.unused_float, df_name=self.name)\n            for cname in roles.unused_float\n        ]\n\n        vars(self)[\"_unused_string_columns\"] = [\n            StringColumn(name=cname, role=roles_.unused_string, df_name=self.name)\n            for cname in roles.unused_string\n        ]\n\n        # ------------------------------------------------------------\n\n        self._check_duplicates()\n\n    # ----------------------------------------------------------------\n\n    @property\n    def _columns(self):\n        return (\n            vars(self)[\"_categorical_columns\"]\n            + vars(self)[\"_join_key_columns\"]\n            + vars(self)[\"_numerical_columns\"]\n            + vars(self)[\"_target_columns\"]\n            + vars(self)[\"_text_columns\"]\n            + vars(self)[\"_time_stamp_columns\"]\n            + vars(self)[\"_unused_float_columns\"]\n            + vars(self)[\"_unused_string_columns\"]\n        )\n\n    # ----------------------------------------------------------------\n\n    def _delete(self, mem_only: bool = False):\n        \"\"\"Deletes the data frame from the getML Engine.\n\n        If called with the `mem_only` option set to True, the data\n        frame corresponding to the handler represented by the current\n        instance can be reloaded using the\n        [`load`][getml.DataFrame.load] method.\n\n        Args:\n            mem_only:\n                If True, the data frame will not be deleted\n                permanently, but just from memory (RAM).\n        \"\"\"\n\n        if not isinstance(mem_only, bool):\n            raise TypeError(\"'mem_only' must be of type bool\")\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.delete\"\n        cmd[\"name_\"] = self.name\n        cmd[\"mem_only_\"] = mem_only\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def __delitem__(self, colname: str):\n        self._drop(colname)\n\n    # ----------------------------------------------------------------\n\n    def __eq__(self, other):\n        if not isinstance(other, DataFrame):\n            raise TypeError(\n                \"A DataFrame can only be compared to another getml.DataFrame\"\n            )\n\n        # ------------------------------------------------------------\n\n        for kkey in self.__dict__:\n            if kkey not in other.__dict__:\n                return False\n\n            # Take special care when comparing numbers.\n            if isinstance(self.__dict__[kkey], numbers.Real):\n                if not np.isclose(self.__dict__[kkey], other.__dict__[kkey]):\n                    return False\n\n            elif self.__dict__[kkey] != other.__dict__[kkey]:\n                return False\n\n        # ------------------------------------------------------------\n\n        return True\n\n    # ----------------------------------------------------------------\n\n    def __getattr__(self, name: str) -> Union[FloatColumn, StringColumn]:\n        try:\n            return self[name]\n        except KeyError:\n            return super().__getattribute__(name)\n\n    # ------------------------------------------------------------\n\n    @overload\n    def __getitem__(self, name: str) -> Union[FloatColumn, StringColumn]: ...\n\n    @overload\n    def __getitem__(\n        self,\n        name: Union[\n            Iterable[str],\n            numbers.Real,\n            slice,\n            BooleanColumnView,\n            FloatColumn,\n            FloatColumnView,\n        ],\n    ) -> View: ...\n\n    def __getitem__(\n        self,\n        name: Union[\n            str,\n            Iterable[str],\n            numbers.Real,\n            slice,\n            BooleanColumnView,\n            FloatColumn,\n            FloatColumnView,\n        ],\n    ) -> Union[FloatColumn, StringColumn, View]:\n        if isinstance(\n            name,\n            (numbers.Integral, slice, BooleanColumnView, FloatColumn, FloatColumnView),\n        ):\n            return self.where(index=name)\n\n        if _is_iterable_not_str(name):\n            not_in_colnames = set(name) - set(self.colnames)\n            if not_in_colnames:\n                raise KeyError(f\"{list(not_in_colnames)} not found.\")\n            dropped = [col for col in self.colnames if col not in name]\n            return View(base=self, dropped=dropped)\n\n        col = _get_column(name, self._columns)\n\n        if col is not None:\n            return col\n\n        raise KeyError(f\"Column named '{name}' not found.\")\n\n    # ------------------------------------------------------------\n\n    def _getml_deserialize(self) -> Dict[str, Any]:\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame\"\n        cmd[\"name_\"] = self.name\n\n        return cmd\n\n    # ------------------------------------------------------------\n\n    def _dir(self) -> List[str]:\n        return sorted(set(dir(type(self)) + list(self.__dict__) + self.colnames))\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return self.nrows()\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        if not _exists_in_memory(self.name):\n            return _empty_data_frame()\n\n        formatted = self._format()\n\n        footer = self._collect_footer_data()\n\n        return formatted._render_string(footer=footer)\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        return self.to_html()\n\n    # ------------------------------------------------------------\n\n    def __setattr__(self, name, value) -> None:\n        if name in vars(self):\n            vars(self)[name] = value\n        else:\n            self.add(value, name)\n\n    # ------------------------------------------------------------\n\n    def __setitem__(self, name, col) -> None:\n        self.add(col, name)\n\n    # ------------------------------------------------------------\n\n    def _add_categorical_column(self, col, name, role, subroles, unit):\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.add_categorical_column\"\n        cmd[\"name_\"] = name\n\n        cmd[\"col_\"] = col.cmd\n        cmd[\"df_name_\"] = self.name\n        cmd[\"role_\"] = role\n        cmd[\"subroles_\"] = subroles\n        cmd[\"unit_\"] = unit\n\n        with comm.send_and_get_socket(cmd) as sock:\n            comm.recv_issues(sock)\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def _add_column(self, col, name, role, subroles, unit):\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.add_column\"\n        cmd[\"name_\"] = name\n\n        cmd[\"col_\"] = col.cmd\n        cmd[\"df_name_\"] = self.name\n        cmd[\"role_\"] = role\n        cmd[\"subroles_\"] = subroles\n        cmd[\"unit_\"] = unit\n\n        with comm.send_and_get_socket(cmd) as sock:\n            comm.recv_issues(sock)\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def _add_numpy_array(self, numpy_array, name, role, subroles, unit):\n        if len(numpy_array.shape) != 1:\n            raise TypeError(\n                \"\"\"numpy.ndarray needs to be one-dimensional!\n                Maybe you can call .ravel().\"\"\"\n            )\n\n        if role is None:\n            temp_df = pd.DataFrame()\n            temp_df[\"column\"] = numpy_array\n            if _is_numerical_type_numpy(temp_df.dtypes.iloc[0]):\n                role = roles_.unused_float\n            else:\n                role = roles_.unused_string\n\n        col = (\n            FloatColumn(name=name, role=role, df_name=self.name)\n            if role in self._numerical_roles\n            else StringColumn(name=name, role=role, df_name=self.name)\n        )\n\n        _send_numpy_array(col, numpy_array)\n\n        if subroles:\n            self._set_subroles(name, append=False, subroles=subroles)\n\n        if unit:\n            self._set_unit(name, unit)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def _check_duplicates(self) -> None:\n        all_colnames: List[str] = []\n\n        all_colnames = _check_if_exists(self._categorical_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._join_key_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._numerical_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._target_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._text_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._time_stamp_names, all_colnames)\n\n        all_colnames = _check_if_exists(self._unused_names, all_colnames)\n\n    # ------------------------------------------------------------\n\n    def _check_plausibility(self, data_frame):\n        self._check_duplicates()\n\n        for col in self._categorical_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._join_key_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._numerical_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._target_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._text_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._time_stamp_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n        for col in self._unused_names:\n            if col not in data_frame.columns:\n                raise ValueError(\"Column named '\" + col + \"' does not exist!\")\n\n    # ------------------------------------------------------------\n\n    def _collect_footer_data(self):\n        footer = namedtuple(\n            \"footer\", [\"n_rows\", \"n_cols\", \"memory_usage\", \"name\", \"type\", \"url\"]\n        )\n\n        return footer(\n            n_rows=self.nrows(),\n            n_cols=self.ncols(),\n            memory_usage=self.memory_usage,\n            name=self.name,\n            type=\"getml.DataFrame\",\n            url=self._monitor_url,\n        )\n\n    # ------------------------------------------------------------\n\n    def _close(self, sock):\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.close\"\n        cmd[\"name_\"] = self.name\n\n        comm.send_string(sock, json.dumps(cmd))\n\n        msg = comm.recv_string(sock)\n\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n    # ------------------------------------------------------------\n\n    def _drop(self, colname: str):\n        if not isinstance(colname, str):\n            raise TypeError(\"'colname' must be either a string.\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.remove_column\"\n        cmd[\"name_\"] = colname\n\n        cmd[\"df_name_\"] = self.name\n\n        comm.send(cmd)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def _format(self):\n        formatted = _DataFrameFormatter(self)\n        return formatted\n\n    # ------------------------------------------------------------\n\n    def _set_role(self, name, role, time_formats):\n        if not isinstance(name, str):\n            raise TypeError(\"Parameter 'name' must be a string!\")\n\n        col = self[name]\n\n        subroles = col.subroles\n\n        unit = TIME_STAMP + COMPARISON_ONLY if role == roles_.time_stamp else col.unit\n\n        self.add(\n            col,\n            name=name,\n            role=role,\n            subroles=subroles,\n            unit=unit,\n            time_formats=time_formats,\n        )\n\n    # ------------------------------------------------------------\n\n    def _set_subroles(\n        self,\n        name: str,\n        append: bool,\n        subroles: Optional[Union[Subrole, Iterable[str]]] = None,\n    ):\n        if not isinstance(name, str):\n            raise TypeError(\"Parameter 'name' must be a string!\")\n\n        col = self[name]\n\n        cmd: Dict[str, Any] = {}\n\n        cmd.update(col.cmd)\n\n        cmd[\"type_\"] += \".set_subroles\"\n\n        cmd[\"subroles_\"] = list(set(col.subroles + subroles)) if append else subroles\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def _set_unit(self, name: str, unit: str):\n        if not isinstance(name, str):\n            raise TypeError(\"Parameter 'name' must be a string!\")\n\n        col = self[name]\n\n        cmd: Dict[str, Any] = {}\n\n        cmd.update(col.cmd)\n\n        cmd[\"type_\"] += \".set_unit\"\n\n        cmd[\"unit_\"] = unit\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def add(\n        self,\n        col: Union[StringColumn, FloatColumn, np.ndarray],\n        name: str,\n        role: Optional[Role] = None,\n        subroles: Optional[Union[Role, Iterable[str]]] = None,\n        unit: str = \"\",\n        time_formats: Optional[Iterable[str]] = None,\n    ):\n        \"\"\"Adds a column to the current [`DataFrame`][getml.DataFrame].\n\n        Args:\n            col:\n                The column or numpy.ndarray to be added.\n\n            name:\n                Name of the new column.\n\n            role:\n                Role of the new column. Must be from [`roles`][getml.data.roles].\n\n            subroles:\n                Subroles of the new column. Must be from [`subroles`][getml.data.subroles].\n\n            unit:\n                Unit of the column.\n\n            time_formats:\n                Formats to be used to parse the time stamps.\n\n                This is only necessary, if an implicit conversion from\n                a [`StringColumn`][getml.data.columns.StringColumn] to a time\n                stamp is taking place.\n\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n        \"\"\"\n\n        if isinstance(col, np.ndarray):\n            self._add_numpy_array(col, name, role, subroles, unit)\n            return\n\n        col, role, subroles = _with_column(\n            col, name, role, subroles, unit, time_formats\n        )\n\n        is_string = isinstance(col, (StringColumnView, StringColumn))\n\n        if is_string:\n            self._add_categorical_column(col, name, role, subroles, unit)\n        else:\n            self._add_column(col, name, role, subroles, unit)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _categorical_names(self) -> List[str]:\n        return [col.name for col in self._categorical_columns]\n\n    # ------------------------------------------------------------\n\n    @property\n    def colnames(self) -> List[str]:\n        \"\"\"\n        List of the names of all columns.\n\n        Returns:\n                List of the names of all columns.\n        \"\"\"\n        return (\n            self._time_stamp_names\n            + self._join_key_names\n            + self._target_names\n            + self._categorical_names\n            + self._numerical_names\n            + self._text_names\n            + self._unused_names\n        )\n\n    # ------------------------------------------------------------\n\n    @property\n    def columns(self) -> List[str]:\n        \"\"\"\n        Alias for [`colnames`][getml.DataFrame.colnames].\n\n        Returns:\n                List of the names of all columns.\n        \"\"\"\n        return self.colnames\n\n    # ------------------------------------------------------------\n\n    def copy(self, name: str) -> DataFrame:\n        \"\"\"\n        Creates a deep copy of the data frame under a new name.\n\n        Args:\n            name:\n                The name of the new data frame.\n\n        Returns:\n                A handle to the deep copy.\n        \"\"\"\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be a string.\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.concat\"\n        cmd[\"name_\"] = name\n\n        cmd[\"data_frames_\"] = [self._getml_deserialize()]\n\n        comm.send(cmd)\n\n        return DataFrame(name=name).refresh()\n\n    # ------------------------------------------------------------\n\n    def delete(self):\n        \"\"\"\n        Permanently deletes the data frame. `delete` first unloads the data frame\n        from memory and then deletes it from disk.\n        \"\"\"\n        # ------------------------------------------------------------\n\n        self._delete()\n\n    # ------------------------------------------------------------\n\n    def drop(\n        self,\n        cols: Union[\n            FloatColumn,\n            StringColumn,\n            str,\n            Union[Iterable[FloatColumn], Iterable[StringColumn], Iterable[str]],\n        ],\n    ) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] that has one or several columns removed.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n        Returns:\n            A new [`View`][getml.data.View] object with the specified columns removed.\n        \"\"\"\n\n        names = _handle_cols(cols)\n\n        return View(base=self, dropped=names)\n\n    # ------------------------------------------------------------\n\n    def freeze(self):\n        \"\"\"Freezes the data frame.\n\n        After you have frozen the data frame, the data frame is immutable\n        and in-place operations are no longer possible. However, you can\n        still create views. In other words, operations like\n        [`set_role`][getml.DataFrame.set_role] are no longer possible,\n        but operations like [`with_role`][getml.DataFrame.with_role] are.\n        \"\"\"\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.freeze\"\n        cmd[\"name_\"] = self.name\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_arrow(\n        cls,\n        table: pa.Table,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_arrow(\n        cls,\n        table: pa.Table,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_arrow(\n        cls,\n        table: pa.Table,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from an Arrow Table.\n\n        This is one of the fastest way to get data into the\n        getML Engine.\n\n        Args:\n            table:\n                The arrow tablelike to be read.\n\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        sniffed_roles = sniff_schema(table.schema)\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_arrow(table=table, append=False)\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_csv(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        num_lines_sniffed: None = None,\n        num_lines_read: int = 0,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Iterable[str] = (),\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n        verbose: bool = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_csv(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        num_lines_sniffed: None = None,\n        num_lines_read: int = 0,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Iterable[str] = (),\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n        verbose: bool = False,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_csv(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        num_lines_sniffed: None = None,\n        num_lines_read: int = 0,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Iterable[str] = (),\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n        verbose: bool = True,\n        block_size: int = DEFAULT_CSV_READ_BLOCK_SIZE,\n        in_batches: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from CSV files.\n\n        The getML Engine will construct a data\n        frame object in the Engine, fill it with the data read from\n        the CSV file(s), and return a corresponding\n        [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            fnames:\n                CSV file paths to be read.\n\n            name:\n                Name of the data frame to be created.\n\n            num_lines_sniffed:\n                Number of lines analyzed by the sniffer.\n\n            num_lines_read:\n                Number of lines read from each file.\n                Set to 0 to read in the entire file.\n\n            quotechar:\n                The character used to wrap strings.\n\n            sep:\n                The separator used for separating fields.\n\n            skip:\n                Number of lines to skip at the beginning of each file.\n\n            colnames: The first line of a CSV file\n                usually contains the column names. When this is not the case,\n                you need to explicitly pass them.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n            verbose:\n                If True, when fnames are urls, the filenames are\n                printed to stdout during the download.\n\n            block_size:\n                The number of bytes read with each batch. Passed down to\n                pyarrow.\n\n            in_batches:\n                If True, read blocks streamwise manner and send those batches to\n                the engine. Blocks are read and sent to the engine sequentially.\n                While more memory efficient, streaming in batches is slower as\n                it is inherently single-threaded. If False (default) the data is\n                read with multiple threads into arrow first and sent to the engine\n                afterwards.\n\n        Returns:\n                Handler of the underlying data.\n\n\n        Deprecated:\n            1.5: The `num_lines_sniffed` parameter is deprecated.\n\n        Note:\n            It is assumed that the first line of each CSV file\n            contains a header with the column names.\n\n        ??? example\n            Let's assume you have two CSV files - *file1.csv* and\n            *file2.csv* - in the current working directory. You can\n            import their data into the getML Engine using.\n            ```python\n            df_expd = data.DataFrame.from_csv(\n                fnames=[\"file1.csv\", \"file2.csv\"],\n                name=\"MY DATA FRAME\",\n                sep=';',\n                quotechar='\"'\n                )\n\n            # However, the CSV format lacks type safety. If you want to\n            # build a reliable pipeline, it is a good idea\n            # to hard-code the roles:\n\n            roles = {\"categorical\": [\"col1\", \"col2\"], \"target\": [\"col3\"]}\n\n            df_expd = data.DataFrame.from_csv(\n                fnames=[\"file1.csv\", \"file2.csv\"],\n                name=\"MY DATA FRAME\",\n                sep=';',\n                quotechar='\"',\n                roles=roles\n                )\n\n            # If you think that typing out all the roles by hand is too\n            # cumbersome, you can use a dry run:\n\n            roles = data.DataFrame.from_csv(\n                fnames=[\"file1.csv\", \"file2.csv\"],\n                name=\"MY DATA FRAME\",\n                sep=';',\n                quotechar='\"',\n                dry=True\n            )\n            ```\n\n            This will return the roles dictionary it would have used. You\n            can now hard-code this.\n\n        \"\"\"\n\n        if num_lines_sniffed is not None:\n            warnings.warn(\n                \"The 'num_lines_sniffed' parameter is deprecated and will be ignored.\",\n                DeprecationWarning,\n            )\n\n        if isinstance(fnames, str):\n            fnames = [fnames]\n\n        if not _is_non_empty_typed_list(fnames, str):\n            raise TypeError(\"'fnames' must be either a str or a list of str.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        if not isinstance(num_lines_read, numbers.Real):\n            raise TypeError(\"'num_lines_read' must be a real number\")\n\n        if not isinstance(quotechar, str):\n            raise TypeError(\"'quotechar' must be str.\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be str.\")\n\n        if not isinstance(skip, numbers.Real):\n            raise TypeError(\"'skip' must be a real number\")\n\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        if colnames:\n            if not _is_iterable_not_str_of_type(colnames, str):\n                raise TypeError(\"'colnames' must be an iterable of str\")\n\n        fnames = _retrieve_urls(fnames, verbose=verbose)\n\n        sniffed_roles = sniff_csv(\n            fnames=fnames,\n            quotechar=quotechar,\n            sep=sep,\n            skip=int(skip),\n            colnames=colnames,\n        )\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_csv(\n            fnames=fnames,\n            append=False,\n            quotechar=quotechar,\n            sep=sep,\n            num_lines_read=num_lines_read,\n            skip=skip,\n            colnames=colnames,\n            block_size=block_size,\n            in_batches=in_batches,\n        )\n\n    # ------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_db(\n        cls,\n        table_name: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n        conn: Optional[Connection] = None,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_db(\n        cls,\n        table_name: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n        conn: Optional[Connection] = None,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_db(\n        cls,\n        table_name: str,\n        name: Optional[str] = None,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n        conn: Optional[Connection] = None,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from a table in a database.\n\n        It will construct a data frame object in the Engine, fill it\n        with the data read from table `table_name` in the connected\n        database (see [`database`][getml.database]), and return a\n        corresponding [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            table_name:\n                Name of the table to be read.\n\n            name:\n                Name of the data frame to be created. If not passed,\n                then the *table_name* will be used.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n            conn:\n                The database connection to be used.\n                If you don't explicitly pass a connection, the Engine\n                will use the default connection.\n\n        Returns:\n                Handler of the underlying data.\n\n        ??? example\n            ```python\n            getml.database.connect_mysql(\n                host=\"relational.fel.cvut.cz\",\n                port=3306,\n                dbname=\"financial\",\n                user=\"guest\",\n                password=\"ctu-relational\"\n            )\n\n            loan = getml.DataFrame.from_db(\n                table_name='loan', name='data_frame_loan')\n            ```\n        \"\"\"\n\n        # -------------------------------------------\n\n        name = name or table_name\n\n        # -------------------------------------------\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be str.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\n                \"'roles' must be a getml.data.Roles object, a dict or None.\"\n            )\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # -------------------------------------------\n\n        conn = conn or database.Connection()\n\n        # ------------------------------------------------------------\n\n        sniffed_roles = _sniff_db(table_name, conn)\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        # ------------------------------------------------------------\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_db(table_name=table_name, append=False, conn=conn)\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_dict(\n        cls,\n        data: Dict[Hashable, Iterable[Any]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_dict(\n        cls,\n        data: Dict[Hashable, Iterable[Any]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_dict(\n        cls,\n        data: Dict[Hashable, Iterable[Any]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a new DataFrame from a dict\n\n        Args:\n            data:\n                The dict containing the data.\n                The data should be in the following format:\n                ```python\n                data = {'col1': [1.0, 2.0, 1.0], 'col2': ['A', 'B', 'C']}\n                ```\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        if not isinstance(data, dict):\n            raise TypeError(\"'data' must be dict.\")\n\n        return cls.from_arrow(\n            table=pa.Table.from_pydict(data),\n            name=name,\n            roles=roles,\n            ignore=ignore,\n            dry=dry,\n        )\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_json(\n        cls,\n        json_str: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_json(\n        cls,\n        json_str: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_json(\n        cls,\n        json_str: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a new DataFrame from a JSON string.\n\n        It will construct a data frame object in the Engine, fill it\n        with the data read from the JSON string, and return a\n        corresponding [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            json_str:\n                The JSON string containing the data.\n                The json_str should be in the following format:\n                ```python\n                json_str = \"{'col1': [1.0, 2.0, 1.0], 'col2': ['A', 'B', 'C']}\"\n                ```\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n            Handler of the underlying data.\n\n        \"\"\"\n\n        if not isinstance(json_str, str):\n            raise TypeError(\"'json_str' must be str.\")\n\n        return cls.from_dict(\n            data=json.loads(json_str),\n            name=name,\n            roles=roles,\n            ignore=ignore,\n            dry=dry,\n        )\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_pandas(\n        cls,\n        pandas_df: pd.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_pandas(\n        cls,\n        pandas_df: pd.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_pandas(\n        cls,\n        pandas_df: pd.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from a `pandas.DataFrame`.\n\n        It will construct a data frame object in the Engine, fill it\n        with the data read from the `pandas.DataFrame`, and\n        return a corresponding [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            pandas_df:\n                The table to be read.\n\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                 roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                          getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n            Handler of the underlying data.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if not isinstance(pandas_df, pd.DataFrame):\n            raise TypeError(\"'pandas_df' must be of type pandas.DataFrame.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        if metadata := pandas_df.attrs.get(\"getml\"):\n            sniffed_roles = Roles.from_dict(metadata[\"roles\"])\n        else:\n            sniffed_roles = sniff_schema(\n                pa.Schema.from_pandas(pandas_df, preserve_index=False)\n            )\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_pandas(pandas_df=pandas_df, append=False)\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_parquet(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n        colnames: Iterable[str] = (),\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_parquet(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n        colnames: Iterable[str] = (),\n    ) -> Roles: ...\n\n    @classmethod\n    def from_parquet(\n        cls,\n        fnames: Union[str, Iterable[str]],\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n        colnames: Iterable[str] = (),\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from parquet files.\n\n        This is one of the fastest way to get data into the\n        getML Engine.\n\n        Args:\n            fnames:\n                The path of the parquet file(s) to be read.\n\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n            Handler of the underlying data.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if isinstance(fnames, str):\n            fnames = [fnames]\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        sniffed_roles = sniff_parquet(fnames, colnames)\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_parquet(fnames=fnames, append=False, colnames=colnames)\n\n    # --------------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_pyspark(\n        cls,\n        spark_df: pyspark.sql.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_pyspark(\n        cls,\n        spark_df: pyspark.sql.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_pyspark(\n        cls,\n        spark_df: pyspark.sql.DataFrame,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from a `pyspark.sql.DataFrame`.\n\n        It will construct a data frame object in the Engine, fill it\n        with the data read from the `pyspark.sql.DataFrame`, and\n        return a corresponding [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            spark_df:\n                The table to be read.\n\n            name:\n                Name of the data frame to be created.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        head = spark_df.limit(2).toPandas()\n\n        sniffed_roles = sniff_schema(pa.Schema.from_pandas(head))\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_pyspark(spark_df=spark_df, append=False)\n\n    # ------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_query(\n        cls,\n        query: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n        conn: Optional[Connection] = None,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_query(\n        cls,\n        query: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n        conn: Optional[Connection] = None,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_query(\n        cls,\n        query: str,\n        name: str,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n        conn: Optional[Connection] = None,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from a query run on a database.\n\n        It will construct a data frame object in the engine, fill it\n        with the data read from the query executed on the connected\n        database (see [`database`][getml.database]), and return a\n        corresponding [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            query:\n                The SQL query to be read.\n\n            name:\n                Name of the data frame to be created.\n\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n            conn:\n                The [`database.Connection`][getml.database.Connection] to be used.\n                If you don't explicitly pass a connection, the engine\n                will use the default connection.\n\n        Returns:\n                Handler of the underlying data.\n\n        ??? example\n            ```python\n            getml.database.connect_mysql(\n                host=\"relational.fel.cvut.cz\"\",\n                port=3306,\n                dbname=\"financial\",\n                user=\"guest\",\n                password=\"ctu-relational\"\n            )\n\n            loan = getml.DataFrame.from_query(\n                query='SELECT * FROM \"loan\";', name='loan')\n            ```\n        \"\"\"\n\n        # -------------------------------------------\n\n        if not isinstance(query, str):\n            raise TypeError(\"'query' must be str.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        # The content of roles is checked in the class constructor called below.\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\n                \"'roles' must be a getml.data.Roles object, a dict or None.\"\n            )\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # -------------------------------------------\n\n        conn = conn or database.Connection()\n\n        # ------------------------------------------------------------\n\n        sniffed_roles = _sniff_query(query, name, conn)\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore_sniffed_roles=ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_query(query=query, append=False, conn=conn)\n\n    # ------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_s3(\n        cls,\n        bucket: str,\n        keys: Iterable[str],\n        region: str,\n        name: str,\n        num_lines_sniffed: int = 1000,\n        num_lines_read: int = 0,\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Optional[Iterable[str]] = None,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_s3(\n        cls,\n        bucket: str,\n        keys: Iterable[str],\n        region: str,\n        name: str,\n        num_lines_sniffed: int = 1000,\n        num_lines_read: int = 0,\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Optional[Iterable[str]] = None,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_s3(\n        cls,\n        bucket: str,\n        keys: Iterable[str],\n        region: str,\n        name: str,\n        num_lines_sniffed: int = 1000,\n        num_lines_read: int = 0,\n        sep: str = \",\",\n        skip: int = 0,\n        colnames: Optional[Iterable[str]] = None,\n        roles: Optional[Union[Dict[Union[Role, str], Iterable[str]], Roles]] = None,\n        ignore: bool = False,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from CSV files located in an S3 bucket.\n\n        This classmethod will construct a data\n        frame object in the Engine, fill it with the data read from\n        the CSV file(s), and return a corresponding\n        [`DataFrame`][getml.DataFrame] handle.\n\n        enterprise-adm: Enterprise edition\n            This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n            For licensing information and technical support, please [contact us][contact-page].\n\n        Note:\n            Note that S3 is not supported on Windows.\n\n        Args:\n            bucket:\n                The bucket from which to read the files.\n\n            keys:\n                The list of keys (files in the bucket) to be read.\n\n            region:\n                The region in which the bucket is located.\n\n            name:\n                Name of the data frame to be created.\n\n            num_lines_sniffed:\n                Number of lines analyzed by the sniffer.\n\n            num_lines_read:\n                Number of lines read from each file.\n                Set to 0 to read in the entire file.\n\n            sep:\n                The separator used for separating fields.\n\n            skip:\n                Number of lines to skip at the beginning of each file.\n\n            colnames:\n                The first line of a CSV file\n                usually contains the column names. When this is not the case,\n                you need to explicitly pass them.\n\n            roles:\n                Maps the [`roles`][getml.data.roles] to the\n                column names (see [`colnames`][getml.DataFrame.colnames]).\n\n                The `roles` dictionary is expected to have the following format:\n                ```python\n                roles = {getml.data.role.numeric: [\"colname1\", \"colname2\"],\n                         getml.data.role.target: [\"colname3\"]}\n                ```\n                Otherwise, you can use the [`Roles`][getml.data.Roles] class.\n\n            ignore:\n                Only relevant when roles is not None.\n                Determines what you want to do with any colnames not\n                mentioned in roles. Do you want to ignore them (True)\n                or read them in as unused columns (False)?\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return the inferred roles.\n\n        Returns:\n                Handler of the underlying data.\n\n        ??? example\n            Let's assume you have two CSV files - *file1.csv* and\n            *file2.csv* - in the bucket. You can\n            import their data into the getML Engine using the following\n            commands:\n            ```python\n            getml.engine.set_s3_access_key_id(\"YOUR-ACCESS-KEY-ID\")\n            getml.engine.set_s3_secret_access_key(\"YOUR-SECRET-ACCESS-KEY\")\n\n            data_frame_expd = data.DataFrame.from_s3(\n                bucket=\"your-bucket-name\",\n                keys=[\"file1.csv\", \"file2.csv\"],\n                region=\"us-east-2\",\n                name=\"MY DATA FRAME\",\n                sep=';'\n            )\n            ```\n\n            You can also set the access credential as environment variables\n            before you launch the getML Engine.\n\n            Also refer to the documentation on [`from_csv`][getml.DataFrame.from_csv]\n            for further information on overriding the CSV sniffer for greater\n            type safety.\n\n        \"\"\"\n\n        if isinstance(keys, str):\n            keys = [keys]\n\n        if not isinstance(bucket, str):\n            raise TypeError(\"'bucket' must be str.\")\n\n        if not _is_non_empty_typed_list(keys, str):\n            raise TypeError(\"'keys' must be either a string or a list of str\")\n\n        if not isinstance(region, str):\n            raise TypeError(\"'region' must be str.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        if not isinstance(num_lines_sniffed, numbers.Real):\n            raise TypeError(\"'num_lines_sniffed' must be a real number\")\n\n        if not isinstance(num_lines_read, numbers.Real):\n            raise TypeError(\"'num_lines_read' must be a real number\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be str.\")\n\n        if not isinstance(skip, numbers.Real):\n            raise TypeError(\"'skip' must be a real number\")\n\n        if roles is not None and not isinstance(roles, (dict, Roles)):\n            raise TypeError(\"'roles' must be a geml.data.Roles object, a dict or None.\")\n\n        if not isinstance(ignore, bool):\n            raise TypeError(\"'ignore' must be bool.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        if colnames is not None and not _is_non_empty_typed_list(colnames, str):\n            raise TypeError(\n                \"'colnames' must be either be None or a non-empty list of str.\"\n            )\n\n        sniffed_roles = _sniff_s3(\n            bucket=bucket,\n            keys=keys,\n            region=region,\n            num_lines_sniffed=int(num_lines_sniffed),\n            sep=sep,\n            skip=int(skip),\n            colnames=colnames,\n        )\n\n        roles = _prepare_roles(roles, sniffed_roles, ignore)\n\n        if dry:\n            return roles\n\n        data_frame = cls(name, roles)\n\n        return data_frame.read_s3(\n            bucket=bucket,\n            keys=keys,\n            region=region,\n            append=False,\n            sep=sep,\n            num_lines_read=int(num_lines_read),\n            skip=int(skip),\n            colnames=colnames,\n        )\n\n    # ------------------------------------------------------------\n\n    @overload\n    @classmethod\n    def from_view(\n        cls,\n        view: View,\n        name: str,\n        dry: Literal[False] = False,\n    ) -> DataFrame: ...\n\n    @overload\n    @classmethod\n    def from_view(\n        cls,\n        view: View,\n        name: str,\n        dry: Literal[True] = True,\n    ) -> Roles: ...\n\n    @classmethod\n    def from_view(\n        cls,\n        view: View,\n        name: str,\n        dry: bool = False,\n    ) -> Union[DataFrame, Roles]:\n        \"\"\"Create a DataFrame from a [`View`][getml.data.View].\n\n        This classmethod will construct a data\n        frame object in the Engine, fill it with the data read from\n        the [`View`][getml.data.View], and return a corresponding\n        [`DataFrame`][getml.DataFrame] handle.\n\n        Args:\n            view:\n                The view from which we want to read the data.\n\n            name:\n                Name of the data frame to be created.\n\n            dry:\n                If set to True, the data will not be read. Instead, the method\n                will return an empty data frame with the roles set as inferred.\n\n        Returns:\n                Handler of the underlying data.\n\n\n        \"\"\"\n        # ------------------------------------------------------------\n\n        if not isinstance(view, View):\n            raise TypeError(\"'view' must be getml.data.View.\")\n\n        if not isinstance(name, str):\n            raise TypeError(\"'name' must be str.\")\n\n        if not isinstance(dry, bool):\n            raise TypeError(\"'dry' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        if dry:\n            return view.roles\n\n        data_frame = cls(name, view.roles)\n\n        # ------------------------------------------------------------\n\n        return data_frame.read_view(view=view, append=False)\n\n    # ------------------------------------------------------------\n\n    def iter_batches(self, batch_size: int = DEFAULT_BATCH_SIZE) -> Iterator[View]:\n        yield from _iter_batches(self, batch_size)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _join_key_names(self) -> Iterable[str]:\n        return [col.name for col in self._join_key_columns]\n\n    # ------------------------------------------------------------\n\n    @property\n    def last_change(self) -> str:\n        \"\"\"\n        A string describing the last time this data frame has been changed.\n        \"\"\"\n        return _last_change(self.name)\n\n    # ------------------------------------------------------------\n\n    def load(self) -> DataFrame:\n        \"\"\"Loads saved data from disk.\n\n        The data frame object holding the same name as the current\n        [`DataFrame`][getml.DataFrame] instance will be loaded from\n        disk into the getML Engine and updates the current handler\n        using [`refresh`][getml.DataFrame.refresh].\n\n        ??? example\n            First, we have to create and import data sets.\n            ```python\n            d, _ = getml.datasets.make_numerical(population_name = 'test')\n            getml.data.list_data_frames()\n            ```\n\n            In the output of [`list_data_frames`][getml.data.list_data_frames] we\n            can find our underlying data frame object 'test' listed\n            under the 'in_memory' key (it was created and imported by\n            [`make_numerical`][getml.datasets.make_numerical]). This means the\n            getML Engine does only hold it in memory (RAM) yet, and we\n            still have to [`save`][getml.DataFrame.save] it to\n            disk in order to [`load`][getml.DataFrame.load] it\n            again or to prevent any loss of information between\n            different sessions.\n            ```python\n            d.save()\n            getml.data.list_data_frames()\n            d2 = getml.DataFrame(name = 'test').load()\n            ```\n\n        Returns:\n                Updated handle the underlying data frame in the getML\n                Engine.\n\n        Note:\n            When invoking [`load`][getml.DataFrame.load] all\n            changes of the underlying data frame object that took\n            place after the last call to the\n            [`save`][getml.DataFrame.save] method will be\n            lost. Thus, this method  enables you to undo changes\n            applied to the [`DataFrame`][getml.DataFrame].\n            ```python\n            d, _ = getml.datasets.make_numerical()\n            d.save()\n\n            # Accidental change we want to undo\n            d.rm('column_01')\n\n            d.load()\n            ```\n            If [`save`][getml.DataFrame.save] hasn't been called\n            on the current instance yet, or it wasn't stored to disk in\n            a previous session, [`load`][getml.DataFrame.load]\n            will throw an exception\n\n                File or directory '../projects/X/data/Y/' not found!\n\n            Alternatively, [`load_data_frame`][getml.data.load_data_frame]\n            offers an easier way of creating\n            [`DataFrame`][getml.DataFrame] handlers to data in the\n            getML Engine.\n\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.load\"\n        cmd[\"name_\"] = self.name\n        comm.send(cmd)\n        return self.refresh()\n\n    # ------------------------------------------------------------\n\n    @property\n    def memory_usage(self):\n        \"\"\"\n        Convenience wrapper that returns the memory usage in MB.\n        \"\"\"\n        return self.nbytes() / 1e06\n\n    # ------------------------------------------------------------\n\n    @property\n    def _monitor_url(self) -> Optional[str]:\n        \"\"\"\n        A link to the data frame in the getML Monitor.\n        \"\"\"\n        url = comm._monitor_url()\n        return (\n            url + \"getdataframe/\" + comm._get_project_name() + \"/\" + self.name + \"/\"\n            if url\n            else None\n        )\n\n    # ------------------------------------------------------------\n\n    def nbytes(self) -> np.uint64:\n        \"\"\"Size of the data stored in the underlying data frame in the getML\n        Engine.\n\n        Returns:\n                Size of the underlying object in bytes.\n\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.nbytes\"\n        cmd[\"name_\"] = self.name\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                sock.close()\n                comm.handle_engine_exception(msg)\n            nbytes = comm.recv_string(sock)\n\n        return np.uint64(nbytes)\n\n    # ------------------------------------------------------------\n\n    def ncols(self) -> int:\n        \"\"\"\n        Number of columns in the current instance.\n\n        Returns:\n                Overall number of columns\n        \"\"\"\n        return len(self.colnames)\n\n    # ------------------------------------------------------------\n\n    def nrows(self) -> int:\n        \"\"\"\n        Number of rows in the current instance.\n\n        Returns:\n                Overall number of rows\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.nrows\"\n        cmd[\"name_\"] = self.name\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                sock.close()\n                comm.handle_engine_exception(msg)\n            nrows = comm.recv_string(sock)\n\n        return int(nrows)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _numerical_names(self):\n        return [col.name for col in self._numerical_columns]\n\n    # --------------------------------------------------------------------------\n\n    def read_arrow(\n        self,\n        table: Union[pa.RecordBatch, pa.Table, Iterable[pa.RecordBatch]],\n        append: bool = False,\n    ) -> DataFrame:\n        \"\"\"Uploads a `pyarrow.Table` or `pyarrow.RecordBatch` to the getML Engine.\n\n        Replaces the actual content of the underlying data frame in\n        the getML Engine with `table`.\n\n        Args:\n            table:\n                The arrow tablelike to be read as a `DataFrame`.\n\n            append:\n                If a data frame object holding the same `name` is\n                already present in the getML Engine, should the content in\n                `query` be appended or replace the existing data?\n\n        Returns:\n                Current instance.\n\n        Note:\n            For columns containing `pandas.Timestamp` there can\n            be small inconsistencies in the order of microseconds\n            when sending the data to the getML Engine. This is due to\n            the way the underlying information is stored.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        inferred_schema, batches = to_arrow_batches(table)\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        # ------------------------------------------------------------\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_pandas(...).\"\"\"\n            )\n\n        # ------------------------------------------------------------\n\n        preprocessed_schema = preprocess_arrow_schema(inferred_schema, self.roles)\n        batches = (cast_arrow_batch(batch, preprocessed_schema) for batch in batches)\n\n        read_arrow_batches(batches, preprocessed_schema, self, append)\n\n        return self.refresh()\n\n    # --------------------------------------------------------------------------\n\n    def read_csv(\n        self,\n        fnames: Iterable[str],\n        append: bool = False,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        num_lines_read: int = 0,\n        skip: int = 0,\n        colnames: Optional[Iterable[str]] = None,\n        time_formats: Optional[Iterable[str]] = None,\n        verbose: bool = True,\n        block_size: int = DEFAULT_CSV_READ_BLOCK_SIZE,\n        in_batches: bool = False,\n    ) -> DataFrame:\n        \"\"\"Read CSV files.\n\n        It is assumed that the first line of each CSV file contains a\n        header with the column names.\n\n        Args:\n            fnames:\n                CSV file paths to be read.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                the CSV files in `fnames` be appended or replace the\n                existing data?\n\n            quotechar:\n                The character used to wrap strings.\n\n            sep:\n                The separator used for separating fields.\n\n            num_lines_read:\n                Number of lines read from each file.\n                Set to 0 to read in the entire file.\n\n            skip:\n                Number of lines to skip at the beginning of each file.\n\n            colnames:\n                The first line of a CSV file\n                usually contains the column names.\n                When this is not the case, you need to explicitly pass them.\n\n            time_formats:\n                The list of formats tried when parsing time stamps.\n\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n\n            verbose:\n                If True, when `fnames` are urls, the filenames are printed to\n                stdout during the download.\n\n            block_size:\n                The number of bytes read with each batch. Passed down to\n                pyarrow.\n\n            in_batches:\n                If True, read blocks streamwise manner and send those batches to\n                the engine. Blocks are read and sent to the engine sequentially.\n                While more memory efficient, streaming in batches is slower as\n                it is inherently single-threaded. If False (default) the data is\n                read with multiple threads into arrow first and sent to the engine\n                afterwards.\n\n        Returns:\n                Handler of the underlying data.\n\n        \"\"\"\n\n        time_formats = time_formats or constants.TIME_FORMATS\n\n        if isinstance(fnames, str):\n            fnames = [fnames]\n\n        if not _is_non_empty_typed_list(fnames, str):\n            raise TypeError(\"'fnames' must be either a string or a list of str\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        if not isinstance(quotechar, str):\n            raise TypeError(\"'quotechar' must be str.\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be str.\")\n\n        if not isinstance(num_lines_read, numbers.Real):\n            raise TypeError(\"'num_lines_read' must be a real number\")\n\n        if not isinstance(skip, numbers.Real):\n            raise TypeError(\"'skip' must be a real number\")\n\n        if not _is_non_empty_typed_list(time_formats, str):\n            raise TypeError(\"'time_formats' must be a non-empty list of str\")\n\n        if colnames:\n            if not _is_iterable_not_str_of_type(colnames, str):\n                raise TypeError(\"'colnames' must be an iterable of str\")\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_csv(...).\"\"\"\n            )\n\n        if not _is_non_empty_typed_list(fnames, str):\n            raise TypeError(\n                \"\"\"'fnames' must be a list containing at\n                least one path to a CSV file\"\"\"\n            )\n\n        fnames_ = _retrieve_urls(fnames, verbose)\n\n        if colnames is None:\n            colnames = ()\n\n        stream_read_csv = stream_csv if in_batches else read_csv\n\n        readers = (\n            stream_read_csv(\n                Path(fname),\n                roles=self.roles,\n                skip_rows=skip,\n                column_names=colnames,\n                delimiter=sep,\n                quote_char=quotechar,\n                block_size=block_size,\n            )\n            for fname in fnames_\n        )\n\n        for batches in readers:\n            first_batch = next(batches)\n            schema = first_batch.schema\n            read_arrow_batches(iter((first_batch, *batches)), schema, self, append)\n            if not append:\n                append = True\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def read_json(\n        self,\n        json_str: str,\n        append: bool = False,\n        time_formats: Optional[Iterable[str]] = None,\n    ) -> DataFrame:\n        \"\"\"Fill from JSON\n\n        Fills the data frame with data from a JSON string.\n\n        Args:\n\n            json_str:\n                The JSON string containing the data.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                `json_str` be appended or replace the existing data?\n\n            time_formats:\n                The list of formats tried when parsing time stamps.\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n\n        Returns:\n                Handler of the underlying data.\n\n        Note:\n            This does not support NaN values. If you want support for NaN,\n            use [`from_json`][getml.DataFrame.from_json] instead.\n\n        \"\"\"\n\n        time_formats = time_formats or constants.TIME_FORMATS\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_json(...).\"\"\"\n            )\n\n        if not isinstance(json_str, str):\n            raise TypeError(\"'json_str' must be of type str\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be of type bool\")\n\n        if not _is_non_empty_typed_list(time_formats, str):\n            raise TypeError(\n                \"\"\"'time_formats' must be a list of strings\n                containing at least one time format\"\"\"\n            )\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.from_json\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"categorical_\"] = self._categorical_names\n        cmd[\"join_keys_\"] = self._join_key_names\n        cmd[\"numerical_\"] = self._numerical_names\n        cmd[\"targets_\"] = self._target_names\n        cmd[\"text_\"] = self._text_names\n        cmd[\"time_stamps_\"] = self._time_stamp_names\n        cmd[\"unused_floats_\"] = self._unused_float_names\n        cmd[\"unused_strings_\"] = self._unused_string_names\n\n        cmd[\"append_\"] = append\n        cmd[\"time_formats_\"] = time_formats\n\n        with comm.send_and_get_socket(cmd) as sock:\n            comm.send_string(sock, json_str)\n            msg = comm.recv_string(sock)\n\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def read_parquet(\n        self,\n        fnames: Union[str, Iterable[str]],\n        append: bool = False,\n        verbose: bool = False,\n        colnames: Iterable[str] = (),\n    ) -> DataFrame:\n        \"\"\"Read a parquet file.\n\n        Args:\n            fnames:\n                The filepath of the parquet file(s) to be read.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                the CSV files in `fnames` be appended or replace the\n                existing data?\n\n            verbose:\n                If True, when `fnames` are urls, the filenames are printed to\n                stdout during the download.\n\n        Returns:\n            Handler of the underlying data.\n        \"\"\"\n\n        if isinstance(fnames, str):\n            fnames = [fnames]\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        if not colnames:\n            colnames = self.colnames\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than\n                zero columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_parquet(...).\"\"\"\n            )\n\n        fnames = _retrieve_urls(fnames, verbose)\n\n        readers = (pq.ParquetFile(fname) for fname in fnames)\n\n        for reader in readers:\n            inferred_schema = reader.schema_arrow\n            preprocessed_schema = preprocess_arrow_schema(inferred_schema, self.roles)\n            cast_batches = (\n                cast_arrow_batch(batch, preprocessed_schema)\n                for batch in reader.iter_batches(columns=colnames)\n            )\n            read_arrow_batches(\n                cast_batches,\n                preprocessed_schema,\n                self,\n                append,\n            )\n            if not append:\n                append = True\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def read_s3(\n        self,\n        bucket: str,\n        keys: Iterable[str],\n        region: str,\n        append: bool = False,\n        sep: str = \",\",\n        num_lines_read: int = 0,\n        skip: int = 0,\n        colnames: Optional[Iterable[str]] = None,\n        time_formats: Optional[Iterable[str]] = None,\n    ) -> DataFrame:\n        \"\"\"Read CSV files from an S3 bucket.\n\n        It is assumed that the first line of each CSV file contains a\n        header with the column names.\n\n        enterprise-adm: Enterprise edition\n            This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n            For licensing information and technical support, please [contact us][contact-page].\n\n        Note:\n            Note that S3 is not supported on Windows.\n\n        Args:\n            bucket:\n                The bucket from which to read the files.\n\n            keys:\n                The list of keys (files in the bucket) to be read.\n\n            region:\n                The region in which the bucket is located.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                the CSV files in `fnames` be appended or replace the\n                existing data?\n\n            sep:\n                The separator used for separating fields.\n\n            num_lines_read:\n                Number of lines read from each file.\n                Set to 0 to read in the entire file.\n\n            skip:\n                Number of lines to skip at the beginning of each file.\n\n            colnames:\n                The first line of a CSV file\n                usually contains the column names.\n                When this is not the case, you need to explicitly pass them.\n\n            time_formats:\n                The list of formats tried when parsing time stamps.\n\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n\n        Returns:\n                Handler of the underlying data.\n\n        \"\"\"\n\n        time_formats = time_formats or constants.TIME_FORMATS\n\n        if isinstance(keys, str):\n            keys = [keys]\n\n        if not isinstance(bucket, str):\n            raise TypeError(\"'bucket' must be str.\")\n\n        if not _is_non_empty_typed_list(keys, str):\n            raise TypeError(\"'keys' must be either a string or a list of str\")\n\n        if not isinstance(region, str):\n            raise TypeError(\"'region' must be str.\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be str.\")\n\n        if not isinstance(num_lines_read, numbers.Real):\n            raise TypeError(\"'num_lines_read' must be a real number\")\n\n        if not isinstance(skip, numbers.Real):\n            raise TypeError(\"'skip' must be a real number\")\n\n        if not _is_non_empty_typed_list(time_formats, str):\n            raise TypeError(\"'time_formats' must be a non-empty list of str\")\n\n        if colnames is not None and not _is_non_empty_typed_list(colnames, str):\n            raise TypeError(\n                \"'colnames' must be either be None or a non-empty list of str.\"\n            )\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_s3(...).\"\"\"\n            )\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.read_s3\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"append_\"] = append\n        cmd[\"bucket_\"] = bucket\n        cmd[\"keys_\"] = keys\n        cmd[\"region_\"] = region\n        cmd[\"sep_\"] = sep\n        cmd[\"time_formats_\"] = time_formats\n        cmd[\"num_lines_read_\"] = num_lines_read\n        cmd[\"skip_\"] = skip\n\n        if colnames is not None:\n            cmd[\"colnames_\"] = colnames\n\n        cmd[\"categorical_\"] = self._categorical_names\n        cmd[\"join_keys_\"] = self._join_key_names\n        cmd[\"numerical_\"] = self._numerical_names\n        cmd[\"targets_\"] = self._target_names\n        cmd[\"text_\"] = self._text_names\n        cmd[\"time_stamps_\"] = self._time_stamp_names\n        cmd[\"unused_floats_\"] = self._unused_float_names\n        cmd[\"unused_strings_\"] = self._unused_string_names\n\n        comm.send(cmd)\n\n        return self\n\n    # ------------------------------------------------------------\n\n    def read_view(\n        self,\n        view: View,\n        append: bool = False,\n    ) -> DataFrame:\n        \"\"\"Read the data from a [`View`][getml.data.View].\n\n        Args:\n            view:\n                The view to read.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                the CSV files in `fnames` be appended or replace the\n                existing data?\n\n        Returns:\n                Handler of the underlying data.\n\n        \"\"\"\n\n        if not isinstance(view, View):\n            raise TypeError(\"'view' must be getml.data.View.\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        view.check()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.from_view\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"view_\"] = view._getml_deserialize()\n\n        cmd[\"append_\"] = append\n\n        comm.send(cmd)\n\n        return self.refresh()\n\n    # --------------------------------------------------------------------------\n\n    def read_db(\n        self, table_name: str, append: bool = False, conn: Optional[Connection] = None\n    ) -> DataFrame:\n        \"\"\"\n        Fill from Database.\n\n        The DataFrame will be filled from a table in the database.\n\n        Args:\n            table_name:\n                Table from which we want to retrieve the data.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML, should the content of\n                `table_name` be appended or replace the existing data?\n\n            conn:\n                The database connection to be used.\n                If you don't explicitly pass a connection,\n                the Engine will use the default connection.\n\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be str.\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_db(...).\"\"\"\n            )\n\n        conn = conn or database.Connection()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.from_db\"\n        cmd[\"name_\"] = self.name\n        cmd[\"table_name_\"] = table_name\n\n        cmd[\"categorical_\"] = self._categorical_names\n        cmd[\"join_keys_\"] = self._join_key_names\n        cmd[\"numerical_\"] = self._numerical_names\n        cmd[\"targets_\"] = self._target_names\n        cmd[\"text_\"] = self._text_names\n        cmd[\"time_stamps_\"] = self._time_stamp_names\n        cmd[\"unused_floats_\"] = self._unused_float_names\n        cmd[\"unused_strings_\"] = self._unused_string_names\n\n        cmd[\"append_\"] = append\n\n        cmd[\"conn_id_\"] = conn.conn_id\n\n        comm.send(cmd)\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def read_pandas(self, pandas_df: pd.DataFrame, append: bool = False) -> DataFrame:\n        \"\"\"Uploads a `pandas.DataFrame`.\n\n        Replaces the actual content of the underlying data frame in\n        the getML Engine with `pandas_df`.\n\n        Args:\n            pandas_df:\n                Data the underlying data frame object in the getML\n                Engine should obtain.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML Engine, should the content in\n                `query` be appended or replace the existing data?\n\n        Returns:\n                Handler of the underlying data.\n        Note:\n            For columns containing `pandas.Timestamp` there can\n            occur small inconsistencies in the order of microseconds\n            when sending the data to the getML Engine. This is due to\n            the way the underlying information is stored.\n        \"\"\"\n\n        if not isinstance(pandas_df, pd.DataFrame):\n            raise TypeError(\"'pandas_df' must be of type pandas.DataFrame.\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_pandas(...).\"\"\"\n            )\n\n        table = pa.Table.from_pandas(pandas_df[self.columns])\n\n        return self.read_arrow(table, append=append)\n\n    # --------------------------------------------------------------------------\n\n    def read_pyspark(\n        self, spark_df: pyspark.sql.DataFrame, append: bool = False\n    ) -> DataFrame:\n        \"\"\"Uploads a `pyspark.sql.DataFrame`.\n\n        Replaces the actual content of the underlying data frame in\n        the getML Engine with `pandas_df`.\n\n        Args:\n            spark_df:\n                Data the underlying data frame object in the getML\n                Engine should obtain.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML Engine, should the content in\n                `query` be appended or replace the existing data?\n\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be bool.\")\n\n        temp_dir = _retrieve_temp_dir()\n        path = temp_dir / str(self.name)\n        spark_df.write.mode(\"overwrite\").parquet(str(path))\n\n        filepaths = [\n            os.path.join(path, filepath)\n            for filepath in os.listdir(path)\n            if filepath[-8:] == \".parquet\"\n        ]\n\n        for i, filepath in enumerate(filepaths):\n            self.read_parquet(filepath, append or i > 0)\n\n        shutil.rmtree(path)\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def read_query(\n        self,\n        query: str,\n        append: Optional[bool] = False,\n        conn: Optional[Connection] = None,\n    ) -> DataFrame:\n        \"\"\"Fill from query\n\n        Fills the data frame with data from a table in the database.\n\n        Args:\n            query:\n                The query used to retrieve the data.\n\n            append:\n                If a data frame object holding the same ``name`` is\n                already present in the getML Engine, should the content in\n                `query` be appended or replace the existing data?\n\n            conn:\n                The database connection to be used.\n                If you don't explicitly pass a connection,\n                the Engine will use the default connection.\n\n        Returns:\n                Handler of the underlying data.\n        \"\"\"\n\n        if self.ncols() == 0:\n            raise Exception(\n                \"\"\"Reading data is only possible in a DataFrame with more than zero\n                columns. You can pre-define columns during\n                initialization of the DataFrame or use the classmethod\n                from_db(...).\"\"\"\n            )\n\n        if not isinstance(query, str):\n            raise TypeError(\"'query' must be of type str\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be of type bool\")\n\n        conn = conn or database.Connection()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.from_query\"\n        cmd[\"name_\"] = self.name\n        cmd[\"query_\"] = query\n\n        cmd[\"categorical_\"] = self._categorical_names\n        cmd[\"join_keys_\"] = self._join_key_names\n        cmd[\"numerical_\"] = self._numerical_names\n        cmd[\"targets_\"] = self._target_names\n        cmd[\"text_\"] = self._text_names\n        cmd[\"time_stamps_\"] = self._time_stamp_names\n        cmd[\"unused_floats_\"] = self._unused_float_names\n        cmd[\"unused_strings_\"] = self._unused_string_names\n\n        cmd[\"append_\"] = append\n\n        cmd[\"conn_id_\"] = conn.conn_id\n\n        comm.send(cmd)\n\n        return self\n\n    # --------------------------------------------------------------------------\n\n    def refresh(self) -> DataFrame:\n        \"\"\"Aligns meta-information of the current instance with the\n                corresponding data frame in the getML Engine.\n\n                This method can be used to avoid encoding conflicts. Note that\n                [`load`][getml.DataFrame.load] as well as several other\n                methods automatically call [`refresh`][getml.DataFrame.refresh].\n\n        Returns:\n                Updated handle the underlying data frame in the getML\n                Engine.\n\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = \"DataFrame.refresh\"\n        cmd[\"name_\"] = self.name\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n\n        if msg[0] != \"{\":\n            comm.handle_engine_exception(msg)\n\n        roles = json.loads(msg)\n\n        self.__init__(name=cast(str, self.name), roles=roles)\n\n        return self\n\n    # ------------------------------------------------------------\n\n    @property\n    def roles(self):\n        \"\"\"\n        The roles of the columns included\n        in this DataFrame.\n        \"\"\"\n        return Roles(\n            categorical=tuple(self._categorical_names),\n            join_key=tuple(self._join_key_names),\n            numerical=tuple(self._numerical_names),\n            target=tuple(self._target_names),\n            text=tuple(self._text_names),\n            time_stamp=tuple(self._time_stamp_names),\n            unused_float=tuple(self._unused_float_names),\n            unused_string=tuple(self._unused_string_names),\n        )\n\n    # ------------------------------------------------------------\n\n    def remove_subroles(\n        self,\n        cols: Union[\n            str, FloatColumn, StringColumn, List[Union[str, FloatColumn, StringColumn]]\n        ],\n    ) -> None:\n        \"\"\"Removes all [`subroles`][getml.data.subroles] from one or more columns.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n        \"\"\"\n\n        names = _handle_cols(cols)\n\n        for name in names:\n            self._set_subroles(name, append=False, subroles=[])\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def remove_unit(\n        self,\n        cols: Union[\n            str, FloatColumn, StringColumn, List[Union[str, FloatColumn, StringColumn]]\n        ],\n    ):\n        \"\"\"Removes the unit from one or more columns.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n        \"\"\"\n\n        names = _handle_cols(cols)\n\n        for name in names:\n            self._set_unit(name, \"\")\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    @property\n    def rowid(self):\n        \"\"\"\n        The rowids for this data frame.\n        \"\"\"\n        return rowid()[: self.nrows()]\n\n    # ------------------------------------------------------------\n\n    def save(self) -> DataFrame:\n        \"\"\"Writes the underlying data in the getML Engine to disk.\n\n        Returns:\n                The current instance.\n\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.save\"\n        cmd[\"name_\"] = self.name\n\n        comm.send(cmd)\n\n        return self\n\n    # ------------------------------------------------------------\n\n    def set_role(\n        self,\n        cols: Union[\n            str, FloatColumn, StringColumn, List[Union[str, FloatColumn, StringColumn]]\n        ],\n        role: str,\n        time_formats: Optional[Iterable[str]] = None,\n    ):\n        \"\"\"Assigns a new role to one or more columns.\n\n        When switching from a role based on type float to a role based on type\n        string or vice verse, an implicit type conversion will be conducted.\n        The `time_formats` argument is used to interpret [Time Stamps][annotating-data-time-stamp]. For more information on\n        roles, please refer to the [User Guide][annotating-data].\n\n        Args:\n            cols:\n                The columns or the names of the columns.\n\n            role:\n                The role to be assigned.\n\n            time_formats:\n                Formats to be used to parse the time stamps.\n                This is only necessary, if an implicit conversion from a StringColumn to\n                a time stamp is taking place.\n\n        ??? example\n            ```python\n            data_df = dict(\n                animal=[\"hawk\", \"parrot\", \"goose\"],\n                votes=[12341, 5127, 65311],\n                date=[\"04/06/2019\", \"01/03/2019\", \"24/12/2018\"])\n            df = getml.DataFrame.from_dict(data_df, \"animal_elections\")\n            df.set_role(['animal'], getml.data.roles.categorical)\n            df.set_role(['votes'], getml.data.roles.numerical)\n            df.set_role(\n                ['date'], getml.data.roles.time_stamp, time_formats=['%d/%m/%Y'])\n\n            df\n            ```\n            ```\n            | date                        | animal      | votes     |\n            | time stamp                  | categorical | numerical |\n            ---------------------------------------------------------\n            | 2019-06-04T00:00:00.000000Z | hawk        | 12341     |\n            | 2019-03-01T00:00:00.000000Z | parrot      | 5127      |\n            | 2018-12-24T00:00:00.000000Z | goose       | 65311     |\n            ```\n        \"\"\"\n        # ------------------------------------------------------------\n\n        time_formats = time_formats or constants.TIME_FORMATS\n\n        # ------------------------------------------------------------\n\n        names = _handle_cols(cols)\n\n        if not isinstance(role, str):\n            raise TypeError(\"'role' must be str.\")\n\n        if not _is_non_empty_typed_list(time_formats, str):\n            raise TypeError(\"'time_formats' must be a non-empty list of str\")\n\n        # ------------------------------------------------------------\n\n        for nname in names:\n            if nname not in self.colnames:\n                raise ValueError(\"No column called '\" + nname + \"' found.\")\n\n        if role not in self._all_roles:\n            raise ValueError(\n                \"'role' must be one of the following values: \" + str(self._all_roles)\n            )\n\n        # ------------------------------------------------------------\n\n        for name in names:\n            if self[name].role != role:\n                self._set_role(name, role, time_formats)\n\n        # ------------------------------------------------------------\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def set_subroles(\n        self,\n        cols: Union[\n            str, FloatColumn, StringColumn, List[Union[str, FloatColumn, StringColumn]]\n        ],\n        subroles: Optional[Union[Subrole, Iterable[str]]] = None,\n        append: Optional[bool] = True,\n    ):\n        \"\"\"Assigns one or several new [`subroles`][getml.data.subroles] to one or more columns.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n            subroles:\n                The subroles to be assigned.\n                Must be from [`subroles`][getml.data.subroles].\n\n            append:\n                Whether you want to append the\n                new subroles to the existing subroles.\n        \"\"\"\n\n        names = _handle_cols(cols)\n\n        if isinstance(subroles, str):\n            subroles = [subroles]\n\n        if not _is_non_empty_typed_list(subroles, str):\n            raise TypeError(\"'subroles' must be either a string or a list of strings.\")\n\n        if not isinstance(append, bool):\n            raise TypeError(\"'append' must be a bool.\")\n\n        for name in names:\n            self._set_subroles(name, append, subroles)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    def set_unit(\n        self,\n        cols: Union[\n            str, FloatColumn, StringColumn, List[Union[str, FloatColumn, StringColumn]]\n        ],\n        unit: str,\n        comparison_only: bool = False,\n    ):\n        \"\"\"Assigns a new unit to one or more columns.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n            unit:\n                The unit to be assigned.\n\n            comparison_only:\n                Whether you want the column to\n                be used for comparison only. This means that the column can\n                only be used in comparison to other columns of the same unit.\n\n                An example might be a bank account number: The number in itself\n                is hardly interesting, but it might be useful to know how often\n                we have seen that same bank account number in another table.\n        \"\"\"\n\n        names = _handle_cols(cols)\n\n        if not isinstance(unit, str):\n            raise TypeError(\"Parameter 'unit' must be a str.\")\n\n        if comparison_only:\n            unit += COMPARISON_ONLY\n\n        for name in names:\n            self._set_unit(name, unit)\n\n        self.refresh()\n\n    # ------------------------------------------------------------\n\n    @property\n    def shape(self):\n        \"\"\"\n        A tuple containing the number of rows and columns of\n        the DataFrame.\n        \"\"\"\n        self.refresh()\n        return (self.nrows(), self.ncols())\n\n    # ------------------------------------------------------------\n\n    @property\n    def _target_names(self):\n        return [col.name for col in self._target_columns]\n\n    # ------------------------------------------------------------\n\n    @property\n    def _text_names(self):\n        return [col.name for col in self._text_columns]\n\n    # ------------------------------------------------------------\n\n    @property\n    def _time_stamp_names(self):\n        return [col.name for col in self._time_stamp_columns]\n\n    # ----------------------------------------------------------------\n\n    def to_arrow(self) -> pa.Table:\n        \"\"\"Creates a `pyarrow.Table` from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        a `pyarrow.Table`.\n\n        Returns:\n                Pyarrow equivalent of the current instance including its underlying data.\n        \"\"\"\n        return to_arrow(self)\n\n    # ------------------------------------------------------------\n\n    @contextmanager\n    def to_arrow_stream(self) -> Iterator[pa.RecordBatchReader]:\n        \"\"\"\n        Streams the dataframe as an Apache Arrow `pa.RecordBatchReader`.\n\n        This method provides a way to access the dataframe as an Apache Arrow\n        stream. Apache Arrow is a cross-language development platform for\n        in-memory data that specifies a standardized language-independent\n        columnar memory format. Using `to_arrow_stream` allows for efficient,\n        zero-copy (or near zero-copy) data exchange with other systems that\n        support Arrow, such as DuckDB, Pandas, Polars, and various data\n        processing engines.\n\n        The method is a context manager (used with a `with` statement). This\n        ensures that any underlying resources associated with the stream,\n        such as network connections or temporary files, are properly initialized\n        when entering the `with` block and cleaned up when exiting.\n\n        The `pa.RecordBatchReader` yielded by this context manager allows you to\n        read the dataset iteratively as a sequence of Arrow `RecordBatch` objects.\n        Each `RecordBatch` represents a chunk of the dataset's columns.\n\n        Yields:\n            pa.RecordBatchReader: An iterator-like object that yields Apache\n                Arrow `RecordBatch` instances.\n\n        ??? example\n            Integrating with DuckDB for SQL-based analysis:\n            ```python\n            import getml\n            import duckdb\n\n            getml.set_project(\"arrow_stream\")\n\n            generated, _ = getml.datasets.make_numerical()\n\n            con = duckdb.connect()\n\n            # Use the context manager to get the Arrow stream\n            with generated.to_arrow_stream() as arrow_stream_reader:\n                # Register the Arrow stream as a duckdb relation\n                con.register(\"generated\", arrow_stream_reader)\n\n                # Now you can query the data using SQL\n                count = con.execute(\"SELECT COUNT(*) FROM generated\").df()\n                print(count)\n            # count_star()\n            # 500\n\n            with generated.to_arrow_stream() as arrow_stream_reader:\n                summary = con.execute(\"SUMMARIZE generated\").df()\n                print(summary)\n            #   column_name   column_type                            min                            max                           q50                         q75 count null_percentage\n            # 0    join_key       varchar                              0                             99                          none                        none   500             0.0\n            # 1   column_01        double            -0.9939631176936072             0.9966827171572035         -0.012749915265777218         0.49808863342423293   500             0.0\n            # 2     targets        double                            0.0                          152.0            111.92857142857143          126.16666666666667   500             0.0\n            # 3  time_stamp  timestamp_ns  1970-01-01 00:00:00.003686084  1970-01-01 00:00:00.998338199    1970-01-01 00:00:00.497298  1970-01-01 00:00:00.747046   500             0.0\n\n            # [4 rows x 12 columns]\n            ```\n        \"\"\"\n        with to_arrow_stream(self) as stream:\n            yield stream\n\n    # ------------------------------------------------------------\n\n    def to_csv(\n        self,\n        fname: str,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        batch_size: int = DEFAULT_BATCH_SIZE,\n        quoting_style: str = \"needed\",\n    ):\n        \"\"\"\n        Writes the underlying data into a newly created CSV file.\n\n        Args:\n            fname:\n                The name of the CSV file.\n                The ending \".csv\" and an optional batch number will\n                be added automatically.\n\n            quotechar:\n                The character used to wrap strings.\n\n            sep:\n                The character used for separating fields.\n\n            batch_size:\n                Maximum number of lines per file. Set to 0 to read\n                the entire data frame into a single file.\n\n            quoting_style (str):\n                The quoting style to use. Delegated to pyarrow.\n\n                The following values are accepted:\n                - `\"needed\"` (default): only enclose values in quotes when needed.\n                - `\"all_valid\"`: enclose all valid values in quotes; nulls are not\n                  quoted.\n                - `\"none\"`: do not enclose any values in quotes; values containing\n                  special characters (such as quotes, cell delimiters or line\n                  endings) will raise an error.\n\n        Deprecated:\n           1.5: The `quotechar` parameter is deprecated.\n        \"\"\"\n\n        if quotechar != '\"':\n            warnings.warn(\n                \"'quotechar' is deprecated, use 'quoting_style' instead.\",\n                DeprecationWarning,\n            )\n\n        to_csv(self, fname, sep, batch_size, quoting_style)\n\n    # ------------------------------------------------------------\n\n    def to_db(self, table_name: str, conn: Optional[Connection] = None):\n        \"\"\"Writes the underlying data into a newly created table in the\n        database.\n\n        Args:\n            table_name:\n                Name of the table to be created.\n\n                If a table of that name already exists, it will be\n                replaced.\n\n            conn:\n                The database connection to be used.\n                If you don't explicitly pass a connection,\n                the Engine will use the default connection.\n        \"\"\"\n\n        conn = conn or database.Connection()\n\n        self.refresh()\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be of type str\")\n\n        cmd = {}\n\n        cmd[\"type_\"] = \"DataFrame.to_db\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"table_name_\"] = table_name\n\n        cmd[\"conn_id_\"] = conn.conn_id\n\n        comm.send(cmd)\n\n    # ----------------------------------------------------------------\n\n    def to_html(self, max_rows: int = 10):\n        \"\"\"\n        Represents the data frame in HTML format, optimized for an\n        iPython notebook.\n\n        Args:\n            max_rows:\n                The maximum number of rows to be displayed.\n        \"\"\"\n\n        if not _exists_in_memory(self.name):\n            return _empty_data_frame().replace(\"\\n\", \"<br>\")\n\n        formatted = self._format()\n        formatted.max_rows = max_rows\n\n        footer = self._collect_footer_data()\n\n        return formatted._render_html(footer=footer)\n\n    # ------------------------------------------------------------\n\n    def to_json(self):\n        \"\"\"Creates a JSON string from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        a JSON string.\n        \"\"\"\n        return self.to_pandas().to_json()\n\n    # ----------------------------------------------------------------\n\n    def to_pandas(self) -> pd.DataFrame:\n        \"\"\"Creates a `pandas.DataFrame` from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        `pandas.DataFrame`.\n\n        Returns:\n                Pandas equivalent of the current instance including\n                its underlying data.\n\n        \"\"\"\n        table = to_arrow(self)\n        df = table.to_pandas()\n        df.attrs = {\"getml\": json.loads(table.schema.metadata[b\"getml\"])}\n        return df\n\n    # ------------------------------------------------------------\n\n    def to_parquet(\n        self,\n        fname: str,\n        compression: Literal[\"brotli\", \"gzip\", \"lz4\", \"snappy\", \"zstd\"] = \"snappy\",\n        coerce_timestamps: Optional[bool] = None,\n    ) -> None:\n        \"\"\"\n        Writes the underlying data into a newly created parquet file.\n\n        Args:\n            fname:\n                The name of the parquet file.\n\n            compression:\n                The compression format to use.\n                Supported values are \"brotli\", \"gzip\", \"lz4\", \"snappy\", \"zstd\"\n            coerce_timestamps:\n                Cast time stamps to a particular resolution.\n                For details, refer to `pyarrow.parquet.ParquetWriter` docs.\n        \"\"\"\n        to_parquet(self, fname, compression, coerce_timestamps=coerce_timestamps)\n\n    # ----------------------------------------------------------------\n\n    def to_placeholder(self, name: Optional[str] = None) -> Placeholder:\n        \"\"\"Generates a [`Placeholder`][getml.data.Placeholder] from the\n        current [`DataFrame`][getml.DataFrame].\n\n        Args:\n            name:\n                The name of the placeholder. If no\n                name is passed, then the name of the placeholder will\n                be identical to the name of the current data frame.\n\n        Returns:\n                A placeholder with the same name as this data frame.\n\n\n        \"\"\"\n        self.refresh()\n        return Placeholder(name=name or self.name, roles=self.roles)\n\n    # ----------------------------------------------------------------\n    def to_pyspark(\n        self, spark: pyspark.sql.SparkSession, name: Optional[str] = None\n    ) -> pyspark.sql.DataFrame:\n        \"\"\"Creates a `pyspark.sql.DataFrame` from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        a `pyspark.sql.DataFrame`.\n\n        Args:\n            spark:\n                The pyspark session in which you want to\n                create the data frame.\n\n            name:\n                The name of the temporary view to be created on top\n                of the `pyspark.sql.DataFrame`,\n                with which it can be referred to\n                in Spark SQL (refer to\n                `pyspark.sql.DataFrame.createOrReplaceTempView`).\n                If None is passed, then the name of this\n                [`DataFrame`][getml.DataFrame] will be used.\n\n        Returns:\n                Pyspark equivalent of the current instance including its underlying data.\n\n        \"\"\"\n        return _to_pyspark(self, name, spark)\n\n    # ------------------------------------------------------------\n\n    def to_s3(\n        self,\n        bucket: str,\n        key: str,\n        region: str,\n        sep: Optional[str] = \",\",\n        batch_size: Optional[int] = 50000,\n    ):\n        \"\"\"\n        Writes the underlying data into a newly created CSV file\n        located in an S3 bucket.\n\n        enterprise-adm: Enterprise edition\n            This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n            For licensing information and technical support, please [contact us][contact-page].\n\n        Note:\n            Note that S3 is not supported on Windows.\n\n        Args:\n            bucket:\n                The bucket from which to read the files.\n\n            key:\n                The key in the S3 bucket in which you want to\n                write the output. The ending \".csv\" and an optional\n                batch number will be added automatically.\n\n            region:\n                The region in which the bucket is located.\n\n            sep:\n                The character used for separating fields.\n\n            batch_size:\n                Maximum number of lines per file. Set to 0 to read\n                the entire data frame into a single file.\n\n        ??? example\n            ```python\n            getml.engine.set_s3_access_key_id(\"YOUR-ACCESS-KEY-ID\")\n            getml.engine.set_s3_secret_access_key(\"YOUR-SECRET-ACCESS-KEY\")\n\n            your_df.to_s3(\n                bucket=\"your-bucket-name\",\n                key=\"filename-on-s3\",\n                region=\"us-east-2\",\n                sep=';'\n            )\n            ```\n\n        \"\"\"\n\n        self.refresh()\n\n        if not isinstance(bucket, str):\n            raise TypeError(\"'bucket' must be of type str\")\n\n        if not isinstance(key, str):\n            raise TypeError(\"'fname' must be of type str\")\n\n        if not isinstance(region, str):\n            raise TypeError(\"'region' must be of type str\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be of type str\")\n\n        if not isinstance(batch_size, numbers.Real):\n            raise TypeError(\"'batch_size' must be a real number\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"DataFrame.to_s3\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"bucket_\"] = bucket\n        cmd[\"key_\"] = key\n        cmd[\"region_\"] = region\n        cmd[\"sep_\"] = sep\n        cmd[\"batch_size_\"] = batch_size\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_float_names(self):\n        return [col.name for col in self._unused_float_columns]\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_names(self):\n        return self._unused_float_names + self._unused_string_names\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_string_names(self):\n        return [col.name for col in self._unused_string_columns]\n\n    # ------------------------------------------------------------\n\n    def unload(self):\n        \"\"\"\n        Unloads the data frame from memory.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        self._delete(mem_only=True)\n\n    # ------------------------------------------------------------\n\n    def where(\n        self,\n        index: Union[\n            numbers.Integral, slice, BooleanColumnView, FloatColumnView, FloatColumn\n        ],\n    ) -> View:\n        \"\"\"Extract a subset of rows.\n\n        Creates a new [`View`][getml.data.View] as a\n        subselection of the current instance.\n\n        Args:\n            index:\n                Indicates the rows you want to select.\n\n        Returns:\n                A new [`View`][getml.data.View] containing the selected rows.\n\n        ??? example\n            Generate example data:\n            ```python\n            data = dict(\n                fruit=[\"banana\", \"apple\", \"cherry\", \"cherry\", \"melon\", \"pineapple\"],\n                price=[2.4, 3.0, 1.2, 1.4, 3.4, 3.4],\n                join_key=[\"0\", \"1\", \"2\", \"2\", \"3\", \"3\"])\n\n            fruits = getml.DataFrame.from_dict(data, name=\"fruits\",\n            roles={\"categorical\": [\"fruit\"], \"join_key\": [\"join_key\"], \"numerical\": [\"price\"]})\n\n            fruits\n            ```\n            ```\n            | join_key | fruit       | price     |\n            | join key | categorical | numerical |\n            --------------------------------------\n            | 0        | banana      | 2.4       |\n            | 1        | apple       | 3         |\n            | 2        | cherry      | 1.2       |\n            | 2        | cherry      | 1.4       |\n            | 3        | melon       | 3.4       |\n            | 3        | pineapple   | 3.4       |\n            ```\n            Apply where condition. This creates a new DataFrame called \"cherries\":\n\n            ```python\n            cherries = fruits.where(\n                fruits[\"fruit\"] == \"cherry\")\n\n            cherries\n            ```\n            ```\n            | join_key | fruit       | price     |\n            | join key | categorical | numerical |\n            --------------------------------------\n            | 2        | cherry      | 1.2       |\n            | 2        | cherry      | 1.4       |\n            ```\n\n        \"\"\"\n\n        return _where(self, index)\n\n    # ------------------------------------------------------------\n\n    def with_column(\n        self,\n        col: Union[\n            bool,\n            str,\n            float,\n            int,\n            np.datetime64,\n            FloatColumn,\n            FloatColumnView,\n            StringColumn,\n            StringColumnView,\n            BooleanColumnView,\n        ],\n        name: str,\n        role: Optional[Role] = None,\n        subroles: Optional[Union[Subrole, Iterable[str]]] = None,\n        unit: str = \"\",\n        time_formats: Optional[Iterable[str]] = None,\n    ):\n        \"\"\"Returns a new [`View`][getml.data.View] that contains an additional column.\n\n        Args:\n            col:\n                The column to be added.\n\n            name:\n                Name of the new column.\n\n            role:\n                Role of the new column. Must be from [`roles`][getml.data.roles].\n\n            subroles:\n                Subroles of the new column. Must be from [`subroles`][getml.data.subroles].\n\n            unit:\n                Unit of the column.\n\n            time_formats:\n                Formats to be used to parse the time stamps.\n\n                This is only necessary, if an implicit conversion from\n                a [`StringColumn`][getml.data.columns.StringColumn] to a time\n                stamp is taking place.\n\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n\n        \"\"\"\n        col, role, subroles = _with_column(\n            col, name, role, subroles, unit, time_formats\n        )\n        return View(\n            base=self,\n            added={\n                \"col_\": col,\n                \"name_\": name,\n                \"role_\": role,\n                \"subroles_\": subroles,\n                \"unit_\": unit,\n            },\n        )\n\n    # ------------------------------------------------------------\n\n    def with_name(self, name: str) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] with a new name.\n\n        Args:\n            name:\n                The name of the new view.\n\n        Returns:\n            A new view with the new name.\n        \"\"\"\n        return View(base=self, name=name)\n\n    # ------------------------------------------------------------\n\n    def with_role(\n        self,\n        cols: Union[\n            str,\n            FloatColumn,\n            StringColumn,\n            Union[Iterable[str], List[FloatColumn], List[StringColumn]],\n        ],\n        role: Role,\n        time_formats: Optional[Iterable[str]] = None,\n    ):\n        \"\"\"Returns a new [`View`][getml.data.View] with modified roles.\n\n        The difference between [`with_role`][getml.DataFrame.with_role] and\n        [`set_role`][getml.DataFrame.set_role] is that\n        [`with_role`][getml.DataFrame.with_role] returns a view that is lazily\n        evaluated when needed whereas [`set_role`][getml.DataFrame.set_role]\n        is an in-place operation. From a memory perspective, in-place operations\n        like [`set_role`][getml.DataFrame.set_role] are preferable.\n\n        When switching from a role based on type float to a role based on type\n        string or vice verse, an implicit type conversion will be conducted.\n        The `time_formats` argument is used to interpret time\n        format string: `annotating_roles_time_stamp`. For more information on\n        roles, please refer to the [User Guide][annotating-data].\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n            role:\n                The role to be assigned.\n\n            time_formats:\n                Formats to be used to\n                parse the time stamps.\n                This is only necessary, if an implicit conversion from a StringColumn to\n                a time stamp is taking place.\n        \"\"\"\n        return _with_role(self, cols, role, time_formats)\n\n    # ------------------------------------------------------------\n\n    def with_subroles(\n        self,\n        cols: Union[\n            str,\n            FloatColumn,\n            StringColumn,\n            Union[Iterable[str], List[FloatColumn], List[StringColumn]],\n        ],\n        subroles: Union[Subrole, Iterable[str]],\n        append: bool = True,\n    ):\n        \"\"\"Returns a new view with one or several new subroles on one or more columns.\n\n        The difference between [`with_subroles`][getml.DataFrame.with_subroles] and\n        [`set_subroles`][getml.DataFrame.set_subroles] is that\n        [`with_subroles`][getml.DataFrame.with_subroles] returns a view that is lazily\n        evaluated when needed whereas [`set_subroles`][getml.DataFrame.set_subroles]\n        is an in-place operation. From a memory perspective, in-place operations\n        like [`set_subroles`][getml.DataFrame.set_subroles] are preferable.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n            subroles:\n                The subroles to be assigned.\n\n            append:\n                Whether you want to append the\n                new subroles to the existing subroles.\n        \"\"\"\n        return _with_subroles(self, cols, subroles, append)\n\n    # ------------------------------------------------------------\n\n    def with_unit(\n        self,\n        cols: Union[\n            str,\n            FloatColumn,\n            StringColumn,\n            Union[Iterable[str], List[FloatColumn], List[StringColumn]],\n        ],\n        unit: str,\n        comparison_only: bool = False,\n    ):\n        \"\"\"Returns a view that contains a new unit on one or more columns.\n\n        The difference between [`with_unit`][getml.DataFrame.with_unit] and\n        [`set_unit`][getml.DataFrame.set_unit] is that\n        [`with_unit`][getml.DataFrame.with_unit] returns a view that is lazily\n        evaluated when needed whereas [`set_unit`][getml.DataFrame.set_unit]\n        is an in-place operation. From a memory perspective, in-place operations\n        like [`set_unit`][getml.DataFrame.set_unit] are preferable.\n\n        Args:\n            cols:\n                The columns or the names thereof.\n\n            unit:\n                The unit to be assigned.\n\n            comparison_only:\n                Whether you want the column to\n                be used for comparison only. This means that the column can\n                only be used in comparison to other columns of the same unit.\n\n                An example might be a bank account number: The number in itself\n                is hardly interesting, but it might be useful to know how often\n                we have seen that same bank account number in another table.\n\n                For more information on units, please refer to the\n                [User Guide][annotating-data-units].\n        \"\"\"\n        return _with_unit(self, cols, unit, comparison_only)\n"
  },
  {
    "path": "src/python-api/getml/data/data_model.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA container for the placeholders.\n\"\"\"\n\nfrom copy import deepcopy\nfrom inspect import cleandoc\nfrom typing import List, Union\n\nfrom getml.utilities.formatting import _Formatter\n\nfrom .diagram import _Diagram\nfrom .helpers import _is_typed_list\nfrom .placeholder import Placeholder\nfrom .staging import _make_staging_overview\n\n\nclass DataModel:\n    \"\"\"\n    Abstract representation of the relationship between tables.\n\n    You might also want to refer to [`Placeholder`][getml.data.Placeholder].\n\n    Attributes:\n        population:\n            The placeholder representing the population table,\n            which defines the\n            [statistical population](https://en.wikipedia.org/wiki/Statistical_population)\n            and contains the targets.\n\n    ??? example\n        This example will construct a data model in which the\n        'population_table' depends on the 'peripheral_table' via\n        the 'join_key' column. In addition, only those rows in\n        'peripheral_table' for which 'time_stamp' is smaller or\n        equal to the 'time_stamp' in 'population_table' are considered:\n        ```python\n        dm = getml.data.DataModel(\n            population_table.to_placeholder(\"POPULATION\")\n        )\n\n        dm.add(peripheral_table.to_placeholder(\"PERIPHERAL\"))\n\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\"\n        )\n        ```\n        If you want to add more than one peripheral table, you can\n        use [`to_placeholder`][getml.data.to_placeholder]:\n        ```python\n        dm = getml.data.DataModel(\n            population_table.to_placeholder(\"POPULATION\")\n        )\n\n        dm.add(\n            getml.data.to_placeholder(\n                PERIPHERAL1=peripheral_table_1,\n                PERIPHERAL2=peripheral_table_2,\n            )\n        )\n        ```\n        If the relationship between two tables is many-to-one or one-to-one\n        you should clearly say so:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            relationship=getml.data.relationship.many_to_one,\n        )\n        ```\n        Please also refer to [`relationship`][getml.data.relationship].\n\n        If the join keys or time stamps are named differently in the two\n        different tables, use a tuple:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=(\"join_key\", \"other_join_key\"),\n            time_stamps=(\"time_stamp\", \"other_time_stamp\"),\n        )\n        ```\n        You can join over more than one join key:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=[\"join_key1\", \"join_key2\", (\"join_key3\", \"other_join_key3\")],\n            time_stamps=\"time_stamp\",\n        )\n        ```\n        You can also limit the scope of your joins using *memory*. This\n        can significantly speed up training time. For instance, if you\n        only want to consider data from the last seven days, you could\n        do something like this:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            memory=getml.data.time.days(7),\n        )\n        ```\n        In some use cases, particularly those involving time series, it\n        might be a good idea to use targets from the past. You can activate\n        this using *lagged_targets*. But if you do that, you must\n        also define a prediction *horizon*. For instance, if you want to\n        predict data for the next hour, using data from the last seven days,\n        you could do this:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            lagged_targets=True,\n            horizon=getml.data.time.hours(1),\n            memory=getml.data.time.days(7),\n        )\n        ```\n        Please also refer to [`time`][getml.data.time].\n\n        If the join involves many matches, it might be a good idea to set the\n        relationship to [`propositionalization`][getml.data.relationship.propositionalization].\n        This forces the pipeline to always use a propositionalization\n        algorithm for this join, which can significantly speed things up.\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            relationship=getml.data.relationship.propositionalization,\n            )\n        ```\n\n        Please also refer to [`relationship`][getml.data.relationship].\n\n        In some cases, it is necessary to have more than one placeholder\n        on the same table. This is necessary to create more complicated\n        data models. In this case, you can do something like this:\n        ```python\n        dm.add(\n            getml.data.to_placeholder(\n                PERIPHERAL=[peripheral_table]*2,\n            )\n        )\n\n        # We can now access our two placeholders like this:\n        placeholder1 = dm.PERIPHERAL[0]\n        placeholder2 = dm.PERIPHERAL[1]\n        ```\n            If you want to check out a real-world example where this\n            is necessary, refer to the\n            [CORA notebook](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/cora.ipynb).\n    \"\"\"\n\n    def __init__(self, population: Union[Placeholder, str]):\n        if isinstance(population, str):\n            population = Placeholder(population)\n\n        if not isinstance(population, Placeholder):\n            raise TypeError(\n                \"'population' must be a getml.data.Placeholder or a str, got \"\n                + type(population).__name__\n                + \".\"\n            )\n\n        self.population = population\n\n        self.peripheral = {}\n\n    def _add(self, placeholder):\n        if placeholder.name in self.peripheral:\n            try:\n                self.peripheral[placeholder.name].append(placeholder)\n            except AttributeError:\n                self.peripheral[placeholder.name] = [\n                    self.peripheral[placeholder.name],\n                    placeholder,\n                ]\n        else:\n            self.peripheral.update({placeholder.name: placeholder})\n\n    def __dir__(self):\n        attrs = dir(type(self)) + list(vars(self))\n        attrs.extend(self.names)\n        return attrs\n\n    def __getattr__(self, key) -> Placeholder:\n        try:\n            return self[key]\n        except KeyError:\n            super().__getattribute__(key)\n\n    def __getitem__(self, key) -> Placeholder:\n        population = vars(self)[\"population\"]\n        peripheral = vars(self)[\"peripheral\"]\n\n        phs = {\n            \"population\": population,\n            population.name: population,\n            **peripheral,\n        }\n\n        return phs[key]\n\n    def _getml_deserialize(self):\n        def deserialize(elem):\n            return (\n                [e._getml_deserialize() for e in elem]\n                if isinstance(elem, list)\n                else elem._getml_deserialize()\n            )\n\n        cmd = self.population._getml_deserialize()\n        cmd[\"peripheral_\"] = {k: deserialize(v) for (k, v) in self.peripheral.items()}\n        return cmd\n\n    def __iter__(self):\n        yield from [self.population.name] + [\"population\"] + list(self.peripheral)\n\n    def __repr__(self):\n        return \"\\n\\n\".join(repr(ph) for ph in self.population.to_list())\n\n    def _make_diagram(self):\n        return _Diagram(self.population).to_html()\n\n    def _make_staging(self):\n        headers = [[\"data frames\", \"staging table\"]]\n        rows = _make_staging_overview(self.population)\n        staging_table = _Formatter(headers=headers, rows=rows)._render_html()\n        return staging_table\n\n    def _repr_html_(self):\n        output = cleandoc(\n            f\"\"\"\n            <div style='margin-top: 15px; margin-bottom: 5px;'>\n            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\n            {self._make_diagram()}\n            </div>\n\n            <div style='margin-top: 15px;'>\n            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\n            {self._make_staging()}\n            </div>\n            \"\"\"\n        )\n\n        return output\n\n    def add(self, *placeholders: Union[Placeholder, List[Placeholder]]):\n        \"\"\"\n        Adds peripheral placeholders to the data model.\n\n        Args:\n            placeholders:\n                The placeholder or placeholders you would like to add.\n        \"\"\"\n\n        def to_list(elem):\n            return elem if isinstance(elem, list) else [elem]\n\n        # We want to be 100% sure that all handles are unique,\n        # so we need deepcopy.\n        placeholders_dc = [\n            deepcopy(ph) for elem in placeholders for ph in to_list(elem)\n        ]\n\n        if not _is_typed_list(placeholders_dc, Placeholder):\n            raise TypeError(\n                \"'placeholders' must consist of getml.data.Placeholders \"\n                + \"or lists thereof.\"\n            )\n\n        for placeholder in placeholders_dc:\n            self._add(placeholder)\n\n    @property\n    def names(self) -> List[str]:\n        \"\"\"\n        A list of the names of all tables contained in the DataModel.\n\n        Returns:\n            A list of names.\n        \"\"\"\n        return [name for name in self]\n"
  },
  {
    "path": "src/python-api/getml/data/diagram.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of functions for visualizing the data model\nthat are not intended to be used by\nthe end-user.\n\"\"\"\n\nimport html\n\nfrom getml import constants\n\nfrom .relationship import many_to_many\n\nTABLE_WIDTH = 150\nTABLE_HEIGHT = 90\n\nLABEL_WIDTH = 150\nLABEL_HEIGHT = 70\n\nLINE_COLOR = \"#808080;\"\n\n\ndef _make_line(origin, target, color, width):\n    line = '<line x1=\"'\n    line += str(origin[1]) + '\"'\n    line += ' y1=\"'\n    line += str(origin[0]) + '\"'\n    line += ' x2=\"'\n    line += str(target[1]) + '\"'\n    line += ' y2=\"'\n    line += str(target[0]) + '\"'\n    line += ' style=\"stroke:'\n    line += color + \";\"\n    line += \"stroke-width:\"\n    line += str(width) + '\" />'\n    return line\n\n\ndef _make_icon(origin, size=48):\n    icon = '<rect x=\"'\n    icon += str(origin[1]) + '\"'\n    icon += ' y=\"'\n    icon += str(origin[0]) + '\"'\n    icon += ' rx=\"4\" ry=\"4\"'\n    icon += ' width=\"'\n    icon += str(size) + '\"'\n    icon += ' height=\"'\n    icon += str(size) + '\"'\n    icon += ' style=\"'\n    icon += \" fill:#6829c2;\"\n    icon += \"stroke:#ffffff;\"\n    icon += \"stroke-width:3;\"\n    icon += '\" />'\n\n    icon += _make_line(\n        origin=(origin[0], origin[1] + size / 3),\n        target=(origin[0] + size, origin[1] + size / 3),\n        color=\"white\",\n        width=3,\n    )\n\n    icon += _make_line(\n        origin=(origin[0], origin[1] + 2 * size / 3),\n        target=(origin[0] + size, origin[1] + 2 * size / 3),\n        color=\"white\",\n        width=3,\n    )\n\n    icon += _make_line(\n        origin=(origin[0] + size / 3, origin[1]),\n        target=(origin[0] + size / 3, origin[1] + size),\n        color=\"white\",\n        width=3,\n    )\n\n    icon += _make_line(\n        origin=(origin[0] + 2 * size / 3, origin[1]),\n        target=(origin[0] + 2 * size / 3, origin[1] + size),\n        color=\"white\",\n        width=3,\n    )\n\n    return icon\n\n\ndef _make_table(name, origin):\n    table = '<rect y=\"'\n    table += str(origin[0])\n    table += '\" x=\"'\n    table += str(origin[1])\n    table += '\" rx=\"10\" ry=\"10\"'\n    table += ' width=\"'\n    table += str(TABLE_WIDTH)\n    table += '\" height=\"'\n    table += str(TABLE_HEIGHT)\n    table += '\" '\n    table += 'style=\"fill:#6829c2;stroke-width:0;\"'\n    table += \" />\"\n\n    table += '<text y=\"'\n    table += str(origin[0] + TABLE_HEIGHT * 0.82) + '\"'\n    table += ' x=\"'\n    table += str(origin[1] + TABLE_WIDTH / 2) + '\"'\n    table += ' dominant-baseline=\"middle\" text-anchor=\"middle\"'\n    table += ' fill=\"white\">'\n    table += name\n    table += \"</text>\"\n\n    table += _make_icon((origin[0] + 10, origin[1] + 51))\n\n    return table\n\n\ndef _make_triangle_down(origin):\n    triangle = '<polygon points=\"'\n    triangle += str(origin[1]) + \", \"\n    triangle += str(origin[0]) + \" \"\n    triangle += str(origin[1] - 6) + \", \"\n    triangle += str(origin[0] - 10) + \" \"\n    triangle += str(origin[1] + 6) + \", \"\n    triangle += str(origin[0] - 10) + \" \"\n    triangle += '\" style=\"fill:'\n    triangle += LINE_COLOR + \";\"\n    triangle += \"stroke-width:0;\"\n    triangle += '\" />'\n\n    return triangle\n\n\ndef _make_triangle_right(origin):\n    triangle = '<polygon points=\"'\n    triangle += str(origin[1]) + \", \"\n    triangle += str(origin[0]) + \" \"\n    triangle += str(origin[1] - 10) + \", \"\n    triangle += str(origin[0] - 6) + \" \"\n    triangle += str(origin[1] - 10) + \", \"\n    triangle += str(origin[0] + 6) + \" \"\n    triangle += '\" style=\"fill:'\n    triangle += LINE_COLOR + \";\"\n    triangle += \"stroke-width:0;\"\n    triangle += '\" />'\n\n    return triangle\n\n\ndef _make_label(content, origin):\n    table = '<rect y=\"'\n    table += str(origin[0])\n    table += '\" x=\"'\n    table += str(origin[1])\n    table += '\" rx=\"10\" ry=\"10\"'\n    table += ' width=\"'\n    table += str(LABEL_WIDTH)\n    table += '\" height=\"'\n    table += str(LABEL_HEIGHT)\n    table += '\"'\n    table += ' style=\"fill:#6829c2;stroke-width:0;\"'\n    table += \" />\"\n\n    table += \"<text\"\n    table += ' dominant-baseline=\"middle\" text-anchor=\"middle\"'\n    table += ' fill=\"white\">'\n\n    x = origin[1] + LABEL_WIDTH / 2\n    y = origin[0] + LABEL_HEIGHT / 2 - (len(content) - 1) * 5\n\n    for c in content:\n        table += '<tspan y=\"'\n        table += str(y) + '\"'\n        table += ' x=\"'\n        table += str(x) + '\"'\n        table += ' font-size=\"7pt\"'\n        table += \" >\"\n        table += c\n        table += \"</tspan>\"\n        y += 10\n\n    table += \"</text>\"\n\n    return table\n\n\ndef _make_arrow(label, origin, target):\n    adj_origin = (origin[0] + TABLE_HEIGHT / 2 - 2, origin[1] + TABLE_WIDTH)\n    adj_target = (target[0], target[1] + TABLE_WIDTH / 2 - 2)\n    if origin[0] == target[0]:\n        adj_target = (target[0] + TABLE_HEIGHT / 2, target[1] - 2)\n\n    label_pos0 = adj_origin[0] - LABEL_HEIGHT / 2 + 2\n\n    if origin[0] == target[0]:\n        label_pos1 = adj_origin[1] + (adj_target[1] - adj_origin[1] - LABEL_WIDTH) / 2\n    else:\n        label_pos1 = (\n            adj_origin[1]\n            + (adj_target[1] - adj_origin[1] - LABEL_WIDTH) / 2\n            - TABLE_WIDTH / 4\n        )\n    if target[0] == origin[0]:\n        arrow = _make_line(\n            origin=adj_origin,\n            target=(adj_origin[0], adj_target[1] - 8),\n            color=LINE_COLOR,\n            width=4,\n        )\n\n        arrow += _make_triangle_right((adj_target[0] - 2, adj_target[1] + 2))\n    else:\n        arrow = _make_line(\n            origin=adj_origin,\n            target=(adj_origin[0], adj_target[1]),\n            color=LINE_COLOR,\n            width=4,\n        )\n\n        arrow += _make_line(\n            origin=(adj_origin[0] - 2, adj_target[1]),\n            target=(adj_target[0] - 10, adj_target[1]),\n            color=LINE_COLOR,\n            width=4,\n        )\n\n        arrow += _make_triangle_down(adj_target)\n\n    arrow += _make_label(label, (label_pos0, label_pos1))\n\n    return arrow\n\n\ndef _split_multiple_join_keys(multiple):\n    multiple = multiple.replace(constants.MULTIPLE_JOIN_KEYS_BEGIN, \"\")\n    multiple = multiple.replace(constants.MULTIPLE_JOIN_KEYS_END, \"\")\n    return multiple.split(constants.JOIN_KEY_SEP)\n\n\ndef _make_join_keys(placeholder, i):\n    if constants.JOIN_KEY_SEP in placeholder[\"join_keys_used_\"][i]:\n        join_keys = _split_multiple_join_keys(placeholder[\"join_keys_used_\"][i])\n        other_join_keys = _split_multiple_join_keys(\n            placeholder[\"other_join_keys_used_\"][i]\n        )\n\n        if len(join_keys) != len(other_join_keys):\n            raise ValueError(\"Number of join keys does not match!\")\n\n        return [other + \" = \" + jk for other, jk in zip(other_join_keys, join_keys)]\n\n    return [\n        placeholder[\"other_join_keys_used_\"][i]\n        + \" = \"\n        + placeholder[\"join_keys_used_\"][i]\n    ]\n\n\ndef _make_time_stamps(placeholder, i):\n    lower = (\n        [\n            placeholder[\"other_time_stamps_used_\"][i]\n            + html.escape(\" <= \")\n            + placeholder[\"time_stamps_used_\"][i]\n        ]\n        if placeholder[\"time_stamps_used_\"][i] != constants.ROWID\n        else [\"rowid <= rowid\"]\n    )\n\n    upper = (\n        [\n            placeholder[\"upper_time_stamps_used_\"][i]\n            + html.escape(\" > \")\n            + placeholder[\"time_stamps_used_\"][i]\n        ]\n        if placeholder[\"upper_time_stamps_used_\"][i]\n        else []\n    )\n\n    return lower + upper\n\n\ndef _make_window(diff, ts_used):\n    if ts_used == constants.ROWID:\n        return str(diff) + \" time steps\"\n\n    seconds_per_day = 24.0 * 60.0 * 60.0\n    seconds_per_hour = 60.0 * 60.0\n    seconds_per_minute = 60.0\n\n    window_formatted = str(diff) + \" seconds\"\n\n    if diff >= seconds_per_day:\n        window_formatted = str(diff / seconds_per_day) + \" days\"\n\n    elif diff >= seconds_per_hour:\n        window_formatted = str(diff / seconds_per_hour) + \" hours\"\n\n    elif diff >= seconds_per_minute:\n        window_formatted = str(diff / seconds_per_minute) + \" minutes\"\n\n    return window_formatted\n\n\ndef _make_label_content(placeholder, i):\n    jk_used = (\n        _make_join_keys(placeholder, i)\n        if placeholder[\"join_keys_used_\"][i] != constants.NO_JOIN_KEY\n        else []\n    )\n\n    ts_used = (\n        _make_time_stamps(placeholder, i) if placeholder[\"time_stamps_used_\"][i] else []\n    )\n\n    memory = (\n        [\n            \"Memory: \"\n            + _make_window(\n                placeholder[\"memory_\"][i], placeholder[\"time_stamps_used_\"][i]\n            )\n        ]\n        if placeholder[\"memory_\"][i] > 0.0\n        else []\n    )\n\n    horizon = (\n        [\n            \"Horizon: \"\n            + _make_window(\n                placeholder[\"horizon_\"][i], placeholder[\"time_stamps_used_\"][i]\n            )\n        ]\n        if placeholder[\"horizon_\"][i] != 0.0\n        else []\n    )\n\n    lagged_targets = (\n        [\"Lagged targets allowed\"] if placeholder[\"allow_lagged_targets_\"][i] else []\n    )\n\n    relationship = (\n        [\"Relationship: \" + placeholder[\"relationship_\"][i]]\n        if placeholder[\"relationship_\"][i] != many_to_many\n        else []\n    )\n\n    return (jk_used + ts_used + memory + horizon + lagged_targets + relationship) or [\n        \"1 = 1\"\n    ]\n\n\nclass _Diagram:\n    def __init__(self, placeholder):\n        self.rectangles = []\n        self.links = []\n\n        self.height = 0\n        self.width = 0\n\n        self._make_structure(0, placeholder._getml_deserialize())\n        self._calc_positions()\n        self._calc_size()\n\n    def _add_children(self, depth, placeholder):\n        child_ids = []\n\n        for joined_table in placeholder[\"joined_tables_\"]:\n            if joined_table[\"joined_tables_\"]:\n                self._make_structure(depth + 1, joined_table)\n            else:\n                self._add_placeholder(depth + 1, joined_table)\n\n            child_ids += [len(self.rectangles) - 1]\n\n        return child_ids\n\n    def _add_links(self, placeholder, child_ids, parent_id):\n        for i, child_id in enumerate(child_ids):\n            label = _make_label_content(placeholder, i)\n            link = dict()\n            link[\"source_id\"] = child_id\n            link[\"target_id\"] = parent_id\n            link[\"label\"] = label\n            self.links += [link]\n\n    def _add_placeholder(self, depth, placeholder):\n        rectangle = dict()\n        rectangle[\"depth\"] = depth\n        rectangle[\"id\"] = len(self.rectangles)\n        rectangle[\"name\"] = placeholder[\"name_\"]\n        self.rectangles += [rectangle]\n        return rectangle[\"id\"]\n\n    def _calc_positions(self):\n        max_depth = self._find_max_depth()\n\n        pos0 = 0\n\n        for i, rectangle in enumerate(self.rectangles):\n            if i > 0 and rectangle[\"depth\"] >= self.rectangles[i - 1][\"depth\"]:\n                pos0 += 110\n\n            pos1 = 500 * (max_depth - rectangle[\"depth\"])\n\n            self.rectangles[i][\"pos\"] = (pos0, pos1)\n\n    def _calc_size(self):\n        last = len(self.rectangles) - 1\n        self.height = self.rectangles[last][\"pos\"][0] + TABLE_HEIGHT\n        self.width = self.rectangles[last][\"pos\"][1] + TABLE_WIDTH\n\n    def _find_max_depth(self):\n        max_depth = 0\n\n        for rectangle in self.rectangles:\n            if rectangle[\"depth\"] > max_depth:\n                max_depth = rectangle[\"depth\"]\n\n        return max_depth\n\n    def _make_structure(self, depth, placeholder):\n        child_ids = self._add_children(depth, placeholder)\n        parent_id = self._add_placeholder(depth, placeholder)\n        self._add_links(placeholder, child_ids, parent_id)\n\n    def to_html(self):\n        \"\"\"\n        Expresses the data model in pure HTML code.\n        \"\"\"\n        output = \"\"\"<div style=\"height:\"\"\" + str(self.height + 10) + \"px;\"\n        output += \"width:\" + str(self.width + 10) + \"px;\"\n        output += \"\"\"position:relative;\">\"\"\"\n        output += '<svg height=\"' + str(self.height) + '\"'\n        output += ' width=\"' + str(self.width) + '\"'\n        output += \">\"\n\n        for rectangle in self.rectangles:\n            output += _make_table(rectangle[\"name\"], rectangle[\"pos\"])\n\n        for link in self.links:\n            origin = self.rectangles[link[\"source_id\"]][\"pos\"]\n            target = self.rectangles[link[\"target_id\"]][\"pos\"]\n            output += _make_arrow(link[\"label\"], origin, target)\n\n        output += \"</svg></div>\"\n\n        return output\n"
  },
  {
    "path": "src/python-api/getml/data/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of helper functions that are not intended to be used by\nthe end-user.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport numbers\nimport os\nimport random\nimport string\nfrom functools import lru_cache\nfrom typing import (\n    TYPE_CHECKING,\n    Any,\n    Dict,\n    Iterable,\n    Iterator,\n    List,\n    Optional,\n    Tuple,\n    Union,\n)\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\n\nimport getml.communication as comm\nfrom getml import communication as comm\nfrom getml import constants, data\nfrom getml.data.columns import (\n    BooleanColumnView,\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n    from_value,\n)\nfrom getml.data.columns.columns import arange\nfrom getml.data.roles import sets as roles_sets\nfrom getml.data.roles import (\n    time_stamp,\n    unused_float,\n    unused_string,\n)\nfrom getml.data.roles.container import Roles\nfrom getml.data.roles.types import Role\nfrom getml.data.subroles import sets as subroles_sets\nfrom getml.data.subroles.types import Subrole\nfrom getml.database import Connection\nfrom getml.database.helpers import _retrieve_temp_dir\nfrom getml.helpers import _is_iterable_not_str_of_type\n\nif TYPE_CHECKING:\n    from getml.data.data_frame import DataFrame\n    from getml.data.view import View\n\n# --------------------------------------------------------------------\n\nOnType = Optional[Union[str, Tuple[str, str], List[Union[str, Tuple[str, str]]]]]\n\"\"\"\nTypes that can be passed to the 'on' argument of the 'join' method.\n\"\"\"\n\n\n# --------------------------------------------------------------------\n\n\ndef list_data_frames() -> Dict[str, List[str]]:\n    \"\"\"Lists all available data frames of the project.\n\n    Returns:\n        dict:\n            Dict containing lists of strings representing the names of\n            the data frames objects\n\n            - 'in_memory'\n                held in memory (RAM).\n            - 'on_disk'\n                stored on disk.\n\n    ??? example\n        ```python\n        d, _ = getml.datasets.make_numerical()\n        getml.data.list_data_frames()\n        d.save()\n        getml.data.list_data_frames()\n        ```\n\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"list_data_frames\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    return json.loads(json_str)\n\n\n# --------------------------------------------------------------------\n\n\ndef _check_if_exists(colnames: List[str], all_colnames: List[str]):\n    for col in colnames:\n        if col in all_colnames:\n            raise ValueError(\"Duplicate column: '\" + col + \"'!\")\n\n        all_colnames.append(col)\n\n    return all_colnames\n\n\n# --------------------------------------------------------------------\n\n\ndef _check_join_key(candidates: Any, roles: Roles, name: str):\n    if isinstance(candidates, str):\n        candidates = [candidates]\n\n    not_existing = [\n        candidate\n        for candidate in candidates\n        if candidate is not None and candidate not in roles.columns\n    ]\n\n    if not_existing:\n        raise ValueError(\n            f\"Columns {not_existing!r} not found in schema of Placeholder {name!r}.\"\n        )\n\n    invalid = [\n        candidate\n        for candidate in candidates\n        if candidate is not None and candidate not in roles.join_key\n    ]\n\n    if invalid:\n        raise ValueError(\n            f\"Columns {invalid!r} don't have the role 'join_key' \"\n            \"In the schema of Placeholder {name!r}.\"\n        )\n\n\n# --------------------------------------------------------------------\n\n\ndef _empty_data_frame() -> str:\n    return \"Empty getml.DataFrame\\nColumns: []\\n\"\n\n\n# --------------------------------------------------------------------\n\n\ndef _exists_in_memory(name: str) -> bool:\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    all_df = list_data_frames()\n\n    return name in all_df[\"in_memory\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef _finditems(key: str, dct: Dict[str, Any]) -> Any:\n    if key in dct:\n        yield dct[key]\n    for k, v in dct.items():\n        if isinstance(v, dict):\n            yield from _finditems(key, v)\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_column(name, columns):\n    for col in columns:\n        if col.name == name:\n            return col\n    return None\n\n\n# --------------------------------------------------------------------\n\n\ndef _handle_cols(\n    cols: Union[\n        str,\n        FloatColumn,\n        StringColumn,\n        Union[Iterable[str], Iterable[FloatColumn], Iterable[StringColumn]],\n    ],\n) -> List[str]:\n    \"\"\"\n    Handles cols as supplied to DataFrame methods. Returns a list of column names.\n    \"\"\"\n\n    if _is_iterable_not_str_of_type(cols, str):\n        cols_ = cols\n    elif isinstance(col := cols, str):\n        cols_ = [col]\n    else:\n        raise TypeError(\n            \"'cols' must be either a string, a FloatColumn, \"\n            \"a StringColumn, or a list thereof.\"\n        )\n\n    names: List[str] = []\n\n    for col in cols_:\n        if isinstance(col, list):\n            names.extend(_handle_cols(col))\n\n        if isinstance(col, str):\n            names.append(col)\n\n        if isinstance(col, (FloatColumn, StringColumn)):\n            names.append(col.name)\n\n    return names\n\n\n# --------------------------------------------------------------------\n\n\ndef _handle_multiple_join_keys(\n    join_keys: List[Union[str, Tuple[str, str]]],\n) -> List[Tuple[str, str]]:\n    return [(jk, jk) if isinstance(jk, str) else jk for jk in join_keys]\n\n\n# --------------------------------------------------------------------\n\n\ndef _handle_on(on: OnType) -> Union[List[Tuple[str, str]], List[Tuple[None, None]]]:\n    if isinstance(on, str):\n        return [(on, on)]\n\n    if isinstance(on, list):\n        return _handle_multiple_join_keys(on)\n\n    if isinstance(on, tuple):\n        return [on]\n\n    if on is None:\n        return [(None, None)]\n\n\n# --------------------------------------------------------------------\n\n\ndef _handle_ts(ts):\n    if ts is None:\n        return (\"\", \"\")\n    return ts\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_numerical_type_numpy(coltype) -> bool:\n    return coltype in [\n        int,\n        float,\n        np.int_,\n        np.int8,\n        np.int16,\n        np.int32,\n        np.int64,\n        np.uint8,\n        np.uint16,\n        np.uint32,\n        np.uint64,\n        np.float16,\n        np.float32,\n        np.float64,\n    ]\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_subclass_list(some_list, parent) -> bool:\n    is_subclass_list = isinstance(some_list, list)\n\n    is_subclass_list = is_subclass_list and all(\n        [issubclass(type(ll), parent) for ll in some_list]\n    )\n\n    return is_subclass_list\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_typed_dict(some_dict, key_types, value_types) -> bool:\n    if not isinstance(key_types, list):\n        key_types = [key_types]\n\n    if not isinstance(value_types, list):\n        value_types = [value_types]\n\n    is_typed_dict = isinstance(some_dict, dict)\n\n    is_typed_dict = is_typed_dict and all(\n        [any([isinstance(key, t) for t in key_types]) for key in some_dict.keys()]\n    )\n\n    is_typed_dict = is_typed_dict and all(\n        [any([isinstance(val, t) for t in value_types]) for val in some_dict.values()]\n    )\n\n    return is_typed_dict\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_typed_list(some_list, types) -> bool:\n    if isinstance(types, list):\n        types = tuple(types)\n\n    is_typed_list = isinstance(some_list, list)\n\n    is_typed_list = is_typed_list and all([isinstance(ll, types) for ll in some_list])\n\n    return is_typed_list\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_non_empty_typed_list(some_list, types) -> bool:\n    return _is_typed_list(some_list, types) and len(some_list) > 0\n\n\n# -----------------------------------------------------------------\n\n\ndef _iter_batches(\n    df_or_view: Union[DataFrame, View], batch_size: int\n) -> Iterator[View]:\n    start, end = 0, batch_size\n    while batch := df_or_view[start:end]:\n        yield batch\n        start, end = end, end + batch_size\n\n\n# -----------------------------------------------------------------\n\n\ndef _make_id() -> str:\n    letters = string.ascii_letters + string.digits\n    return \"\".join([random.choice(letters) for _ in range(6)])\n\n\n# --------------------------------------------------------------------\n\n\ndef _infer_arange_args_from_slice(\n    slc: slice, df_or_view: Union[DataFrame, View]\n) -> Tuple[int, int, int]:\n    @lru_cache(maxsize=None)\n    def len_df_or_view():\n        return len(df_or_view)\n\n    if slc.step == 0:\n        raise ValueError(\"slice step cannot be zero\")\n\n    start = slc.start\n    stop = slc.stop\n    step = slc.step or 1\n\n    # as len forces an evaluation of the view, we want to call\n    # it only if necessary and only once\n\n    if start is None:\n        if step > 0:\n            start = 0\n        elif step < 0:\n            start = len_df_or_view() - 1\n    elif start < 0:\n        start = max(len_df_or_view() + start, 0)\n\n    if stop is None:\n        if step > 0:\n            stop = len_df_or_view()\n        elif step < 0:\n            stop = -1\n    elif stop < 0:\n        stop = max(len_df_or_view() + stop, 0)\n\n    if (stop < start and step > 0) or (stop > start and step < 0):\n        return 0, 0, 1\n\n    return start, stop, step\n\n\n# --------------------------------------------------------------------\n\n\ndef _serialize_join_keys(on: OnType) -> Tuple[str, str]:\n    if on == [(None, None)]:\n        return constants.NO_JOIN_KEY, constants.NO_JOIN_KEY\n\n    join_key, other_join_key = zip(*on)\n    begin = constants.MULTIPLE_JOIN_KEYS_BEGIN\n    end = constants.MULTIPLE_JOIN_KEYS_END\n    sep = constants.JOIN_KEY_SEP\n    len_jk = len_other_jk = 1\n\n    if not other_join_key:\n        other_join_key = join_key\n\n    len_jk = len(join_key)\n    if len_jk > 1:\n        join_key_merged = begin + sep.join(join_key) + end\n    else:\n        join_key_merged = join_key[0]\n\n    len_other_jk = len(other_join_key)\n    if len_other_jk > 1:\n        other_join_key_merged = begin + sep.join(other_join_key) + end\n    else:\n        other_join_key_merged = other_join_key[0]\n\n    if len_jk != len_other_jk:\n        raise ValueError(\n            \"The number of join keys passed to \"\n            + \"'join_key' and 'other_join_key' \"\n            + \"must match!\"\n        )\n\n    return join_key_merged, other_join_key_merged\n\n\n# --------------------------------------------------------------------\n\n\ndef _prepare_roles(\n    roles: Optional[Union[Roles, Dict[Role, Iterable[str]]]],\n    sniffed_roles: Roles,\n    ignore_sniffed_roles: bool = False,\n) -> Roles:\n    if roles is None:\n        roles = {}\n\n    if isinstance(roles, dict):\n        roles = Roles.from_dict(roles)\n\n    if ignore_sniffed_roles:\n        return roles\n\n    roles = sniffed_roles.update(roles)\n\n    return roles\n\n\n# --------------------------------------------------------------------\n\n\n# --------------------------------------------------------------------\n\n\ndef _remove_trailing_underscores(some_dict: Dict[str, Any]) -> Dict[str, Any]:\n    new_dict: Dict[str, Any] = {}\n\n    for kkey in some_dict:\n        new_key = kkey\n\n        if kkey[-1] == \"_\":\n            new_key = kkey[:-1]\n\n        if isinstance(some_dict[kkey], dict):\n            new_dict[new_key] = _remove_trailing_underscores(some_dict[kkey])\n\n        elif isinstance(some_dict[kkey], list):\n            new_dict[new_key] = [\n                _remove_trailing_underscores(elem) if isinstance(elem, dict) else elem\n                for elem in some_dict[kkey]\n            ]\n\n        else:\n            new_dict[new_key] = some_dict[kkey]\n\n    return new_dict\n\n\n# --------------------------------------------------------------------\n\n\ndef _replace_non_alphanumeric(string: str) -> str:\n    replaced = \"\".join([\" \" if not c.isalnum() else c for c in list(string)])\n    stripped = replaced.strip()\n    result = stripped.replace(\" \", \"_\")\n    while \"___\" in result:\n        result.replace(\"___\", \"__\")\n    return result\n\n\n# --------------------------------------------------------------------\n\n\ndef _replace_non_alphanumeric_cols(df_or_view):\n    \"\"\"\n    Many databases only accept alphanumeric characters\n    and underscores. This is meant to handle these\n    problems.\n    \"\"\"\n    non_alnum_colnames = [\n        cname\n        for cname in df_or_view.colnames\n        if not cname.isalnum() and _replace_non_alphanumeric(cname) != cname\n    ]\n\n    if not non_alnum_colnames:\n        return df_or_view\n\n    for old_name in non_alnum_colnames:\n        col = df_or_view[old_name]\n        new_name = _replace_non_alphanumeric(old_name)\n        if new_name != old_name:\n            df_or_view = df_or_view.with_column(\n                col=col,\n                name=new_name,\n                role=df_or_view.roles.column(old_name),\n                subroles=col.subroles,\n                unit=col.unit,\n                time_formats=None,\n            )\n\n    return df_or_view.drop(non_alnum_colnames)\n\n\n# --------------------------------------------------------------------\n\n\ndef _make_table(col, numpy_array) -> pa.Table:\n    data_frame = pd.DataFrame()\n    data_frame[col.name] = numpy_array\n    return pa.Table.from_pandas(data_frame)\n\n\n# ------------------------------------------------------------\n\n\ndef _send_numpy_array(col, numpy_array: np.ndarray):\n    table = _make_table(col, numpy_array)\n\n    with comm.send_and_get_socket(col.cmd) as sock:\n        with sock.makefile(mode=\"wb\") as sink:\n            batches = table.to_batches()\n            with pa.ipc.new_stream(sink, table.schema) as writer:\n                for batch in batches:\n                    writer.write_batch(batch)\n\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n\n# --------------------------------------------------------------------\n\n\ndef _sniff_db(table_name: str, conn: Optional[Connection] = None) -> Roles:\n    \"\"\"\n    Sniffs a table in the database and returns a dictionary of roles.\n\n    Args:\n        table_name: Name of the table to be sniffed.\n\n        conn: The database\n            connection to be used. If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        Keyword arguments (kwargs) that can be used to construct a DataFrame.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = table_name\n    cmd[\"type_\"] = \"Database.sniff_table\"\n\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            raise Exception(msg)\n        roles = comm.recv_string(sock)\n\n    return Roles.from_dict(json.loads(roles))\n\n\n# --------------------------------------------------------------------\n\n\ndef _sniff_query(query: str, name: str, conn: Optional[Connection] = None) -> Roles:\n    \"\"\"\n    Sniffs a table in the database and returns a dictionary of roles.\n\n    Args:\n        name: Name of the resulting DataFrame.\n\n        conn: The database connection to be used. If you don't explicitly pass a\n            connection, the engine will use the default connection.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, str] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Database.sniff_query\"\n\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.send_string(sock, query)\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            sock.close()\n            raise Exception(msg)\n        roles = comm.recv_string(sock)\n\n    return Roles.from_dict(json.loads(roles))\n\n\n# --------------------------------------------------------------------\n\n\ndef _sniff_s3(\n    bucket: str,\n    keys: Iterable[str],\n    region: str,\n    num_lines_sniffed: Optional[int] = 1000,\n    sep: Optional[str] = \",\",\n    skip: Optional[int] = 0,\n    colnames: Optional[Iterable[str]] = None,\n) -> Roles:\n    \"\"\"Sniffs a list of CSV files located in an S3 bucket\n    and returns the result as a dictionary of roles.\n\n    Args:\n        bucket:\n            The bucket from which to read the files.\n\n        keys: The list of keys (files in the bucket) to be read.\n\n        region:\n            The region in which the bucket is located.\n\n        num_lines_sniffed:\n            Number of lines analysed by the sniffer.\n\n        sep:\n            The character used for separating fields.\n\n        skip:\n            Number of lines to skip at the beginning of each file.\n\n        colnames: The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n    Returns:\n        Keyword arguments (kwargs) that can be used to construct a DataFrame.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.sniff_s3\"\n\n    cmd[\"bucket_\"] = bucket\n    cmd[\"dialect_\"] = \"python\"\n    cmd[\"keys_\"] = keys\n    cmd[\"num_lines_sniffed_\"] = num_lines_sniffed\n    cmd[\"region_\"] = region\n    cmd[\"sep_\"] = sep\n    cmd[\"skip_\"] = skip\n    cmd[\"conn_id_\"] = \"default\"\n\n    if colnames is not None:\n        cmd[\"colnames_\"] = colnames\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            raise OSError(msg)\n        roles = comm.recv_string(sock)\n\n    return Roles.from_dict(json.loads(roles))\n\n\n# ------------------------------------------------------------\n\n\ndef _to_pyspark(df_or_view: Any, name: str, spark: Any):\n    if df_or_view.ncols() == 0:\n        raise ValueError(\n            \"Cannot transform '\"\n            + df_or_view.name\n            + \"' to pyspark.DataFrame, because it contains no columns.\"\n        )\n    temp_dir = _retrieve_temp_dir()\n    path = (temp_dir / (name + \".parquet\")).as_posix()\n\n    _replace_non_alphanumeric_cols(df_or_view).to_parquet(path, coerce_timestamps=\"ms\")\n    spark_df = spark.read.parquet(path)\n    spark_df.createOrReplaceTempView(name)\n    spark.sql(\"CACHE TABLE `\" + name + \"`;\")\n    os.remove(path)\n    return spark_df\n\n\n# ------------------------------------------------------------\n\n\ndef _transform_col(col, role, is_boolean, is_float, is_string, time_formats):\n    if (is_boolean or is_float) and role in roles_sets.categorical:\n        return col.as_str()  # pytype: disable=attribute-error\n\n    if (is_boolean or is_string) and role in roles_sets.numerical:\n        if role == time_stamp:\n            return col.as_ts(  # pytype: disable=attribute-error\n                time_formats=time_formats\n            )\n\n        return col.as_num()  # pytype: disable=attribute-error\n\n    return col\n\n\n# --------------------------------------------------------------------\n\n\ndef _where(\n    df_or_view: Union[DataFrame, View],\n    indeces_or_mask: Union[\n        numbers.Real, slice, BooleanColumnView, FloatColumnView, FloatColumn\n    ],\n):\n    if isinstance(indeces_or_mask, numbers.Real):\n        index = int(indeces_or_mask)\n        index = index if index >= 0 else len(df_or_view) + index\n        selection = arange(index, index + 1)\n        candidate_view = data.View(base=df_or_view, subselection=selection)\n\n        # in python, a slice's stop can exceed the length of the container,\n        # so we can carry out the bounds check on a minimum subselection\n        # (1 row) to avoid evaluation of the full view\n        if len(candidate_view[:1]) == 0:\n            raise IndexError(\"Index out of bounds.\")\n\n        return candidate_view\n\n    if isinstance(slice_ := indeces_or_mask, slice):\n        start, stop, step = _infer_arange_args_from_slice(slice_, df_or_view)\n        selection = arange(start, stop, step)\n        return data.View(base=df_or_view, subselection=selection)\n\n    if isinstance(\n        mask := indeces_or_mask, (BooleanColumnView, FloatColumn, FloatColumnView)\n    ):\n        return data.View(base=df_or_view, subselection=mask)\n\n    raise TypeError(\n        \"Unsupported type for a subselection: \" + type(indeces_or_mask).__name__\n    )\n\n\n# --------------------------------------------------------------------\n\n\ndef _with_column(\n    col: Union[\n        bool,\n        str,\n        float,\n        int,\n        np.datetime64,\n        FloatColumn,\n        FloatColumnView,\n        StringColumn,\n        StringColumnView,\n        BooleanColumnView,\n    ],\n    name: str,\n    role: Optional[Role] = None,\n    subroles: Optional[Union[Subrole, Iterable[str]]] = None,\n    unit: str = \"\",\n    time_formats: Optional[Iterable[str]] = None,\n):\n    # ------------------------------------------------------------\n\n    if isinstance(col, (bool, str, int, float, numbers.Number, np.datetime64)):\n        col = from_value(col)\n\n    # ------------------------------------------------------------\n\n    if not isinstance(\n        col,\n        (\n            FloatColumn,\n            FloatColumnView,\n            StringColumn,\n            StringColumnView,\n            BooleanColumnView,\n        ),\n    ):\n        raise TypeError(\"'col' must be a getml.data.Column or a value!\")\n\n    # ------------------------------------------------------------\n\n    is_boolean = isinstance(col, BooleanColumnView)\n\n    if not is_boolean:\n        subroles = subroles or col.subroles  # type: ignore\n        unit = unit or col.unit  # type: ignore\n\n    # ------------------------------------------------------------\n\n    subroles = subroles or []\n\n    if isinstance(subroles, str):\n        subroles = [subroles]\n\n    # ------------------------------------------------------------\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    if not _is_typed_list(subroles, str):\n        raise TypeError(\"'subroles' must be of type str, List[str] or None.\")\n\n    if not isinstance(unit, str):\n        raise TypeError(\"'unit' must be of type str\")\n\n    # ------------------------------------------------------------\n\n    invalid_subroles = [r for r in subroles if r not in subroles_sets.all_]\n\n    if invalid_subroles:\n        raise ValueError(\n            \"'subroles' must be from getml.data.subroles, \"\n            + \"meaning it is one of the following: \"\n            + str(subroles_sets.all_)\n            + \", got \"\n            + str(invalid_subroles)\n        )\n\n    # ------------------------------------------------------------\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    # ------------------------------------------------------------\n\n    is_string = \"StringColumn\" in col.cmd[\"type_\"]\n\n    is_float = \"FloatColumn\" in col.cmd[\"type_\"]\n\n    # ------------------------------------------------------------\n\n    correct_coltype = is_boolean or is_string or is_float\n\n    if not correct_coltype:\n        raise TypeError(\"\"\"'col' must be a getml.data.Column.\"\"\")\n\n    # ------------------------------------------------------------\n\n    if role is None:\n        role = unused_float if is_float else unused_string\n\n    Roles.from_dict({role: [name]}).validate()\n\n    # ------------------------------------------------------------\n\n    return (\n        _transform_col(col, role, is_boolean, is_float, is_string, time_formats),\n        role,\n        subroles,\n    )\n\n\n# --------------------------------------------------------------------\n\n\ndef _with_role(\n    base,\n    cols: Union[\n        str,\n        FloatColumn,\n        StringColumn,\n        Union[List[str], List[FloatColumn], List[StringColumn]],\n    ],\n    role: str,\n    time_formats: Optional[List[str]],\n):\n    # ------------------------------------------------------------\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    # ------------------------------------------------------------\n\n    names = _handle_cols(cols)\n\n    if not isinstance(role, str):\n        raise TypeError(\"'role' must be str.\")\n\n    if not _is_non_empty_typed_list(time_formats, str):\n        raise TypeError(\"'time_formats' must be a non-empty list of str\")\n\n    # ------------------------------------------------------------\n\n    for nname in names:\n        if nname not in base.colnames:\n            raise ValueError(\"No column called '\" + nname + \"' found.\")\n\n    if role not in roles_sets.all_:\n        raise ValueError(\n            \"'role' must be one of the following values: \" + str(roles_sets.all_)\n        )\n\n    # ------------------------------------------------------------\n\n    view = base\n\n    for name in names:\n        col = view[name]\n        unit = (\n            constants.TIME_STAMP + constants.COMPARISON_ONLY\n            if role == time_stamp\n            else col.unit\n        )\n        view = view.with_column(\n            col=col,\n            name=name,\n            role=role,\n            subroles=col.subroles,\n            unit=unit,\n            time_formats=time_formats,\n        )\n\n    # ------------------------------------------------------------\n\n    return view\n\n\n# ------------------------------------------------------------\n\n\ndef _with_subroles(\n    base,\n    cols: Union[\n        str,\n        FloatColumn,\n        StringColumn,\n        Union[List[str], List[FloatColumn], List[StringColumn]],\n    ],\n    subroles: Union[Subrole, Iterable[str]],\n    append: bool = False,\n):\n    names = _handle_cols(cols)\n\n    if isinstance(subroles, str):\n        subroles = [subroles]\n\n    if not _is_non_empty_typed_list(names, str):\n        raise TypeError(\"'names' must be either a string or a list of strings.\")\n\n    if not _is_typed_list(subroles, str):\n        raise TypeError(\"'subroles' must be either a string or a list of strings.\")\n\n    if not isinstance(append, bool):\n        raise TypeError(\"'append' must be a bool.\")\n\n    # ------------------------------------------------------------\n\n    if [r for r in subroles if r not in subroles_sets.all_]:\n        raise ValueError(\n            \"'subroles' must be from getml.data.subroles, \"\n            + \"meaning it is one of the following: \"\n            + str(subroles_sets.all_)\n        )\n\n    # ------------------------------------------------------------\n\n    view = base\n\n    for name in names:\n        col = view[name]\n        view = view.with_column(\n            col=col,\n            name=name,\n            role=view.roles.column(name),\n            subroles=subroles,\n            unit=col.unit,\n            time_formats=None,\n        )\n\n    # ------------------------------------------------------------\n\n    return view\n\n\n# ------------------------------------------------------------\n\n\ndef _with_unit(\n    base,\n    cols: Union[\n        str,\n        FloatColumn,\n        StringColumn,\n        Union[List[str], List[FloatColumn], List[StringColumn]],\n    ],\n    unit: str,\n    comparison_only=False,\n):\n    names = _handle_cols(cols)\n\n    if not isinstance(unit, str):\n        raise TypeError(\"Parameter 'unit' must be a str.\")\n\n    # ------------------------------------------------------------\n\n    if comparison_only:\n        unit += constants.COMPARISON_ONLY\n\n    # ------------------------------------------------------------\n\n    view = base\n\n    for name in names:\n        col = view[name]\n        view = view.with_column(\n            col=col,\n            name=name,\n            role=view.roles.column(name),\n            subroles=col.subroles,\n            unit=unit,\n            time_formats=None,\n        )\n\n    # ------------------------------------------------------------\n\n    return view\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/data/helpers2.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Helper functions that depend on the DataFrame class.\"\"\"\n\nimport warnings\nfrom inspect import cleandoc\nfrom typing import Any, Dict, List, Tuple, Union\n\nfrom getml.constants import ANNOTATIONS_DOCS_URL, MULTIPLE_JOIN_KEYS_BEGIN, NO_JOIN_KEY\nfrom getml.data.columns import (\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n    _parse,\n)\nfrom getml.data.data_frame import DataFrame\nfrom getml.data.data_model import DataModel\nfrom getml.data.diagram import _split_multiple_join_keys\nfrom getml.data.helpers import _make_id, list_data_frames\nfrom getml.data.placeholder import Placeholder\nfrom getml.data.roles import target\nfrom getml.data.view import View\nfrom getml.log import logger\n\n# --------------------------------------------------------------------\n\n\ndef _decode_data_model(cmd: Dict[str, Any]):\n    def decode(elem):\n        return (\n            [_decode_placeholder(e) for e in elem]\n            if isinstance(elem, list)\n            else _decode_placeholder(elem)\n        )\n\n    population = _decode_placeholder(cmd)\n    peripheral = (\n        {k: decode(v) for (k, v) in cmd[\"peripherals_\"].items()}\n        if \"peripherals_\" in cmd\n        else {}\n    )\n    data_model = DataModel(population)\n    data_model.peripheral = peripheral\n    return data_model\n\n\n# --------------------------------------------------------------------\n\n\ndef _decode_multiple_join_keys(jk_used, other_jk_used):\n    jks1 = _split_multiple_join_keys(jk_used)\n    jks2 = _split_multiple_join_keys(other_jk_used)\n\n    if len(jks1) != len(jks2):\n        raise ValueError(\n            \"Number of multiple join keys does not match: \"\n            + str(jks1)\n            + \" vs. \"\n            + str(jks2)\n            + \".\"\n        )\n\n    return list(zip(jks1, jks2))\n\n\n# --------------------------------------------------------------------\n\n\ndef _decode_placeholder(dictionary):\n    ph_new = Placeholder(dictionary[\"name_\"], dictionary.get(\"roles_\", None))\n\n    def ts_map(ts):\n        return None if ts == (\"\", \"\") else ts\n\n    def jk_map(jk):\n        if MULTIPLE_JOIN_KEYS_BEGIN in jk[0]:\n            return _decode_multiple_join_keys(jk[0], jk[1])\n        return None if jk == (NO_JOIN_KEY, NO_JOIN_KEY) else jk\n\n    for i, perph in enumerate(dictionary[\"joined_tables_\"]):\n        perph_new = _decode_placeholder(perph)\n\n        ph_new.join(\n            perph_new,\n            on=jk_map(\n                (\n                    dictionary[\"join_keys_used_\"][i],\n                    dictionary[\"other_join_keys_used_\"][i],\n                )\n            ),\n            time_stamps=ts_map(\n                (\n                    dictionary[\"time_stamps_used_\"][i],\n                    dictionary[\"other_time_stamps_used_\"][i],\n                )\n            ),\n            upper_time_stamp=dictionary[\"upper_time_stamps_used_\"][i],\n            relationship=dictionary[\"relationship_\"][i],\n            memory=dictionary[\"memory_\"][i],\n            horizon=dictionary[\"horizon_\"][i],\n            lagged_targets=dictionary[\"allow_lagged_targets_\"][i],\n        )\n\n    return ph_new\n\n\n# -----------------------------------------------------------------\n\n\ndef _deep_copy(df_or_view, container_id):\n    if isinstance(df_or_view, DataFrame):\n        return df_or_view.copy(\"container-\" + container_id + \"-\" + df_or_view.name)\n\n    if isinstance(df_or_view, View):\n        return df_or_view.to_df(\"container-\" + container_id + \"-\" + _make_id())\n\n\n# -----------------------------------------------------------------\n\n\ndef _get_last_change(population, peripheral, subsets):\n    return max(\n        ([population.last_change] if population is not None else [])\n        + [v.last_change for (k, v) in peripheral.items()]\n        + [v.last_change for (k, v) in subsets.items()]\n    )\n\n\n# -----------------------------------------------------------------\n\n\ndef _make_subsets_from_kwargs(train, validation, test, **kwargs):\n    tvt = {\n        \"train\": train,\n        \"validation\": validation,\n        \"test\": test,\n    }\n    tvt = {k: v for (k, v) in tvt.items() if v is not None}\n    return {**tvt, **kwargs}\n\n\n# -----------------------------------------------------------------\n\n\ndef _make_subsets_from_split(\n    population: Union[DataFrame, View], split: Union[StringColumn, StringColumnView]\n) -> Dict[str, Tuple[int, Union[DataFrame, View]]]:\n    nrows = len(population)\n    unique = set(split[:nrows].unique())  # type: ignore\n\n    subsets = {}\n\n    for name in unique:\n        len_set = int((split[:nrows] == name).as_num().sum())\n        subset_data = population.drop(population.roles.unused)[split == name]\n        if not subset_data:\n            msg = f\"\\nSubset '{name}' is empty.\"\n            if not subset_data.columns:\n                msg = cleandoc(\n                    \"\"\"\n                    {msg} Have you forgotten to set the roles? Refer to\n                    {annotations_docs_url} for more information on\n                    annotating data for getML.\n                    \"\"\"\n                ).format(msg=msg, annotations_docs_url=ANNOTATIONS_DOCS_URL)\n\n            warnings.warn(msg, UserWarning)\n        else:\n            subsets[name] = (len_set, subset_data)\n\n    if not subsets:\n        raise ValueError(\"All subsets are empty.\")\n\n    return subsets\n\n\n# -----------------------------------------------------------------\n\n\ndef load_data_frame(name: str) -> DataFrame:\n    \"\"\"Retrieves a [`DataFrame`][getml.DataFrame] handler of data in the\n    getML Engine.\n\n    A data frame object can be loaded regardless if it is held in\n    memory or not. It only has to be present in the current project\n    and thus listed in the output of\n    [`list_data_frames`][getml.data.list_data_frames].\n\n    Args:\n        name:\n            Name of the data frame.\n\n    Returns:\n            Handle the underlying data frame in the getML Engine.\n\n    ??? example\n        ```python\n        d, _ = getml.datasets.make_numerical(population_name = 'test')\n        d2 = getml.data.load_data_frame('test')\n        ```\n    \"\"\"\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    data_frames_available = list_data_frames()\n\n    if name in data_frames_available[\"in_memory\"]:\n        return DataFrame(name).refresh()\n\n    if name in data_frames_available[\"on_disk\"]:\n        return DataFrame(name).load()\n\n    raise ValueError(\n        \"No data frame holding the name '\" + name + \"' present on the getML Engine.\"\n    )\n\n\n# --------------------------------------------------------------------\n\n\ndef _load_dependent_data_frames(cmd):\n    if \"df_name_\" in cmd:\n        load_data_frame(cmd[\"df_name_\"])\n    for _, value in cmd.items():\n        if isinstance(value, dict):\n            _load_dependent_data_frames(value)\n\n\n# --------------------------------------------------------------------\n\n\ndef _load_added(cmd):\n    if \"added_\" not in cmd:\n        return None\n    _load_dependent_data_frames(cmd[\"added_\"][\"col_\"])\n    col = _parse(cmd[\"added_\"][\"col_\"])\n    added = cmd[\"added_\"]\n    added[\"col_\"] = col\n    return added\n\n\n# --------------------------------------------------------------------\n\n\ndef _load_subselection(cmd):\n    if \"subselection_\" not in cmd:\n        return None\n    _load_dependent_data_frames(cmd[\"subselection_\"])\n    return _parse(cmd[\"subselection_\"])\n\n\n# --------------------------------------------------------------------\n\n\ndef _load_view(cmd):\n    typ = cmd[\"type_\"]\n\n    if typ == \"DataFrame\":\n        return load_data_frame(cmd[\"name_\"])\n\n    if typ != \"View\":\n        raise ValueError(\"Unknown type: '\" + typ + \"'.\")\n\n    added = _load_added(cmd)\n    subselection = _load_subselection(cmd)\n    dropped = cmd[\"dropped_\"]\n    name = cmd[\"name_\"]\n    base = _load_view(cmd[\"base_\"])\n\n    return View(\n        base=base, name=name, subselection=subselection, added=added, dropped=dropped\n    )\n\n\n# --------------------------------------------------------------------\n\n\ndef exists(name: str):\n    \"\"\"\n    Returns true if a data frame named 'name' exists.\n\n    Args:\n        name:\n            Name of the data frame.\n    \"\"\"\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    all_df = list_data_frames()\n\n    return name in (all_df[\"in_memory\"] + all_df[\"on_disk\"])\n\n\n# --------------------------------------------------------------------\n\n\ndef delete(name: str):\n    \"\"\"\n    If a data frame named 'name' exists, it is deleted.\n\n    Args:\n        name:\n            Name of the data frame.\n    \"\"\"\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    if exists(name):\n        DataFrame(name).delete()\n\n\n# --------------------------------------------------------------------\n\n\ndef make_target_columns(base: Union[DataFrame, View], colname: str) -> View:\n    \"\"\"\n    Returns a view containing binary target columns.\n\n    getML expects binary target columns for classification problems. This\n    helper function allows you to split up a column into such binary\n    target columns.\n\n    Args:\n        base:\n            The original view or data frame. `base` will remain unaffected\n            by this function, instead you will get a view with the appropriate\n            changes.\n\n        colname: The column you would like to split. A column named\n            `colname` should appear on `base`.\n\n    Returns:\n        A view containing binary target columns.\n    \"\"\"\n    if not isinstance(\n        base[colname], (FloatColumn, FloatColumnView, StringColumn, StringColumnView)\n    ):\n        raise TypeError(\n            \"'\"\n            + colname\n            + \"' must be a FloatColumn, a FloatColumnView, \"\n            + \"a StringColumn or a StringColumnView.\"\n        )\n\n    unique_values = base[colname].unique()\n\n    if len(unique_values) > 10:\n        logger.warning(\n            \"You are splitting the column into more than 10 target \"\n            + \"columns. This might take a long time to fit.\"\n        )\n\n    view = base\n\n    for label in unique_values:\n        col = (base[colname] == label).as_num()\n        name = colname + \"=\" + label\n        view = view.with_column(col=col, name=name, role=target)\n\n    return view.drop(colname)\n\n\n# --------------------------------------------------------------------\n\n\ndef to_placeholder(\n    *args: Union[DataFrame, View, List[Union[DataFrame, View]]],\n    **kwargs: Union[DataFrame, View, List[Union[DataFrame, View]]],\n) -> List[Placeholder]:\n    \"\"\"\n    Factory function for extracting placeholders from a\n    [`DataFrame`][getml.DataFrame] or [`View`][getml.data.View].\n\n    Args:\n        args:\n            The data frames or views you would like to convert to placeholders.\n\n        kwargs:\n            The data frames or views you would like to convert to placeholders.\n\n    Returns:\n        A list of placeholders.\n\n    ??? example\n        Suppose we wanted to create a [`DataModel`][getml.data.DataModel]:\n\n\n\n            dm = getml.data.DataModel(\n                population_train.to_placeholder(\"population\")\n            )\n\n            # Add placeholders for the peripheral tables.\n            dm.add(meta.to_placeholder(\"meta\"))\n            dm.add(order.to_placeholder(\"order\"))\n            dm.add(trans.to_placeholder(\"trans\"))\n\n        But this is a bit repetitive. So instead, we can do\n        the following:\n        ```python\n        dm = getml.data.DataModel(\n            population_train.to_placeholder(\"population\")\n        )\n\n        # Add placeholders for the peripheral tables.\n        dm.add(getml.data.to_placeholder(\n            meta=meta, order=order, trans=trans))\n        ```\n    \"\"\"\n\n    def to_ph_list(list_or_elem, key=None):\n        as_list = list_or_elem if isinstance(list_or_elem, list) else [list_or_elem]\n        return [elem.to_placeholder(key) for elem in as_list]\n\n    return [elem for item in args for elem in to_ph_list(item)] + [\n        elem for (k, v) in kwargs.items() for elem in to_ph_list(v, k)\n    ]\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/data/load_container.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Loads a container.\"\"\"\n\nimport json\nfrom typing import Any, Dict\n\nimport getml.communication as comm\nfrom getml.data.columns import _parse\n\nfrom .container import Container\nfrom .helpers2 import _load_view\n\n\ndef load_container(container_id: str) -> Container:\n    \"\"\"\n    Loads a container and all associated data frames from disk.\n\n    Args:\n        container_id:\n            The id of the container you would like to load.\n\n    Returns:\n        The container with the given id.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"DataContainer.load\"\n    cmd[\"name_\"] = container_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    cmd = json.loads(json_str)\n\n    population = _load_view(cmd[\"population_\"]) if \"population_\" in cmd else None\n\n    peripheral = {k: _load_view(v) for (k, v) in cmd[\"peripheral_\"].items()}\n\n    subsets = {k: _load_view(v) for (k, v) in cmd[\"subsets_\"].items()}\n\n    split = _parse(cmd[\"split_\"]) if \"split_\" in cmd else None\n\n    deep_copy = cmd[\"deep_copy_\"]\n    frozen_time = cmd[\"frozen_time_\"] if \"frozen_time_\" in cmd else None\n    last_change = cmd[\"last_change_\"]\n\n    container = Container(\n        population=population, peripheral=peripheral, deep_copy=deep_copy, **subsets\n    )\n\n    container._id = container_id\n    container._frozen_time = frozen_time\n    container._split = split\n    container._last_change = last_change\n\n    return container\n"
  },
  {
    "path": "src/python-api/getml/data/placeholder.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Abstract representation of tables and their relations.\"\"\"\n\n# ------------------------------------------------------------------------------\n\nfrom dataclasses import dataclass, field, fields\nfrom inspect import cleandoc\nfrom textwrap import indent\nfrom typing import Dict, List, Optional, Tuple, Union, cast\n\nfrom getml.utilities.formatting import _SignatureFormatter\n\nfrom .helpers import (\n    OnType,\n    _check_join_key,\n    _handle_on,\n    _handle_ts,\n    _serialize_join_keys,\n)\nfrom .relationship import many_to_many\nfrom .roles.container import Roles\n\n# --------------------------------------------------------------------\n\nTimeStampsType = Optional[Union[str, Tuple[str, str]]]\n\"\"\"\nTypes of time stamps used in joins.\n\"\"\"\n\n# ------------------------------------------------------------------------------\n\n\nclass Placeholder:\n    \"\"\"Abstract representation of tables and their relations.\n\n    This class is an abstract representation of the\n    [`DataFrame`][getml.DataFrame] or [`View`][getml.data.View].\n    However, it does not contain any actual data.\n\n    You might also want to refer to [`DataModel`][getml.data.DataModel].\n\n    Attributes:\n        name:\n            The name used for this placeholder. This name will appear\n            in the generated SQL code.\n\n        roles:\n            The roles of the columns in this placeholder. If you pass\n            a dictionary, the keys must be the column names and the\n            values must be lists of roles. If you pass a [`Roles`][getml.data.Roles]\n            object, it will be used as is.\n\n    ??? example\n        This example will construct a data model in which the\n        'population_table' depends on the 'peripheral_table' via\n        the 'join_key' column. In addition, only those rows in\n        'peripheral_table' for which 'time_stamp' is smaller or\n        equal to the 'time_stamp' in 'population_table' are considered:\n        ```python\n        dm = getml.data.DataModel(\n            population_table.to_placeholder(\"POPULATION\")\n        )\n\n        dm.add(peripheral_table.to_placeholder(\"PERIPHERAL\"))\n\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\"\n        )\n        ```\n        If you want to add more than one peripheral table, you can\n        use [`to_placeholder`][getml.data.to_placeholder]:\n        ```python\n        dm = getml.data.DataModel(\n            population_table.to_placeholder(\"POPULATION\")\n        )\n\n        dm.add(\n            getml.data.to_placeholder(\n                PERIPHERAL1=peripheral_table_1,\n                PERIPHERAL2=peripheral_table_2,\n            )\n        )\n        ```\n        If the relationship between two tables is many-to-one or one-to-one\n        you should clearly say so:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            relationship=getml.data.relationship.many_to_one,\n        )\n        ```\n        Please also refer to [`relationship`][getml.data.relationship].\n\n        If the join keys or time stamps are named differently in the two\n        different tables, use a tuple:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=(\"join_key\", \"other_join_key\"),\n            time_stamps=(\"time_stamp\", \"other_time_stamp\"),\n        )\n        ```\n        You can join over more than one join key:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=[\"join_key1\", \"join_key2\", (\"join_key3\", \"other_join_key3\")],\n            time_stamps=\"time_stamp\",\n        )\n        ```\n        You can also limit the scope of your joins using *memory*. This\n        can significantly speed up training time. For instance, if you\n        only want to consider data from the last seven days, you could\n        do something like this:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            memory=getml.data.time.days(7),\n        )\n        ```\n        In some use cases, particularly those involving time series, it\n        might be a good idea to use targets from the past. You can activate\n        this using *lagged_targets*. But if you do that, you must\n        also define a prediction *horizon*. For instance, if you want to\n        predict data for the next hour, using data from the last seven days,\n        you could do this:\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            lagged_targets=True,\n            horizon=getml.data.time.hours(1),\n            memory=getml.data.time.days(7),\n        )\n        ```\n        Please also refer to [`time`][getml.data.time].\n\n        If the join involves many matches, it might be a good idea to set the\n        relationship to [`propositionalization`][getml.data.relationship.propositionalization].\n        This forces the pipeline to always use a propositionalization\n        algorithm for this join, which can significantly speed things up.\n        ```python\n        dm.POPULATION.join(\n            dm.PERIPHERAL,\n            on=\"join_key\",\n            time_stamps=\"time_stamp\",\n            relationship=getml.data.relationship.propositionalization,\n        )\n        ```\n        Please also refer to [`relationship`][getml.data.relationship].\n\n        In some cases, it is necessary to have more than one placeholder\n        on the same table. This is necessary to create more complicated\n        data models. In this case, you can do something like this:\n        ```python\n        dm.add(\n            getml.data.to_placeholder(\n                PERIPHERAL=[peripheral_table]*2,\n            )\n        )\n\n        # We can now access our two placeholders like this:\n        placeholder1 = dm.PERIPHERAL[0]\n        placeholder2 = dm.PERIPHERAL[1]\n        ```\n        If you want to check out a real-world example where this\n        is necessary, refer to the\n        [CORA notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/cora.ipynb).\n    \"\"\"\n\n    def __init__(\n        self, name: str, roles: Optional[Union[Roles, Dict[str, List[str]]]] = None\n    ):\n        self._name = name\n\n        if roles is None:\n            self._roles: Roles = Roles()\n        elif isinstance(roles, dict):\n            self._roles = Roles(**roles)\n        else:\n            self._roles = roles\n\n        self.joins: List[Join] = []\n        self.parent = None\n\n    def __dir__(self):\n        attrs = dir(type(self)) + list(self.__dict__.keys())\n        attrs.extend(col for col in self.columns if col.isidentifier())\n        return attrs\n\n    def __getattr__(self, key):\n        try:\n            return self[key]\n        except KeyError:\n            super().__getattribute__(key)\n\n    def __getitem__(self, key):\n        if key in vars(self)[\"_roles\"].columns:\n            return key\n        else:\n            raise KeyError(\n                f\"No column with with name {key!r} on the Placeholder's signature.\"\n            )\n\n    def _getml_deserialize(self):\n        allow_lagged_targets = []\n        horizon = []\n        join_keys_used = []\n        joined_tables = []\n        memory = []\n        other_join_keys_used = []\n        other_time_stamps_used = []\n        relationship = []\n        time_stamps_used = []\n        upper_time_stamps_used = []\n\n        for join in self.joins:\n            allow_lagged_targets.append(join.lagged_targets)\n            horizon.append(join.horizon or 0.0)\n            left_keys, right_keys = _serialize_join_keys(join.on)\n            join_keys_used.append(left_keys)\n            other_join_keys_used.append(right_keys)\n            joined_tables.append(join.right._getml_deserialize())\n            memory.append(join.memory or 0.0)\n            left_ts, right_ts = _handle_ts(join.time_stamps)\n            time_stamps_used.append(left_ts)\n            other_time_stamps_used.append(right_ts)\n            relationship.append(join.relationship)\n            upper_time_stamps_used.append(join.upper_time_stamp or \"\")\n\n        cmd = {\n            \"name_\": self.name,\n            \"roles_\": self.roles.to_dict(),\n            \"allow_lagged_targets_\": allow_lagged_targets,\n            \"horizon_\": horizon,\n            \"join_keys_used_\": join_keys_used,\n            \"joined_tables_\": joined_tables,\n            \"memory_\": memory,\n            \"other_join_keys_used_\": other_join_keys_used,\n            \"other_time_stamps_used_\": other_time_stamps_used,\n            \"relationship_\": relationship,\n            \"time_stamps_used_\": time_stamps_used,\n            \"upper_time_stamps_used_\": upper_time_stamps_used,\n        }\n\n        return cmd\n\n    def __repr__(self) -> str:\n        template = cleandoc(\n            \"\"\"\n            {name}:\n              columns:\n            {cols}\n            \"\"\"\n        )\n\n        if self.joins:\n            template += \"\\n\\n\" + cleandoc(\n                \"\"\"\n                  joins:\n                {joins}\n                \"\"\"\n            )\n\n        def format_on(on, join: Join):\n            template = \"({left.name}.{on[0]}, {join.right.name}.{on[1]})\"\n\n            if isinstance(on, list) and all(isinstance(key, tuple) for key in on):\n                formatted = \"\\n- \" + \"\\n- \".join(\n                    template.format(on=keys, left=self, join=join) for keys in on\n                )\n                return indent(formatted, \" \" * 2)\n\n            return template.format(on=on, left=self, join=join)\n\n        cols = [\n            f\"- {col}: {role}\" for col, role in zip(self.columns, self.roles.to_list())\n        ]\n\n        if len(cols) > 5:\n            cols = cols[:5] + [\"- ...\"]\n\n        joins = []\n\n        for join in self.joins:\n            for param, value in vars(join).items():\n                if param == \"right\":\n                    joins.append(f\"- right: {join.right.name!r}\")\n                    continue\n\n                if value is not None:\n                    if param == \"on\":\n                        joins.append(f\"  on: {format_on(value, join)}\")\n                    elif param == \"time_stamps\":\n                        joins.append(\n                            f\"  {param}: ({self.name}.{value[0]}, {join.right.name}.{value[1]})\"\n                        )\n                    else:\n                        joins.append(f\"  {param}: {value!r}\")\n\n        joins = indent(\"\\n\".join(joins), \" \" * 2)  # type: ignore\n\n        cols = indent(\"\\n\".join(cols), \" \" * 2)  # type: ignore\n\n        return template.format(name=self.name, cols=cols, joins=joins)\n\n    def _ipython_key_completions_(self):\n        return self.columns\n\n    @property\n    def children(self):\n        return set([self]) ^ set(self.to_list())\n\n    @property\n    def name(self) -> str:\n        return self._name\n\n    def join(\n        self,\n        right: \"Placeholder\",\n        on: OnType = None,\n        time_stamps: TimeStampsType = None,\n        relationship: str = many_to_many,\n        memory: Optional[float] = None,\n        horizon: Optional[float] = None,\n        lagged_targets: bool = False,\n        upper_time_stamp: Optional[str] = None,\n    ):\n        \"\"\"\n        Joins another to placeholder to this placeholder.\n\n        Args:\n            right:\n                The placeholder you would like to join.\n\n            on:\n                The join keys to use. If none is passed, then everything\n                will be joined to everything else.\n\n            time_stamps:\n                The time stamps used to limit the join.\n\n            relationship:\n                The relationship between the two tables. Must be from\n                [`relationship`][getml.data.relationship].\n\n            memory:\n                The difference between the time stamps until data is 'forgotten'.\n                Limiting your joins using memory can significantly speed up\n                training time. Also refer to [`time`][getml.data.time].\n\n            horizon:\n                The prediction horizon to apply to this join.\n                Also refer to [`time`][getml.data.time].\n\n            lagged_targets:\n                Whether you want to allow lagged targets. If this is set to True,\n                you must also pass a positive, non-zero *horizon*.\n\n            upper_time_stamp:\n                Name of a time stamp in *right* that serves as an upper limit\n                on the join.\n        \"\"\"\n\n        if not isinstance(right, type(self)):\n            msg = (\n                \"'right' must be a getml.data.Placeholder. \"\n                + \"You can create a placeholder by calling .to_placeholder() \"\n                + \"on DataFrames or Views.\"\n            )\n            raise TypeError(msg)\n\n        if self in right.to_list():\n            raise ValueError(\n                \"Cicular references to other placeholders are not allowed.\"\n            )\n\n        sanitized_on = _handle_on(on)\n\n        if isinstance(time_stamps, str):\n            time_stamps = (time_stamps, time_stamps)\n\n        keys_by_ph = list(zip(*sanitized_on))\n\n        for i, ph in enumerate([self, right]):\n            if ph.roles.join_key:\n                if ph_keys := keys_by_ph[i]:\n                    _check_join_key(ph_keys, ph.roles, ph.name)\n\n            if ph.roles.time_stamp and time_stamps:\n                if time_stamps[i] not in ph.roles.time_stamp:\n                    raise ValueError(f\"Not a time stamp: {time_stamps[i]}.\")\n\n        if lagged_targets and horizon in (0.0, None):\n            raise ValueError(\n                \"Setting 'lagged_targets' to True requires a positive, non-zero \"\n                \"'horizon'  to avoid data leakage.\"\n            )\n\n        if horizon not in (0.0, None) and time_stamps is None:\n            raise ValueError(\n                \"Setting 'horizon' (i.e. a relative look-back window) requires \"\n                \"setting 'time_stamps'.\"\n            )\n\n        if memory not in (0.0, None) and time_stamps is None:\n            raise ValueError(\"Setting 'horizon' requires setting 'time_stamps'.\")\n\n        join = Join(\n            right=right,\n            on=sanitized_on,\n            time_stamps=time_stamps,\n            relationship=relationship,\n            memory=memory,\n            horizon=horizon,\n            lagged_targets=lagged_targets,\n            upper_time_stamp=upper_time_stamp,\n        )\n\n        if any(join == existing for existing in self.joins):\n            raise ValueError(\n                \"A join with the following set of parameters already exists on \"\n                f\"the placeholder {self.name!r}:\"\n                f\"\\n\\n{join}\\n\\n\"\n                \"Redundant joins are not allowed.\"\n            )\n\n        self.joins.append(join)\n        right.parent = self  # type: ignore\n\n    @property\n    def population(self):\n        if self.parent is None:\n            return self\n        return self.parent.population\n\n    @property\n    def roles(self) -> Roles:\n        return self._roles\n\n    @roles.setter\n    def roles(self, roles):\n        if not isinstance(roles, (Roles, dict)):\n            raise TypeError(\"'roles' must be a dict or getml.data.Roles\")\n        if isinstance(roles, dict):\n            self._roles = Roles(**roles)\n        else:\n            self._roles = roles\n\n    def to_list(self):\n        \"\"\"\n        Returns a list of this placeholder and all of its descendants.\n        \"\"\"\n        return [self] + [ph for join in self.joins for ph in join.right.to_list()]\n\n    def to_dict(self):\n        \"\"\"\n        Expresses this placeholder and all of its descendants as a dictionary.\n        \"\"\"\n        phs = {}\n        for ph in self.to_list():\n            key = ph.name\n            if ph.children:\n                i = 2\n                while key in phs:\n                    key = f\"{ph.name}{i}\"\n                    i += 1\n            phs[key] = ph\n        return phs\n\n    @property\n    def columns(self):\n        return self.roles.columns\n\n\n# ------------------------------------------------------------------------------\n\n\n@dataclass\nclass Join:\n    right: Placeholder\n    on: Union[List[Tuple[str, str]], List[Tuple[None, None]]] = field(\n        default_factory=lambda: [(None, None)]\n    )\n    time_stamps: TimeStampsType = None\n    upper_time_stamp: Optional[str] = \"\"\n    relationship: Optional[str] = many_to_many\n    memory: Optional[float] = None\n    horizon: Optional[float] = None\n    lagged_targets: Optional[bool] = None\n\n    def __eq__(self, other):\n        return vars(self) == vars(other)\n\n    def __getitem__(self, key):\n        try:\n            return getattr(self, key)\n        except TypeError:\n            raise KeyError(key)\n\n    def __iter__(self):\n        yield from vars(self)\n\n    def __len__(self):\n        return len(fields(self))\n\n    def __repr__(self):\n        sig = _SignatureFormatter(self)\n        sig.data[\"right\"] = self.right.name\n\n        return sig._format()\n"
  },
  {
    "path": "src/python-api/getml/data/relationship.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nMarks the relationship between joins in [`Placeholder`][getml.data.Placeholder]\n\"\"\"\n\nfrom typing import Literal, Union\n\nManyToMany = Literal[\"many-to-many\"]\nmany_to_many: ManyToMany = \"many-to-many\"\n\"\"\"Used for one-to-many or many-to-many relationships.\n\nWhen there is such a relationship, feature learning\nis necessary and meaningful. If you mark a join as\na default relationship, but that assumption is violated\nfor the training data, the pipeline will raise a\nwarning.\n\"\"\"\n\nManyToONE = Literal[\"many-to-one\"]\nmany_to_one: ManyToONE = \"many-to-one\"\n\"\"\"Used for many-to-one relationships.\n\nIf two tables are guaranteed to be in a\nmany-to-one relationship, then feature\nlearning is not necessary as they can\nsimply be joined. If a relationship is\nmarked many-to-one, but the assumption is\nviolated, the pipeline will raise\nan exception.\n\"\"\"\n\nOneToMany = Literal[\"one-to-many\"]\none_to_many: OneToMany = \"one-to-many\"\n\"\"\"Used for one-to-many or many-to-many relationships.\n\nWhen there is such a relationship, feature learning\nis necessary and meaningful. If you mark a join as\na default relationship, but that assumption is violated\nfor the training data, the pipeline will raise a\nwarning.\n\"\"\"\n\nOneToOne = Literal[\"one-to-one\"]\none_to_one: OneToOne = \"one-to-one\"\n\"\"\"Used for one-to-one relationships.\n\nIf two tables are guaranteed to be in a\none-to-one relationship, then feature\nlearning is not necessary as they can\nsimply be joined. If a relationship is\nmarked one-to-one, but the assumption is\nviolated, the pipeline will raise\nan exception. If you are unsure whether\nyou want to use many_to_one or one_to_one,\nuser many_to_one.\n\"\"\"\n\nPropositionalization = Literal[\"propositionalization\"]\npropositionalization: Propositionalization = \"propositionalization\"\n\"\"\"Used for one-to-many or many-to-many relationships.\n\nThe flag means that you want a propositionalization\nalgorithm to be used for this particular join.\nThis is recommended when there are very many matches\nwithin the join and normal algorithms would take\ntoo long.\n\"\"\"\n\n_all_relationships_types = Union[\n    ManyToMany, ManyToONE, OneToMany, OneToOne, Propositionalization\n]\n_all_relationships = [\n    many_to_many,\n    many_to_one,\n    one_to_many,\n    one_to_one,\n    propositionalization,\n]\n"
  },
  {
    "path": "src/python-api/getml/data/roles/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom getml.data.roles.roles import (\n    categorical,\n    join_key,\n    numerical,\n    target,\n    text,\n    time_stamp,\n    unused_float,\n    unused_string,\n)\n\n__all__ = (\n    \"categorical\",\n    \"join_key\",\n    \"numerical\",\n    \"target\",\n    \"text\",\n    \"time_stamp\",\n    \"unused_float\",\n    \"unused_string\",\n)\n"
  },
  {
    "path": "src/python-api/getml/data/roles/container.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDataclass for handling the roles.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport warnings\nfrom dataclasses import dataclass, field, fields\nfrom inspect import cleandoc\nfrom typing import Dict, Iterable, Iterator, List, Mapping, Tuple, Union, cast\n\nfrom getml.data.roles import sets as roles_sets\nfrom getml.data.roles.types import Role\nfrom getml.helpers import _is_iterable_not_str_of_type\n\nINVALID_ROLE_ERROR_MESSAGE_TEMPLATE = cleandoc(\n    \"\"\"\n    '{{candidate_role}}' is not a proper role. Please provide a dictionary with valid\n    roles as keys and lists of column names as values.\n\n    Possible roles are: {valid_roles}\n    \"\"\"\n).format(valid_roles=f\"{{{set(roles_sets.all_)!r}}}\")\n\n\n@dataclass(frozen=True)\nclass Roles:\n    \"\"\"\n    Roles can be passed to [`DataFrame`][getml.DataFrame] to\n    predefine the roles assigned to certain columns.\n\n    Attributes:\n        categorical:\n            Names of the categorical columns.\n\n        join_key:\n            Names of the join key columns.\n\n        numerical:\n            Names of the numerical columns.\n\n        target:\n            Names of the target columns.\n\n        text:\n            Names of the text columns.\n\n        time_stamp:\n            Names of the time stamp columns.\n\n        unused_float:\n            Names of the unused float columns.\n\n        unused_string:\n            Names of the unused string columns.\n\n\n\n    ??? example\n        ```python\n        roles = getml.data.Roles(\n            categorical=[\"col1\", \"col2\"], target=[\"col3\"]\n        )\n\n        df_expd = data.DataFrame.from_csv(\n            fnames=[\"file1.csv\", \"file2.csv\"],\n            name=\"MY DATA FRAME\",\n            sep=';',\n            quotechar='\"',\n            roles=roles\n        )\n        ```\n    \"\"\"\n\n    categorical: Iterable[str] = field(default_factory=tuple)\n    join_key: Iterable[str] = field(default_factory=tuple)\n    numerical: Iterable[str] = field(default_factory=tuple)\n    target: Iterable[str] = field(default_factory=tuple)\n    text: Iterable[str] = field(default_factory=tuple)\n    time_stamp: Iterable[str] = field(default_factory=tuple)\n    unused_float: Iterable[str] = field(default_factory=tuple)\n    unused_string: Iterable[str] = field(default_factory=tuple)\n\n    def __post_init__(self):\n        self.validate()\n\n        for field in fields(self):\n            if not isinstance(value := getattr(self, field.name), list):\n                object.__setattr__(self, field.name, list(value))\n\n    def __getitem__(self, key) -> Tuple[str, ...]:\n        try:\n            return getattr(self, key)\n        except TypeError:\n            raise KeyError(key)\n\n    def __iter__(self) -> Iterator[Role]:\n        return cast(Iterator[Role], (field_.name for field_ in fields(self)))\n\n    def __len__(self) -> int:\n        return len(fields(self))\n\n    def __repr__(self) -> str:\n        template = cleandoc(\n            \"\"\"\n            {role}:\n              - {cols}\n            \"\"\"\n        )\n\n        blocks = []\n\n        for role in self:\n            if self[role]:\n                cols = \"\\n  - \".join(self[role])\n                blocks.append(template.format(role=role, cols=cols))\n\n        return \"\\n\".join(blocks)\n\n    @property\n    def columns(self) -> Tuple[str, ...]:\n        \"\"\"\n        The name of all columns contained in the roles object.\n\n        Returns:\n            The names of all columns.\n        \"\"\"\n        return tuple(column for role in self for column in self[role])\n\n    def column(self, colname: str) -> Role:\n        \"\"\"\n        Gets the role of a column by its column name.\n\n        Args:\n            colname:\n                The name of the column.\n\n        Returns:\n            The role of the column as a string.\n        \"\"\"\n        for role in self:\n            if colname in self[role]:\n                return role\n        raise ValueError(\"Column named '\" + colname + \"' not found.\")\n\n    @classmethod\n    def from_dict(cls, roles_dict: Mapping[Union[Role, str], Iterable[str]]) -> Roles:\n        \"\"\"\n        Creates a roles object from a dictionary.\n\n        Args:\n            roles_dict:\n                A dictionary where keys are role names and values are lists of column names.\n\n        Returns:\n            A roles object.\n        \"\"\"\n        roles: Dict[Role, List[str]] = {}\n        for role in roles_dict:\n            if role not in roles_sets.all_:\n                raise ValueError(\n                    INVALID_ROLE_ERROR_MESSAGE_TEMPLATE.format(candidate_role=role)\n                )\n            roles[role] = list(roles_dict[role])\n\n        return cls(**roles)\n\n    @classmethod\n    def from_mapping(cls, roles_mapping: Mapping[str, Role]) -> Roles:\n        \"\"\"\n        Creates a roles object from a mapping of column names to roles.\n\n        Args:\n            roles_mapping:\n                A dictionary where keys are column names and values are role names.\n\n        Returns:\n            A roles object.\n        \"\"\"\n        roles: Dict[Role, List[str]] = {\n            cast(Role, field.name): [] for field in fields(cls)\n        }\n        for column, role in roles_mapping.items():\n            roles[role].append(column)\n        return cls.from_dict(roles)\n\n    def infer(self, colname: str) -> Role:\n        \"\"\"\n        Infers the role of a column by its name.\n\n        Args:\n            colname:\n                The name of the column to be inferred.\n\n        Returns:\n            The role of the column as a string.\n        \"\"\"\n        warnings.warn(\n            \"The 'infer' method is deprecated and will be removed in a future \"\n            \"release. To get a specific column's role, use 'column' instead.\",\n            DeprecationWarning,\n        )\n        return self.column(colname)\n\n    def to_dict(self) -> Dict[Role, List[str]]:\n        \"\"\"\n        Expresses the roles object as a dictionary.\n\n        Returns:\n            A dictionary where keys are role names and values are lists of column names.\n        \"\"\"\n        return {role: list(self[role]) for role in self}\n\n    def to_list(self) -> List[Role]:\n        \"\"\"\n        Returns a list containing the roles, without the corresponding\n        columns names.\n\n        Returns:\n            A list where each element is a role name, repeated by the number of columns in that role.\n        \"\"\"\n        return [role for role in self for _ in self[role]]\n\n    def to_mapping(self) -> Dict[str, Role]:\n        \"\"\"\n        Maps column names to their roles.\n\n        Returns:\n            A dictionary where keys are column names and values are role names.\n        \"\"\"\n        return {column: role for role in self for column in self[role]}\n\n    @property\n    def unused(self) -> List[str]:\n        \"\"\"\n        Names of all unused columns (unused_float + unused_string).\n\n        Returns:\n            A list of column names that are categorized as unused, combining both float and string types.\n        \"\"\"\n        return [*self.unused_float, *self.unused_string]\n\n    def update(self, other: Roles) -> Roles:\n        \"\"\"\n        Merges the roles of two roles objects.\n\n        Args:\n            other:\n                The roles object to be merged with the current one.\n\n        Returns:\n            A new roles object containing the merged roles.\n        \"\"\"\n\n        current = self.to_mapping()\n        new = other.to_mapping()\n\n        updated: dict[str, Role] = {**current, **new}\n\n        return Roles.from_mapping(updated)\n\n    def validate(self) -> None:\n        \"\"\"\n        Checks if the roles are consistent.\n\n        Raises:\n            ValueError:\n                If the roles are inconsistent.\n        \"\"\"\n\n        seen = dict()\n\n        for role in self:\n            if not _is_iterable_not_str_of_type(self[role], type=str):\n                raise TypeError(\n                    f\"Argument for '{role}' must be an iterable of column names \"\n                    \"(strings): Iterable[str].\"\n                )\n\n            for column in self[role]:\n                if (already_defined_role := seen.get(column)) is not None:\n                    raise ValueError(\n                        f\"Column names must be unique across all roles. Found \"\n                        f\"duplicate roles set for column '{column}': '{role}' and \"\n                        f\"'{already_defined_role}'.\"\n                    )\n                else:\n                    seen.update({column: role})\n"
  },
  {
    "path": "src/python-api/getml/data/roles/roles.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import Final\n\nfrom getml.data.roles.types import (\n    Categorical,\n    JoinKey,\n    Numerical,\n    Target,\n    Text,\n    TimeStamp,\n    UnusedFloat,\n    UnusedString,\n)\n\n\"\"\"A role determines if and how\n[`columns`][getml.data.columns] are handled during the construction of the\n[`DataModel`][getml.data.DataModel] and used by the feature learning\nalgorithm (see [`feature_learning`][getml.feature_learning]).\n\n??? example\n    ```python\n    data_frame = getml.DataFrame.from_db(\n        name=name,\n        table_name=table_name,\n        conn=conn\n    )\n\n    data_frame.set_role(\n        [\"store_nbr\", \"item_nbr\"], getml.data.roles.join_key)\n    data_frame.set_role(\"date\", getml.data.roles.time_stamp)\n    data_frame.set_role(\"units\", getml.data.roles.target)\n    ```\n\n\"\"\"\n\ncategorical: Final[Categorical] = \"categorical\"\n\"\"\"Marks categorical columns.\n\nThis role tells the getML Engine to include the associated\n[`StringColumn`][getml.data.columns.StringColumn] during feature\nlearning.\n\nIt should be used for all data with no inherent ordering, even if the\ncategories are encoded as integer instead of strings in your provided\ndata set.\n\"\"\"\n\njoin_key: Final[JoinKey] = \"join_key\"\n\"\"\"Marks join keys.\n\nRole required to establish a relation between two\n[`Placeholder`][getml.data.Placeholder], the abstract representation of the\n[`DataFrame`][getml.DataFrame], by using the\n[`join`][getml.data.Placeholder.join] method. Please refer to the\nchapter [Data Model][data-model-concepts] for details.\n\nThe content of this column is allowed to contain NULL values. But\nbeware, columns with NULL in their join keys won't be matched to\nanything, not even to NULL in other join keys.\n\n[`columns`][getml.data.columns] of this role will *not* be handled by\nthe feature learning algorithm.\n\n\"\"\"\n\nnumerical: Final[Numerical] = \"numerical\"\n\"\"\"Marks numerical columns.\n\nThis role tells the getML Engine to include the associated\n[`FloatColumn`][getml.data.columns.FloatColumn] during feature\nlearning.\n\nIt should be used for all data with an inherent ordering, regardless\nof whether it is sampled from a continuous quantity, like passed time or the\ntotal amount of rainfall, or a discrete one, like the number of sugary\nmulberries one has eaten since lunch.\n\"\"\"\n\ntarget: Final[Target] = \"target\"\n\"\"\"\nMarks the column(s) we would like to predict.\n\nThe associated [`columns`][getml.data.columns] contain the variables we\nwant to predict. They are not used by the feature learning\nalgorithm unless we explicitly tell it to do so\n(refer to ``lagged_target`` in [`join`][getml.data.Placeholder.join]).\nBut they\nare such an important part of the analysis that the population table is required\nto contain at least one of them (refer to [Data Model Tables][data-model-tables]).\n\nThe content of the target columns needs to be numerical.\nFor classification problems, target variables can only assume the values\n0 or 1. Target variables can never be `NULL`.\n\"\"\"\n\ntext: Final[Text] = \"text\"\n\"\"\"Marks text columns.\n\nThis role tells the getML Engine to include the associated\n[`StringColumn`][getml.data.columns.StringColumn] during feature\nlearning.\n\nIt should be used for all data with no inherent ordering.\nUnlike categorical columns, text columns can not be used\nas a whole. Instead, the feature learners have to apply\nbasic text mining techniques before they are able to use them.\n\"\"\"\n\ntime_stamp: Final[TimeStamp] = \"time_stamp\"\n\"\"\"\nMarks time stamps.\n\nThis role is used to prevent data leaks. When you join one table onto another,\nyou usually want to make sure that no data from the future is used. Time stamps\ncan be used to limit your joins.\n\nIn addition, the feature learning algorithm can aggregate time stamps or use them\nfor conditions. However, they will not be compared to fixed values unless you explicitly\nchange their units. This means\nthat conditions like this are not possible by default:\n\n```python\nWHERE time_stamp > some_fixed_date\n```\nInstead, time stamps will always be compared to other time stamps:\n```python\nWHERE time_stamp1 - time_stamp2 > some_value\n```\n\nThis is because it is unlikely that comparing time stamps to a fixed date performs\nwell out-of-sample.\n\nWhen assigning the role time stamp to a column that is currently a\n[`StringColumn`][getml.data.columns.StringColumn],\nyou need to specify the format of this string. You can do so by using\nthe `time_formats` argument of\n[`set_role`][getml.DataFrame.set_role]. You can pass a list of time formats\nthat is used to try to interpret the input strings. Possible format options are\n\n* %w - abbreviated weekday (Mon, Tue, ...)\n* %W - full weekday (Monday, Tuesday, ...)\n* %b - abbreviated month (Jan, Feb, ...)\n* %B - full month (January, February, ...)\n* %d - zero-padded day of month (01 .. 31)\n* %e - day of month (1 .. 31)\n* %f - space-padded day of month ( 1 .. 31)\n* %m - zero-padded month (01 .. 12)\n* %n - month (1 .. 12)\n* %o - space-padded month ( 1 .. 12)\n* %y - year without century (70)\n* %Y - year with century (1970)\n* %H - hour (00 .. 23)\n* %h - hour (00 .. 12)\n* %a - am/pm\n* %A - AM/PM\n* %M - minute (00 .. 59)\n* %S - second (00 .. 59)\n* %s - seconds and microseconds (equivalent to %S.%F)\n* %i - millisecond (000 .. 999)\n* %c - centisecond (0 .. 9)\n* %F - fractional seconds/microseconds (000000 - 999999)\n* %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n* %Z - time zone differential in RFC format (GMT or +NNNN)\n* %% - percent sign\n\nIf none of the formats works, the getML Engine will try to interpret\nthe time stamps as numerical values. If this fails, the time stamp will be set\nto NULL.\n\n??? example\n    ```python\n    data_df = dict(\n            date1=[getml.data.time.days(365), getml.data.time.days(366), getml.data.time.days(367)],\n            date2=['1971-01-01', '1971-01-02', '1971-01-03'],\n            date3=['1|1|71', '1|2|71', '1|3|71'],\n        )\n    df = getml.DataFrame.from_dict(data_df, name='dates')\n    df.set_role(['date1', 'date2', 'date3'], getml.data.roles.time_stamp, time_formats=['%Y-%m-%d', '%n|%e|%y'])\n    df\n    ```\n    ```\n    | date1                       | date2                       | date3                       |\n    | time stamp                  | time stamp                  | time stamp                  |\n    -------------------------------------------------------------------------------------------\n    | 1971-01-01T00:00:00.000000Z | 1971-01-01T00:00:00.000000Z | 1971-01-01T00:00:00.000000Z |\n    | 1971-01-02T00:00:00.000000Z | 1971-01-02T00:00:00.000000Z | 1971-01-02T00:00:00.000000Z |\n    | 1971-01-03T00:00:00.000000Z | 1971-01-03T00:00:00.000000Z | 1971-01-03T00:00:00.000000Z |\n    ```\n\nNote:\n    getML time stamps are actually floats expressing the number of seconds since\n    UNIX time (1970-01-01T00:00:00).\n\"\"\"\n\nunused_float: Final[UnusedFloat] = \"unused_float\"\n\"\"\"Marks a [`FloatColumn`][getml.data.columns.columns.FloatColumn] as unused.\n\nThe associated [`column`][getml.data.columns] will be neither used in the\ndata model nor during feature learning or prediction.\n\"\"\"\n\nunused_string: Final[UnusedString] = \"unused_string\"\n\"\"\"Marks a [`StringColumn`][getml.data.columns.columns.StringColumn] as unused.\n\nThe associated [`column`][getml.data.columns] will be neither used in the\ndata model nor during feature learning or prediction.\n\"\"\"\n"
  },
  {
    "path": "src/python-api/getml/data/roles/sets.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import FrozenSet\n\nfrom getml.data.roles import roles\nfrom getml.data.roles.types import CategoricalLike, NumericalLike, Role\n\ncategorical: FrozenSet[CategoricalLike] = frozenset(\n    {\n        roles.categorical,\n        roles.join_key,\n        roles.text,\n        roles.unused_string,\n    }\n)\n\"\"\"\nSet of roles that are interpreted as categorical.\n\"\"\"\n\nnumerical: FrozenSet[NumericalLike] = frozenset(\n    {\n        roles.numerical,\n        roles.target,\n        roles.time_stamp,\n        roles.unused_float,\n    }\n)\n\"\"\"\nSet of roles that are interpreted as numerical.\n\"\"\"\n\nall_: FrozenSet[Role] = categorical | numerical\n\"\"\"\nSet of all possible roles.\n\"\"\"\n"
  },
  {
    "path": "src/python-api/getml/data/roles/types.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import Literal\n\n\"\"\"\nThe Literal types that define all possible roles a column can have.\n\nSee [`getml.data.roles.roles`][getml.data.roles.roles] for the actual constants.\n\"\"\"\n\nCategorical = Literal[\"categorical\"]\nJoinKey = Literal[\"join_key\"]\nNumerical = Literal[\"numerical\"]\nTarget = Literal[\"target\"]\nText = Literal[\"text\"]\nTimeStamp = Literal[\"time_stamp\"]\nUnusedFloat = Literal[\"unused_float\"]\nUnusedString = Literal[\"unused_string\"]\n\nCategoricalLike = Literal[Categorical, JoinKey, Text, UnusedString]\nNumericalLike = Literal[Numerical, Target, TimeStamp, UnusedFloat]\n\nRole = Literal[\n    Categorical,\n    JoinKey,\n    Numerical,\n    Target,\n    Text,\n    TimeStamp,\n    UnusedFloat,\n    UnusedString,\n]\n"
  },
  {
    "path": "src/python-api/getml/data/split/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSplits data into a training, testing, validation or other sets.\n\"\"\"\n\nfrom .concat import concat\nfrom .random import random\nfrom .time import time\n\n__all__ = (\"concat\", \"random\", \"time\")\n"
  },
  {
    "path": "src/python-api/getml/data/split/concat.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nConcatenates data.\n\"\"\"\n\nfrom typing import Tuple\n\nfrom getml.data.columns import StringColumnView\nfrom getml.data.columns.columns import rowid\nfrom getml.data.columns.from_value import from_value\nfrom getml.data.concat import concat as _concat\nfrom getml.data.data_frame import DataFrame\nfrom getml.data.helpers import _is_non_empty_typed_list\nfrom getml.data.view import View\n\n\ndef concat(name: str, **kwargs: DataFrame) -> Tuple[DataFrame, StringColumnView]:\n    \"\"\"\n    Concatenates several data frames into and produces a split\n    column that keeps track of their origin.\n\n    Args:\n        name:\n            The name of the data frame you would like to create.\n\n        kwargs:\n            The data frames you would like\n            to concat with the name in which they should appear\n            in the split column.\n\n    Returns:\n        A tuple containing the concatenated data frame and the split column.\n\n    ??? example\n        A common use case for this functionality are [`TimeSeries`][getml.data.TimeSeries]:\n        ```python\n        data_train = getml.DataFrame.from_pandas(\n            datatraining_pandas, name='data_train')\n\n        data_validate = getml.DataFrame.from_pandas(\n            datatest_pandas, name='data_validate')\n\n        data_test = getml.DataFrame.from_pandas(\n            datatest2_pandas, name='data_test')\n\n        population, split = getml.data.split.concat(\n            \"population\", train=data_train, validate=data_validate, test=data_test)\n\n        ...\n\n        time_series = getml.data.TimeSeries(\n            population=population, split=split)\n\n        my_pipeline.fit(time_series.train)\n        ```\n    \"\"\"\n\n    if not _is_non_empty_typed_list(list(kwargs.values()), [DataFrame, View]):\n        raise ValueError(\n            \"'kwargs' must be non-empty and contain getml.DataFrames \"\n            + \"or getml.data.Views.\"\n        )\n\n    names = list(kwargs.keys())\n\n    first = kwargs[names[0]]\n\n    population = first.copy(name) if isinstance(first, DataFrame) else first.to_df(name)\n\n    split = from_value(names[0])\n\n    assert isinstance(split, StringColumnView), \"Should be a StringColumnView\"\n\n    for new_df_name in names[1:]:\n        split = split.update(rowid() > population.nrows(), new_df_name)  # type: ignore\n        population = _concat(name, [population, kwargs[new_df_name]])\n\n    return population, split[: population.nrows()]  # type: ignore\n"
  },
  {
    "path": "src/python-api/getml/data/split/random.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSplits data at random.\n\"\"\"\n\nimport numbers\n\nimport numpy as np\n\nfrom getml.data.columns import StringColumnView\nfrom getml.data.columns import random as random_col\nfrom getml.data.columns.from_value import from_value\nfrom getml.data.helpers import _is_typed_list\n\n\ndef random(\n    seed: int = 5849,\n    train: float = 0.8,\n    test: float = 0.2,\n    validation: float = 0,\n    **kwargs: float,\n) -> StringColumnView:\n    \"\"\"\n    Returns a [`StringColumnView`][getml.data.columns.StringColumnView] that\n    can be used to randomly divide data into training, testing,\n    validation or other sets.\n\n    Args:\n        seed:\n            Seed used for the random number generator.\n\n        train:\n            The share of random samples assigned to\n            the training set.\n\n        validation:\n            The share of random samples assigned to\n            the validation set.\n\n        test:\n            The share of random samples assigned to\n            the test set.\n\n        kwargs:\n            Any other sets you would like to assign.\n            You can name these sets whatever you want to (in our example,\n            we called it 'other').\n\n    ??? example\n        ```python\n        split = getml.data.split.random(\n            train=0.8, test=0.1, validation=0.05, other=0.05\n        )\n\n        train_set = data_frame[split=='train']\n        validation_set = data_frame[split=='validation']\n        test_set = data_frame[split=='test']\n        other_set = data_frame[split=='other']\n        ```\n\n    \"\"\"\n\n    values = np.asarray([train, validation, test] + list(kwargs.values()))\n\n    if not _is_typed_list(values.tolist(), numbers.Real):\n        raise ValueError(\"All values must be real numbers.\")\n\n    if np.abs(np.sum(values) - 1.0) > 0.0001:\n        raise ValueError(\n            \"'train', 'validation', 'test' and all other sets must add up to 1, \"\n            + \"but add up to \"\n            + str(np.sum(values))\n            + \".\"\n        )\n\n    upper_bounds = np.cumsum(values)\n    lower_bounds = upper_bounds - values\n\n    names = [\"train\", \"validation\", \"test\"] + list(kwargs.keys())\n\n    col: StringColumnView = from_value(\"train\")  # type: ignore\n\n    assert isinstance(col, StringColumnView), \"Should be a StringColumnView\"\n\n    for i in range(len(names)):\n        col = col.update(  # type: ignore\n            (random_col(seed=seed) >= lower_bounds[i])  # type: ignore\n            & (random_col(seed=seed) < upper_bounds[i]),\n            names[i],\n        )\n\n    return col\n"
  },
  {
    "path": "src/python-api/getml/data/split/time.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSplits data at random.\n\"\"\"\n\nimport numbers\nfrom typing import Dict, Optional, Union\n\nimport numpy as np\n\nfrom getml.data.columns import FloatColumn, FloatColumnView, StringColumnView\nfrom getml.data.columns.from_value import from_value\nfrom getml.data.data_frame import DataFrame\nfrom getml.data.helpers import _is_typed_list\nfrom getml.data.view import View\n\n\ndef time(\n    population: DataFrame,\n    time_stamp: Union[str, FloatColumn, FloatColumnView],\n    validation: Optional[Union[float, int, np.datetime64]] = None,\n    test: Optional[Union[float, int, np.datetime64]] = None,\n    **kwargs: Union[float, int, np.datetime64],\n) -> StringColumnView:\n    \"\"\"\n    Returns a [`StringColumnView`][getml.data.columns.StringColumnView] that can be used to divide\n    data into training, testing, validation or other sets.\n\n    The arguments are\n    `key=value` pairs of names (`key`) and starting points (`value`).\n    The starting point defines the left endpoint of the subset. Intervals are left\n    closed and right open, such that $[value, next value)$.  The (unnamed) subset\n    left from the first named starting point, i.e.  $[0, first value)$, is always\n    considered to be the training set.\n\n    Args:\n        population:\n            The population table you would like to split.\n\n        time_stamp:\n            The name of the time stamp column in the population table\n            you want to use. Ideally, the role of said column would be\n            [`time_stamp`][getml.data.roles.time_stamp]. If you want to split on the rowid,\n            then pass \"rowid\" to `time_stamp`.\n\n        validation:\n            The start date of the validation set.\n\n        test:\n            The start date of the test set.\n\n        kwargs:\n            Any other sets you would like to assign.\n            You can name these sets whatever you want to (in our example,\n            we called it 'other').\n\n    ??? example\n        ```python\n        validation_begin = getml.data.time.datetime(2010, 1, 1)\n        test_begin = getml.data.time.datetime(2011, 1, 1)\n        other_begin = getml.data.time.datetime(2012, 1, 1)\n\n        split = getml.data.split.time(\n            population=data_frame,\n            time_stamp=\"ds\",\n            test=test_begin,\n            validation=validation_begin,\n            other=other_begin\n        )\n\n        # Contains all data before 2010-01-01 (not included)\n        train_set = data_frame[split=='train']\n\n        # Contains all data between 2010-01-01 (included) and 2011-01-01 (not included)\n        validation_set = data_frame[split=='validation']\n\n        # Contains all data between 2011-01-01 (included) and 2012-01-01 (not included)\n        test_set = data_frame[split=='test']\n\n        # Contains all data after 2012-01-01 (included)\n        other_set = data_frame[split=='other']\n        ```\n    \"\"\"\n    if not isinstance(population, (DataFrame, View)):\n        raise ValueError(\"'population' must be a DataFrame or a View.\")\n\n    if not isinstance(time_stamp, (str, FloatColumn, FloatColumnView)):\n        raise ValueError(\n            \"'time_stamp' must be a string, a FloatColumn, or a FloatColumnView.\"\n        )\n\n    if not test and not validation and not kwargs:\n        raise ValueError(\"You have to supply at least one starting point.\")\n\n    defaults: Dict[str, Optional[Union[float, int, np.datetime64]]] = {\n        \"test\": test,\n        \"validation\": validation,\n    }\n\n    sets = {name: value for name, value in defaults.items() if value is not None}\n\n    sets.update({**kwargs})\n\n    values = np.asarray(list(sets.values()))\n    index = np.argsort(values)\n    values = values[index]\n\n    if not _is_typed_list(values.tolist(), numbers.Real):\n        raise ValueError(\"All values must be real numbers.\")\n\n    names = np.asarray(list(sets.keys()))\n    names = names[index]\n\n    if isinstance(time_stamp, str):\n        time_stamp_col = (\n            population[time_stamp] if time_stamp != \"rowid\" else population.rowid\n        )\n    else:\n        time_stamp_col = time_stamp\n\n    col: StringColumnView = from_value(\"train\")  # type: ignore\n\n    assert isinstance(col, StringColumnView), \"Should be a StringColumnView\"\n\n    for i in range(len(names)):\n        col = col.update(  # type: ignore\n            time_stamp_col >= values[i],\n            names[i],\n        )\n\n    return col\n"
  },
  {
    "path": "src/python-api/getml/data/staging.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of functions for simulating the effects of staging,\nnot meant to be used by the end user.\n\"\"\"\n\nfrom typing import List, Optional, Tuple\n\nfrom .placeholder import Join, Placeholder\nfrom .relationship import _all_relationships, many_to_one, one_to_one\n\n# ------------------------------------------------------------------\n\nPair = Tuple[List[str], List[Join]]\n\n# ------------------------------------------------------------------\n\n\ndef _compare_join(join1: Join, join2: Join) -> bool:\n    return (\n        (join1.right.name == join2.right.name)\n        and (join1.on == join2.on)\n        and (join1.time_stamps == join2.time_stamps)\n        and (join1.upper_time_stamp == join2.upper_time_stamp)\n        and (join1.relationship == join2.relationship)\n        and (join1.memory == join2.memory)\n        and (join1.horizon == join2.horizon)\n        and (join1.lagged_targets == join2.lagged_targets)\n    )\n\n\n# ------------------------------------------------------------------\n\n\ndef _compare_joins(pair1: Pair, pair2: Pair) -> bool:\n    return all(_compare_join(j1, j2) for (j1, j2) in zip(pair1[1], pair2[1]))\n\n\n# ------------------------------------------------------------------\n\n\ndef _is_same(pair1: Pair, pair2: Pair) -> bool:\n    if pair1[0] != pair2[0]:\n        return False\n\n    if not _compare_joins(pair1, pair2):\n        return False\n\n    return True\n\n\n# ------------------------------------------------------------------\n\n\ndef _is_to_one(relationship: Optional[str]) -> bool:\n    if not relationship:\n        raise ValueError(\"'relationship' is not set!\")\n    if relationship not in _all_relationships:\n        raise ValueError(\n            \"'relationship' must be from getml.data.relationship, \"\n            + \"meaning it must be one of the following: \"\n            + str(_all_relationships)\n            + \".\"\n        )\n    return relationship in [many_to_one, one_to_one]\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_names(placeholder: Placeholder) -> List[str]:\n    return [placeholder.name] + [\n        name\n        for j in placeholder.joins\n        if _is_to_one(j.relationship)\n        for name in _make_names(j.right)\n    ]\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_joins(placeholder: Placeholder) -> List[Join]:\n    return [join for join in placeholder.joins if _is_to_one(join.relationship)] + [\n        subjoin\n        for join in placeholder.joins\n        if _is_to_one(join.relationship)\n        for subjoin in _make_joins(join.right)\n    ]\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_pair(placeholder: Placeholder) -> Pair:\n    names = _make_names(placeholder)\n    joins = _make_joins(placeholder)\n    assert len(names) == len(joins) + 1, \"Lengths don't match\"\n    return (names, joins)\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_list_of_pairs(placeholder: Placeholder, include_head=True) -> List[Pair]:\n    head = [_make_pair(placeholder)] if include_head else []\n    return head + [\n        pair\n        for j in placeholder.joins\n        for pair in _make_list_of_pairs(j.right, not _is_to_one(j.relationship))\n    ]\n\n\n# ------------------------------------------------------------------\n\n\ndef _make_staging_overview(placeholder: Placeholder) -> List[List[str]]:\n    \"\"\"\n    All joins that involve many-to-one or one-to-one joins are handled\n    during the staging phase. Therefore, we need to:\n    1) Identify any such joins (_make_list_of_pairs)\n    2) Sort the peripheral tables by the name of the first table.\n    3) Remove any duplicates (_remove_duplicates).\n    4) Return a list that maps the names of the original tables\n       to the name of the generated staging tables.\n    \"\"\"\n    list_of_pairs = _make_list_of_pairs(placeholder)\n    peripheral = list_of_pairs[1:]\n    peripheral.sort(key=lambda pairs: pairs[0][0])\n    peripheral = _remove_duplicates(peripheral)\n    list_of_names = [list_of_pairs[0][0]] + [p[0] for p in peripheral]\n    return [\n        [\", \".join(lis), lis[0].upper() + \"__STAGING_TABLE_\" + str(i + 1)]\n        for (i, lis) in enumerate(list_of_names)\n    ]\n\n\n# ------------------------------------------------------------------\n\n\ndef _remove_duplicates(sorted_list_of_pairs: List[Pair]) -> List[Pair]:\n    if not sorted_list_of_pairs:\n        return []\n\n    if len(sorted_list_of_pairs) == 1:\n        return sorted_list_of_pairs\n\n    head = sorted_list_of_pairs[0]\n\n    tail = sorted_list_of_pairs[1:]\n\n    if _is_same(head, tail[0]):\n        return _remove_duplicates(tail)\n\n    return [head] + _remove_duplicates(tail)\n\n\n# ------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/data/star_schema.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSpecial container for star schemata.\n\"\"\"\n\nfrom inspect import cleandoc\nfrom textwrap import indent\nfrom typing import Dict, Optional, Union\n\nfrom getml.data.columns import StringColumnView\nfrom getml.data.placeholder import OnType, TimeStampsType\n\nfrom .columns import FloatColumn, StringColumn\nfrom .container import Container\nfrom .data_frame import DataFrame\nfrom .data_model import DataModel\nfrom .relationship import many_to_many\nfrom .view import View\n\n\nclass StarSchema:\n    \"\"\"\n    A StarSchema is a simplifying abstraction that can be used\n    for machine learning problems that can be organized in a simple\n    [star schema](https://en.wikipedia.org/wiki/Star_schema).\n\n    It unifies [`Container`][getml.data.Container] and\n    [`DataModel`][getml.data.DataModel] thus abstracting away the need to\n    differentiate between the concrete data and the abstract data model.\n\n    The class is designed using\n    [composition ](https://en.wikipedia.org/wiki/Composition_over_inheritance)\n    - it *is* neither [`Container`][getml.data.Container] nor [`DataModel`][getml.data.DataModel],\n    but *has* both of them.\n\n    This means that you can always fall back to the more flexible methods using\n    [`Container`][getml.data.Container] and [`DataModel`][getml.data.DataModel] by directly\n    accessing the attributes `container` and `data_model`.\n\n    Attributes:\n        population:\n            The population table defines the\n            [statistical population ](https://en.wikipedia.org/wiki/Statistical_population)\n            of the machine learning problem and contains the target variables.\n\n        alias:\n            The alias to be used for the population table. This is required,\n            if *population* is a [`View`][getml.data.View].\n\n        peripheral:\n            The peripheral tables are joined onto *population* or other\n            peripheral tables. Note that you can also pass them using\n            [`join`][getml.data.StarSchema.join].\n\n        split:\n            Contains information on how you want to split *population* into\n            different [`Subset`][getml.data.Subset] s.\n            Also refer to [`split`][getml.data.split].\n\n        deep_copy:\n            Whether you want to create deep copies or your tables.\n\n        train:\n            The population table used in the *train*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        validation:\n            The population table used in the *validation*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        test:\n            The population table used in the *test*\n            [`Subset`][getml.data.Subset].\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n        kwargs:\n            The population table used in [`Subset`][getml.data.Subset] s\n            other than the predefined *train*, *validation* and *test* subsets.\n            You can call these subsets anything you want to and can access them\n            just like *train*, *validation* and *test*.\n            You can either pass *population* and *split* or you can pass\n            the subsets separately using *train*, *validation*, *test*\n            and *kwargs*.\n\n            ??? example\n                ```python\n                # Pass the subset.\n                star_schema = getml.data.StarSchema(\n                    my_subset=my_data_frame)\n\n                # You can access the subset just like train,\n                # validation or test\n                my_pipeline.fit(star_schema.my_subset)\n                ```\n\n    ??? example\n        Note that this example is taken from the\n        [loans notebook](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/loans.ipynb).\n\n        You might also want to refer to\n        [`DataFrame`][getml.DataFrame], [`View`][getml.data.View]\n        and [`Pipeline`][getml.Pipeline].\n\n        ```python\n        # First, we insert our data.\n        # population_train and population_test are either\n        # DataFrames or Views. The population table\n        # defines the statistical population of your\n        # machine learning problem and contains the\n        # target variables.\n        star_schema = getml.data.StarSchema(\n            train=population_train,\n            test=population_test\n        )\n\n        # meta, order and trans are either\n        # DataFrames or Views.\n        # Because this is a star schema,\n        # all joins take place on the population\n        # table.\n        star_schema.join(\n            trans,\n            on=\"account_id\",\n            time_stamps=(\"date_loan\", \"date\")\n        )\n\n        star_schema.join(\n            order,\n            on=\"account_id\",\n        )\n\n        star_schema.join(\n            meta,\n            on=\"account_id\",\n        )\n\n        # Now you can insert your data model,\n        # your preprocessors, feature learners,\n        # feature selectors and predictors\n        # into the pipeline.\n        # Note that the pipeline only knows\n        # the abstract data model, but hasn't\n        # seen the actual data yet.\n        pipe = getml.Pipeline(\n            data_model=star_schema.data_model,\n            preprocessors=[mapping],\n            feature_learners=[fast_prop],\n            feature_selectors=[feature_selector],\n            predictors=predictor,\n        )\n\n        # Now, we pass the actual data.\n        # This passes 'population_train' and the\n        # peripheral tables (meta, order and trans)\n        # to the pipeline.\n        pipe.check(star_schema.train)\n\n        pipe.fit(star_schema.train)\n\n        pipe.score(star_schema.test)\n\n        # To generate predictions on new data,\n        # it is sufficient to use a Container.\n        # You don't have to recreate the entire\n        # StarSchema, because the abstract data model\n        # is stored in the pipeline.\n        container = getml.data.Container(\n            population=population_new)\n\n        container.add(\n            trans=trans_new,\n            order=order_new,\n            meta=meta_new)\n\n        predictions = pipe.predict(container.full)\n        ```\n        If you don't already have a train and test set,\n        you can use a function from the\n        [`split`][getml.data.split] module.\n\n        ```python\n        split = getml.data.split.random(\n            train=0.8, test=0.2)\n\n        star_schema = getml.data.StarSchema(\n            population=population_all,\n            split=split,\n        )\n\n        # The remaining code is the same as in\n        # the example above. In particular,\n        # star_schema.train and star_schema.test\n        # work just like above.\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        population: Optional[Union[DataFrame, View]] = None,\n        alias: Optional[str] = None,\n        peripheral: Optional[Dict[str, Union[DataFrame, View]]] = None,\n        split: Optional[Union[StringColumn, StringColumnView]] = None,\n        deep_copy: Optional[bool] = False,\n        train: Optional[Union[DataFrame, View]] = None,\n        validation: Optional[Union[DataFrame, View]] = None,\n        test: Optional[Union[DataFrame, View]] = None,\n        **kwargs: Optional[Union[DataFrame, View]],\n    ):\n        if (population is None or isinstance(population, View)) and alias is None:\n            raise ValueError(\n                \"If 'population' is None or a getml.data.View, you must set an alias.\"\n            )\n\n        self._alias = alias or population.name\n\n        self._container = Container(\n            population=population,\n            peripheral=peripheral,\n            split=split,\n            deep_copy=deep_copy,\n            train=train,\n            validation=validation,\n            test=test,\n            **kwargs,\n        )\n\n        def get_placeholder():\n            if population is not None:\n                return population.to_placeholder(alias)\n            if train is not None:\n                return train.to_placeholder(alias)\n            if validation is not None:\n                return validation.to_placeholder(alias)\n            if test is not None:\n                return test.to_placeholder(alias)\n            assert (\n                len(kwargs) > 0\n            ), \"This should have been checked by Container.__init__.\"\n            return kwargs[list(kwargs.keys())[0]].to_placeholder(alias)\n\n        self._data_model = DataModel(get_placeholder())\n\n    def __dir__(self):\n        attrs = dir(type(self)) + [key[1:] for key in list(vars(self))]\n        attrs += dir(self.container)\n        attrs += dir(self.data_model)\n        return list(set(attrs))\n\n    def __iter__(self):\n        yield from [self.population] + list(self.peripheral.values())\n\n    def __getattr__(self, key):\n        try:\n            return self[key]\n        except KeyError:\n            return super().__getattribute__(key)\n\n    def __getitem__(self, key):\n        attrs = vars(self)\n\n        if key in attrs:\n            return attrs[key]\n\n        if \"_\" + key in attrs:\n            return attrs[\"_\" + key]\n\n        try:\n            return attrs[\"_container\"][key]\n        except KeyError:\n            return attrs[\"_data_model\"][key]\n\n    def __repr__(self):\n        template = cleandoc(\n            \"\"\"\n            data model\n\n            {data_model}\n\n\n            container\n\n            {container}\n            \"\"\"\n        )\n        return template.format(\n            data_model=indent(repr(self.data_model), \"  \"),\n            container=indent(repr(self.container), \"  \"),\n        )\n\n    def _repr_html_(self):\n        template = cleandoc(\n            \"\"\"\n            <span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\n            {data_model}\n            <span style='font-size: 1.2rem; font-weight: 500;'>container</span>\n            {container}\n            \"\"\"\n        )\n        return template.format(\n            data_model=self.data_model._repr_html_(),\n            container=self.container._repr_html_(),\n        )\n\n    @property\n    def container(self) -> Container:\n        \"\"\"\n        The underlying [`Container`][getml.data.Container].\n\n        Returns:\n            The underlying container.\n        \"\"\"\n        return self._container\n\n    @property\n    def data_model(self) -> DataModel:\n        \"\"\"\n        The underlying [`DataModel`][getml.data.DataModel].\n\n        Returns:\n            The underlying data model.\n        \"\"\"\n        return self._data_model\n\n    def join(\n        self,\n        right_df: Union[DataFrame, View],\n        alias: Optional[str] = None,\n        on: OnType = None,\n        time_stamps: TimeStampsType = None,\n        relationship: str = many_to_many,\n        memory: Optional[float] = None,\n        horizon: Optional[float] = None,\n        lagged_targets: bool = False,\n        upper_time_stamp: Optional[str] = None,\n    ):\n        \"\"\"\n        Joins a [`DataFrame`][getml.DataFrame] or [`View`][getml.data.View]\n        to the population table.\n\n        In a [`StarSchema`][getml.data.StarSchema] or [`TimeSeries`][getml.data.TimeSeries],\n        all joins take place on the population table. If you want to create more\n        complex data models, use [`DataModel`][getml.data.DataModel] instead.\n\n        ??? example\n            This example will construct a data model in which the\n            'population_table' depends on the 'peripheral_table' via\n            the 'join_key' column. In addition, only those rows in\n            'peripheral_table' for which 'time_stamp' is smaller or\n            equal to the 'time_stamp' in 'population_table' are considered:\n\n            ```python\n            star_schema = getml.data.StarSchema(\n                population=population_table, split=split)\n\n            star_schema.join(\n                peripheral_table,\n                on=\"join_key\",\n                time_stamps=\"time_stamp\"\n            )\n            ```\n\n            If the relationship between two tables is many-to-one or one-to-one\n            you should clearly say so:\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=\"join_key\",\n                time_stamps=\"time_stamp\",\n                relationship=getml.data.relationship.many_to_one,\n            )\n            ```\n            Please also refer to [`relationship`][getml.data.relationship].\n\n            If the join keys or time stamps are named differently in the two\n            different tables, use a tuple:\n\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=(\"join_key\", \"other_join_key\"),\n                time_stamps=(\"time_stamp\", \"other_time_stamp\"),\n            )\n            ```\n\n            You can join over more than one join key:\n\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=[\"join_key1\", \"join_key2\", (\"join_key3\", \"other_join_key3\")],\n                time_stamps=\"time_stamp\",\n                )\n            ```\n\n            You can also limit the scope of your joins using *memory*. This\n            can significantly speed up training time. For instance, if you\n            only want to consider data from the last seven days, you could\n            do something like this:\n\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=\"join_key\",\n                time_stamps=\"time_stamp\",\n                memory=getml.data.time.days(7),\n            )\n            ```\n\n            In some use cases, particularly those involving time series, it\n            might be a good idea to use targets from the past. You can activate\n            this using *lagged_targets*. But if you do that, you must\n            also define a prediction *horizon*. For instance, if you want to\n            predict data for the next hour, using data from the last seven days,\n            you could do this:\n\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=\"join_key\",\n                time_stamps=\"time_stamp\",\n                lagged_targets=True,\n                horizon=getml.data.time.hours(1),\n                memory=getml.data.time.days(7),\n            )\n            ```\n\n            Please also refer to [`time`][getml.data.time].\n\n            If the join involves many matches, it might be a good idea to set the\n            relationship to [`propositionalization`][getml.data.relationship.propositionalization].\n            This forces the pipeline to always use a propositionalization\n            algorithm for this join, which can significantly speed things up.\n\n            ```python\n            star_schema.join(\n                peripheral_table,\n                on=\"join_key\",\n                time_stamps=\"time_stamp\",\n                relationship=getml.data.relationship.propositionalization,\n            )\n            ```\n\n            Please also refer to [`relationship`][getml.data.relationship].\n\n        Args:\n            right_df:\n                The data frame or view you would like to join.\n\n            alias:\n                The name as which you want *right_df* to be referred to in\n                the generated SQL code.\n\n            on:\n                The join keys to use. If none is passed, then everything\n                will be joined to everything else.\n\n            time_stamps:\n                The time stamps used to limit the join.\n\n            relationship:\n                The relationship between the two tables. Must be from\n                [`relationship`][getml.data.relationship].\n\n            memory:\n                The difference between the time stamps until data is 'forgotten'.\n                Limiting your joins using memory can significantly speed up\n                training time. Also refer to [`time`][getml.data.time].\n\n            horizon:\n                The prediction horizon to apply to this join.\n                Also refer to [`time`][getml.data.time].\n\n            lagged_targets:\n                Whether you want to allow lagged targets. If this is set to True,\n                you must also pass a positive, non-zero *horizon*.\n\n            upper_time_stamp:\n                Name of a time stamp in *right_df* that serves as an upper limit\n                on the join.\n        \"\"\"\n\n        if not isinstance(right_df, (DataFrame, View)):\n            raise TypeError(\n                f\"Expected a {DataFrame} as 'right_df', got: {type(right_df)}.\"\n            )\n\n        if isinstance(right_df, View):\n            if alias is None:\n                raise ValueError(\n                    \"Setting an 'alias' is required if a getml.data.View is supplied \"\n                    \"as a peripheral table.\"\n                )\n\n        def modify_join_keys(on):\n            if isinstance(on, list):\n                return [modify_join_keys(jk) for jk in on]\n\n            if isinstance(on, (str, StringColumn)):\n                on = (on, on)\n\n            if on is not None and on:\n                on = tuple(\n                    jkey.name if isinstance(jkey, StringColumn) else jkey for jkey in on\n                )\n\n            return on\n\n        def modify_time_stamps(time_stamps):\n            if isinstance(time_stamps, (str, FloatColumn)):\n                time_stamps = (time_stamps, time_stamps)\n\n            if time_stamps is not None:\n                time_stamps = tuple(\n                    time_stamp.name\n                    if isinstance(time_stamp, FloatColumn)\n                    else time_stamp\n                    for time_stamp in time_stamps\n                )\n\n            return time_stamps\n\n        on = modify_join_keys(on)\n\n        time_stamps = modify_time_stamps(time_stamps)\n\n        upper_time_stamp = (\n            upper_time_stamp.name\n            if isinstance(upper_time_stamp, FloatColumn)\n            else upper_time_stamp\n        )\n\n        right = right_df.to_placeholder(alias)\n\n        self.data_model.population.join(\n            right=right,\n            on=on,\n            time_stamps=time_stamps,\n            relationship=relationship,\n            memory=memory,\n            horizon=horizon,\n            lagged_targets=lagged_targets,\n            upper_time_stamp=upper_time_stamp,\n        )\n\n        alias = alias or right_df.name\n\n        self.container.add(**{alias: right_df})\n\n    def sync(self):\n        \"\"\"\n        Synchronizes the last change with the data to avoid warnings that the data\n        has been changed.\n\n        This is only a problem when `deep_copy=False`.\n        \"\"\"\n        self.container.sync()\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"Subroles allow for more fine-granular control of how certain columns\nwill be used by the pipeline.\n\nA column can have no subrole, one subrole or several subroles.\n\n??? example\n    ```python\n    # The Relboost feature learning algorithm will\n    # ignore this column.\n    my_data_frame.set_subroles(\n        \"my_column\", getml.data.subroles.exclude.relboost)\n\n    # The Substring preprocessor will be applied to this column.\n    # But other preprocessors, feature learners or predictors\n    # are not excluded from using it as well.\n    my_data_frame.set_subroles(\n        \"ucc\", getml.data.subroles.include.substring)\n\n    # Only the EmailDomain preprocessor will be applied\n    # to \"emails\". All other preprocessors, feature learners,\n    # feature selectors and predictors will ignore this column.\n    my_data_frame.set_subroles(\"emails\", getml.data.subroles.only.email)\n    ```\n\"\"\"\n\nfrom getml.data.subroles import exclude, include, only\n\n__all__ = (\"exclude\", \"include\", \"only\")\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/exclude.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import Final\n\nfrom getml.data.subroles.types import (\n    ExcludeCategoryTrimmer,\n    ExcludeFastProp,\n    ExcludeFeatureLearners,\n    ExcludeImputation,\n    ExcludeMapping,\n    ExcludeMultirel,\n    ExcludePredictors,\n    ExcludePreprocessors,\n    ExcludeRelboost,\n    ExcludeRelMT,\n    ExcludeSeasonal,\n    ExcludeTextFieldSplitter,\n)\n\n\"\"\"\nColumns marked with a subrole in this submodule will not be used\nfor the specified purpose.\n\n??? example\n    ```python\n    # The Relboost feature learning algorithm will\n    # ignore this column.\n    my_data_frame.set_subroles(\n        \"my_column\", getml.data.subroles.exclude.relboost)\n    ```\n\"\"\"\ncategory_trimmer: Final[ExcludeCategoryTrimmer] = \"exclude category trimmer\"\n\"\"\"\nThe [`CategoryTrimmer`][getml.preprocessors.CategoryTrimmer] preprocessor\nwill ignore this column.\n\"\"\"\n\nfastprop: Final[ExcludeFastProp] = \"exclude fastprop\"\n\"\"\"\n[`FastProp`][getml.feature_learning.FastProp] will ignore\nthis column.\n\"\"\"\n\nfeature_learners: Final[ExcludeFeatureLearners] = \"exclude feature learners\"\n\"\"\"\nAll feature learners ([`feature_learning`][getml.feature_learning])\nwill ignore this column.\n\"\"\"\n\nimputation: Final[ExcludeImputation] = \"exclude imputation\"\n\"\"\"\nThe [`Imputation`][getml.preprocessors.Imputation] preprocessor\nwill ignore this column.\n\"\"\"\n\nmapping: Final[ExcludeMapping] = \"exclude mapping\"\n\"\"\"\nThe [`Mapping`][getml.preprocessors.Mapping] preprocessor\nwill ignore this column.\n\"\"\"\n\nmultirel: Final[ExcludeMultirel] = \"exclude multirel\"\n\"\"\"\n[`Multirel`][getml.feature_learning.Multirel] will ignore\nthis column.\n\"\"\"\n\npredictors: Final[ExcludePredictors] = \"exclude predictors\"\n\"\"\"\nAll [`predictors`][getml.predictors] will ignore this column.\n\"\"\"\n\npreprocessors: Final[ExcludePreprocessors] = \"exclude preprocessors\"\n\"\"\"\nAll [`preprocessors`][getml.preprocessors] will ignore this column.\n\"\"\"\n\nrelboost: Final[ExcludeRelboost] = \"exclude relboost\"\n\"\"\"\n[`Relboost`][getml.feature_learning.Relboost] will ignore\nthis column.\n\"\"\"\n\nrelmt: Final[ExcludeRelMT] = \"exclude relmt\"\n\"\"\"\n[`RelMT`][getml.feature_learning.RelMT] will ignore\nthis column.\n\"\"\"\n\nseasonal: Final[ExcludeSeasonal] = \"exclude seasonal\"\n\"\"\"\nThe [`Seasonal`][getml.preprocessors.Seasonal] preprocessor\nwill ignore this column.\n\"\"\"\n\ntext_field_splitter: Final[ExcludeTextFieldSplitter] = \"exclude text field splitter\"\n\"\"\"\nThe [`TextFieldSplitter`][getml.preprocessors.TextFieldSplitter] will ignore this column.\n\"\"\"\n\n__all__ = (\n    \"category_trimmer\",\n    \"fastprop\",\n    \"feature_learners\",\n    \"imputation\",\n    \"mapping\",\n    \"multirel\",\n    \"predictors\",\n    \"preprocessors\",\n    \"relboost\",\n    \"relmt\",\n    \"seasonal\",\n    \"text_field_splitter\",\n)\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/include.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nfrom typing import Final\n\nfrom getml.data.subroles.types import IncludeEmail, IncludeSubstring\n\n\"\"\"\nColumns marked with a subrole in this submodule will be used for the\nspecified purpose without excluding other purposes.\n\n??? example\n    ```python\n    # The Substring preprocessor will be applied to this column.\n    # But other preprocessors, feature learners or predictors\n    # are not excluded from using it as well.\n    my_data_frame.set_subroles(\n        \"ucc\", getml.data.subroles.include.substring)\n    ```\n\"\"\"\n\nemail: Final[IncludeEmail] = \"include email\"\n\"\"\"\nA column with this subrole will be\nused for the [`EmailDomain`][getml.preprocessors.EmailDomain] preprocessor.\n\"\"\"\n\nsubstring: Final[IncludeSubstring] = \"include substring\"\n\"\"\"\nA column with this subrole will be\nused for the [`Substring`][getml.preprocessors.Substring] preprocessor.\n\"\"\"\n\n__all__ = (\"email\", \"substring\")\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/only.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nfrom typing import Final\n\nfrom getml.data.subroles.types import OnlyEmail, OnlySubstring\n\n\"\"\"\nColumns marked with a subrole in this submodule will only be used\nfor the specified purpose and nothing else.\n\n??? example\n    ```python\n    # Only the EmailDomain preprocessor will be applied\n    # to \"emails\". All other preprocessors, feature learners,\n    # feature selectors and predictors will ignore this column.\n    my_data_frame.set_subroles(\"emails\", getml.data.subroles.only.email)\n    ```\n\n\"\"\"\n\nemail: Final[OnlyEmail] = \"only email\"\n\"\"\"\nA column with this subrole will only be\nused for the [`EmailDomain`][getml.preprocessors.EmailDomain]\npreprocessor and nothing else. It will be ignored by all other preprocessors,\nfeature learners and predictors.\n\"\"\"\n\nsubstring: Final[OnlySubstring] = \"only substring\"\n\"\"\"\nA column with this subrole will only be\nused for the [`Substring`][getml.preprocessors.Substring]\npreprocessor and nothing else. It will be ignored by all other preprocessors,\nfeature learners and predictors.\n\"\"\"\n\n__all__ = (\"email\", \"substring\")\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/sets.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import FrozenSet\n\nfrom getml.data import subroles\nfrom getml.data.subroles.types import ExcludeLike, IncludeLike, OnlyLike, Subrole\n\nexclude: FrozenSet[ExcludeLike] = frozenset(\n    {\n        subroles.exclude.category_trimmer,\n        subroles.exclude.fastprop,\n        subroles.exclude.feature_learners,\n        subroles.exclude.imputation,\n        subroles.exclude.mapping,\n        subroles.exclude.multirel,\n        subroles.exclude.predictors,\n        subroles.exclude.preprocessors,\n        subroles.exclude.relboost,\n        subroles.exclude.relmt,\n        subroles.exclude.seasonal,\n        subroles.exclude.text_field_splitter,\n    }\n)\n\"\"\"\nSet of subroles that exclude columns from certain operations.\n\"\"\"\n\ninclude: FrozenSet[IncludeLike] = frozenset(\n    {\n        subroles.include.email,\n        subroles.include.substring,\n    }\n)\n\"\"\"\nSet of subroles that explicitly include columns for certain operations.\n\"\"\"\n\nonly: FrozenSet[OnlyLike] = frozenset(\n    {\n        subroles.only.email,\n        subroles.only.substring,\n    }\n)\n\"\"\"\nSet of subroles that restrict the operations that can be performed on columns.\n\"\"\"\n\nall_: FrozenSet[Subrole] = exclude | include | only\n\"\"\"\nSet of all possible subroles.\n\"\"\"\n"
  },
  {
    "path": "src/python-api/getml/data/subroles/types.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import Literal\n\nExcludeCategoryTrimmer = Literal[\"exclude category trimmer\"]\nExcludeFastProp = Literal[\"exclude fastprop\"]\nExcludeFeatureLearners = Literal[\"exclude feature learners\"]\nExcludeImputation = Literal[\"exclude imputation\"]\nExcludeMapping = Literal[\"exclude mapping\"]\nExcludeMultirel = Literal[\"exclude multirel\"]\nExcludePredictors = Literal[\"exclude predictors\"]\nExcludePreprocessors = Literal[\"exclude preprocessors\"]\nExcludeRelboost = Literal[\"exclude relboost\"]\nExcludeRelMT = Literal[\"exclude relmt\"]\nExcludeSeasonal = Literal[\"exclude seasonal\"]\nExcludeTextFieldSplitter = Literal[\"exclude text field splitter\"]\n\nExcludeLike = Literal[\n    ExcludeCategoryTrimmer,\n    ExcludeFastProp,\n    ExcludeFeatureLearners,\n    ExcludeImputation,\n    ExcludeMapping,\n    ExcludeMultirel,\n    ExcludePredictors,\n    ExcludePreprocessors,\n    ExcludeRelboost,\n    ExcludeRelMT,\n    ExcludeSeasonal,\n    ExcludeTextFieldSplitter,\n]\n\nIncludeEmail = Literal[\"include email\"]\nIncludeSubstring = Literal[\"include substring\"]\n\nIncludeLike = Literal[IncludeEmail, IncludeSubstring]\n\n\nOnlyEmail = Literal[\"only email\"]\nOnlySubstring = Literal[\"only substring\"]\n\nOnlyLike = Literal[OnlyEmail, OnlySubstring]\n\nSubrole = Literal[ExcludeLike, IncludeLike, OnlyLike]\n"
  },
  {
    "path": "src/python-api/getml/data/subset.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSubset class intended to be passed to the pipeline.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom inspect import cleandoc\nfrom typing import Dict, Union\n\nfrom getml.utilities.formatting import _Formatter\n\nfrom .data_frame import DataFrame\nfrom .view import View\n\n\n@dataclass\nclass Subset:\n    \"\"\"\n    A Subset consists of a population table and one or several peripheral tables.\n\n    It is passed by a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n    and [`TimeSeries`][getml.data.TimeSeries] to the [`Pipeline`][getml.Pipeline].\n\n    Attributes:\n        container_id:\n            The ID of the container the subset belongs to.\n\n        peripheral:\n            A dictionary containing the peripheral tables.\n\n        population:\n            The population table.\n\n    ??? example\n        ```python\n        container = getml.data.Container(\n            train=population_train,\n            test=population_test\n        )\n\n        container.add(\n            meta=meta,\n            order=order,\n            trans=trans\n        )\n\n        # train and test are Subsets.\n        # They contain population_train\n        # and population_test respectively,\n        # as well as their peripheral tables\n        # meta, order and trans.\n        my_pipeline.fit(container.train)\n\n        my_pipeline.score(container.test)\n        ```\n    \"\"\"\n\n    container_id: str\n    peripheral: Dict[str, Union[DataFrame, View]]\n    population: Union[DataFrame, View]\n\n    def _format(self):\n        headers_perph = [[\"name\", \"rows\", \"type\"]]\n\n        rows_perph = [\n            [perph.name, perph.nrows(), type(perph).__name__]\n            for perph in self.peripheral.values()\n        ]\n\n        names = [perph.name for perph in self.peripheral.values()]\n        aliases = list(self.peripheral.keys())\n\n        if any(alias not in names for alias in aliases):\n            headers_perph[0].insert(0, \"alias\")\n\n            for alias, row in zip(aliases, rows_perph):\n                row.insert(0, alias)\n\n        return self.population._format(), _Formatter(\n            headers=headers_perph, rows=rows_perph\n        )\n\n    def __repr__(self):\n        pop, perph = self._format()\n        pop_footer = self.population._collect_footer_data()\n\n        template = cleandoc(\n            \"\"\"\n            population\n            {pop}\n\n            peripheral\n            {perph}\n            \"\"\"\n        )\n\n        return template.format(\n            pop=pop._render_string(footer=pop_footer), perph=perph._render_string()\n        )\n\n    def _repr_html_(self):\n        pop, perph = self._format()\n        pop_footer = self.population._collect_footer_data()\n\n        template = cleandoc(\n            \"\"\"\n            <div>\n                <div style='margin-bottom: 10px; font-size: 1rem'>population</div>\n                {pop}\n            </div>\n            <div>\n                <div style='margin-bottom: 10px; font-size: 1rem'>peripheral</div>\n                {perph}\n            </div>\n            \"\"\"\n        )\n\n        return template.format(\n            pop=pop._render_html(footer=pop_footer), perph=perph._render_html()\n        )\n"
  },
  {
    "path": "src/python-api/getml/data/time.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Convenience functions for the handling of time stamps.\n\nIn getML, time stamps are always expressed as a floating point value. This float\nmeasures the number of seconds since UNIX time (January 1, 1970, 00:00:00).\nSmaller units of time are expressed as fractions of a second.\n\nTo make this a bit easier to handle, this module contains simple convenience\nfunctions that express other time units in terms of seconds.\"\"\"\n\nimport datetime as dt\n\n# --------------------------------------------------------------------------\n\n\ndef seconds(num: float) -> float:\n    \"\"\"\n    Returns the number of seconds.\n\n    Args:\n        num:\n            The number of seconds.\n\n    Returns:\n        *num*\n    \"\"\"\n    return num\n\n\n# --------------------------------------------------------------------------\n\n\ndef minutes(num: float) -> float:\n    \"\"\"\n    Expresses *num* minutes in terms of seconds.\n\n    Args:\n        num:\n            The number of minutes.\n\n    Returns:\n        *num* minutes expressed in terms of seconds.\n    \"\"\"\n    return seconds(num) * 60.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef hours(num: float) -> float:\n    \"\"\"\n    Expresses *num* hours in terms of seconds.\n\n    Args:\n        num:\n            The number of hours.\n\n    Returns:\n        *num* hours expressed in terms of seconds.\n    \"\"\"\n    return minutes(num) * 60.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef days(num: float) -> float:\n    \"\"\"\n    Expresses *num* days in terms of seconds.\n\n    Args:\n        num:\n            The number of days.\n\n    Returns:\n        *num* days expressed in terms of seconds.\n    \"\"\"\n    return hours(num) * 24.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef weeks(num: float) -> float:\n    \"\"\"\n    Expresses *num* weeks in terms of seconds.\n\n    Args:\n        num:\n            The number of weeks.\n\n    Returns:\n        *num* weeks expressed in terms of seconds.\n    \"\"\"\n    return days(num) * 7.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef milliseconds(num: float) -> float:\n    \"\"\"\n    Expresses *num* milliseconds in terms of fractions of a second.\n\n    Args:\n        num:\n            The number of milliseconds.\n\n    Returns:\n        *num* milliseconds expressed in terms of seconds.\n    \"\"\"\n    return seconds(num) / 1000.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef microseconds(num: float) -> float:\n    \"\"\"\n    Expresses *num* microseconds in terms of fractions of a second.\n\n    Args:\n        num:\n            The number of microseconds.\n\n    Returns:\n        *num* microseconds expressed in terms of seconds.\n    \"\"\"\n    return milliseconds(num) / 1000.0\n\n\n# --------------------------------------------------------------------------\n\n\ndef datetime(\n    year: int,\n    month: int,\n    day: int,\n    hour: int = 0,\n    minute: int = 0,\n    second: int = 0,\n    microsecond: int = 0,\n) -> float:\n    \"\"\"\n    Returns the number of seconds since UNIX time (January 1, 1970, 00:00:00).\n\n    Args:\n        year:\n            Year component of the date.\n\n        month:\n            Month component of the date.\n\n        day:\n            Day component of the date.\n\n        hour:\n            Hour component of the date.\n\n        minute:\n            Minute component of the date.\n\n        second:\n            Second component of the date.\n\n        microsecond:\n            Microsecond component of the date.\n\n    Returns:\n        The number of seconds since UNIX time (January 1, 1970, 00:00:00).\n    \"\"\"\n    return dt.datetime(\n        year,\n        month,\n        day,\n        hour,\n        minute,\n        second,\n        microsecond,\n        tzinfo=dt.timezone.utc,\n    ).timestamp()\n\n\n# --------------------------------------------------------------------------\n\n__all__ = (\n    \"seconds\",\n    \"minutes\",\n    \"hours\",\n    \"days\",\n    \"weeks\",\n    \"milliseconds\",\n    \"microseconds\",\n    \"datetime\",\n)\n"
  },
  {
    "path": "src/python-api/getml/data/time_series.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSpecial container for time series - abstracts away self-joins.\n\"\"\"\n\nfrom typing import Dict, Optional, Union\n\nfrom getml.data.columns import StringColumn, StringColumnView\nfrom getml.data.helpers import OnType\n\nfrom .columns import FloatColumn, FloatColumnView\nfrom .data_frame import DataFrame\nfrom .helpers import _finditems\nfrom .roles import time_stamp\nfrom .star_schema import StarSchema\nfrom .view import View\n\n\nclass TimeSeries(StarSchema):\n    \"\"\"\n    A TimeSeries is a simplifying abstraction that can be used\n    for machine learning problems on time series data.\n\n    It unifies [`Container`][getml.data.Container] and\n    [`DataModel`][getml.data.DataModel] thus abstracting away the need to\n    differentiate between the concrete data and the abstract data model.\n    It also abstracts away the need for\n    [self joins](https://en.wikipedia.org/wiki/Join_(SQL)#Self-join).\n\n    Attributes:\n        time_stamps:\n            The time stamps used to limit the self-join.\n\n        population:\n            The population table defines the\n            [statistical population ](https://en.wikipedia.org/wiki/Statistical_population)\n            of the machine learning problem and contains the target variables.\n\n        alias:\n            The alias to be used for the population table. If it isn't set, the 'population'\n            will be used as the alias. To explicitly set an alias for the\n            peripheral table, use [`with_name`][getml.DataFrame.with_name].\n\n        peripheral:\n            The peripheral tables are joined onto *population* or other\n            peripheral tables. Note that you can also pass them using\n            [`add`][getml.data.Container.add].\n\n        split:\n            Contains information on how you want to split *population* into\n            different [`Subset`][getml.data.Subset] s.\n            Also refer to [`split`][getml.data.split].\n\n        deep_copy:\n            Whether you want to create deep copies or your tables.\n\n        on:\n            The join keys to use. If none is passed, then everything\n            will be joined to everything else.\n\n        memory:\n            The difference between the time stamps until data is 'forgotten'.\n            Limiting your joins using memory can significantly speed up\n            training time. Provide the value in seconds, alternatively use\n             the convenience functions from [`time`][getml.data.time].\n\n        horizon:\n            The prediction horizon to apply to this join.\n            Provide the value in seconds, alternatively use\n            the convenience functions from [`time`][getml.data.time].\n\n        lagged_targets:\n            Whether you want to allow lagged targets. If this is set to True,\n            you must also pass a positive, non-zero *horizon*.\n\n        upper_time_stamp:\n            Name of a time stamp in *right_df* that serves as an upper limit\n            on the join.\n\n    ??? example\n        ```python\n        # All rows before row 10500 will be used for training.\n        split = getml.data.split.time(data_all, \"rowid\", test=10500)\n\n        time_series = getml.data.TimeSeries(\n            population=data_all,\n            time_stamps=\"rowid\",\n            split=split,\n            lagged_targets=False,\n            memory=30,\n        )\n\n        pipe = getml.Pipeline(\n            data_model=time_series.data_model,\n            feature_learners=[...],\n            predictors=...\n        )\n\n        pipe.check(time_series.train)\n\n        pipe.fit(time_series.train)\n\n        pipe.score(time_series.test)\n\n        # To generate predictions on new data,\n        # it is sufficient to use a Container.\n        # You don't have to recreate the entire\n        # TimeSeries, because the abstract data model\n        # is stored in the pipeline.\n        container = getml.data.Container(\n            population=population_new,\n        )\n\n        # Add the data as a peripheral table, for the\n        # self-join.\n        container.add(population=population_new)\n\n        predictions = pipe.predict(container.full)\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        population: Union[DataFrame, View],\n        time_stamps: str,\n        alias: Optional[str] = None,\n        peripheral: Optional[Dict[str, Union[DataFrame, View]]] = None,\n        split: Optional[Union[StringColumn, StringColumnView]] = None,\n        deep_copy: Optional[bool] = False,\n        on: OnType = None,\n        memory: Optional[float] = None,\n        horizon: Optional[float] = None,\n        lagged_targets: bool = False,\n        upper_time_stamp: Optional[str] = None,\n    ):\n        if not isinstance(population, (DataFrame, View)):\n            raise TypeError(\n                \"'population' must be a getml.DataFrame or a getml.data.View\"\n            )\n\n        if isinstance(time_stamps, FloatColumn):\n            time_stamps = time_stamps.name\n\n        if isinstance(time_stamps, FloatColumnView):\n            if \"rowid\" in _finditems(\"operator_\", time_stamps.cmd):\n                time_stamps = \"rowid\"\n\n        population = (\n            population.with_column(\n                population.rowid, name=\"rowid\", role=time_stamp\n            ).with_unit(names=\"rowid\", unit=\"rowid\", comparison_only=True)\n            if time_stamps == \"rowid\"\n            else population\n        )\n\n        alias = \"population\" if alias is None else alias\n\n        super().__init__(\n            population=population,\n            alias=alias,\n            peripheral=peripheral,\n            split=split,\n            deep_copy=deep_copy,\n        )\n\n        self.on = on\n        self.time_stamps = time_stamps\n        self.memory = memory\n        self.horizon = horizon\n        self.lagged_targets = lagged_targets\n        self.upper_time_stamp = upper_time_stamp\n\n        if not isinstance(on, list):\n            on = [on]\n\n        for o in on:\n            self._add_joins(o)\n\n    def _add_joins(self, on):\n        self.join(\n            right_df=self.population,\n            alias=self.population.name,\n            on=self.on,\n            time_stamps=self.time_stamps,\n            memory=self.memory,\n            horizon=self.horizon,\n            lagged_targets=self.lagged_targets,\n            upper_time_stamp=self.upper_time_stamp,\n        )\n"
  },
  {
    "path": "src/python-api/getml/data/view.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Contains the view.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport numbers\nimport warnings\nfrom collections import namedtuple\nfrom collections.abc import Iterator\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom typing import (\n    TYPE_CHECKING,\n    Any,\n    Dict,\n    Iterable,\n    List,\n    Literal,\n    Optional,\n    Tuple,\n    Union,\n    cast,\n)\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\n\nimport getml.communication as comm\nfrom getml import data\nfrom getml.constants import DEFAULT_BATCH_SIZE\nfrom getml.data._io.arrow import to_arrow, to_arrow_stream\nfrom getml.data._io.csv import to_csv\nfrom getml.data._io.parquet import to_parquet\nfrom getml.data.columns import (\n    BooleanColumnView,\n    FloatColumn,\n    FloatColumnView,\n    StringColumn,\n    StringColumnView,\n    rowid,\n)\nfrom getml.data.helpers import (\n    _is_typed_list,\n    _iter_batches,\n    _to_pyspark,\n    _where,\n    _with_column,\n    _with_role,\n    _with_subroles,\n    _with_unit,\n)\nfrom getml.data.placeholder import Placeholder\nfrom getml.data.roles import (\n    categorical,\n    join_key,\n    numerical,\n    target,\n    text,\n    time_stamp,\n    unused_float,\n    unused_string,\n)\nfrom getml.data.roles.container import Roles\nfrom getml.data.roles.types import Role\nfrom getml.data.subroles.types import Subrole\nfrom getml.database import Connection\nfrom getml.log import logger\nfrom getml.utilities.formatting import _ViewFormatter\n\nif TYPE_CHECKING:\n    import pyspark.sql\n\n    from getml.data import DataFrame\n\n# --------------------------------------------------------------------\n\n\nclass View:\n    \"\"\"A view is a lazily evaluated, immutable representation of a [`DataFrame`][getml.DataFrame].\n\n    There are important differences between a\n    [`DataFrame`][getml.DataFrame] and a view:\n\n    - Views are lazily evaluated. That means that views do not\n      contain any data themselves. Instead, they just refer to\n      an underlying data frame. If the underlying data frame changes,\n      so will the view (but such behavior will result in a warning).\n\n    - Views are immutable. In-place operations on a view are not\n      possible. Any operation on a view will result in a new view.\n\n    - Views have no direct representation on the getML Engine, and\n      therefore they do not need to have an identifying name.\n\n    Attributes:\n        base:\n            A data frame or view used as the basis for this view.\n\n        name:\n            The name assigned to this view.\n\n        subselection:\n            Indicates which rows we would like to keep.\n\n        added:\n            A dictionary that describes a new column\n            that has been added to the view.\n\n        dropped:\n            A list of columns that have been dropped.\n\n    ??? example\n        You hardly ever directly create views. Instead, it is more likely\n        that you will encounter them as a result of some operation on a\n        [`DataFrame`][getml.DataFrame]:\n\n        ```python\n\n        # Creates a view on the first 100 lines\n        view1 = data_frame[:100]\n\n        # Creates a view without some columns.\n        view2 = data_frame.drop([\"col1\", \"col2\"])\n\n        # Creates a view in which some roles are reassigned.\n        view3 = data_frame.with_role([\"col1\", \"col2\"], getml.data.roles.categorical)\n        ```\n        A recommended pattern is to assign 'baseline roles' to your data frames\n        and then using views to tweak them:\n\n        ```python\n        # Assign baseline roles\n        data_frame.set_role([\"jk\"], getml.data.roles.join_key)\n        data_frame.set_role([\"col1\", \"col2\"], getml.data.roles.categorical)\n        data_frame.set_role([\"col3\", \"col4\"], getml.data.roles.numerical)\n        data_frame.set_role([\"col5\"], getml.data.roles.target)\n\n        # Make the data frame immutable, so in-place operations are\n        # no longer possible.\n        data_frame.freeze()\n\n        # Save the data frame.\n        data_frame.save()\n\n        # I suspect that col1 leads to overfitting, so I will drop it.\n        view = data_frame.drop([\"col1\"])\n\n        # Insert the view into a container.\n        container = getml.data.Container(...)\n        container.add(some_alias=view)\n        container.save()\n        ```\n\n        The advantage of using such a pattern is that it enables you to\n        always completely retrace your entire pipeline without creating\n        deep copies of the data frames whenever you have made a small\n        change like the one in our example. Note that the pipeline will\n        record which [`Container`][getml.data.Container] you have used.\n\n    \"\"\"\n\n    # ------------------------------------------------------------\n\n    def __init__(\n        self,\n        base: Union[DataFrame, View],\n        name: Optional[str] = None,\n        subselection: Optional[\n            Union[BooleanColumnView, FloatColumn, FloatColumnView]\n        ] = None,\n        added: Optional[Dict] = None,\n        dropped: Optional[List[str]] = None,\n    ):\n        self._added = added\n        self._base = deepcopy(base)\n        self._dropped = dropped or []\n        self._name = name\n        self._subselection = subselection\n\n        self._initial_timestamp: str = (\n            self._base._initial_timestamp\n            if isinstance(self._base, View)\n            else self._base.last_change\n        )\n\n        self._base.refresh()\n\n    # ------------------------------------------------------------\n\n    def _apply_subselection(self, col):\n        if self._subselection is not None:\n            return col[self._subselection]\n        return col\n\n    # ------------------------------------------------------------\n\n    def _collect_footer_data(self):\n        footer = namedtuple(\"footer\", [\"n_rows\", \"n_cols\", \"type\"])\n\n        n_rows = \"unknown number of\" if self.nrows() == \"unknown\" else self.nrows()\n\n        return footer(\n            n_rows=n_rows,\n            n_cols=len(self.colnames),\n            type=\"getml.data.View\",\n        )\n\n    # ------------------------------------------------------------\n\n    def _format(self):\n        return _ViewFormatter(self)\n\n    # ----------------------------------------------------------------\n\n    def __getattr__(self, name):\n        try:\n            return self[name]\n        except KeyError:\n            return super().__getattribute__(name)\n\n    # ------------------------------------------------------------\n\n    def __getitem__(self, name):\n        if isinstance(\n            name,\n            (numbers.Integral, slice, BooleanColumnView, FloatColumn, FloatColumnView),\n        ):\n            return self.where(index=name)\n\n        if isinstance(name, list):\n            not_in_colnames = set(name) - set(self.colnames)\n            if not_in_colnames:\n                raise KeyError(f\"{list(not_in_colnames)} not found.\")\n            dropped = [col for col in self.colnames if col not in name]\n            return View(base=self, dropped=dropped)\n\n        if name in self.__dict__[\"_dropped\"]:\n            raise KeyError(\n                \"Cannot retrieve column '\" + name + \"'. It has been dropped.\"\n            )\n\n        if (\n            self.__dict__[\"_added\"] is not None\n            and self.__dict__[\"_added\"][\"name_\"] == name\n        ):\n            return self._apply_subselection(\n                self.__dict__[\"_added\"][\"col_\"]\n                .with_subroles(self.__dict__[\"_added\"][\"subroles_\"], append=False)\n                .with_unit(self.__dict__[\"_added\"][\"unit_\"])\n            )\n\n        return self._apply_subselection(self.__dict__[\"_base\"][name])\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return cast(int, self.nrows(force=True))\n\n    # ------------------------------------------------------------\n\n    def _getml_deserialize(self) -> Dict[str, Any]:\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"View\"\n\n        if self._added is not None:\n            added = deepcopy(self._added)\n            col = deepcopy(added[\"col_\"].cmd)\n            col[\"last_change_\"] = added[\"col_\"].last_change\n            added[\"col_\"] = col\n            cmd[\"added_\"] = added\n\n        if self._subselection is not None:\n            subselection = deepcopy(self._subselection.cmd)\n            subselection[\"last_change_\"] = self.subselection.last_change\n            cmd[\"subselection_\"] = subselection\n\n        cmd[\"base_\"] = self._base._getml_deserialize()\n        cmd[\"dropped_\"] = self._dropped\n        cmd[\"name_\"] = self.name\n        cmd[\"last_change_\"] = self.last_change\n\n        return cmd\n\n    # ------------------------------------------------------------\n\n    def _modify_colnames(self, base_names, role):\n        remove_dropped = [name for name in base_names if name not in self._dropped]\n\n        if self._added is not None and self._added[\"role_\"] != role:\n            return [name for name in remove_dropped if name != self._added[\"name_\"]]\n\n        if (\n            self._added is not None\n            and self._added[\"role_\"] == role\n            and self._added[\"name_\"] not in remove_dropped\n        ):\n            return remove_dropped + [self._added[\"name_\"]]\n\n        return remove_dropped\n\n    # ------------------------------------------------------------\n\n    def __repr__(self):\n        formatted = self._format()\n\n        footer = self._collect_footer_data()\n\n        return formatted._render_string(footer=footer)\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self):\n        formatted = self._format()\n\n        footer = self._collect_footer_data()\n\n        return formatted._render_html(footer=footer)\n\n    # ------------------------------------------------------------\n\n    @property\n    def added(self) -> Dict:\n        \"\"\"\n        The column that has been added to the view.\n\n        Returns:\n                The column that has been added to the view.\n        \"\"\"\n        return deepcopy(self._added)\n\n    # ------------------------------------------------------------\n\n    @property\n    def base(self) -> Union[DataFrame, View]:\n        \"\"\"\n        The basis on which the view is created. Must be a\n        [`DataFrame`][getml.DataFrame] or a [`View`][getml.data.View].\n\n        Returns:\n                The basis on which the view is created.\n        \"\"\"\n        return deepcopy(self._base)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _categorical_names(self):\n        return self._modify_colnames(self._base._categorical_names, categorical)\n\n    # ------------------------------------------------------------\n\n    def check(self):\n        \"\"\"\n        Checks whether the underlying data frame has been changed\n        after the creation of the view.\n        \"\"\"\n        last_change = self.last_change\n        if last_change != self.__dict__[\"_initial_timestamp\"]:\n            logger.warning(\n                \"The data frame underlying view '\"\n                + self.name\n                + \"' was last changed at \"\n                + last_change\n                + \", which was after the creation of the view. \"\n                + \"This might lead to unexpected results. You might \"\n                + \"want to recreate the view. (Views are lazily \"\n                + \"evaluated, so recreating them is a very \"\n                + \"inexpensive operation).\"\n            )\n\n    # ------------------------------------------------------------\n\n    @property\n    def colnames(self) -> List[str]:\n        \"\"\"\n        List of the names of all columns.\n\n        Returns:\n                List of the names of all columns.\n        \"\"\"\n        return (\n            self._time_stamp_names\n            + self._join_key_names\n            + self._target_names\n            + self._categorical_names\n            + self._numerical_names\n            + self._text_names\n            + self._unused_names\n        )\n\n    # ------------------------------------------------------------\n\n    @property\n    def columns(self) -> List[str]:\n        \"\"\"\n        Alias for [`colnames`][getml.data.View.colnames].\n\n        Returns:\n                List of the names of all columns.\n        \"\"\"\n        return self.colnames\n\n    # ------------------------------------------------------------\n\n    def drop(self, cols: Union[str, List[str]]) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] that has one or several columns removed.\n\n        Args:\n            cols:\n                The names of the columns to be dropped.\n\n        Returns:\n                A new view with the specified columns removed.\n        \"\"\"\n        if isinstance(cols, str):\n            cols = [cols]\n\n        if not _is_typed_list(cols, str):\n            raise TypeError(\"'cols' must be a string or a list of strings.\")\n\n        return View(base=self, dropped=cols)\n\n    # ------------------------------------------------------------\n\n    @property\n    def dropped(self) -> List[str]:\n        \"\"\"\n        The names of the columns that has been dropped.\n\n        Returns:\n                The names of the columns that has been dropped.\n        \"\"\"\n        return deepcopy(self._dropped)\n\n    # ------------------------------------------------------------\n\n    def iter_batches(self, batch_size: int = DEFAULT_BATCH_SIZE) -> Iterator[View]:\n        yield from _iter_batches(self, batch_size)\n\n    # ------------------------------------------------------------\n\n    @property\n    def last_change(self) -> str:\n        \"\"\"\n        A string describing the last time this data frame has been changed.\n\n        Returns:\n                A string describing the last time this data frame has been changed.\n        \"\"\"\n        return self.__dict__[\"_base\"].last_change\n\n    # ------------------------------------------------------------\n\n    @property\n    def _join_key_names(self):\n        return self._modify_colnames(self._base._join_key_names, join_key)\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self) -> str:\n        \"\"\"\n        The name of the view. If no name is explicitly set,\n        the name will be identical to the name of the base.\n\n        Returns:\n                The name of the view.\n\n        \"\"\"\n        if self.__dict__[\"_name\"] is None:\n            return self.__dict__[\"_base\"].name\n        return deepcopy(self.__dict__[\"_name\"])\n\n    # ------------------------------------------------------------\n\n    def ncols(self) -> int:\n        \"\"\"\n        Number of columns in the current instance.\n\n        Returns:\n                Overall number of columns\n        \"\"\"\n        return len(self.colnames)\n\n    # ------------------------------------------------------------\n\n    def nrows(self, force: bool = False) -> Union[int, str]:\n        \"\"\"\n        Returns the number of rows in the current instance.\n\n        Args:\n            force:\n                If the number of rows is unknown,\n                do you want to force the Engine to calculate it anyway?\n                This is a relatively expensive operation, therefore\n                you might not necessarily want this.\n\n        Returns:\n                The number of rows in the current instance.\n        \"\"\"\n\n        self.refresh()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"View.get_nrows\"\n        cmd[\"name_\"] = \"\"\n\n        cmd[\"cols_\"] = [self[cname].cmd for cname in self.colnames]\n        cmd[\"force_\"] = force\n\n        with comm.send_and_get_socket(cmd) as sock:\n            json_str = comm.recv_string(sock)\n\n        if json_str[0] != \"{\":\n            comm.handle_engine_exception(json_str)\n\n        response = json.loads(json_str)\n\n        if \"recordsTotal\" in response:\n            return response[\"recordsTotal\"]\n\n        # ensure that we do not display \"unknown\" if the number of rows is\n        # less than or equal to the maximum number of diplayed rows\n        nrows_to_display = len(self[: _ViewFormatter.max_rows + 1])\n        if nrows_to_display <= _ViewFormatter.max_rows:\n            return nrows_to_display\n\n        return \"unknown\"\n\n    # ------------------------------------------------------------\n\n    @property\n    def _numerical_names(self):\n        return self._modify_colnames(self._base._numerical_names, numerical)\n\n    # --------------------------------------------------------------------------\n\n    def refresh(self) -> View:\n        \"\"\"Aligns meta-information of the current instance with the\n        corresponding data frame in the getML Engine.\n\n        Returns:\n                Updated handle the underlying data frame in the getML\n                Engine.\n\n        \"\"\"\n        self._base = self.__dict__[\"_base\"].refresh()\n        return self\n\n    # ------------------------------------------------------------\n\n    @property\n    def roles(self) -> Roles:\n        \"\"\"\n        The roles of the columns included\n        in this View.\n\n        Returns:\n                The roles of the columns included in this View.\n        \"\"\"\n        return Roles(\n            categorical=tuple(self._categorical_names),\n            join_key=tuple(self._join_key_names),\n            numerical=tuple(self._numerical_names),\n            target=tuple(self._target_names),\n            text=tuple(self._text_names),\n            time_stamp=tuple(self._time_stamp_names),\n            unused_float=tuple(self._unused_float_names),\n            unused_string=tuple(self._unused_string_names),\n        )\n\n    # ------------------------------------------------------------\n\n    @property\n    def rowid(self) -> List[int]:\n        \"\"\"\n        The rowids for this view.\n\n        Returns:\n                The rowids for this view.\n        \"\"\"\n        return rowid()[: len(self)]\n\n    # ------------------------------------------------------------\n\n    @property\n    def subselection(self) -> Union[BooleanColumnView, FloatColumn, FloatColumnView]:\n        \"\"\"\n        The subselection that is applied to this view.\n\n        Returns:\n                The subselection that is applied to this view.\n        \"\"\"\n        return deepcopy(self._subselection)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _target_names(self):\n        return self._modify_colnames(self._base._target_names, target)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _text_names(self):\n        return self._modify_colnames(self._base._text_names, text)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _time_stamp_names(self):\n        return self._modify_colnames(self._base._time_stamp_names, time_stamp)\n\n    # ------------------------------------------------------------\n\n    @property\n    def shape(self) -> Tuple[Union[int, str], int]:\n        \"\"\"\n        A tuple containing the number of rows and columns of\n        the View.\n        \"\"\"\n        self.refresh()\n        return self.nrows(), self.ncols()\n\n    # ------------------------------------------------------------\n    def to_arrow(self) -> pa.Table:\n        \"\"\"Creates a `pyarrow.Table` from the view.\n\n        Loads the underlying data from the getML Engine and constructs\n        a `pyarrow.Table`.\n\n        Returns:\n                Pyarrow equivalent of the current instance including\n                its underlying data.\n        \"\"\"\n        return to_arrow(self)\n\n    # ------------------------------------------------------------\n\n    @contextmanager\n    def to_arrow_stream(self) -> Iterator[pa.RecordBatchReader]:\n        \"\"\"\n        Streams the view as an Apache Arrow `pa.RecordBatchReader`.\n\n        This method provides a way to access the view as an Apache Arrow\n        stream. Apache Arrow is a cross-language development platform for\n        in-memory data that specifies a standardized language-independent\n        columnar memory format. Using `to_arrow_stream` allows for efficient,\n        zero-copy (or near zero-copy) data exchange with other systems that\n        support Arrow, such as DuckDB, Pandas, Polars, and various data\n        processing engines.\n\n        The method is a context manager (used with a `with` statement). This\n        ensures that any underlying resources associated with the stream,\n        such as network connections or temporary files, are properly initialized\n        when entering the `with` block and cleaned up when exiting.\n\n        The `pa.RecordBatchReader` yielded by this context manager allows you to\n        read the dataset iteratively as a sequence of Arrow `RecordBatch` objects.\n        Each `RecordBatch` represents a chunk of the dataset's columns.\n\n        Yields:\n            pa.RecordBatchReader: An iterator-like object that yields Apache\n                Arrow `RecordBatch` instances.\n\n        ??? Example\n            ```python\n            Integrating with DuckDB for SQL-based analysis:\n\n            import getml\n            import duckdb\n\n            getml.set_project(\"arrow_stream\")\n\n            generated, _ = getml.datasets.make_numerical()\n            generated_view = generated[:100]\n\n            con = duckdb.connect()\n\n            # Use the context manager to get the Arrow stream\n            with generated_view.to_arrow_stream() as arrow_stream_reader:\n                # Register the Arrow stream as a duckdb relation\n                con.register(\"generated\", arrow_stream_reader)\n\n                # Now you can query the data using SQL\n                count = con.execute(\"SELECT COUNT(*) FROM generated\").df()\n                print(count)\n            # count_star()\n            # 100\n            ```\n        \"\"\"\n        with to_arrow_stream(self) as stream:\n            yield stream\n\n    # ------------------------------------------------------------\n\n    def to_json(self) -> str:\n        \"\"\"Creates a JSON string from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        a JSON string.\n\n        Returns:\n                JSON string of the current instance including its\n                underlying data.\n        \"\"\"\n        return self.to_pandas().to_json()\n\n    def to_csv(\n        self,\n        fname: str,\n        quotechar: str = '\"',\n        sep: str = \",\",\n        batch_size: int = 0,\n        quoting_style: str = \"needed\",\n    ):\n        \"\"\"\n        Writes the underlying data into a newly created CSV file.\n\n        Args:\n            fname:\n                The name of the CSV file.\n                The ending \".csv\" and an optional batch number will\n                be added automatically.\n\n            quotechar:\n                The character used to wrap strings.\n\n            sep:\n                The character used for separating fields.\n\n            batch_size:\n                Maximum number of lines per file. Set to 0 to read\n                the entire data frame into a single file.\n\n            quoting_style (str):\n                The quoting style to use. Delegated to pyarrow.\n\n                The following values are accepted:\n                - `\"needed\"` (default): only enclose values in quotes when needed.\n                - `\"all_valid\"`: enclose all valid values in quotes; nulls are not\n                  quoted.\n                - `\"none\"`: do not enclose any values in quotes; values containing\n                  special characters (such as quotes, cell delimiters or line\n                  endings) will raise an error.\n\n        Deprecated:\n           1.5: The `quotechar` parameter is deprecated.\n        \"\"\"\n\n        if quotechar != '\"':\n            warnings.warn(\n                \"'quotechar' is deprecated, use 'quoting_style' instead.\",\n                DeprecationWarning,\n            )\n\n        to_csv(self, fname, sep, batch_size, quoting_style)\n\n    # ------------------------------------------------------------\n\n    def to_db(self, table_name: str, conn: Optional[Connection] = None):\n        \"\"\"Writes the underlying data into a newly created table in the\n        database.\n\n        Args:\n            table_name:\n                Name of the table to be created.\n\n                If a table of that name already exists, it will be\n                replaced.\n\n            conn:\n                The database connection to be used.\n                If you don't explicitly pass a connection,\n                the Engine will use the default connection.\n        \"\"\"\n\n        conn = conn or Connection()\n\n        self.refresh()\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be of type str\")\n\n        if not isinstance(conn, Connection):\n            raise TypeError(\"'conn' must be a getml.database.Connection object or None\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"View.to_db\"\n        cmd[\"name_\"] = \"\"\n\n        cmd[\"view_\"] = self._getml_deserialize()\n        cmd[\"table_name_\"] = table_name\n        cmd[\"conn_id_\"] = conn.conn_id\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def to_df(self, name) -> DataFrame:\n        \"\"\"Creates a [`DataFrame`][getml.DataFrame] from the view.\"\"\"\n        self.check()\n        self = self.refresh()\n        df = data.DataFrame(name)\n        return df.read_view(self)\n\n    # ------------------------------------------------------------\n\n    def to_pandas(self) -> pd.DataFrame:\n        \"\"\"Creates a `pandas.DataFrame` from the view.\n\n        Loads the underlying data from the getML Engine and constructs\n        a `pandas.DataFrame`.\n\n        Returns:\n                Pandas equivalent of the current instance including\n                its underlying data.\n        \"\"\"\n        return to_arrow(self).to_pandas()\n\n    # ------------------------------------------------------------\n\n    def to_placeholder(self, name: Optional[str] = None) -> Placeholder:\n        \"\"\"Generates a [`Placeholder`][getml.data.Placeholder] from the\n        current [`View`][getml.data.View].\n\n        Args:\n            name:\n                The name of the placeholder. If no\n                name is passed, then the name of the placeholder will\n                be identical to the name of the current view.\n\n        Returns:\n                A placeholder with the same name as this data frame.\n\n\n        \"\"\"\n        self.refresh()\n        return Placeholder(name=name or self.name, roles=self.roles)\n\n    # ------------------------------------------------------------\n\n    def to_parquet(\n        self,\n        fname: str,\n        compression: Literal[\"brotli\", \"gzip\", \"lz4\", \"snappy\", \"zstd\"] = \"snappy\",\n        coerce_timestamps: Optional[bool] = None,\n    ):\n        \"\"\"\n        Writes the underlying data into a newly created parquet file.\n\n        Args:\n            fname:\n                The name of the parquet file.\n\n            compression:\n                The compression format to use.\n                Supported values are \"brotli\", \"gzip\", \"lz4\", \"snappy\", \"zstd\"\n            coerce_timestamps:\n                Cast time stamps to a particular resolution.\n                For details, refer to `pyarrow.parquet.ParquetWriter` docs.\n        \"\"\"\n        to_parquet(self, fname, compression, coerce_timestamps=coerce_timestamps)\n\n    # ----------------------------------------------------------------\n\n    def to_pyspark(\n        self, spark: pyspark.sql.SparkSession, name: Optional[str] = None\n    ) -> pyspark.sql.DataFrame:\n        \"\"\"Creates a `pyspark.sql.DataFrame` from the current instance.\n\n        Loads the underlying data from the getML Engine and constructs\n        a `pyspark.sql.DataFrame`.\n\n        Args:\n            spark:\n                The pyspark session in which you want to\n                create the data frame.\n\n            name:\n                The name of the temporary view to be created on top\n                of the `pyspark.sql.DataFrame`,\n                with which it can be referred to\n                in Spark SQL (refer to\n                `pyspark.sql.DataFrame.createOrReplaceTempView`).\n                If none is passed, then the name of this\n                [`DataFrame`][getml.DataFrame] will be used.\n\n        Returns:\n                Pyspark equivalent of the current instance including\n                its underlying data.\n\n        \"\"\"\n        return _to_pyspark(self, name, spark)\n\n    # ------------------------------------------------------------\n\n    def to_s3(\n        self,\n        bucket: str,\n        key: str,\n        region: str,\n        sep: str = \",\",\n        batch_size: int = 50000,\n    ):\n        \"\"\"\n        Writes the underlying data into a newly created CSV file\n        located in an S3 bucket.\n\n        Note:\n            S3 is not supported on Windows.\n\n        Args:\n            bucket:\n                The bucket from which to read the files.\n\n            key:\n                The key in the S3 bucket in which you want to\n                write the output. The ending \".csv\" and an optional\n                batch number will be added automatically.\n\n            region:\n                The region in which the bucket is located.\n\n            sep:\n                The character used for separating fields.\n\n            batch_size:\n                Maximum number of lines per file. Set to 0 to read\n                the entire data frame into a single file.\n\n        ??? example\n            ```python\n            getml.engine.set_s3_access_key_id(\"YOUR-ACCESS-KEY-ID\")\n            getml.engine.set_s3_secret_access_key(\"YOUR-SECRET-ACCESS-KEY\")\n\n            your_view.to_s3(\n                bucket=\"your-bucket-name\",\n                key=\"filename-on-s3\",\n                region=\"us-east-2\",\n                sep=';'\n            )\n            ```\n        \"\"\"\n\n        self.refresh()\n\n        if not isinstance(bucket, str):\n            raise TypeError(\"'bucket' must be of type str\")\n\n        if not isinstance(key, str):\n            raise TypeError(\"'fname' must be of type str\")\n\n        if not isinstance(region, str):\n            raise TypeError(\"'region' must be of type str\")\n\n        if not isinstance(sep, str):\n            raise TypeError(\"'sep' must be of type str\")\n\n        if not isinstance(batch_size, numbers.Real):\n            raise TypeError(\"'batch_size' must be a real number\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"View.to_s3\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"view_\"] = self._getml_deserialize()\n        cmd[\"bucket_\"] = bucket\n        cmd[\"key_\"] = key\n        cmd[\"region_\"] = region\n        cmd[\"sep_\"] = sep\n        cmd[\"batch_size_\"] = batch_size\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_float_names(self):\n        return self._modify_colnames(self._base._unused_float_names, unused_float)\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_names(self):\n        return self._unused_float_names + self._unused_string_names\n\n    # ------------------------------------------------------------\n\n    @property\n    def _unused_string_names(self):\n        return self._modify_colnames(self._base._unused_string_names, unused_string)\n\n    # ------------------------------------------------------------\n\n    def where(\n        self, index: Optional[Union[BooleanColumnView, FloatColumn, FloatColumnView]]\n    ) -> View:\n        \"\"\"Extract a subset of rows.\n\n        Creates a new [`View`][getml.data.View] as a\n        subselection of the current instance.\n\n        Args:\n            index:\n                Boolean column indicating the rows you want to select.\n\n        Returns:\n            A new view containing only the rows that satisfy the condition.\n\n        ??? example\n            Generate example data:\n            ```python\n            data = dict(\n                fruit=[\"banana\", \"apple\", \"cherry\", \"cherry\", \"melon\", \"pineapple\"],\n                price=[2.4, 3.0, 1.2, 1.4, 3.4, 3.4],\n                join_key=[\"0\", \"1\", \"2\", \"2\", \"3\", \"3\"])\n\n            fruits = getml.DataFrame.from_dict(data, name=\"fruits\",\n            roles={\"categorical\": [\"fruit\"], \"join_key\": [\"join_key\"], \"numerical\": [\"price\"]})\n\n            fruits\n            ```\n            ```\n            | join_key | fruit       | price     |\n            | join key | categorical | numerical |\n            --------------------------------------\n            | 0        | banana      | 2.4       |\n            | 1        | apple       | 3         |\n            | 2        | cherry      | 1.2       |\n            | 2        | cherry      | 1.4       |\n            | 3        | melon       | 3.4       |\n            | 3        | pineapple   | 3.4       |\n            ```\n            Apply where condition. This creates a new DataFrame called \"cherries\":\n\n            ```python\n\n            cherries = fruits.where(\n                fruits[\"fruit\"] == \"cherry\")\n\n            cherries\n            ```\n            ```\n            | join_key | fruit       | price     |\n            | join key | categorical | numerical |\n            --------------------------------------\n            | 2        | cherry      | 1.2       |\n            | 2        | cherry      | 1.4       |\n            ```\n        \"\"\"\n        return _where(self, index)\n\n    # ------------------------------------------------------------\n\n    def with_column(\n        self,\n        col: Union[\n            bool,\n            str,\n            float,\n            int,\n            np.datetime64,\n            FloatColumn,\n            FloatColumnView,\n            StringColumn,\n            StringColumnView,\n            BooleanColumnView,\n        ],\n        name: str,\n        role: Optional[Role] = None,\n        unit: str = \"\",\n        subroles: Optional[Union[Subrole, Iterable[str]]] = None,\n        time_formats: Optional[List[str]] = None,\n    ) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] that contains an additional column.\n\n        Args:\n            col:\n                The column to be added.\n\n            name:\n                Name of the new column.\n\n            role:\n                Role of the new column. Must be from [`roles`][getml.data.roles].\n\n            subroles:\n                Subroles of the new column. Must be from [`subroles`][getml.data.subroles].\n\n            unit:\n                Unit of the column.\n\n            time_formats:\n                Formats to be used to parse the time stamps.\n\n                This is only necessary, if an implicit conversion from\n                a [`StringColumn`][getml.data.columns.StringColumn] to a time\n                stamp is taking place.\n\n                The formats are allowed to contain the following\n                special characters:\n\n                * %w - abbreviated weekday (Mon, Tue, ...)\n                * %W - full weekday (Monday, Tuesday, ...)\n                * %b - abbreviated month (Jan, Feb, ...)\n                * %B - full month (January, February, ...)\n                * %d - zero-padded day of month (01 .. 31)\n                * %e - day of month (1 .. 31)\n                * %f - space-padded day of month ( 1 .. 31)\n                * %m - zero-padded month (01 .. 12)\n                * %n - month (1 .. 12)\n                * %o - space-padded month ( 1 .. 12)\n                * %y - year without century (70)\n                * %Y - year with century (1970)\n                * %H - hour (00 .. 23)\n                * %h - hour (00 .. 12)\n                * %a - am/pm\n                * %A - AM/PM\n                * %M - minute (00 .. 59)\n                * %S - second (00 .. 59)\n                * %s - seconds and microseconds (equivalent to %S.%F)\n                * %i - millisecond (000 .. 999)\n                * %c - centisecond (0 .. 9)\n                * %F - fractional seconds/microseconds (000000 - 999999)\n                * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n                * %Z - time zone differential in RFC format (GMT or +NNNN)\n                * %% - percent sign\n\n        Returns:\n            A new view containing the additional column.\n        \"\"\"\n        col, role, subroles = _with_column(\n            col, name, role, subroles, unit, time_formats\n        )\n        return View(\n            base=self,\n            added={\n                \"col_\": col,\n                \"name_\": name,\n                \"role_\": role,\n                \"subroles_\": subroles,\n                \"unit_\": unit,\n            },\n        )\n\n    # ------------------------------------------------------------\n\n    def with_name(self, name: str) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] with a new name.\n\n        Args:\n            name (str):\n                The name of the new view.\n\n        Returns:\n            A new view with the new name.\n        \"\"\"\n        return View(base=self, name=name)\n\n    # ------------------------------------------------------------\n\n    def with_role(\n        self,\n        names: Union[str, List[str]],\n        role: str,\n        time_formats: Optional[List[str]] = None,\n    ) -> View:\n        \"\"\"Returns a new [`View`][getml.data.View] with modified roles.\n\n        When switching from a role based on type float to a role based on type\n        string or vice verse, an implicit type conversion will be conducted.\n        The `time_formats` argument is used to interpret time\n        format string: `annotating_roles_time_stamp`. For more information on\n        roles, please refer to the [User Guide][annotating-data].\n\n        Args:\n            names:\n                The name or names of the column.\n\n            role:\n                The role to be assigned.\n\n            time_formats:\n                Formats to be used to parse the time stamps.\n                This is only necessary, if an implicit conversion from a StringColumn to\n                a time stamp is taking place.\n\n        Returns:\n            A new view with the modified roles.\n        \"\"\"\n        return _with_role(self, names, role, time_formats)\n\n    # ------------------------------------------------------------\n\n    def with_subroles(\n        self,\n        names: Union[str, List[str]],\n        subroles: Union[Subrole, Iterable[str]],\n        append: bool = True,\n    ) -> View:\n        \"\"\"Returns a new view with one or several new subroles on one or more columns.\n\n        Args:\n            names:\n                The name or names of the column.\n\n            subroles:\n                The subroles to be assigned.\n\n            append:\n                Whether you want to append the\n                new subroles to the existing subroles.\n\n        Returns:\n            A new view with the modified subroles.\n        \"\"\"\n        return _with_subroles(self, names, subroles, append)\n\n    # ------------------------------------------------------------\n\n    def with_unit(\n        self, names: Union[str, List[str]], unit: str, comparison_only: bool = False\n    ) -> View:\n        \"\"\"Returns a view that contains a new unit on one or more columns.\n\n        Args:\n            names:\n                The name or names of the column.\n\n            unit:\n                The unit to be assigned.\n\n            comparison_only:\n                Whether you want the column to\n                be used for comparison only. This means that the column can\n                only be used in comparison to other columns of the same unit.\n\n                An example might be a bank account number: The number in itself\n                is hardly interesting, but it might be useful to know how often\n                we have seen that same bank account number in another table.\n\n                If True, this will append \", comparison only\" to the unit.\n                The feature learning algorithms and the feature selectors will\n                interpret this accordingly.\n\n        Returns:\n            A new view with the modified unit.\n        \"\"\"\n        return _with_unit(self, names, unit, comparison_only)\n\n    # ------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/database/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"This module contains communication routines to access various databases.\n\nThe [`connect_bigquery`][getml.database.connect_bigquery.connect_bigquery],\n[`connect_hana`][getml.database.connect_hana.connect_hana],\n[`connect_greenplum`][getml.database.connect_greenplum.connect_greenplum],\n[`connect_mariadb`][getml.database.connect_mariadb.connect_mariadb],\n[`connect_mysql`][getml.database.connect_mysql.connect_mysql],\n[`connect_postgres`][getml.database.connect_postgres.connect_postgres], and\n[`connect_sqlite3`][getml.database.connect_sqlite3.connect_sqlite3] functions establish a\nconnection between a database and the getML Engine. During the data\nimport using either the [`read_db`][getml.DataFrame.read_db] or\n[`read_query`][getml.DataFrame.read_query] methods of a\n[`DataFrame`][getml.DataFrame] instance or the corresponding\n[`from_db`][getml.DataFrame.from_db] class method all data will be\ndirectly loaded from the database into the Engine without ever passing\nthe Python interpreter.\n\nIn addition, several auxiliary functions that might be handy during\nthe analysis and interaction with the database are provided.\n\n\"\"\"\n\nimport getml.database.exceptions\nfrom getml.database.connect_bigquery import connect_bigquery\nfrom getml.database.connect_duckdb import connect_duckdb\nfrom getml.database.connect_greenplum import connect_greenplum\nfrom getml.database.connect_hana import connect_hana\nfrom getml.database.connect_mariadb import connect_mariadb\nfrom getml.database.connect_mysql import connect_mysql\nfrom getml.database.connect_odbc import connect_odbc\nfrom getml.database.connect_postgres import connect_postgres\nfrom getml.database.connect_sqlite3 import connect_sqlite3\nfrom getml.database.connection import Connection\nfrom getml.database.copy_table import copy_table\nfrom getml.database.drop_table import drop_table\nfrom getml.database.execute import execute\nfrom getml.database.get import get\nfrom getml.database.get_colnames import get_colnames\nfrom getml.database.helpers import _retrieve_temp_dir, _retrieve_url, _retrieve_urls\nfrom getml.database.list_connections import list_connections\nfrom getml.database.list_tables import list_tables\nfrom getml.database.read_csv import read_csv\nfrom getml.database.read_s3 import read_s3\nfrom getml.database.sniff_csv import sniff_csv\nfrom getml.database.sniff_s3 import sniff_s3\n\n__all__ = (\n    \"Connection\",\n    \"connect_bigquery\",\n    \"connect_duckdb\",\n    \"connect_greenplum\",\n    \"connect_hana\",\n    \"connect_mariadb\",\n    \"connect_mysql\",\n    \"connect_odbc\",\n    \"connect_postgres\",\n    \"connect_sqlite3\",\n    \"copy_table\",\n    \"drop_table\",\n    \"execute\",\n    \"get\",\n    \"get_colnames\",\n    \"_retrieve_temp_dir\",\n    \"_retrieve_url\",\n    \"_retrieve_urls\",\n    \"list_connections\",\n    \"list_tables\",\n    \"read_csv\",\n    \"read_s3\",\n    \"sniff_csv\",\n    \"sniff_s3\",\n)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_bigquery.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new BigQuery database connection.\n\"\"\"\n\nimport os\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Union\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_bigquery(\n    database_id: str,\n    project_id: str,\n    google_application_credentials: Union[str, Path],\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new BigQuery database connection.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        database_id:\n            The ID of the database to connect to.\n\n        project_id:\n            The ID of the project to connect to.\n\n        google_application_credentials:\n            The path of the Google application credentials.\n            (Must be located on the machine hosting the getML Engine).\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"database_id_\"] = database_id\n    cmd[\"project_id_\"] = project_id\n    cmd[\"google_application_credentials_\"] = os.path.abspath(\n        str(google_application_credentials)\n    )\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"bigquery\"\n\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The API expects a password, but in this case there is none\n        comm.send_string(sock, \"\")\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_duckdb.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new DuckDB database connection.\n\"\"\"\n\nimport os\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_duckdb(\n    name: Optional[str] = None,\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n):\n    \"\"\"Creates a new DuckDB database connection.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available\n        in the Community edition. Discover the [benefits of the Enterprise\n        edition][enterprise-benefits] and [compare their\n        features][enterprise-feature-list].\n\n        For licensing information and technical support, please\n        [contact us][contact-page].\n\n    Args:\n        name:\n            Name of the DuckDB file.  If the file does not exist, it\n            will be created. Set to None for a purely in-memory DuckDB\n            database.\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create a new\n        [`DataFrame`][getml.DataFrame] containing all its data. Alternatively\n        you can use the [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace the\n        content of the current [`DataFrame`][getml.DataFrame] instance or append\n        further rows based on either a table or a specific query.\n\n        You can also write your results back into the database. By passing\n        the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features generated from\n        your raw data will be written back. Passing them into\n        [`predict`][getml.Pipeline.predict], instead, makes predictions of the\n        target variables to new, unseen data and stores the result into the\n        corresponding table.\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    if name is not None:\n        cmd[\"name_\"] = os.path.abspath(name)\n\n    cmd[\"type_\"] = \"Database.new\"\n\n    cmd[\"db_\"] = \"duckdb\"\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is usually sent separately,\n        # so it doesn't\n        # end up in the logs. However, DuckDB does not\n        # need a password, so we just send a dummy.\n        comm.send_string(sock, \"none\")\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_greenplum.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new Greenplum database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_greenplum(\n    dbname: str,\n    user: str,\n    password: str,\n    host: str,\n    hostaddr: str,\n    port: int = 5432,\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"Creates a new Greenplum database connection.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        dbname:\n            The name of the database to which you want to connect.\n\n        user:\n            Username with which to log into the Greenplum database.\n\n        password:\n            Password with which to log into the Greenplum database.\n\n        host:\n            Host of the Greenplum database.\n\n        hostaddr:\n            IP address of the Greenplum database.\n\n        port:\n            Port of the Greenplum database.\n\n            The default port used by Greenplum is 5432.\n\n            If you do not know, which port to use, type the following into your\n            Greenplum client:\n\n            ```sql\n            SELECT setting FROM pg_settings WHERE name = 'port';\n            ```\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create\n        a new [`DataFrame`][getml.DataFrame] containing all its data.\n        Alternatively you can use the\n        [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace\n        the content of the current [`DataFrame`][getml.DataFrame]\n        instance or append further rows based on either a table or a\n        specific query.\n\n        You can also write your results back into the Greenplum\n        database. By passing the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features\n        generated from your raw data will be written back. Passing\n        them into [`predict`][getml.Pipeline.predict], instead,\n        makes predictions of the target variables to new, unseen data\n        and stores the result into the corresponding table.\n\n\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"greenplum\"\n\n    cmd[\"host_\"] = host\n    cmd[\"hostaddr_\"] = hostaddr\n    cmd[\"port_\"] = port\n    cmd[\"dbname_\"] = dbname\n    cmd[\"user_\"] = user\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_hana.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new HANA database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_hana(\n    user: str,\n    password: str,\n    host: str,\n    port: int = 39017,\n    default_schema: Optional[str] = \"public\",\n    ping_interval: int = 0,\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new HANA database connection.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        user:\n            Username with which to log into the HANA database.\n\n        password:\n            Password with which to log into the HANA database.\n\n        host:\n            Host of the HANA database.\n\n        port:\n            Port of the database.\n\n        default_schema:\n            The schema within the database you want to connect\n            use unless another schema is explicitly set.\n\n        ping_interval:\n            The interval at which you want to ping the database,\n            in seconds. Set to 0 for no pings at all.\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id (str, optional):\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"sap_hana\"\n\n    cmd[\"host_\"] = host\n    cmd[\"port_\"] = port\n    cmd[\"default_schema_\"] = default_schema\n    cmd[\"user_\"] = user\n    cmd[\"ping_interval_\"] = ping_interval\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_mariadb.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new MariaDB database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_mariadb(\n    dbname: str,\n    user: str,\n    password: str,\n    host: str,\n    port: int = 3306,\n    unix_socket: str = \"/var/run/mysqld/mysqld.sock\",\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new MariaDB database connection.\n\n    But first, make sure your database is running and you can reach it\n    from via your command line.\n\n    Args:\n        dbname:\n            The name of the database to which you want to connect.\n\n        user:\n            Username with which to log into the MariaDB database.\n\n        password:\n            Password with which to log into the MariaDB database.\n\n        host:\n            Host of the MariaDB database.\n\n        port:\n            Port of the MariaDB database.\n\n            The default port for MariaDB is 3306.\n\n            If you do not know which port to use, type\n\n            ```sql\n            SELECT @@port;\n            ```\n            into your MariaDB client.\n\n        unix_socket:\n            The UNIX socket used to connect to the MariaDB database.\n\n            If you do not know which UNIX socket to use, type\n\n            ```sql\n            SELECT @@socket;\n            ```\n            into your MariaDB client.\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create\n        a new [`DataFrame`][getml.DataFrame] containing all its data.\n        Alternatively you can use the\n        [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace\n        the content of the current [`DataFrame`][getml.DataFrame]\n        instance or append further rows based on either a table or a\n        specific query.\n\n        You can also write your results back into the MariaDB\n        database. By passing the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features\n        generated from your raw data will be written back. Passing\n        them into [`predict`][getml.Pipeline.predict], instead,\n        makes predictions\n        of the target variables to new, unseen data and stores the result into\n        the corresponding table.\n\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"mariadb\"\n\n    cmd[\"host_\"] = host\n    cmd[\"port_\"] = port\n    cmd[\"dbname_\"] = dbname\n    cmd[\"user_\"] = user\n    cmd[\"unix_socket_\"] = unix_socket\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_mysql.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new MySQL database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_mysql(\n    dbname: str,\n    user: str,\n    password: str,\n    host: str,\n    port: int = 3306,\n    unix_socket: str = \"/var/run/mysqld/mysqld.sock\",\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new MySQL database connection.\n\n    But first, make sure your database is running and you can reach it\n    from via your command line.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        dbname:\n            The name of the database to which you want to connect.\n\n        user:\n            Username with which to log into the MySQL database.\n\n        password:\n            Password with which to log into the MySQL database.\n\n        host:\n            Host of the MySQL database.\n\n        port:\n            Port of the MySQL database.\n\n            The default port for MySQL is 3306.\n\n            If you do not know which port to use, type\n\n            ```sql\n            SELECT @@port;\n            ```\n            into your mysql client.\n\n        unix_socket:\n            The UNIX socket used to connect to the MySQL database.\n\n            If you do not know which UNIX socket to use, type\n\n            ```sql\n            SELECT @@socket;\n            ```\n            into your mysql client.\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create\n        a new [`DataFrame`][getml.DataFrame] containing all its data.\n        Alternatively you can use the\n        [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace\n        the content of the current [`DataFrame`][getml.DataFrame]\n        instance or append further rows based on either a table or a\n        specific query.\n\n        You can also write your results back into the MySQL\n        database. By passing the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features\n        generated from your raw data will be written back. Passing\n        them into [`predict`][getml.Pipeline.predict], instead,\n        makes predictions of the target variables to new, unseen data\n        and stores the result into the corresponding table.\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"mariadb\"\n\n    cmd[\"host_\"] = host\n    cmd[\"port_\"] = port\n    cmd[\"dbname_\"] = dbname\n    cmd[\"user_\"] = user\n    cmd[\"unix_socket_\"] = unix_socket\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_odbc.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new ODBC database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_odbc(\n    server_name: str,\n    user: str = \"\",\n    password: str = \"\",\n    escape_chars: str = '\"',\n    double_precision: str = \"DOUBLE PRECISION\",\n    integer: str = \"INTEGER\",\n    text: str = \"TEXT\",\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new ODBC database connection.\n\n    ODBC is standardized format that can be used to connect to almost any\n    database.\n\n    Before you use the ODBC connector, make sure the database is up and\n    running and that the appropriate ODBC drivers are installed.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        server_name:\n            The server name, as referenced in your .obdc.ini file.\n\n        user:\n            Username with which to log into the database.\n            You do not need to pass this, if it is already contained in your\n            .odbc.ini.\n\n        password:\n            Password with which to log into the database.\n            You do not need to pass this, if it is already contained in your\n            .odbc.ini.\n\n        escape_chars:\n            ODBC drivers are supposed to support\n            escaping table and column names using '\"' characters irrespective of the\n            syntax in the target database. Unfortunately, not all ODBC drivers\n            follow this standard. This is why some\n            tuning might be necessary.\n\n            The escape_chars value behaves as follows:\n\n            * If you pass an empty string, schema, table and column names will not\n              be escaped at all. This is not a problem unless some table\n              or column names are identical to SQL keywords.\n\n            * If you pass a single character, schema, table and column names will\n              be enveloped in that character: \"TABLE_NAME\".\"COLUMN_NAME\" (standard SQL)\n              or `TABLE_NAME`.`COLUMN_NAME` (MySQL/MariaDB style).\n\n            * If you pass two characters, table, column and schema names will be\n              enveloped between these to characters. For instance, if you pass \"[]\",\n              the produced queries look as follows:\n              [TABLE_NAME].[COLUMN_NAME] (MS SQL Server style).\n\n            * If you pass more than two characters, the Engine will throw an exception.\n\n        double_precision:\n            The keyword used for double precision columns.\n\n        integer:\n            The keyword used for integer columns.\n\n        text:\n            The keyword used for text columns.\n\n        time_formats (List[str], optional):\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"odbc\"\n\n    cmd[\"server_name_\"] = server_name\n    cmd[\"user_\"] = user\n    cmd[\"escape_chars_\"] = escape_chars\n    cmd[\"double_precision_\"] = double_precision\n    cmd[\"integer_\"] = integer\n    cmd[\"text_\"] = text\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_postgres.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new PostgreSQL database connection.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_postgres(\n    dbname: str,\n    user: str,\n    password: str,\n    host: Optional[str] = None,\n    hostaddr: Optional[str] = None,\n    port: int = 5432,\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"\n    Creates a new PostgreSQL database connection.\n\n    But first, make sure your database is running, and you can reach it\n    from via your command line.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        dbname:\n            The name of the database to which you want to connect.\n\n        user:\n            Username with which to log into the PostgreSQL database.\n\n        password:\n            Password with which to log into the PostgreSQL database.\n\n        host:\n            Host of the PostgreSQL database.\n\n        hostaddr:\n            IP address of the PostgreSQL database.\n            This should be in the standard IPv4 address format, e.g., 172.28.40.9.\n            If your machine supports IPv6, you can also use those addresses.\n            TCP/IP communication is always used when a nonempty string is specified\n            for this parameter.\n\n        port:\n            Port of the PostgreSQL database.\n\n            The default port used by PostgreSQL is 5432.\n\n            If you do not know, which port to use, type the following into your\n            PostgreSQL client\n\n            ```sql\n            SELECT setting FROM pg_settings WHERE name = 'port';\n            ```\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The connection object.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create\n        a new [`DataFrame`][getml.DataFrame] containing all its data.\n        Alternatively you can use the\n        [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace\n        the content of the current [`DataFrame`][getml.DataFrame]\n        instance or append further rows based on either a table or a\n        specific query.\n\n        You can also write your results back into the PostgreSQL\n        database. By passing the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features\n        generated from your raw data will be written back. Passing\n        them into [`predict`][getml.Pipeline.predict], instead,\n        makes predictions of the target variables to new, unseen data\n        and stores the result into the corresponding table.\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.new\"\n    cmd[\"db_\"] = \"postgres\"\n\n    if host is not None:\n        cmd[\"host_\"] = host\n\n    if hostaddr is not None:\n        cmd[\"hostaddr_\"] = hostaddr\n\n    cmd[\"port_\"] = port\n    cmd[\"dbname_\"] = dbname\n    cmd[\"user_\"] = user\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is sent separately, so it doesn't\n        # end up in the logs.\n        comm.send_string(sock, password)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connect_sqlite3.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCreates a new SQLite3 database connection.\n\"\"\"\n\nimport os\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\nfrom getml import constants\n\nfrom .connection import Connection\n\n\ndef connect_sqlite3(\n    name: str = \":memory:\",\n    time_formats: Optional[List[str]] = None,\n    conn_id: str = \"default\",\n) -> Connection:\n    \"\"\"Creates a new SQLite3 database connection.\n\n    SQLite3 is a popular in-memory database. It is faster than\n    distributed databases, like PostgreSQL, but less stable under massive\n    parallel access, consumes more memory and requires all contained\n    data sets to be loaded into memory, which might fill up too much\n    of your RAM, especially for large data sets.\n\n    Args:\n        name:\n            Name of the sqlite3 file.  If the file does not exist, it\n            will be created. Set to \":memory:\" for a purely in-memory SQLite3\n            database.\n\n        time_formats:\n            The list of formats tried when parsing time stamps.\n\n            The formats are allowed to contain the following\n            special characters:\n\n            * %w - abbreviated weekday (Mon, Tue, ...)\n            * %W - full weekday (Monday, Tuesday, ...)\n            * %b - abbreviated month (Jan, Feb, ...)\n            * %B - full month (January, February, ...)\n            * %d - zero-padded day of month (01 .. 31)\n            * %e - day of month (1 .. 31)\n            * %f - space-padded day of month ( 1 .. 31)\n            * %m - zero-padded month (01 .. 12)\n            * %n - month (1 .. 12)\n            * %o - space-padded month ( 1 .. 12)\n            * %y - year without century (70)\n            * %Y - year with century (1970)\n            * %H - hour (00 .. 23)\n            * %h - hour (00 .. 12)\n            * %a - am/pm\n            * %A - AM/PM\n            * %M - minute (00 .. 59)\n            * %S - second (00 .. 59)\n            * %s - seconds and microseconds (equivalent to %S.%F)\n            * %i - millisecond (000 .. 999)\n            * %c - centisecond (0 .. 9)\n            * %F - fractional seconds/microseconds (000000 - 999999)\n            * %z - time zone differential in ISO 8601 format (Z or +NN.NN)\n            * %Z - time zone differential in RFC format (GMT or +NNNN)\n            * %% - percent sign\n\n        conn_id:\n            The name to be used to reference the connection.\n            If you do not pass anything, this will create a new default connection.\n\n    Returns:\n        The new SQLite3 database connection.\n\n    Note:\n        By selecting an existing table of your database in\n        [`from_db`][getml.DataFrame.from_db] function, you can create\n        a new [`DataFrame`][getml.DataFrame] containing all its data.\n        Alternatively you can use the\n        [`read_db`][getml.DataFrame.read_db] and\n        [`read_query`][getml.DataFrame.read_query] methods to replace\n        the content of the current [`DataFrame`][getml.DataFrame]\n        instance or append further rows based on either a table or a\n        specific query.\n\n        You can also write your results back into the SQLite3\n        database. By passing the name for the destination table to\n        [`transform`][getml.Pipeline.transform], the features\n        generated from your raw data will be written back. Passing\n        them into [`predict`][getml.Pipeline.predict], instead,\n        makes predictions of the target variables to new, unseen data\n        and stores the result into the corresponding table.\n\n    \"\"\"\n\n    time_formats = time_formats or constants.TIME_FORMATS\n\n    # We want to use the absolute path, unless it is a pure\n    # in-memory version.\n    name_ = name\n\n    if name_ != \":memory:\":\n        name_ = os.path.abspath(name)\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name_\n    cmd[\"type_\"] = \"Database.new\"\n\n    cmd[\"db_\"] = \"sqlite3\"\n    cmd[\"time_formats_\"] = time_formats\n    cmd[\"conn_id_\"] = conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        # The password is usually sent separately,\n        # so it doesn't\n        # end up in the logs. However, Sqlite3 does not\n        # need a password, so we just send a dummy.\n        comm.send_string(sock, \"none\")\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg, extra={\"name\": name_})\n\n    return Connection(conn_id=conn_id)\n"
  },
  {
    "path": "src/python-api/getml/database/connection.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA handle to a database connection on the getML Engine.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict\n\nimport getml.communication as comm\nfrom getml.utilities.formatting import _SignatureFormatter\n\n\nclass Connection:\n    \"\"\"\n    A handle to a database connection on the getML Engine.\n\n    Attributes:\n        conn_id:\n            The name you want to use to reference the connection.\n            You can call it\n            anything you want to. If a database\n            connection with the same conn_id already exists, that connection\n            will be removed automatically and the new connection will take its place.\n            The default conn_id is \"default\", which refers to the default connection.\n            If you do not explicitly pass a connection handle to any function that\n            relates to a database, the default connection will be used automatically.\n    \"\"\"\n\n    def __init__(self, conn_id: str = \"default\"):\n        self.conn_id = conn_id\n\n    def __repr__(self):\n        return str(self)\n\n    def __str__(self):\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"name_\"] = self.conn_id\n        cmd[\"type_\"] = \"Database.describe_connection\"\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            description = comm.recv_string(sock)\n\n        json_obj = json.loads(description)\n\n        json_obj[\"type\"] = \"Connection\"\n\n        sig = _SignatureFormatter(data=json_obj)\n\n        return sig._format()\n"
  },
  {
    "path": "src/python-api/getml/database/copy_table.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCopies a table from one database connection to another.\n\"\"\"\n\nfrom typing import Any, Dict, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef copy_table(\n    source_conn: Connection,\n    target_conn: Connection,\n    source_table: str,\n    target_table: Optional[str] = None,\n):\n    \"\"\"\n    Copies a table from one database connection to another.\n\n    Args:\n        source_conn:\n            The database connection to be copied from.\n\n        target_conn:\n            The database connection to be copied to.\n\n        source_table:\n            The name of the table in the source connection.\n\n        target_table:\n            The name of the table in the target\n            connection. If you do not explicitly pass a target_table, the\n            name will be identical to the source_table.\n\n    ??? example\n        A frequent use case for this function is to copy data from a data source into\n        sqlite. This is a good idea, because sqlite is faster than most standard,\n        ACID-compliant databases, and also you want to avoid messing up a productive\n        environment.\n\n        It is important to explicitly pass conn_id, otherwise the source connection\n        will be closed\n        when you create the target connection. What you pass as conn_id is entirely\n        up to you,\n        as long as the conn_id of the source and the target are different. It can\n        be any name you see fit.\n\n        ```python\n        source_conn = getml.database.connect_odbc(\n            \"MY-SERVER-NAME\", conn_id=\"MY-SOURCE\")\n\n        target_conn = getml.database.connect_sqlite3(\n            \"MY-SQLITE.db\", conn_id=\"MY-TARGET\")\n\n        data.database.copy_table(\n                source_conn=source_conn,\n                target_conn=target_conn,\n                source_table=\"MY-TABLE\"\n        )\n        ```\n\n    \"\"\"\n\n    # -------------------------------------------\n\n    target_table = target_table or source_table\n\n    # -------------------------------------------\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.copy_table\"\n\n    cmd[\"source_conn_id_\"] = source_conn.conn_id\n    cmd[\"target_conn_id_\"] = target_conn.conn_id\n    cmd[\"source_table_\"] = source_table\n    cmd[\"target_table_\"] = target_table\n\n    # -------------------------------------------\n\n    comm.send(cmd)\n"
  },
  {
    "path": "src/python-api/getml/database/drop_table.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDrops a table from the database.\n\"\"\"\n\nfrom typing import Any, Dict, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef drop_table(name: str, conn: Optional[Connection] = None):\n    \"\"\"\n    Drops a table from the database.\n\n    Args:\n        name:\n            The table to be dropped.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n    \"\"\"\n\n    # -------------------------------------------\n\n    conn = conn or Connection()\n\n    # -------------------------------------------\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Database.drop_table\"\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    # -------------------------------------------\n\n    comm.send(cmd)\n"
  },
  {
    "path": "src/python-api/getml/database/exceptions.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport re\nfrom inspect import cleandoc\nfrom typing import Any, Dict\n\nfrom getml.exceptions import engine_exception_handler\n\nFILE_SYSTEM_MISMATCH_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    Engine could not retrieve file:\n    {file_name!r}\n\n    Is the Engine running inside docker? When working with getML's database\n    module, file paths are interpreted as paths inside the docker container.\n\n    Hint: Use `docker cp <container>:/home/getml/assets` to copy the files into\n    the docker container and read the data from the container's filesystem\n    directly, e.g.:\n\n    `getml.database.read_csv('table_name', '/home/getml/assets/file.csv')`\n    `getml.database.connect_sqlite3(\"/home/getml/assets/database.db\")`\n    \"\"\"\n)\n\n\n@engine_exception_handler\ndef handle_database_file_system_mismatch_error(msg: str, extra: Dict[str, Any]):\n    if msg.endswith(\"unable to open database file\"):\n        file_name = extra.get(\"name\", \"\")\n        raise OSError(\n            FILE_SYSTEM_MISMATCH_ERROR_MSG_TEMPLATE.format(file_name=file_name)\n        )\n    could_not_be_opened_msg = re.match(\"'(.+)' could not be opened!\", msg)\n    if could_not_be_opened_msg:\n        file_name = could_not_be_opened_msg.group(1)\n        raise OSError(\n            FILE_SYSTEM_MISMATCH_ERROR_MSG_TEMPLATE.format(file_name=file_name)\n        )\n"
  },
  {
    "path": "src/python-api/getml/database/execute.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nExecutes an SQL query on the database.\n\"\"\"\n\nfrom typing import Any, Dict, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef execute(query: str, conn: Optional[Connection] = None):\n    \"\"\"\n    Executes an SQL query on the database.\n\n    Please note that this is not meant to return results. If you want to\n    get results, use `database.get()` instead.\n\n    Args:\n        query:\n            The SQL query to be executed.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = conn.conn_id\n    cmd[\"type_\"] = \"Database.execute\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        comm.send_string(sock, query)\n        msg = comm.recv_string(sock)\n\n    if msg != \"Success!\":\n        comm.handle_engine_exception(msg)\n"
  },
  {
    "path": "src/python-api/getml/database/get.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nExecutes an SQL query on the database and returns the result as\na pandas dataframe.\n\"\"\"\n\nimport io\nfrom typing import Any, Dict, Optional\n\nimport pandas as pd\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef get(query: str, conn: Optional[Connection] = None) -> pd.DataFrame:\n    \"\"\"\n    Executes an SQL query on the database and returns the result as\n    a pandas dataframe.\n\n    Args:\n        query:\n            The SQL query to be executed.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        The result of the query as a pandas dataframe.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = conn.conn_id\n    cmd[\"type_\"] = \"Database.get\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        comm.send_string(sock, query)\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    return pd.read_json(io.StringIO(json_str))\n"
  },
  {
    "path": "src/python-api/getml/database/get_colnames.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nLists the colnames of a table held in the database.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef get_colnames(name: str, conn: Optional[Connection] = None) -> List[str]:\n    \"\"\"\n    Lists the colnames of a table held in the database.\n\n    Args:\n        name:\n            The name of the table in the database.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        A list of strings containing the names of the columns in the table.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Database.get_colnames\"\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        arr = json.loads(comm.recv_string(sock))\n\n    return arr\n"
  },
  {
    "path": "src/python-api/getml/database/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of helper functions not meant to be used by the enduser.\n\"\"\"\n\nfrom enum import Enum\nfrom inspect import cleandoc\nfrom pathlib import Path\nfrom tempfile import gettempdir\nfrom typing import Any, Dict, Iterable, List, Literal, Optional, Union, overload\nfrom urllib import request\nfrom urllib.parse import urlparse\n\nfrom rich import print\n\nimport getml.communication as comm\nfrom getml.database.connection import Connection\nfrom getml.utilities.progress import Progress, ProgressType\n\nFILE_SYSTEM_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    Engine could not retrieve files:\n    {fnames}\n\n    Is the Engine running inside docker? When ingesting csvs to databases all\n    paths in `fnames` are interpreted as paths w.r.t. the container's filesystem.\n\n    Hint: Use `docker cp <container>:/home/getml/assets` to copy the files into\n    the docker container and read the data from the container's filesystem\n    directly:\n\n    `getml.database.read_csv('table_name', '/home/getml/assets/file.csv')`\n    \"\"\"\n)\n\n# -----------------------------------------------------------------------------\n\n\nclass CSVCmdType(Enum):\n    SNIFF = \"sniff\"\n    READ = \"read\"\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _load_to_file(url: str, file_path: Path, description: Optional[str] = None):\n    with request.urlopen(url) as response:\n        content_length = int(response.getheader(\"content-length\", 0))\n        block_size = max(4096, content_length // 100)\n        with Progress(\n            progress_type=ProgressType.DOWNLOAD,\n        ) as progress:\n            task_id = progress.new_task(\n                f\"Downloading {description or file_path.name}...\", total=content_length\n            )\n            with file_path.open(\"wb\") as file:\n                while True:\n                    block = response.read(block_size)\n                    if not block:\n                        break\n                    file.write(block)\n                    progress.advance(task_id, steps=len(block))\n\n\n# --------------------------------------------------------------------\n\n\ndef _retrieve_temp_dir() -> Path:\n    temp_dir = Path(gettempdir()) / \"getml\"\n    temp_dir.mkdir(exist_ok=True)\n    return temp_dir\n\n\n# --------------------------------------------------------------------\n\n\ndef _retrieve_url(\n    url: str,\n    verbose: bool = False,\n    target_path: Optional[Path] = None,\n    description: Optional[str] = None,\n) -> str:\n    parse_result = urlparse(url)\n\n    if target_path is None:\n        target_path = _retrieve_temp_dir()\n\n    target_path = target_path / parse_result.netloc / parse_result.path[1:]\n\n    if target_path.exists():\n        return target_path.as_posix()\n\n    target_path.parent.mkdir(parents=True, exist_ok=True)\n\n    if verbose:\n        print(f\"Downloading {url} to {target_path.as_posix()}...\")\n\n    _load_to_file(url, target_path, description)\n\n    return target_path.as_posix()\n\n\n# --------------------------------------------------------------------\n\n\ndef _retrieve_urls(\n    fnames: Iterable[str],\n    verbose: bool = False,\n    target_path: Optional[Path] = None,\n    description: Optional[str] = None,\n) -> List[str]:\n    def is_url(fname):\n        return urlparse(fname).scheme != \"\"\n\n    return [\n        _retrieve_url(\n            url=fname, verbose=verbose, target_path=target_path, description=description\n        )\n        if is_url(fname)\n        else Path(fname).expanduser().absolute().as_posix()\n        for fname in fnames\n    ]\n\n\n# --------------------------------------------------------------------\n\n\ndef _retrieve_urls_for_engine(fnames: Iterable[str], verbose: bool = True) -> List[str]:\n    try:\n        fnames = _retrieve_urls(fnames, verbose)\n    except FileNotFoundError as e:\n        raise OSError(FILE_SYSTEM_ERROR_MSG_TEMPLATE.format(fnames=fnames)) from e\n\n    return fnames\n\n\n# --------------------------------------------------------------------\n\n\n@overload\ndef _read_csv(\n    type: Literal[CSVCmdType.SNIFF],\n    name: str,\n    fnames: Union[str, List[str]],\n    num_lines_sniffed: int,\n    quotechar: str,\n    sep: str,\n    skip: int,\n    colnames: Optional[List[str]],\n    conn: Optional[Connection],\n) -> str: ...\n\n\n@overload\ndef _read_csv(\n    type: Literal[CSVCmdType.READ],\n    name: str,\n    fnames: Union[str, List[str]],\n    num_lines_sniffed: int,\n    quotechar: str,\n    sep: str,\n    skip: int,\n    colnames: Optional[List[str]],\n    conn: Optional[Connection],\n) -> None: ...\n\n\ndef _read_csv(\n    type: CSVCmdType,\n    name: str,\n    fnames: Union[str, List[str]],\n    num_lines_sniffed: int,\n    quotechar: str,\n    sep: str,\n    skip: int,\n    colnames: Optional[List[str]],\n    conn: Optional[Connection] = None,\n) -> Union[str, None]:\n    conn = conn or Connection()\n\n    if not isinstance(fnames, list):\n        fnames = [fnames]\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = f\"Database.{type.value}_csv\"\n\n    cmd[\"fnames_\"] = _retrieve_urls_for_engine(fnames)\n    cmd[\"num_lines_sniffed_\"] = num_lines_sniffed\n    cmd[\"quotechar_\"] = quotechar\n    cmd[\"sep_\"] = sep\n    cmd[\"skip_\"] = skip\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    if colnames is not None:\n        cmd[\"colnames_\"] = colnames\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        if type == CSVCmdType.SNIFF:\n            return comm.recv_string(sock)\n"
  },
  {
    "path": "src/python-api/getml/database/list_connections.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns a list handles to all connections that are currently active on the\nEngine.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict, List\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef list_connections() -> List[Connection]:\n    \"\"\"\n    Returns a list handles to all connections\n    that are currently active on the Engine.\n\n    Returns:\n        A list of Connection objects.\n    \"\"\"\n\n    cmd: Dict[Any, str] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Database.list_connections\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        arr = json.loads(comm.recv_string(sock))\n\n    return [Connection(elem) for elem in arr]\n"
  },
  {
    "path": "src/python-api/getml/database/list_tables.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nLists all tables and views currently held in the database.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef list_tables(conn: Optional[Connection] = None) -> List[str]:\n    \"\"\"\n    Lists all tables and views currently held in the database.\n\n    Args:\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        A list of strings containing the names of the tables and views.\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = conn.conn_id\n    cmd[\"type_\"] = \"Database.list_tables\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        return json.loads(comm.recv_string(sock))\n"
  },
  {
    "path": "src/python-api/getml/database/read_csv.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReads a CSV file into the database.\n\"\"\"\n\nfrom typing import List, Optional, Union\n\nfrom .connection import Connection\nfrom .helpers import (\n    CSVCmdType,\n    _read_csv,\n)\n\n\ndef read_csv(\n    name: str,\n    fnames: Union[str, List[str]],\n    quotechar: str = '\"',\n    sep: str = \",\",\n    num_lines_read: int = 0,\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n    conn: Optional[Connection] = None,\n) -> None:\n    \"\"\"\n    Reads a CSV file into the database.\n\n    Args:\n        name:\n            Name of the table in which the data is to be inserted.\n\n        fnames:\n            The list of CSV file names to be read.\n\n        quotechar:\n            The character used to wrap strings. Default:`\"`\n\n        sep:\n            The separator used for separating fields. Default:`,`\n\n        num_lines_read:\n            Number of lines read from each file.\n            Set to 0 to read in the entire file.\n\n        skip:\n            Number of lines to skip at the beginning of each\n            file (Default: 0).\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    ??? example\n        Let's assume you have two CSV files - *file1.csv* and\n        *file2.csv* . You can import their data into the database\n        using the following commands:\n\n        ```python\n        stmt = data.database.sniff_csv(\n                fnames=[\"file1.csv\", \"file2.csv\"],\n                name=\"MY_TABLE\",\n                sep=';'\n        )\n\n        getml.database.execute(stmt)\n\n        data.database.read_csv(\n            fnames=[\"file1.csv\", \"file2.csv\"],\n            name=\"MY_TABLE\",\n            sep=';'\n        )\n        ```\n\n    \"\"\"\n\n    return _read_csv(\n        CSVCmdType.READ,\n        name,\n        fnames,\n        num_lines_read,\n        quotechar,\n        sep,\n        skip,\n        colnames,\n        conn,\n    )\n"
  },
  {
    "path": "src/python-api/getml/database/read_s3.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReads a list of CSV files located in an S3 bucket.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef read_s3(\n    name: str,\n    bucket: str,\n    keys: List[str],\n    region: str,\n    sep: str = \",\",\n    num_lines_read: int = 0,\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n    conn: Optional[Connection] = None,\n) -> None:\n    \"\"\"\n    Reads a list of CSV files located in an S3 bucket.\n\n    Args:\n        name:\n            Name of the table in which the data is to be inserted.\n\n        bucket:\n            The bucket from which to read the files.\n\n        keys:\n            The list of keys (files in the bucket) to be read.\n\n        region:\n            The region in which the bucket is located.\n\n        sep:\n            The separator used for separating fields. Default:`,`\n\n        num_lines_read:\n            Number of lines read from each file.\n            Set to 0 to read in the entire file.\n\n        skip:\n            Number of lines to skip at the beginning of each\n            file (Default: 0).\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    ??? example\n        Let's assume you have two CSV files - *file1.csv* and\n        *file2.csv* - in the bucket. You can\n        import their data into the getML Engine using the following\n        commands:\n        ```python\n        getml.engine.set_s3_access_key_id(\"YOUR-ACCESS-KEY-ID\")\n\n        getml.engine.set_s3_secret_access_key(\"YOUR-SECRET-ACCESS-KEY\")\n\n        stmt = data.database.sniff_s3(\n                bucket=\"your-bucket-name\",\n                keys=[\"file1.csv\", \"file2.csv\"],\n                region=\"us-east-2\",\n                name=\"MY_TABLE\",\n                sep=';'\n        )\n\n        getml.database.execute(stmt)\n\n        data.database.read_s3(\n            bucket=\"your-bucket-name\",\n            keys=[\"file1.csv\", \"file2.csv\"],\n            region=\"us-east-2\",\n            name=\"MY_TABLE\",\n            sep=';'\n        )\n        ```\n        You can also set the access credential as environment variables\n        before you launch the getML Engine.\n\n    \"\"\"\n    # -------------------------------------------\n\n    conn = conn or Connection()\n\n    # -------------------------------------------\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Database.read_s3\"\n\n    cmd[\"bucket_\"] = bucket\n    cmd[\"keys_\"] = keys\n    cmd[\"num_lines_read_\"] = num_lines_read\n    cmd[\"region_\"] = region\n    cmd[\"sep_\"] = sep\n    cmd[\"skip_\"] = skip\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    if colnames is not None:\n        cmd[\"colnames_\"] = colnames\n\n    # -------------------------------------------\n\n    comm.send(cmd)\n"
  },
  {
    "path": "src/python-api/getml/database/sniff_csv.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSniffs a list of CSV files.\n\"\"\"\n\nfrom typing import List, Optional, Union\n\nfrom .connection import Connection\nfrom .helpers import CSVCmdType, _read_csv, _retrieve_urls_for_engine\n\n\ndef sniff_csv(\n    name: str,\n    fnames: Union[str, List[str]],\n    num_lines_sniffed: int = 1000,\n    quotechar: str = '\"',\n    sep: str = \",\",\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n    conn: Optional[Connection] = None,\n) -> str:\n    \"\"\"\n    Sniffs a list of CSV files.\n\n    Args:\n        name:\n            Name of the table in which the data is to be inserted.\n\n        fnames:\n            The list of CSV file names to be read.\n\n        num_lines_sniffed:\n            Number of lines analyzed by the sniffer.\n\n        quotechar:\n            The character used to wrap strings. Default:`\"`\n\n        sep:\n            The separator used for separating fields. Default:`,`\n\n        skip:\n            Number of lines to skip at the beginning of each\n            file (Default: 0).\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        Appropriate `CREATE TABLE` statement.\n    \"\"\"\n\n    return _read_csv(\n        CSVCmdType.SNIFF,\n        name,\n        fnames,\n        num_lines_sniffed,\n        quotechar,\n        sep,\n        skip,\n        colnames,\n        conn,\n    )\n"
  },
  {
    "path": "src/python-api/getml/database/sniff_s3.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSniffs a list of CSV files located in an S3 bucket.\n\"\"\"\n\nfrom typing import Any, Dict, List, Optional\n\nimport getml.communication as comm\n\nfrom .connection import Connection\n\n\ndef sniff_s3(\n    name: str,\n    bucket: str,\n    keys: List[str],\n    region: str,\n    num_lines_sniffed: int = 1000,\n    sep: str = \",\",\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n    conn: Optional[Connection] = None,\n) -> str:\n    \"\"\"\n    Sniffs a list of CSV files located in an S3 bucket.\n\n\n    Args:\n        name:\n            Name of the table in which the data is to be inserted.\n\n        bucket:\n            The bucket from which to read the files.\n\n        keys:\n            The list of keys (files in the bucket) to be read.\n\n        region:\n            The region in which the bucket is located.\n\n        num_lines_sniffed:\n            Number of lines analyzed by the sniffer.\n\n        sep:\n            The character used for separating fields.\n\n        skip:\n            Number of lines to skip at the beginning of each file.\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you need to\n            explicitly pass them.\n\n        conn:\n            The database connection to be used.\n            If you don't explicitly pass a connection,\n            the Engine will use the default connection.\n\n    Returns:\n        Appropriate `CREATE TABLE` statement.\n\n    ??? example\n        Let's assume you have two CSV files - *file1.csv* and\n        *file2.csv* - in the bucket. You can\n        import their data into the getML Engine using the following\n        commands:\n        ```python\n        getml.engine.set_s3_access_key_id(\"YOUR-ACCESS-KEY-ID\")\n\n        getml.engine.set_s3_secret_access_key(\"YOUR-SECRET-ACCESS-KEY\")\n\n        stmt = data.database.sniff_s3(\n                bucket=\"your-bucket-name\",\n                keys=[\"file1.csv\", \"file2.csv\"],\n                region=\"us-east-2\",\n                name=\"MY_TABLE\",\n                sep=';'\n        )\n        ```\n        You can also set the access credential as environment variables\n        before you launch the getML Engine.\n\n    \"\"\"\n\n    conn = conn or Connection()\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Database.sniff_s3\"\n\n    cmd[\"bucket_\"] = bucket\n    cmd[\"keys_\"] = keys\n    cmd[\"num_lines_sniffed_\"] = num_lines_sniffed\n    cmd[\"region_\"] = region\n    cmd[\"sep_\"] = sep\n    cmd[\"skip_\"] = skip\n    cmd[\"conn_id_\"] = conn.conn_id\n\n    if colnames is not None:\n        cmd[\"colnames_\"] = colnames\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        return comm.recv_string(sock)\n"
  },
  {
    "path": "src/python-api/getml/datasets/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThe [`datasets`][getml.datasets] module includes utilities to load datasets,\nincluding methods to load and fetch popular reference datasets. It also\nfeatures some artificial data generators.\n\"\"\"\n\nfrom .base import (\n    load_air_pollution,\n    load_atherosclerosis,\n    load_biodegradability,\n    load_consumer_expenditures,\n    load_interstate94,\n    load_loans,\n    load_occupancy,\n)\nfrom .samples_generator import (\n    _aggregate,\n    make_categorical,\n    make_discrete,\n    make_numerical,\n    make_same_units_categorical,\n    make_same_units_numerical,\n    make_snowflake,\n)\n\n__all__ = [\n    \"load_air_pollution\",\n    \"load_atherosclerosis\",\n    \"load_biodegradability\",\n    \"load_consumer_expenditures\",\n    \"load_interstate94\",\n    \"load_loans\",\n    \"load_occupancy\",\n    \"_aggregate\",\n    \"make_categorical\",\n    \"make_discrete\",\n    \"make_numerical\",\n    \"make_same_units_categorical\",\n    \"make_same_units_numerical\",\n    \"make_snowflake\",\n]\n"
  },
  {
    "path": "src/python-api/getml/datasets/base.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nLoad preprocessed datasets\n\"\"\"\n\nimport json\nimport warnings\nfrom typing import Dict, List, Optional, Tuple, Union\nfrom urllib.request import urlopen\n\nimport pandas as pd\n\nfrom getml.data import DataFrame\nfrom getml.database.helpers import _retrieve_url\n\nDataFrameT = Union[DataFrame, pd.DataFrame]\n\"\"\"\nDataFrame types for builtin demonstration datasets\n\"\"\"\n\nVERSION: str = \"v1\"\n\nBUCKET: str = \"https://static.getml.com/datasets\"\n\"\"\"S3 bucket containing the CSV files\"\"\"\n\nASSETS: Dict[str, List[str]] = {\n    \"air_pollution\": [\"population\"],\n    \"atherosclerosis\": [\"population\", \"contr\"],\n    \"biodegradability\": [\n        \"molecule_train\",\n        \"molecule_test\",\n        \"molecule_validation\",\n        \"atom\",\n        \"bond\",\n        \"gmember\",\n        \"group\",\n    ],\n    \"consumer_expenditures\": [\n        \"population_training\",\n        \"population_testing\",\n        \"population_validation\",\n        \"expd\",\n        \"fmld\",\n        \"memd\",\n    ],\n    \"interstate94\": [\"traffic\"],\n    \"loans\": [\n        \"population_train\",\n        \"population_test\",\n        \"order\",\n        \"trans\",\n        \"meta\",\n    ],\n    \"loans_new\": [\n        \"account\",\n        \"card\",\n        \"client\",\n        \"disp\",\n        \"district\",\n        \"loan\",\n        \"order\",\n        \"trans\",\n    ],\n    \"occupancy\": [\"population_train\", \"population_test\", \"population_validation\"],\n}\n\n\ndef _load_dataset(\n    ds_name: str,\n    assets: Optional[List[str]] = None,\n    roles: bool = False,\n    units: bool = False,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"Helper function to load a dataset\n\n    Args:\n        ds_name:\n            name of the dataset\n\n        assets:\n            CSV files to be loaded from the S3 bucket\n\n        roles:\n            Return getml.DataFrame with roles set\n\n        units:\n            Return getml.DataFrame with units set\n\n        as_pandas:\n            Return data as `pandas.DataFrame` s\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n            * Tuple containing (sorted alphabetically by `df.name`) the data as [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas` is True) or\n\n            * Dict containing the with (`df.name` as keys and `df` as values) as [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas` is True) if `as_dict` is `True`.\n    \"\"\"\n    base = f\"{BUCKET}/{VERSION}/{ds_name}/preprocessed\"\n\n    if assets is None:\n        assets = ASSETS[ds_name]\n\n    if roles:\n        filename = base + \"/\" + ds_name + \"_\" + \"roles.json\"\n        response = urlopen(filename)\n        roles_ = json.loads(response.read())\n    else:\n        roles_ = {}\n\n    dfs = {}\n    for asset in assets:\n        asset_url = base + \"/\" + ds_name + \"_\" + asset + \".csv\"\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            csv_file = _retrieve_url(asset_url, description=asset)\n            dfs[asset] = DataFrame.from_csv(\n                name=asset,\n                fnames=csv_file,\n                roles=roles_.get(asset),\n                verbose=False,\n            )\n\n    if units:\n        filename = base + \"/\" + ds_name + \"_\" + \"units.json\"\n        response = urlopen(filename)\n        units_ = json.loads(response.read())\n\n        for key, df in dfs.items():\n            for unit, columns in units_.get(key, {}).items():\n                if unit:\n                    df.set_unit(columns, unit)\n\n    if as_pandas:\n        dfs = {key: df.to_pandas() for key, df in dfs.items()}\n\n    if as_dict:\n        return dfs\n\n    return tuple(dfs.values())\n\n\ndef load_air_pollution(\n    roles: bool = True,\n    as_pandas: bool = False,\n) -> DataFrameT:\n    \"\"\"\n    Regression dataset on air pollution in Beijing, China\n\n    The dataset consists of a single table split into train and test sets\n    around 2014-01-01.\n\n    !!! abstract \"Reference\"\n        Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H. and\n        Chen, S. X. (2015). Assessing Beijing's PM2.5 pollution: severity, weather\n        impact, APEC and winter heating. Proceedings of the Royal Society A, 471,\n        20150257.\n\n    Args:\n        as_pandas:\n            Return data as `pandas.DataFrame`\n\n        roles:\n            Return data with roles set\n\n    Returns:\n        A DataFrame holding the data described above.\n\n            The following DataFrames are returned:\n\n            * `air_pollution`\n\n    ??? example\n        ```python\n        air_pollution = getml.datasets.load_air_pollution()\n        type(air_pollution)\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the atherosclerosis dataset including all necessary\n        preprocessing steps please refer to [getml-demo\n        ](https://github.com/getml/getml-demo/blob/master/air_pollution.ipynb).\n\n\n    Note:\n        Roles can be set ad-hoc by supplying the respective flag. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. This dataset contains no units.\n        Before using them in an analysis, a data model needs to be constructed\n        using [`Placeholder`][getml.data.Placeholder].\n\n    \"\"\"\n\n    ds_name = \"air_pollution\"\n\n    dataset = _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        as_pandas=as_pandas,\n    )\n    assert isinstance(dataset, tuple), \"Expected a tuple\"\n    return dataset[0]\n\n\ndef load_atherosclerosis(\n    roles: bool = True,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"\n    Binary classification dataset on the lethality of atherosclerosis\n\n    The atherosclerosis dataset is a medical dataset from the [Relational Dataset Repository (former CTU Prague\n    Relational Learning Repository)\n    ](https://relational-data.org/dataset/Atherosclerosis). It contains\n    information from a longitudinal study on 1417 middle-aged men observed over\n    the course of 20 years. After preprocessing, it consists of 2 tables with 76\n    and 66 columns:\n\n    * `population`: Data on the study's participants\n\n    * `contr`: Data on control dates\n\n    The population table is split into a training (70%), a testing (15%) set and a\n    validation (15%) set.\n\n    Args:\n        as_pandas:\n            Return data as `pandas.DataFrame` s\n\n        roles:\n            Return data with roles set\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n        Tuple containing (sorted alphabetically by `df.name`) the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True) or\n            if `as_dict` is `True`: Dictionary containing the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True). The keys correspond to the name of the DataFrame on the\n            [`engine`][getml.engine].\n\n            The following DataFrames are returned:\n\n            - `population`\n            - `contr`\n\n    ??? example\n        ```python\n        population, contr = getml.datasets.load_atherosclerosis()\n        type(population)\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the atherosclerosis dataset including all necessary\n        preprocessing steps please refer to [getml-examples\n        ](https://github.com/getml/getml-demo/blob/master/atherosclerosis.ipynb).\n\n\n    Note:\n        Roles can be set ad-hoc by supplying the respective flag. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. This dataset contains no units.\n        Before using them in an analysis, a data model needs to be constructed\n        using [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"atherosclerosis\"\n\n    return _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        as_pandas=as_pandas,\n        as_dict=as_dict,\n    )\n\n\ndef load_biodegradability(\n    roles: bool = True,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"\n    Regression dataset on molecule weight prediction\n\n    The QSAR biodegradation dataset was built in the Milano Chemometrics and\n    QSAR Research Group (Universita degli Studi Milano-Bicocca, Milano, Italy).\n    The data have been used to develop QSAR (Quantitative Structure Activity\n    Relationships) models for the study of the relationships between chemical\n    structure and biodegradation of molecules. Biodegradation experimental\n    values of 1055 chemicals were collected from the webpage of the National\n    Institute of Technology and Evaluation of Japan (NITE).\n\n    !!! abstract \"Reference\"\n        Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V.\n        (2013). Quantitative Structure - Activity Relationship models for ready\n        biodegradability of chemicals. Journal of Chemical Information and Modeling,\n        53, 867-878\n\n    The dataset was collected through the [Relational Dataset Repository (former CTU Prague\n    Relational Learning Repository)](https://relational-data.org/dataset/Biodegradability)\n\n    It contains information on 1309 molecules with 6166 bonds. It consists of 5\n    tables.\n\n    The population table is split into a training (50 %) and a testing (25%) and\n    validation (25%) sets.\n\n    Args:\n        as_pandas:\n            Return data as `pandas.DataFrame`\n\n        roles:\n            Return data with roles set\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n        Tuple containing (sorted alphabetically by `df.name`) the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True) or if `as_dict` is `True`: Dictionary containing the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True). The keys correspond to the name of the DataFrame on the\n            [`engine`][getml.engine].\n\n            The following DataFrames are returned:\n\n            * `molecule`\n            * `atom`\n            * `bond`\n            * `gmember`\n            * `group`\n\n    ??? example\n        ```python\n        biodegradability = getml.datasets.load_biodegradability(as_dict=True)\n        type(biodegradability[\"molecule_train\"])\n        getml.data.data_frame.DataFrame\n        ```\n\n    Note:\n        Roles can be set ad-hoc by supplying the respective flag. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. This dataset contains no units.\n        Before using them in an analysis, a data model needs to be constructed\n        using [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"biodegradability\"\n\n    return _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        as_pandas=as_pandas,\n        as_dict=as_dict,\n    )\n\n\ndef load_consumer_expenditures(\n    roles: bool = True,\n    units: bool = True,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"\n    Binary classification dataset on consumer expenditures\n\n    The Consumer Expenditure Data Set is a public domain data set provided by\n    the [American Bureau of Labor Statistics](https://www.bls.gov/cex/pumd.htm).\n    It includes the diary entries, where American consumers are asked to keep\n    record of the products they have purchased each month.\n\n    We use this dataset to classify whether an item was purchased as a gift or not.\n\n    Args:\n        roles:\n            Return data with roles set\n\n        units:\n            Return data with units set\n\n        as_pandas:\n            Return data as `pandas.DataFrame`\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n        Tuple containing (sorted alphabetically by `df.name`) the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True) or\n            if `as_dict` is `True`: Dictionary containing the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True). The keys correspond to the name of the DataFrame on the\n            [`engine`][getml.engine].\n\n            The following DataFrames are returned:\n\n            * `population`\n            * `expd`\n            * `fmld`\n            * `memd`\n\n    ??? example\n        ```python\n        ce = getml.datasets.load_consumer_expenditures(as_dict=True)\n        type(ce[\"expd\"])\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the occupancy dataset including all necessary\n        preprocessing steps please refer to [getml-examples\n        ](https://github.com/getml/getml-demo/blob/master/consumer_expenditures.ipynb).\n\n    Note:\n        Roles and units can be set ad-hoc by supplying the respective flag. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float].\n        Before using them in an analysis, a data model needs to be constructed\n        using [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"consumer_expenditures\"\n\n    return _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        units=units,\n        as_pandas=as_pandas,\n        as_dict=as_dict,\n    )\n\n\ndef load_interstate94(\n    roles: bool = True,\n    units: bool = True,\n    as_pandas: bool = False,\n) -> DataFrameT:\n    \"\"\"\n    Regression dataset on traffic volume prediction\n\n    The interstate94 dataset is a multivariate time series containing the\n    hourly traffic volume on I-94 westbound from Minneapolis-St Paul. It is\n    based on data provided by the\n    [MN Department of Transportation](https://www.dot.state.mn.us/).\n    Some additional data preparation done by\n    [John Hogue](https://github.com/dreyco676/Anomaly_Detection_A_to_Z/). The\n    dataset features some particular interesting characteristics common for\n    time series, which classical models may struggle to appropriately deal\n    with. Such characteristics are:\n\n    * High frequency (hourly)\n    * Dependence on irregular events (holidays)\n    * Strong and overlapping cycles (daily, weekly)\n    * Anomalies\n    * Multiple seasonalities\n\n    Args:\n        roles:\n            Return data with roles set\n\n        units:\n            Return data with units set\n\n        as_pandas:\n            Return data as `pandas.DataFrame`\n\n    Returns:\n        A DataFrame holding the data described above.\n\n            The following DataFrames are returned:\n\n            * `traffic`\n\n    ??? example\n        ```python\n        traffic = getml.datasets.load_interstate94()\n        type(traffic)\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the interstate94 dataset including all necessary\n        preprocessing steps please refer to [getml-examples](https://github.com/getml/getml-demo/blob/master/interstate94.ipynb).\n\n    Note:\n        Roles and units can be set ad-hoc by supplying the respective flags. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. Before using them in an\n        analysis, a data model needs to be constructed using\n        [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"interstate94\"\n    dataset = _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        units=units,\n        as_pandas=as_pandas,\n    )\n    assert isinstance(dataset, tuple), \"Expected a tuple\"\n    return dataset[0]\n\n\ndef load_loans(\n    roles: bool = True,\n    units: bool = True,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"\n    Binary classification dataset on loan default\n\n    The loans dataset is based on a financial dataset from the [Relational Dataset Repository (former CTU Prague\n    Relational Learning Repository)](https://relational-data.org/dataset/Financial).\n\n    !!! abstract \"Reference\"\n        Berka, Petr (1999). Workshop notes on Discovery Challenge PKDD'99.\n\n    The dataset contains information on 606 successful and 76 unsuccessful\n    loans. After some preprocessing it contains 5 tables\n\n    * `account`: Information about the borrower(s) of a given loan.\n\n    * `loan`: Information about the loans themselves, such as the date of creation, the amount, and the planned duration of the loan. The target variable is the status of the loan (default/no default)\n\n    * `meta`: Meta information about the obligor, such as gender and geo-information\n\n    * `order`: Information about permanent orders, debited payments and account balances.\n\n    * `trans`: Information about transactions and accounts balances.\n\n    The population table is split into a training and a testing set at 80% of the main population.\n\n    Args:\n        roles:\n            Return data with roles set\n\n        units:\n            Return data with units set\n\n        as_pandas:\n            Return data as `pandas.DataFrame`\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n        Tuple containing (sorted alphabetically by `df.name`) the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True) or\n            if `as_dict` is `True`: Dictionary containing the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True). The keys correspond to the name of the DataFrame on the\n            [`engine`][getml.engine].\n\n            The following DataFrames are returned:\n\n            * `account`\n            * `loan`\n            * `meta`\n            * `order`\n            * `trans`\n\n    ??? example\n        ```python\n        loans = getml.datasets.load_loans(as_dict=True)\n        type(loans[\"population_train\"])\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the loans dataset including all necessary\n        preprocessing steps please refer to [getml-examples\n        ](https://github.com/getml/getml-demo/blob/master/loans.ipynb).\n\n    Note:\n        Roles and units can be set ad-hoc by supplying the respective flags. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. Before using them in an\n        analysis, a data model needs to be constructed using\n        [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"loans\"\n\n    return _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        units=units,\n        as_pandas=as_pandas,\n        as_dict=as_dict,\n    )\n\n\ndef load_occupancy(\n    roles: bool = True,\n    as_pandas: bool = False,\n    as_dict: bool = False,\n) -> Union[Tuple[DataFrameT, ...], Dict[str, DataFrameT]]:\n    \"\"\"\n    Binary classification dataset on occupancy detection\n\n    The occupancy detection dataset is a very simple multivariate time series\n    from the [UCI Machine Learning Repository\n    ](https://archive.ics.uci.edu/dataset/357/occupancy+detection). It is a\n    binary classification problem. The task is to predict room occupancy\n    from Temperature, Humidity, Light and CO2.\n\n    !!! abstract \"Reference\"\n        Candanedo, L. M., & Feldheim, V. (2016). Accurate occupancy detection of an\n        office room from light, temperature, humidity and CO2 measurements using\n        statistical learning models. Energy and Buildings, 112, 28-39.\n\n    Args:\n        roles:\n            Return data with roles set\n\n        as_pandas:\n            Return data as `pandas.DataFrame` s\n\n        as_dict:\n            Return data as dict with `df.name` as keys and\n            `df` as values.\n\n    Returns:\n        Tuple containing (sorted alphabetically by `df.name`) the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True) or\n            if `as_dict` is `True`: Dictionary containing the data as\n            [`DataFrame`][getml.DataFrame] or `pandas.DataFrame` (if `as_pandas`\n            is True). The keys correspond to the name of the DataFrame on the\n            [`engine`][getml.engine].\n\n            The following DataFrames are returned:\n\n            * `population_train`\n            * `population_test`\n            * `population_validation`\n\n    ??? example\n        ```python\n        population_train, population_test, _ = getml.datasets.load_occupancy()\n        type(occupancy_train)\n        getml.data.data_frame.DataFrame\n        ```\n        For a full analysis of the occupancy dataset including all necessary\n        preprocessing steps please refer to [getml-examples\n        ](https://github.com/getml/getml-demo/blob/master/occupancy.ipynb).\n\n\n    Note:\n        Roles can be set ad-hoc by supplying the respective flag. If\n        `roles` is `False`, all columns in the returned\n        [`DataFrame`][getml.data.DataFrame] have roles\n        [`unused_string`][getml.data.roles.unused_string] or\n        [`unused_float`][getml.data.roles.unused_float]. This dataset contains no units.\n        Before using them in an analysis, a data model needs to be constructed\n        using [`Placeholder`][getml.data.Placeholder].\n    \"\"\"\n\n    ds_name = \"occupancy\"\n\n    return _load_dataset(\n        ds_name=ds_name,\n        roles=roles,\n        as_pandas=as_pandas,\n        as_dict=as_dict,\n    )\n"
  },
  {
    "path": "src/python-api/getml/datasets/samples_generator.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nGenerate samples of artificial data sets.\n\"\"\"\n\nimport datetime\nfrom typing import Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\n\nfrom getml import data\nfrom getml.data.data_frame import DataFrame\nfrom getml.feature_learning import aggregations\n\n# -----------------------------------------------------------------------------\n\n\ndef _aggregate(\n    table: pd.DataFrame, aggregation: str, col: str, join_key: str\n) -> pd.DataFrame:\n    \"\"\"\n    Implements the aggregation.\"\"\"\n\n    if aggregation == aggregations.AVG:\n        return table[[col, join_key]].groupby([join_key], as_index=False).mean()\n\n    if aggregation == aggregations.COUNT:\n        return table[[col, join_key]].groupby([join_key], as_index=False).count()\n\n    if aggregation == aggregations.COUNT_DISTINCT:\n        series = (\n            table[[col, join_key]].groupby([join_key], as_index=False)[col].nunique()\n        )\n\n        output = table[[col, join_key]].groupby([join_key], as_index=False).count()\n\n        output[col] = series\n\n        return output\n\n    if aggregation == aggregations.COUNT_MINUS_COUNT_DISTINCT:\n        series = (\n            table[[col, join_key]].groupby([join_key], as_index=False)[col].nunique()\n        )\n\n        output = table[[col, join_key]].groupby([join_key], as_index=False).count()\n\n        output[col] -= series\n\n        return output\n\n    if aggregation == aggregations.MAX:\n        return table[[col, join_key]].groupby([join_key], as_index=False).max()\n\n    if aggregation == aggregations.MEDIAN:\n        return table[[col, join_key]].groupby([join_key], as_index=False).median()\n\n    if aggregation == aggregations.MIN:\n        return table[[col, join_key]].groupby([join_key], as_index=False).min()\n\n    if aggregation == aggregations.STDDEV:\n        return table[[col, join_key]].groupby([join_key], as_index=False).std()\n\n    if aggregation == aggregations.SUM:\n        return table[[col, join_key]].groupby([join_key], as_index=False).sum()\n\n    if aggregation == aggregations.VAR:\n        return table[[col, join_key]].groupby([join_key], as_index=False).var()\n\n    raise Exception(\"Aggregation '\" + aggregation + \"' not known!\")\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_categorical(\n    n_rows_population: int = 500,\n    n_rows_peripheral: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name: str = \"\",\n    aggregation: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with categorical variables\n\n    The dataset consists of a population table and one peripheral table.\n\n    The peripheral table has 3 columns:\n\n    * `column_01`: random categorical variable between '0' and '9'\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`: random categorical variable between '0' and '9'\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable. Defined as the number of matching entries in\n      the peripheral table for which ``time_stamp_peripheral <\n      time_stamp_population`` and the category in the peripheral table is not\n      1, 2 or 9. The SQL definition of the target variable read like this\n\n    ```sql\n    SELECT aggregation( column_01 )\n    FROM POPULATION_TABLE t1\n    LEFT JOIN PERIPHERAL_TABLE t2\n    ON t1.join_key = t2.join_key\n    WHERE (\n       ( t2.column_01 != '1' AND t2.column_01 != '2' AND t2.column_01 != '9' )\n    ) AND t2.time_stamps <= t1.time_stamps\n    GROUP BY t1.join_key,\n         t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral:\n            Number of rows in the peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `categorical_population_` and the seed of the random\n            number generator.\n\n        peripheral_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the peripheral\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `categorical_peripheral_` and the seed of the random\n            number generator.\n\n        aggregation:\n            [`aggregations`][getml.feature_learning.aggregations] used to generate the 'target'\n            column.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n    population_table = pd.DataFrame()\n    population_table[\"column_01\"] = random.randint(0, 10, n_rows_population).astype(str)\n    population_table[\"join_key\"] = np.arange(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01\"] = random.randint(0, 10, n_rows_peripheral).astype(str)\n    peripheral_table[\"join_key\"] = random.randint(\n        0, n_rows_population, n_rows_peripheral\n    )\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral)\n\n    # Compute targets\n    temp = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral\"] <= temp[\"time_stamp_population\"])\n        & (temp[\"column_01\"] != \"1\")\n        & (temp[\"column_01\"] != \"2\")\n        & (temp[\"column_01\"] != \"9\")\n    ]\n\n    # Define the aggregation\n    temp = _aggregate(temp, aggregation, \"column_01\", \"join_key\")\n\n    temp = temp.rename(index=str, columns={\"column_01\": \"targets\"})\n\n    population_table = population_table.merge(temp, how=\"left\", on=\"join_key\")\n\n    del temp\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table.targets = np.where(\n        np.isnan(population_table[\"targets\"]), 0, population_table[\"targets\"]\n    )\n\n    # Set default names if none where provided.\n    if not population_name:\n        population_name = \"categorical_population_\" + str(random_state)\n    if not peripheral_name:\n        peripheral_name = \"categorical_peripheral_\" + str(random_state)\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"categorical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"categorical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    return population_on_engine, peripheral_on_engine\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_discrete(\n    n_rows_population: int = 500,\n    n_rows_peripheral: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name: str = \"\",\n    aggregation: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with categorical variables\n\n    The dataset consists of a population table and one peripheral table.\n\n    The peripheral table has 3 columns:\n\n    * `column_01`: random integer between -10 and 10\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`: random number between -1 and 1\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable. Defined as the minimum value greater than 0\n      in the peripheral table for which\n      ``time_stamp_peripheral < time_stamp_population``\n      and the join key matches\n    ```sql\n    SELECT aggregation( column_01 )\n    FROM POPULATION t1\n    LEFT JOIN PERIPHERAL t2\n    ON t1.join_key = t2.join_key\n    WHERE (\n       ( t2.column_01 > 0 )\n    ) AND t2.time_stamp <= t1.time_stamp\n    GROUP BY t1.join_key,\n             t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral:\n            Number of rows in the peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `discrete_population_` and the seed of the random\n            number generator.\n\n        peripheral_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the peripheral\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `discrete_peripheral_` and the seed of the random\n            number generator.\n\n        aggregation:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n\n    population_table = pd.DataFrame()\n    population_table[\"column_01\"] = random.randint(0, 10, n_rows_population).astype(str)\n    population_table[\"join_key\"] = np.arange(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01\"] = random.randint(-11, 11, n_rows_peripheral)\n    peripheral_table[\"join_key\"] = random.randint(\n        0, n_rows_population, n_rows_peripheral\n    )\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral)\n\n    # Compute targets\n    temp = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral\"] <= temp[\"time_stamp_population\"])\n        & (temp[\"column_01\"] > 0.0)\n    ]\n\n    # Define the aggregation\n    temp = _aggregate(temp, aggregation, \"column_01\", \"join_key\")\n\n    temp = temp.rename(index=str, columns={\"column_01\": \"targets\"})\n\n    population_table = population_table.merge(temp, how=\"left\", on=\"join_key\")\n\n    del temp\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table.targets = np.where(\n        np.isnan(population_table[\"targets\"]), 0, population_table[\"targets\"]\n    )\n\n    # Set default names if none where provided.\n    if not population_name:\n        population_name = \"discrete_population_\" + str(random_state)\n    if not peripheral_name:\n        peripheral_name = \"discrete_peripheral_\" + str(random_state)\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    return population_on_engine, peripheral_on_engine\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_numerical(\n    n_rows_population: int = 500,\n    n_rows_peripheral: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name: str = \"\",\n    aggregation: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with continuous numerical variables\n\n    The dataset consists of a population table and one peripheral table.\n\n    The peripheral table has 3 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable. Defined as the number of matching entries in\n      the peripheral table for which ``time_stamp_peripheral <\n      time_stamp_population < time_stamp_peripheral + 0.5``\n\n    ```sql\n    SELECT aggregation( column_01 )\n    FROM POPULATION t1\n    LEFT JOIN PERIPHERAL t2\n    ON t1.join_key = t2.join_key\n    WHERE (\n       ( t1.time_stamp - t2.time_stamp <= 0.5 )\n    ) AND t2.time_stamp <= t1.time_stamp\n    GROUP BY t1.join_key,\n         t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral:\n            Number of rows in the peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `numerical_population_` and the seed of the random\n            number generator.\n\n        peripheral_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the peripheral\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `numerical_peripheral_` and the seed of the random\n            number generator.\n\n        aggregation:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n\n    population_table = pd.DataFrame()\n    population_table[\"column_01\"] = random.rand(n_rows_population) * 2.0 - 1.0\n    population_table[\"join_key\"] = np.arange(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01\"] = random.rand(n_rows_peripheral) * 2.0 - 1.0\n    peripheral_table[\"join_key\"] = random.randint(\n        0, n_rows_population, n_rows_peripheral\n    )\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral)\n\n    # Compute targets\n    temp = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral\"] <= temp[\"time_stamp_population\"])\n        & (temp[\"time_stamp_peripheral\"] >= temp[\"time_stamp_population\"] - 0.5)\n    ]\n\n    # Define the aggregation\n    temp = _aggregate(temp, aggregation, \"column_01\", \"join_key\")\n\n    temp = temp.rename(index=str, columns={\"column_01\": \"targets\"})\n\n    population_table = population_table.merge(temp, how=\"left\", on=\"join_key\")\n\n    del temp\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table.targets = np.where(\n        np.isnan(population_table[\"targets\"]), 0, population_table[\"targets\"]\n    )\n\n    # Set default names if none where provided.\n    if not population_name:\n        population_name = \"numerical_population_\" + str(random_state)\n    if not peripheral_name:\n        peripheral_name = \"numerical_peripheral_\" + str(random_state)\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    return population_on_engine, peripheral_on_engine\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_same_units_categorical(\n    n_rows_population: int = 500,\n    n_rows_peripheral: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name: str = \"\",\n    aggregation: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with categorical variables\n\n    The dataset consists of a population table and one peripheral table.\n\n    The peripheral table has 3 columns:\n\n    * `column_01`: random categorical variable between '0' and '9'\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`: random categorical variable between '0' and '9'\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable. Defined as the number of matching entries in\n      the peripheral table for which ``time_stamp_peripheral <\n      time_stamp_population`` and the category in the peripheral table is not\n      1, 2 or 9\n\n    ```sql\n    SELECT aggregation( column_02 )\n    FROM POPULATION_TABLE t1\n    LEFT JOIN PERIPHERAL_TABLE t2\n    ON t1.join_key = t2.join_key\n    WHERE (\n       ( t1.column_01 == t2.column_01 )\n    ) AND t2.time_stamps <= t1.time_stamps\n    GROUP BY t1.join_key,\n         t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral:\n            Number of rows in the peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `make_same_units_categorical_population_` and the seed of the random\n            number generator.\n\n        peripheral_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the peripheral\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `make_same_units_categorical_peripheral_` and the seed of the random\n            number generator.\n\n        aggregation:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n\n    population_table = pd.DataFrame()\n    population_table[\"column_01_population\"] = (\n        (random.rand(n_rows_population) * 10.0).astype(np.int32).astype(str)\n    )\n    population_table[\"join_key\"] = range(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01_peripheral\"] = (\n        (random.rand(n_rows_peripheral) * 10.0).astype(np.int32).astype(str)\n    )\n    peripheral_table[\"column_02\"] = random.rand(n_rows_peripheral) * 2.0 - 1.0\n    peripheral_table[\"join_key\"] = [\n        int(float(n_rows_population) * random.rand(1)[0])\n        for i in range(n_rows_peripheral)\n    ]\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral)\n\n    # ----------------\n\n    temp = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\", \"column_01_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral\"] <= temp[\"time_stamp_population\"])\n        & (temp[\"column_01_peripheral\"] == temp[\"column_01_population\"])\n    ]\n\n    # Define the aggregation\n    temp = _aggregate(temp, aggregation, \"column_02\", \"join_key\")\n\n    temp = temp.rename(index=str, columns={\"column_02\": \"targets\"})\n\n    population_table = population_table.merge(temp, how=\"left\", on=\"join_key\")\n\n    population_table = population_table.rename(\n        index=str, columns={\"column_01_population\": \"column_01\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"column_01_peripheral\": \"column_01\"}\n    )\n\n    del temp\n\n    # ----------------\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    # ----------------\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table[\"targets\"] = [\n        0.0 if val != val else val for val in population_table[\"targets\"]\n    ]\n\n    # ----------------\n\n    # Set default names if none where provided.\n    population_name = (\n        population_name\n        or \"make_same_units_categorical_population__\" + str(random_state)\n    )\n\n    peripheral_name = (\n        peripheral_name\n        or \"make_same_units_categorical_peripheral__\" + str(random_state)\n    )\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"categorical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"categorical\": [\"column_01\"],\n            \"numerical\": [\"column_02\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    # ----------------\n\n    return population_on_engine, peripheral_on_engine\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_same_units_numerical(\n    n_rows_population: int = 500,\n    n_rows_peripheral: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name: str = \"\",\n    aggregation: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with continuous numerical variables\n\n    The dataset consists of a population table and one peripheral table.\n\n    The peripheral table has 3 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable. Defined as the number of matching entries in\n      the peripheral table for which ``time_stamp_peripheral <\n      time_stamp_population < time_stamp_peripheral + 0.5``\n\n    ```sql\n    SELECT aggregation( column_01 )\n    FROM POPULATION t1\n    LEFT JOIN PERIPHERAL t2\n    ON t1.join_key = t2.join_key\n    WHERE (\n       ( t1.column_01 - t2.column_01 <= 0.5 )\n    ) AND t2.time_stamp <= t1.time_stamp\n    GROUP BY t1.join_key,\n         t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral:\n            Number of rows in the peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `make_same_units_numerical_population_` and the seed of the random\n            number generator.\n\n        peripheral_name:\n            Name assigned to\n            [`DataFrame`][getml.DataFrame] holding the peripheral\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `make_same_units_numerical_peripheral_` and the seed of the random\n            number generator.\n\n        aggregation:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n\n    population_table = pd.DataFrame()\n    population_table[\"column_01_population\"] = (\n        random.rand(n_rows_population) * 2.0 - 1.0\n    )\n    population_table[\"join_key\"] = range(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01_peripheral\"] = (\n        random.rand(n_rows_peripheral) * 2.0 - 1.0\n    )\n    peripheral_table[\"join_key\"] = [\n        int(float(n_rows_population) * random.rand(1)[0])\n        for i in range(n_rows_peripheral)\n    ]\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral)\n\n    # ----------------\n\n    temp = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\", \"column_01_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral\"] <= temp[\"time_stamp_population\"])\n        & (temp[\"column_01_peripheral\"] > temp[\"column_01_population\"] - 0.5)\n    ]\n\n    # Define the aggregation\n    temp = (\n        temp[[\"column_01_peripheral\", \"join_key\"]]\n        .groupby([\"join_key\"], as_index=False)\n        .count()\n    )\n\n    temp = temp.rename(index=str, columns={\"column_01_peripheral\": \"targets\"})\n\n    population_table = population_table.merge(temp, how=\"left\", on=\"join_key\")\n\n    population_table = population_table.rename(\n        index=str, columns={\"column_01_population\": \"column_01\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"column_01_peripheral\": \"column_01\"}\n    )\n\n    del temp\n\n    # ----------------\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    # ----------------\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table[\"targets\"] = [\n        0.0 if val != val else val for val in population_table[\"targets\"]\n    ]\n\n    # ----------------\n\n    # Set default names if none where provided.\n    if not population_name:\n        population_name = \"same_unit_numerical_population_\" + str(random_state)\n    if not peripheral_name:\n        peripheral_name = \"same_unit_numerical_peripheral_\" + str(random_state)\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    return population_on_engine, peripheral_on_engine\n\n\n# -----------------------------------------------------------------------------\n\n\ndef make_snowflake(\n    n_rows_population: int = 500,\n    n_rows_peripheral1: int = 5000,\n    n_rows_peripheral2: int = 125000,\n    random_state: Optional[int] = None,\n    population_name: str = \"\",\n    peripheral_name1: str = \"\",\n    peripheral_name2: str = \"\",\n    aggregation1: str = aggregations.SUM,\n    aggregation2: str = aggregations.COUNT,\n) -> Tuple[DataFrame, DataFrame, DataFrame]:\n    \"\"\"\n    Generate a random dataset with continuous numerical variables\n\n    The dataset consists of a population table and two peripheral tables.\n\n    The first peripheral table has 4 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: random integer in the range from 0 to ``n_rows_population``\n    * `join_key2`: unique integer in the range from 0 to ``n_rows_peripheral1``\n    * `time_stamp`: random number between 0 and 1\n\n    The second peripheral table has 3 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key2`: random integer in the range from 0 to ``n_rows_peripheral1``\n    * `time_stamp`: random number between 0 and 1\n\n    The population table has 4 columns:\n\n    * `column_01`:  random number between -1 and 1\n    * `join_key`: unique integer in the range from 0 to ``n_rows_population``\n    * `time_stamp`: random number between 0 and 1\n    * `targets`: target variable as defined by the SQL block below:\n\n    ```sql\n    SELECT aggregation1( feature_1_1 )\n    FROM POPULATION t1\n    LEFT JOIN (\n        SELECT aggregation2( t4.column_01 ) AS feature_1_1\n        FROM PERIPHERAL t3\n        LEFT JOIN PERIPHERAL2 t4\n        ON t3.join_key2 = t4.join_key2\n        WHERE (\n           ( t3.time_stamp - t4.time_stamp <= 0.5 )\n        ) AND t4.time_stamp <= t3.time_stamp\n        GROUP BY t3.join_key,\n             t3.time_stamp\n    ) t2\n    ON t1.join_key = t2.join_key\n    WHERE t2.time_stamp <= t1.time_stamp\n    GROUP BY t1.join_key,\n         t1.time_stamp;\n    ```\n\n    Args:\n        n_rows_population:\n            Number of rows in the population table.\n\n        n_rows_peripheral1:\n            Number of rows in the first peripheral table.\n\n        n_rows_peripheral2:\n            Number of rows in the second peripheral table.\n\n        random_state:\n            Seed to initialize the random number generator used for\n            the dataset creation. If set to None, the seed will be the\n            'microsecond' component of\n            `datetime.datetime.now()`.\n\n        population_name:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the population\n            table. If set to a name already existing on the getML\n            Engine, the corresponding [`DataFrame`][getml.DataFrame]\n            will be overwritten. If set to an empty string, a unique\n            name will be generated by concatenating\n            `snowflake_population_` and the seed of the random\n            number generator.\n\n        peripheral_name1:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the first\n            peripheral table. If set to a name already existing on the\n            getML Engine, the corresponding\n            [`DataFrame`][getml.DataFrame] will be overwritten. If\n            set to an empty string, a unique name will be generated by\n            concatenating `snowflake_peripheral_1_` and the seed of the\n            random number generator.\n\n        peripheral_name2:\n            Name assigned to the\n            [`DataFrame`][getml.DataFrame] holding the second\n            peripheral table. If set to a name already existing on the\n            getML Engine, the corresponding\n            [`DataFrame`][getml.DataFrame] will be overwritten. If\n            set to an empty string, a unique name will be generated by\n            concatenating `snowflake_peripheral_2_` and the seed of the\n            random number generator.\n\n        aggregation1:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column in the first peripheral table.\n\n        aggregation2:\n            [aggregations][getml.feature_learning.aggregations] used to generate the 'target'\n            column in the second peripheral table.\n\n    Returns:\n        The dataframes are:\n\n            * population ([`DataFrame`][getml.DataFrame]): Population table\n            * peripheral ([`DataFrame`][getml.DataFrame]): Peripheral table\n            * peripheral_2 ([`DataFrame`][getml.DataFrame]): Peripheral table\n    \"\"\"\n\n    if random_state is None:\n        random_state = datetime.datetime.now().microsecond\n\n    random = np.random.RandomState(random_state)  # pylint: disable=E1101\n\n    population_table = pd.DataFrame()\n    population_table[\"column_01\"] = random.rand(n_rows_population) * 2.0 - 1.0\n    population_table[\"join_key\"] = range(n_rows_population)\n    population_table[\"time_stamp_population\"] = random.rand(n_rows_population)\n\n    peripheral_table = pd.DataFrame()\n    peripheral_table[\"column_01\"] = random.rand(n_rows_peripheral1) * 2.0 - 1.0\n    peripheral_table[\"join_key\"] = [\n        int(float(n_rows_population) * random.rand(1)[0])\n        for i in range(n_rows_peripheral1)\n    ]\n    peripheral_table[\"join_key2\"] = range(n_rows_peripheral1)\n    peripheral_table[\"time_stamp_peripheral\"] = random.rand(n_rows_peripheral1)\n\n    peripheral_table2 = pd.DataFrame()\n    peripheral_table2[\"column_01\"] = random.rand(n_rows_peripheral2) * 2.0 - 1.0\n    peripheral_table2[\"join_key2\"] = [\n        int(float(n_rows_peripheral1) * random.rand(1)[0])\n        for i in range(n_rows_peripheral2)\n    ]\n    peripheral_table2[\"time_stamp_peripheral2\"] = random.rand(n_rows_peripheral2)\n\n    # ----------------\n    # Merge peripheral_table with peripheral_table2\n\n    temp = peripheral_table2.merge(\n        peripheral_table[[\"join_key2\", \"time_stamp_peripheral\"]],\n        how=\"left\",\n        on=\"join_key2\",\n    )\n\n    # Apply some conditions\n    temp = temp[\n        (temp[\"time_stamp_peripheral2\"] <= temp[\"time_stamp_peripheral\"])\n        & (temp[\"time_stamp_peripheral2\"] >= temp[\"time_stamp_peripheral\"] - 0.5)\n    ]\n\n    # Define the aggregation\n    temp = _aggregate(temp, aggregation2, \"column_01\", \"join_key2\")\n\n    temp = temp.rename(index=str, columns={\"column_01\": \"temporary\"})\n\n    peripheral_table = peripheral_table.merge(temp, how=\"left\", on=\"join_key2\")\n\n    del temp\n\n    # Replace NaN with 0.0\n    peripheral_table[\"temporary\"] = [\n        0.0 if val != val else val for val in peripheral_table[\"temporary\"]\n    ]\n\n    # ----------------\n    # Merge population_table with peripheral_table\n\n    temp2 = peripheral_table.merge(\n        population_table[[\"join_key\", \"time_stamp_population\"]],\n        how=\"left\",\n        on=\"join_key\",\n    )\n\n    # Apply some conditions\n    temp2 = temp2[(temp2[\"time_stamp_peripheral\"] <= temp2[\"time_stamp_population\"])]\n\n    # Define the aggregation\n    temp2 = _aggregate(temp2, aggregation1, \"temporary\", \"join_key\")\n\n    temp2 = temp2.rename(index=str, columns={\"temporary\": \"targets\"})\n\n    population_table = population_table.merge(temp2, how=\"left\", on=\"join_key\")\n\n    del temp2\n\n    # Replace NaN targets with 0.0 - target values may never be NaN!.\n    population_table[\"targets\"] = [\n        0.0 if val != val else val for val in population_table[\"targets\"]\n    ]\n\n    # Remove temporary column.\n    del peripheral_table[\"temporary\"]\n\n    # ----------------\n\n    population_table = population_table.rename(\n        index=str, columns={\"time_stamp_population\": \"time_stamp\"}\n    )\n\n    peripheral_table = peripheral_table.rename(\n        index=str, columns={\"time_stamp_peripheral\": \"time_stamp\"}\n    )\n\n    peripheral_table2 = peripheral_table2.rename(\n        index=str, columns={\"time_stamp_peripheral2\": \"time_stamp\"}\n    )\n\n    # ----------------\n\n    # Set default names if none where provided.\n    if not population_name:\n        population_name = \"snowflake_population_\" + str(random_state)\n    if not peripheral_name1:\n        peripheral_name1 = \"snowflake_peripheral_1_\" + str(random_state)\n    if not peripheral_name2:\n        peripheral_name2 = \"snowflake_peripheral_2_\" + str(random_state)\n\n    # Create the data.DataFrame counterpart.\n    population_on_engine = data.DataFrame(\n        name=population_name,\n        roles={\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n            \"target\": [\"targets\"],\n        },\n    ).read_pandas(population_table)\n\n    peripheral_on_engine = data.DataFrame(\n        name=peripheral_name1,\n        roles={\n            \"join_key\": [\"join_key\", \"join_key2\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table)\n\n    peripheral_on_engine2 = data.DataFrame(\n        name=peripheral_name2,\n        roles={\n            \"join_key\": [\"join_key2\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_pandas(peripheral_table2)\n\n    # ----------------\n\n    return population_on_engine, peripheral_on_engine, peripheral_on_engine2\n\n\n# -----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/engine/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"This module is a collection of utility functions for the overall\ncommunication and the session management of the getML Engine.\n\nIn order to log into the Engine, you have to open your favorite\ninternet browser and enter [http://localhost:1709](http://localhost:1709) in the navigation bar. This tells it to\nconnect to a local TCP socket at port 1709 opened by the getML\nMonitor. This will only be possible from within the same device!\n\n??? example\n    First of all, you need to start the getML Engine.\n    Next, you need to create a new project or\n    load an existing one.\n\n    ```python\n    getml.engine.list_projects()\n    getml.engine.set_project('test')\n    ```\n    After doing all calculations for today you can shut down the getML\n    Engine.\n\n    ```python\n    print(getml.engine.is_alive())\n    getml.engine.shutdown()\n    ```\n\nNote:\n    The Python process and the getML Engine must be located on\n    the same machine. If you\n    intend to run the Engine on a remote host, make sure to start your\n    Python session on that device as well. Also, when using SSH sessions,\n    make sure to start Python using `python &` followed by `disown` or\n    using `nohup python`. This ensures the Python process and all the\n    script it has to run won't be killed the moment your remote\n    connection becomes unstable, and\n    you are able to recover them later on (see\n    [`remote_access`][remote-access-anchor]).\n\n    All data frame objects and models in the getML Engine are\n    bundled in projects. When loading an existing project, the\n    current memory of the Engine will be flushed and all changes\n    applied to [`DataFrame`][getml.DataFrame] instances after\n    calling their [`save`][getml.DataFrame.save] method will\n    be lost. Afterwards, all\n    [`Pipeline`][getml.Pipeline] will be loaded into\n    memory automatically. The data frame objects\n    will not be loaded automatically since they consume significantly\n    more\n    memory than the pipelines. They can be loaded manually using\n    [`load_data_frame`][getml.data.load_data_frame] or\n    [`load`][getml.DataFrame.load].\n\n    The getML Engine reflects the separation of data into individual\n    projects on the level of the filesystem too. All data\n    belonging to a single project is stored in a dedicated folder in\n    the 'projects' directory located in '.getML' in your home\n    folder. These projects can be copied and shared between different\n    platforms and architectures without any loss of information. However,\n    you must copy the entire project and not just individual data frames\n    or pipelines.\n\"\"\"\n\nfrom getml.communication import is_engine_alive, is_monitor_alive\n\nfrom ._launch import EXECUTABLE_NAME, Edition, launch\nfrom .helpers import (\n    delete_project,\n    is_alive,\n    list_projects,\n    list_running_projects,\n    set_project,\n    shutdown,\n    suspend_project,\n)\n\n__all__ = (\n    \"delete_project\",\n    \"EXECUTABLE_NAME\",\n    \"Edition\",\n    \"is_alive\",\n    \"is_engine_alive\",\n    \"is_monitor_alive\",\n    \"launch\",\n    \"list_projects\",\n    \"list_running_projects\",\n    \"set_project\",\n    \"shutdown\",\n    \"suspend_project\",\n)\n"
  },
  {
    "path": "src/python-api/getml/engine/_launch.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nimport json\nimport os\nimport platform\nimport re\nimport subprocess\nfrom dataclasses import asdict, dataclass\nfrom datetime import datetime\nfrom enum import Enum\nfrom inspect import cleandoc\nfrom os import PathLike\nfrom pathlib import Path\nfrom time import sleep\nfrom typing import Dict, List, Optional, Union\n\nimport getml.communication as comm\nfrom getml.constants import COMPOSE_FILE_URL, DOCKER_DOCS_URL, INSTALL_DOCS_URL\nfrom getml.version import __version__\n\nOS_NOT_SUPPORTED_NATIVELY_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    The operating system '{os}' is not supported natively by getML.\n\n    You can use the dockerized version of getML to run it on your OS.\n\n    Refer to the documentation for more information:\n    {docker_docs_url}\n\n    The simplest way to get started is to use the curated compose runtime:\n    curl -L {compose_file_url} | docker compose -f - up\n    \"\"\"\n)\n\n\nclass Edition(str, Enum):\n    ENTERPRISE = \"enterprise\"\n    COMMUNITY = \"community\"\n\n\nclass Arch(str, Enum):\n    AMD64 = \"amd64\"\n    ARM64 = \"arm64\"\n\n\nclass Os(str, Enum):\n    LINUX = \"linux\"\n    MACOS = \"darwin\"\n    WINDOWS = \"windows\"\n\n\nNATIVELY_SUPPORTED_OSES = (Os.LINUX,)\n\nEXECUTABLE_NAME = \"getML\"\n\nSEMVER_REGEX = re.compile(\n    r\"\"\"\n    (?P<version_major>0|[1-9]\\d*)\n    \\.(?P<version_minor>0|[1-9]\\d*)\n    \\.(?P<version_patch>0|[1-9]\\d*)\n    (?:-(?P<version_prerelease>(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)\n    (?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?\n    (?:\\+(?P<version_buildmetadata>[0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?\n    \"\"\",\n    re.VERBOSE,\n)\n\"\"\"\nRegex to match semantic version strings. Adapted from:\nhttps://semver.org/#is-there-a-suggested-regular-expression-regex-to-check-a-semver-string\n\"\"\"\n\nPACKAGE_NAME_REGEX = re.compile(\n    rf\"\"\"\n    getml-(?P<edition>{'|'.join(Edition)})\n    -(?P<version>{SEMVER_REGEX.pattern})\n    -(?P<arch>{'|'.join(Arch)})\n    -(?P<os>{'|'.join(NATIVELY_SUPPORTED_OSES)})\n    \"\"\",\n    re.VERBOSE,\n)\n\"\"\"\nRegex to match getML package names. The package name is expected to have the following format:\ngetml-{edition}-{version}-{arch}-{os}\n\"\"\"\n\nINSTALL_LOCATIONS = (\n    Path.home() / \".getML\",\n    Path(\"/usr/local\") / \"getML\",\n    Path(__file__).parent.parent / \".getML\",\n)\n\"\"\"\nLocations where getML is expected to be installed.\n\"\"\"\n\nCOULD_NOT_FIND_EXECUTABLE_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    Could not find getML executable in any of the following locations:\n    {install_locations}\n\n    Refer to the installation documentation for more information:\n    {install_docs_url}\n    \"\"\"\n)\n\nMAX_LAUNCH_WAIT_TIME = 10\n\"\"\"\nMaximum time to wait for the getML subprocess to start (in seconds).\n\"\"\"\n\nHEALTH_CHECK_INTERVAL = 0.1\n\"\"\"\nInterval between health checks during launch (in seconds).\n\"\"\"\n\nENGINE_DID_NOT_RESPOND_IN_TIME_ERROR_MSG_TEMPLATE = cleandoc(\n    \"\"\"\n    getML Engine did not respond within {max_launch_wait_time} seconds.\n\n    For furher information, check the log file at {{log_file}}.\n    \"\"\"\n).format(max_launch_wait_time=MAX_LAUNCH_WAIT_TIME)\n\n\ndef _handle_option_name(name: str) -> str:\n    return name.replace(\"_\", \"-\")\n\n\ndef _handle_option_value(value: Union[bool, int, str, PathLike, None]) -> str:\n    return json.dumps(value, default=str).strip('\"')\n\n\n@dataclass\nclass _Options:\n    allow_push_notifications: bool\n    allow_remote_ips: bool\n    home_directory: PathLike\n    http_port: Optional[int]\n    in_memory: bool\n    install: bool\n    launch_browser: bool\n    log: bool\n    project_directory: Optional[PathLike]\n    proxy_url: Optional[str]\n    token: Optional[str]\n\n    def to_cmd(self) -> List[str]:\n        \"\"\"\n        Generates the cmd (args) passed to subprocess.Popen.\n        \"\"\"\n        options = [\n            f\"--{_handle_option_name(name)}={_handle_option_value(value)}\"\n            for (name, value) in asdict(self).items()\n            if value is not None\n        ]\n        return [\"./getML\", *options]\n\n\ndef create_log_dir(getml_home_directory: Path) -> Path:\n    log_path = getml_home_directory / \"logs\"\n    log_path.mkdir(parents=True, exist_ok=True)\n    return log_path\n\n\ndef locate_installed_packages_by_edition() -> Dict[Edition, List[Path]]:\n    \"\"\"\n    Locate all installed getML packages that match the python api's version\n    grouped by edition.\n    \"\"\"\n    installed_packages = {edition: [] for edition in Edition}\n    for location in INSTALL_LOCATIONS:\n        for package in location.glob(\"getml-*\"):\n            if package.is_dir():\n                match = PACKAGE_NAME_REGEX.match(package.name)\n                if match and match.group(\"version\") == __version__:\n                    installed_packages[Edition(match.group(\"edition\"))].append(package)\n    return installed_packages\n\n\ndef locate_executable() -> Optional[Path]:\n    \"\"\"\n    Locates the getML executable respecting the prioitized order of:\n    1. editions (enterprise > community)\n    2. instalation locations (home > /usr/local > getml package directory)\n\n    Returns the first found executable or None if none is found.\n    \"\"\"\n    installed_packages = locate_installed_packages_by_edition()\n    for edition in Edition:\n        packages = installed_packages[edition]\n        for package in packages:\n            for executable in package.glob(EXECUTABLE_NAME):\n                if (\n                    executable.name == EXECUTABLE_NAME\n                    and executable.is_file()\n                    and os.access(executable, os.X_OK)\n                ):\n                    return executable\n\n\ndef launch(\n    allow_push_notifications: bool = True,\n    allow_remote_ips: bool = False,\n    home_directory: Optional[str] = None,\n    http_port: Optional[int] = None,\n    in_memory: bool = True,\n    install: bool = False,\n    launch_browser: bool = True,\n    log: bool = False,\n    project_directory: Optional[Union[PathLike, str]] = None,\n    proxy_url: Optional[str] = None,\n    token: Optional[str] = None,\n    quiet: bool = False,\n):\n    \"\"\"\n    Launches the getML Engine.\n\n    Args:\n      allow_push_notifications:\n        Whether you want to allow the getML Monitor to send push notifications to your desktop.\n\n      allow_remote_ips:\n        Whether you want to allow remote IPs to access the http-port.\n\n      home_directory:\n        The directory which should be treated as the home directory by getML.\n        getML will create a hidden folder named '.getML' in said directory.\n        All binaries will be installed there.\n\n      http_port:\n        The local port of the getML Monitor.\n        This port can only be accessed from your local computer,\n        unless you set `allow_remote_ips=True`.\n\n      in_memory:\n        Whether you want the Engine to process everything in memory.\n\n      install:\n        Reinstalls getML, even if it is already installed.\n\n      launch_browser:\n        Whether you want to automatically launch your browser.\n\n      log:\n        Whether you want the Engine log to appear in the logfile (more detailed logging).\n        The Engine log also appears in the 'Log' page of the Monitor.\n\n      project_directory:\n        The directory in which to store all of your projects.\n\n      proxy_url:\n        The URL of any proxy server that that redirects to the getML Monitor.\n\n      token:\n        The token used for authentication.\n        Authentication is required when remote IPs are allowed to access the Monitor.\n        If authentication is required and no token is passed,\n        a random hexcode will be generated as the token.\n\n      quiet:\n        Whether to suppress output.\n    \"\"\"\n\n    if comm.is_monitor_alive():\n        print(\"getML Engine is already running.\")\n        return\n    if (os_ := platform.system().lower()) not in NATIVELY_SUPPORTED_OSES:\n        raise OSError(\n            OS_NOT_SUPPORTED_NATIVELY_ERROR_MSG_TEMPLATE.format(\n                os=os_,\n                docker_docs_url=DOCKER_DOCS_URL,\n                compose_file_url=COMPOSE_FILE_URL,\n            )\n        )\n    executable_path = locate_executable()\n    if not executable_path:\n        raise OSError(\n            COULD_NOT_FIND_EXECUTABLE_ERROR_MSG_TEMPLATE.format(\n                install_locations=[str(p) for p in INSTALL_LOCATIONS],\n                install_docs_url=INSTALL_DOCS_URL,\n            )\n        )\n    getml_dir = (\n        Path(home_directory) if home_directory is not None else Path.home() / \".getML\"\n    )\n    project_dir = (\n        Path(project_directory)\n        if project_directory is not None\n        else getml_dir / \"projects\"\n    )\n    log_dir = create_log_dir(getml_dir)\n    log_file = log_dir / f\"getml_{datetime.now():%Y%m%d%H%M%S}.log\"\n    cmd = _Options(\n        allow_push_notifications=allow_push_notifications,\n        allow_remote_ips=allow_remote_ips,\n        home_directory=getml_dir,\n        http_port=http_port,\n        in_memory=in_memory,\n        install=install,\n        launch_browser=launch_browser,\n        log=log,\n        project_directory=project_dir,\n        proxy_url=proxy_url,\n        token=token,\n    ).to_cmd()\n    if not quiet:\n        print(f\"Launching {' '.join(cmd)} in {executable_path.parent}...\")\n    subprocess.Popen(\n        cmd,\n        cwd=executable_path.parent,\n        shell=False,\n        stdout=log_file.open(\"w\"),\n        stderr=subprocess.STDOUT,\n    )\n    for _ in range(int(MAX_LAUNCH_WAIT_TIME / HEALTH_CHECK_INTERVAL)):\n        if comm.is_monitor_alive():\n            if not quiet:\n                print(\n                    f\"Launched the getML Engine. The log output will be stored in {log_file}\"\n                )\n            return\n        sleep(HEALTH_CHECK_INTERVAL)\n    raise TimeoutError(\n        ENGINE_DID_NOT_RESPOND_IN_TIME_ERROR_MSG_TEMPLATE.format(log_file=log_file)\n    )\n"
  },
  {
    "path": "src/python-api/getml/engine/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains various helper functions related to the getML Engine.\n\"\"\"\n\nimport json\nimport socket\nfrom typing import Dict, List\n\nimport getml.communication as comm\nfrom getml.communication import (\n    _delete_project,\n    _list_projects_impl,\n    _set_project,\n    _shutdown,\n    _suspend_project,\n)\n\n# backward compatibility alias\nfrom getml.communication import is_engine_alive as is_alive\n\n# --------------------------------------------------------------------\n\n\ndef delete_project(name: str):\n    \"\"\"Deletes a project.\n\n    Args:\n        name:\n            Name of your project.\n\n    Note:\n        All data and models contained in the project directory will be\n        permanently lost.\n\n    \"\"\"\n    _delete_project(name)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef list_projects() -> List[str]:\n    \"\"\"\n    List all projects on the getML Engine.\n\n    Returns:\n            Lists the name of all the projects.\n    \"\"\"\n    return _list_projects_impl(running_only=False)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef list_running_projects() -> List[str]:\n    \"\"\"\n    List all projects on the getML Engine that are currently running.\n\n    Returns:\n        Lists the name of all the projects currently running.\n    \"\"\"\n    return _list_projects_impl(running_only=True)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef set_project(name: str):\n    \"\"\"Creates a new project or loads an existing one.\n\n    If there is no project called `name` present on the Engine, a new one will\n    be created.\n\n    Args:\n           name: Name of the new project.\n    \"\"\"\n    _set_project(name)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef shutdown():\n    \"\"\"Shuts down the getML Engine.\n\n    Warning:\n        All changes applied to the [`DataFrame`][getml.DataFrame]\n        after calling their [`save`][getml.DataFrame.save]\n        method will be lost.\n\n    \"\"\"\n    _shutdown()\n\n\n# --------------------------------------------------------------------\n\n\ndef suspend_project(name: str):\n    \"\"\"Suspends a project that is currently running.\n\n    Args:\n        name:\n            Name of your project.\n    \"\"\"\n    _suspend_project(name)\n\n\n# -----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/events/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom getml.events.dispatchers import PoolEventDispatcher\nfrom getml.events.emitters import DispatcherEventEmitter\nfrom getml.events.handlers import engine_event_handler, monitor_event_handler\nfrom getml.events.parsers import LogMessageEventParser\n\n__all__ = [\n    \"PoolEventDispatcher\",\n    \"DispatcherEventEmitter\",\n    \"engine_event_handler\",\n    \"monitor_event_handler\",\n    \"LogMessageEventParser\",\n]\n"
  },
  {
    "path": "src/python-api/getml/events/dispatchers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDispatchers for events.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport queue\nimport warnings\nfrom concurrent.futures import Executor, Future, ThreadPoolExecutor\nfrom types import TracebackType\nfrom typing import Dict, Optional, Sequence, Type, Union\n\nfrom getml.events.handlers import EventHandlerRegistry, HandlerMetadata\nfrom getml.events.types import Event, EventSource, Shutdown\n\n\nclass PoolEventDispatcher:\n    \"\"\"\n    Receives events and dispatches them to registered event handlers inside an executor\n    managed pool.\n\n    Creates one queue per registered event handler and one thread per queue inside the\n    pool. The dispatcher manages the lifecycle of the pool and the queues.\n\n    The number of threads/processes spawned is equal to the number of handlers\n    registered over all event sources.\n    \"\"\"\n\n    def __init__(\n        self,\n        executor: Optional[Executor] = None,\n    ):\n        self.queues = {\n            source: {\n                metadata: queue.Queue()\n                for metadata in EventHandlerRegistry.handlers[source]\n            }\n            for source in EventSource\n        }\n\n        if not executor:\n            executor = ThreadPoolExecutor()\n        self.executor = executor\n\n        self.futures: Dict[HandlerMetadata, Future] = {}\n\n    def __enter__(self):\n        self.start()\n        return self\n\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]],\n        exc_value: Optional[BaseException],\n        traceback: Optional[TracebackType],\n    ):\n        if exc_type:\n            self.stop(wait=False)\n\n        self.stop()\n\n    def dispatch(self, events: Sequence[Event]):\n        for event in events:\n            for metadata, handler_queue in self.queues[event.source].items():\n                if metadata.sync:\n                    # Execute synchronously in the calling thread\n                    try:\n                        metadata.handler(event)  # type: ignore\n                    except Exception as e:\n                        warnings.warn(\n                            f\"An exception occurred while dispatching event {event} to handler {metadata.handler}: {e}\",\n                            RuntimeWarning,\n                        )\n                else:\n                    # Dispatch to worker thread via queue\n                    handler_queue.put(event)\n\n    def process(\n        self,\n        metadata: HandlerMetadata,\n        handler_queue: queue.Queue[Union[Event, Shutdown]],\n    ):\n        while True:\n            try:\n                event = handler_queue.get(timeout=1)\n            except queue.Empty:\n                continue\n\n            if event is Shutdown:\n                handler_queue.task_done()\n                return\n\n            try:\n                metadata.handler(event)  # type: ignore\n            except Exception as e:\n                warnings.warn(\n                    f\"An exception occurred while dispatching event {event} to handler {metadata.handler}: {e}\",\n                    RuntimeWarning,\n                )\n            finally:\n                handler_queue.task_done()\n\n    def start(self):\n        for source in self.queues:\n            for metadata, handler_queue in self.queues[source].items():\n                if not metadata.sync:\n                    # Only submit async handlers to the thread pool\n                    self.futures[metadata] = self.executor.submit(\n                        self.process, metadata, handler_queue\n                    )\n\n    def stop(self, wait: bool = True):\n        if not wait:\n            self.executor.shutdown(wait=False)\n            return\n\n        for source in self.queues:\n            for metadata, handler_queue in self.queues[source].items():\n                if not metadata.sync:\n                    # Only wait for and shutdown async handlers\n                    handler_queue.join()\n                    handler_queue.put(Shutdown)\n        self.executor.shutdown(wait=True)\n"
  },
  {
    "path": "src/python-api/getml/events/emitters.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nEmitters for the event system.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom types import TracebackType\nfrom typing import Optional, Sequence, Type\n\nfrom getml.events.types import Event, EventDispatcher\n\n\nclass DispatcherEventEmitter:\n    \"\"\"\n    Emits events to a dispatcher.\n    \"\"\"\n\n    def __init__(\n        self,\n        dispatcher: EventDispatcher,\n    ):\n        self.dispatcher = dispatcher\n\n    def __enter__(self):\n        self.dispatcher.__enter__()\n        return self\n\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]],\n        exc_value: Optional[BaseException],\n        traceback: Optional[TracebackType],\n    ):\n        self.dispatcher.__exit__(exc_type, exc_value, traceback)\n\n    def emit(self, events: Sequence[Event]):\n        self.dispatcher.dispatch(events)\n"
  },
  {
    "path": "src/python-api/getml/events/handlers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nEvent system.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Callable, Dict, List, Optional, TypeVar, Union, overload\n\nfrom getml.events.types import Event, EventSource\n\nEventHandler = Callable[[Event], None]\nH = TypeVar(\"H\", bound=EventHandler)\n\n\n@dataclass(frozen=True)\nclass HandlerMetadata:\n    \"\"\"Metadata for an event handler.\"\"\"\n\n    handler: EventHandler\n    sync: bool = False\n\n\nclass EventHandlerRegistry:\n    handlers: Dict[EventSource, List[HandlerMetadata]] = {}\n\n    def __init__(self, source: EventSource):\n        self.source = source\n        type(self).handlers[source] = []\n\n    @overload\n    def __call__(self, handler: H) -> H: ...\n\n    @overload\n    def __call__(self, *, sync: bool = False) -> Callable[[H], H]: ...\n\n    def __call__(\n        self, handler: Optional[H] = None, *, sync: bool = False\n    ) -> Union[Callable[[H], H], H]:\n        \"\"\"\n        Register an event handler for the source associated with the instance.\n\n        Can be used as a decorator with or without arguments:\n        - @event_handler\n        - @event_handler(sync=True)\n\n        Args:\n            handler: The event handler function\n            sync: If True, handler executes synchronously in the calling thread\n                  (no dispatching). If False, handler executes in a worker thread pool.\n\n        Returns:\n            The handler function (for decorator usage)\n        \"\"\"\n        if handler is None:\n            # Called with arguments: @event_handler(sync=...)\n            def decorator(func: H) -> H:\n                self.register_handler(self.source, func, sync=sync)\n                return func\n\n            return decorator\n\n        # Called without arguments: @event_handler\n        self.register_handler(self.source, handler, sync=sync)\n        return handler\n\n    @classmethod\n    def register_handler(\n        cls, source: EventSource, handler: EventHandler, sync: bool = False\n    ):\n        \"\"\"\n        Register an event handler for a specific source.\n\n        Args:\n            source: The event source\n            handler: The event handler function\n            sync: If True, handler executes synchronously in the calling thread\n        \"\"\"\n        metadata = HandlerMetadata(handler=handler, sync=sync)\n        cls.handlers[source].append(metadata)\n\n    @classmethod\n    def unregister_handler(cls, source: EventSource, handler: EventHandler):\n        \"\"\"\n        Unregister an event handler for a specific source.\n\n        Args:\n            source: The event source\n            handler: The event handler function to remove\n        \"\"\"\n        cls.handlers[source] = [\n            metadata for metadata in cls.handlers[source] if metadata.handler != handler\n        ]\n\n\nengine_event_handler = EventHandlerRegistry(EventSource.ENGINE)\nmonitor_event_handler = EventHandlerRegistry(EventSource.MONITOR)\n"
  },
  {
    "path": "src/python-api/getml/events/parsers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nParsers for creating structured events from raw log messages.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Dict, List, Optional, Union\n\nfrom getml.events.regex import (\n    LOG_MESSAGE_REGEX,\n    UNSPECIFIED_PROGRESS_MESSAGE_REGEX,\n    EventMessageRegex,\n)\nfrom getml.events.types import (\n    Event,\n    EventContext,\n    EventSource,\n    EventType,\n    EventTypeState,\n)\n\n\ndef _coerce_attribute_values(attributes: Dict[str, str]) -> Dict[str, Union[str, int]]:\n    \"\"\"\n    Coerce attribute values to the correct types.\n    \"\"\"\n    coerced_attributes = {}\n    for key, value in attributes.items():\n        if key in {\"progress\", \"n_features\", \"rows\", \"tree\"} and value is not None:\n            coerced_attributes[key] = int(value)\n        else:\n            coerced_attributes[key] = value\n    return coerced_attributes\n\n\ndef _process_attributes(\n    attributes: Dict[str, Union[str, str]],\n) -> Dict[str, Union[str, int]]:\n    processed_attributes = {}\n    for key, value in attributes.items():\n        if key == \"type\":\n            processed_attributes[key] = value.replace(\" \", \"_\")\n        else:\n            processed_attributes[key] = value\n    return _coerce_attribute_values(processed_attributes)\n\n\ndef _create_attributes(\n    groupdict: Dict[str, str],\n    context: EventContext,\n    body: Optional[str] = None,\n) -> Dict[str, Union[str, int]]:\n    \"\"\"\n    Create attributes from a match group dict, the context and the body of the\n    log message.\n    \"\"\"\n    attributes = {**groupdict}\n\n    if \"id_\" in context.cmd:\n        attributes[\"id\"] = context.cmd[\"id_\"]\n\n    if body:\n        description, *_ = body.split(\" Progress: \")\n        attributes[\"description\"] = description\n\n    return _process_attributes(attributes)\n\n\nclass LogMessageEventParser:\n    \"\"\"\n    Parses raw log messages from engine and monitor into zero, one or multiple\n    structured events.\n\n    The parser is stateful because progress messages can be ambiguous\n    without context.\n    \"\"\"\n\n    def __init__(self, context: EventContext):\n        # The context of the message that is being parsed. Contains the\n        # command that was sent to either the engine or the monitor and\n        # the source of the event (engine or monitor).\n        self.context = context\n        # The parser needs to keep track of the current event because\n        # unspecified progress messages are ambiguous.\n        # Also, there are ambiguous progress messages for feature learners\n        # and predictors.\n        self.current_event: Optional[Event] = None\n\n    def parse(self, message: str) -> List[Event]:\n        \"\"\"\n        Parse a log message into an event.\n        \"\"\"\n        events = []\n\n        if not (log_match := LOG_MESSAGE_REGEX.match(message)):\n            return []\n\n        body = log_match.group(\"body\")\n\n        if progress_event := self._try_parse_unspecified_progress_event(body):\n            events.append(progress_event)\n        elif typed_event := self._try_parse_typed_event(body):\n            events.append(typed_event)\n\n        if end_event := self._try_create_end_event():\n            events.append(end_event)\n\n        return events\n\n    def _try_parse_unspecified_progress_event(self, body: str) -> Optional[Event]:\n        \"\"\"\n        Process an unspecified progress message.\n        \"\"\"\n        if progress_match := UNSPECIFIED_PROGRESS_MESSAGE_REGEX.match(body):\n            attributes = _create_attributes(progress_match.groupdict(), self.context)\n            if self.current_event:\n                event = Event(\n                    source=EventSource.ENGINE,\n                    type=self.current_event.type.transition_to(EventTypeState.PROGRESS),\n                    attributes={**self.current_event.attributes, **attributes},\n                )\n                self.current_event = event\n                return event\n\n    def _try_reattach_model(self, event: Event) -> Event:\n        \"\"\"\n        Re-attach model to progressed feature learner events.\n        \"\"\"\n        if self.current_event and event.type in {\n            EventType.PIPELINE_FIT_FEATURE_LEARNER_TRAIN_PROGRESS,\n            EventType.PIPELINE_FIT_FEATURE_LEARNER_BUILD_PROGRESS,\n        }:\n            event.attributes[\"model\"] = self.current_event.attributes[\"model\"]\n        return event\n\n    def _try_reattach_type(self, event: Event) -> Event:\n        \"\"\"\n        Re-attach type to progressed predictor events.\n        \"\"\"\n        if self.current_event and event.type in {\n            EventType.PIPELINE_FIT_PREDICTOR_TRAIN_PROGRESS,\n        }:\n            event.attributes[\"type\"] = self.current_event.attributes[\"type\"]\n        return event\n\n    def _try_parse_typed_event(self, body: str) -> Optional[Event]:\n        \"\"\"\n        Process a specific event, a match maps to a specific event type.\n        \"\"\"\n        prefix = self.context.cmd.get(\"type_\", \"\").replace(\".\", \"_\").upper()\n\n        for event_type, regex in EventMessageRegex.with_prefix(prefix):\n            if event_match := regex.match(body):\n                attributes = _create_attributes(\n                    event_match.groupdict(), self.context, body\n                )\n\n                event = Event(\n                    source=self.context.source,\n                    type=event_type,\n                    attributes=attributes,\n                )\n                event = self._try_reattach_model(event)\n                event = self._try_reattach_type(event)\n                self.current_event = event\n                return event\n\n    def _try_create_end_event(self) -> Optional[Event]:\n        \"\"\"\n        Add an end event if the current event is a progress event that has\n        reached the end state (100%).\n        \"\"\"\n        if (\n            (event := self.current_event)\n            and event.type.state == EventTypeState.PROGRESS\n            and event.attributes.get(\"progress\") == 100\n        ):\n            end_event = event.progress()\n            self.current_event = end_event\n            return end_event\n"
  },
  {
    "path": "src/python-api/getml/events/regex.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nRegex patterns for parsing engine messages.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport re\nfrom enum import Enum\nfrom typing import Iterator, Tuple\n\nfrom getml.events.types import EventType\n\nLOG_MESSAGE_REGEX = re.compile(r\"log: (?P<body>.*)\")\nUNSPECIFIED_PROGRESS_MESSAGE_REGEX = re.compile(r\"Progress: (?P<progress>\\d+)%.*\")\n\n\n_PIPELINE_STAGE_START = re.compile(r\"Staging...\")\n_PIPELINE_CHECK_START = re.compile(r\"Checking...\")\n_PIPELINE_PREPROCESS_START = re.compile(r\"Preprocessing...\")\n\n\nclass EventMessageRegex(Enum):\n    PIPELINE_CHECK_STAGE_START = _PIPELINE_STAGE_START\n    PIPELINE_CHECK_CHECK_START = _PIPELINE_CHECK_START\n    PIPELINE_CHECK_PREPROCESS_START = _PIPELINE_PREPROCESS_START\n    PIPELINE_FIT_STAGE_START = _PIPELINE_STAGE_START\n    PIPELINE_FIT_CHECK_START = _PIPELINE_CHECK_START\n    PIPELINE_FIT_PREPROCESS_START = _PIPELINE_PREPROCESS_START\n    PIPELINE_FIT_FEATURE_LEARNER_TRAIN_START = re.compile(\n        r\"(?P<model>FastProp|Multirel|Relboost|Fastboost|RelMT): \"\n        r\"(Training (features|subfeatures)|Trying (?P<n_features>\\d+) features\\.\\.\\.)\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_TRAIN_PROGRESS = re.compile(\n        r\"(Trained (?P<feature>FEATURE_\\d+)|Built (?P<n_features>\\d+) features|Trained new features)\\. \"\n        r\"Progress: (?P<progress>\\d+)%\\.\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_BUILD_START = re.compile(\n        r\"(?P<model>FastProp|Multirel|Relboost|Fastboost|RelMT): \"\n        r\"Building (features|subfeatures)\\.\\.\\.\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_BUILD_PROGRESS = re.compile(\n        r\"Built ((?P<feature>FEATURE_\\d+)|(?P<n_rows>\\d+) rows)\\. \"\n        r\"Progress: (?P<progress>\\d+)%\\.\"\n    )\n    PIPELINE_FIT_PREDICTOR_TRAIN_START = re.compile(\n        r\"(?P<model>XGBoost|ScaleGBM|LinearRegression|LogisticRegression): Training as \"\n        r\"(?P<type>feature selector|predictor)\\.\\.\\.\"\n    )\n    PIPELINE_FIT_PREDICTOR_TRAIN_PROGRESS = re.compile(\n        r\"(?P<model>XGBoost|ScaleGBM): Trained tree (?P<tree>\\d+)\\. \"\n        r\"Progress: (?P<progress>\\d+)%\\.\"\n    )\n    PIPELINE_TRANSFORM_STAGE_START = _PIPELINE_STAGE_START\n    PIPELINE_TRANSFORM_PREPROCESS_START = _PIPELINE_PREPROCESS_START\n    PIPELINE_TRANSFORM_FEATURE_LEARNER_BUILD_START = re.compile(\n        r\"(?P<model>FastProp|Multirel|Relboost|Fastboost|RelMT): \"\n        r\"Building (features|subfeatures)\\.\\.\\.\"\n    )\n    PIPELINE_TRANSFORM_FEATURE_LEARNER_BUILD_PROGRESS = re.compile(\n        r\"Built ((?P<feature>FEATURE_\\d+)|(?P<n_rows>\\d+) rows)\\. \"\n        r\"Progress: (?P<progress>\\d+)%\\.\"\n    )\n    HYPEROPT_TUNE_FEATURE_LEARNER_START = re.compile(\n        r\"Tuning (?P<model>FastProp|Multirel|Relboost|Fastboost|RelMT)\\.\\.\\.\"\n    )\n    HYPEROPT_TUNE_PREDICTOR_START = re.compile(\n        r\"Tuning (?P<model>XGBoost|ScaleGBM|LinearRegression|LogisticRegression)\\.\\.\\.\"\n    )\n    SETPROJECT_LOADING_PIPELINES_START = re.compile(r\"Loading pipelines\\.\\.\\.\")\n    SETPROJECT_LOADING_HYPEROPTS_START = re.compile(r\"Loading hyperopts\\.\\.\\.\")\n    LOADPROJECTBUNDLE_LOADING_PROJECT_START = re.compile(r\"Loading project\\.\\.\\.\")\n\n    @classmethod\n    def with_prefix(cls, prefix: str) -> Iterator[Tuple[EventType, re.Pattern]]:\n        for name, member in cls.__members__.items():\n            if name.startswith(prefix):\n                yield EventType[name], member.value\n"
  },
  {
    "path": "src/python-api/getml/events/types.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom __future__ import annotations\n\nimport re\nfrom dataclasses import dataclass, field, replace\nfrom datetime import datetime\nfrom enum import Enum\nfrom types import TracebackType\nfrom typing import Any, Dict, Literal, Optional, Protocol, Sequence, Type\n\n\nclass Shutdown:\n    \"\"\"\n    Sentinel object to signal that a queue should shut down.\n    \"\"\"\n\n    ...\n\n\nclass EventTypeState(str, Enum):\n    START = \"start\"\n    PROGRESS = \"progress\"\n    END = \"end\"\n\n\nclass EventType(str, Enum):\n    COMMAND_FINISHED = \"command.finished\"\n    PIPELINE_CHECK_STAGE_START = \"pipeline.check.stage.start\"\n    PIPELINE_CHECK_STAGE_PROGRESS = \"pipeline.check.stage.progress\"\n    PIPELINE_CHECK_STAGE_END = \"pipeline.check.stage.end\"\n    PIPELINE_CHECK_CHECK_START = \"pipeline.check.check.start\"\n    PIPELINE_CHECK_CHECK_PROGRESS = \"pipeline.check.check.progress\"\n    PIPELINE_CHECK_CHECK_END = \"pipeline.check.check.end\"\n    PIPELINE_CHECK_PREPROCESS_START = \"pipeline.check.preprocess.start\"\n    PIPELINE_CHECK_PREPROCESS_PROGRESS = \"pipeline.check.preprocess.progress\"\n    PIPELINE_CHECK_PREPROCESS_END = \"pipeline.check.preprocess.end\"\n    PIPELINE_FIT_STAGE_START = \"pipeline.fit.stage.start\"\n    PIPELINE_FIT_STAGE_PROGRESS = \"pipeline.fit.stage.progress\"\n    PIPELINE_FIT_STAGE_END = \"pipeline.fit.stage.end\"\n    PIPELINE_FIT_CHECK_START = \"pipeline.fit.check.start\"\n    PIPELINE_FIT_CHECK_PROGRESS = \"pipeline.fit.check.progress\"\n    PIPELINE_FIT_CHECK_END = \"pipeline.fit.check.end\"\n    PIPELINE_FIT_PREPROCESS_START = \"pipeline.fit.preprocess.start\"\n    PIPELINE_FIT_PREPROCESS_PROGRESS = \"pipeline.fit.preprocess.progress\"\n    PIPELINE_FIT_PREPROCESS_END = \"pipeline.fit.preprocess.end\"\n    PIPELINE_FIT_FEATURE_LEARNER_TRAIN_START = (\n        \"pipeline.fit.feature_learner.train.start\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_TRAIN_PROGRESS = (\n        \"pipeline.fit.feature_learner.train.progress\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_TRAIN_END = \"pipeline.fit.feature_learner.train.end\"\n    PIPELINE_FIT_FEATURE_LEARNER_BUILD_START = (\n        \"pipeline.fit.feature_learner.build.start\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_BUILD_PROGRESS = (\n        \"pipeline.fit.feature_learner.build.progress\"\n    )\n    PIPELINE_FIT_FEATURE_LEARNER_BUILD_END = \"pipeline.fit.feature_learner.build.end\"\n    PIPELINE_FIT_PREDICTOR_TRAIN_START = \"pipeline.fit.predictor.train.start\"\n    PIPELINE_FIT_PREDICTOR_TRAIN_PROGRESS = \"pipeline.fit.predictor.train.progress\"\n    PIPELINE_FIT_PREDICTOR_TRAIN_END = \"pipeline.fit.predictor.train.end\"\n    PIPELINE_TRANSFORM_STAGE_START = \"pipeline.transform.stage.start\"\n    PIPELINE_TRANSFORM_STAGE_PROGRESS = \"pipeline.transform.stage.progress\"\n    PIPELINE_TRANSFORM_STAGE_END = \"pipeline.transform.stage.end\"\n    PIPELINE_TRANSFORM_PREPROCESS_START = \"pipeline.transform.preprocess.start\"\n    PIPELINE_TRANSFORM_PREPROCESS_PROGRESS = \"pipeline.transform.preprocess.progress\"\n    PIPELINE_TRANSFORM_PREPROCESS_END = \"pipeline.transform.preprocess.end\"\n    PIPELINE_TRANSFORM_FEATURE_LEARNER_BUILD_START = (\n        \"pipeline.transform.feature_learner.build.start\"\n    )\n    PIPELINE_TRANSFORM_FEATURE_LEARNER_BUILD_PROGRESS = (\n        \"pipeline.transform.feature_learner.build.progress\"\n    )\n    PIPELINE_TRANSFORM_FEATURE_LEARNER_BUILD_END = (\n        \"pipeline.transform.feature_learner.build.end\"\n    )\n    HYPEROPT_TUNE_FEATURE_LEARNER_START = \"hyperopt.tune.feature_learner.start\"\n    HYPEROPT_TUNE_FEATURE_LEARNER_PROGRESS = \"hyperopt.tune.feature_learner.progress\"\n    HYPEROPT_TUNE_FEATURE_LEARNER_END = \"hyperopt.tune.feature_learner.end\"\n    HYPEROPT_TUNE_PREDICTOR_START = \"hyperopt.tune.predictor.start\"\n    HYPEROPT_TUNE_PREDICTOR_PROGRESS = \"hyperopt.tune.predictor.progress\"\n    HYPEROPT_TUNE_PREDICTOR_END = \"hyperopt.tune.predictor.end\"\n    SETPROJECT_LOADING_PIPELINES_START = \"setproject.loading_pipelines.start\"\n    SETPROJECT_LOADING_PIPELINES_PROGRESS = \"setproject.loading_pipelines.progress\"\n    SETPROJECT_LOADING_PIPELINES_END = \"setproject.loading_pipelines.end\"\n    SETPROJECT_LOADING_HYPEROPTS_START = \"setproject.loading_hyperopts.start\"\n    SETPROJECT_LOADING_HYPEROPTS_PROGRESS = \"setproject.loading_hyperopts.progress\"\n    SETPROJECT_LOADING_HYPEROPTS_END = \"setproject.loading_hyperopts.end\"\n    LOADPROJECTBUNDLE_LOADING_PROJECT_START = \"loadprojectbundle.loading_project.start\"\n    LOADPROJECTBUNDLE_LOADING_PROJECT_PROGRESS = (\n        \"loadprojectbundle.loading_project.progress\"\n    )\n    LOADPROJECTBUNDLE_LOADING_PROJECT_END = \"loadprojectbundle.loading_project.end\"\n\n    def progress(self) -> EventType:\n        \"\"\"\n        Progress (transition) event type to the succeeding state.\n\n        If the event type is already in the end state, no transition is\n        performed and the end state is returned.\n        \"\"\"\n        if self.value.endswith(\".start\"):\n            return EventType(self.value.replace(\".start\", \".progress\"))\n        if self.value.endswith(\".progress\"):\n            return EventType(self.value.replace(\".progress\", \".end\"))\n        return self\n\n    @property\n    def state(self) -> EventTypeState:\n        \"\"\"\n        The state of the event type.\n        \"\"\"\n        if self.value.endswith(\".start\"):\n            return EventTypeState.START\n        if self.value.endswith(\".progress\"):\n            return EventTypeState.PROGRESS\n        return EventTypeState.END\n\n    def transition_to(self, state: EventTypeState) -> EventType:\n        \"\"\"\n        Return the event type with the state suffix.\n        \"\"\"\n        return EventType(re.sub(r\"\\.\\w+$\", f\".{state.value}\", self.value))\n\n\nclass EventSource(str, Enum):\n    ENGINE = \"engine\"\n    MONITOR = \"monitor\"\n\n\n@dataclass\nclass EventContext:\n    cmd: Dict[str, Any]\n    source: EventSource\n\n\n@dataclass\nclass Event:\n    source: EventSource\n    type: EventType\n    attributes: Dict[str, Any]\n    timestamp: datetime = field(default_factory=datetime.now)\n    _version: Literal[1] = 1\n\n    def progress(self) -> Event:\n        \"\"\"\n        Progress (transition) event to the succeeding state.\n\n        If the event is already in the end state, no transition is\n        performed and the end state is returned.\n        \"\"\"\n        return replace(self, type=self.type.progress())\n\n\nclass EventParser(Protocol):\n    context: EventContext\n\n    def parse(self, message: str) -> Sequence[Event]: ...\n\n\nclass EventDispatcher(Protocol):\n    def __enter__(self) -> EventDispatcher: ...\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]],\n        exc_value: Optional[BaseException],\n        traceback: Optional[TracebackType],\n    ): ...\n    def start(self): ...\n    def stop(self, wait: bool = True): ...\n    def dispatch(self, events: Sequence[Event]): ...\n\n\nclass EventEmitter(Protocol):\n    dispatcher: EventDispatcher\n\n    def __enter__(self) -> EventEmitter: ...\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]],\n        exc_value: Optional[BaseException],\n        traceback: Optional[TracebackType],\n    ): ...\n    def emit(self, events: Sequence[Event]): ...\n"
  },
  {
    "path": "src/python-api/getml/exceptions.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport re\nfrom inspect import cleandoc\nfrom typing import Any, Callable, Dict, Optional, Protocol, Tuple\n\nimport pyarrow as pa\nfrom pyarrow.lib import ArrowInvalid\n\nfrom getml.constants import ENTERPRISE_DOCS_URL\n\n\nclass ArrowExceptionHandler(Protocol):\n    def __call__(\n        self, exc: ArrowInvalid, source_array: pa.Array, target_field: pa.Field\n    ) -> None: ...\n\n\nENTERPRISE_FEATURE_NOT_AVAILABLE_REGEX = re.compile(\n    r\"The (.*) (.*) is not supported in the community edition. Please upgrade \"\n    r\"to getML enterprise to use this. An overview of what is supported in the \"\n    r\"community edition can be found in the official getML documentation.\"\n)\n\n\nENTERPRISE_FEATURE_NOT_AVAILABLE_ERROR_MSG_TEMPLATE = cleandoc(\n    \"The {missing_feature_name} {missing_feature_type} \"\n    \"is unique to getML Enterprise and is not available \"\n    \"in the getML Community edition you are currently using.\\n\\n\"\n    \"Please visit {enterprise_docs_url} to learn about getML's \"\n    \"advanced algorithms, extended feature set, commercial plans, and available \"\n    \"trial options. \"\n)\n\n\ndef _nop_arrow_cast_exception_handler(exc: Exception, field: pa.Field) -> None:\n    raise exc\n\n\nclass ArrowCastExceptionHandlerRegistry:\n    \"\"\"\n    A registry for handlers of exceptions originating from the Arrow library.\n\n    Instances of this class can be used as a decorator to register exception\n    handlers. Handlers are registered for specific target arrow types.\n\n    The handlers are called with the exception (exc) and the field (field) which\n    cast caused the exception.\n\n    As there is only a single Exception type raised by Arrow (`ArrowInvalid`),\n    the handlers have to parse the exception themselves.\n    \"\"\"\n\n    handlers: Dict[Tuple[pa.DataType, pa.DataType], ArrowExceptionHandler] = {}\n\n    def __call__(\n        self,\n        source_type: pa.DataType,\n        target_type: pa.DataType,\n    ) -> Callable[[ArrowExceptionHandler], ArrowExceptionHandler]:\n        return self.register(source_type, target_type)\n\n    @classmethod\n    def register(\n        cls,\n        source_type: pa.DataType,\n        target_type: pa.DataType,\n    ) -> Callable[[ArrowExceptionHandler], ArrowExceptionHandler]:\n        def decorator(handler: ArrowExceptionHandler):\n            cls.handlers[(source_type, target_type)] = handler\n            return handler\n\n        return decorator\n\n    @classmethod\n    def retrieve(\n        cls, source_type: pa.DataType, target_type: pa.DataType\n    ) -> ArrowExceptionHandler:\n        if (source_type, target_type) not in cls.handlers:\n            return _nop_arrow_cast_exception_handler\n        return cls.handlers[(source_type, target_type)]\n\n\narrow_cast_exception_handler = ArrowCastExceptionHandlerRegistry()\n\n\nclass EngineExceptionHandlerRegistry:\n    \"\"\"\n    A registry for handlers of exceptions originating from the getML Engine.\n    Instances of this class can be used as a decorator to register exception\n    handlers. As all exceptions are sent as raw strings, the handlers have to\n    parse the exceptions themselves.\n\n    The handlers are called with the raw exception string (msg) and an possibly\n    empty dictionary of extra information.\n    \"\"\"\n\n    handlers = []\n\n    def __call__(\n        self, handler: Callable[[str, Dict[str, Any]], None]\n    ) -> Callable[[str, Dict[str, Any]], None]:\n        return self.register(handler)\n\n    @classmethod\n    def register(cls, handler: Callable[[str, Dict[str, Any]], None]):\n        cls.handlers.append(handler)\n        return handler\n\n\nengine_exception_handler = EngineExceptionHandlerRegistry()\n\n\ndef handle_engine_exception(msg: str, extra: Optional[Dict[str, Any]] = None) -> None:\n    \"\"\"\n    Handles exceptions raised by the getML Engine. Iterates over all\n    registered handlers and calls them with the error message.\n    Registered handlers parse the message and may raise an exception\n    themselves.\n\n    If no registered handler can handle the exception, the message is\n    raised inside and OSError as is.\n\n    Args:\n        msg: Error message returned by the getML Engine.\n    \"\"\"\n\n    if extra is None:\n        extra = {}\n\n    for handler in EngineExceptionHandlerRegistry.handlers:\n        handler(msg, extra=extra)\n\n    raise OSError(msg)\n\n\n@engine_exception_handler\ndef handle_enterprise_feature_not_available_error(msg: str, extra: Dict[str, Any]):\n    \"\"\"\n    Handler for the \"Enterprise feature not available\" error message.\n    \"\"\"\n    match = ENTERPRISE_FEATURE_NOT_AVAILABLE_REGEX.match(msg)\n    if match:\n        missing_feature_name = match.group(1)\n        missing_feature_type = match.group(2)\n        raise OSError(\n            ENTERPRISE_FEATURE_NOT_AVAILABLE_ERROR_MSG_TEMPLATE.format(\n                enterprise_docs_url=ENTERPRISE_DOCS_URL,\n                missing_feature_name=missing_feature_name,\n                missing_feature_type=missing_feature_type,\n            )\n        )\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis module contains relational learning algorithms to learn features\nfrom relational data or time series.\n\nNote:\n    All feature learners need to be passed to [`Pipeline`][getml.Pipeline].\n\"\"\"\n\nfrom . import aggregations, loss_functions\nfrom .fastboost import Fastboost\nfrom .fastprop import FastProp\nfrom .feature_learner import _FeatureLearner\nfrom .multirel import Multirel\nfrom .relboost import Relboost\nfrom .relmt import RelMT\n\n__all__ = (\n    \"_FeatureLearner\",\n    \"Fastboost\",\n    \"FastProp\",\n    \"Multirel\",\n    \"Relboost\",\n    \"RelMT\",\n    \"aggregations\",\n    \"loss_functions\",\n)\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/aggregations/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom .aggregations import (\n    AVG,\n    COUNT,\n    COUNT_DISTINCT,\n    COUNT_DISTINCT_OVER_COUNT,\n    COUNT_MINUS_COUNT_DISTINCT,\n    EWMA_1D,\n    EWMA_1H,\n    EWMA_1M,\n    EWMA_1S,\n    EWMA_7D,\n    EWMA_30D,\n    EWMA_90D,\n    EWMA_365D,\n    EWMA_TREND_1D,\n    EWMA_TREND_1H,\n    EWMA_TREND_1M,\n    EWMA_TREND_1S,\n    EWMA_TREND_7D,\n    EWMA_TREND_30D,\n    EWMA_TREND_90D,\n    EWMA_TREND_365D,\n    FIRST,\n    KURTOSIS,\n    LAST,\n    MAX,\n    MEDIAN,\n    MIN,\n    MODE,\n    NUM_MAX,\n    NUM_MIN,\n    Q_1,\n    Q_5,\n    Q_10,\n    Q_25,\n    Q_75,\n    Q_90,\n    Q_95,\n    Q_99,\n    SKEW,\n    STDDEV,\n    SUM,\n    TIME_SINCE_FIRST_MAXIMUM,\n    TIME_SINCE_FIRST_MINIMUM,\n    TIME_SINCE_LAST_MAXIMUM,\n    TIME_SINCE_LAST_MINIMUM,\n    TREND,\n    VAR,\n    VARIATION_COEFFICIENT,\n)\nfrom .sets import (\n    AGGREGATIONS,\n    FASTPROP,\n    FASTPROP_AGGREGATIONS,\n    MAPPING,\n    MAPPING_AGGREGATIONS,\n    MULTIREL,\n    MULTIREL_AGGREGATIONS,\n    _all_aggregations,\n    _multirel_subset,\n)\n\n# Compatibility aliases\nAvg = AVG\nCount = COUNT\nCountDistinct = COUNT_DISTINCT\nCountDistinctOverCount = COUNT_DISTINCT_OVER_COUNT\nCountMinusCountDistinct = COUNT_MINUS_COUNT_DISTINCT\nFirst = FIRST\nKurtosis = KURTOSIS\nLast = LAST\nMax = MAX\nMedian = MEDIAN\nMin = MIN\nMode = MODE\nNumMax = NUM_MAX\nNumMin = NUM_MIN\nSkew = SKEW\nStddev = STDDEV\nSum = SUM\nTimeSinceFirstMaximum = TIME_SINCE_FIRST_MAXIMUM\nTimeSinceFirstMinimum = TIME_SINCE_FIRST_MINIMUM\nTimeSinceLastMaximum = TIME_SINCE_LAST_MAXIMUM\nTimeSinceLastMinimum = TIME_SINCE_LAST_MINIMUM\nTrend = TREND\nVar = VAR\nVariationCoefficient = VARIATION_COEFFICIENT\n\n__all__ = (\n    \"FASTPROP\",\n    \"MULTIREL\",\n    \"MAPPING\",\n    \"FASTPROP_AGGREGATIONS\",\n    \"MULTIREL_AGGREGATIONS\",\n    \"MAPPING_AGGREGATIONS\",\n    \"AGGREGATIONS\",\n    \"AVG\",\n    \"COUNT\",\n    \"COUNT_DISTINCT\",\n    \"COUNT_DISTINCT_OVER_COUNT\",\n    \"COUNT_MINUS_COUNT_DISTINCT\",\n    \"EWMA_1S\",\n    \"EWMA_1M\",\n    \"EWMA_1H\",\n    \"EWMA_1D\",\n    \"EWMA_7D\",\n    \"EWMA_30D\",\n    \"EWMA_90D\",\n    \"EWMA_365D\",\n    \"EWMA_TREND_1S\",\n    \"EWMA_TREND_1M\",\n    \"EWMA_TREND_1H\",\n    \"EWMA_TREND_1D\",\n    \"EWMA_TREND_7D\",\n    \"EWMA_TREND_30D\",\n    \"EWMA_TREND_90D\",\n    \"EWMA_TREND_365D\",\n    \"FIRST\",\n    \"KURTOSIS\",\n    \"LAST\",\n    \"MAX\",\n    \"MEDIAN\",\n    \"MIN\",\n    \"MODE\",\n    \"NUM_MAX\",\n    \"NUM_MIN\",\n    \"Q_1\",\n    \"Q_5\",\n    \"Q_10\",\n    \"Q_25\",\n    \"Q_75\",\n    \"Q_90\",\n    \"Q_95\",\n    \"Q_99\",\n    \"SKEW\",\n    \"STDDEV\",\n    \"SUM\",\n    \"TIME_SINCE_FIRST_MAXIMUM\",\n    \"TIME_SINCE_FIRST_MINIMUM\",\n    \"TIME_SINCE_LAST_MAXIMUM\",\n    \"TIME_SINCE_LAST_MINIMUM\",\n    \"TREND\",\n    \"VAR\",\n    \"VARIATION_COEFFICIENT\",\n    \"Avg\",\n    \"Count\",\n    \"CountDistinct\",\n    \"CountDistinctOverCount\",\n    \"CountMinusCountDistinct\",\n    \"First\",\n    \"Kurtosis\",\n    \"Last\",\n    \"Max\",\n    \"Median\",\n    \"Min\",\n    \"Mode\",\n    \"NumMax\",\n    \"NumMin\",\n    \"Skew\",\n    \"Stddev\",\n    \"Sum\",\n    \"TimeSinceFirstMaximum\",\n    \"TimeSinceFirstMinimum\",\n    \"TimeSinceLastMaximum\",\n    \"TimeSinceLastMinimum\",\n    \"Trend\",\n    \"Var\",\n    \"VariationCoefficient\",\n    \"_all_aggregations\",\n    \"_multirel_subset\",\n)\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/aggregations/aggregations.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThis module contains all possible aggregations to be used with\n[`Multirel`][getml.feature_learning.Multirel],\n[`FastProp`][getml.feature_learning.FastProp],\n[`Mapping`][getml.preprocessors.Mapping].\n\n\nRefer to the [feature learning section][feature-engineering-design-principles] in the user guide for details about how\nthese aggregations are used in the context of feature learning.\n\"\"\"\n\nfrom typing import Final\n\nfrom getml.feature_learning.aggregations.types import (\n    Q1,\n    Q5,\n    Q10,\n    Q25,\n    Q75,\n    Q90,\n    Q95,\n    Q99,\n    Avg,\n    Count,\n    CountDistinct,\n    CountDistinctOverCount,\n    CountMinusCountDistinct,\n    EWMA_1d,\n    EWMA_1h,\n    EWMA_1m,\n    EWMA_1s,\n    EWMA_7d,\n    EWMA_30d,\n    EWMA_90d,\n    EWMA_365d,\n    EWMA_TREND_1d,\n    EWMA_TREND_1h,\n    EWMA_TREND_1m,\n    EWMA_TREND_1s,\n    EWMA_TREND_7d,\n    EWMA_TREND_30d,\n    EWMA_TREND_90d,\n    EWMA_TREND_365d,\n    First,\n    Kurtosis,\n    Last,\n    Max,\n    Median,\n    Min,\n    Mode,\n    NumMax,\n    NumMin,\n    Skew,\n    Stddev,\n    Sum,\n    TimeSinceFirstMaximum,\n    TimeSinceFirstMinimum,\n    TimeSinceLastMaximum,\n    TimeSinceLastMinimum,\n    Trend,\n    Var,\n    VariationCoefficient,\n)\n\n# ------------------------------------------------------------------------------\nAVG: Final[Avg] = \"AVG\"\n\"\"\"  \nAverage value of a given numerical column.  \n\"\"\"\n\nCOUNT: Final[Count] = \"COUNT\"\n\"\"\"  \nNumber of rows in a given column.  \n\"\"\"\n\nCOUNT_DISTINCT: Final[CountDistinct] = \"COUNT DISTINCT\"\n\"\"\"Count function with distinct clause. This only counts unique elements.\"\"\"\n\nCOUNT_DISTINCT_OVER_COUNT: Final[CountDistinctOverCount] = \"COUNT DISTINCT OVER COUNT\"\n\"\"\"COUNT DISTINCT divided by COUNT. Please note that this aggregation is not \nsupported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nCOUNT_MINUS_COUNT_DISTINCT: Final[CountMinusCountDistinct] = (\n    \"COUNT MINUS COUNT DISTINCT\"\n)\n\"\"\"  \nCounts minus counts distinct. Substracts COUNT DISTINCT from COUNT.  \n\"\"\"\n\nEWMA_1S: Final[EWMA_1s] = \"EWMA_1S\"\n\"\"\"  \nExponentially weighted moving average with a half-life of 1 second.  \n\"\"\"\n\nEWMA_1M: Final[EWMA_1m] = \"EWMA_1M\"\n\"\"\"Exponentially weighted moving average with a half-life of 1 minute. Please note \nthat this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_1H: Final[EWMA_1h] = \"EWMA_1H\"\n\"\"\"Exponentially weighted moving average with a half-life of 1 hour. Please note that \nthis aggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_1D: Final[EWMA_1d] = \"EWMA_1D\"\n\"\"\"Exponentially weighted moving average with a half-life of 1 day. Please note that \nthis aggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_7D: Final[EWMA_7d] = \"EWMA_7D\"\n\"\"\"Exponentially weighted moving average with a half-life of 7 days. Please note that \nthis aggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_30D: Final[EWMA_30d] = \"EWMA_30D\"\n\"\"\"Exponentially weighted moving average with a half-life of 30 days. Please note \nthat this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_90D: Final[EWMA_90d] = \"EWMA_90D\"\n\"\"\"Exponentially weighted moving average with a half-life of 90 days. Please note \nthat this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_365D: Final[EWMA_365d] = \"EWMA_365D\"\n\"\"\"Exponentially weighted moving average with a half-life of 365 days. Please note \nthat this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_1S: Final[EWMA_TREND_1s] = \"EWMA_TREND_1S\"\n\"\"\"  \nExponentially weighted trend with a half-life of 1 second.  \n\"\"\"\n\nEWMA_TREND_1M: Final[EWMA_TREND_1m] = \"EWMA_TREND_1M\"\n\"\"\"Exponentially weighted trend with a half-life of 1 minute. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_1H: Final[EWMA_TREND_1h] = \"EWMA_TREND_1H\"\n\"\"\"Exponentially weighted trend with a half-life of 1 hour. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_1D: Final[EWMA_TREND_1d] = \"EWMA_TREND_1D\"\n\"\"\"Exponentially weighted trend with a half-life of 1 day. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_7D: Final[EWMA_TREND_7d] = \"EWMA_TREND_7D\"\n\"\"\"Exponentially weighted trend with a half-life of 7 days. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_30D: Final[EWMA_TREND_30d] = \"EWMA_TREND_30D\"\n\"\"\"Exponentially weighted trend with a half-life of 30 days. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_90D: Final[EWMA_TREND_90d] = \"EWMA_TREND_90D\"\n\"\"\"Exponentially weighted trend with a half-life of 90 days. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nEWMA_TREND_365D: Final[EWMA_TREND_365d] = \"EWMA_TREND_365D\"\n\"\"\"Exponentially weighted trend with a half-life of 365 days. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nFIRST: Final[First] = \"FIRST\"\n\"\"\"  \nFirst value of a given column, when ordered by the time stamp.  \n\"\"\"\n\nKURTOSIS: Final[Kurtosis] = \"KURTOSIS\"\n\"\"\"The kurtosis of a given column. Please note that this aggregation is not supported \nby [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nLAST: Final[Last] = \"LAST\"\n\"\"\"Last value of a given column, when ordered by the time stamp.\"\"\"\n\nMAX: Final[Max] = \"MAX\"\n\"\"\"Largest value of a given column.\"\"\"\n\nMEDIAN: Final[Median] = \"MEDIAN\"\n\"\"\"Median of a given column.\"\"\"\n\nMIN: Final[Min] = \"MIN\"\n\"\"\"Smallest value of a given column.\"\"\"\n\nMODE: Final[Mode] = \"MODE\"\n\"\"\"Most frequent value of a given column. Please note that this aggregation is not \nsupported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nNUM_MAX: Final[NumMax] = \"NUM MAX\"\n\"\"\"The number of times we observe the maximum value. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nNUM_MIN: Final[NumMin] = \"NUM MIN\"\n\"\"\"The number of times we observe the minimum value. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_1: Final[Q1] = \"Q1\"\n\"\"\"The 1%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_5: Final[Q5] = \"Q5\"\n\"\"\"The 5%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_10: Final[Q10] = \"Q10\"\n\"\"\"The 10%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_25: Final[Q25] = \"Q25\"\n\"\"\"The 25%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_75: Final[Q75] = \"Q75\"\n\"\"\"The 75%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_90: Final[Q90] = \"Q90\"\n\"\"\"The 90%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_95: Final[Q95] = \"Q95\"\n\"\"\"The 95%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nQ_99: Final[Q99] = \"Q99\"\n\"\"\"The 99%-quantile. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nSKEW: Final[Skew] = \"SKEW\"\n\"\"\"Skewness of a given column. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nSTDDEV: Final[Stddev] = \"STDDEV\"\n\"\"\"Standard deviation of a given column.\"\"\"\n\nSUM: Final[Sum] = \"SUM\"\n\"\"\"Total sum of a given numerical column.\"\"\"\n\nTIME_SINCE_FIRST_MAXIMUM: Final[TimeSinceFirstMaximum] = \"TIME SINCE FIRST MAXIMUM\"\n\"\"\"The time difference between the first time we see the maximum value and the time \nstamp in the population table. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nTIME_SINCE_FIRST_MINIMUM: Final[TimeSinceFirstMinimum] = \"TIME SINCE FIRST MINIMUM\"\n\"\"\"The time difference between the first time we see the minimum value and the time \nstamp in the population table. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nTIME_SINCE_LAST_MAXIMUM: Final[TimeSinceLastMaximum] = \"TIME SINCE LAST MAXIMUM\"\n\"\"\"The time difference between the last time we see the maximum value and the time \nstamp in the population table. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nTIME_SINCE_LAST_MINIMUM: Final[TimeSinceLastMinimum] = \"TIME SINCE LAST MINIMUM\"\n\"\"\"The time difference between the last time we see the minimum value and the time \nstamp in the population table. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nTREND: Final[Trend] = \"TREND\"\n\"\"\"Extracts a linear trend from a variable over time and extrapolates this trend to \nthe current time stamp. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nVAR: Final[Var] = \"VAR\"\n\"\"\"Statistical variance of a given numerical column. Please note that this \naggregation is not supported by [`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\nVARIATION_COEFFICIENT: Final[VariationCoefficient] = \"VARIATION COEFFICIENT\"\n\"\"\"VAR divided by MEAN. Please note that this aggregation is not supported by \n[`Multirel`][getml.feature_learning.Multirel].\"\"\"\n\n# ------------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/aggregations/sets.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom dataclasses import dataclass\nfrom typing import FrozenSet\n\nfrom getml.feature_learning.aggregations.aggregations import (\n    AVG,\n    COUNT,\n    COUNT_DISTINCT,\n    COUNT_DISTINCT_OVER_COUNT,\n    COUNT_MINUS_COUNT_DISTINCT,\n    EWMA_1D,\n    EWMA_1H,\n    EWMA_1M,\n    EWMA_1S,\n    EWMA_7D,\n    EWMA_30D,\n    EWMA_90D,\n    EWMA_365D,\n    EWMA_TREND_1D,\n    EWMA_TREND_1H,\n    EWMA_TREND_1M,\n    EWMA_TREND_1S,\n    EWMA_TREND_7D,\n    EWMA_TREND_30D,\n    EWMA_TREND_90D,\n    EWMA_TREND_365D,\n    FIRST,\n    KURTOSIS,\n    LAST,\n    MAX,\n    MEDIAN,\n    MIN,\n    MODE,\n    NUM_MAX,\n    NUM_MIN,\n    Q_1,\n    Q_5,\n    Q_10,\n    Q_25,\n    Q_75,\n    Q_90,\n    Q_95,\n    Q_99,\n    SKEW,\n    STDDEV,\n    SUM,\n    TIME_SINCE_FIRST_MAXIMUM,\n    TIME_SINCE_FIRST_MINIMUM,\n    TIME_SINCE_LAST_MAXIMUM,\n    TIME_SINCE_LAST_MINIMUM,\n    TREND,\n    VAR,\n    VARIATION_COEFFICIENT,\n)\nfrom getml.feature_learning.aggregations.types import (\n    Aggregations,\n    FastPropAggregations,\n    MappingAggregations,\n    MultirelAggregations,\n)\n\nMULTIREL_AGGREGATIONS: FrozenSet[MultirelAggregations] = frozenset(\n    {\n        AVG,\n        COUNT,\n        COUNT_DISTINCT,\n        COUNT_MINUS_COUNT_DISTINCT,\n        FIRST,\n        LAST,\n        MAX,\n        MEDIAN,\n        MIN,\n        STDDEV,\n        SUM,\n        VAR,\n    }\n)\n\"\"\"Set of all aggregations supported by Multirel.\"\"\"\n\nFASTPROP_AGGREGATIONS: FrozenSet[FastPropAggregations] = frozenset(\n    {\n        AVG,\n        COUNT,\n        COUNT_DISTINCT,\n        COUNT_MINUS_COUNT_DISTINCT,\n        FIRST,\n        LAST,\n        MAX,\n        MEDIAN,\n        MIN,\n        STDDEV,\n        SUM,\n        VAR,\n        COUNT_DISTINCT_OVER_COUNT,\n        EWMA_1S,\n        EWMA_1M,\n        EWMA_1H,\n        EWMA_1D,\n        EWMA_7D,\n        EWMA_30D,\n        EWMA_90D,\n        EWMA_365D,\n        EWMA_TREND_1S,\n        EWMA_TREND_1M,\n        EWMA_TREND_1H,\n        EWMA_TREND_1D,\n        EWMA_TREND_7D,\n        EWMA_TREND_30D,\n        EWMA_TREND_90D,\n        EWMA_TREND_365D,\n        KURTOSIS,\n        MODE,\n        NUM_MAX,\n        NUM_MIN,\n        Q_1,\n        Q_5,\n        Q_10,\n        Q_25,\n        Q_75,\n        Q_90,\n        Q_95,\n        Q_99,\n        SKEW,\n        TIME_SINCE_FIRST_MAXIMUM,\n        TIME_SINCE_FIRST_MINIMUM,\n        TIME_SINCE_LAST_MAXIMUM,\n        TIME_SINCE_LAST_MINIMUM,\n        TREND,\n        VARIATION_COEFFICIENT,\n    }\n)\n\"\"\"Set of all aggregations supported by FastProp.\"\"\"\n\n# ------------------------------------------------------------------------------\n\nMAPPING_AGGREGATIONS: FrozenSet[MappingAggregations] = frozenset(\n    {\n        AVG,\n        COUNT,\n        COUNT_DISTINCT,\n        COUNT_DISTINCT_OVER_COUNT,\n        COUNT_MINUS_COUNT_DISTINCT,\n        KURTOSIS,\n        MAX,\n        MEDIAN,\n        MIN,\n        MODE,\n        NUM_MAX,\n        NUM_MIN,\n        Q_1,\n        Q_5,\n        Q_10,\n        Q_25,\n        Q_75,\n        Q_90,\n        Q_95,\n        Q_99,\n        SKEW,\n        STDDEV,\n        SUM,\n        VAR,\n        VARIATION_COEFFICIENT,\n    }\n)\n\"\"\"Set of all aggregations supported by the mapping preprocessor.\"\"\"\n\n# ------------------------------------------------------------------------------\n\nAGGREGATIONS: FrozenSet[Aggregations] = (\n    MULTIREL_AGGREGATIONS | FASTPROP_AGGREGATIONS | MAPPING_AGGREGATIONS\n)\n\"\"\"Set of all possible aggregations.\"\"\"\n\n# ------------------------------------------------------------------------------\n\n\n@dataclass(frozen=True)\nclass AggregationsSets:\n    \"\"\"\n    Base Class for aggregations sets\n    \"\"\"\n\n    all: FrozenSet[Aggregations]\n    default: FrozenSet[Aggregations]\n    minimal: FrozenSet[Aggregations]\n\n    # Introduce CamelCase aliases to keep backward compatibility\n    @property\n    def All(self) -> FrozenSet[Aggregations]:\n        return self.all\n\n    @property\n    def Default(self) -> FrozenSet[Aggregations]:\n        return self.default\n\n    @property\n    def Minimal(self) -> FrozenSet[Aggregations]:\n        return self.minimal\n\n\n@dataclass(frozen=True)\nclass MappingAggregationsSets(AggregationsSets):\n    \"\"\"\n    Base class for Mapping aggregation sets\n    \"\"\"\n\n    all: FrozenSet[MappingAggregations]\n    default: FrozenSet[MappingAggregations]\n    minimal: FrozenSet[MappingAggregations]\n\n\n@dataclass(frozen=True)\nclass FastPropAggregationsSets(AggregationsSets):\n    \"\"\"\n    Base class for FastProp aggregation sets\n    \"\"\"\n\n    all: FrozenSet[FastPropAggregations]\n    default: FrozenSet[FastPropAggregations]\n    minimal: FrozenSet[FastPropAggregations]\n\n\n@dataclass(frozen=True)\nclass MultirelAggregationsSets(AggregationsSets):\n    \"\"\"\n    Base class for Multirel aggregation sets\n    \"\"\"\n\n    all: FrozenSet[MultirelAggregations]\n    default: FrozenSet[MultirelAggregations]\n    minimal: FrozenSet[MultirelAggregations]\n\n\nFASTPROP = FastPropAggregationsSets(\n    all=FASTPROP_AGGREGATIONS,\n    default=frozenset(\n        {\n            AVG,\n            COUNT,\n            COUNT_DISTINCT,\n            COUNT_MINUS_COUNT_DISTINCT,\n            FIRST,\n            LAST,\n            MAX,\n            MEDIAN,\n            MIN,\n            MODE,\n            STDDEV,\n            SUM,\n            TREND,\n        }\n    ),\n    minimal=frozenset([AVG, COUNT, MAX, MIN, SUM]),\n)\n\"\"\"Set of default aggregations for [`FastProp`][getml.feature_learning.FastProp]. \n`all` contains all aggregations supported by FastProp, `default` contains the subset \nof reasonable default aggregations, `minimal` is minimal set.\"\"\"\n\n# ------------------------------------------------------------------------------\n\nMULTIREL = MultirelAggregationsSets(\n    all=MULTIREL_AGGREGATIONS,\n    default=frozenset(\n        {\n            AVG,\n            COUNT,\n            MAX,\n            MIN,\n            SUM,\n        }\n    ),\n    minimal=frozenset([AVG, COUNT, SUM]),\n)\n\"\"\"Set of default aggregations for [`Multirel`][getml.feature_learning.Multirel]. \n`all` contains all aggregations supported by Multirel, `default` contains the subset \nof reasonable default aggregations, `minimal` is minimal set.\"\"\"\n\n# ------------------------------------------------------------------------------\n\nMAPPING = MappingAggregationsSets(\n    all=MAPPING_AGGREGATIONS,\n    default=frozenset({AVG}),\n    minimal=frozenset({AVG}),\n)\n\"\"\"Set of default aggregations for [`Mapping`][getml.preprocessors.Mapping]. `all` \ncontains all aggregations supported by the mapping preprocessor. `default` and \n`minimal` are identical and include only the AVG aggregation, which is the \nrecommended setting for classification problems.\"\"\"\n\n# for backward compatibility of internal tests\n_all_aggregations = AGGREGATIONS\n_multirel_subset = MULTIREL_AGGREGATIONS\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/aggregations/types.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom typing import Literal, Union\n\n# ------------------------------------------------------------------------------\nAvg = Literal[\"AVG\"]\nCount = Literal[\"COUNT\"]\nCountDistinct = Literal[\"COUNT DISTINCT\"]\nCountDistinctOverCount = Literal[\"COUNT DISTINCT OVER COUNT\"]\nCountMinusCountDistinct = Literal[\"COUNT MINUS COUNT DISTINCT\"]\nEWMA_1s = Literal[\"EWMA_1S\"]\nEWMA_1m = Literal[\"EWMA_1M\"]\nEWMA_1h = Literal[\"EWMA_1H\"]\nEWMA_1d = Literal[\"EWMA_1D\"]\nEWMA_7d = Literal[\"EWMA_7D\"]\nEWMA_30d = Literal[\"EWMA_30D\"]\nEWMA_90d = Literal[\"EWMA_90D\"]\nEWMA_365d = Literal[\"EWMA_365D\"]\nEWMA_TREND_1s = Literal[\"EWMA_TREND_1S\"]\nEWMA_TREND_1m = Literal[\"EWMA_TREND_1M\"]\nEWMA_TREND_1h = Literal[\"EWMA_TREND_1H\"]\nEWMA_TREND_1d = Literal[\"EWMA_TREND_1D\"]\nEWMA_TREND_7d = Literal[\"EWMA_TREND_7D\"]\nEWMA_TREND_30d = Literal[\"EWMA_TREND_30D\"]\nEWMA_TREND_90d = Literal[\"EWMA_TREND_90D\"]\nEWMA_TREND_365d = Literal[\"EWMA_TREND_365D\"]\nFirst = Literal[\"FIRST\"]\nKurtosis = Literal[\"KURTOSIS\"]\nLast = Literal[\"LAST\"]\nMax = Literal[\"MAX\"]\nMedian = Literal[\"MEDIAN\"]\nMin = Literal[\"MIN\"]\nMode = Literal[\"MODE\"]\nNumMax = Literal[\"NUM MAX\"]\nNumMin = Literal[\"NUM MIN\"]\nQ1 = Literal[\"Q1\"]\nQ5 = Literal[\"Q5\"]\nQ10 = Literal[\"Q10\"]\nQ25 = Literal[\"Q25\"]\nQ75 = Literal[\"Q75\"]\nQ90 = Literal[\"Q90\"]\nQ95 = Literal[\"Q95\"]\nQ99 = Literal[\"Q99\"]\nSkew = Literal[\"SKEW\"]\nStddev = Literal[\"STDDEV\"]\nSum = Literal[\"SUM\"]\nTimeSinceFirstMaximum = Literal[\"TIME SINCE FIRST MAXIMUM\"]\nTimeSinceFirstMinimum = Literal[\"TIME SINCE FIRST MINIMUM\"]\nTimeSinceLastMaximum = Literal[\"TIME SINCE LAST MAXIMUM\"]\nTimeSinceLastMinimum = Literal[\"TIME SINCE LAST MINIMUM\"]\nTrend = Literal[\"TREND\"]\nVar = Literal[\"VAR\"]\nVariationCoefficient = Literal[\"VARIATION COEFFICIENT\"]\n\n\nMultirelAggregations = Union[\n    Avg,\n    Count,\n    CountDistinct,\n    CountMinusCountDistinct,\n    First,\n    Last,\n    Max,\n    Median,\n    Min,\n    Stddev,\n    Sum,\n    Var,\n]\n\"\"\"\nUnion of all Multirel aggregation types.\n\"\"\"\n\n# ------------------------------------------------------------------------------\n\nFastPropAggregations = Union[\n    Avg,\n    Count,\n    CountDistinct,\n    CountMinusCountDistinct,\n    First,\n    Last,\n    Max,\n    Median,\n    Min,\n    Stddev,\n    Sum,\n    Var,\n    CountDistinctOverCount,\n    EWMA_1s,\n    EWMA_1m,\n    EWMA_1h,\n    EWMA_1d,\n    EWMA_7d,\n    EWMA_30d,\n    EWMA_90d,\n    EWMA_365d,\n    EWMA_TREND_1s,\n    EWMA_TREND_1m,\n    EWMA_TREND_1h,\n    EWMA_TREND_1d,\n    EWMA_TREND_7d,\n    EWMA_TREND_30d,\n    EWMA_TREND_90d,\n    EWMA_TREND_365d,\n    Kurtosis,\n    Mode,\n    NumMax,\n    NumMin,\n    Q1,\n    Q5,\n    Q10,\n    Q25,\n    Q75,\n    Q90,\n    Q95,\n    Q99,\n    Skew,\n    TimeSinceFirstMaximum,\n    TimeSinceFirstMinimum,\n    TimeSinceLastMaximum,\n    TimeSinceLastMinimum,\n    Trend,\n    VariationCoefficient,\n]\n\"\"\"\nUnion of all FastProp aggregation types.\n\"\"\"\n\n# ------------------------------------------------------------------------------\n\nMappingAggregations = Union[\n    Avg,\n    Count,\n    CountDistinct,\n    CountDistinctOverCount,\n    CountMinusCountDistinct,\n    Kurtosis,\n    Max,\n    Median,\n    Min,\n    Mode,\n    NumMax,\n    NumMin,\n    Q1,\n    Q5,\n    Q10,\n    Q25,\n    Q75,\n    Q90,\n    Q95,\n    Q99,\n    Skew,\n    Stddev,\n    Sum,\n    Var,\n    VariationCoefficient,\n]\n\"\"\"\nUnion of all Mapping aggregation types.\n\"\"\"\n\n# ------------------------------------------------------------------------------\n\nAggregations = Union[MultirelAggregations, FastPropAggregations, MappingAggregations]\n\"\"\"\nUnion of all possible aggregation types.\n\"\"\"\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/fastboost.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFeature learning based on fast gradient boosting.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional, Union\n\nfrom .feature_learner import _FeatureLearner\nfrom .loss_functions import CrossEntropyLossType, SquareLossType\nfrom .validation import _validate_fastboost_parameters\n\n\n@dataclass(repr=False)\nclass Fastboost(_FeatureLearner):\n    \"\"\"\n    Feature learning based on Gradient Boosting.\n\n    [`Fastboost`][getml.feature_learning.Fastboost] automates feature learning\n    for relational data and time series. The algorithm used is slightly\n    simpler than [`Relboost`][getml.feature_learning.Relboost] and much faster.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        gamma:\n            During the training of Fastboost, which is based on\n            gradient tree boosting, this value serves as the minimum\n            improvement in terms of the `loss_function` required for a\n            split of the tree to be applied. Larger `gamma` will lead\n            to fewer partitions of the tree and a more conservative\n            algorithm. Range: [0, ∞]\n\n        loss_function:\n            Objective function used by the feature learning algorithm\n            to optimize your features. For regression problems use\n            [`SquareLoss`][getml.feature_learning.loss_functions.SQUARELOSS] and for\n            classification problems use\n            [`CrossEntropyLoss`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS].\n\n        max_depth:\n            Maximum depth of the trees generated during the gradient\n            tree boosting. Deeper trees will result in more complex\n            models and increase the risk of overfitting. Range: [0, ∞]\n\n        min_child_weights:\n            Determines the minimum sum of the weights a subcondition\n            should apply to in order for it to be considered. Higher\n            values lead to less complex statements and less danger of\n            overfitting. Range: [1, ∞]\n\n        num_features:\n            Number of features generated by the feature learning\n            algorithm. Range: [1, ∞]\n\n        num_threads:\n            Number of threads used by the feature learning algorithm. If set to\n            zero or a negative value, the number of threads will be\n            determined automatically by the getML Engine. Range:\n            [0, ∞]\n\n        reg_lambda:\n            L2 regularization on the weights in the gradient boosting\n            routine. This is one of the most important hyperparameters\n            in the [`Relboost`][getml.feature_learning.Relboost] as it allows\n            for the most direct regularization. Larger values will\n            make the resulting model more conservative. Range: [0,\n            ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n        shrinkage:\n            Since Fastboost works using a gradient-boosting-like\n            algorithm, `shrinkage` (or learning rate) scales down the\n            weights and thus the impact of each new tree. This gives\n            more room for future ones to improve the overall\n            performance of the model in this greedy algorithm. It must\n            be between 0.0 and 1.0 with higher values leading to a higher\n            danger of overfitting. Range: [0, 1]\n\n        silent:\n            Controls the logging during training.\n\n        subsample:\n            Fastboost uses a bootstrapping procedure (sampling with\n            replacement) to train each of the features. The sampling\n            factor is proportional to the share of the samples\n            randomly drawn from the population table every time\n            Fastboost generates a new feature. A lower sampling factor\n            (but still greater than 0.0), will lead to less danger of\n            overfitting, less complex statements and faster\n            training. When set to 1.0, the number of samples drawn will\n            be identical to the size of the population table.\n            When set to 0.0, there will be no sampling at all.\n            Range: [0, 1]\n\n\n    \"\"\"\n\n    gamma: float = 0.0\n    loss_function: Optional[Union[CrossEntropyLossType, SquareLossType]] = None\n    max_depth: int = 5\n    min_child_weights: float = 1.0\n    num_features: int = 100\n    num_threads: int = 1\n    reg_lambda: float = 1.0\n    seed: int = 5543\n    shrinkage: float = 0.1\n    silent: bool = True\n    subsample: float = 1.0\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"\n        Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing the parameters to validate.\n                params can hold the full set or a subset of the\n                parameters explained in\n                [`Fastboost`][getml.feature_learning.Fastboost].\n                If params is None, the\n                current set of parameters in the\n                instance dictionary will be validated.\n\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        unsupported_params = [\n            k for k in params if k not in type(self)._supported_params\n        ]\n\n        if unsupported_params:\n            raise KeyError(\n                \"The following instance variables are not supported \"\n                + f\"in {self.type}: {unsupported_params}\"\n            )\n\n        _validate_fastboost_parameters(**params)\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/fastprop.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFeature learning based on propositionalization.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, ClassVar, Dict, Iterable, Optional, Union\n\nfrom .aggregations.sets import (\n    FASTPROP,\n    FastPropAggregationsSets,\n)\nfrom .aggregations.types import FastPropAggregations\nfrom .feature_learner import _FeatureLearner\nfrom .loss_functions import CrossEntropyLossType, SquareLossType\nfrom .validation import _validate_fastprop_parameters\n\n\n@dataclass(repr=False)\nclass FastProp(_FeatureLearner):\n    \"\"\"\n    Generates simple features based on propositionalization.\n\n    [`FastProp`][getml.feature_learning.FastProp] generates simple and easily\n    interpretable features for relational data and time series. It is based on a\n    propositionalization approach and has been optimized for speed and memory efficiency.\n    [`FastProp`][getml.feature_learning.FastProp] generates a large number\n    of features and selects the most relevant ones based on the pair-wise correlation\n    with the target(s).\n\n    It is recommended to combine [`FastProp`][getml.feature_learning.FastProp] with\n    the [`Mapping`][getml.preprocessors.Mapping] and [`Seasonal`][getml.preprocessors.Seasonal]\n    preprocessors, which can drastically improve predictive accuracy.\n\n    For more information on the underlying feature learning algorithm, check\n    out the User guide: [FastProp][feature-engineering-algorithms-fastprop].\n\n    Attributes:\n        agg_sets:\n            It is a class variable holding the available aggregation sets for the\n            FastProp feature learner.\n            Value: [`FASTPROP`][getml.feature_learning.aggregations.FASTPROP].\n\n    Parameters:\n        aggregation:\n            Mathematical operations used by the automated feature\n            learning algorithm to create new features.\n\n            Must be an aggregation supported by FastProp feature learner\n            ([`FASTPROP_AGGREGATIONS`][getml.feature_learning.aggregations.FASTPROP_AGGREGATIONS]).\n\n        delta_t:\n            Frequency with which lag variables will be explored in a\n            time series setting. When set to 0.0, there will be no lag\n            variables. Please note that you must also pass a value to\n            max_lag.\n\n            For more information please refer to\n            [Data Model Time Series][data-model-time-series]. Range: [0, ∞]\n\n        loss_function:\n            Objective function used by the feature learning algorithm\n            to optimize your features. For regression problems use\n            [`SquareLoss`][getml.feature_learning.loss_functions.SQUARELOSS] and for\n            classification problems use\n            [`CrossEntropyLoss`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS].\n\n        max_lag:\n            Maximum number of steps taken into the past to form lag variables. The\n            step size is determined by delta_t. Please note that you must also pass\n            a value to delta_t.\n\n            For more information please refer to\n            [Time Series][data-model-time-series]. Range: [0, ∞]\n\n        min_df:\n            Only relevant for columns with role [`text`][getml.data.roles.text].\n            The minimum\n            number of fields (i.e. rows) in [`text`][getml.data.roles.text] column a\n            given word is required to appear in to be included in the bag of words.\n            Range: [1, ∞]\n\n        num_features:\n            Number of features generated by the feature learning\n            algorithm. Range: [1, ∞]\n\n        n_most_frequent:\n            [`FastProp`][getml.feature_learning.FastProp] can find the N most frequent\n            categories in a categorical column and derive features from them.\n            The parameter determines how many categories should be used.\n            Range: [0, ∞]\n\n        num_threads:\n            Number of threads used by the feature learning algorithm. If set to\n            zero or a negative value, the number of threads will be\n            determined automatically by the getML Engine. Range:\n            [0, ∞]\n\n        sampling_factor:\n            FastProp uses a bootstrapping procedure (sampling with replacement) to train\n            each of the features. The sampling factor is proportional to the share of\n            the samples randomly drawn from the population table every time Multirel\n            generates a new feature. A lower sampling factor (but still greater than\n            0.0), will lead to less danger of overfitting, less complex statements and\n            faster training. When set to 1.0, roughly 2,000 samples are drawn from the\n            population table. If the population table contains less than 2,000 samples,\n            it will use standard bagging. When set to 0.0, there will be no sampling at\n            all. Range: [0, ∞]\n\n        silent:\n            Controls the logging during training.\n\n        vocab_size:\n            Determines the maximum number\n            of words that are extracted in total from [`text`][getml.data.roles.text]\n            columns. This can be interpreted as the maximum size of the bag of words.\n            Range: [0, ∞]\n\n    \"\"\"\n\n    agg_sets: ClassVar[FastPropAggregationsSets] = FASTPROP\n\n    aggregation: Iterable[FastPropAggregations] = FASTPROP.default\n    delta_t: float = 0.0\n    loss_function: Optional[Union[CrossEntropyLossType, SquareLossType]] = None\n    max_lag: int = 0\n    min_df: int = 30\n    n_most_frequent: int = 0\n    num_features: int = 200\n    num_threads: int = 0\n    sampling_factor: float = 1.0\n    silent: bool = True\n    vocab_size: int = 500\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"\n        Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing the parameters to validate.\n                params can hold the full set or a subset of the\n                parameters explained in\n                [`FastProp`][getml.feature_learning.FastProp].\n                If params is None, the\n                current set of parameters in the\n                instance dictionary will be validated.\n\n\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        for kkey in params:\n            if kkey not in type(self)._supported_params:\n                raise KeyError(\n                    f\"Instance variable '{kkey}' is not supported in {self.type}.\"\n                )\n\n        _validate_fastprop_parameters(**params)\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/feature_learner.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nBase class. Should not ever be directly initialized!\n\"\"\"\n\nimport numbers\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nfrom typing import Any, ClassVar, Dict, Optional, Set\n\nimport numpy as np\n\nfrom getml.utilities.formatting import _SignatureFormatter\n\nfrom .validation import Validator\n\n# --------------------------------------------------------------------\n\n\n@dataclass\nclass _FeatureLearner(ABC):\n    \"\"\"\n    Base class. Should not ever be directly initialized!\n    \"\"\"\n\n    _supported_params: ClassVar[Set]\n\n    def __post_init__(self) -> None:\n        type(self)._supported_params = set(vars(self).keys())\n        for param in self._supported_params:\n            setattr(type(self), param, Validator(param))\n\n    # ----------------------------------------------------------------\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Compares the current instance with another one.\n\n        Returns:\n            Indicating whether the current instance and `other` are the same.\n        \"\"\"\n\n        # Allow for in comparing for feature_learner names\n        if isinstance(other, str):\n            return other == self.type\n\n        if not isinstance(other, _FeatureLearner):\n            raise TypeError(\n                \"\"\"A feature learning algorithm can only\n                compared to another feature learning algorithm!\"\"\"\n            )\n\n        # Check whether both objects have the same number of instance\n        # variables.\n        if len(set(self.__dict__.keys())) != len(set(other.__dict__.keys())):\n            return False\n\n        for kkey in self.__dict__:\n            if kkey not in other.__dict__:\n                return False\n\n            # Take special care when comparing numbers.\n            if isinstance(self.__dict__[kkey], numbers.Real):\n                if not np.isclose(self.__dict__[kkey], other.__dict__[kkey]):\n                    return False\n\n            elif self.__dict__[kkey] != other.__dict__[kkey]:\n                return False\n\n        return True\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        return str(self)\n\n    # ----------------------------------------------------------------\n\n    def __setattr__(self, name: str, value: Any) -> None:\n        if name in self.__dataclass_fields__:  # pylint: disable=E1101\n            super().__setattr__(name, value)\n        else:\n            raise AttributeError(\n                f\"Instance variable '{name}' is not supported in {self.type}.\"\n            )\n\n    # ----------------------------------------------------------------\n\n    def __str__(self) -> str:\n        sig = _SignatureFormatter(self)\n        return sig._format()\n\n    # ----------------------------------------------------------------\n\n    def _getml_deserialize(self) -> Dict[str, Any]:\n        # To ensure the getML can handle all keys, we have to add\n        # a trailing underscore.\n        self_dict = self.__dict__\n        encoding_dict = dict()\n\n        for kkey in self_dict:\n            encoding_dict[kkey + \"_\"] = self_dict[kkey]\n\n        encoding_dict[\"type_\"] = self.type\n\n        return encoding_dict\n\n    # ---------------------------------------------------------------\n\n    @property\n    def type(self) -> str:\n        return type(self).__name__\n\n    # ---------------------------------------------------------------\n\n    @abstractmethod\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        pass\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/loss_functions.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nLoss functions used by the feature learning algorithms.\n\nThe getML Python API contains two different loss\nfunctions. We recommend using\n[`SQUARELOSS`][getml.feature_learning.loss_functions.SQUARELOSS] for regression problems and\n[`CROSSENTROPYLOSS`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS] for classification\nproblems.\n\nPlease note that these loss functions will only be used by the feature\nlearning algorithms and not by the [`predictors`][getml.predictors].\n\n\"\"\"\n\nfrom typing import Final, Literal\n\nCrossEntropyLossType = Literal[\"CrossEntropyLoss\"]\n\"\"\"Type of the cross entropy loss function.\"\"\"\nCROSSENTROPYLOSS: Final[CrossEntropyLossType] = \"CrossEntropyLoss\"\n\"\"\"\nThe cross entropy between two probability distributions\n$p(x)$ and $q(x)$ is a combination of the information\ncontained in $p(x)$ and the additional information stored in\n$q(x)$ with respect to $p(x)$. In technical terms: it\nis the entropy of $p(x)$ plus the Kullback-Leibler\ndivergence - a distance in probability space - from $q(x)$\nto $p(x)$.\n\n$$\nH(p,q) = H(p) + D_{KL}(p||q)\n$$\n\nFor discrete probability distributions the cross entropy loss can\nbe calculated by\n\n$$\nH(p,q) = - \\\\sum_{x \\\\in X} p(x) \\\\log q(x)\n$$\n\nand for continuous probability distributions by\n\n$$\nH(p,q) = - \\\\int_{X} p(x) \\\\log q(x) dx\n$$\n\nwith $X$ being the support of the samples and $p(x)$\nand $q(x)$ being two discrete or continuous probability\ndistributions over $X$.\n\nNote:\n    Recommended loss function for classification problems.\n\"\"\"\n\nSquareLossType = Literal[\"SquareLoss\"]\n\"\"\"Type of the square loss function.\"\"\"\nSQUARELOSS: Final[SquareLossType] = \"SquareLoss\"\n\"\"\"\nThe Square loss (aka mean squared error (MSE)) measures the loss by calculating the average of all squared\ndeviations of the predictions $\\\\hat{y}$ from the observed\n(given) outcomes $y$. Depending on the context this measure\nis also known as mean squared error (MSE) or mean squared\ndeviation (MSD).\n\n$$\nL(y,\\\\hat{y}) = \\\\frac{1}{n} \\\\sum_{i=1}^{n} (y_i -\\\\hat{y}_i)^2 \n$$\n\nwith $n$ being the number of samples, $y$ the observed\noutcome, and $\\\\hat{y}$ the estimate.\n\nNote:\n    Recommended loss function for regression problems.\n\"\"\"\n\n_all_loss_functions = [CROSSENTROPYLOSS, SQUARELOSS]\n\n_classification_loss = [CROSSENTROPYLOSS]\n\n# For backward compatibility\nCrossEntropyLoss = CROSSENTROPYLOSS\nSquareLoss = SQUARELOSS\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/multirel.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFeature learning based on Multi-Relational Decision Tree Learning.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Any, ClassVar, Dict, Iterable, Optional, Union\n\nfrom .aggregations.sets import (\n    MULTIREL,\n    MultirelAggregationsSets,\n)\nfrom .aggregations.types import MultirelAggregations\nfrom .fastprop import FastProp\nfrom .feature_learner import _FeatureLearner\nfrom .loss_functions import CrossEntropyLossType, SquareLossType\nfrom .validation import _validate_multirel_parameters\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass Multirel(_FeatureLearner):\n    \"\"\"\n    Feature learning based on Multi-Relational Decision Tree Learning.\n\n    [`Multirel`][getml.feature_learning.Multirel] automates feature learning\n    for relational data and time series. It is based on an efficient\n    variation of the Multi-Relational Decision Tree Learning (MRDTL).\n\n    For more information on the underlying feature learning algorithm, check\n    out the User guide: [Multirel][feature-engineering-algorithms-multirel].\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Attributes:\n        agg_sets:\n            It is a class variable holding the available aggregation sets for the\n            Multirel feature learner.\n            Value: [`MULTIREL`][getml.feature_learning.aggregations.MULTIREL].\n\n    Parameters:\n        aggregation:\n            Mathematical operations used by the automated feature\n            learning algorithm to create new features.\n\n            Must be an aggregation supported by Multirel feature learner\n            ([`MULTIREL_AGGREGATIONS`][getml.feature_learning.aggregations.MULTIREL_AGGREGATIONS]).\n\n        allow_sets:\n            Multirel can summarize different categories into sets for\n            producing conditions. When expressed as SQL statements these\n            sets might look like this:\n            ```sql\n            t2.category IN ( 'value_1', 'value_2', ... )\n            ```\n            This can be very powerful, but it can also produce\n            features that are hard to read and might be prone to\n            overfitting when the `sampling_factor` is too low.\n\n        delta_t:\n            Frequency with which lag variables will be explored in a\n            time series setting. When set to 0.0, there will be no lag\n            variables.\n\n            For more information please refer to\n            [Time Series][data-model-time-series] in the User Guide. Range: [0, ∞]\n\n        grid_factor:\n            Multirel will try a grid of critical values for your\n            numerical features. A higher `grid_factor` will lead to a\n            larger number of critical values being considered. This\n            can increase the training time, but also lead to more\n            accurate features. Range: (0, ∞]\n\n        loss_function:\n            Objective function used by the feature learning algorithm\n            to optimize your features. For regression problems use\n            [`SquareLoss`][getml.feature_learning.loss_functions.SQUARELOSS] and for\n            classification problems use\n            [`CrossEntropyLoss`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS].\n\n        max_length:\n            The maximum length a subcondition might have. Multirel\n            will create conditions in the form\n            ```sql\n            (condition 1.1 AND condition 1.2 AND condition 1.3 )\n            OR ( condition 2.1 AND condition 2.2 AND condition 2.3 )\n            ...\n            ```\n            Using this parameter you can set the maximum number of\n            conditions allowed in the brackets. Range: [0, ∞]\n\n        min_df:\n            Only relevant for columns with role [`text`][getml.data.roles.text].\n            The minimum\n            number of fields (i.e. rows) in [`text`][getml.data.roles.text] column a\n            given word is required to appear in to be included in the bag of words.\n            Range: [1, ∞]\n\n        min_num_samples:\n            Determines the minimum number of samples a subcondition\n            should apply to in order for it to be considered. Higher\n            values lead to less complex statements and less danger of\n            overfitting. Range: [1, ∞]\n\n        num_features:\n            Number of features generated by the feature learning\n            algorithm. Range: [1, ∞]\n\n        num_subfeatures:\n            The number of subfeatures you would like to extract in a\n            subensemble (for snowflake data model only). See\n            [Snowflake Schema][data-model-snowflake-schema] for more\n            information. Range: [1, ∞]\n\n        num_threads:\n            Number of threads used by the feature learning algorithm. If set to\n            zero or a negative value, the number of threads will be\n            determined automatically by the getML Engine. Range:\n            [0, ∞]\n\n        propositionalization:\n            The feature learner used for joins which are flagged to be\n            propositionalized (by setting a join's `relationship` parameter to\n            [`propositionalization`][getml.data.relationship.propositionalization])\n\n        regularization:\n            Most important regularization parameter for the quality of\n            the features produced by Multirel. Higher values will lead\n            to less complex features and less danger of overfitting. A\n            `regularization` of 1.0 is very strong and allows no\n            conditions. Range: [0, 1]\n\n        round_robin:\n            If True, the Multirel picks a different `aggregation`\n            every time a new feature is generated.\n\n        sampling_factor:\n            Multirel uses a bootstrapping procedure (sampling with\n            replacement) to train each of the features. The sampling\n            factor is proportional to the share of the samples\n            randomly drawn from the population table every time\n            Multirel generates a new feature. A lower sampling factor\n            (but still greater than 0.0), will lead to less danger of\n            overfitting, less complex statements and faster\n            training. When set to 1.0, roughly 20,000 samples are drawn\n            from the population table. If the population table\n            contains less than 20,000 samples, it will use standard\n            bagging. When set to 0.0, there will be no sampling at\n            all. Range: [0, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n        share_aggregations:\n            Every time a new feature is generated, the `aggregation`\n            will be taken from a random subsample of possible\n            aggregations and values to be aggregated. This parameter\n            determines the size of that subsample. Only relevant when\n            `round_robin` is False. Range: [0, 1]\n\n        share_conditions:\n            Every time a new column is tested for applying conditions,\n            it might be skipped at random. This parameter determines\n            the probability that a column will *not* be\n            skipped. Range: [0, 1]\n\n        shrinkage:\n            Since Multirel works using a gradient-boosting-like\n            algorithm, `shrinkage` (or learning rate) scales down the\n            weights and thus the impact of each new tree. This gives\n            more room for future ones to improve the overall\n            performance of the model in this greedy algorithm. Higher\n            values will lead to more danger of overfitting. Range: [0,\n            1]\n\n        silent:\n            Controls the logging during training.\n\n        vocab_size:\n            Determines the maximum number\n            of words that are extracted in total from [`text`][getml.data.roles.text]\n            columns. This can be interpreted as the maximum size of the bag of words.\n            Range: [0, ∞]\n\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    agg_sets: ClassVar[MultirelAggregationsSets] = MULTIREL\n\n    # ----------------------------------------------------------------\n\n    aggregation: Iterable[MultirelAggregations] = MULTIREL.default\n    allow_sets: bool = True\n    delta_t: float = 0.0\n    grid_factor: float = 1.0\n    loss_function: Optional[Union[CrossEntropyLossType, SquareLossType]] = None\n    max_length: int = 4\n    min_df: int = 30\n    min_num_samples: int = 1\n    num_features: int = 100\n    num_subfeatures: int = 5\n    num_threads: int = 0\n    propositionalization: FastProp = field(default_factory=FastProp)\n    regularization: float = 0.01\n    round_robin: bool = False\n    sampling_factor: float = 1.0\n    seed: int = 5543\n    share_aggregations: float = 0.0\n    share_conditions: float = 1.0\n    shrinkage: float = 0.0\n    silent: bool = True\n    vocab_size: int = 500\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"\n        Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        # ------------------------------------------------------------\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        # ------------------------------------------------------------\n\n        for kkey in params:\n            if kkey not in type(self)._supported_params:\n                raise KeyError(\n                    f\"Instance variable '{kkey}' is not supported in {self.type}.\"\n                )\n\n        # ------------------------------------------------------------\n\n        _validate_multirel_parameters(**params)\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/relboost.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFeature learning based on Gradient Boosting.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, Optional, Union\n\nfrom .fastprop import FastProp\nfrom .feature_learner import _FeatureLearner\nfrom .loss_functions import CrossEntropyLossType, SquareLossType\nfrom .validation import _validate_relboost_parameters\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass Relboost(_FeatureLearner):\n    \"\"\"\n       Feature learning based on Gradient Boosting.\n\n    [`Relboost`][getml.feature_learning.Relboost] automates feature learning\n    for relational data and time series. It is based on a\n    generalization of the XGBoost algorithm to relational data, hence\n    the name.\n\n    For more information on the underlying feature learning\n    algorithm, check out the User Guide: [Relboost][feature-engineering-algorithms-relboost].\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        allow_null_weights:\n            Whether you want to allow\n            [`Relboost`][getml.feature_learning.Relboost] to set weights to\n            NULL.\n\n        delta_t:\n            Frequency with which lag variables will be explored in a\n            time series setting. When set to 0.0, there will be no lag\n            variables.\n\n            For more information, please refer to\n            [Time Series][data-model-time-series] in the User Guide. Range: [0, ∞]\n\n        gamma:\n            During the training of Relboost, which is based on\n            gradient tree boosting, this value serves as the minimum\n            improvement in terms of the `loss_function` required for a\n            split of the tree to be applied. Larger `gamma` will lead\n            to fewer partitions of the tree and a more conservative\n            algorithm. Range: [0, ∞]\n\n        loss_function:\n            Objective function used by the feature learning algorithm\n            to optimize your features. For regression problems use\n            [`SquareLoss`][getml.feature_learning.loss_functions.SQUARELOSS] and for\n            classification problems use\n            [`CrossEntropyLoss`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS].\n\n        max_depth:\n            Maximum depth of the trees generated during the gradient\n            tree boosting. Deeper trees will result in more complex\n            models and increase the risk of overfitting. Range: [0,\n            ∞]\n\n        min_df:\n            Only relevant for columns with role [`text`][getml.data.roles.text].\n            The minimum\n            number of fields (i.e. rows) in [`text`][getml.data.roles.text] column a\n            given word is required to appear in to be included in the bag of words.\n            Range: [1, ∞]\n\n        min_num_samples:\n            Determines the minimum number of samples a subcondition\n            should apply to in order for it to be considered. Higher\n            values lead to less complex statements and less danger of\n            overfitting. Range: [1, ∞]\n\n        num_features:\n            Number of features generated by the feature learning\n            algorithm. Range: [1, ∞]\n\n        num_subfeatures:\n            The number of subfeatures you would like to extract in a\n            subensemble (for snowflake data model only). See\n            [Snowflake Schema][data-model-snowflake-schema] for more\n            information. Range: [1, ∞]\n\n        num_threads:\n            Number of threads used by the feature learning algorithm. If set to\n            zero or a negative value, the number of threads will be\n            determined automatically by the getML Engine. Range:\n            [0, ∞]\n\n        propositionalization:\n            The feature learner used for joins which are flagged to be\n            propositionalized (by setting a join's `relationship` parameter to\n            [`propositionalization`][getml.data.relationship.propositionalization])\n\n        reg_lambda:\n            L2 regularization on the weights in the gradient boosting\n            routine. This is one of the most important hyperparameters\n            in the [`Relboost`][getml.feature_learning.Relboost] as it allows\n            for the most direct regularization. Larger values will\n            make the resulting model more conservative. Range: [0,\n            ∞]\n\n        sampling_factor:\n            Relboost uses a bootstrapping procedure (sampling with\n            replacement) to train each of the features. The sampling\n            factor is proportional to the share of the samples\n            randomly drawn from the population table every time\n            Relboost generates a new feature. A lower sampling factor\n            (but still greater than 0.0), will lead to less danger of\n            overfitting, less complex statements and faster\n            training. When set to 1.0, roughly 20,000 samples are drawn\n            from the population table. If the population table\n            contains less than 20,000 samples, it will use standard\n            bagging. When set to 0.0, there will be no sampling at\n            all. Range: [0, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n        shrinkage:\n            Since Relboost works using a gradient-boosting-like\n            algorithm, `shrinkage` (or learning rate) scales down the\n            weights and thus the impact of each new tree. This gives\n            more room for future ones to improve the overall\n            performance of the model in this greedy algorithm. It must\n            be between 0.0 and 1.0 with higher values leading to a higher\n            danger of overfitting. Range: [0, 1]\n\n        silent:\n            Controls the logging during training.\n\n        vocab_size:\n            Determines the maximum number\n            of words that are extracted in total from [`text`][getml.data.roles.text]\n            columns. This can be interpreted as the maximum size of the bag of words.\n            Range: [0, ∞]\n\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    allow_null_weights: bool = False\n    delta_t: float = 0.0\n    gamma: float = 0.0\n    loss_function: Optional[Union[CrossEntropyLossType, SquareLossType]] = None\n    max_depth: int = 3\n    min_df: int = 30\n    min_num_samples: int = 1\n    num_features: int = 100\n    num_subfeatures: int = 100\n    num_threads: int = 0\n    propositionalization: FastProp = field(default_factory=FastProp)\n    reg_lambda: float = 0.0\n    sampling_factor: float = 1.0\n    seed: int = 5543\n    shrinkage: float = 0.1\n    silent: bool = True\n    vocab_size: int = 500\n\n    # ------------------------------------------------------------\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"\n        Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        # ------------------------------------------------------------\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        # ------------------------------------------------------------\n\n        for kkey in params:\n            if kkey not in type(self)._supported_params:\n                raise KeyError(\n                    f\"Instance variable '{kkey}' is not supported in {self.type}.\"\n                )\n\n        # ------------------------------------------------------------\n\n        if not isinstance(params[\"silent\"], bool):\n            raise TypeError(\"'silent' must be of type bool\")\n\n        # ------------------------------------------------------------\n\n        _validate_relboost_parameters(**params)\n\n        # ------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/relmt.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFeature learning based on Gradient Boosting.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, Optional, Union\n\nfrom .fastprop import FastProp\nfrom .feature_learner import _FeatureLearner\nfrom .loss_functions import CrossEntropyLossType, SquareLossType\nfrom .validation import _validate_relboost_parameters\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass RelMT(_FeatureLearner):\n    \"\"\"Feature learning based on relational linear model trees.\n\n    [`RelMT`][getml.feature_learning.RelMT] automates feature learning\n    for relational data and time series. It is based on a\n    generalization of linear model trees to relational data, hence\n    the name. A linear model tree is a decision tree\n    with linear models on its leaves.\n\n\n    For more information on the underlying feature learning\n    algorithm, check out the User Guide: [RelMT][feature-engineering-algorithms-relmt].\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        allow_avg:\n            Whether to allow an AVG aggregation. Particularly for time\n            series problems, AVG aggregations are not necessary and you\n            can save some time by taking them out.\n\n        delta_t:\n            Frequency with which lag variables will be explored in a\n            time series setting. When set to 0.0, there will be no lag\n            variables.\n\n            For more information, please refer to [Time Series][data-model-time-series] in the User Guide. Range: [0, ∞]\n\n        gamma:\n            During the training of RelMT, which is based on\n            gradient tree boosting, this value serves as the minimum\n            improvement in terms of the `loss_function` required for a\n            split of the tree to be applied. Larger `gamma` will lead\n            to fewer partitions of the tree and a more conservative\n            algorithm. Range: [0, ∞]\n\n        loss_function:\n            Objective function used by the feature learning algorithm\n            to optimize your features. For regression problems use\n            [`SquareLoss`][getml.feature_learning.loss_functions.SQUARELOSS] and for\n            classification problems use\n            [`CrossEntropyLoss`][getml.feature_learning.loss_functions.CROSSENTROPYLOSS].\n\n        max_depth:\n            Maximum depth of the trees generated during the gradient\n            tree boosting. Deeper trees will result in more complex\n            models and increase the risk of overfitting. Range: [0,\n            ∞]\n\n        min_df:\n            Only relevant for columns with role [`text`][getml.data.roles.text].\n            The minimum\n            number of fields (i.e. rows) in [`text`][getml.data.roles.text] column a\n            given word is required to appear in to be included in the bag of words.\n            Range: [1, ∞]\n\n        min_num_samples:\n            Determines the minimum number of samples a subcondition\n            should apply to in order for it to be considered. Higher\n            values lead to less complex statements and less danger of\n            overfitting. Range: [1, ∞]\n\n        num_features:\n            Number of features generated by the feature learning\n            algorithm. Range: [1, ∞]\n\n        num_subfeatures:\n            The number of subfeatures you would like to extract in a\n            subensemble (for snowflake data model only). See\n            :ref:`data_model_snowflake_schema` for more\n            information. Range: [1, ∞]\n\n        num_threads:\n            Number of threads used by the feature learning algorithm. If set to\n            zero or a negative value, the number of threads will be\n            determined automatically by the getML Engine. Range:\n            [-∞, ∞]\n\n        propositionalization:\n            The feature learner used for joins which are flagged to be\n            propositionalized (by setting a join's `relationship` parameter to\n            [`propositionalization`][getml.data.relationship.propositionalization])\n\n        reg_lambda:\n            L2 regularization on the weights in the gradient boosting\n            routine. This is one of the most important hyperparameters\n            in the [`RelMT`][getml.feature_learning.RelMT] as it allows\n            for the most direct regularization. Larger values will\n            make the resulting model more conservative. Range: [0,\n            ∞]\n\n        sampling_factor:\n            RelMT uses a bootstrapping procedure (sampling with\n            replacement) to train each of the features. The sampling\n            factor is proportional to the share of the samples\n            randomly drawn from the population table every time\n            RelMT generates a new feature. A lower sampling factor\n            (but still greater than 0.0), will lead to less danger of\n            overfitting, less complex statements and faster\n            training. When set to 1.0, roughly 20,000 samples are drawn\n            from the population table. If the population table\n            contains less than 20,000 samples, it will use standard\n            bagging. When set to 0.0, there will be no sampling at\n            all. Range: [0, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n        shrinkage:\n            Since RelMT works using a gradient-boosting-like\n            algorithm, `shrinkage` (or learning rate) scales down the\n            weights and thus the impact of each new tree. This gives\n            more room for future ones to improve the overall\n            performance of the model in this greedy algorithm. It must\n            be between 0.0 and 1.0 with higher values leading to more\n            danger of overfitting. Range: [0, 1]\n\n        silent:\n            Controls the logging during training.\n\n        vocab_size:\n            Determines the maximum number\n            of words that are extracted in total from [`text`][getml.data.roles.text]\n            columns. This can be interpreted as the maximum size of the bag of words.\n            Range: [0, ∞]\n\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    allow_avg: bool = True\n    delta_t: float = 0.0\n    gamma: float = 0.0\n    loss_function: Optional[Union[CrossEntropyLossType, SquareLossType]] = None\n    max_depth: int = 2\n    min_df: int = 30\n    min_num_samples: int = 1\n    num_features: int = 30\n    num_subfeatures: int = 30\n    num_threads: int = 0\n    propositionalization: FastProp = field(default_factory=FastProp)\n    reg_lambda: float = 0.0\n    sampling_factor: float = 1.0\n    seed: int = 5543\n    shrinkage: float = 0.1\n    silent: bool = True\n    vocab_size: int = 500\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"\n        Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        # ------------------------------------------------------------\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        # ------------------------------------------------------------\n\n        for kkey in params:\n            if kkey not in type(self)._supported_params:\n                raise KeyError(\n                    f\"Instance variable '{kkey}' is not supported in {self.type}.\"\n                )\n\n        # ------------------------------------------------------------\n\n        if not isinstance(params[\"silent\"], bool):\n            raise TypeError(\"'silent' must be of type bool\")\n\n        # ------------------------------------------------------------\n\n        _validate_relboost_parameters(**params)\n\n        # ------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/feature_learning/validation.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains helper functions for validating the feature learning algorithms.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport numbers\nfrom typing import Any, cast\n\nimport numpy as np\n\nfrom getml import feature_learning\nfrom getml.data.helpers import _is_non_empty_typed_list, _is_typed_list\nfrom getml.helpers import _check_parameter_bounds, _is_iterable_not_str_of_type\nfrom getml.log import MIGHT_TAKE_LONG, logger\n\nfrom .aggregations.sets import _all_aggregations, _multirel_subset\n\n# --------------------------------------------------------------------\n\n\nclass Validator:\n    def __init__(self, name: str) -> None:\n        self.name = name\n\n    def __set__(self, instance: feature_learning._FeatureLearner, value: Any) -> None:\n        # dont call validate upon initialization\n        if self.name in instance.__dict__:\n            instance.validate({self.name: value})\n        instance.__dict__[self.name] = value\n\n\n# --------------------------------------------------------------------\n\n\ndef _validate_fastprop_parameters(**kwargs: Any) -> None:\n    aggregation = kwargs[\"aggregation\"]\n    delta_t = kwargs[\"delta_t\"]\n    loss_function = kwargs[\"loss_function\"]\n    max_lag = kwargs[\"max_lag\"]\n    min_df = kwargs[\"min_df\"]\n    n_most_frequent = kwargs[\"n_most_frequent\"]\n    num_features = kwargs[\"num_features\"]\n    num_threads = kwargs[\"num_threads\"]\n    sampling_factor = kwargs[\"sampling_factor\"]\n    silent = kwargs[\"silent\"]\n    vocab_size = kwargs[\"vocab_size\"]\n\n    if not _is_iterable_not_str_of_type(aggregation, str):\n        raise TypeError(\n            \"\"\"\n            'aggregation' must be a non-empty\n            non-string iterable of str found in getml.feature_learning.aggregations\n            \"\"\"\n        )\n\n    if not isinstance(delta_t, numbers.Real):\n        raise TypeError(\"'delta_t' must be a real number\")\n\n    if not isinstance(loss_function, str) or loss_function is None:\n        raise TypeError(\"'loss_function' must be a str\")\n\n    if not isinstance(max_lag, int):\n        raise TypeError(\"'max_lag' must be an integer\")\n\n    if not isinstance(min_df, numbers.Real):\n        raise TypeError(\"'min_df' must be a real number\")\n\n    if not isinstance(num_features, numbers.Real):\n        raise TypeError(\"'num_features' must be a real number\")\n\n    if not isinstance(n_most_frequent, numbers.Real):\n        raise TypeError(\"'n_most_frequent' must be a real number\")\n\n    if not isinstance(num_threads, numbers.Real):\n        raise TypeError(\"'num_threads' must be a real number\")\n\n    if not isinstance(sampling_factor, numbers.Real):\n        raise TypeError(\"'sampling_factor' must be a real number\")\n\n    if not isinstance(silent, bool):\n        raise TypeError(\"'silent' must be of type bool\")\n\n    if not isinstance(vocab_size, numbers.Real):\n        raise TypeError(\"'vocab_size' must be a real number\")\n\n    if not all([aa in _all_aggregations for aa in aggregation]):\n        raise ValueError(\n            f\"\"\"\n            'aggregation' must be a list of the composed of the following\n               aggregations defined in getml.feature_learning.aggregations:\n            {_all_aggregations}.\n            \"\"\"\n        )\n\n    _check_parameter_bounds(delta_t, \"delta_t\", [0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(max_lag, \"max_lag\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(min_df, \"min_df\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        n_most_frequent, \"n_most_frequent\", [0, np.iinfo(np.int32).max]\n    )\n\n    _check_parameter_bounds(num_features, \"num_features\", [1, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(num_threads, \"num_threads\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        sampling_factor, \"sampling_factor\", [0, np.finfo(np.float64).max]\n    )\n\n    _check_parameter_bounds(vocab_size, \"vocab_size\", [0, np.finfo(np.float64).max])\n\n    if (cast(float, delta_t) > 0.0) ^ (max_lag > 0):\n        raise ValueError(\n            \"\"\"\n            If you pass a non-zero value to delta_t, you must also\n            pass a non-zero\n            value to max_lag and vice-versa.\n            \"\"\"\n        )\n\n\n# --------------------------------------------------------------------\n\n\ndef _validate_fastboost_parameters(**kwargs: Any) -> None:\n    \"\"\"\n    Checks both the types and values of the `parameters` belonging to\n    [`Fastboost`][getml.feature_learning.Fastboost] and raises an exception if\n    something is off.\n    \"\"\"\n\n    gamma = kwargs[\"gamma\"]\n    loss_function = kwargs[\"loss_function\"]\n    max_depth = kwargs[\"max_depth\"]\n    min_child_weights = kwargs[\"min_child_weights\"]\n    num_features = kwargs[\"num_features\"]\n    num_threads = kwargs[\"num_threads\"]\n    reg_lambda = kwargs[\"reg_lambda\"]\n    seed = kwargs[\"seed\"]\n    shrinkage = kwargs[\"shrinkage\"]\n    subsample = kwargs[\"subsample\"]\n\n    if not isinstance(gamma, numbers.Real):\n        raise TypeError(\"'gamma' must be a real number\")\n\n    if not isinstance(loss_function, str) or loss_function is None:\n        raise TypeError(\"'loss_function' must be a str\")\n\n    if not isinstance(max_depth, numbers.Real):\n        raise TypeError(\"'max_depth' must be a real number\")\n\n    if not isinstance(min_child_weights, numbers.Real):\n        raise TypeError(\"'min_child_weights' must be a real number\")\n\n    if not isinstance(num_features, numbers.Real):\n        raise TypeError(\"'num_features' must be a real number\")\n\n    if not isinstance(num_threads, numbers.Real):\n        raise TypeError(\"'num_threads' must be a real number\")\n\n    if not isinstance(reg_lambda, numbers.Real):\n        raise TypeError(\"'reg_lambda' must be a real number\")\n\n    if not isinstance(seed, numbers.Real):\n        raise TypeError(\"'seed' must be a real number or None\")\n\n    if not isinstance(shrinkage, numbers.Real):\n        raise TypeError(\"'shrinkage' must be a real number\")\n\n    if not isinstance(subsample, numbers.Real):\n        raise TypeError(\"'subsample' must be a real number\")\n\n    _check_parameter_bounds(gamma, \"gamma\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(max_depth, \"max_depth\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        min_child_weights, \"min_child_weights\", [0.0, np.finfo(np.float64).max]\n    )\n\n    _check_parameter_bounds(num_features, \"num_features\", [1, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(num_threads, \"num_threads\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(reg_lambda, \"reg_lambda\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(seed, \"seed\", [0.0, np.iinfo(np.uint64).max])\n\n    _check_parameter_bounds(shrinkage, \"shrinkage\", [0.0, 1.0])\n\n    _check_parameter_bounds(subsample, \"subsample\", [0.0, np.finfo(np.float64).max])\n\n\n# --------------------------------------------------------------------\n\n\ndef _validate_multirel_parameters(**kwargs: Any) -> None:\n    # ----------------------------------------------------------------\n\n    aggregation = kwargs[\"aggregation\"]\n    allow_sets = kwargs[\"allow_sets\"]\n    delta_t = kwargs[\"delta_t\"]\n    grid_factor = kwargs[\"grid_factor\"]\n    loss_function = kwargs[\"loss_function\"]\n    max_length = kwargs[\"max_length\"]\n    min_df = kwargs[\"min_df\"]\n    min_num_samples = kwargs[\"min_num_samples\"]\n    num_features = kwargs[\"num_features\"]\n    num_subfeatures = kwargs[\"num_subfeatures\"]\n    num_threads = kwargs[\"num_threads\"]\n    propositionalization = kwargs[\"propositionalization\"]\n    regularization = kwargs[\"regularization\"]\n    round_robin = kwargs[\"round_robin\"]\n    sampling_factor = kwargs[\"sampling_factor\"]\n    seed = kwargs[\"seed\"]\n    share_aggregations = kwargs[\"share_aggregations\"]\n    share_conditions = kwargs[\"share_conditions\"]\n    shrinkage = kwargs[\"shrinkage\"]\n    vocab_size = kwargs[\"vocab_size\"]\n\n    # ----------------------------------------------------------------\n\n    if not _is_iterable_not_str_of_type(aggregation, str):\n        raise TypeError(\n            \"\"\"\n            'aggregation' must be a non-empty\n            non-string iterable of str found in getml.feature_learning.aggregations\n            \"\"\"\n        )\n    if not isinstance(allow_sets, bool):\n        raise TypeError(\"'allow_sets' must be of type bool\")\n\n    if not isinstance(delta_t, numbers.Real):\n        raise TypeError(\"'delta_t' must be a real number\")\n\n    if not isinstance(grid_factor, numbers.Real):\n        raise TypeError(\"'grid_factor' must be a real number\")\n\n    if not isinstance(loss_function, str) or loss_function is None:\n        raise TypeError(\"'loss_function' must be a str\")\n\n    if not isinstance(max_length, numbers.Real):\n        raise TypeError(\"'max_length' must be a real number\")\n\n    if not isinstance(min_df, numbers.Real):\n        raise TypeError(\"'min_df' must be a real number\")\n\n    if not isinstance(min_num_samples, numbers.Real):\n        raise TypeError(\"'min_num_samples' must be a real number\")\n\n    if not isinstance(num_features, numbers.Real):\n        raise TypeError(\"'num_features' must be a real number\")\n\n    if not isinstance(num_subfeatures, numbers.Real):\n        raise TypeError(\"'num_subfeatures' must be a real number\")\n\n    if not isinstance(num_threads, numbers.Real):\n        raise TypeError(\"'num_threads' must be a real number\")\n\n    if type(propositionalization).__name__ != \"FastProp\":\n        raise TypeError(\"'propositionalization' must be a FastProp\")\n\n    if not isinstance(regularization, numbers.Real):\n        raise TypeError(\"'regularization' must be a real number\")\n\n    if not isinstance(round_robin, bool):\n        raise TypeError(\"'round_robin' must be of type bool\")\n\n    if not isinstance(sampling_factor, numbers.Real):\n        raise TypeError(\"'sampling_factor' must be a real number\")\n\n    if not isinstance(seed, numbers.Real):\n        raise TypeError(\"'seed' must be a real number or None\")\n\n    if not isinstance(share_aggregations, numbers.Real):\n        raise TypeError(\"'share_aggregations' must be a real number\")\n\n    if not isinstance(share_conditions, numbers.Real):\n        raise TypeError(\"'share_conditions' must be a real number\")\n\n    if not isinstance(shrinkage, numbers.Real):\n        raise TypeError(\"'shrinkage' must be a real number\")\n\n    if not isinstance(vocab_size, numbers.Real):\n        raise TypeError(\"'vocab_size' must be a real number\")\n\n    _check_parameter_bounds(vocab_size, \"vocab_size\", [0, np.finfo(np.float64).max])\n\n    # ----------------------------------------------------------------\n\n    if not all([aa in _multirel_subset for aa in aggregation]):\n        raise ValueError(\n            f\"\"\"\n            'aggregation' must be a list of the composed of the following\n            aggregations defined in getml.feature_learning.aggregations:\n            {_multirel_subset}.\n            \"\"\"\n        )\n\n    _check_parameter_bounds(delta_t, \"delta_t\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(\n        grid_factor,\n        \"grid_factor\",\n        [\n            np.finfo(np.float64).resolution,  # pylint: disable=E1101\n            np.finfo(np.float64).max,\n        ],\n    )\n\n    _check_parameter_bounds(max_length, \"max_length\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(min_df, \"min_df\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        min_num_samples, \"min_num_samples\", [1, np.iinfo(np.int32).max]\n    )\n\n    _check_parameter_bounds(num_features, \"num_features\", [1, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        num_subfeatures, \"num_subfeatures\", [1, np.iinfo(np.int32).max]\n    )\n\n    _check_parameter_bounds(num_threads, \"num_threads\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(regularization, \"regularization\", [0.0, 1.0])\n\n    _check_parameter_bounds(\n        sampling_factor, \"sampling_factor\", [0.0, np.finfo(np.float64).max]\n    )\n\n    _check_parameter_bounds(seed, \"seed\", [0.0, np.iinfo(np.uint64).max])\n\n    _check_parameter_bounds(share_aggregations, \"share_aggregations\", [0.0, 1.0])\n\n    _check_parameter_bounds(share_conditions, \"share_conditions\", [0.0, 1.0])\n\n    _check_parameter_bounds(shrinkage, \"shrinkage\", [0.0, 1.0])\n\n    # ----------------------------------------------------------------\n\n    if cast(int, num_subfeatures) > 10:\n        logger.info(\n            f\"\"\"\n            {MIGHT_TAKE_LONG}\n            You have set num_subfeatures to {num_subfeatures}.\n            The multirel algorithm does not scale well to many columns.\n            You should consider using Relboost instead.\n            \"\"\"\n        )\n\n\n# --------------------------------------------------------------------\n\n\ndef _validate_relboost_parameters(**kwargs: Any) -> None:\n    \"\"\"\n    Checks both the types and values of the `parameters` belonging to\n    [`Relboost`][getml.feature_learning.Relboost] and raises an exception if\n    something is off.\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    allow_avg = kwargs.get(\"allow_avg\", False)\n    allow_null_weights = kwargs.get(\"allow_null_weights\", False)\n\n    # ----------------------------------------------------------------\n\n    delta_t = kwargs[\"delta_t\"]\n    gamma = kwargs[\"gamma\"]\n    loss_function = kwargs[\"loss_function\"]\n    max_depth = kwargs[\"max_depth\"]\n    min_df = kwargs[\"min_df\"]\n    min_num_samples = kwargs[\"min_num_samples\"]\n    num_features = kwargs[\"num_features\"]\n    num_subfeatures = kwargs[\"num_subfeatures\"]\n    num_threads = kwargs[\"num_threads\"]\n    propositionalization = kwargs[\"propositionalization\"]\n    reg_lambda = kwargs[\"reg_lambda\"]\n    sampling_factor = kwargs[\"sampling_factor\"]\n    seed = kwargs[\"seed\"]\n    shrinkage = kwargs[\"shrinkage\"]\n    vocab_size = kwargs[\"vocab_size\"]\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(allow_avg, bool):\n        raise TypeError(\"'allow_avg' must be of type bool\")\n\n    if not isinstance(allow_null_weights, bool):\n        raise TypeError(\"'allow_null_weights' must be of type bool\")\n\n    if not isinstance(delta_t, numbers.Real):\n        raise TypeError(\"'delta_t' must be a real number\")\n\n    if not isinstance(gamma, numbers.Real):\n        raise TypeError(\"'gamma' must be a real number\")\n\n    if not isinstance(loss_function, str) or loss_function is None:\n        raise TypeError(\"'loss_function' must be a str\")\n\n    if not isinstance(max_depth, numbers.Real):\n        raise TypeError(\"'max_depth' must be a real number\")\n\n    if not isinstance(min_df, numbers.Real):\n        raise TypeError(\"'min_df' must be a real number\")\n\n    if not isinstance(min_num_samples, numbers.Real):\n        raise TypeError(\"'min_num_samples' must be a real number\")\n\n    if not isinstance(num_features, numbers.Real):\n        raise TypeError(\"'num_features' must be a real number\")\n\n    if not isinstance(num_subfeatures, numbers.Real):\n        raise TypeError(\"'num_subfeatures' must be a real number\")\n\n    if not isinstance(num_threads, numbers.Real):\n        raise TypeError(\"'num_threads' must be a real number\")\n\n    if type(propositionalization).__name__ != \"FastProp\":\n        raise TypeError(\"'propositionalization' must be a FastProp\")\n\n    if not isinstance(reg_lambda, numbers.Real):\n        raise TypeError(\"'reg_lambda' must be a real number\")\n\n    if not isinstance(sampling_factor, numbers.Real):\n        raise TypeError(\"'sampling_factor' must be a real number\")\n\n    if not isinstance(seed, numbers.Real):\n        raise TypeError(\"'seed' must be a real number or None\")\n\n    if not isinstance(shrinkage, numbers.Real):\n        raise TypeError(\"'shrinkage' must be a real number\")\n\n    if not isinstance(vocab_size, numbers.Real):\n        raise TypeError(\"'vocab_size' must be a real number\")\n\n    # ----------------------------------------------------------------\n\n    _check_parameter_bounds(delta_t, \"delta_t\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(gamma, \"gamma\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(max_depth, \"max_depth\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(min_df, \"min_df\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        min_num_samples, \"min_num_samples\", [1, np.iinfo(np.int32).max]\n    )\n\n    _check_parameter_bounds(num_features, \"num_features\", [1, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(\n        num_subfeatures, \"num_subfeatures\", [1, np.iinfo(np.int32).max]\n    )\n\n    _check_parameter_bounds(num_threads, \"num_threads\", [0, np.iinfo(np.int32).max])\n\n    _check_parameter_bounds(reg_lambda, \"reg_lambda\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(\n        sampling_factor, \"sampling_factor\", [0.0, np.finfo(np.float64).max]\n    )\n\n    _check_parameter_bounds(seed, \"seed\", [0.0, np.iinfo(np.uint64).max])\n\n    _check_parameter_bounds(shrinkage, \"shrinkage\", [0.0, 1.0])\n\n    _check_parameter_bounds(vocab_size, \"vocab_size\", [0, np.finfo(np.float64).max])\n\n\n# --------------------------------------------------------------------\n\n\ndef _validate_time_series_parameters(**kwargs: Any) -> None:\n    \"\"\"\n    Validates the parameters that are specific to the time series\n    models and raises and exception if something is off.\n    \"\"\"\n    # ----------------------------------------------------------------\n\n    horizon = kwargs[\"horizon\"]\n    memory = kwargs[\"memory\"]\n    self_join_keys = kwargs[\"self_join_keys\"]\n    ts_name = kwargs[\"ts_name\"]\n    lagged_targets = kwargs[\"lagged_targets\"]\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(horizon, numbers.Real):\n        raise TypeError(\"'horizon' must be a real number\")\n\n    if not isinstance(memory, numbers.Real):\n        raise TypeError(\"'memory' must be a real number\")\n\n    if not _is_typed_list(self_join_keys, str):\n        raise TypeError(\"'self_join_keys' must be list of str.\")\n\n    if not isinstance(ts_name, str):\n        raise TypeError(\"'ts_name' must be a str\")\n\n    if not isinstance(lagged_targets, bool):\n        raise TypeError(\"'lagged_targets' must be a bool\")\n\n    # ----------------------------------------------------------------\n\n    _check_parameter_bounds(horizon, \"horizon\", [0.0, np.finfo(np.float64).max])\n\n    _check_parameter_bounds(memory, \"memory\", [0.0, np.finfo(np.float64).max])\n\n    if horizon == 0.0 and lagged_targets:\n        raise ValueError(\n            \"\"\"\n            If your horizon is 0.0, then you cannot use\n            lagged_targets. This is a data leak.\n            \"\"\"\n        )\n\n    # --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of helper classes that are relevant\nfor many submodules.\n\"\"\"\n\nfrom typing import (\n    Collection,\n    Iterable,\n    Type,\n    TypeVar,\n    Union,\n)\n\nfrom typing_extensions import TypeGuard  # py3.10\n\nT_co = TypeVar(\"T_co\", covariant=True)\n\n\n# --------------------------------------------------------------------\n\n\ndef _check_parameter_bounds(parameter, parameter_name, bounds):\n    \"\"\"Checks whether a particular parameter does lie within the provided\n    `bounds`.\n\n    Args:\n        parameter (numeric): Particular value of an instance variable.\n        key_name (string): Name of the parameter used for an\n            expressive Exception\n        bounds (list[numeric]): Numerical list of length 2\n            specifying the lower and upper bound (in that order)\n            of `parameter`.\n    \"\"\"\n    if parameter < bounds[0] or parameter > bounds[1]:\n        raise ValueError(\n            \"'\"\n            + parameter_name\n            + \"' is only defined for range [\"\n            + str(bounds[0])\n            + \", \"\n            + str(bounds[1])\n            + \"]\"\n        )\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_iterable_not_str(\n    obj: Union[T_co, Iterable[T_co]],\n) -> TypeGuard[Iterable[T_co]]:\n    \"\"\"\n    Check whether an object is an iterable (supports `__iter__`) but not a\n    string.\n    \"\"\"\n    return isinstance(obj, Iterable) and not isinstance(obj, str)\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_iterable_not_str_of_type(\n    obj: Union[T_co, Iterable[T_co]], type: Type[T_co]\n) -> TypeGuard[Iterable[T_co]]:\n    \"\"\"\n    Check whether an object is an iterable (supports `__iter__`) but not a\n    string and all elements are of type `type`.\n    \"\"\"\n    if _is_iterable_not_str(obj):\n        return all(isinstance(x, type) for x in obj)\n    return False\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_collection_not_str(\n    obj: Union[T_co, Collection[T_co]],\n) -> TypeGuard[Collection[T_co]]:\n    \"\"\"\n    Check whether an object is a collection (supports `__iter__`, `__len__` and\n    `__contains__`) but not a string.\n    \"\"\"\n    return isinstance(obj, Collection) and not isinstance(obj, str)\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_collection_not_str_of_type(\n    obj: Union[T_co, Collection[T_co]], type: Type[T_co]\n) -> TypeGuard[Collection[T_co]]:\n    \"\"\"\n    Check whether an object is a collection (supports `__iter__`, `__len__` and\n    `__contains__`) but not a string and all elements are of type `type`.\n    \"\"\"\n    if _is_collection_not_str(obj):\n        return all(isinstance(x, type) for x in obj)\n    return False\n\n\n# --------------------------------------------------------------------\n\n\ndef _is_nonempty_collection_not_str(\n    obj: Union[T_co, Collection[T_co]],\n) -> TypeGuard[Collection[T_co]]:\n    \"\"\"\n    Check whether an object is a collection (supports `__iter__`, `__len__` and\n    `__contains__`) but not a string and is not empty.\n    \"\"\"\n    if _is_collection_not_str(obj):\n        return len(obj) > 0\n    return False\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Automatically find the best parameters for\n\n* [`Multirel`][getml.feature_learning.Multirel]\n* [`Relboost`][getml.feature_learning.Relboost]\n* [`RelMT`][getml.feature_learning.RelMT]\n* [`FastProp`][getml.feature_learning.FastProp]\n* [`FastBoost`][getml.feature_learning.Fastboost]\n* [`LinearRegression`][getml.predictors.LinearRegression]\n* [`LogisticRegression`][getml.predictors.LogisticRegression]\n* [`XGBoostClassifier`][getml.predictors.XGBoostClassifier]\n* [`XGBoostRegressor`][getml.predictors.XGBoostRegressor]\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\n??? example\n    The easiest way to conduct a hyperparameter optimization\n    is to use the built-in tuning routines. Note that these\n    tuning routines usually take a day to complete unless\n    we use very small data sets as we do in this example.\n\n    ```python\n    from getml import data\n    from getml import datasets\n    from getml import engine\n    from getml import feature_learning\n    from getml.feature_learning import aggregations\n    from getml.feature_learning import loss_functions\n    from getml import hyperopt\n    from getml import pipeline\n    from getml import predictors\n\n    # ----------------\n\n    engine.set_project(\"examples\")\n\n    # ----------------\n\n    population_table, peripheral_table = datasets.make_numerical()\n\n    # ----------------\n    # Construct placeholders\n\n    population_placeholder = data.Placeholder(\"POPULATION\")\n    peripheral_placeholder = data.Placeholder(\"PERIPHERAL\")\n    population_placeholder.join(peripheral_placeholder, \"join_key\", \"time_stamp\")\n\n    # ----------------\n\n    feature_learner1 = feature_learning.Multirel(\n        aggregation=[\n            aggregations.COUNT,\n            aggregations.SUM\n        ],\n        loss_function=loss_functions.SquareLoss,\n        num_features=10,\n        share_aggregations=1.0,\n        max_length=1,\n        num_threads=0\n    )\n\n    # ----------------\n\n    feature_learner2 = feature_learning.Relboost(\n        loss_function=loss_functions.SquareLoss,\n        num_features=10\n    )\n\n    # ----------------\n\n    predictor = predictors.LinearRegression()\n\n    # ----------------\n\n    pipe = pipeline.Pipeline(\n        population=population_placeholder,\n        peripheral=[peripheral_placeholder],\n        feature_learners=[feature_learner1, feature_learner2],\n        predictors=[predictor]\n    )\n\n    # ----------------\n\n    tuned_pipeline = getml.hyperopt.tune_feature_learners(\n        pipeline=base_pipeline,\n        population_table_training=population_table,\n        population_table_validation=population_table,\n        peripheral_tables=[peripheral_table]\n    )\n\n    # ----------------\n\n    tuned_pipeline = getml.hyperopt.tune_predictors(\n        pipeline=tuned_pipeline,\n        population_table_training=population_table,\n        population_table_validation=population_table,\n        peripheral_tables=[peripheral_table]\n    )\n    ```\n        If you want to define the hyperparameter space and\n        the tuning routing yourself, this is how you\n        can do that:\n\n\n    ```python\n    from getml import data\n    from getml import datasets\n    from getml import engine\n    from getml import feature_learning\n    from getml.feature_learning import aggregations\n    from getml.feature_learning import loss_functions\n    from getml import hyperopt\n    from getml import pipeline\n    from getml import predictors\n\n    # ----------------\n\n    engine.set_project(\"examples\")\n\n    # ----------------\n\n    population_table, peripheral_table = datasets.make_numerical()\n\n    # ----------------\n    # Construct placeholders\n\n    population_placeholder = data.Placeholder(\"POPULATION\")\n    peripheral_placeholder = data.Placeholder(\"PERIPHERAL\")\n    population_placeholder.join(peripheral_placeholder, \"join_key\", \"time_stamp\")\n\n    # ----------------\n    # Base model - any parameters not included\n    # in param_space will be taken from this.\n\n    feature_learner1 = feature_learning.Multirel(\n        aggregation=[\n            aggregations.COUNT,\n            aggregations.SUM\n        ],\n        loss_function=loss_functions.SquareLoss,\n        num_features=10,\n        share_aggregations=1.0,\n        max_length=1,\n        num_threads=0\n    )\n\n    # ----------------\n    # Base model - any parameters not included\n    # in param_space will be taken from this.\n\n    feature_learner2 = feature_learning.Relboost(\n        loss_function=loss_functions.SquareLoss,\n        num_features=10\n    )\n\n    # ----------------\n    # Base model - any parameters not included\n    # in param_space will be taken from this.\n\n    predictor = predictors.LinearRegression()\n\n    # ----------------\n\n    pipe = pipeline.Pipeline(\n        population=population_placeholder,\n        peripheral=[peripheral_placeholder],\n        feature_learners=[feature_learner1, feature_learner2],\n        predictors=[predictor]\n    )\n\n    # ----------------\n    # Build a hyperparameter space.\n    # We have two feature learners and one\n    # predictor, so this is how we must\n    # construct our hyperparameter space.\n    # If we only wanted to optimize the predictor,\n    # we could just leave out the feature_learners.\n\n    param_space = {\n        \"feature_learners\": [\n            {\n                \"num_features\": [10, 50],\n            },\n            {\n                \"max_depth\": [1, 10],\n                \"min_num_samples\": [100, 500],\n                \"num_features\": [10, 50],\n                \"reg_lambda\": [0.0, 0.1],\n                \"shrinkage\": [0.01, 0.4]\n            }],\n        \"predictors\": [\n            {\n                \"reg_lambda\": [0.0, 10.0]\n            }\n        ]\n    }\n\n    # ----------------\n    # Wrap a GaussianHyperparameterSearch around the reference model\n\n    gaussian_search = hyperopt.GaussianHyperparameterSearch(\n        pipeline=pipe,\n        param_space=param_space,\n        n_iter=30,\n        score=pipeline.scores.rsquared\n    )\n\n    gaussian_search.fit(\n        population_table_training=population_table,\n        population_table_validation=population_table,\n        peripheral_tables=[peripheral_table]\n    )\n\n    # ----------------\n\n    # We want 5 additional iterations.\n    gaussian_search.n_iter = 5\n\n    # We do not want another burn-in-phase,\n    # so we set ratio_iter to 0.\n    gaussian_search.ratio_iter = 0.0\n\n    # This widens the hyperparameter space.\n    gaussian_search.param_space[\"feature_learners\"][1][\"num_features\"] = [10, 100]\n\n    # This narrows the hyperparameter space.\n    gaussian_search.param_space[\"predictors\"][0][\"reg_lambda\"] = [0.0, 0.0]\n\n    # This continues the hyperparameter search using the previous iterations as\n    # prior knowledge.\n    gaussian_search.fit(\n        population_table_training=population_table,\n        population_table_validation=population_table,\n        peripheral_tables=[peripheral_table]\n    )\n\n    # ----------------\n\n    all_hyp = hyperopt.list_hyperopts()\n\n    best_pipeline = gaussian_search.best_pipeline\n    ```\n\"\"\"\n\nfrom .helpers import delete, exists, list_hyperopts\nfrom .hyperopt import GaussianHyperparameterSearch, LatinHypercubeSearch, RandomSearch\nfrom .load_hyperopt import load_hyperopt\nfrom .tuning import tune_feature_learners, tune_predictors\n\n__all__ = (\n    \"delete\",\n    \"exists\",\n    \"list_hyperopts\",\n    \"load_hyperopt\",\n    \"GaussianHyperparameterSearch\",\n    \"LatinHypercubeSearch\",\n    \"RandomSearch\",\n    \"tune_feature_learners\",\n    \"tune_predictors\",\n)\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/burn_in.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of burn-in algorithms to be used by the\nhyperparameter optimizations.\n\"\"\"\n\nlatin_hypercube = \"latinHypercube\"\n\"\"\"\nSamples from the hyperparameter space\nalmost randomly, but ensures that the\ndifferent draws are sufficiently different\nfrom each other.\n\"\"\"\n\nrandom = \"random\"\n\"\"\"\nSamples from the hyperparameter space\nat random.\n\"\"\"\n\n_all_burn_ins = [latin_hypercube, random]\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Lists all hyperparameter optimization objects present in the Engine.\"\"\"\n\nimport json\nfrom typing import Any, Dict, List\n\nimport getml.communication as comm\n\n# --------------------------------------------------------------------\n\n\ndef list_hyperopts() -> List[str]:\n    \"\"\"Lists all hyperparameter optimization objects present in the Engine.\n\n    Note that this function only lists hyperopts which are part of the\n    current project. See [`set_project`][getml.engine.set_project] for\n    changing projects.\n\n    To subsequently load one of them, use\n    [`load_hyperopt`][getml.hyperopt.load_hyperopt.load_hyperopt].\n\n    Returns:\n        list containing the names of all hyperopts.\n\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n    cmd[\"type_\"] = \"list_hyperopts\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    return json.loads(json_str)[\"names\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef exists(name: str) -> bool:\n    \"\"\"Determines whether a hyperopt exists.\n\n    Args:\n        name: The name of the hyperopt.\n\n    Returns:\n        A boolean indicating whether a hyperopt named 'name' exists.\n    \"\"\"\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    return name in list_hyperopts()\n\n\n# --------------------------------------------------------------------\n\n\ndef delete(name: str) -> None:\n    \"\"\"\n    If a hyperopt named 'name' exists, it is deleted.\n\n    Args:\n        name: The name of the hyperopt.\n    \"\"\"\n\n    if not exists(name):\n        return\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"Hyperopt.delete\"\n    cmd[\"name_\"] = name\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/hyperopt.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains hyperparameter optimization routines.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport copy\nimport json\nimport time\nfrom typing import Any, Dict, List, Union\n\nfrom rich import print\n\nimport getml.communication as comm\nfrom getml.data import Container, StarSchema, TimeSeries\nfrom getml.data.helpers import _remove_trailing_underscores\nfrom getml.pipeline import Pipeline, delete, exists, load, metrics\nfrom getml.pipeline.helpers import _make_id, _print_time_taken, _transform_peripheral\nfrom getml.utilities.formatting import _SignatureFormatter\n\nfrom .burn_in import latin_hypercube, random\nfrom .kernels import matern52\nfrom .optimization import nelder_mead\nfrom .validation import _validate_hyperopt\n\n# -----------------------------------------------------------------------------\n\n\ndef _get_json_obj(name):\n    \"\"\"\n    Retrieves a JSON representation of the hyperopt object *name*\n    from the Engine.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = name\n    cmd[\"type_\"] = \"Hyperopt.refresh\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n\n    if msg[0] != \"{\":\n        comm.handle_engine_exception(msg)\n\n    return json.loads(msg)\n\n\n# -----------------------------------------------------------------------------\n\n\nclass _Hyperopt:\n    \"\"\"\n    Base class that is not meant to be called directly by the user.\n    \"\"\"\n\n    def __init__(\n        self,\n        param_space: Dict[str, Any],\n        pipeline: Pipeline,\n        score: str,\n        n_iter: int,\n        seed: int,\n        ratio_iter=1.0,\n        optimization_algorithm=nelder_mead,\n        optimization_burn_in_algorithm=latin_hypercube,\n        optimization_burn_ins=15,\n        surrogate_burn_in_algorithm=latin_hypercube,\n        gaussian_kernel=matern52,\n        gaussian_optimization_burn_in_algorithm=latin_hypercube,\n        gaussian_optimization_algorithm=nelder_mead,\n        gaussian_optimization_burn_ins=50,\n        gaussian_nugget=50,\n        early_stopping=True,\n    ):\n        self._id = \"NOT SENT TO ENGINE\"\n        self._type = \"_Hyperopt\"\n        self._score = score\n\n        self._original_param_space = param_space\n\n        self.evaluations: List[Any] = []\n\n        self.pipeline = copy.deepcopy(pipeline)\n        self.param_space = param_space\n        self.n_iter = n_iter\n        self.seed = seed\n\n        self.ratio_iter = ratio_iter\n        self.optimization_algorithm = optimization_algorithm\n        self.optimization_burn_in_algorithm = optimization_burn_in_algorithm\n        self.optimization_burn_ins = optimization_burn_ins\n        self.surrogate_burn_in_algorithm = surrogate_burn_in_algorithm\n        self.gaussian_kernel = gaussian_kernel\n        self.gaussian_optimization_algorithm = gaussian_optimization_algorithm\n        self.gaussian_optimization_burn_in_algorithm = (\n            gaussian_optimization_burn_in_algorithm\n        )\n        self.gaussian_optimization_burn_ins = gaussian_optimization_burn_ins\n        self.gaussian_nugget = gaussian_nugget\n        self.early_stopping = early_stopping\n\n        _Hyperopt._supported_params = list(self.__dict__.keys())  # type: ignore\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self):\n        return str(self)\n\n    # ------------------------------------------------------------\n\n    def _append_underscore(self, some_dict):\n        \"\"\"Helper functions that returns a trailing underscore to all keys in a dict\"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        for kkey in some_dict:\n            if kkey == \"evaluations\":\n                cmd[kkey + \"_\"] = some_dict[kkey]\n\n            elif isinstance(some_dict[kkey], dict):\n                cmd[kkey + \"_\"] = self._append_underscore(some_dict[kkey])\n\n            elif isinstance(some_dict[kkey], list):\n                cmd[kkey + \"_\"] = [\n                    self._append_underscore(elem) if isinstance(elem, dict) else elem\n                    for elem in some_dict[kkey]\n                ]\n\n            else:\n                cmd[kkey + \"_\"] = some_dict[kkey]\n\n        return cmd\n\n    # ------------------------------------------------------------\n\n    def _best_pipeline_name(self):\n        if not self.evaluations:\n            raise ValueError(\"The hyperparameter optimization has not been fitted!\")\n\n        def key(x):\n            return x[\"evaluation\"][\"score\"]\n\n        # The hyperparameter optimization always minimizes.\n        # Scores like AUC or RSquared are multiplied by -1.\n        return min(self.evaluations, key=key)[\"pipeline_name\"]\n\n    # ------------------------------------------------------------\n\n    def _getml_deserialize(self) -> Dict[str, Any]:\n        \"\"\"\n        Expresses the hyperparameter optimization in a form the Engine can understand.\n        \"\"\"\n        cmd = self._append_underscore(self.__dict__)\n\n        del cmd[\"_id_\"]\n        del cmd[\"_score_\"]\n        del cmd[\"_type_\"]\n        del cmd[\"_original_param_space_\"]\n\n        cmd[\"name_\"] = self.id\n        cmd[\"score_\"] = self.score\n        cmd[\"type_\"] = self.type\n\n        cmd[\"pipeline_\"] = self.pipeline._getml_deserialize()\n\n        return cmd\n\n    # ----------------------------------------------------------------\n\n    def _parse_json_obj(self, json_obj: Dict[str, Any]) -> _Hyperopt:\n        pipeline = self.pipeline._parse_cmd(json_obj[\"pipeline_\"])\n\n        del json_obj[\"pipeline_\"]\n\n        kwargs = _remove_trailing_underscores(json_obj)\n\n        evaluations: List[Any] = []\n\n        if \"evaluations\" in kwargs:\n            evaluations = kwargs[\"evaluations\"]\n            del kwargs[\"evaluations\"]\n\n        param_space = kwargs[\"param_space\"]\n\n        del kwargs[\"param_space\"]\n\n        del kwargs[\"name\"]\n        del kwargs[\"type\"]\n\n        id_ = self.id\n\n        self.__init__(param_space=param_space, pipeline=pipeline, **kwargs)  # type: ignore\n\n        self._id = id_\n\n        self.evaluations = evaluations\n\n        return self\n\n    # ----------------------------------------------------------------\n\n    def _save(self):\n        cmd = dict()\n        cmd[\"type_\"] = \"Hyperopt.save\"\n        cmd[\"name_\"] = self.id\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def _send(self):\n        self._id = _make_id()\n        self.pipeline._id = self._id\n        cmd = self._getml_deserialize()\n        comm.send(cmd)\n        return self\n\n    # ------------------------------------------------------------\n\n    @property\n    def best_pipeline(self) -> Pipeline:\n        \"\"\"\n        The best pipeline that is part of the hyperparameter optimization.\n\n        This is always based on the validation\n        data you have passed even if you have chosen to\n        score the pipeline on other data afterwards.\n\n        Returns:\n            The best pipeline.\n        \"\"\"\n        name = self._best_pipeline_name()\n        return load(name)\n\n    # ------------------------------------------------------------\n\n    def clean_up(self) -> None:\n        \"\"\"\n        Deletes all pipelines associated with hyperparameter optimization,\n        but the best pipeline.\n        \"\"\"\n        best_pipeline = self._best_pipeline_name()\n        names = [obj[\"pipeline_name\"] for obj in self.evaluations]\n        for name in names:\n            if name == best_pipeline:\n                continue\n            if exists(name):\n                delete(name)\n\n    # ------------------------------------------------------------\n\n    def fit(\n        self,\n        container: Union[Container, StarSchema, TimeSeries],\n        train: str = \"train\",\n        validation: str = \"validation\",\n    ) -> _Hyperopt:\n        \"\"\"Launches the hyperparameter optimization.\n\n        Args:\n            container:\n                The data container used for the hyperparameter tuning.\n\n            train:\n                The name of the subset in 'container' used for training.\n\n            validation:\n                The name of the subset in 'container' used for validation.\n\n        Returns:\n            The current instance.\n        \"\"\"\n\n        if isinstance(container, (StarSchema, TimeSeries)):\n            container = container.container\n\n        if not isinstance(container, Container):\n            raise TypeError(\n                \"'container' must be a `~getml.data.Container`, \"\n                + \"a `~getml.data.StarSchema` or a `~getml.data.TimeSeries`\"\n            )\n\n        if not isinstance(train, str):\n            raise TypeError(\"\"\"'train' must be a string\"\"\")\n\n        if not isinstance(validation, str):\n            raise TypeError(\"\"\"'validation' must be a string\"\"\")\n\n        self.pipeline.check(container[train])\n\n        population_table_training = container[train].population\n\n        population_table_validation = container[validation].population\n\n        peripheral_tables = _transform_peripheral(\n            container[train].peripheral, self.pipeline.peripheral\n        )\n\n        self._send()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"name_\"] = self.id\n        cmd[\"type_\"] = \"Hyperopt.launch\"\n\n        cmd[\"population_training_df_\"] = population_table_training._getml_deserialize()\n\n        cmd[\"population_validation_df_\"] = (\n            population_table_validation._getml_deserialize()\n        )\n\n        cmd[\"peripheral_dfs_\"] = [\n            elem._getml_deserialize() for elem in peripheral_tables\n        ]\n\n        with comm.send_and_get_socket(cmd) as sock:\n            begin = time.monotonic()\n            msg = comm.log(sock, extra={\"cmd\": cmd})\n            end = time.monotonic()\n\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n        _print_time_taken(begin, end, \"Time taken: \")\n\n        self._save()\n\n        return self.refresh()\n\n    # ------------------------------------------------------------\n\n    @property\n    def id(self) -> str:\n        \"\"\"\n        Name of the hyperparameter optimization.\n        This is used to uniquely identify it on the engine.\n\n        Returns:\n            The name of the hyperparameter optimization.\n        \"\"\"\n        return self._id\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self) -> str:\n        \"\"\"\n        Returns the ID of the hyperparameter optimization.\n        The name property is kept for backward compatibility.\n\n        Returns:\n            The name of the hyperparameter optimization.\n        \"\"\"\n        return self._id\n\n    # ------------------------------------------------------------\n\n    def refresh(self) -> _Hyperopt:\n        \"\"\"Reloads the hyperparameter optimization from the Engine.\n\n        Returns:\n                Current instance\n\n        \"\"\"\n        json_obj = _get_json_obj(self.id)\n        return self._parse_json_obj(json_obj)\n\n    # ------------------------------------------------------------\n\n    @property\n    def score(self) -> str:\n        \"\"\"\n        The score to be optimized.\n\n        Returns:\n            The score to be optimized.\n        \"\"\"\n        return self._score\n\n    # ------------------------------------------------------------\n\n    @property\n    def type(self) -> str:\n        \"\"\"\n        The algorithm used for the hyperparameter optimization.\n\n        Returns:\n            The algorithm used for the hyperparameter optimization.\n        \"\"\"\n        return self._type\n\n\n# -----------------------------------------------------------------------------\n\n\nclass GaussianHyperparameterSearch(_Hyperopt):\n    \"\"\"\n    Bayesian hyperparameter optimization using a Gaussian process.\n\n    After a burn-in period,\n    a Gaussian process is used to pick the most promising\n    parameter combination to be evaluated next based on the knowledge gathered\n    throughout previous evaluations. Assessing the quality of potential\n    combinations will be done using the expected information (EI).\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        param_space:\n            Dictionary containing numerical arrays of length two\n            holding the lower and upper bounds of all parameters which\n            will be altered in `pipeline` during the hyperparameter\n            optimization.\n\n            If we have two feature learners and one predictor,\n            the hyperparameter space might look like this:\n\n            ```python\n            param_space = {\n                \"feature_learners\": [\n                    {\n                        \"num_features\": [10, 50],\n                    },\n                    {\n                        \"max_depth\": [1, 10],\n                        \"min_num_samples\": [100, 500],\n                        \"num_features\": [10, 50],\n                        \"reg_lambda\": [0.0, 0.1],\n                        \"shrinkage\": [0.01, 0.4]\n                    }],\n                \"predictors\": [\n                    {\n                        \"reg_lambda\": [0.0, 10.0]\n                    }\n                ]\n            }\n            ```\n\n            If we only want to optimize the predictor, then\n            we can leave out the feature learners.\n\n        pipeline:\n            Base pipeline used to derive all models fitted and scored\n            during the hyperparameter optimization. Be careful when\n            constructing it since only the parameters present in\n            `param_space` will be overwritten. It defines the data\n            schema and any hyperparameters that are not optimized.\n\n        score:\n            The score to optimize. Must be from\n            [`metrics`][getml.pipeline.metrics].\n\n        n_iter:\n            Number of iterations in the hyperparameter optimization\n            and thus the number of parameter combinations to draw and\n            evaluate. Range: [1, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Due to nature of the underlying algorithm,\n            this is only the case if the fit is done without\n            multithreading. To reflect this, a `seed` of None\n            is only allowed to be set\n            to an actual integer if both ``num_threads`` and\n            ``n_jobs`` instance variables of the ``predictor`` and\n            ``feature_selector`` in `model` - if they are instances of\n            either [`XGBoostRegressor`][getml.predictors.XGBoostRegressor] or\n            [`XGBoostClassifier`][getml.predictors.XGBoostClassifier] - are set to\n            1. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n        ratio_iter:\n            Ratio of the iterations used for the burn-in.\n            For a `ratio_iter` of 1.0, all iterations will be\n            spent in the burn-in period resulting in an equivalence of\n            this class to\n            [`LatinHypercubeSearch`][getml.hyperopt.LatinHypercubeSearch] or\n            [`RandomSearch`][getml.hyperopt.RandomSearch] - depending on\n            `surrogate_burn_in_algorithm`. Range: [0, 1]\n\n            As a *rule of thumb* at least 70 percent of the evaluations\n            should be spent in the burn-in phase. The more comprehensive\n            the exploration of the `param_space` during the burn-in,\n            the less likely it is that the Gaussian process gets stuck in\n            local minima.\n\n        optimization_algorithm:\n            Determines the optimization algorithm used for the local\n            search in the optimization of the expected information\n            (EI). Must be from\n            [`optimization`][getml.hyperopt.optimization].\n\n        optimization_burn_in_algorithm :\n            Specifies the algorithm used to draw initial points in the\n            burn-in period of the optimization of the expected\n            information (EI). Must be from [`burn_in`][getml.hyperopt.burn_in].\n\n        optimization_burn_ins:\n            Number of random evaluation points used during the burn-in\n            of the minimization of the expected information (EI).\n            After the surrogate model - the Gaussian process - was\n            successfully fitted to the previous parameter combination,\n            the algorithm is able to calculate the EI for a given point. In\n            order to get to the next combination, the EI has to be\n            maximized over the whole parameter space. Much like the\n            GaussianProcess itself, this requires a burn-in phase.\n            Range: [3, ∞]\n\n        surrogate_burn_in_algorithm:\n            Specifies the algorithm used to draw new parameter\n            combinations during the burn-in period.\n            Must be from [`burn_in`][getml.hyperopt.burn_in].\n\n        gaussian_kernel:\n            Specifies the 1-dimensional kernel of the Gaussian process\n            which will be used along each dimension of the parameter\n            space. All the choices below will result in continuous\n            sample paths and their main difference is the degree of\n            smoothness of the results with 'exp' yielding the least\n            and 'gauss' yielding the most smooth paths.\n            Must be from [`kernels`][getml.hyperopt.kernels].\n\n        gaussian_optimization_burn_in_algorithm:\n            Specifies the algorithm used to draw new parameter\n            combinations during the burn-in period of the optimization\n            of the Gaussian process.\n            Must be from [`burn_in`][getml.hyperopt.burn_in].\n\n        gaussian_optimization_algorithm:\n            Determines the optimization algorithm used for the local\n            search in the fitting of the Gaussian process to the\n            previous parameter combinations. Must be from\n            [`optimization`][getml.hyperopt.optimization].\n\n        gaussian_optimization_burn_ins:\n            Number of random evaluation points used during the burn-in\n            of the fitting of the Gaussian process. Range: [3,\n            ∞]\n\n        early_stopping:\n            Whether you want to apply early stopping to the predictors.\n\n    Note:\n        A Gaussian hyperparameter search works like this:\n\n        - It begins with a burn-in phase, usually about 70% to 90%\n          of all iterations. During that burn-in phase, the hyperparameter\n          space is sampled more or less at random. You can control\n          this phase using ``ratio_iter`` and ``surrogate_burn_in_algorithm``.\n\n        - Once enough information has been collected, it fits a\n          Gaussian process on the hyperparameters with the ``score`` we want to\n          maximize or minimize as the predicted variable. Note that the\n          Gaussian process has hyperparameters itself, which are also optimized.\n          You can control this phase using ``gaussian_kernel``,\n          ``gaussian_optimization_algorithm``,\n          ``gaussian_optimization_burn_in_algorithm`` and\n          ``gaussian_optimization_burn_ins``.\n\n        - It then uses the Gaussian process to predict the expected information\n          (EI), which is how much additional information it might get from\n          evaluating\n          a particular point in the hyperparameter space. The expected information\n          is to be maximized. The point in the hyperparameter space with\n          the maximum expected information is the next point that is actually\n          evaluated (meaning a new pipeline with these hyperparameters is trained).\n          You can control this phase using ``optimization_algorithm``,\n          ``optimization_burn_ins`` and ``optimization_burn_in_algorithm``.\n\n        In a nutshell, the GaussianHyperparameterSearch behaves like human data scientists:\n\n        - At first, it picks random hyperparameter combinations.\n\n        - Once it has gained a better understanding of the hyperparameter space,\n          it starts evaluating hyperparameter combinations that are\n          particularly interesting.\n\n    References:\n        * [Carl Edward Rasmussen and Christopher K. I. Williams, MIT\n          Press, 2006 ](http://www.gaussianprocess.org/gpml/)\n        * [Julien Villemonteix, Emmanuel Vazquez, and Eric Walter, 2009\n          ](https://arxiv.org/pdf/cs/0611143.pdf)\n\n    ??? example\n        ```python\n        from getml import data\n        from getml import datasets\n        from getml import engine\n        from getml import feature_learning\n        from getml.feature_learning import aggregations\n        from getml.feature_learning import loss_functions\n        from getml import hyperopt\n        from getml import pipeline\n        from getml import predictors\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n\n        population_table, peripheral_table = datasets.make_numerical()\n\n        # ----------------\n        # Construct placeholders\n\n        population_placeholder = data.Placeholder(\"POPULATION\")\n        peripheral_placeholder = data.Placeholder(\"PERIPHERAL\")\n        population_placeholder.join(peripheral_placeholder, \"join_key\", \"time_stamp\")\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe1 = feature_learning.Multirel(\n            aggregation=[\n                aggregations.COUNT,\n                aggregations.SUM\n            ],\n            loss_function=loss_functions.SquareLoss,\n            num_features=10,\n            share_aggregations=1.0,\n            max_length=1,\n            num_threads=0\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe2 = feature_learning.Relboost(\n            loss_function=loss_functions.SquareLoss,\n            num_features=10\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        predictor = predictors.LinearRegression()\n\n        # ----------------\n\n        pipe = pipeline.Pipeline(\n            population=population_placeholder,\n            peripheral=[peripheral_placeholder],\n            feature_learners=[fe1, fe2],\n            predictors=[predictor]\n        )\n\n        # ----------------\n        # Build a hyperparameter space.\n        # We have two feature learners and one\n        # predictor, so this is how we must\n        # construct our hyperparameter space.\n        # If we only wanted to optimize the predictor,\n        # we could just leave out the feature_learners.\n\n        param_space = {\n            \"feature_learners\": [\n                {\n                    \"num_features\": [10, 50],\n                },\n                {\n                    \"max_depth\": [1, 10],\n                    \"min_num_samples\": [100, 500],\n                    \"num_features\": [10, 50],\n                    \"reg_lambda\": [0.0, 0.1],\n                    \"shrinkage\": [0.01, 0.4]\n                }],\n            \"predictors\": [\n                {\n                    \"reg_lambda\": [0.0, 10.0]\n                }\n            ]\n        }\n\n        # ----------------\n        # Wrap a GaussianHyperparameterSearch around the reference model\n\n        gaussian_search = hyperopt.GaussianHyperparameterSearch(\n            pipeline=pipe,\n            param_space=param_space,\n            n_iter=30,\n            score=pipeline.metrics.rsquared\n        )\n\n        gaussian_search.fit(\n            population_table_training=population_table,\n            population_table_validation=population_table,\n            peripheral_tables=[peripheral_table]\n        )\n\n        # ----------------\n\n        # We want 5 additional iterations.\n        gaussian_search.n_iter = 5\n\n        # We do not want another burn-in-phase,\n        # so we set ratio_iter to 0.\n        gaussian_search.ratio_iter = 0.0\n\n        # This widens the hyperparameter space.\n        gaussian_search.param_space[\"feature_learners\"][1][\"num_features\"] = [10, 100]\n\n        # This narrows the hyperparameter space.\n        gaussian_search.param_space[\"predictors\"][0][\"reg_lambda\"] = [0.0, 0.0]\n\n        # This continues the hyperparameter search using the previous iterations as\n        # prior knowledge.\n        gaussian_search.fit(\n            population_table_training=population_table,\n            population_table_validation=population_table,\n            peripheral_tables=[peripheral_table]\n        )\n\n        # ----------------\n\n        all_hyp = hyperopt.list_hyperopts()\n\n        best_pipeline = gaussian_search.best_pipeline\n        ```\n\n    \"\"\"\n\n    def __init__(\n        self,\n        param_space: Dict[str, Any],\n        pipeline: Pipeline,\n        score: str = metrics.rmse,\n        n_iter: int = 100,\n        seed: int = 5483,\n        ratio_iter: float = 0.80,\n        optimization_algorithm: str = nelder_mead,\n        optimization_burn_in_algorithm: str = latin_hypercube,\n        optimization_burn_ins: int = 500,\n        surrogate_burn_in_algorithm: str = latin_hypercube,\n        gaussian_kernel: str = matern52,\n        gaussian_optimization_burn_in_algorithm: str = latin_hypercube,\n        gaussian_optimization_algorithm: str = nelder_mead,\n        gaussian_optimization_burn_ins: int = 500,\n        gaussian_nugget: int = 50,\n        early_stopping: bool = True,\n    ):\n        super().__init__(\n            param_space=param_space,\n            pipeline=pipeline,\n            score=score,\n            n_iter=n_iter,\n            seed=seed,\n            ratio_iter=ratio_iter,\n            optimization_algorithm=optimization_algorithm,\n            optimization_burn_in_algorithm=optimization_burn_in_algorithm,\n            optimization_burn_ins=optimization_burn_ins,\n            surrogate_burn_in_algorithm=surrogate_burn_in_algorithm,\n            gaussian_kernel=gaussian_kernel,\n            gaussian_optimization_algorithm=gaussian_optimization_algorithm,\n            gaussian_optimization_burn_in_algorithm=gaussian_optimization_burn_in_algorithm,\n            gaussian_optimization_burn_ins=gaussian_optimization_burn_ins,\n            gaussian_nugget=gaussian_nugget,\n            early_stopping=early_stopping,\n        )\n\n        self._type = \"GaussianHyperparameterSearch\"\n\n        self.validate()\n\n    # ----------------------------------------------------------------\n\n    def __str__(self):\n        obj_dict = copy.deepcopy(self.__dict__)\n        del obj_dict[\"pipeline\"]\n        del obj_dict[\"param_space\"]\n        del obj_dict[\"evaluations\"]\n        obj_dict[\"type\"] = self.type\n        obj_dict[\"score\"] = self.score\n        sig = _SignatureFormatter(data=obj_dict)\n        return sig._format()\n\n    # ------------------------------------------------------------\n\n    def validate(self) -> None:\n        \"\"\"\n        Validate the parameters of the hyperparameter optimization.\n        \"\"\"\n        _validate_hyperopt(_Hyperopt._supported_params, **self.__dict__)  # type: ignore\n\n\n# -----------------------------------------------------------------------------\n\n\nclass LatinHypercubeSearch(_Hyperopt):\n    \"\"\"\n    Latin hypercube sampling of the hyperparameters.\n\n    Uses a multidimensional, uniform cumulative distribution function\n    to draw the random numbers from. For drawing `n_iter` samples,\n    the distribution will be divided in `n_iter`*`n_iter` hypercubes\n    of equal size (`n_iter` per dimension). `n_iter` of them will be\n    selected in such a way only one per dimension is used and an\n    independent and identically-distributed (iid) random number is\n    drawn within the boundaries of the hypercube.\n\n    A latin hypercube search can be seen as a compromise between\n    a grid search, which iterates through the entire hyperparameter\n    space, and a random search, which draws completely random samples\n    from the hyperparameter space.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        param_space:\n            Dictionary containing numerical arrays of length two\n            holding the lower and upper bounds of all parameters which\n            will be altered in `pipeline` during the hyperparameter\n            optimization.\n\n            If we have two feature learners and one predictor,\n            the hyperparameter space might look like this:\n\n            ```python\n            param_space = {\n                \"feature_learners\": [\n                    {\n                        \"num_features\": [10, 50],\n                    },\n                    {\n                        \"max_depth\": [1, 10],\n                        \"min_num_samples\": [100, 500],\n                        \"num_features\": [10, 50],\n                        \"reg_lambda\": [0.0, 0.1],\n                        \"shrinkage\": [0.01, 0.4]\n                    }],\n                \"predictors\": [\n                    {\n                        \"reg_lambda\": [0.0, 10.0]\n                    }\n                ]\n            }\n            ```\n\n            If we only want to optimize the predictor, then\n            we can leave out the feature learners.\n\n        pipeline:\n            Base pipeline used to derive all models fitted and scored\n            during the hyperparameter optimization. Be careful in\n            constructing it since only those parameters present in\n            `param_space` will be overwritten. It defines the data\n            schema and any hyperparameters that are not optimized.\n\n        score:\n            The score to optimize. Must be from\n            [`metrics`][getml.pipeline.metrics].\n\n        n_iter:\n            Number of iterations in the hyperparameter optimization\n            and thus the number of parameter combinations to draw and\n            evaluate. Range: [1, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Due to nature of the underlying algorithm\n            this is only the case if the fit is done without\n            multithreading. To reflect this, a `seed` of None\n            represents an unreproducible and is only allowed to be set\n            to an actual integer if both ``num_threads`` and\n            ``n_jobs`` instance variables of the ``predictor`` and\n            ``feature_selector`` in `model` - if they are instances of\n            either [`XGBoostRegressor`][getml.predictors.XGBoostRegressor] or\n            [`XGBoostClassifier`][getml.predictors.XGBoostClassifier] - are set to\n            1. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n    ??? example\n        ```python\n        from getml import data\n        from getml import datasets\n        from getml import engine\n        from getml import feature_learning\n        from getml.feature_learning import aggregations\n        from getml.feature_learning import loss_functions\n        from getml import hyperopt\n        from getml import pipeline\n        from getml import predictors\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n\n        population_table, peripheral_table = datasets.make_numerical()\n\n        # ----------------\n        # Construct placeholders\n\n        population_placeholder = data.Placeholder(\"POPULATION\")\n        peripheral_placeholder = data.Placeholder(\"PERIPHERAL\")\n        population_placeholder.join(peripheral_placeholder, \"join_key\", \"time_stamp\")\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe1 = feature_learning.Multirel(\n            aggregation=[\n                aggregations.COUNT,\n                aggregations.SUM\n            ],\n            loss_function=loss_functions.SquareLoss,\n            num_features=10,\n            share_aggregations=1.0,\n            max_length=1,\n            num_threads=0\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe2 = feature_learning.Relboost(\n            loss_function=loss_functions.SquareLoss,\n            num_features=10\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        predictor = predictors.LinearRegression()\n\n        # ----------------\n\n        pipe = pipeline.Pipeline(\n            population=population_placeholder,\n            peripheral=[peripheral_placeholder],\n            feature_learners=[fe1, fe2],\n            predictors=[predictor]\n        )\n\n        # ----------------\n        # Build a hyperparameter space.\n        # We have two feature learners and one\n        # predictor, so this is how we must\n        # construct our hyperparameter space.\n        # If we only wanted to optimize the predictor,\n        # we could just leave out the feature_learners.\n\n        param_space = {\n            \"feature_learners\": [\n                {\n                    \"num_features\": [10, 50],\n                },\n                {\n                    \"max_depth\": [1, 10],\n                    \"min_num_samples\": [100, 500],\n                    \"num_features\": [10, 50],\n                    \"reg_lambda\": [0.0, 0.1],\n                    \"shrinkage\": [0.01, 0.4]\n                }],\n            \"predictors\": [\n                {\n                    \"reg_lambda\": [0.0, 10.0]\n                }\n            ]\n        }\n\n        # ----------------\n        # Wrap a LatinHypercubeSearch around the reference model\n\n        latin_search = hyperopt.LatinHypercubeSearch(\n            pipeline=pipe,\n            param_space=param_space,\n            n_iter=30,\n            score=pipeline.metrics.rsquared\n        )\n\n        latin_search.fit(\n            population_table_training=population_table,\n            population_table_validation=population_table,\n            peripheral_tables=[peripheral_table]\n        )\n        ```\n\n    \"\"\"\n\n    def __init__(\n        self,\n        param_space: Dict[str, Any],\n        pipeline: Pipeline,\n        score: str = metrics.rmse,\n        n_iter: int = 100,\n        seed: int = 5483,\n        **kwargs,\n    ):\n        super().__init__(\n            param_space=param_space,\n            pipeline=pipeline,\n            score=score,\n            n_iter=n_iter,\n            seed=seed,\n            **kwargs,\n        )\n\n        self._type = \"LatinHypercubeSearch\"\n\n        self.surrogate_burn_in_algorithm = latin_hypercube\n\n        self.validate()\n\n    # ----------------------------------------------------------------\n\n    def __str__(self):\n        obj_dict = dict()\n        obj_dict[\"type\"] = self.type\n        obj_dict[\"score\"] = self.score\n        obj_dict[\"n_iter\"] = self.n_iter\n        obj_dict[\"seed\"] = self.seed\n        sig = _SignatureFormatter(data=obj_dict)\n        return sig._format()\n\n    # ------------------------------------------------------------\n\n    def validate(self) -> None:\n        \"\"\"\n        Validate the parameters of the hyperparameter optimization.\n        \"\"\"\n        _validate_hyperopt(_Hyperopt._supported_params, **self.__dict__)  # type: ignore\n\n        if self.surrogate_burn_in_algorithm != latin_hypercube:\n            raise ValueError(\n                \"'surrogate_burn_in_algorithm' must be '\" + latin_hypercube + \"'.\"\n            )\n\n        if self.ratio_iter != 1.0:\n            raise ValueError(\"'ratio_iter' must be 1.0.\")\n\n\n# -----------------------------------------------------------------------------\n\n\nclass RandomSearch(_Hyperopt):\n    \"\"\"\n    Uniformly distributed sampling of the hyperparameters.\n\n    During every iteration, a new set of hyperparameters is chosen at random\n    by uniformly drawing a random value in between the lower and upper\n    bound for each dimension of `param_space` independently.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        param_space:\n            Dictionary containing numerical arrays of length two\n            holding the lower and upper bounds of all parameters which\n            will be altered in `pipeline` during the hyperparameter\n            optimization.\n\n            If we have two feature learners and one predictor,\n            the hyperparameter space might look like this:\n\n            ```python\n            param_space = {\n                \"feature_learners\": [\n                    {\n                        \"num_features\": [10, 50],\n                    },\n                    {\n                        \"max_depth\": [1, 10],\n                        \"min_num_samples\": [100, 500],\n                        \"num_features\": [10, 50],\n                        \"reg_lambda\": [0.0, 0.1],\n                        \"shrinkage\": [0.01, 0.4]\n                    }],\n                \"predictors\": [\n                    {\n                        \"reg_lambda\": [0.0, 10.0]\n                    }\n                ]\n            }\n            ```\n            If we only want to optimize the predictor, then\n            we can leave out the feature learners.\n\n        pipeline:\n            Base pipeline used to derive all models fitted and scored\n            during the hyperparameter optimization. Be careful in\n            constructing it since only those parameters present in\n            `param_space` will be overwritten. It defines the data\n            schema and any hyperparameters that are not optimized.\n\n        score:\n            The score to optimize. Must be from\n            [`metrics`][getml.pipeline.metrics].\n\n        n_iter:\n            Number of iterations in the hyperparameter optimization\n            and thus the number of parameter combinations to draw and\n            evaluate. Range: [1, ∞]\n\n        seed:\n            Seed used for the random number generator that underlies\n            the sampling procedure to make the calculation\n            reproducible. Due to nature of the underlying algorithm\n            this is only the case if the fit is done without\n            multithreading. To reflect this, a `seed` of None\n            represents an unreproducible and is only allowed to be set\n            to an actual integer if both ``num_threads`` and\n            ``n_jobs`` instance variables of the ``predictor`` and\n            ``feature_selector`` in `model` - if they are instances of\n            either [`XGBoostRegressor`][getml.predictors.XGBoostRegressor] or\n            [`XGBoostClassifier`][getml.predictors.XGBoostClassifier] - are set to\n            1. Internally, a `seed` of None will be mapped to\n            5543. Range: [0, ∞]\n\n    ??? example\n        ```python\n\n        from getml import data\n        from getml import datasets\n        from getml import engine\n        from getml import feature_learning\n        from getml.feature_learning import aggregations\n        from getml.feature_learning import loss_functions\n        from getml import hyperopt\n        from getml import pipeline\n        from getml import predictors\n\n        # ----------------\n\n        engine.set_project(\"examples\")\n\n        # ----------------\n\n        population_table, peripheral_table = datasets.make_numerical()\n\n        # ----------------\n        # Construct placeholders\n\n        population_placeholder = data.Placeholder(\"POPULATION\")\n        peripheral_placeholder = data.Placeholder(\"PERIPHERAL\")\n        population_placeholder.join(peripheral_placeholder, \"join_key\", \"time_stamp\")\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe1 = feature_learning.Multirel(\n            aggregation=[\n                aggregations.COUNT,\n                aggregations.SUM\n            ],\n            loss_function=loss_functions.SquareLoss,\n            num_features=10,\n            share_aggregations=1.0,\n            max_length=1,\n            num_threads=0\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        fe2 = feature_learning.Relboost(\n            loss_function=loss_functions.SquareLoss,\n            num_features=10\n        )\n\n        # ----------------\n        # Base model - any parameters not included\n        # in param_space will be taken from this.\n\n        predictor = predictors.LinearRegression()\n\n        # ----------------\n\n        pipe = pipeline.Pipeline(\n            population=population_placeholder,\n            peripheral=[peripheral_placeholder],\n            feature_learners=[fe1, fe2],\n            predictors=[predictor]\n        )\n\n        # ----------------\n        # Build a hyperparameter space.\n        # We have two feature learners and one\n        # predictor, so this is how we must\n        # construct our hyperparameter space.\n        # If we only wanted to optimize the predictor,\n        # we could just leave out the feature_learners.\n\n        param_space = {\n            \"feature_learners\": [\n                {\n                    \"num_features\": [10, 50],\n                },\n                {\n                    \"max_depth\": [1, 10],\n                    \"min_num_samples\": [100, 500],\n                    \"num_features\": [10, 50],\n                    \"reg_lambda\": [0.0, 0.1],\n                    \"shrinkage\": [0.01, 0.4]\n                }],\n            \"predictors\": [\n                {\n                    \"reg_lambda\": [0.0, 10.0]\n                }\n            ]\n        }\n\n        # ----------------\n        # Wrap a RandomSearch around the reference model\n\n        random_search = hyperopt.RandomSearch(\n            pipeline=pipe,\n            param_space=param_space,\n            n_iter=30,\n            score=pipeline.metrics.rsquared\n        )\n\n        random_search.fit(\n            population_table_training=population_table,\n            population_table_validation=population_table,\n            peripheral_tables=[peripheral_table]\n        )\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        param_space: Dict[str, Any],\n        pipeline: Pipeline,\n        score: str = metrics.rmse,\n        n_iter: int = 100,\n        seed: int = 5483,\n        **kwargs,\n    ):\n        super().__init__(\n            param_space=param_space,\n            pipeline=pipeline,\n            score=score,\n            n_iter=n_iter,\n            seed=seed,\n            **kwargs,\n        )\n\n        self._type = \"RandomSearch\"\n\n        self.surrogate_burn_in_algorithm = random\n\n        self.validate()\n\n    # ----------------------------------------------------------------\n\n    def __str__(self):\n        obj_dict: Dict[str, Any] = {}\n        obj_dict[\"type\"] = self.type\n        obj_dict[\"score\"] = self.score\n        obj_dict[\"n_iter\"] = self.n_iter\n        obj_dict[\"seed\"] = self.seed\n        sig = _SignatureFormatter(data=obj_dict)\n        return sig._format()\n\n    # ------------------------------------------------------------\n\n    def validate(self) -> None:\n        \"\"\"\n        Validate the parameters of the hyperparameter optimization.\n        \"\"\"\n        _validate_hyperopt(_Hyperopt._supported_params, **self.__dict__)  # type: ignore\n\n        if self.surrogate_burn_in_algorithm != random:\n            raise ValueError(\"'surrogate_burn_in_algorithm' must be '\" + random + \"'.\")\n\n        if self.ratio_iter != 1.0:\n            raise ValueError(\"'ratio_iter' must be 1.0.\")\n\n\n# -----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/kernels.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of kernel functions to be used by the\nhyperparameter optimizations.\n\"\"\"\n\nexp = \"exp\"\n\"\"\"\nAn exponential kernel yielding non-differentiable\nsample paths.\n\"\"\"\n\ngauss = \"gauss\"\n\"\"\"\nA Gaussian kernel yielding analytic\n(infinitely--differentiable) sample paths.\n\"\"\"\n\nmatern32 = \"matern32\"\n\"\"\"\nA Matérn 3/2 kernel yielding once-differentiable\nsample paths.\n\"\"\"\n\nmatern52 = \"matern52\"\n\"\"\"\nA Matérn 5/2 kernel yielding twice-differentiable\nsample paths.\n\"\"\"\n\n\n_all_kernels = [matern32, matern52, exp, gauss]\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/load_hyperopt.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Loads a hyperparameter optimization object from the getML Engine into Python.\"\"\"\n\nfrom typing import Union\n\nfrom getml.pipeline.helpers2 import _make_dummy\n\nfrom .hyperopt import (\n    GaussianHyperparameterSearch,\n    LatinHypercubeSearch,\n    RandomSearch,\n    _get_json_obj,\n)\n\n\ndef load_hyperopt(\n    name: str,\n) -> Union[GaussianHyperparameterSearch, LatinHypercubeSearch, RandomSearch]:\n    \"\"\"Loads a hyperparameter optimization object from the getML Engine into Python.\n\n    Args:\n        name:\n            The name of the hyperopt to be loaded.\n\n    Returns:\n        The hyperopt object.\n\n    \"\"\"\n    # This will be overwritten by .refresh(...) anyway\n    dummy_pipeline = _make_dummy(\"123456\")\n\n    dummy_param_space = {\"predictors\": [{\"reg_lambda\": [0.0, 1.0]}]}\n\n    json_obj = _get_json_obj(name)\n\n    if json_obj[\"type_\"] == \"GaussianHyperparameterSearch\":\n        return GaussianHyperparameterSearch(\n            param_space=dummy_param_space, pipeline=dummy_pipeline\n        )._parse_json_obj(json_obj)\n\n    if json_obj[\"type_\"] == \"LatinHypercubeSearch\":\n        return LatinHypercubeSearch(\n            param_space=dummy_param_space, pipeline=dummy_pipeline\n        )._parse_json_obj(json_obj)\n\n    if json_obj[\"type_\"] == \"RandomSearch\":\n        return RandomSearch(\n            param_space=dummy_param_space, pipeline=dummy_pipeline\n        )._parse_json_obj(json_obj)\n\n    raise ValueError(\"Unknown type: '\" + json_obj[\"type_\"] + \"'!\")\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/optimization.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of optimization algorithms to be used by the\nhyperparameter optimizations.\n\"\"\"\n\nbfgs = \"bfgs\"\n\"\"\"\nBroyden-Fletcher-Goldbarb-Shanno optimization algorithm.\n\nThe BFGS algorithm is a quasi-Newton method that\nrequires the function to be differentiable.\n\"\"\"\n\nnelder_mead = \"nelderMead\"\n\"\"\"\nNelder-Mead optimization algorithm.\n\nNelder-Mead is a direct search method that\ndoes not require functions to be differentiable.\n\"\"\"\n\n_all_optimizations = [bfgs, nelder_mead]\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/tuning.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTuning routines simplify the hyperparameter optimizations and\nare the recommended way of tuning hyperparameters.\n\"\"\"\n\nimport copy\nimport numbers\nimport time\nfrom typing import Any, Dict, Optional\n\nfrom rich import print\n\nimport getml.communication as comm\nimport getml.pipeline\nfrom getml.data import Container, StarSchema, TimeSeries\nfrom getml.pipeline import Pipeline, metrics\nfrom getml.pipeline.helpers import _print_time_taken, _transform_peripheral\n\n# -----------------------------------------------------------------------------\n\n\ndef _infer_score(pipeline):\n    if pipeline.is_classification:\n        return getml.pipeline.metrics.auc\n    return getml.pipeline.metrics.rmse\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _make_final_pipeline(\n    pipeline: Pipeline,\n    tuned_feature_learners,\n    tuned_predictors,\n    container,\n    train,\n    validation,\n) -> Pipeline:\n    print(\"Building final pipeline...\")\n\n    final_pipeline = copy.deepcopy(pipeline)\n\n    final_pipeline.feature_learners = tuned_feature_learners\n    final_pipeline.predictors = tuned_predictors\n\n    final_pipeline.fit(\n        population_table=container[train], validation_table=container[validation]\n    )\n\n    final_pipeline.score(container[validation])\n\n    return final_pipeline\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _tune(\n    what,\n    pipeline,\n    container,\n    train,\n    validation,\n    n_iter=111,\n    score=metrics.rmse,\n    num_threads=0,\n) -> Pipeline:\n    \"\"\"\n    Internal base tuning function that is called by other tuning functions.\n    \"\"\"\n\n    pipeline = copy.deepcopy(pipeline)\n\n    if isinstance(container, (StarSchema, TimeSeries)):\n        container = container.container\n\n    pipeline.check(container[train])\n\n    if not isinstance(container, Container):\n        raise TypeError(\n            \"'container' must be a `~getml.data.Container`, \"\n            + \"a `~getml.data.StarSchema` or a `~getml.data.TimeSeries`\"\n        )\n\n    if not isinstance(train, str):\n        raise TypeError(\"\"\"'train' must be a string\"\"\")\n\n    if not isinstance(validation, str):\n        raise TypeError(\"\"\"'validation' must be a string\"\"\")\n\n    if not isinstance(n_iter, numbers.Real):\n        raise TypeError(\"\"\"'n_iter' must be a real number\"\"\")\n\n    if not isinstance(num_threads, numbers.Real):\n        raise TypeError(\"\"\"'num_threads' must be a real number\"\"\")\n\n    population_table_training = container[train].population\n\n    population_table_validation = container[validation].population\n\n    peripheral_tables = _transform_peripheral(\n        container[train].peripheral, pipeline.peripheral\n    )\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"name_\"] = \"\"\n    cmd[\"type_\"] = \"Hyperopt.tune\"\n\n    cmd[\"n_iter_\"] = n_iter\n    cmd[\"num_threads_\"] = num_threads\n    cmd[\"pipeline_\"] = pipeline._getml_deserialize()\n    cmd[\"score_\"] = score\n    cmd[\"what_\"] = what\n\n    cmd[\"population_training_df_\"] = population_table_training._getml_deserialize()\n    cmd[\"population_validation_df_\"] = population_table_validation._getml_deserialize()\n    cmd[\"peripheral_dfs_\"] = [elem._getml_deserialize() for elem in peripheral_tables]\n\n    with comm.send_and_get_socket(cmd) as sock:\n        begin = time.monotonic()\n        msg = comm.log(sock, extra={\"cmd\": cmd})\n        end = time.monotonic()\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        _print_time_taken(begin, end, \"Time taken: \")\n        pipeline_name = comm.recv_string(sock)\n\n    return getml.pipeline.load(pipeline_name)\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _tune_feature_learner(\n    feature_learner,\n    pipeline,\n    container,\n    train,\n    validation,\n    n_iter,\n    score,\n    num_threads,\n) -> Pipeline:\n    if feature_learner.type not in [\n        \"Fastboost\",\n        \"FastProp\",\n        \"Multirel\",\n        \"Relboost\",\n        \"RelMT\",\n    ]:\n        raise ValueError(\"Unknown feature learner: \" + feature_learner.type + \"!\")\n\n    return _tune(\n        feature_learner.type,\n        pipeline,\n        container,\n        train,\n        validation,\n        n_iter,\n        score,\n        num_threads,\n    )\n\n\n# -----------------------------------------------------------------------------\n\n\ndef _tune_predictor(\n    predictor,\n    pipeline,\n    container,\n    train,\n    validation,\n    n_iter,\n    score,\n    num_threads,\n) -> Pipeline:\n    if \"ScaleGBM\" in predictor.type:\n        return _tune(\n            \"ScaleGBM\",\n            pipeline,\n            container,\n            train,\n            validation,\n            n_iter,\n            score,\n            num_threads,\n        )\n\n    if \"XGBoost\" in predictor.type:\n        return _tune(\n            \"XGBoost\",\n            pipeline,\n            container,\n            train,\n            validation,\n            n_iter,\n            score,\n            num_threads,\n        )\n\n    if \"Regression\" in predictor.type:\n        return _tune(\n            \"Linear\",\n            pipeline,\n            container,\n            train,\n            validation,\n            n_iter,\n            score,\n            num_threads,\n        )\n\n    raise ValueError(\"Unknown predictor: '\" + predictor.type + \"'!\")\n\n\n# -----------------------------------------------------------------------------\n\n\ndef tune_feature_learners(\n    pipeline: Pipeline,\n    container: Container,\n    train: str = \"train\",\n    validation: str = \"validation\",\n    n_iter: int = 0,\n    score: Optional[str] = None,\n    num_threads: int = 0,\n) -> Pipeline:\n    \"\"\"\n    A high-level interface for optimizing the feature learners of a\n    [`Pipeline`][getml.pipeline.Pipeline].\n\n    Efficiently optimizes the hyperparameters for the set of feature learners\n    (from [`feature_learning`][getml.feature_learning]) of a given pipeline by breaking each\n    feature learner's hyperparameter space down into carefully curated\n    subspaces: `hyperopt_tuning_subspaces` and optimizing the hyperparameters for\n    each subspace in a sequential multi-step process.  For further details about\n    the actual recipes behind the tuning routines refer\n    to tuning routines: `hyperopt_tuning`.\n\n    Args:\n        pipeline:\n            Base pipeline used to derive all models fitted and scored\n            during the hyperparameter optimization. It defines the data\n            schema and any hyperparameters that are not optimized.\n\n        container:\n            The data container used for the hyperparameter tuning.\n\n        train:\n            The name of the subset in 'container' used for training.\n\n        validation:\n            The name of the subset in 'container' used for validation.\n\n        n_iter:\n            The number of iterations.\n\n        score:\n            The score to optimize. Must be from\n            [`metrics`][getml.pipeline.metrics].\n\n        num_threads:\n            The number of parallel threads to use. If set to 0,\n            the number of threads will be inferred.\n\n    Returns:\n        Pipeline containing tuned versions of the feature learners.\n\n    ??? example\n        We assume that you have already set up your\n        [`Pipeline`][getml.Pipeline] and\n        [`Container`][getml.data.Container].\n\n        ```python\n        tuned_pipeline = getml.hyperopt.tune_predictors(\n            pipeline=base_pipeline,\n            container=container)\n        ```\n    \"\"\"\n\n    if not isinstance(pipeline, getml.pipeline.Pipeline):\n        raise TypeError(\"'pipeline' must be a pipeline!\")\n\n    pipeline._validate()\n\n    if not score:\n        score = _infer_score(pipeline)\n\n    tuned_feature_learners = []\n\n    for feature_learner in pipeline.feature_learners:\n        tuned_pipeline = _tune_feature_learner(\n            feature_learner=feature_learner,\n            pipeline=pipeline,\n            container=container,\n            train=train,\n            validation=validation,\n            n_iter=n_iter,\n            score=score,\n            num_threads=num_threads,\n        )\n\n        assert len(tuned_pipeline.feature_learners) == 1, (\n            \"Expected exactly one feature learner, got \"\n            + str(len(tuned_pipeline.feature_learners))\n        )\n\n        tuned_feature_learners.append(tuned_pipeline.feature_learners[0])\n\n    return _make_final_pipeline(\n        pipeline,\n        tuned_feature_learners,\n        copy.deepcopy(pipeline.predictors),\n        container,\n        train,\n        validation,\n    )\n\n\n# -----------------------------------------------------------------------------\n\n\ndef tune_predictors(\n    pipeline: Pipeline,\n    container: Container,\n    train: str = \"train\",\n    validation: str = \"validation\",\n    n_iter: int = 0,\n    score: Optional[str] = None,\n    num_threads: int = 0,\n) -> Pipeline:\n    \"\"\"\n    A high-level interface for optimizing the predictors of a\n    [`Pipeline`][getml.Pipeline].\n\n    Efficiently optimizes the hyperparameters for the set of predictors (from\n    [`getml.predictors`][getml.predictors]) of a given pipeline by breaking each\n    predictor's\n    hyperparameter space down into carefully curated\n    subspaces: `hyperopt_tuning_subspaces` and optimizing the hyperparameters for\n    each subspace in a sequential multi-step process.  For further details about\n    the actual recipes behind the tuning routines refer to\n    tuning routines: `hyperopt_tuning`.\n\n    Args:\n        pipeline:\n            Base pipeline used to derive all models fitted and scored\n            during the hyperparameter optimization. It defines the data\n            schema and any hyperparameters that are not optimized.\n\n        container:\n            The data container used for the hyperparameter tuning.\n\n        train:\n            The name of the subset in 'container' used for training.\n\n        validation:\n            The name of the subset in 'container' used for validation.\n\n        n_iter:\n            The number of iterations.\n\n        score:\n            The score to optimize. Must be from\n            [`metrics`][getml.pipeline.metrics].\n\n        num_threads:\n            The number of parallel threads to use. If set to 0,\n            the number of threads will be inferred.\n\n    ??? example\n        We assume that you have already set up your\n        [`Pipeline`][getml.Pipeline] and\n        [`Container`][getml.data.Container].\n\n        ```python\n        tuned_pipeline = getml.hyperopt.tune_predictors(\n            pipeline=base_pipeline,\n            container=container)\n        ```\n\n    Returns:\n        Pipeline containing tuned predictors.\n\n    \"\"\"\n\n    if not isinstance(pipeline, getml.pipeline.Pipeline):\n        raise TypeError(\"'pipeline' must be a pipeline!\")\n\n    pipeline._validate()\n\n    if not score:\n        score = _infer_score(pipeline)\n\n    tuned_predictors = []\n\n    for predictor in pipeline.predictors:\n        tuned_pipeline = _tune_predictor(\n            predictor=predictor,\n            pipeline=pipeline,\n            container=container,\n            train=train,\n            validation=validation,\n            n_iter=n_iter,\n            score=score,\n            num_threads=num_threads,\n        )\n\n        assert len(tuned_pipeline.predictors) == 1, (\n            \"Expected exactly one predictor, got \" + str(len(tuned_pipeline.predictors))\n        )\n\n        tuned_predictors.append(tuned_pipeline.predictors[0])\n\n    return _make_final_pipeline(\n        pipeline,\n        copy.deepcopy(pipeline.feature_learners),\n        tuned_predictors,\n        container,\n        train,\n        validation,\n    )\n"
  },
  {
    "path": "src/python-api/getml/hyperopt/validation.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nChecks both the types and values of the `kwargs` and raises an\nexception is something is off.\n\"\"\"\n\nimport numbers\n\nimport numpy as np\n\nfrom getml.data.helpers import _is_typed_list\nfrom getml.helpers import _check_parameter_bounds\nfrom getml.pipeline.metrics import _all_metrics\n\nfrom .burn_in import _all_burn_ins\nfrom .kernels import _all_kernels\nfrom .optimization import _all_optimizations\n\n# -------------------------------------------------------------------------------\n\n\ndef _validate_bounds(subspace, component):\n    \"\"\"Validates the lower and upper bound using a component.\n\n    A component can be a feature_learner, a feature_selector\n    or a predictor.\n    \"\"\"\n    params = component.__dict__\n\n    # Make sure that the parameter is supported.\n    for key in subspace:\n        if key not in params:\n            raise ValueError(\n                component.type + \" does not support a parameter called '\" + key + \"'.\"\n            )\n\n    # Check the lower bounds.\n    for key in subspace:\n        assert len(subspace[key]) == 2, \"Unexpected bound length\"\n        params[key] = subspace[key][0]\n\n    component.validate(params)\n\n    # Check the upper bounds.\n    for key in subspace:\n        assert len(subspace[key]) == 2, \"Unexpected bound length\"\n        params[key] = subspace[key][1]\n\n    component.validate(params)\n\n\n# -------------------------------------------------------------------------------\n\n\ndef _validate_dimension(subspace, original_subspace):\n    \"\"\"Makes sure that no dimensions have been removed from the hyperparameter space.\"\"\"\n    for key in original_subspace:\n        if key not in subspace:\n            raise ValueError(\n                \"\"\"It appears you have removed a dimension from the\n                original param_space: '\"\"\"\n                + key\n                + \"'.\"\n            )\n\n\n# -------------------------------------------------------------------------------\n\n\ndef _validate_components(param_space, original_param_space, components, ltype):\n    if ltype in original_param_space:\n        if len(param_space[ltype]) != len(original_param_space[ltype]):\n            raise ValueError(\"Number of \" + ltype + \" must stay the same.\")\n\n    if len(param_space[ltype]) != len(components):\n        raise ValueError(\n            \"Number of subspaces for the \"\n            + ltype\n            + \"must match the number of \"\n            + ltype\n            + \".\"\n        )\n\n    for i, subspace in enumerate(param_space[ltype]):\n        if not isinstance(subspace, dict):\n            raise TypeError(\"subspace must be a dict\")\n\n        if len(subspace) == 0:\n            raise ValueError(\"subspace must contain at least one key-value pair\")\n\n        for ddimension in subspace:\n            if len(subspace[ddimension]) != 2:\n                raise ValueError(\n                    \"In key '\"\n                    + ddimension\n                    + \"' of subspace: Must be a numerical array of length 2.\"\n                )\n\n            if not _is_typed_list(subspace[ddimension], numbers.Real):\n                raise ValueError(\n                    \"In key '\"\n                    + ddimension\n                    + \"' of 'param_space': All values must be numerical.\"\n                )\n\n            if subspace[ddimension][0] > subspace[ddimension][1]:\n                raise ValueError(\n                    \"\"\"In key '\"\"\"\n                    + ddimension\n                    + \"\"\"' of 'param_space':\n                    The lower bound cannot be greater than the upper bound!\n                    Syntax 'key: [lower, upper]'\"\"\"\n                )\n\n        _validate_bounds(subspace, components[i])\n\n        if ltype in original_param_space:\n            _validate_dimension(subspace, original_param_space[ltype][i])\n\n\n# -------------------------------------------------------------------------------\n\n\ndef _validate_param_space(kwargs):\n    # ----------------------------------------------------------------\n\n    pipeline = kwargs[\"pipeline\"]\n\n    param_space = kwargs[\"param_space\"]\n\n    original_param_space = kwargs[\"_original_param_space\"]\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"param_space\"], dict):\n        raise TypeError(\"'param_space' must be a dict\")\n\n    if len(kwargs[\"param_space\"]) == 0:\n        raise ValueError(\"'param_space' must contain at least something.\")\n\n    # ----------------------------------------------------------------\n\n    if \"feature_learns\" in param_space:\n        _validate_components(\n            param_space,\n            original_param_space,\n            pipeline.feature_learners,\n            \"feature_learns\",\n        )\n    elif \"feature_learns\" in original_param_space:\n        raise ValueError(\"'feature_learns' has been removed from the param_space.\")\n\n    # ----------------------------------------------------------------\n\n    if \"feature_selectors\" in param_space:\n        _validate_components(\n            param_space,\n            original_param_space,\n            pipeline.feature_selectors,\n            \"feature_selectors\",\n        )\n    elif \"feature_selectors\" in original_param_space:\n        raise ValueError(\"'feature_selectors' has been removed from the param_space.\")\n\n    # ----------------------------------------------------------------\n\n    if \"predictors\" in param_space:\n        _validate_components(\n            param_space, original_param_space, pipeline.predictors, \"predictors\"\n        )\n    elif \"predictors\" in original_param_space:\n        raise ValueError(\"'predictors' has been removed from the param_space.\")\n\n\n# -------------------------------------------------------------------------------\n\n\ndef _validate_hyperopt(supported_params, **kwargs):\n    \"\"\"\n    Checks both the types and values of the `kwargs` and raises an\n    exception is something is off.\n\n    Examples:\n    ```python\n    getml.helpers.validation.validate_Hyperopt_kwargs(\n        {'n_iter': 10, 'score': 'auc'})\n    ```\n    Args:\n        kwargs (dict): Dictionary containing some of all\n            kwargs supported in\n            [`RandomSearch`][getml.predictors.RandomSearch],\n            [`GaussianHyperparameterSearch`][getml.predictors.GaussianHyperparameterSearch], and\n            [`LatinHypercubeSearch`][getml.predictors.LatinHypercubeSearch].\n\n    Note:\n        In addition to checking the types and values of the\n        `kwargs`, the function also accesses whether:\n\n            - `seed` is only used in a single-threaded context\n            - `param_space` is both valid and consistent\n            - `session_name` is set\n            - `n_iter` matches the requirements of the algorithm\n            - `score` is compatible with the loss function provided\n                in `model`\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    for key in kwargs:\n        if key not in supported_params:\n            raise ValueError(\n                \"Parameter '\" + key + \"' is not supported for \" + kwargs[\"_type\"] + \".\"\n            )\n\n    # ----------------------------------------------------------------\n\n    pipeline = kwargs[\"pipeline\"]\n\n    pipeline._validate()\n\n    # ----------------------------------------------------------------\n\n    _validate_param_space(kwargs)\n\n    # ----------------------------------------------------------------\n\n    if kwargs[\"seed\"] is not None and not isinstance(kwargs[\"seed\"], numbers.Real):\n        raise TypeError(\"'seed' must be either None or a real number\")\n\n    if kwargs[\"seed\"] is not None:\n        _check_parameter_bounds(kwargs[\"seed\"], \"seed\", [0, np.iinfo(np.uint64).max])\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"ratio_iter\"], numbers.Real):\n        raise TypeError(\"'ratio_iter' must be a real number\")\n\n    _check_parameter_bounds(kwargs[\"ratio_iter\"], \"ratio_iter\", [0.0, 1.0])\n\n    if not isinstance(kwargs[\"n_iter\"], numbers.Real):\n        raise TypeError(\"'n_iter' must be a real number\")\n\n    # ----------------------------------------------------------------\n\n    if kwargs[\"ratio_iter\"] == 1:\n        _check_parameter_bounds(kwargs[\"n_iter\"], \"n_iter\", [1, np.iinfo(np.int32).max])\n    else:\n        _check_parameter_bounds(kwargs[\"n_iter\"], \"n_iter\", [4, np.iinfo(np.int32).max])\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"optimization_algorithm\"], str):\n        raise TypeError(\"'optimization_algorithm' must be a str\")\n\n    if kwargs[\"optimization_algorithm\"] not in _all_optimizations:\n        raise ValueError(\n            \"'optimization_algorithm' must be one of the following: \"\n            + str(_all_optimizations)\n            + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"optimization_burn_in_algorithm\"], str):\n        raise TypeError(\"'optimization_burn_in_algorithm' must be a str\")\n\n    if kwargs[\"optimization_burn_in_algorithm\"] not in _all_burn_ins:\n        raise ValueError(\n            \"'optimization_burn_in_algorithm' must one of the following: \"\n            + str(_all_burn_ins)\n            + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"optimization_burn_ins\"], numbers.Real):\n        raise TypeError(\"'optimization_burn_ins' must be a real number\")\n\n    _check_parameter_bounds(\n        kwargs[\"optimization_burn_ins\"],\n        \"optimization_burn_ins\",\n        [3, np.iinfo(np.int32).max],\n    )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"surrogate_burn_in_algorithm\"], str):\n        raise TypeError(\"'surrogate_burn_in_algorithm' must be a str\")\n\n    if kwargs[\"surrogate_burn_in_algorithm\"] not in _all_burn_ins:\n        raise ValueError(\n            \"'surrogate_burn_in_algorithm' must one of the following: \"\n            + str(_all_burn_ins)\n            + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"gaussian_kernel\"], str):\n        raise TypeError(\"'gaussian_kernel' must be a str\")\n\n    if kwargs[\"gaussian_kernel\"] not in _all_kernels:\n        raise ValueError(\n            \"'gaussian_kernel' must one of the following: \" + str(_all_kernels) + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"gaussian_optimization_algorithm\"], str):\n        raise TypeError(\"'gaussian_optimization_algorithm' must be a str\")\n\n    if kwargs[\"gaussian_optimization_algorithm\"] not in _all_optimizations:\n        raise ValueError(\n            \"'gaussian_optimization_algorithm' must be one of the following: \"\n            + str(_all_optimizations)\n            + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"gaussian_optimization_burn_in_algorithm\"], str):\n        raise TypeError(\"'gaussian_optimization_burn_in_algorithm' must be a str\")\n\n    if kwargs[\"gaussian_optimization_burn_in_algorithm\"] not in _all_burn_ins:\n        raise ValueError(\n            \"'gaussian_optimization_burn_in_algorithm' must be one of the following: \"\n            + str(_all_burn_ins)\n            + \".\"\n        )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"gaussian_optimization_burn_ins\"], numbers.Real):\n        raise TypeError(\"'gaussian_optimization_burn_ins' must be a real number\")\n\n    _check_parameter_bounds(\n        kwargs[\"gaussian_optimization_burn_ins\"],\n        \"gaussian_optimization_burn_ins\",\n        [3, np.iinfo(np.int32).max],\n    )\n\n    # ----------------------------------------------------------------\n\n    if not isinstance(kwargs[\"_score\"], str):\n        raise TypeError(\"'score' must be a str\")\n\n    if kwargs[\"_score\"] not in _all_metrics:\n        raise ValueError(\n            \"'score' must be one of the following: \" + str(_all_metrics) + \".\"\n        )\n\n    # ----------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/log.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDetermines the logging level of the getML library.\n\nLogs generated by getML are either on the level WARNING or\nINFO.\n\"\"\"\n\nimport logging\n\nMIGHT_TAKE_LONG = \"INFO [MIGHT TAKE LONG] \"\n\nlogger = logging.getLogger(\"getML logger\")\nlogger.setLevel(logging.DEBUG)\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\nformatter = logging.Formatter(\"%(message)s\")\nch.setFormatter(formatter)\nlogger.addHandler(ch)\n"
  },
  {
    "path": "src/python-api/getml/pipeline/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Contains handlers for all steps involved in a data science project after\ndata preparation:\n\n- Automated feature learning\n- Automated feature selection\n- Training and evaluation of machine learning (ML) algorithms\n- Deployment of the fitted models\n\n??? example\n    We assume that you have already set up your\n    preprocessors (refer to [`preprocessors`][getml.preprocessors]),\n    your feature learners (refer to [`feature_learning`][getml.feature_learning])\n    as well as your feature selectors and predictors\n    (refer to [`predictors`][getml.predictors], which can be used\n    for prediction and feature selection).\n\n    You might also want to refer to\n    [`DataFrame`][getml.DataFrame], [`View`][getml.data.View],\n    [`DataModel`][getml.data.DataModel], [`Container`][getml.data.Container],\n    [`Placeholder`][getml.data.Placeholder] and\n    [`StarSchema`][getml.data.StarSchema].\n\n    If you want to create features for a time series problem,\n    the easiest way to do so is to use the [`TimeSeries`][getml.data.TimeSeries]\n    abstraction.\n\n    Note that this example is taken from the\n    [robot notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/robot.ipynb).\n\n    ```python\n\n    # All rows before row 10500 will be used for training.\n    split = getml.data.split.time(data_all, \"rowid\", test=10500)\n\n    time_series = getml.data.TimeSeries(\n        population=data_all,\n        time_stamps=\"rowid\",\n        split=split,\n        lagged_targets=False,\n        memory=30,\n    )\n\n    pipe = getml.Pipeline(\n        data_model=time_series.data_model,\n        feature_learners=[...],\n        predictors=...\n    )\n\n    pipe.check(time_series.train)\n\n    pipe.fit(time_series.train)\n\n    pipe.score(time_series.test)\n\n    # To generate predictions on new data,\n    # it is sufficient to use a Container.\n    # You don't have to recreate the entire\n    # TimeSeries, because the abstract data model\n    # is stored in the pipeline.\n    container = getml.data.Container(\n        population=population_new,\n    )\n\n    # Add the data as a peripheral table, for the\n    # self-join.\n    container.add(population=population_new)\n\n    predictions = pipe.predict(container.full)\n    ```\n\n??? example\n    If your data can be organized in a simple star schema,\n    you can use [`StarSchema`][getml.data.StarSchema].\n    [`StarSchema`][getml.data.StarSchema] unifies\n    [`Container`][getml.data.Container] and [`DataModel`][getml.data.DataModel]:\n    Note that this example is taken from the\n    [loans notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/loans.ipynb).\n\n    ```python\n\n    # First, we insert our data into a StarSchema.\n    # population_train and population_test are either\n    # DataFrames or Views. The population table\n    # defines the statistical population of your\n    # machine learning problem and contains the\n    # target variables.\n    star_schema = getml.data.StarSchema(\n        train=population_train,\n        test=population_test\n    )\n\n    # meta, order and trans are either\n    # DataFrames or Views.\n    # Because this is a star schema,\n    # all joins take place on the population\n    # table.\n    star_schema.join(\n        trans,\n        on=\"account_id\",\n        time_stamps=(\"date_loan\", \"date\")\n    )\n\n    star_schema.join(\n        order,\n        on=\"account_id\",\n    )\n\n    star_schema.join(\n        meta,\n        on=\"account_id\",\n    )\n\n    # Now you can insert your data model,\n    # your preprocessors, feature learners,\n    # feature selectors and predictors\n    # into the pipeline.\n    # Note that the pipeline only knows\n    # the abstract data model, but hasn't\n    # seen the actual data yet.\n    pipe = getml.Pipeline(\n        data_model=star_schema.data_model,\n        preprocessors=[mapping],\n        feature_learners=[fast_prop],\n        feature_selectors=[feature_selector],\n        predictors=predictor,\n    )\n\n    # Now, we pass the actual data.\n    # This passes 'population_train' and the\n    # peripheral tables (meta, order and trans)\n    # to the pipeline.\n    pipe.check(star_schema.train)\n\n    pipe.fit(star_schema.train)\n\n    pipe.score(star_schema.test)\n    ```\n\n??? example\n    [`StarSchema`][getml.data.StarSchema] is simpler,\n    but cannot be used for more complex data models.\n    The general approach is to use\n    [`Container`][getml.data.Container] and [`DataModel`][getml.data.DataModel]:\n\n    ```python\n\n    # First, we insert our data into a Container.\n    # population_train and population_test are either\n    # DataFrames or Views.\n    container = getml.data.Container(\n        train=population_train,\n        test=population_test\n    )\n\n    # meta, order and trans are either\n    # DataFrames or Views. They are given\n    # aliases, so we can refer to them in the\n    # DataModel.\n    container.add(\n        meta=meta,\n        order=order,\n        trans=trans\n    )\n\n    # Freezing makes the container immutable.\n    # This is not required, but often a good idea.\n    container.freeze()\n\n    # The abstract data model is constructed\n    # using the DataModel class. A data model\n    # does not contain any actual data. It just\n    # defines the abstract relational structure.\n    dm = getml.data.DataModel(\n        population_train.to_placeholder(\"population\")\n    )\n\n    dm.add(getml.data.to_placeholder(\n        meta=meta,\n        order=order,\n        trans=trans)\n    )\n\n    dm.population.join(\n        dm.trans,\n        on=\"account_id\",\n        time_stamps=(\"date_loan\", \"date\")\n    )\n\n    dm.population.join(\n        dm.order,\n        on=\"account_id\",\n    )\n\n    dm.population.join(\n        dm.meta,\n        on=\"account_id\",\n    )\n\n    # Now you can insert your data model,\n    # your preprocessors, feature learners,\n    # feature selectors and predictors\n    # into the pipeline.\n    # Note that the pipeline only knows\n    # the abstract data model, but hasn't\n    # seen the actual data yet.\n    pipe = getml.Pipeline(\n        data_model=dm,\n        preprocessors=[mapping],\n        feature_learners=[fast_prop],\n        feature_selectors=[feature_selector],\n        predictors=predictor,\n    )\n\n    # This passes 'population_train' and the\n    # peripheral tables (meta, order and trans)\n    # to the pipeline.\n    pipe.check(container.train)\n\n    pipe.fit(container.train)\n\n    pipe.score(container.test)\n    ```\n    Technically, you don't actually have to use a\n    [`Container`][getml.data.Container]. You might as well do this\n    (in fact, a [`Container`][getml.data.Container] is just\n    syntactic sugar for this approach):\n\n    ```python\n\n    pipe.check(\n        population_train,\n        {\"meta\": meta, \"order\": order, \"trans\": trans},\n    )\n\n    pipe.fit(\n        population_train,\n        {\"meta\": meta, \"order\": order, \"trans\": trans},\n    )\n\n    pipe.score(\n        population_test,\n        {\"meta\": meta, \"order\": order, \"trans\": trans},\n    )\n    ```\n    Or you could even do this. The order of the peripheral tables\n    can be inferred from the `__repr__()` method of the pipeline,\n    and it is usually in alphabetical order.\n\n    ```python\n\n    pipe.check(\n        population_train,\n        [meta, order, trans],\n    )\n\n    pipe.fit(\n        population_train,\n        [meta, order, trans],\n    )\n\n    pipe.score(\n        population_test,\n        [meta, order, trans],\n    )\n    ```\n\"\"\"\n\nfrom . import dialect, metrics\nfrom .columns import Columns\nfrom .features import Features\nfrom .helpers2 import delete, exists, list_pipelines, load\nfrom .pipeline import Pipeline\nfrom .plots import Plots\nfrom .scores_container import Scores\nfrom .sql_code import SQLCode\nfrom .tables import Tables\n\n__all__ = (\n    \"delete\",\n    \"dialect\",\n    \"exists\",\n    \"list_pipelines\",\n    \"load\",\n    \"metrics\",\n    \"Columns\",\n    \"Tables\",\n    \"Features\",\n    \"Plots\",\n    \"Pipeline\",\n    \"Scores\",\n    \"SQLCode\",\n)\n"
  },
  {
    "path": "src/python-api/getml/pipeline/column.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nClass representing metadata on a column used by the features of a pipeline.\n\"\"\"\n\nfrom dataclasses import dataclass\n\nimport numpy as np\n\n\n@dataclass\nclass Column:\n    \"\"\"\n    Dataclass that holds data about a single column.\n\n    Args:\n        index:\n            The index of the column.\n        name:\n            The name of the column.\n        marker:\n            The marker of the column.\n        table:\n            The table the column is from.\n        target:\n            The target the column is associated with.\n        importance:\n            The importance of the column.\n    \"\"\"\n\n    index: int\n    name: str\n    marker: str\n    table: str\n    target: str\n    importance: float = np.nan\n"
  },
  {
    "path": "src/python-api/getml/pipeline/columns.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCustom class for handling the columns of a pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport numbers\nimport re\nfrom copy import deepcopy\nfrom typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.typing import NDArray\n\nimport getml.communication as comm\nfrom getml.data import Container, StarSchema, TimeSeries\nfrom getml.data.helpers import _is_typed_list\nfrom getml.data.placeholder import Placeholder\nfrom getml.utilities.formatting import _Formatter\n\nfrom .column import Column\nfrom .helpers import PERIPHERAL, POPULATION, _drop\n\n\nclass Columns:\n    \"\"\"\n    Container which holds a pipeline's columns. These include the columns for\n    which importance can be calculated, such as the ones with\n    [`roles`][getml.data.roles] as [`categorical`][getml.data.roles.categorical],\n    [`numerical`][getml.data.roles.numerical] and [`text`][getml.data.roles.text].\n    The rest of the columns with roles [`time_stamp`][getml.data.roles.time_stamp],\n    [`join_key`][getml.data.roles.join_key], [`target`][getml.data.roles.target],\n    [`unused_float`][getml.data.roles.unused_float] and\n    [`unused_string`][getml.data.roles.unused_string] can not have importance of course.\n\n    Columns can be accessed by name, index or with a NumPy array. The container\n    supports slicing and is sort- and filterable. Further, the container holds\n    global methods to request columns' importances and apply a column selection\n    to data frames provided to the pipeline.\n\n    Args:\n        pipeline:\n            The id of the pipeline.\n        targets:\n            The names of the targets used for this pipeline.\n        peripheral:\n            The abstract representation of peripheral tables used for this pipeline.\n        data:\n            The columns to be stored in the container. If not provided, they are obtained from the Engine.\n\n    Note:\n        The container is an iterable. So, in addition to\n        [`filter`][getml.pipeline.Columns.filter] you can also use python list\n        comprehensions for filtering.\n\n    ??? example\n        ```python\n        all_my_columns = my_pipeline.columns\n\n        first_column = my_pipeline.columns[0]\n\n        all_but_last_10_columns = my_pipeline.columns[:-10]\n\n        important_columns = [column for column in my_pipeline.columns if\n        column.importance > 0.1]\n\n        names, importances = my_pipeline.columns.importances()\n\n        # Drops all categorical and numerical columns that are not # in the\n        top 20%. new_container = my_pipeline.columns.select(\n            container, share_selected_columns=0.2,\n        )\n        ```\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(\n        self,\n        pipeline: str,\n        targets: Sequence[str],\n        peripheral: Sequence[Placeholder],\n        data: Optional[Sequence[Column]] = None,\n    ) -> None:\n        if not isinstance(pipeline, str):\n            raise ValueError(\"'pipeline' must be a str.\")\n\n        if not _is_typed_list(targets, str):\n            raise TypeError(\"'targets' must be a list of str.\")\n\n        self.pipeline = pipeline\n\n        self.targets = targets\n\n        self.peripheral = peripheral\n\n        self.peripheral_names = [p.name for p in self.peripheral]\n\n        if data is not None:\n            self.data = data\n        else:\n            self._load_columns()\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def __iter__(self) -> Iterator[Column]:\n        yield from self.data\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(\n        self, key: Union[str, int, slice, Union[NDArray[np.int_], NDArray[np.bool_]]]\n    ) -> Union[Column, Columns, List[Column]]:\n        if not self.data:\n            raise AttributeError(\"Columns container not fully initialised.\")\n\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            columns_subset = self.data[key]\n            return self._make_columns(columns_subset)\n        if isinstance(key, str):\n            if key in self.names:\n                return [column for column in self.data if column.name == key][0]\n            raise AttributeError(f\"No Column with name: {key}\")\n        if isinstance(key, np.ndarray):\n            columns_subset = np.array(self.data)[key].tolist()\n            return list(columns_subset)\n        raise TypeError(\n            \"Columns can only be indexed by: int, slices, or str,\"\n            f\" not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        return self._format()._render_string()\n\n    # ------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        return self._format()._render_html()\n\n    # ----------------------------------------------------------------\n\n    def _get_column_importances(\n        self, target_num: int, sort: bool\n    ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]:\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.column_importances\"\n        cmd[\"name_\"] = self.pipeline\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        descriptions = np.asarray(json_obj[\"column_descriptions_\"])\n        importances = np.asarray(json_obj[\"column_importances_\"])\n\n        if hasattr(self, \"data\"):\n            indices = np.asarray(\n                [\n                    column.index\n                    for column in self.data\n                    if column.target == self.targets[target_num]\n                    and column.index < len(importances)\n                ]\n            )\n\n            descriptions = descriptions[indices]\n            importances = importances[indices]\n\n        if not sort:\n            return descriptions, importances\n\n        indices = np.argsort(importances)[::-1]\n\n        return (descriptions[indices], importances[indices])\n\n    # ----------------------------------------------------------------\n\n    def _format(self) -> _Formatter:\n        rows = [\n            [\n                column.name,\n                column.marker,\n                column.table,\n                column.importance,\n                column.target,\n            ]\n            for column in self.data\n        ]\n\n        headers = [\n            [\n                \"name\",\n                \"marker\",\n                \"table\",\n                \"importance\",\n                \"target\",\n            ]\n        ]\n\n        return _Formatter(headers, rows)\n\n    # ----------------------------------------------------------------\n\n    def _load_columns(self) -> None:\n        \"\"\"\n        Loads the actual column data from the Engine.\n        \"\"\"\n        columns = []\n\n        for target_num, target in enumerate(self.targets):\n            descriptions, importances = self._get_column_importances(\n                target_num=target_num, sort=False\n            )\n\n            columns.extend(\n                [\n                    Column(\n                        index=index,\n                        name=description.get(\"name_\"),\n                        marker=description.get(\"marker_\"),\n                        table=description.get(\"table_\"),\n                        importance=importances[index],\n                        target=target,\n                    )\n                    for index, description in enumerate(descriptions)\n                ]\n            )\n\n        self.data = columns\n\n    # ----------------------------------------------------------------\n\n    def _make_columns(self, data: Sequence[Column]) -> Columns:\n        \"\"\"\n        A factory to construct a [`Columns`][getml.pipeline.Columns] container from a list\n        of [`Columns`][getml.pipeline.Columns]s.\n        \"\"\"\n        return Columns(self.pipeline, self.targets, self.peripheral, data)\n\n    # ----------------------------------------------------------------\n\n    def _pivot(self, field: str) -> Any:\n        \"\"\"\n        Pivots the data for a given field. Returns a list of values of the field's type.\n        \"\"\"\n        return [getattr(column, field) for column in self.data]\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable[[Column], bool]) -> Columns:\n        \"\"\"\n        Filters the columns container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n            A container of filtered Columns.\n\n        ??? example\n            ```python\n            important_columns = my_pipeline.columns.filter(lambda column: column.importance > 0.1)\n            peripheral_columns = my_pipeline.columns.filter(lambda column: column.marker == \"[PERIPHERAL]\")\n            ```\n        \"\"\"\n        columns_filtered = [column for column in self.data if conditional(column)]\n        return self._make_columns(columns_filtered)\n\n    # ----------------------------------------------------------------\n\n    def importances(\n        self, target_num: int = 0, sort: bool = True\n    ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]:\n        \"\"\"\n        Returns the data for the column importances.\n\n        Column importances extend the idea of column importances\n        to the columns originally inserted into the pipeline.\n        Each column is assigned an importance value that measures\n        its contribution to the predictive performance. All\n        columns importances add up to 1.\n\n        The importances can be calculated for columns with\n        [`roles`][getml.data.roles] such as [`categorical`][getml.data.roles.categorical],\n        [`numerical`][getml.data.roles.numerical] and [`text`][getml.data.roles.text].\n        The rest of the columns with roles [`time_stamp`][getml.data.roles.time_stamp],\n        [`join_key`][getml.data.roles.join_key], [`target`][getml.data.roles.target],\n        [`unused_float`][getml.data.roles.unused_float] and\n        [`unused_string`][getml.data.roles.unused_string] can not have importance of course.\n\n        Args:\n            target_num:\n                Indicates for which target you want to view the\n                importances.\n                (Pipelines can have more than one target.)\n\n            sort:\n                Whether you want the results to be sorted.\n\n        Returns:\n            The first array contains the names of the columns.\n            The second array contains their importances. By definition, all importances add up to 1.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        descriptions, importances = self._get_column_importances(\n            target_num=target_num, sort=sort\n        )\n\n        # ------------------------------------------------------------\n\n        names = np.asarray(\n            [d[\"marker_\"] + \" \" + d[\"table_\"] + \".\" + d[\"name_\"] for d in descriptions]\n        )\n\n        # ------------------------------------------------------------\n\n        return names, importances\n\n    # ----------------------------------------------------------------\n\n    @property\n    def names(self) -> List[str]:\n        \"\"\"\n        Holds the names of a [`Pipeline`][getml.Pipeline]'s columns.\n\n        Returns:\n            List containing the names.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return [column.name for column in self.data]\n\n    # ----------------------------------------------------------------\n\n    def select(\n        self,\n        container: Union[Container, StarSchema, TimeSeries],\n        share_selected_columns: float = 0.5,\n    ) -> Container:\n        \"\"\"\n        Returns a new data container with all insufficiently important columns dropped.\n\n        Args:\n            container:\n                The container containing the data you want to use.\n\n            share_selected_columns: The share of columns\n                to keep. Must be between 0.0 and 1.0.\n\n        Returns:\n            A new container with the columns dropped.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if isinstance(container, (StarSchema, TimeSeries)):\n            data = self.select(\n                container.container, share_selected_columns=share_selected_columns\n            )\n            new_container = deepcopy(container)\n            new_container._container = data\n            return new_container\n\n        # ------------------------------------------------------------\n\n        if not isinstance(container, Container):\n            raise TypeError(\n                \"'container' must be a getml.data.Container, \"\n                + \"a getml.data.StarSchema or a getml.data.TimeSeries\"\n            )\n\n        if not isinstance(share_selected_columns, numbers.Real):\n            raise TypeError(\"'share_selected_columns' must be a real number!\")\n\n        if share_selected_columns < 0.0 or share_selected_columns > 1.0:\n            raise ValueError(\"'share_selected_columns' must be between 0 and 1!\")\n\n        # ------------------------------------------------------------\n\n        descriptions, _ = self._get_column_importances(target_num=-1, sort=True)\n\n        # ------------------------------------------------------------\n\n        num_keep = int(np.ceil(share_selected_columns * len(descriptions)))\n\n        keep_columns = descriptions[:num_keep]\n\n        # ------------------------------------------------------------\n\n        subsets = {\n            k: _drop(v, keep_columns, k, POPULATION)\n            for (k, v) in container.subsets.items()\n        }\n\n        peripheral = {\n            k: _drop(v, keep_columns, k, PERIPHERAL)\n            for (k, v) in container.peripheral.items()\n        }\n\n        # ------------------------------------------------------------\n\n        new_container = Container(**subsets)\n        new_container.add(**peripheral)\n        new_container.freeze()\n\n        # ------------------------------------------------------------\n\n        return new_container\n\n    # ----------------------------------------------------------------\n\n    def sort(\n        self,\n        by: Optional[str] = None,\n        key: Optional[Callable[[Column], Any]] = None,\n        descending: Optional[bool] = None,\n    ) -> Columns:\n        \"\"\"\n        Sorts the Columns container. If no arguments are provided the\n        container is sorted by target and name.\n\n        Args:\n            by:\n                The name of field to sort by. Possible fields:\n                    - name(s)\n                    - table(s)\n                    - importances(s)\n            key:\n                A callable that evaluates to a sort key for a given item.\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted columns.\n\n        ??? example\n            ```python\n            by_importance = my_pipeline.columns.sort(key=lambda column: column.importance)\n            ```\n        \"\"\"\n\n        reverse = False if descending is None else descending\n\n        if (by is not None) and (key is not None):\n            raise ValueError(\"Only one of `by` and `key` can be provided.\")\n\n        if key is not None:\n            columns_sorted = sorted(self.data, key=key, reverse=reverse)\n            return self._make_columns(columns_sorted)\n\n        if by is None:\n            columns_sorted = sorted(\n                self.data, key=lambda column: column.name, reverse=reverse\n            )\n            columns_sorted.sort(key=lambda column: column.target)\n            return self._make_columns(columns_sorted)\n\n        if re.match(pattern=\"names?$\", string=by):\n            columns_sorted = sorted(\n                self.data, key=lambda column: column.name, reverse=reverse\n            )\n            return self._make_columns(columns_sorted)\n\n        if re.match(pattern=\"tables?$\", string=by):\n            columns_sorted = sorted(\n                self.data,\n                key=lambda column: column.table,\n            )\n            return self._make_columns(columns_sorted)\n\n        if re.match(pattern=\"importances?$\", string=by):\n            reverse = True if descending is None else descending\n            columns_sorted = sorted(\n                self.data, key=lambda column: column.importance, reverse=reverse\n            )\n            return self._make_columns(columns_sorted)\n\n        raise ValueError(f\"Cannot sort by: {by}.\")\n\n    # ----------------------------------------------------------------\n\n    def to_pandas(self) -> pd.DataFrame:\n        \"\"\"Returns all information related to the columns in a pandas data frame.\"\"\"\n\n        names, markers, tables, importances, targets = (\n            self._pivot(field)\n            for field in [\"name\", \"marker\", \"table\", \"importance\", \"target\"]\n        )\n\n        data_frame = pd.DataFrame(index=np.arange(len(self.data)))\n\n        data_frame[\"name\"] = names\n\n        data_frame[\"marker\"] = markers\n\n        data_frame[\"table\"] = tables\n\n        data_frame[\"importance\"] = importances\n\n        data_frame[\"target\"] = targets\n\n        return data_frame\n\n    # ----------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/pipeline/dialect.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSQL dialects that can be used for the generated code.\n\nOne way to productionize a [`Pipeline`][getml.Pipeline] is\nto transpile its features to production-ready SQL code.\nThis SQL code can be run on standard cloud infrastructure.\nPlease also refer to [`SQLCode`][getml.pipeline.SQLCode].\n\n??? example\n    ```python\n    sql_code = my_pipeline.features.to_sql(\n        getml.pipeline.dialect.spark_sql)\n\n    # Creates a folder called \"my_pipeline\"\n    # which contains the SQL scripts.\n    sql_code.save(\"my_pipeline\")\n    ```\n\"\"\"\n\nimport re\nfrom typing import Pattern\n\n# --------------------------------------------------------------\n\n_all_dialects = [\n    \"bigquery\",\n    \"duckdb\",\n    \"human-readable sql\",\n    \"mysql\",\n    \"postgres\",\n    \"spark sql\",\n    \"sqlite3\",\n    \"tsql\",\n]\n\n# --------------------------------------------------------------\n\nbigquery = _all_dialects[0]\n\"\"\"\nBigQuery is a proprietary database system used by the Google Cloud.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\nduckdb = _all_dialects[1]\n\"\"\"\nDuckDB is an columnar database system that is designed for OLAP workloads.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\n\nhuman_readable_sql = _all_dialects[2]\n\"\"\"\nSQL that is not meant to be executed, but for interpretation by humans.\n\"\"\"\n\nmysql = _all_dialects[3]\n\"\"\"\nMySQL and its fork MariaDB are among the most popular open-source\ndatabase systems.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\npostgres = _all_dialects[4]\n\"\"\"\nThe PostgreSQL or postgres dialect is a popular SQL dialect\nused by PostgreSQL and its many derivatives like Redshift\nor Greenplum.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\nspark_sql = _all_dialects[5]\n\"\"\"\nSpark SQL is the SQL dialect used by Apache Spark.\n\nApache Spark is an open-source, distributed, in-memory\nengine for large-scale data processing and a popular\nchoice for productionizing machine learning pipelines.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\nsqlite3 = _all_dialects[6]\n\"\"\"\nSQLite3 is a light-weight and widely used database system.\n\nIt is recommended for live prediction systems or when the amount\nof data handled is unlikely to be too large.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\ntsql = _all_dialects[7]\n\"\"\"\nTSQL or Transact-SQL is the dialect used by most Microsoft\ndatabases.\n\nenterprise-adm: Enterprise edition\n    This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n    For licensing information and technical support, please [contact us][contact-page].\n\"\"\"\n\n\n# --------------------------------------------------------------\n\n\ndef _drop_table(dialect: str, key: str) -> str:\n    if dialect in (bigquery, mysql, spark_sql):\n        return f\"DROP TABLE IF EXISTS `{key.upper()}`\"\n\n    if dialect in (duckdb, human_readable_sql, postgres, sqlite3):\n        return 'DROP TABLE IF EXISTS \"' + key.upper() + '\"'\n\n    if dialect == tsql:\n        return f\"DROP TABLE IF EXISTS [{key.upper()}]\"\n\n    raise ValueError(\n        \"Unknown dialect: '\"\n        + dialect\n        + \"'. Please choose one of the following: \"\n        + str(_all_dialects)\n    )\n\n\n# --------------------------------------------------------------\n\n\ndef _table_pattern(dialect: str) -> Pattern:\n    if dialect in (bigquery, mysql, spark_sql):\n        return re.compile(\"CREATE TABLE `(.+)`\")\n\n    if dialect in (duckdb, human_readable_sql, postgres, sqlite3):\n        return re.compile('CREATE TABLE \"(.+)\"')\n\n    if dialect == tsql:\n        return re.compile(r\"INTO \\[(.+)\\]\")\n\n    raise ValueError(\n        \"Unknown dialect: '\"\n        + dialect\n        + \"'. Please choose one of the following: \"\n        + str(_all_dialects)\n    )\n\n\n# --------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/pipeline/feature.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCustom representing a sole feature.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Sequence\n\nfrom .sql_string import SQLString\n\n\n@dataclass\nclass Feature:\n    \"\"\"\n    Dataclass that holds data about a single feature.\n\n    Args:\n        index:\n            The index of the feature.\n        name:\n            The name of the feature.\n        pipeline:\n            The pipeline the feature is from.\n        target:\n            The target the feature is associated with.\n        targets:\n            The targets the feature is associated with.\n        importance:\n            The importance of the feature.\n        correlation:\n            The correlation of the feature with the target.\n        sql:\n            The SQL code of the feature.\n    \"\"\"\n\n    index: int\n    name: str\n    pipeline: str\n    target: str\n    targets: Sequence[str]\n    importance: float\n    correlation: float\n    sql: SQLString\n"
  },
  {
    "path": "src/python-api/getml/pipeline/features.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContainer for the features associated with a pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport re\nfrom typing import (\n    Any,\n    Callable,\n    Dict,\n    Iterator,\n    List,\n    Optional,\n    Sequence,\n    Tuple,\n    Union,\n)\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.typing import NDArray\n\nimport getml.communication as comm\nfrom getml.data.helpers import _is_typed_list\nfrom getml.utilities.formatting import _Formatter\n\nfrom .dialect import _all_dialects, sqlite3\nfrom .feature import Feature\nfrom .helpers import _attach_empty\nfrom .sql_code import SQLCode\nfrom .sql_string import SQLString\n\n# --------------------------------------------------------------------\n\n\nclass Features:\n    \"\"\"\n    Container which holds a pipeline's features. Features can be accessed\n    by name, index or with a numpy array. The container supports slicing and\n    is sort- and filterable.\n\n    Further, the container holds global methods to request features' importances,\n    correlations and their respective transpiled sql representation.\n\n    Args:\n        pipeline:\n            The name of the pipeline the features are associated with.\n\n        targets:\n            The targets the features are associated with.\n\n        data:\n            The features to be stored in the container.\n\n    Note:\n        The container is an iterable. So, in addition to\n        [`filter`][getml.pipeline.Features.filter] you can also use python list\n        comprehensions for filtering.\n\n    ??? example\n        ```python\n        all_my_features = my_pipeline.features\n\n        first_feature = my_pipeline.features[0]\n\n        second_feature = my_pipeline.features[\"feature_1_2\"]\n\n        all_but_last_10_features = my_pipeline.features[:-10]\n\n        important_features = [feature for feature in my_pipeline.features if feature.importance > 0.1]\n\n        names, importances = my_pipeline.features.importances()\n\n        names, correlations = my_pipeline.features.correlations()\n\n        sql_code = my_pipeline.features.to_sql()\n        ```\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(\n        self,\n        pipeline: str,\n        targets: Sequence[str],\n        data: Optional[Sequence[Feature]] = None,\n    ) -> None:\n        if not isinstance(pipeline, str):\n            raise ValueError(\"'pipeline' must be a str.\")\n\n        if not _is_typed_list(targets, str):\n            raise TypeError(\"'targets' must be a list of str.\")\n\n        self.pipeline = pipeline\n\n        self.targets = targets\n\n        if data is None:\n            self.data = self._load_features()\n\n        else:\n            self.data = list(data)\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        return self._format()._render_string()\n\n    # ------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        return self._format()._render_html()\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(\n        self, key: Union[int, slice, str, NDArray[np.int_]]\n    ) -> Union[Feature, Features, Sequence[Feature]]:\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            return self._make_features(self.data[key])\n        if isinstance(key, str):\n            if key in self.names:\n                return [feature for feature in self.data if feature.name == key][0]\n            raise AttributeError(f\"No Feature with name: {key}\")\n        if isinstance(key, np.ndarray):\n            features_subset = np.array(self.data)[key].tolist()\n            return features_subset\n        raise TypeError(\n            f\"Features can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __iter__(self) -> Iterator[Feature]:\n        yield from self.data\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def _pivot(self, field: str) -> List[Any]:\n        \"\"\"\n        Pivots the data for a given field. Returns a list of values of the field's type.\n        \"\"\"\n        return [getattr(feature, field) for feature in self.data]\n\n    # ----------------------------------------------------------------\n\n    def _load_features(self) -> List[Feature]:\n        \"\"\"\n        Loads the actual feature data from the Engine.\n        \"\"\"\n        features = []\n\n        for target_num, target in enumerate(self.targets):\n            names = self.correlations(target_num, sort=False)[0].tolist()\n            indices = range(len(names))\n            correlations = _attach_empty(\n                self.correlations(target_num, sort=False)[1].tolist(),\n                len(names),\n                np.nan,\n            )\n            importances = _attach_empty(\n                self.importances(target_num, sort=False)[1].tolist(), len(names), np.nan\n            )\n            sql_transpilations = _attach_empty(\n                self.to_sql(subfeatures=False).code[:-1], len(names), \"\"\n            )\n\n            features.extend(\n                [\n                    Feature(\n                        index=index,\n                        name=names[index],\n                        pipeline=self.pipeline,\n                        target=target,\n                        targets=self.targets,\n                        importance=importances[index],\n                        correlation=correlations[index],\n                        sql=SQLString(sql_transpilations[index]),\n                    )\n                    for index in indices\n                ]\n            )\n        return features\n\n    # ----------------------------------------------------------------\n\n    def _format(self) -> _Formatter:\n        rows = [\n            [\n                feature.target,\n                feature.name,\n                feature.correlation,\n                feature.importance,\n            ]\n            for feature in self.data\n        ]\n\n        headers = [[\"target\", \"name\", \"correlation\", \"importance\"]]\n\n        return _Formatter(headers, rows)\n\n    # ----------------------------------------------------------------\n\n    def _make_features(self, data: Sequence[Feature]) -> Features:\n        \"\"\"\n        A factory to construct a `Features` container from a list of\n        sole `Feature`s.\n        \"\"\"\n        return Features(self.pipeline, self.targets, data)\n\n    # ----------------------------------------------------------------\n\n    def _to_pandas(self) -> pd.DataFrame:\n        names, correlations, importances, sql, target = (\n            self._pivot(field)\n            for field in [\"name\", \"correlation\", \"importance\", \"sql\", \"target\"]\n        )\n\n        data_frame = pd.DataFrame(index=range(len(names)))\n\n        data_frame[\"names\"] = names\n\n        data_frame[\"correlations\"] = correlations\n\n        data_frame[\"importances\"] = importances\n\n        data_frame[\"target\"] = target\n\n        data_frame[\"sql\"] = sql\n\n        return data_frame\n\n    # ----------------------------------------------------------------\n\n    @property\n    def correlation(self) -> List[float]:\n        \"\"\"\n        Holds the correlations of a [`Pipeline`][getml.Pipeline]'s features.\n\n        Returns:\n            List containing the correlations.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return self._pivot(\"correlation\")\n\n    # ------------------------------------------------------------\n\n    def correlations(\n        self, target_num: int = 0, sort: bool = True\n    ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]:\n        \"\"\"\n        Returns the data for the feature correlations,\n        as displayed in the getML Monitor.\n\n        Args:\n            target_num:\n                Indicates for which target you want to view the\n                importances.\n                (Pipelines can have more than one target.)\n\n            sort:\n                Whether you want the results to be sorted.\n\n        Returns:\n            The first array contains the names of the features.\n            The second array contains the correlations with the target.\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.feature_correlations\"\n        cmd[\"name_\"] = self.pipeline\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        names = np.asarray(json_obj[\"feature_names_\"])\n        correlations = np.asarray(json_obj[\"feature_correlations_\"])\n\n        assert len(correlations) <= len(names), \"Correlations must be <= names\"\n\n        if hasattr(self, \"data\"):\n            indices = np.asarray(\n                [\n                    feature.index\n                    for feature in self.data\n                    if feature.target == self.targets[target_num]\n                    and feature.index < len(correlations)\n                ]\n            )\n\n            names = names[indices]\n            correlations = correlations[indices]\n\n        if not sort:\n            return names, correlations\n\n        indices = np.argsort(np.abs(correlations))[::-1]\n\n        return (names[indices], correlations[indices])\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable[[Feature], bool]) -> Features:\n        \"\"\"\n         Filters the Features container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered Features.\n\n        ??? example\n            ```python\n            important_features = my_pipeline.features.filter(lambda feature: feature.importance > 0.1)\n            correlated_features = my_pipeline.features.filter(lambda feature: feature.correlation > 0.3)\n            ```\n        \"\"\"\n        features_filtered = [feature for feature in self.data if conditional(feature)]\n        return Features(self.pipeline, self.targets, data=features_filtered)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def importance(self) -> List[float]:\n        \"\"\"\n         Holds the correlations of a [`Pipeline`][getml.Pipeline]'s features.\n\n        Returns:\n            List containing the correlations.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return self._pivot(\"importance\")\n\n    # ----------------------------------------------------------------\n\n    def importances(\n        self, target_num: int = 0, sort: bool = True\n    ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]:\n        \"\"\"\n        Returns the data for the feature importances,\n        as displayed in the getML Monitor.\n\n        Args:\n            target_num:\n                Indicates for which target you want to view the\n                importances.\n                (Pipelines can have more than one target.)\n\n            sort:\n                Whether you want the results to be sorted.\n\n        Returns:\n            The first array contains the names of the features.\n            The second array contains their importances. By definition, all importances add up to 1.\n\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.feature_importances\"\n        cmd[\"name_\"] = self.pipeline\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        names = np.asarray(json_obj[\"feature_names_\"])\n        importances = np.asarray(json_obj[\"feature_importances_\"])\n\n        if hasattr(self, \"data\"):\n            assert len(importances) <= len(names), \"Importances must be <= names\"\n\n            indices = np.asarray(\n                [\n                    feature.index\n                    for feature in self.data\n                    if feature.target == self.targets[target_num]\n                    and feature.index < len(importances)\n                ]\n            )\n\n            names = names[indices]\n            importances = importances[indices]\n\n        if not sort:\n            return names, importances\n\n        assert len(importances) <= len(names), \"Must have the same length\"\n\n        indices = np.argsort(importances)[::-1]\n\n        return (names[indices], importances[indices])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def name(self) -> List[str]:\n        \"\"\"\n        Holds the names of a [`Pipeline`][getml.Pipeline]'s features.\n\n        Returns:\n            List containing the names.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return self._pivot(\"name\")\n\n    # ----------------------------------------------------------------\n\n    @property\n    def names(self) -> List[str]:\n        \"\"\"\n        Holds the names of a [`Pipeline`][getml.Pipeline]'s features.\n\n        Returns:\n            List containing the names.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return self._pivot(\"name\")\n\n    # ----------------------------------------------------------------\n\n    def sort(\n        self,\n        by: Optional[str] = None,\n        key: Optional[\n            Callable[\n                [Feature],\n                Union[\n                    float,\n                    int,\n                    str,\n                ],\n            ]\n        ] = None,\n        descending: Optional[bool] = None,\n    ) -> Features:\n        \"\"\"\n        Sorts the Features container. If no arguments are provided the\n        container is sorted by target and name.\n\n        Args:\n            by:\n                The name of field to sort by. Possible fields:\n                    - name(s)\n                    - correlation(s)\n                    - importances(s)\n            key:\n                A callable that evaluates to a sort key for a given item.\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted Features.\n\n        ??? example\n            ```python\n            by_correlation = my_pipeline.features.sort(by=\"correlation\")\n\n            by_importance = my_pipeline.features.sort(key=lambda feature: feature.importance)\n            ```\n        \"\"\"\n\n        reverse = False if descending is None else descending\n\n        if (by is not None) and (key is not None):\n            raise ValueError(\"Only one of `by` and `key` can be provided.\")\n\n        if key is not None:\n            features_sorted = sorted(self.data, key=key, reverse=reverse)\n            return self._make_features(features_sorted)\n\n        else:\n            if by is None:\n                features_sorted = sorted(\n                    self.data, key=lambda feature: feature.index, reverse=reverse\n                )\n                features_sorted.sort(key=lambda feature: feature.target)\n                return self._make_features(features_sorted)\n\n            if re.match(pattern=\"names?$\", string=by):\n                features_sorted = sorted(\n                    self.data, key=lambda feature: feature.name, reverse=reverse\n                )\n                return self._make_features(features_sorted)\n\n            if re.match(pattern=\"correlations?$\", string=by):\n                reverse = True if descending is None else descending\n                features_sorted = sorted(\n                    self.data,\n                    key=lambda feature: abs(feature.correlation),\n                    reverse=reverse,\n                )\n                return self._make_features(features_sorted)\n\n            if re.match(pattern=\"importances?$\", string=by):\n                reverse = True if descending is None else descending\n                features_sorted = sorted(\n                    self.data,\n                    key=lambda feature: feature.importance,\n                    reverse=reverse,\n                )\n                return self._make_features(features_sorted)\n\n            raise ValueError(f\"Cannot sort by: {by}.\")\n\n    # ----------------------------------------------------------------\n\n    def to_pandas(self) -> pd.DataFrame:\n        \"\"\"\n        Returns all information related to the features in a pandas data frame.\n\n        Returns:\n            A pandas data frame containing the features' names, importances, correlations, and SQL code.\n        \"\"\"\n\n        return self._to_pandas()\n\n    # ----------------------------------------------------------------\n\n    def to_sql(\n        self,\n        targets: bool = True,\n        subfeatures: bool = True,\n        dialect: str = sqlite3,\n        schema: Optional[str] = None,\n        nchar_categorical: int = 128,\n        nchar_join_key: int = 128,\n        nchar_text: int = 4096,\n        size_threshold: Optional[int] = 50000,\n    ) -> SQLCode:\n        \"\"\"\n        Returns SQL statements visualizing the features.\n\n        Args:\n            targets:\n                Whether you want to include the target columns\n                in the main table.\n\n            subfeatures:\n                Whether you want to include the code for the\n                subfeatures of a snowflake schema.\n\n            dialect:\n                The SQL dialect to use. Must be from\n                [`dialect`][getml.pipeline.dialect]. Please\n                note that not all dialects are supported\n                in the getML Community edition.\n\n            schema:\n                The schema in which to wrap all generated tables and\n                indices. None for no schema. Not applicable to all dialects.\n                For the BigQuery and MySQL dialects, the schema is identical\n                to the database ID.\n\n            nchar_categorical:\n                The maximum number of characters used in the\n                VARCHAR for categorical columns. Not applicable\n                to all dialects.\n\n            nchar_join_key:\n                The maximum number of characters used in the\n                VARCHAR for join keys. Not applicable\n                to all dialects.\n\n            nchar_text:\n                The maximum number of characters used in the\n                VARCHAR for text columns. Not applicable\n                to all dialects.\n\n            size_threshold:\n                The maximum number of characters to display\n                in a single feature. Displaying extremely\n                complicated features can crash your iPython\n                notebook or lead to unexpectedly high memory\n                consumption, which is why a reasonable\n                upper limit is advantageous. Set to None\n                for no upper limit.\n\n        Returns:\n                Object representing the features.\n\n        ??? example\n            ```python\n            my_pipeline.features.to_sql()\n            ```\n\n        Note:\n            Only fitted pipelines\n            ([`fit`][getml.Pipeline.fit]) can hold trained\n            features which can be returned as SQL statements.\n\n        Note:\n            The getML Community edition only supports\n            transpilation to human-readable SQL. Passing\n            'sqlite3' will also produce human-readable SQL.\n\n        \"\"\"\n\n        if not isinstance(targets, bool):\n            raise TypeError(\"'targets' must be a bool!\")\n\n        if not isinstance(subfeatures, bool):\n            raise TypeError(\"'subfeatures' must be a bool!\")\n\n        if not isinstance(dialect, str):\n            raise TypeError(\"'dialect' must be a string!\")\n\n        if not isinstance(nchar_categorical, int):\n            raise TypeError(\"'nchar_categorical' must be an int!\")\n\n        if not isinstance(nchar_join_key, int):\n            raise TypeError(\"'nchar_join_key' must be an int!\")\n\n        if not isinstance(nchar_text, int):\n            raise TypeError(\"'nchar_text' must be an int!\")\n\n        if dialect not in _all_dialects:\n            raise ValueError(\n                \"'dialect' must from getml.pipeline.dialect, \"\n                + \"meaning that is must be one of the following: \"\n                + str(_all_dialects)\n                + \".\"\n            )\n\n        if size_threshold is not None and not isinstance(size_threshold, int):\n            raise TypeError(\"'size_threshold' must be an int or None!\")\n\n        if size_threshold is not None and size_threshold <= 0:\n            raise ValueError(\"'size_threshold' must be a positive number!\")\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.to_sql\"\n        cmd[\"name_\"] = self.pipeline\n\n        cmd[\"targets_\"] = targets\n        cmd[\"subfeatures_\"] = subfeatures\n        cmd[\"dialect_\"] = dialect\n        cmd[\"schema_\"] = schema or \"\"\n        cmd[\"nchar_categorical_\"] = nchar_categorical\n        cmd[\"nchar_join_key_\"] = nchar_join_key\n        cmd[\"nchar_text_\"] = nchar_text\n\n        if size_threshold is not None:\n            cmd[\"size_threshold_\"] = size_threshold\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n            sql = comm.recv_string(sock)\n\n        return SQLCode(sql.split(\"\\n\\n\\n\"), dialect)\n"
  },
  {
    "path": "src/python-api/getml/pipeline/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of helper functions not intended to be used by the end-user.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport builtins\nimport random\nimport string\nfrom copy import deepcopy\nfrom datetime import timedelta\nfrom typing import Any, Dict, List, Optional, Sequence, Union\n\nimport numpy as np\nfrom rich import print\n\nfrom getml.data import DataFrame, Placeholder, Roles, View\nfrom getml.data.helpers import _is_typed_list, _remove_trailing_underscores\nfrom getml.feature_learning import (\n    Fastboost,\n    FastProp,\n    Multirel,\n    Relboost,\n    RelMT,\n)\nfrom getml.feature_learning.loss_functions import _all_loss_functions\nfrom getml.predictors import (\n    LinearRegression,\n    LogisticRegression,\n    ScaleGBMClassifier,\n    ScaleGBMRegressor,\n    XGBoostClassifier,\n    XGBoostRegressor,\n)\nfrom getml.preprocessors import (\n    CategoryTrimmer,\n    EmailDomain,\n    Imputation,\n    Mapping,\n    Seasonal,\n    Substring,\n    TextFieldSplitter,\n)\n\nfrom .metadata import Metadata\n\nPOPULATION = \"[POPULATION]\"\n\"\"\"\nPopulation marker - the names of the population and peripheral\ntables may overlap, so markers are necessary.\n\"\"\"\n\nPERIPHERAL = \"[PERIPHERAL]\"\n\"\"\"\nPeripheral marker - the names of the population and peripheral\ntables may overlap, so markers are necessary.\n\"\"\"\n\n\ndef _attach_empty(my_list: List, max_length: int, empty_val: Any) -> List:\n    assert len(my_list) <= max_length, \"length exceeds max_length!\"\n    diff = max_length - len(my_list)\n    return my_list + [empty_val] * diff\n\n\n# --------------------------------------------------------------------\n\n\ndef _check_df_types(\n    population_table: Union[DataFrame, View],\n    peripheral_tables: Sequence[Union[DataFrame, View]],\n    validation_table: Optional[Union[DataFrame, View]] = None,\n) -> None:\n    if not isinstance(population_table, (DataFrame, View)):\n        raise TypeError(\n            \"'population_table' must be a getml.DataFrame or a \"\n            + \"getml.data.View, got \"\n            + type(population_table).__name__\n        )\n\n    if not _is_typed_list(peripheral_tables, (DataFrame, View)):\n        raise TypeError(\n            \"\"\"'peripheral_tables' must be a getml.DataFrame, a getml.data.View,\n                          or a list of those\"\"\"\n        )\n\n    if validation_table is not None and not isinstance(\n        validation_table, (DataFrame, View)\n    ):\n        raise TypeError(\n            \"'validation_table' must be a getml.DataFrame or a getml.data.View.\"\n        )\n\n    if isinstance(population_table, View):\n        population_table.check()\n\n    for table in peripheral_tables:\n        if isinstance(table, View):\n            table.check()\n\n    if isinstance(validation_table, View):\n        validation_table.check()\n\n\n# --------------------------------------------------------------------\n\n\ndef _drop(\n    base: Union[DataFrame, View],\n    keep_columns: List[Dict[str, str]],\n    table: Optional[str],\n    marker: str,\n) -> View:\n    assert isinstance(base, (DataFrame, View)), \"Wrong type\"\n    assert isinstance(table, str) or table is None, \"Wrong type\"\n    assert marker in (POPULATION, PERIPHERAL), \"Unknown marker\"\n\n    keep_colnames = [\n        desc[\"name_\"]\n        for desc in keep_columns\n        if (desc[\"table_\"] == table and desc[\"marker_\"] == marker)\n        or (marker == POPULATION and desc[\"marker_\"] == POPULATION)\n    ]\n\n    base.refresh()\n\n    drop = (\n        [col for col in base._categorical_names if col not in keep_colnames]\n        + [col for col in base._numerical_names if col not in keep_colnames]\n        + [col for col in base._text_names if col not in keep_colnames]\n        + base._unused_names\n    )\n\n    return base.drop(drop)\n\n\n# --------------------------------------------------------------------\n\n\ndef _edit_table_name(table_name: str) -> str:\n    for char in '\"`[]':\n        table_name = table_name.split(char)[0]\n    return table_name\n\n\n# --------------------------------------------------------------------\n\n\ndef _edit_windows_filename(file_name: str) -> str:\n    return \"\".join([c for c in file_name if c not in '<>:\"\\\\/|?*'])\n\n\n# --------------------------------------------------------------------\n\n\ndef _extract_df_by_name(\n    df_dict: Dict[str, Union[DataFrame, View]], name: str\n) -> Union[DataFrame, View]:\n    \"\"\"\n    Extracts a data frame signified by name from df_dict.\n    \"\"\"\n\n    if name not in df_dict:\n        raise ValueError(\n            \"\"\"The dictionary you passed contains no key\n               called '\"\"\"\n            + name\n            + \"'.\"\n        )\n\n    if not isinstance(df_dict[name], (DataFrame, View)):\n        raise TypeError(\n            \"'\"\n            + name\n            + \"' must be a getml.DataFrame or a getml.data.View, got \"\n            + type(df_dict[name]).__name__\n            + \".\"\n        )\n\n    return df_dict[name]\n\n\n# --------------------------------------------------------------------\n\n\ndef _extract_dfs(\n    df_dict: Dict[str, Union[DataFrame, View]], placeholders: List[Placeholder]\n) -> List[Union[DataFrame, View]]:\n    \"\"\"\n    Transforms df_dict into a list of data frames\n    corresponding to the placeholders.\n    \"\"\"\n    if not _is_typed_list(placeholders, Placeholder):\n        raise TypeError(\"'placeholders' must be a list of Placeholders\")\n    return [_extract_df_by_name(df_dict, elem.name) for elem in placeholders]\n\n\n# --------------------------------------------------------------------\n\n\ndef _infer_peripheral(population: Placeholder) -> List[Placeholder]:\n    def _get_names(population):\n        return [j.right.name for j in population.joins] + [\n            name for j in population.joins for name in _get_names(j.right)\n        ]\n\n    names = _get_names(population)\n    names = np.unique(names)\n    np.sort(names)\n    return [Placeholder(name) for name in names.tolist()]\n\n\n# --------------------------------------------------------------------\n\n\ndef _handle_loss_function(\n    feature_learner: Union[Fastboost, FastProp, Multirel, Relboost, RelMT],  # type: ignore\n    loss_function: Optional[str],\n) -> Union[Fastboost, FastProp, Multirel, Relboost, RelMT]:  # type: ignore\n    if not isinstance(loss_function, str) and loss_function is not None:\n        raise TypeError(\"'loss_function' must be str or None.\")\n\n    if loss_function is not None and loss_function not in _all_loss_functions:\n        raise ValueError(\n            \"'loss_function' must be from \"\n            + \"getml.feature_learning.loss_functions, \"\n            + \"meaning that it is one of the following: \"\n            + str(_all_loss_functions)\n            + \".\"\n        )\n\n    if loss_function is None and feature_learner.loss_function is None:\n        raise ValueError(\n            \"You must set a loss function either on the feature learner \"\n            \"or the pipeline\"\n        )\n\n    feature_learner = deepcopy(feature_learner)\n\n    feature_learner.loss_function = feature_learner.loss_function or loss_function\n\n    if isinstance(feature_learner, (Multirel, Relboost, RelMT)):\n        feature_learner.propositionalization.loss_function = (\n            feature_learner.propositionalization.loss_function\n            or feature_learner.loss_function\n        )\n\n    return feature_learner\n\n\n# --------------------------------------------------------------------\n\n\ndef _make_id() -> str:\n    letters = string.ascii_letters + string.digits\n    return \"\".join([random.choice(letters) for _ in range(6)])\n\n\n# --------------------------------------------------------------------\n\n\ndef _parse_fe(\n    dict_: Dict[str, Any],\n) -> Union[FastProp, Fastboost, Multirel, Relboost, RelMT]:\n    kwargs = _remove_trailing_underscores(dict_)\n\n    fe_type = kwargs[\"type\"]\n\n    del kwargs[\"type\"]\n\n    if \"propositionalization\" in kwargs:\n        kwargs[\"propositionalization\"] = _parse_fe(kwargs[\"propositionalization\"])\n\n    if fe_type == \"Fastboost\":\n        return Fastboost(**kwargs)\n\n    if fe_type == \"FastProp\":\n        return FastProp(**kwargs)\n\n    if fe_type == \"Multirel\":\n        return Multirel(**kwargs)\n\n    if fe_type == \"Relboost\":\n        return Relboost(**kwargs)\n\n    if fe_type == \"RelMT\":\n        return RelMT(**kwargs)\n\n    raise ValueError(\"Unknown feature learning algorithm: \" + fe_type)\n\n\n# --------------------------------------------------------------------\n\n\ndef _parse_metadata(dict_: Dict[str, Any]) -> Metadata:\n    return Metadata(name=dict_[\"name\"], roles=Roles(**dict_[\"roles\"]))\n\n\n# --------------------------------------------------------------------\n\n\ndef _parse_pred(\n    dict_: Dict[str, Any],\n) -> Union[\n    ScaleGBMClassifier,\n    ScaleGBMRegressor,\n    LinearRegression,\n    LogisticRegression,\n    XGBoostClassifier,\n    XGBoostRegressor,\n]:\n    kwargs = _remove_trailing_underscores(dict_)\n\n    pred_type = kwargs[\"type\"]\n\n    del kwargs[\"type\"]\n\n    if pred_type == \"LinearRegression\":\n        return LinearRegression(**kwargs)\n\n    if pred_type == \"LogisticRegression\":\n        return LogisticRegression(**kwargs)\n\n    if pred_type == \"ScaleGBMClassifier\":\n        return ScaleGBMClassifier(**kwargs)\n\n    if pred_type == \"ScaleGBMRegressor\":\n        return ScaleGBMRegressor(**kwargs)\n\n    if pred_type == \"XGBoostClassifier\":\n        return XGBoostClassifier(**kwargs)\n\n    if pred_type == \"XGBoostRegressor\":\n        return XGBoostRegressor(**kwargs)\n\n    raise ValueError(\"Unknown predictor: \" + pred_type)\n\n\n# --------------------------------------------------------------------\n\n\ndef _parse_preprocessor(\n    dict_: Dict[str, Any],\n) -> Union[\n    CategoryTrimmer,\n    EmailDomain,\n    Imputation,\n    Mapping,\n    Seasonal,\n    Substring,\n    TextFieldSplitter,\n]:\n    kwargs = _remove_trailing_underscores(dict_)\n\n    pre_type = kwargs[\"type\"]\n\n    del kwargs[\"type\"]\n\n    if pre_type == \"CategoryTrimmer\":\n        return CategoryTrimmer(**kwargs)\n\n    if pre_type == \"EmailDomain\":\n        return EmailDomain()\n\n    if pre_type == \"Imputation\":\n        return Imputation(**kwargs)\n\n    if pre_type == \"Mapping\":\n        return Mapping(**kwargs)\n\n    if pre_type == \"Seasonal\":\n        return Seasonal()\n\n    if pre_type == \"Substring\":\n        return Substring(**kwargs)\n\n    if pre_type == \"TextFieldSplitter\":\n        return TextFieldSplitter()\n\n    raise ValueError(\"Unknown preprocessor: \" + pre_type)\n\n\n# --------------------------------------------------------------------\n\n\ndef _print_time_taken(begin: float, end: float, msg: str) -> None:\n    \"\"\"Prints time required to fit a model.\n\n    Args:\n        begin (float): [`time`][time.time] output marking the beginning\n            of the training.\n        end (float): [`time`][time.time] output marking the end of the\n            training.\n        msg (str): Message to display along the duration.\n\n    \"\"\"\n\n    if not isinstance(begin, float):\n        raise TypeError(\"'begin' must be a float as returned by time.time().\")\n\n    if not isinstance(end, float):\n        raise TypeError(\"'end' must be a float as returned by time.time().\")\n\n    if not isinstance(msg, str):\n        raise TypeError(\"'msg' must be a str.\")\n\n    seconds = end - begin\n\n    delta = timedelta(seconds=seconds)\n\n    builtins.print(f\"Time taken: {delta}.\\n\")\n\n\n# --------------------------------------------------------------------\n\n\ndef _replace_with_nan_maybe(dict_: Dict[str, Any]) -> Dict[str, Any]:\n    dict_ = deepcopy(dict_)\n\n    for key, values in dict_.items():\n        if isinstance(values, list):\n            dict_[key] = [np.nan if value == -1 else value for value in values]\n\n    return dict_\n\n\n# --------------------------------------------------------------------\n\n\ndef _transform_peripheral(\n    peripheral_tables: Optional[\n        Union[\n            Sequence[Union[DataFrame, View]],\n            Dict[str, Union[DataFrame, View]],\n            Union[DataFrame, View],\n        ]\n    ],\n    placeholders: List[Placeholder],\n) -> Sequence[Union[DataFrame, View]]:\n    \"\"\"\n    Transforms the peripheral_tables into the desired list\n    format\n    \"\"\"\n    peripheral_tables = peripheral_tables or []\n\n    if isinstance(peripheral_tables, dict):\n        peripheral_tables = _extract_dfs(peripheral_tables, placeholders)\n\n    if isinstance(peripheral_tables, (DataFrame, View)):\n        peripheral_tables = [peripheral_tables]\n\n    return peripheral_tables\n\n\n# --------------------------------------------------------------------\n\n\ndef _unlist_maybe(list_: list) -> Union[List[Any], Any]:\n    \"\"\"\n    `_unlist_maybe` has an unstable return type and should therefore only\n    be used in interactive contexts. An example are a pipeline's atomic scores\n    properties (auc, accuracy, ...) that are just convenience wrappers for\n    interactive sessions.\n    \"\"\"\n    if len(list_) == 1:\n        return list_[0]\n    return list_\n\n\n# ------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/pipeline/helpers2.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHelper functions that depend on Pipeline.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict, List\n\nimport getml.communication as comm\nfrom getml.data import DataModel\n\nfrom .pipeline import Pipeline\n\n# --------------------------------------------------------------------\n\n\ndef _make_dummy(name: str) -> Pipeline:\n    data_model = DataModel(\"dummy\")\n    pipeline = Pipeline(data_model=data_model)\n    pipeline._id = name\n    return pipeline\n\n\n# --------------------------------------------------------------------\n\n\ndef _from_json(json_obj: Dict[str, Any]) -> Pipeline:\n    pipe = _make_dummy(\"dummy\")\n    pipe._parse_json_obj(json_obj)\n    return pipe\n\n\n# --------------------------------------------------------------------\n\n\ndef _refresh_all() -> List[Pipeline]:\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"Pipeline.refresh_all\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    json_obj = json.loads(json_str)\n\n    return [_from_json(obj) for obj in json_obj[\"pipelines\"]]\n\n\n# --------------------------------------------------------------------\n\n\ndef list_pipelines() -> List[str]:\n    \"\"\"Lists all pipelines present in the Engine.\n\n    Note that this function only lists pipelines which are part of the\n    current project. See [`set_project`][getml.engine.set_project] for\n    changing projects and [`pipelines`][getml.pipeline] for more details about\n    the lifecycles of the pipelines.\n\n    To subsequently load one of them, use\n    [`load`][getml.pipeline.load].\n\n    Returns:\n        List containing the names of all pipelines.\n    \"\"\"\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"list_pipelines\"\n    cmd[\"name_\"] = \"\"\n\n    with comm.send_and_get_socket(cmd) as sock:\n        msg = comm.recv_string(sock)\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n        json_str = comm.recv_string(sock)\n\n    return json.loads(json_str)[\"names\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef load(name: str) -> Pipeline:\n    \"\"\"Loads a pipeline from the getML Engine into Python.\n\n    Args:\n        name: The name of the pipeline to be loaded.\n\n    Returns:\n        Pipeline that is a handler for the pipeline signified by name.\n    \"\"\"\n\n    return _make_dummy(name).refresh()\n\n\n# --------------------------------------------------------------------\n\n\ndef exists(name: str) -> bool:\n    \"\"\"\n    Returns true if a pipeline named 'name' exists.\n\n    Args:\n        name (str):\n            Name of the pipeline.\n\n    Returns:\n            True if the pipeline exists, False otherwise.\n    \"\"\"\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    all_pipelines = list_pipelines()\n\n    return name in all_pipelines\n\n\n# --------------------------------------------------------------------\n\n\ndef delete(name: str) -> None:\n    \"\"\"\n    If a pipeline named 'name' exists, it is deleted.\n\n    Args:\n        name:\n            Name of the pipeline.\n    \"\"\"\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be of type str\")\n\n    if exists(name):\n        _make_dummy(name).delete()\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/pipeline/issues.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA helper class meant to represent the\nwarnings generated by the check method of\nthe pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Sequence\n\nfrom getml.communication import _Issue\nfrom getml.utilities.formatting import _Formatter\n\n\nclass Issues:\n    \"\"\"\n    A helper class meant to represent the\n    warnings generated by the check method of\n    the pipeline.\n\n    Args:\n        data:\n            The warnings generated by the check method\n            of the pipeline.\n    \"\"\"\n\n    def __init__(self, data: Sequence[_Issue]) -> None:\n        self.data = data\n\n    def __iter__(self):\n        yield from self.data\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    def __repr__(self) -> str:\n        return self._format()._render_string()\n\n    def _repr_html_(self) -> str:\n        return self._format()._render_html()\n\n    def _format(self) -> _Formatter:\n        headers = [\"type\", \"label\", \"message\"]\n        rows = [[w.warning_type, w.label, w.message] for w in self.data]\n        return _Formatter([headers], rows)\n\n    @property\n    def info(self) -> Issues:\n        \"\"\"\n        Returns a new container with all warnings\n        labeled INFO.\n\n        Returns:\n            A new container with all warnings\n            labeled INFO.\n        \"\"\"\n        return Issues([d for d in self.data if d.warning_type == \"INFO\"])\n\n    @property\n    def warnings(self) -> Issues:\n        \"\"\"\n        Returns a new container with all warnings\n        labeled WARNING.\n\n        Returns:\n            A new container with all warnings\n            labeled WARNING.\n        \"\"\"\n        return Issues([d for d in self.data if d.warning_type == \"WARNING\"])\n"
  },
  {
    "path": "src/python-api/getml/pipeline/metadata.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains the metadata related\nto the data frames that were originally\npassed to .fit(...).\n\"\"\"\n\nfrom typing import List, NamedTuple\n\nfrom getml.data import Roles\n\n\nclass Metadata(NamedTuple):\n    \"\"\"\n    Contains the metadata related\n    to a data frame that\n    were originally passed to .fit(...).\n\n    Attributes:\n        name:\n            The name of the data frame.\n        roles:\n            The roles of the columns in the data frame.\n    \"\"\"\n\n    name: str\n    roles: Roles\n\n\nclass AllMetadata(NamedTuple):\n    \"\"\"\n    Contains the metadata related\n    to all the data frames that\n    were originally passed to .fit(...).\n\n    Attributes:\n        peripheral:\n            The metadata of the peripheral tables.\n        population:\n            The metadata of the population table.\n    \"\"\"\n\n    peripheral: List[Metadata]\n    population: Metadata\n"
  },
  {
    "path": "src/python-api/getml/pipeline/metrics.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSignifies different scoring methods.\n\"\"\"\n\nfrom typing import List\n\n# --------------------------------------------------------------------\n\n_all_metrics: List[str] = [\n    \"auc\",\n    \"accuracy\",\n    \"cross_entropy\",\n    \"mae\",\n    \"rmse\",\n    \"rsquared\",\n]\n\n# --------------------------------------------------------------------\n# Scores for classification problems.\n\nauc = _all_metrics[0]\n\"\"\"Area under the curve - refers to the area under the receiver\noperating characteristic (ROC) curve.\n\nUsed for classification problems.\n\nWhen handling a classification problem, the ROC curve maps the\nrelationship between two conflicting goals:\n\nOn the hand, we want a high *true positive rate*. The true positive\nrate, sometimes referred to as *recall*, measures the share of\ntrue positive predictions over all positives:\n\n$$\nTPR = \\\\frac{number \\\\; of \\\\; true \\\\; positives}{number \\\\; of \\\\; all \\\\; positives}\n$$\n\nIn other words, we want our classification algorithm to \"catch\" as\nmany positives as possible.\n\nOn the other hand, we also want a low *false positive rate* (FPR). The\nfalse positive rate measures the share of false positives over all\nnegatives.\n\n$$\nFPR = \\\\frac{number \\\\; of \\\\; false \\\\; positives}{number \\\\; of \\\\; all \\\\; negatives}\n$$\n\nIn other words, we want as few \"false alarms\" as possible.\n\nHowever, unless we have a perfect classifier, these two goals\nconflict with each other.\n\nThe ROC curve maps the TPR against the FPR. We now measure the area\nunder said curve (AUC). A higher AUC implies that the trade-off between\nTPR and FPR is more beneficial. A perfect model would have an AUC of\n1. An AUC of 0.5 implies that the model has no predictive value.\n\n\"\"\"\n\naccuracy = _all_metrics[1]\n\"\"\"Accuracy - measures the share of accurate predictions as of total\nsamples in the testing set.\n\nUsed for classification problems.\n\n$$\naccuracy = \\\\frac{number \\\\; of \\\\; correct \\\\; predictions}{number \\\\; of \\\\; all \\\\; predictions}\n$$\n\nThe number of correct predictions depends on the threshold used:\nFor instance, we could interpret all predictions for which the probability\nis greater than 0.5 as a positive and all others as a negative.\nBut we do not have to use a threshold of 0.5 - we might as well\nuse any other threshold. Which threshold we choose will impact\nthe calculated accuracy.\n\nWhen calculating the accuracy, the value returned is the\naccuracy returned by the *best threshold*.\n\nEven though accuracy is the most intuitive way to measure a classification\nalgorithm, it can also be very misleading when the samples are very skewed.\nFor instance, if only 2% of the samples are positive, a predictor that\nalways predicts negative outcomes will have an accuracy of 98%. This sounds\nvery good to the layman, but the predictor in this example\nactually has no predictive value.\n\"\"\"\n\ncross_entropy = _all_metrics[2]\n\"\"\"Cross entropy, also referred to as log-loss, is a measure of the likelihood\nof the classification model.\n\nUsed for classification problems.\n\nMathematically speaking, cross-entropy for a binary classification problem\nis defined as follows:\n\n$$\ncross \\\\; entropy = - \\\\frac{1}{N} \\\\sum_{i}^{N} (y_i \\\\log p_i + (1 - y_i) \\\\log(1 - p_i),\n$$\n\nwhere $p_i$ is the probability of a positive outcome as predicted\nby the classification algorithm and $y_i$ is the target value,\nwhich is 1 for a positive outcome and 0 otherwise.\n\nThere are several ways to justify the use of cross entropy to evaluate\nclassification algorithms. But the most intuitive way is to think of\nit as a measure of *likelihood*. When we have a classification algorithm\nthat gives us probabilities, we would like to know how likely it is\nthat we observe a particular state of the world given the probabilities.\n\nWe can calculate this likelihood as follows:\n\n$$\nlikelihood = \\\\prod_{i}^{N} (p_i^{y_i} * (1 - p_i)^{1 - y_i}).\n$$\n\n(Recall that $y_i$ can only be 0 or 1.)\n\nIf we take the logarithm of the likelihood as defined above, divide by\n$N$ and then multiply by `-1` (because we want lower to mean\nbetter and 0 to mean perfect), the outcome will be cross entropy.\n\n\"\"\"\n\n# --------------------------------------------------------------------\n# Scores for regression problems.\n\nmae = _all_metrics[3]\n\"\"\"Mean Absolute Error - measure of distance between two\nnumerical targets.\n\nUsed for regression problems.\n\n$$\nMAE = \\\\frac{\\\\sum_{i=1}^n | \\\\mathbf{y}_i - \\\\mathbf{\\\\hat{y}}_i |}{n},\n$$\n\nwhere $\\\\mathbf{y}_i$ and $\\\\mathbf{\\\\hat{y}}_i$ are the target\nvalues or prediction respectively for a particular data sample\n$i$ (both multidimensional in case of using multiple targets)\nwhile $n$ is the number of samples we consider during the\nscoring.\n\"\"\"\n\nrmse = _all_metrics[4]\n\"\"\"Root Mean Squared Error - measure of distance between two\nnumerical targets.\n\nUsed for regression problems.\n\n$$\nRMSE = \\\\sqrt{\\\\frac{\\\\sum_{i=1}^n ( \\\\mathbf{y}_i - \\\\mathbf{\\\\hat{y}}_i )^2}{n}},\n$$\n\nwhere $\\\\mathbf{y}_i$ and $\\\\mathbf{\\\\hat{y}}_i$ are the target\nvalues or prediction respectively for a particular data sample\n$i$ (both multidimensional in case of using multiple targets)\nwhile $n$ is the number of samples we consider during the\nscoring.\n\"\"\"\n\n\nrsquared = _all_metrics[5]\n\"\"\"$R^{2}$ - squared correlation coefficient between predictions and targets.\n\nUsed for regression problems.\n\n$R^{2}$ is defined as follows:\n\n$$\nR^{2} = \\\\frac{(\\\\sum_{i=1}^n ( y_i - \\\\bar{y_i} ) *  ( \\\\hat{y_i} - \\\\bar{\\\\hat{y_i}} ))^2 }{\\\\sum_{i=1}^n ( y_i - \\\\bar{y_i} )^2 \\\\sum_{i=1}^n ( \\\\hat{y_i} - \\\\bar{\\\\hat{y_i}} )^2 },\n$$\n\nwhere $y_i$ are the true values, $\\\\hat{y_i}$ are\nthe predictions and $\\\\bar{...}$ denotes the mean operator.\n\nAn $R^{2}$ of 1 implies perfect correlation between the predictions\nand the targets and an $R^{2}$ of 0 implies no correlation\nat all.\n\"\"\"\n\n\n# --------------------------------------------------------------------\n\n_classification_metrics: List[str] = [auc, accuracy, cross_entropy]\n\n# --------------------------------------------------------------------\n\n_minimizing_metrics: List[str] = [cross_entropy, mae, rmse]\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/pipeline/pipeline.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThis submodule contains the Pipeline, which is the main\nclass for feature learning and prediction.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport copy\nimport json\nimport numbers\nimport socket\nimport time\nfrom datetime import datetime\nfrom typing import Any, Dict, List, Optional, Sequence, Union\n\nimport numpy as np\nfrom numpy.typing import NDArray\nfrom rich import print\n\nimport getml.communication as comm\nfrom getml import data\nfrom getml.data import (\n    DataModel,\n    Placeholder,\n    View,\n    _decode_data_model,\n    _decode_placeholder,\n)\nfrom getml.data.data_frame import DataFrame\nfrom getml.data.helpers import (\n    _is_subclass_list,\n    _is_typed_list,\n    _remove_trailing_underscores,\n)\nfrom getml.feature_learning import (\n    Fastboost,\n    FastProp,\n    Multirel,\n    Relboost,\n    RelMT,\n    _FeatureLearner,\n)\nfrom getml.feature_learning.loss_functions import _classification_loss\nfrom getml.predictors import (\n    LinearRegression,\n    LogisticRegression,\n    ScaleGBMClassifier,\n    ScaleGBMRegressor,\n    XGBoostClassifier,\n    XGBoostRegressor,\n    _classification_types,\n    _Predictor,\n)\nfrom getml.preprocessors import (\n    CategoryTrimmer,\n    EmailDomain,\n    Imputation,\n    Mapping,\n    Seasonal,\n    Substring,\n    TextFieldSplitter,\n)\nfrom getml.preprocessors.preprocessor import _Preprocessor\nfrom getml.utilities.formatting import _SignatureFormatter\n\nfrom .columns import Columns\nfrom .features import Features\nfrom .helpers import (\n    _check_df_types,\n    _handle_loss_function,\n    _infer_peripheral,\n    _make_id,\n    _parse_fe,\n    _parse_metadata,\n    _parse_pred,\n    _parse_preprocessor,\n    _print_time_taken,\n    _replace_with_nan_maybe,\n    _transform_peripheral,\n)\nfrom .issues import Issues\nfrom .metadata import AllMetadata\nfrom .metrics import (\n    _all_metrics,\n    _classification_metrics,\n    accuracy,\n    auc,\n    cross_entropy,\n    mae,\n    rmse,\n    rsquared,\n)\nfrom .plots import Plots\nfrom .score import ClassificationScore, RegressionScore\nfrom .scores_container import Scores\nfrom .tables import Tables\nfrom .tags import Tags\n\nNOT_FITTED = \"NOT FITTED\"\n\n\nclass Pipeline:\n    \"\"\"\n    A Pipeline is the main class for feature learning and prediction.\n\n    Args:\n        data_model:\n            Abstract representation of the data_model,\n            which defines the abstract relationships between the tables.\n            Required for the feature learners.\n\n        peripheral:\n            Abstract representations of the additional tables used to\n            augment the information provided in `population`. These\n            have to be the same objects that were\n            [`join`][getml.data.Placeholder.join] ed onto the\n            `population` [`Placeholder`][getml.data.Placeholder].\n            Their order determines the order of the\n            peripheral [`DataFrame`][getml.DataFrame] passed to\n            the 'peripheral_tables' argument in\n            [`check`][getml.Pipeline.check],\n            [`fit`][getml.Pipeline.fit],\n            [`predict`][getml.Pipeline.predict],\n            [`score`][getml.Pipeline.score], and\n            [`transform`][getml.Pipeline.transform], if you\n            pass the data frames as a list.\n            If you omit the peripheral placeholders, they will\n            be inferred from the data model and ordered\n            alphabetically.\n\n        preprocessors:\n            The preprocessor(s) to be used.\n            Must be from [`preprocessors`][getml.preprocessors].\n            A single preprocessor does not have to be wrapped in a list.\n\n        feature_learners:\n            The feature learner(s) to be used.\n            Must be from [`feature_learning`][getml.feature_learning].\n            A single feature learner does not have to be wrapped\n            in a list.\n\n        feature_selectors:\n            Predictor(s) used to select the best features.\n            Must be from [`predictors`][getml.predictors].\n            A single feature selector does not have to be wrapped\n            in a list.\n            Make sure to also set *share_selected_features*.\n\n        predictors:\n            Predictor(s) used to generate the predictions.\n            If more than one predictor is passed, the predictions\n            generated will be averaged.\n            Must be from [`predictors`][getml.predictors].\n            A single predictor does not have to be wrapped\n            in a list.\n\n        loss_function:\n            The loss function to use for the feature learners.\n\n        tags: Tags exist to help you organize your pipelines.\n            You can add any tags that help you remember what you were\n            trying to do.\n\n        include_categorical:\n            Whether you want to pass categorical columns\n            in the population table to the predictor.\n\n        share_selected_features:\n            The share of features you want the feature\n            selection to keep. When set to 0.0, then all features will be kept.\n\n    ??? example\n        We assume that you have already set up your\n        preprocessors (refer to [`preprocessors`][getml.preprocessors]),\n        your feature learners (refer to [`feature_learning`][getml.feature_learning])\n        as well as your feature selectors and predictors\n        (refer to [`predictors`][getml.predictors], which can be used\n        for prediction and feature selection).\n\n        You might also want to refer to\n        [`DataFrame`][getml.DataFrame], [`View`][getml.data.View],\n        [`DataModel`][getml.data.DataModel], [`Container`][getml.data.Container],\n        [`Placeholder`][getml.data.Placeholder] and\n        [`StarSchema`][getml.data.StarSchema].\n\n        If you want to create features for a time series problem,\n        the easiest way to do so is to use the [`TimeSeries`][getml.data.TimeSeries]\n        abstraction.\n\n        Note that this example is taken from the\n        [robot notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/robot.ipynb).\n\n        ```python\n        # All rows before row 10500 will be used for training.\n        split = getml.data.split.time(data_all, \"rowid\", test=10500)\n\n        time_series = getml.data.TimeSeries(\n            population=data_all,\n            time_stamps=\"rowid\",\n            split=split,\n            lagged_targets=False,\n            memory=30,\n        )\n\n        pipe = getml.Pipeline(\n            data_model=time_series.data_model,\n            feature_learners=[...],\n            predictors=...\n        )\n\n        pipe.check(time_series.train)\n\n        pipe.fit(time_series.train)\n\n        pipe.score(time_series.test)\n\n        # To generate predictions on new data,\n        # it is sufficient to use a Container.\n        # You don't have to recreate the entire\n        # TimeSeries, because the abstract data model\n        # is stored in the pipeline.\n        container = getml.data.Container(\n            population=population_new,\n        )\n\n        # Add the data as a peripheral table, for the\n        # self-join.\n        container.add(population=population_new)\n\n        predictions = pipe.predict(container.full)\n        ```\n\n    ??? example\n        If your data can be organized in a simple star schema,\n        you can use [`StarSchema`][getml.data.StarSchema].\n        [`StarSchema`][getml.data.StarSchema] unifies\n        [`Container`][getml.data.Container] and [`DataModel`][getml.data.DataModel]:\n\n        Note that this example is taken from the\n        [loans notebook ](https://nbviewer.getml.com/github/getml/getml-demo/blob/master/loans.ipynb).\n\n        ```python\n\n        # First, we insert our data into a StarSchema.\n        # population_train and population_test are either\n        # DataFrames or Views. The population table\n        # defines the statistical population of your\n        # machine learning problem and contains the\n        # target variables.\n        star_schema = getml.data.StarSchema(\n            train=population_train,\n            test=population_test\n        )\n\n        # meta, order and trans are either\n        # DataFrames or Views.\n        # Because this is a star schema,\n        # all joins take place on the population\n        # table.\n        star_schema.join(\n            trans,\n            on=\"account_id\",\n            time_stamps=(\"date_loan\", \"date\")\n        )\n\n        star_schema.join(\n            order,\n            on=\"account_id\",\n        )\n\n        star_schema.join(\n            meta,\n            on=\"account_id\",\n        )\n\n        # Now you can insert your data model,\n        # your preprocessors, feature learners,\n        # feature selectors and predictors\n        # into the pipeline.\n        # Note that the pipeline only knows\n        # the abstract data model, but hasn't\n        # seen the actual data yet.\n        pipe = getml.Pipeline(\n            data_model=star_schema.data_model,\n            preprocessors=[mapping],\n            feature_learners=[fast_prop],\n            feature_selectors=[feature_selector],\n            predictors=predictor,\n        )\n\n        # Now, we pass the actual data.\n        # This passes 'population_train' and the\n        # peripheral tables (meta, order and trans)\n        # to the pipeline.\n        pipe.check(star_schema.train)\n\n        pipe.fit(star_schema.train)\n\n        pipe.score(star_schema.test)\n        ```\n\n    ??? example\n        [`StarSchema`][getml.data.StarSchema] is simpler,\n        but cannot be used for more complex data models.\n        The general approach is to use\n        [`Container`][getml.data.Container] and [`DataModel`][getml.data.DataModel]:\n\n        ```python\n\n        # First, we insert our data into a Container.\n        # population_train and population_test are either\n        # DataFrames or Views.\n        container = getml.data.Container(\n            train=population_train,\n            test=population_test\n        )\n\n        # meta, order and trans are either\n        # DataFrames or Views. They are given\n        # aliases, so we can refer to them in the\n        # DataModel.\n        container.add(\n            meta=meta,\n            order=order,\n            trans=trans\n        )\n\n        # Freezing makes the container immutable.\n        # This is not required, but often a good idea.\n        container.freeze()\n\n        # The abstract data model is constructed\n        # using the DataModel class. A data model\n        # does not contain any actual data. It just\n        # defines the abstract relational structure.\n        dm = getml.data.DataModel(\n            population_train.to_placeholder(\"population\")\n        )\n\n        dm.add(getml.data.to_placeholder(\n            meta=meta,\n            order=order,\n            trans=trans)\n        )\n\n        dm.population.join(\n            dm.trans,\n            on=\"account_id\",\n            time_stamps=(\"date_loan\", \"date\")\n        )\n\n        dm.population.join(\n            dm.order,\n            on=\"account_id\",\n        )\n\n        dm.population.join(\n            dm.meta,\n            on=\"account_id\",\n        )\n\n        # Now you can insert your data model,\n        # your preprocessors, feature learners,\n        # feature selectors and predictors\n        # into the pipeline.\n        # Note that the pipeline only knows\n        # the abstract data model, but hasn't\n        # seen the actual data yet.\n        pipe = getml.Pipeline(\n            data_model=dm,\n            preprocessors=[mapping],\n            feature_learners=[fast_prop],\n            feature_selectors=[feature_selector],\n            predictors=predictor,\n        )\n\n        # This passes 'population_train' and the\n        # peripheral tables (meta, order and trans)\n        # to the pipeline.\n        pipe.check(container.train)\n\n        pipe.fit(container.train)\n\n        pipe.score(container.test)\n        ```\n\n        Technically, you don't actually have to use a\n        [`Container`][getml.data.Container]. You might as well do this\n        (in fact, a [`Container`][getml.data.Container] is just\n        syntactic sugar for this approach):\n\n        ```python\n\n        pipe.check(\n            population_train,\n            {\"meta\": meta, \"order\": order, \"trans\": trans},\n        )\n\n        pipe.fit(\n            population_train,\n            {\"meta\": meta, \"order\": order, \"trans\": trans},\n        )\n\n        pipe.score(\n            population_test,\n            {\"meta\": meta, \"order\": order, \"trans\": trans},\n        )\n        ```\n        Or you could even do this. The order of the peripheral tables\n        can be inferred from the `__repr__` method of the pipeline,\n        and it is usually in alphabetical order.\n\n        ```python\n        pipe.check(\n            population_train,\n            [meta, order, trans],\n        )\n\n        pipe.fit(\n            population_train,\n            [meta, order, trans],\n        )\n\n        pipe.score(\n            population_test,\n            [meta, order, trans],\n        )\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        data_model: Optional[DataModel] = None,\n        peripheral: Optional[List[Placeholder]] = None,\n        preprocessors: Optional[\n            Union[\n                CategoryTrimmer,\n                EmailDomain,\n                Imputation,\n                Mapping,\n                Seasonal,\n                Substring,\n                TextFieldSplitter,\n                List[\n                    Union[\n                        CategoryTrimmer,\n                        EmailDomain,\n                        Imputation,\n                        Mapping,\n                        Seasonal,\n                        Substring,\n                        TextFieldSplitter,\n                    ]\n                ],\n            ],\n        ] = None,\n        feature_learners: Optional[\n            Union[\n                Union[Fastboost, FastProp, Multirel, Relboost, RelMT],\n                List[Union[Fastboost, FastProp, Multirel, Relboost, RelMT]],\n            ]\n        ] = None,\n        feature_selectors: Optional[\n            Union[\n                Union[\n                    LinearRegression,\n                    LogisticRegression,\n                    XGBoostClassifier,\n                    XGBoostRegressor,\n                    ScaleGBMClassifier,\n                    ScaleGBMRegressor,\n                ],\n                List[\n                    Union[\n                        LinearRegression,\n                        LogisticRegression,\n                        XGBoostClassifier,\n                        XGBoostRegressor,\n                        ScaleGBMClassifier,\n                        ScaleGBMRegressor,\n                    ]\n                ],\n            ],\n        ] = None,\n        predictors: Optional[\n            Union[\n                LinearRegression,\n                LogisticRegression,\n                XGBoostClassifier,\n                XGBoostRegressor,\n                ScaleGBMClassifier,\n                ScaleGBMRegressor,\n                List[\n                    Union[\n                        LinearRegression,\n                        LogisticRegression,\n                        XGBoostClassifier,\n                        XGBoostRegressor,\n                        ScaleGBMClassifier,\n                        ScaleGBMRegressor,\n                    ]\n                ],\n            ]\n        ] = None,\n        loss_function: Optional[str] = None,\n        tags: Optional[List[str]] = None,\n        include_categorical: bool = False,\n        share_selected_features: float = 0.5,\n    ) -> None:\n        data_model = data_model or DataModel(\"population\")\n\n        if not isinstance(data_model, DataModel):\n            raise TypeError(\"'data_model' must be a getml.data.DataModel.\")\n\n        peripheral = peripheral or _infer_peripheral(data_model.population)\n\n        preprocessors = preprocessors or []\n\n        feature_learners = feature_learners or []\n\n        feature_selectors = feature_selectors or []\n\n        predictors = predictors or []\n\n        tags = tags or []\n\n        if not isinstance(preprocessors, list):\n            preprocessors = [preprocessors]\n\n        if not isinstance(feature_learners, list):\n            feature_learners = [feature_learners]\n\n        if not isinstance(feature_selectors, list):\n            feature_selectors = [feature_selectors]\n\n        if not isinstance(predictors, list):\n            predictors = [predictors]\n\n        if not isinstance(peripheral, list):\n            peripheral = [peripheral]\n\n        if not isinstance(tags, list):\n            tags = [tags]\n\n        self._id: str = NOT_FITTED\n\n        self.type = \"Pipeline\"\n\n        loss_function = (\n            loss_function\n            or (\n                [fl.loss_function for fl in feature_learners if fl.loss_function]\n                or [\"SquareLoss\"]\n            )[0]\n        )\n\n        feature_learners = [\n            _handle_loss_function(fl, loss_function) for fl in feature_learners\n        ]\n\n        self.data_model = data_model\n        self.feature_learners = feature_learners\n        self.feature_selectors = feature_selectors\n        self.include_categorical = include_categorical\n        self.loss_function = loss_function\n        self.peripheral = peripheral\n        self.predictors = predictors\n        self.preprocessors = preprocessors\n        self.share_selected_features = share_selected_features\n        self.tags = Tags(tags)\n\n        self._metadata: Optional[AllMetadata] = None\n\n        self._scores: Dict[str, Any] = {}\n\n        self._targets: List[str] = []\n\n        setattr(type(self), \"_supported_params\", list(self.__dict__.keys()))\n\n        self._validate()\n\n    # ----------------------------------------------------------------\n\n    def __eq__(self, other: object) -> bool:\n        if not isinstance(other, Pipeline):\n            raise TypeError(\"A Pipeline can only be compared to another Pipeline\")\n\n        if len(set(self.__dict__.keys())) != len(set(other.__dict__.keys())):\n            return False\n\n        for kkey in self.__dict__:\n            if kkey not in other.__dict__:\n                return False\n\n            # Take special care when comparing numbers.\n            if isinstance(self.__dict__[kkey], numbers.Real):\n                if not np.isclose(self.__dict__[kkey], other.__dict__[kkey]):\n                    return False\n\n            elif self.__dict__[kkey] != other.__dict__[kkey]:\n                return False\n\n        return True\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        obj_dict = self._make_object_dict()\n\n        sig = _SignatureFormatter(data=obj_dict)\n        repr_str = sig._format()\n\n        if self.fitted:\n            url = self._make_url()\n            repr_str += \"\\n\\nurl: \" + url if url else \"\"\n\n        return repr_str\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        obj_dict = self._make_object_dict()\n\n        sig = _SignatureFormatter(data=obj_dict)\n        repr_str = sig._format()\n        html = f\"<pre>{repr_str}</pre>\"\n\n        if self.fitted:\n            url = self._make_url()\n            html += (\n                (\n                    \"<br><pre>\"\n                    + \"url: <a href='\"\n                    + url\n                    + '\\' target=\"_blank\">'\n                    + url\n                    + \"</a>\"\n                    + \"</pre>\"\n                )\n                if url\n                else \"\"\n            )\n\n        return html\n\n    # ------------------------------------------------------------\n\n    def _check_classification_or_regression(self) -> bool:\n        \"\"\"\n        Checks whether there are inconsistencies in the algorithms used\n        (mixing classification and regression algorithms).\n        \"\"\"\n\n        all_classifiers = all(\n            [\n                elem.loss_function in _classification_loss\n                for elem in self.feature_learners\n            ]\n        )\n\n        all_classifiers = all_classifiers and all(\n            [elem.type in _classification_types for elem in self.feature_selectors]\n        )\n\n        all_classifiers = all_classifiers and all(\n            [elem.type in _classification_types for elem in self.predictors]\n        )\n\n        all_regressors = all(\n            [\n                elem.loss_function not in _classification_loss\n                for elem in self.feature_learners\n            ]\n        )\n\n        all_regressors = all_regressors and all(\n            [elem.type not in _classification_types for elem in self.feature_selectors]\n        )\n\n        all_regressors = all_regressors and all(\n            [elem.type not in _classification_types for elem in self.predictors]\n        )\n\n        if not all_classifiers and not all_regressors:\n            raise ValueError(\n                \"\"\"You are mixing classification and regression\n                                algorithms. Please make sure that your feature learning\n                                algorithms consistently have classification loss functions\n                                (like CrossEntropyLoss) or consistently have regression\n                                loss functions (like SquareLoss). Also make sure that your\n                                feature selectors and predictors are consistently classifiers\n                                (like XGBoostClassifier or LogisticRegression) or consistently\n                                regressors (like XGBoostRegressor or LinearRegression).\n                             \"\"\"\n            )\n\n        return all_classifiers\n\n    # ------------------------------------------------------------\n\n    def _check_whether_fitted(self) -> None:\n        if not self.fitted:\n            raise ValueError(\"Pipeline has not been fitted!\")\n\n    # ------------------------------------------------------------\n\n    def _close(self, sock: socket.socket) -> None:\n        if not isinstance(sock, socket.socket):\n            raise TypeError(\"'sock' must be a socket.\")\n\n        cmd = dict()\n        cmd[\"type_\"] = self.type + \".close\"\n        cmd[\"name_\"] = self.id\n\n        comm.send_string(sock, json.dumps(cmd))\n\n        msg = comm.recv_string(sock)\n\n        if msg != \"Success!\":\n            comm.handle_engine_exception(msg)\n\n    # ------------------------------------------------------------\n\n    def _get_latest_score(self, score: str) -> List[float]:\n        nan_ = [np.nan] * len(self.targets)\n\n        if score not in _all_metrics:\n            raise AttributeError(f\"Not a valid score name: {score}\")\n\n        if not self.scored:\n            return nan_\n\n        if self.is_classification and score not in _classification_metrics:\n            return nan_\n\n        if self.is_regression and score in _classification_metrics:\n            return nan_\n\n        return self._scores[score]\n\n    # ------------------------------------------------------------\n\n    def _getml_deserialize(self) -> Dict[str, Any]:\n        \"\"\"\n        Expresses the pipeline in a form the Engine can understand.\n        \"\"\"\n\n        cmd = dict()\n\n        self_dict = self.__dict__\n\n        cmd[\"name_\"] = self.id\n\n        for key, value in self_dict.items():\n            cmd[key + \"_\"] = value\n\n        del cmd[\"_id_\"]\n        del cmd[\"_metadata_\"]\n        del cmd[\"_scores_\"]\n        del cmd[\"_targets_\"]\n\n        return cmd\n\n    # ----------------------------------------------------------------\n\n    def _make_object_dict(self) -> Dict[str, Any]:\n        obj_dict = copy.deepcopy(self.__dict__)\n\n        obj_dict[\"data_model\"] = self.data_model.population.name\n\n        obj_dict[\"peripheral\"] = [elem.name for elem in self.peripheral]\n\n        obj_dict[\"preprocessors\"] = [elem.type for elem in self.preprocessors]\n\n        obj_dict[\"feature_learners\"] = [elem.type for elem in self.feature_learners]\n\n        obj_dict[\"feature_selectors\"] = [elem.type for elem in self.feature_selectors]\n\n        obj_dict[\"predictors\"] = [elem.type for elem in self.predictors]\n\n        return obj_dict\n\n    # ----------------------------------------------------------------\n\n    def _make_score_history(self) -> List[Union[ClassificationScore, RegressionScore]]:\n        scores: List[Dict[str, Any]] = self._scores[\"history\"]\n        scores = [_replace_with_nan_maybe(score) for score in scores]\n\n        if self.is_classification:\n            return [\n                ClassificationScore(\n                    date_time=datetime.strptime(\n                        score.get(\"date_time\", \"\"), \"%Y-%m-%d %H:%M:%S\"\n                    ),\n                    set_used=score.get(\"set_used\", \"\"),\n                    target=target,\n                    accuracy=score.get(accuracy, [np.nan])[target_num],\n                    auc=score.get(auc, [np.nan])[target_num],\n                    cross_entropy=score.get(cross_entropy, [np.nan])[target_num],\n                )\n                for score in scores\n                for target_num, target in enumerate(self.targets)\n            ]\n\n        return [\n            RegressionScore(\n                date_time=datetime.strptime(\n                    score.get(\"date_time\", \"\"), \"%Y-%m-%d %H:%M:%S\"\n                ),\n                set_used=score.get(\"set_used\", \"\"),\n                target=target,\n                mae=score.get(mae, [np.nan])[target_num],\n                rmse=score.get(rmse, [np.nan])[target_num],\n                rsquared=score.get(rsquared, [np.nan])[target_num],\n            )\n            for score in scores\n            for target_num, target in enumerate(self.targets)\n        ]\n\n    # ----------------------------------------------------------------\n\n    def _make_url(self) -> Optional[str]:\n        url = comm._monitor_url()\n        if not url:\n            return None\n        url += \"getpipeline/\" + comm._get_project_name() + \"/\" + self.id + \"/0/\"\n        return url\n\n    # ----------------------------------------------------------------\n\n    def _parse_cmd(self, json_obj: Dict[str, Any]) -> Pipeline:\n        ptype = json_obj[\"type_\"]\n\n        del json_obj[\"type_\"]\n\n        if ptype != \"Pipeline\":\n            raise ValueError(\"Expected type 'Pipeline', got '\" + ptype + \"'.\")\n\n        preprocessors = [\n            _parse_preprocessor(elem) for elem in json_obj[\"preprocessors_\"]\n        ]\n\n        del json_obj[\"preprocessors_\"]\n\n        feature_learners = [_parse_fe(elem) for elem in json_obj[\"feature_learners_\"]]\n\n        del json_obj[\"feature_learners_\"]\n\n        feature_selectors = [\n            _parse_pred(elem) for elem in json_obj[\"feature_selectors_\"]\n        ]\n\n        del json_obj[\"feature_selectors_\"]\n\n        predictors = [_parse_pred(elem) for elem in json_obj[\"predictors_\"]]\n\n        del json_obj[\"predictors_\"]\n\n        data_model = _decode_data_model(json_obj[\"data_model_\"])\n\n        del json_obj[\"data_model_\"]\n\n        peripheral = [_decode_placeholder(elem) for elem in json_obj[\"peripheral_\"]]\n\n        del json_obj[\"peripheral_\"]\n\n        id_ = json_obj[\"name_\"]\n\n        del json_obj[\"name_\"]\n\n        kwargs = _remove_trailing_underscores(json_obj)\n\n        self.__init__(\n            data_model=data_model,\n            peripheral=peripheral,\n            preprocessors=preprocessors,\n            feature_learners=feature_learners,\n            feature_selectors=feature_selectors,\n            predictors=predictors,\n            **kwargs,\n        )\n\n        self._id = id_\n\n        return self\n\n    # ----------------------------------------------------------------\n\n    def _parse_json_obj(self, all_json_objs: Dict[str, Any]) -> Pipeline:\n        obj = all_json_objs[\"obj\"]\n\n        scores = all_json_objs[\"scores\"]\n\n        targets = all_json_objs[\"targets\"]\n\n        self._parse_cmd(obj)\n\n        scores = _remove_trailing_underscores(scores)\n        scores = _replace_with_nan_maybe(scores)\n\n        self._scores = scores\n\n        self._targets = targets\n\n        peripheral_metadata = [\n            _parse_metadata(m) for m in all_json_objs[\"peripheral_metadata\"]\n        ]\n        population_metadata = _parse_metadata(all_json_objs[\"population_metadata\"])\n\n        self._metadata = AllMetadata(\n            peripheral=peripheral_metadata,\n            population=population_metadata,\n        )\n\n        return self\n\n    # ----------------------------------------------------------------\n\n    def _save(self) -> None:\n        \"\"\"\n        Saves the pipeline as a JSON file.\n        \"\"\"\n\n        cmd = dict()\n        cmd[\"type_\"] = self.type + \".save\"\n        cmd[\"name_\"] = self.id\n\n        comm.send(cmd)\n\n    # ------------------------------------------------------------\n\n    def _send(self, additional_tags: Optional[List[str]] = None) -> Pipeline:\n        self._validate()\n\n        self._id = _make_id()\n\n        cmd = self._getml_deserialize()\n\n        if additional_tags is not None:\n            cmd[\"tags_\"] += additional_tags\n\n        comm.send(cmd)\n\n        return self\n\n    # ------------------------------------------------------------\n\n    def _transform(\n        self,\n        peripheral_data_frames: Sequence[Union[DataFrame, View]],\n        population_data_frame: Union[DataFrame, View],\n        sock: socket.socket,\n        score: bool = False,\n        predict: bool = False,\n        df_name: str = \"\",\n        table_name: str = \"\",\n    ) -> Union[NDArray[np.float_], None]:\n        _check_df_types(population_data_frame, peripheral_data_frames)\n\n        if not isinstance(sock, socket.socket):\n            raise TypeError(\"'sock' must be a socket.\")\n\n        if not isinstance(score, bool):\n            raise TypeError(\"'score' must be of type bool\")\n\n        if not isinstance(predict, bool):\n            raise TypeError(\"'predict' must be of type bool\")\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be of type str\")\n\n        if not isinstance(df_name, str):\n            raise TypeError(\"'df_name' must be of type str\")\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".transform\"\n        cmd[\"name_\"] = self.id\n\n        cmd[\"score_\"] = score\n        cmd[\"predict_\"] = predict\n\n        cmd[\"peripheral_dfs_\"] = [\n            df._getml_deserialize() for df in peripheral_data_frames\n        ]\n        cmd[\"population_df_\"] = population_data_frame._getml_deserialize()\n\n        cmd[\"df_name_\"] = df_name\n        cmd[\"table_name_\"] = table_name\n\n        comm.send_string(sock, json.dumps(cmd))\n\n        msg = comm.log(sock, extra={\"cmd\": cmd})\n\n        if msg == \"Success!\":\n            if table_name == \"\" and df_name == \"\" and not score:\n                yhat = comm.recv_float_matrix(sock)\n            else:\n                yhat = None\n        else:\n            comm.handle_engine_exception(msg)\n\n        return yhat\n\n    # ----------------------------------------------------------------\n\n    @property\n    def accuracy(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the accuracy of the latest scoring run (the\n        last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The accuracy of the pipeline.\n        \"\"\"\n        return self.scores.accuracy\n\n    # ----------------------------------------------------------------\n\n    @property\n    def auc(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the auc of the latest scoring run (the\n        last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The auc of the pipeline.\n        \"\"\"\n        return self.scores.auc\n\n    # ----------------------------------------------------------------\n\n    def check(\n        self,\n        population_table: Union[DataFrame, View, data.Subset],\n        peripheral_tables: Optional[\n            Union[\n                Dict[str, Union[DataFrame, View]],\n                Sequence[Union[DataFrame, View]],\n            ]\n        ] = None,\n    ) -> Optional[Issues]:\n        \"\"\"\n        Checks the validity of the data model.\n\n        Args:\n            population_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable.\n\n            peripheral_tables:\n                Additional tables corresponding to the ``peripheral``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If passed as a list, the order needs to\n                match the order of the corresponding placeholders passed\n                to ``peripheral``.\n\n                If you pass a [`Subset`][getml.data.Subset] to `population_table`,\n                the peripheral tables from that subset will be used. If you use\n                a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n                or [`TimeSeries`][getml.data.TimeSeries], that means you are passing\n                a [`Subset`][getml.data.Subset].\n\n        \"\"\"\n\n        if isinstance(population_table, data.Subset):\n            peripheral_tables = population_table.peripheral\n            population_table = population_table.population\n\n        peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)\n\n        _check_df_types(population_table, peripheral_tables)\n\n        temp = copy.deepcopy(self)\n\n        temp._send()\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = temp.type + \".check\"\n        cmd[\"name_\"] = temp.id\n\n        cmd[\"peripheral_dfs_\"] = [df._getml_deserialize() for df in peripheral_tables]\n        cmd[\"population_df_\"] = population_table._getml_deserialize()\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n            print(\"Checking data model...\")\n            msg = comm.log(sock, extra={\"cmd\": cmd})\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            issues = Issues(comm.recv_issues(sock))\n            if len(issues) == 0:\n                print(\"OK.\")\n            else:\n                print(\n                    f\"The pipeline check generated {len(issues.info)} \"\n                    + f\"issues labeled INFO and {len(issues.warnings)} \"\n                    + \"issues labeled WARNING.\"\n                )\n\n        temp.delete()\n\n        return None if len(issues) == 0 else issues\n\n    # ------------------------------------------------------------\n\n    @property\n    def columns(self) -> Columns:\n        \"\"\"\n        [`Columns`][getml.pipeline.Columns] object that\n        can be used to handle information about the original\n        columns utilized by the feature learners.\n\n        Returns:\n            The columns object.\n        \"\"\"\n        self._check_whether_fitted()\n        return Columns(self.id, self.targets, self.peripheral)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def cross_entropy(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the cross entropy of the latest scoring\n        run (the last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The cross entropy of the pipeline.\n        \"\"\"\n        return self.scores.cross_entropy\n\n    # ----------------------------------------------------------------\n\n    def delete(self) -> None:\n        \"\"\"\n        Deletes the pipeline from the Engine.\n\n        Warning:\n            You can not undo this action!\n        \"\"\"\n        self._check_whether_fitted()\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".delete\"\n        cmd[\"name_\"] = self.id\n        cmd[\"mem_only_\"] = False\n\n        comm.send(cmd)\n\n        self._id = NOT_FITTED\n\n    # ------------------------------------------------------------\n\n    def deploy(self, deploy: bool) -> None:\n        \"\"\"Allows a fitted pipeline to be addressable via an HTTP request.\n        See [deployment][deployment] for details.\n\n        Args:\n            deploy: If `True`, the deployment of the pipeline\n                will be triggered.\n        \"\"\"\n        self._check_whether_fitted()\n\n        if not isinstance(deploy, bool):\n            raise TypeError(\"'deploy' must be of type bool\")\n\n        self._validate()\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".deploy\"\n        cmd[\"name_\"] = self.id\n        cmd[\"deploy_\"] = deploy\n\n        comm.send(cmd)\n\n        self._save()\n\n    # ------------------------------------------------------------\n\n    @property\n    def features(self) -> Features:\n        \"\"\"\n        [`Features`][getml.pipeline.Features] object that\n        can be used to handle the features generated\n        by the feature learners.\n\n        Returns:\n            The features object.\n        \"\"\"\n        self._check_whether_fitted()\n        return Features(self.id, self.targets)\n\n    # ------------------------------------------------------------\n\n    def fit(\n        self,\n        population_table: Union[DataFrame, View, data.Subset],\n        peripheral_tables: Optional[\n            Union[\n                Sequence[Union[DataFrame, View]],\n                Dict[str, Union[DataFrame, View]],\n            ]\n        ] = None,\n        validation_table: Optional[Union[DataFrame, View, data.Subset]] = None,\n        check: bool = True,\n    ) -> Pipeline:\n        \"\"\"Trains the feature learning algorithms, feature selectors\n        and predictors.\n\n        Args:\n            population_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable.\n\n            peripheral_tables:\n                Additional tables corresponding to the ``peripheral``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If passed as a list, the order needs to\n                match the order of the corresponding placeholders passed\n                to ``peripheral``.\n\n                If you pass a [`Subset`][getml.data.Subset] to `population_table`,\n                the peripheral tables from that subset will be used. If you use\n                a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n                or [`TimeSeries`][getml.data.TimeSeries], that means you are passing\n                a [`Subset`][getml.data.Subset].\n\n            validation_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If you are passing a subset, that subset\n                must be derived from the same container as *population_table*.\n\n                Only used for early stopping in [`XGBoostClassifier`][getml.predictors.XGBoostClassifier]\n                and [`XGBoostRegressor`][getml.predictors.XGBoostRegressor].\n\n            check:\n                Whether you want to check the data model before fitting. The checks are\n                equivalent to the checks run by [`check`][getml.Pipeline.check].\n\n        Returns:\n            The fitted pipeline.\n        \"\"\"\n\n        additional_tags = (\n            [\"container-\" + population_table.container_id]\n            if isinstance(population_table, data.Subset)\n            else []\n        )\n\n        if (\n            isinstance(population_table, data.Subset)\n            and isinstance(validation_table, data.Subset)\n            and validation_table.container_id != population_table.container_id\n        ):\n            raise ValueError(\n                \"The subset used for validation must be from the same container \"\n                + \"as the subset used for training.\"\n            )\n\n        if isinstance(population_table, data.Subset):\n            peripheral_tables = population_table.peripheral\n            population_table = population_table.population\n\n        if isinstance(validation_table, data.Subset):\n            validation_table = validation_table.population\n\n        peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)\n\n        _check_df_types(population_table, peripheral_tables)\n\n        if check:\n            warnings = self.check(population_table, peripheral_tables)\n            if warnings:\n                print(\"To see the issues in full, run .check() on the pipeline.\")\n\n        self._send(additional_tags)\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = self.type + \".fit\"\n        cmd[\"name_\"] = self.id\n\n        cmd[\"peripheral_dfs_\"] = [df._getml_deserialize() for df in peripheral_tables]\n        cmd[\"population_df_\"] = population_table._getml_deserialize()\n\n        if validation_table is not None:\n            cmd[\"validation_df_\"] = validation_table._getml_deserialize()\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n\n            begin = time.monotonic()\n\n            msg = comm.log(sock, extra={\"cmd\": cmd})\n\n            end = time.monotonic()\n\n            if \"Trained\" in msg:\n                print(msg)\n                _print_time_taken(begin, end, \"Time taken: \")\n            else:\n                comm.handle_engine_exception(msg)\n\n        self._save()\n\n        return self.refresh()\n\n    # ------------------------------------------------------------\n\n    @property\n    def fitted(self) -> bool:\n        \"\"\"\n        Whether the pipeline has already been fitted.\n\n        Returns:\n            Whether the pipeline has already been fitted.\n        \"\"\"\n        return self._id != NOT_FITTED\n\n    # ----------------------------------------------------------------\n\n    @property\n    def mae(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the mae of the latest scoring run (the\n        last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The mae of the pipeline.\n        \"\"\"\n        return self.scores.mae\n\n    # ------------------------------------------------------------\n\n    @property\n    def plots(self) -> Plots:\n        \"\"\"\n        [`Plots`][getml.pipeline.Plots] object that\n        can be used to generate plots like an ROC\n        curve or a lift curve.\n\n        Returns:\n            The plots object.\n        \"\"\"\n        self._check_whether_fitted()\n        return Plots(self.id)\n\n    # ------------------------------------------------------------\n\n    @property\n    def id(self) -> str:\n        \"\"\"\n        ID of the pipeline. This is used to uniquely identify\n        the pipeline on the Engine.\n\n        Returns:\n            The ID of the pipeline.\n        \"\"\"\n        return self._id\n\n    # ------------------------------------------------------------\n\n    @property\n    def is_classification(self) -> bool:\n        \"\"\"\n        Whether the pipeline can be used for classification problems.\n\n        Returns:\n            Whether the pipeline can be used for classification problems.\n        \"\"\"\n        return self._check_classification_or_regression()\n\n    # ------------------------------------------------------------\n\n    @property\n    def is_regression(self) -> bool:\n        \"\"\"\n        Whether the pipeline can be used for regression problems.\n\n        Returns:\n            Whether the pipeline can be used for regression problems.\n        \"\"\"\n        return not self.is_classification\n\n    # ------------------------------------------------------------\n\n    @property\n    def metadata(self) -> Optional[AllMetadata]:\n        \"\"\"\n        Contains information on the data frames\n        that were passed to .fit(...). The roles\n        contained therein can be directly passed\n        to existing data frames to correctly reassign\n        the roles of existing columns. If the pipeline\n        has not been fitted, this is None.\n\n        Returns:\n            The metadata of the pipeline.\n        \"\"\"\n        return self._metadata\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self) -> str:\n        \"\"\"\n        Returns the ID of the pipeline. The name property is\n        kept for backward compatibility.\n\n        Returns:\n            The ID of the pipeline.\n        \"\"\"\n        return self._id\n\n    # ------------------------------------------------------------\n\n    def predict(\n        self,\n        population_table: Union[DataFrame, View, data.Subset],\n        peripheral_tables: Optional[\n            Union[\n                Sequence[Union[DataFrame, View]],\n                Dict[str, Union[DataFrame, View]],\n            ]\n        ] = None,\n        table_name: str = \"\",\n    ) -> Union[NDArray[np.float_], None]:\n        \"\"\"Forecasts on new, unseen data using the trained ``predictor``.\n\n        Returns the predictions generated by the pipeline based on\n        `population_table` and `peripheral_tables` or writes them into\n        a database named `table_name`.\n\n        Args:\n            population_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable.\n\n            peripheral_tables:\n                Additional tables corresponding to the ``peripheral``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If passed as a list, the order needs to\n                match the order of the corresponding placeholders passed\n                to ``peripheral``.\n\n                If you pass a [`Subset`][getml.data.Subset] to `population_table`,\n                the peripheral tables from that subset will be used. If you use\n                a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n                or [`TimeSeries`][getml.data.TimeSeries], that means you are passing\n                a [`Subset`][getml.data.Subset].\n\n            table_name:\n                If not an empty string, the resulting predictions will\n                be written into a table in a [`database`][getml.database].\n                Refer to [Unified import interface][importing-data-unified-interface] for further information.\n\n        Returns:\n            Resulting predictions provided in an array of the (number of rows in `population_table`, number of targets in `population_table`).\n\n        Note:\n            Only fitted pipelines\n            ([`fit`][getml.Pipeline.fit]) can be used for\n            prediction.\n\n\n        \"\"\"\n\n        self._check_whether_fitted()\n\n        if isinstance(population_table, data.Subset):\n            peripheral_tables = population_table.peripheral\n            population_table = population_table.population\n\n        peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)\n\n        _check_df_types(population_table, peripheral_tables)\n\n        if not isinstance(table_name, str):\n            raise TypeError(\"'table_name' must be of type str\")\n\n        self._validate()\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".transform\"\n        cmd[\"name_\"] = self.id\n        cmd[\"http_request_\"] = False\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n            y_hat = self._transform(\n                peripheral_tables,\n                population_table,\n                sock,\n                predict=True,\n                table_name=table_name,\n            )\n\n        return y_hat\n\n    # ------------------------------------------------------------\n\n    def refresh(self) -> Pipeline:\n        \"\"\"Reloads the pipeline from the Engine.\n\n        This discards all local changes you have made since the\n        last time you called [`fit`][getml.Pipeline.fit].\n\n        Returns:\n                Current instance\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".refresh\"\n        cmd[\"name_\"] = self.id\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n\n        if msg[0] != \"{\":\n            comm.handle_engine_exception(msg)\n\n        json_obj = json.loads(msg)\n\n        self._parse_json_obj(json_obj)\n\n        return self\n\n    # ----------------------------------------------------------------\n\n    @property\n    def rmse(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the rmse of the latest scoring run\n        (the last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The rmse of the pipeline.\n        \"\"\"\n        return self.scores.rmse\n\n    # ----------------------------------------------------------------\n\n    @property\n    def rsquared(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the rsquared of the latest scoring run\n        (the last time `.score()` was called) on the pipeline.\n\n        For programmatic access use [`metrics`][getml.pipeline.metrics].\n\n        Returns:\n            The rsquared of the pipeline.\n        \"\"\"\n        return self.scores.rsquared\n\n    # ----------------------------------------------------------------\n\n    def score(\n        self,\n        population_table: Union[DataFrame, View, data.Subset],\n        peripheral_tables: Optional[\n            Union[\n                Sequence[Union[DataFrame, View]],\n                Dict[str, Union[DataFrame, View]],\n            ]\n        ] = None,\n    ) -> Scores:\n        \"\"\"Calculates the performance of the ``predictor``.\n\n        Returns different scores calculated on `population_table` and\n        `peripheral_tables`.\n\n        Args:\n            population_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable.\n\n            peripheral_tables:\n                Additional tables corresponding to the ``peripheral``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If passed as a list, the order needs to\n                match the order of the corresponding placeholders passed\n                to ``peripheral``.\n\n                If you pass a [`Subset`][getml.data.Subset] to `population_table`,\n                the peripheral tables from that subset will be used. If you use\n                a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n                or [`TimeSeries`][getml.data.TimeSeries], that means you are passing\n                a [`Subset`][getml.data.Subset].\n\n        Returns:\n            The scores of the pipeline.\n\n        Note:\n            Only fitted pipelines\n            ([`fit`][getml.Pipeline.fit]) can be\n            scored.\n\n        \"\"\"\n\n        self._check_whether_fitted()\n\n        if isinstance(population_table, data.Subset):\n            peripheral_tables = population_table.peripheral\n            population_table = population_table.population\n\n        peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)\n\n        _check_df_types(population_table, peripheral_tables)\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".transform\"\n        cmd[\"name_\"] = self.id\n        cmd[\"http_request_\"] = False\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n\n            self._transform(\n                peripheral_tables, population_table, sock, predict=True, score=True\n            )\n\n            msg = comm.recv_string(sock)\n\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n\n            scores = comm.recv_string(sock)\n\n            scores = json.loads(scores)\n\n        self.refresh()\n\n        self._save()\n\n        return self.scores\n\n    # ----------------------------------------------------------------\n\n    @property\n    def scores(self) -> Scores:\n        \"\"\"\n        Contains all scores generated by [`score`][getml.Pipeline.score]\n\n        Returns:\n                A container that holds the scores for the pipeline.\n\n        \"\"\"\n        self._check_whether_fitted()\n\n        scores = self._make_score_history()\n\n        latest = {score: self._get_latest_score(score) for score in _all_metrics}\n\n        return Scores(scores, latest)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def scored(self) -> bool:\n        \"\"\"\n        Whether the pipeline has been scored.\n\n        Returns:\n            Whether the pipeline has been scored.\n        \"\"\"\n        if self._scores is None:\n            return False\n        return len(self._scores) > 1\n\n    # ----------------------------------------------------------------\n\n    @property\n    def tables(self) -> Tables:\n        \"\"\"\n        [`Tables`][getml.pipeline.Tables] object that\n        can be used to handle information about the original\n        tables utilized by the feature learners.\n\n        Returns:\n            The tables object.\n        \"\"\"\n        self._check_whether_fitted()\n        return Tables(self.targets, self.columns)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def targets(self) -> List[str]:\n        \"\"\"\n        Contains the names of the targets used for this pipeline.\n\n        Returns:\n            The names of the targets.\n        \"\"\"\n        self._check_whether_fitted()\n        return copy.deepcopy(self._targets)\n\n    # ----------------------------------------------------------------\n\n    def transform(\n        self,\n        population_table: Union[DataFrame, View, data.Subset],\n        peripheral_tables: Optional[\n            Union[\n                Sequence[Union[DataFrame, View]],\n                Dict[str, Union[DataFrame, View]],\n            ]\n        ] = None,\n        df_name: str = \"\",\n        table_name: str = \"\",\n    ) -> Union[DataFrame, NDArray[np.float_], None]:\n        \"\"\"Translates new data into the trained features.\n\n        Transforms the data passed in `population_table` and\n        `peripheral_tables` into features, which can be inserted into\n        machine learning models.\n\n\n        Args:\n            population_table:\n                Main table containing the target variable(s) and\n                corresponding to the ``population``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable.\n\n            peripheral_tables:\n                Additional tables corresponding to the ``peripheral``\n                [`Placeholder`][getml.data.Placeholder] instance\n                variable. If passed as a list, the order needs to\n                match the order of the corresponding placeholders passed\n                to ``peripheral``.\n\n                If you pass a [`Subset`][getml.data.Subset] to `population_table`,\n                the peripheral tables from that subset will be used. If you use\n                a [`Container`][getml.data.Container], [`StarSchema`][getml.data.StarSchema]\n                or [`TimeSeries`][getml.data.TimeSeries], that means you are passing\n                a [`Subset`][getml.data.Subset].\n\n            df_name:\n                If not an empty string, the resulting features will be\n                written into a newly created DataFrame.\n\n            table_name:\n                If not an empty string, the resulting features will\n                be written into a table in a [`database`][getml.database].\n                Refer to [Unified import interface][importing-data-unified-interface] for further information.\n\n        Returns:\n            The features generated by the pipeline.\n\n        ??? example\n            By default, `transform` returns a [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html):\n            ```python\n            my_features_array = pipe.transform()\n            ```\n            You can also export your features as a [`DataFrame`][getml.DataFrame]\n            by providing the `df_name` argument:\n            ```python\n            my_features_df = pipe.transform(df_name=\"my_features\")\n            ```\n            Or you can write the results directly into a database:\n            ```python\n            getml.database.connect_odbc(...)\n            pipe.transform(table_name=\"MY_FEATURES\")\n            ```\n\n        Note:\n            Only fitted pipelines\n            ([`fit`][getml.Pipeline.fit]) can transform\n            data into features.\n\n        \"\"\"\n\n        self._check_whether_fitted()\n\n        if isinstance(population_table, data.Subset):\n            peripheral_tables = population_table.peripheral\n            population_table = population_table.population\n\n        peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)\n\n        _check_df_types(population_table, peripheral_tables)\n\n        self._validate()\n\n        cmd: Dict[str, Any] = {}\n        cmd[\"type_\"] = self.type + \".transform\"\n        cmd[\"name_\"] = self.id\n        cmd[\"http_request_\"] = False\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Found!\":\n                comm.handle_engine_exception(msg)\n            y_hat = self._transform(\n                peripheral_tables,\n                population_table,\n                sock,\n                df_name=df_name,\n                table_name=table_name,\n            )\n\n        if df_name != \"\":\n            return data.DataFrame(name=df_name).refresh()\n\n        return y_hat\n\n    # ----------------------------------------------------------------\n\n    def _validate(self) -> None:\n        if not isinstance(self.id, str):\n            raise TypeError(\"'name' must be of type str\")\n\n        if not isinstance(self.data_model, DataModel):\n            raise TypeError(\"'data_model' must be a getml.data.DataModel.\")\n\n        if not _is_typed_list(self.peripheral, data.Placeholder):\n            raise TypeError(\n                \"'peripheral' must be either a getml.data.Placeholder or a list thereof\"\n            )\n\n        if not _is_subclass_list(self.preprocessors, _Preprocessor):\n            raise TypeError(\"'preprocessor' must be a list of _Preprocessor.\")\n\n        if not _is_subclass_list(self.feature_learners, _FeatureLearner):\n            raise TypeError(\"'feature_learners' must be a list of _FeatureLearners.\")\n\n        if not _is_subclass_list(self.feature_selectors, _Predictor):\n            raise TypeError(\n                \"'feature_selectors' must be a list of getml.predictors._Predictors.\"\n            )\n\n        if not _is_subclass_list(self.predictors, _Predictor):\n            raise TypeError(\n                \"'predictors' must be a list of getml.predictors._Predictors.\"\n            )\n\n        if not isinstance(self.include_categorical, bool):\n            raise TypeError(\"'include_categorical' must be a bool!\")\n\n        if not isinstance(self.share_selected_features, numbers.Real):\n            raise TypeError(\"'share_selected_features' must be number!\")\n\n        if not _is_typed_list(self.tags, str):\n            raise TypeError(\"'tags' must be a list of str.\")\n\n        if self.type != \"Pipeline\":\n            raise ValueError(\"'type' must be 'Pipeline'\")\n\n        for kkey in self.__dict__:\n            if kkey not in Pipeline._supported_params:  # pylint: disable=E1101\n                raise KeyError(\n                    \"\"\"Instance variable [\"\"\"\n                    + kkey\n                    + \"\"\"]\n                       is not supported in Pipeline.\"\"\"\n                )\n\n        for elem in self.feature_learners:\n            elem.validate()\n\n        for elem in self.feature_selectors:\n            elem.validate()\n\n        for elem in self.predictors:\n            elem.validate()\n\n        self._check_classification_or_regression()\n"
  },
  {
    "path": "src/python-api/getml/pipeline/plots.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCustom class for handling the plots of a pipeline.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict, Tuple\n\nimport numpy as np\n\nimport getml.communication as comm\n\n\nclass Plots:\n    \"\"\"\n    Custom class for handling the\n    plots generated by the pipeline.\n\n    Args:\n        name:\n            The id of the pipeline the plots are associated with.\n\n    ??? example\n        ```python\n        recall, precision = my_pipeline.plots.precision_recall_curve()\n        fpr, tpr = my_pipeline.plots.roc_curve()\n        ```\n\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(self, name: str) -> None:\n        if not isinstance(name, str):\n            raise ValueError(\"'name' must be a str.\")\n\n        self.name = name\n\n    # ------------------------------------------------------------\n\n    def lift_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"\n        Returns the data for the lift curve, as displayed in the getML Monitor.\n\n        This requires that you call\n        [`score`][getml.Pipeline.score] first. The data used\n        for the curve will always be the data from the *last* time\n        you called [`score`][getml.Pipeline.score].\n\n        Args:\n            target_num:\n                Indicates for which target you want to plot the lift\n                curve. (Pipelines can have more than one target.)\n\n        Returns:\n            The first array is the proportion of samples, usually displayed on the x-axis.\n                The second array is the lift, usually displayed on the y-axis.\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.lift_curve\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        return (np.asarray(json_obj[\"proportion_\"]), np.asarray(json_obj[\"lift_\"]))\n\n    # ------------------------------------------------------------\n\n    def precision_recall_curve(\n        self, target_num: int = 0\n    ) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"\n        Returns the data for the precision-recall curve, as displayed in the getML\n        Monitor.\n\n        This requires that you call\n        [`score`][getml.Pipeline.score] first. The data used\n        for the curve will always be the data from the *last* time\n        you called [`score`][getml.Pipeline.score].\n\n        Args:\n            target_num:\n                Indicates for which target you want to plot the lift\n                curve. (Pipelines can have more than one target.)\n\n        Returns:\n            The first array is the recall (a.k.a. true positive rate), usually displayed on the x-axis.\n                The second array is the precision, usually displayed on the y-axis.\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.precision_recall_curve\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        return (np.asarray(json_obj[\"tpr_\"]), np.asarray(json_obj[\"precision_\"]))\n\n    # ------------------------------------------------------------\n\n    def roc_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"\n        Returns the data for the ROC curve, as displayed in the getML Monitor.\n\n        This requires that you call\n        [`score`][getml.Pipeline.score] first. The data used\n        for the curve will always be the data from the *last* time\n        you called [`score`][getml.Pipeline.score].\n\n        Args:\n            target_num:\n                Indicates for which target you want to plot the lift\n                curve. (Pipelines can have more than one target.)\n\n        Returns:\n            The first array is the false positive rate, usually displayed on the x-axis.\n                The second array is the true positive rate, usually displayed on the y-axis.\n        \"\"\"\n\n        cmd: Dict[str, Any] = {}\n\n        cmd[\"type_\"] = \"Pipeline.roc_curve\"\n        cmd[\"name_\"] = self.name\n\n        cmd[\"target_num_\"] = target_num\n\n        with comm.send_and_get_socket(cmd) as sock:\n            msg = comm.recv_string(sock)\n            if msg != \"Success!\":\n                comm.handle_engine_exception(msg)\n            msg = comm.recv_string(sock)\n\n        json_obj = json.loads(msg)\n\n        return (np.asarray(json_obj[\"fpr_\"]), np.asarray(json_obj[\"tpr_\"]))\n"
  },
  {
    "path": "src/python-api/getml/pipeline/score.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nfrom abc import ABC, abstractmethod\nfrom dataclasses import asdict, dataclass\nfrom datetime import datetime\n\n# --------------------------------------------------------------------\n\n\n@dataclass\nclass Score(ABC):\n    date_time: datetime\n    set_used: str\n    target: str\n\n    @abstractmethod\n    def __repr__(self):\n        pass\n\n    def __iter__(self):\n        yield from asdict(self).items()\n\n\n# --------------------------------------------------------------------\n\n\n@dataclass\nclass ClassificationScore(Score):\n    \"\"\"\n    Dataclass that holds data of a scoring run for a classification pipeline.\n\n    Args:\n        accuracy:\n            The [`accuracy`][getml.pipeline.metrics.accuracy] of the classification.\n        auc:\n            The area under the curve: [`auc`][getml.pipeline.metrics.auc].\n        cross_entropy:\n            The [`cross_entropy`][getml.pipeline.metrics.cross_entropy].\n    \"\"\"\n\n    accuracy: float\n    auc: float\n    cross_entropy: float\n\n    def __repr__(self) -> str:\n        return f\"{self.date_time:%Y-%m-%d %H:%M:%S} {self.set_used} {self.target} {self.accuracy} {self.auc} {self.cross_entropy}\"\n\n\n# --------------------------------------------------------------------\n\n\n@dataclass\nclass RegressionScore(Score):\n    \"\"\"\n    Dataclass that holds data of a scoring run for a regression pipeline.\n\n    Args:\n        mae:\n            The mean absolute error: [`mae`][getml.pipeline.metrics.mae]\n        rmse:\n            The root mean squared error: [`rmse`][getml.pipeline.metrics.rmse]\n        rsquared:\n            The squared correlation coefficient: [`rsquared`][getml.pipeline.metrics.rsquared]\n    \"\"\"\n\n    mae: float\n    rmse: float\n    rsquared: float\n\n    def __repr__(self) -> str:\n        return f\"{self.date_time:%Y-%m-%d %H:%M:%S} {self.set_used} {self.target} {self.mae} {self.rmse} {self.rsquared}\"\n"
  },
  {
    "path": "src/python-api/getml/pipeline/scores_container.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA container for storing a pipeline's scoring history.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Callable, Dict, List, Sequence, Union\n\nfrom getml.utilities.formatting import _Formatter\n\nfrom .helpers import _unlist_maybe\nfrom .metrics import _all_metrics, accuracy, auc, cross_entropy, mae, rmse, rsquared\nfrom .score import ClassificationScore, Score\n\n\nclass Scores:\n    \"\"\"\n    Container which holds the history of all scores associated with a given pipeline.\n    The container supports slicing and is sort- and filterable.\n\n    Args:\n        data:\n            A list of [`Score`][getml.pipeline.score] objects.\n        latest:\n            A dictionary containing the latest scores for each metric.\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(self, data: Sequence[Score], latest: Dict[str, List[float]]) -> None:\n        self._latest = latest\n\n        self.is_classification = all(\n            isinstance(score, ClassificationScore) for score in data\n        )\n\n        self.is_regression = not self.is_classification\n\n        self.data = data\n\n        self.sets_used = [score.set_used for score in data]\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(self, key: Union[int, slice, str]):\n        if isinstance(key, int):\n            return self.data[key]\n\n        if isinstance(key, slice):\n            scores_subset = self.data[key]\n            return Scores(scores_subset, self._latest)\n\n        if isinstance(key, str):\n            # allow to access latest scores via their name for backward compatibility\n            if key in _all_metrics:\n                return self._latest[key]\n\n            scores_subset = [score for score in self.data if score.set_used == key]\n\n            return Scores(scores_subset, self._latest)\n\n        raise TypeError(\n            f\"Scores can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __iter__(self):\n        yield from self.data\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    # ------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        return self._format()._render_string()\n\n    # ------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        return self._format()._render_html()\n\n    # ------------------------------------------------------------\n\n    def _format(self) -> _Formatter:\n        headers = [\"date time\", \"set used\", \"target\"]\n        if self.is_classification:\n            headers += [\"accuracy\", \"auc\", \"cross entropy\"]\n        if self.is_regression:\n            headers += [\"mae\", \"rmse\", \"rsquared\"]\n\n        rows = [list(vars(score).values()) for score in self.data]\n\n        return _Formatter([headers], rows)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def accuracy(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `accuracy` from the latest scoring run.\n        \"\"\"\n        return _unlist_maybe(self._latest[accuracy])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def auc(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `auc` from the latest scoring run.\n        \"\"\"\n        return _unlist_maybe(self._latest[auc])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def cross_entropy(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `cross entropy` from the latest scoring run.\n        \"\"\"\n        return _unlist_maybe(self._latest[cross_entropy])\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable[[Score], bool]) -> Scores:\n        \"\"\"\n        Filters the scores container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered scores.\n\n        ??? example\n            ```python\n            from datetime import datetime, timedelta\n            one_week_ago = datetime.today() - timedelta(days=7)\n            scores_last_week = pipe.scores.filter(lambda score: score.date_time >= one_week_ago)\n            ```\n        \"\"\"\n        scores_filtered = [score for score in self.data if conditional(score)]\n\n        return Scores(scores_filtered, self._latest)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def mae(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `mae` from the latest scoring run.\n\n        Returns:\n                The mean absolute error.\n        \"\"\"\n        return _unlist_maybe(self._latest[mae])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def rmse(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `rmse` from the latest scoring run.\n\n        Returns:\n                The root mean squared error.\n        \"\"\"\n        return _unlist_maybe(self._latest[rmse])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def rsquared(self) -> Union[float, List[float]]:\n        \"\"\"\n        A convenience wrapper to retrieve the `rsquared` from the latest scoring run.\n\n        Returns:\n                The squared correlation coefficient.\n        \"\"\"\n        return _unlist_maybe(self._latest[rsquared])\n\n    # ----------------------------------------------------------------\n\n    def sort(\n        self, key: Callable[[Score], Union[float, int, str]], descending: bool = False\n    ) -> Scores:\n        \"\"\"\n        Sorts the scores container.\n\n        Args:\n            key:\n                A callable that evaluates to a sort key for a given item.\n\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted scores.\n\n        ??? example\n            ```python\n            by_auc = pipe.scores.sort(key=lambda score: score.auc)\n            most_recent_first = pipe.scores.sort(key=lambda score: score.date_time, descending=True)\n            ```\n        \"\"\"\n\n        scores_sorted = sorted(self.data, key=key, reverse=descending)\n        return Scores(scores_sorted, self._latest)\n"
  },
  {
    "path": "src/python-api/getml/pipeline/sql_code.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCustom class for handling the SQL code of the features.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nimport re\nfrom pathlib import Path\nfrom typing import Iterator, Sequence, Union\n\nimport numpy as np\nfrom rich import print\n\nfrom getml.data.helpers import _is_typed_list\n\nfrom .dialect import _drop_table, _table_pattern, sqlite3\nfrom .helpers import _edit_table_name, _edit_windows_filename\nfrom .sql_string import SQLString\n\n\nclass SQLCode:\n    \"\"\"\n    Custom class for handling the SQL code of the\n    features generated by the pipeline.\n\n    Args:\n        code:\n            The SQL code of the features.\n\n        dialect:\n            The SQL dialect used in the code. Default is 'sqlite3'.\n\n    ??? example\n        ```python\n        sql_code = my_pipeline.features.to_sql()\n\n        # You can access individual features\n        # by index.\n        feature_1_1 = sql_code[0]\n\n        # You can also access them by name.\n        feature_1_10 = sql_code[\"FEATURE_1_10\"]\n\n        # You can also type the name of\n        # a table or column to find all\n        # features related to that table\n        # or column.\n        features = sql_code.find(\"SOME_TABLE\")\n\n        # HINT: The generated SQL code always\n        # escapes table and column names using\n        # quotation marks. So if you want exact\n        # matching, you can do this:\n        features = sql_code.find('\"SOME_TABLE\"')\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        code: Sequence[Union[str, SQLString]],\n        dialect: str = sqlite3,\n    ) -> None:\n        if not _is_typed_list(code, str):\n            raise TypeError(\"'code' must be a list of str.\")\n\n        self.code = [SQLString(elem) for elem in code]\n\n        self.dialect = dialect\n\n        self.tables = [\n            _edit_table_name(table_name)\n            for table_name in re.findall(_table_pattern(self.dialect), \"\".join(code))\n        ]\n\n    def __getitem__(self, key: Union[int, slice, str]) -> Union[SQLCode, SQLString]:\n        if isinstance(key, int):\n            return self.code[key]\n\n        if isinstance(key, slice):\n            return SQLCode(self.code[key], self.dialect)\n\n        if isinstance(key, str):\n            if key.upper() in self.tables:\n                return self.find(_drop_table(self.dialect, key))[0]\n            return SQLString(\"\")\n\n        raise TypeError(\n            \"Features can only be indexed by: int, slices, \"\n            f\"or str, not {type(key).__name__}\"\n        )\n\n    def __iter__(self) -> Iterator[SQLString]:\n        yield from self.code\n\n    def __len__(self) -> int:\n        return len(self.code)\n\n    def __repr__(self) -> str:\n        return \"\\n\\n\\n\".join(self.code)\n\n    def _repr_markdown_(self) -> str:\n        return \"```sql\\n\" + self.__repr__() + \"\\n```\"\n\n    def find(self, keyword: str) -> SQLCode:\n        \"\"\"\n        Returns the SQLCode for all features\n        containing the keyword.\n\n        Args:\n            keyword: The keyword to be found.\n\n        Returns:\n            The SQL code for all features containing the keyword.\n        \"\"\"\n        if not isinstance(keyword, str):\n            raise TypeError(\"'keyword' must be a str.\")\n\n        return SQLCode([elem for elem in self.code if keyword in elem], self.dialect)\n\n    def save(self, fname: str, split: bool = True, remove: bool = False) -> None:\n        \"\"\"\n        Saves the SQL code to a file.\n\n        Args:\n            fname:\n                The name of the file or folder (if `split==True`)\n                in which you want to save the features.\n\n            split:\n                If True, the code will be split into multiple files, one for\n                each feature and saved into a folder `fname`.\n\n            remove:\n                If True, the existing SQL files in `fname` folder generated\n                previously with the save method will be removed.\n        \"\"\"\n        if not split:\n            with open(fname, \"w\", encoding=\"utf-8\") as sqlfile:\n                sqlfile.write(str(self))\n            return\n\n        directory = Path(fname)\n\n        if directory.exists():\n            iter_dir = os.listdir(fname)\n\n            pattern = r\"^\\d{4}.*\\_.*\\.sql$\"\n\n            exist_files_path = [fp for fp in iter_dir if re.search(pattern, fp)]\n\n            if not remove and exist_files_path:\n                print(f\"The following files already exist in the directory ({fname}):\")\n                for fp in np.sort(exist_files_path):\n                    print(fp)\n                print(\"Please set 'remove=True' to remove them.\")\n                return\n\n            if remove and exist_files_path:\n                for fp in exist_files_path:\n                    os.remove(fname + \"/\" + fp)\n\n        directory.mkdir(exist_ok=True)\n\n        for index, code in enumerate(self.code, 1):\n            match = re.search(_table_pattern(self.dialect), str(code))\n            name = _edit_table_name(match.group(1).lower()) if match else \"feature\"\n            name = _edit_windows_filename(name).replace(\".\", \"_\").replace(\"`\", \"\")\n            file_path = directory / f\"{index:04d}_{name}.sql\"\n            with open(file_path, \"w\", encoding=\"utf-8\") as sqlfile:\n                sqlfile.write(str(code))\n\n    def to_str(self) -> str:\n        \"\"\"\n        Returns a raw string representation of the SQL code.\n\n        Returns:\n            A raw string representation of the SQL code.\n        \"\"\"\n        return str(self)\n"
  },
  {
    "path": "src/python-api/getml/pipeline/sql_string.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCustom str type that holds SQL Source code.\n\"\"\"\n\n\nclass SQLString(str):\n    \"\"\"\n    A custom string type that handles the representation of SQL code strings.\n    \"\"\"\n\n    def _repr_markdown_(self) -> str:\n        return \"```sql\\n\" + self + \"\\n```\"\n"
  },
  {
    "path": "src/python-api/getml/pipeline/table.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nContains class representing data for a table of a pipeline.\n\"\"\"\n\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass Table:\n    \"\"\"\n    A dataclass that holds data about a single table.\n\n    Args:\n        name:\n            The name of the table.\n        importance:\n            The importance of the table.\n        target:\n            The target of the table.\n        marker:\n            The marker of the table.\n    \"\"\"\n\n    name: str\n    importance: float\n    target: str\n    marker: str\n"
  },
  {
    "path": "src/python-api/getml/pipeline/tables.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nContains custom class for handling the tables of a pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport re\nfrom typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.typing import NDArray\n\nfrom getml.utilities.formatting import _Formatter\n\nfrom .columns import Columns\nfrom .table import Table\n\n\nclass Tables:\n    \"\"\"\n    This container holds a pipeline's tables. These tables are build from the\n    columns for which importances can be calculated. The motivation behind this\n    container is to determine which tables are more important than others.\n\n    Tables can be accessed by name, index or with a NumPy array. The container\n    supports slicing and can be sorted and filtered. Further, the container\n    holds global methods to request tables' importances.\n\n    Args:\n        targets:\n            The targets associated with the pipeline.\n        columns:\n            The columns with which the tables are built.\n        data:\n            A list of [`Table`][getml.pipeline.table.Table] objects.\n\n    Note:\n        The container is an iterable. So, in addition to\n        [`filter`][getml.pipeline.Tables.filter] you can also use python list\n        comprehensions for filtering.\n\n    ??? example\n        ```python\n        all_my_tables = my_pipeline.tables\n        first_table = my_pipeline.tables[0]\n        all_but_last_10_tables = my_pipeline.tables[:-10]\n        important_tables = [table for table in my_pipeline.tables if table.importance > 0.1]\n        names, importances = my_pipeline.tables.importances()\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        targets: Sequence[str],\n        columns: Columns,\n        data: Optional[Sequence[Table]] = None,\n    ) -> None:\n        self._targets = targets\n        self._columns = columns\n\n        if data is not None:\n            self.data = data\n\n        else:\n            self._load_tables()\n\n        if not (targets and columns) and not data:\n            raise ValueError(\n                \"Missing required arguments. Either provide `targets` & \"\n                \"`columns` or else provide `data`.\"\n            )\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(\n        self, key: Union[str, int, slice, Union[NDArray[np.int_], NDArray[np.bool_]]]\n    ) -> Union[Table, Tables, List[Table]]:\n        if not self.data:\n            raise AttributeError(\"Tables container not fully initialized.\")\n\n        if isinstance(key, int):\n            return self.data[key]\n\n        if isinstance(key, slice):\n            tables_subset = self.data[key]\n            return self._make_tables(tables_subset)\n\n        if isinstance(key, str):\n            if key in self.names:\n                return [table for table in self.data if table.name == key][0]\n            raise AttributeError(f\"No Table with name: {key}\")\n\n        if isinstance(key, np.ndarray):\n            tables_subset = np.array(self.data)[key].tolist()\n            return self._make_tables(tables_subset)\n\n        raise TypeError(\n            \"Columns can only be indexed by: int, slices, str or np.ndarray,\"\n            f\" not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __iter__(self) -> Iterator[Table]:\n        yield from self.data\n\n    # ----------------------------------------------------------------\n\n    def __len__(self) -> int:\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self) -> str:\n        return self._format()._render_string()\n\n    # ------------------------------------------------------------\n\n    def _repr_html_(self) -> str:\n        return self._format()._render_html()\n\n    # ----------------------------------------------------------------\n\n    def _format(self) -> _Formatter:\n        headers = [[\"name\", \"importance\", \"target\", \"marker\"]]\n\n        rows = [\n            [\n                table.name,\n                table.importance,\n                table.target,\n                table.marker,\n            ]\n            for table in self.data\n        ]\n\n        return _Formatter(headers, rows)\n\n    # ----------------------------------------------------------------\n\n    def _load_tables(self) -> None:\n        \"\"\"\n        Gets tables data from columns\n        \"\"\"\n\n        tables = []\n\n        for table_target in self._targets:\n            importances: Dict[str, float] = {\n                column.table: 0.0\n                for column in self._columns\n                if column.target == table_target\n            }\n\n            targets: Dict[str, str] = {}\n            markers: Dict[str, str] = {}\n\n            for column in self._columns:\n                if column.target == table_target:\n                    importances[column.table] += column.importance\n                    targets[column.table] = column.target\n                    markers[column.table] = column.marker\n\n            tables_zip = zip(\n                importances.keys(),\n                importances.values(),\n                targets.values(),\n                markers.values(),\n            )\n\n            for name, importance, target, marker in tables_zip:\n                tables.append(\n                    Table(\n                        name=name, importance=importance, target=target, marker=marker\n                    )\n                )\n\n        self.data = tables\n\n    # ----------------------------------------------------------------\n\n    def _make_tables(self, data: Sequence[Table]) -> Tables:\n        \"\"\"\n        A factory to construct a [`Tables`][getml.pipeline.Tables] container\n        from a list of [`Table`][getml.pipeline.Table]s.\n        \"\"\"\n\n        return Tables(self._targets, self._columns, data=data)\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable[[Table], bool]) -> Tables:\n        \"\"\"\n        Filters the tables container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered tables.\n\n        ??? example\n            ```python\n            important_tables = my_pipeline.table.filter(lambda table: table.importance > 0.1)\n            peripheral_tables = my_pipeline.tables.filter(lambda table: table.marker == \"[PERIPHERAL]\")\n            ```\n        \"\"\"\n        tables_filtered = [table for table in self.data if conditional(table)]\n        return self._make_tables(tables_filtered)\n\n    # ----------------------------------------------------------------\n\n    def importances(\n        self, target_num: int = 0, sort: bool = True\n    ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]:\n        \"\"\"\n        Returns the importances of tables.\n\n        Table importances are calculated by summing up the importances of the\n        columns belonging to the tables. Each column is assigned an importance\n        value that measures its contribution to the predictive performance. For\n        each target, the importances add up to 1.\n\n        Args:\n            target_num:\n                Indicates for which target you want to view the\n                importances. (Pipelines can have more than one target.)\n\n            sort:\n                Whether you want the results to be sorted.\n\n        Returns:\n            The first array contains the names of the tables.\n            The second array contains their importances. By definition, all importances add up to 1.\n        \"\"\"\n\n        target_name = self._targets[target_num]\n\n        names = np.empty(0, dtype=str)\n        importances = np.empty(0, dtype=float)\n\n        for table in self.data:\n            if table.target == target_name:\n                names = np.append(names, table.name)\n                importances = np.append(importances, table.importance)\n\n        if not sort:\n            return names, importances\n\n        indices = np.argsort(importances)[::-1]\n\n        return (names[indices], importances[indices])\n\n    # ----------------------------------------------------------------\n\n    @property\n    def names(self) -> List[str]:\n        \"\"\"\n        Holds the names of a [`Pipeline`][getml.Pipeline]'s tables.\n\n        Returns:\n            List containing the names.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return [table.name for table in self.data]\n\n    # ----------------------------------------------------------------\n\n    def sort(\n        self,\n        by: Optional[str] = None,\n        key: Optional[Callable[[Table], Any]] = None,\n        descending: Optional[bool] = None,\n    ) -> Tables:\n        \"\"\"\n        Sorts the Tables container. If no arguments are provided the\n        container is sorted by target and name.\n\n        Args:\n            by:\n                The name of field to sort by. Possible fields:\n                    - name(s)\n                    - importances(s)\n            key:\n                A callable that evaluates to a sort key for a given item.\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted tables.\n\n        ??? example\n            ```python\n            by_importance = my_pipeline.tables.sort(key=lambda table: table.importance)\n            ```\n        \"\"\"\n\n        reverse = False if descending is None else descending\n\n        if (by is not None) and (key is not None):\n            raise ValueError(\"Only one of `by` and `key` can be provided.\")\n\n        if key is not None:\n            tables_sorted = sorted(self.data, key=key, reverse=reverse)\n            return self._make_tables(tables_sorted)\n\n        if by is None:\n            tables_sorted = sorted(\n                self.data, key=lambda table: table.name, reverse=reverse\n            )\n            tables_sorted.sort(key=lambda table: table.target)\n            return self._make_tables(tables_sorted)\n\n        if re.match(pattern=\"names?$\", string=by):\n            tables_sorted = sorted(\n                self.data, key=lambda table: table.name, reverse=reverse\n            )\n            return self._make_tables(tables_sorted)\n\n        if re.match(pattern=\"importances?$\", string=by):\n            reverse = True if descending is None else descending\n            tables_sorted = sorted(\n                self.data, key=lambda table: table.importance, reverse=reverse\n            )\n            return self._make_tables(tables_sorted)\n\n        raise ValueError(f\"Cannot sort by: {by}.\")\n\n    # ----------------------------------------------------------------\n\n    @property\n    def targets(self) -> List[str]:\n        \"\"\"\n        Holds the targets of a [`Pipeline`][getml.Pipeline]'s tables.\n\n        Returns:\n            List containing the names.\n\n        Note:\n            The order corresponds to the current sorting of the container.\n        \"\"\"\n        return [table.target for table in self.data]\n\n    # ----------------------------------------------------------------\n\n    def to_pandas(self) -> pd.DataFrame:\n        \"\"\"\n        Returns all information related to the tables in a pandas DataFrame.\n\n        Returns:\n            A pandas DataFrame containing the tables' names, importances, targets and markers.\n        \"\"\"\n\n        data_frame = pd.DataFrame()\n\n        for i, table in enumerate(self.data):\n            data_frame.loc[i, \"name\"] = table.name\n            data_frame.loc[i, \"importance\"] = table.importance\n            data_frame.loc[i, \"target\"] = table.target\n            data_frame.loc[i, \"marker\"] = table.marker\n\n        return data_frame\n"
  },
  {
    "path": "src/python-api/getml/pipeline/tags.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA small lists-type class that allows to search for arbitrary substrings within its items.\n\"\"\"\n\n\nclass Tags(list):\n    \"\"\"\n    A small lists-type class that allows to search for arbitrary substrings within its items.\n    \"\"\"\n\n    def __contains__(self, substr: object) -> bool:\n        if not isinstance(substr, str):\n            raise ValueError(\"Tags can only contain strings.\")\n        return any(substr in tag for tag in self)\n"
  },
  {
    "path": "src/python-api/getml/predictors/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\n\nThe predictor classes defined in this module **serve two purposes**.\n\nFirst, a predictor can be used as a **feature selector**\nin [`Pipeline`][getml.Pipeline] to only select the best features\ngenerated during the automated feature learning and to get rid of\nany redundancies.\n\nSecond, by using it as a **predictor**, it will\nbe trained on the features of the supplied data set and used to\npredict unknown results.\n\nEvery time a new data set is passed to\nthe [`predict`][getml.Pipeline.predict] method of one of the\nmodels, the raw relational data is interpreted in the\ndata model, which was provided during the construction of the model,\ntransformed into features using the trained feature learning\nalgorithm, and, finally, its [target][annotating-data-target]\nwill be predicted using the trained predictor.\n\nThe algorithms can be grouped according to their finesse and\nwhether you want to use them for a classification or\nregression problem. For memory intensive applications, the getML\nEnterprise edition offers predictors with memory mapping .\n\n\n\n\n|   | simple  | sophisticated  | memory intensive  |\n----|---------|---------------|--------------------|\n|  __regression__ | [`LinearRegression`][getml.predictors.LinearRegression]  |  [`XGBoostRegressor`][getml.predictors.XGBoostRegressor] | [`ScaleGBMRegressor`][getml.predictors.ScaleGBMRegressor] |\n| __classification__ |  [`LogisticRegression`][getml.predictors.LogisticRegression] | [`XGBoostClassifier`][getml.predictors.XGBoostClassifier]  | [`ScaleGBMClassifier`][getml.predictors.ScaleGBMClassifier] |\n\n\nNote:\n    All predictors need to be passed to [`Pipeline`][getml.Pipeline].\n\"\"\"\n\nfrom .linear_regression import LinearRegression\nfrom .logistic_regression import LogisticRegression\nfrom .predictor import _Predictor\nfrom .scale_gbm_classifier import ScaleGBMClassifier\nfrom .scale_gbm_regressor import ScaleGBMRegressor\nfrom .xgboost_classifier import XGBoostClassifier\nfrom .xgboost_regressor import XGBoostRegressor\n\n__all__ = (\n    \"ScaleGBMClassifier\",\n    \"ScaleGBMRegressor\",\n    \"LinearRegression\",\n    \"LogisticRegression\",\n    \"_Predictor\",\n    \"XGBoostClassifier\",\n    \"XGBoostRegressor\",\n)\n\n\n_classification_types = [\n    LogisticRegression().type,\n    XGBoostClassifier().type,\n    ScaleGBMClassifier().type,\n]\n"
  },
  {
    "path": "src/python-api/getml/predictors/linear_regression.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA simple linear regression model for predicting regression problems.\n\"\"\"\n\nimport numbers\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nimport numpy as np\n\nfrom getml.helpers import _check_parameter_bounds\n\nfrom .predictor import _Predictor\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass LinearRegression(_Predictor):\n    \"\"\"\n    Simple predictor for regression problems.\n\n    Learns a simple linear relationship using ordinary least squares (OLS)\n    regression:\n\n    $$\n    \\\\hat{y} = w_0 + w_1 * feature_1 + w_2 * feature_2 + ...\n    $$\n\n    The weights are optimized by minimizing the squared loss of the\n    predictions $\\\\hat{y}$ w.r.t. the [target][annotating-data-target] $y$.\n\n    $$\n    L(y,\\\\hat{y}) = \\\\frac{1}{n} \\\\sum_{i=1}^{n} (y_i -\\\\hat{y}_i)^2\n    $$\n\n    Linear regressions can be trained arithmetically or numerically.\n    Training arithmetically is more accurate, but suffers worse\n    scalability.\n\n    If you decide to pass [categorical features][annotating-data-categorical] to the\n    [`LinearRegression`][getml.predictors.LinearRegression], it will be trained\n    numerically. Otherwise, it will be trained arithmetically.\n\n    Args:\n        learning_rate:\n            The learning rate used for training numerically (only\n            relevant when categorical features are included). Range:\n            (0, ∞]\n\n        reg_lambda:\n            L2 regularization parameter. Range: [0, ∞]\n\n\n\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    learning_rate: float = 0.9\n    reg_lambda: float = 1e-10\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[dict] = None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If nothing is passed,\n                the default parameters will be validated.\n\n        ??? example\n            ```python\n            l = getml.predictors.LinearRegression()\n            l.learning_rate = 8.1\n            l.validate()\n            ```\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        _validate_linear_model_parameters(params)\n\n\n# ------------------------------------------------------------------------------\n\n\ndef _validate_linear_model_parameters(parameters: dict):\n    \"\"\"Checks both the types and values of the `parameters` and raises an\n    exception if something is off.\n\n    Examples:\n    ```python\n            getml.helpers.validation._validate_linear_model_parameters(\n                {'learning_rate': 0.1})\n    ```\n\n    Args:\n        parameters (dict): Dictionary containing some of all\n            parameters supported in\n            [`LinearRegression`][getml.predictors.LinearRegression] and\n            [`LogisticRegression`][getml.predictors.LogisticRegression].\n\n    Note:\n        Both [`LinearRegression`][getml.predictors.LinearRegression] and\n        [`LogisticRegression`][getml.predictors.LogisticRegression] have an instance\n        variable called ``type``, which is not checked in this\n        function but in the corresponding\n        [`validate`][getml.predictors.LinearRegression.validate] method. If\n        it is supplied to this function, it won't cause harm but will\n        be ignored instead of checked.\n    \"\"\"\n\n    allowed_parameters = {\"learning_rate\", \"reg_lambda\", \"type\"}\n\n    # ----------------------------------------------------------------\n\n    for kkey in parameters:\n        if kkey not in allowed_parameters:\n            raise KeyError(\"'unknown parameter: \" + kkey)\n\n        if kkey == \"learning_rate\":\n            if not isinstance(parameters[\"learning_rate\"], numbers.Real):\n                raise TypeError(\"'learning_rate' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"learning_rate\"],\n                \"learning_rate\",\n                [\n                    np.finfo(np.float64).resolution,  # pylint: disable=E1101\n                    np.finfo(np.float64).max,\n                ],\n            )\n\n        if kkey == \"reg_lambda\":\n            if not isinstance(parameters[\"reg_lambda\"], numbers.Real):\n                raise TypeError(\"'reg_lambda' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"reg_lambda\"], \"reg_lambda\", [0.0, np.finfo(np.float64).max]\n            )\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/predictors/logistic_regression.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA simple logistic regression model for predicting classification problems.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nfrom .linear_regression import _validate_linear_model_parameters\nfrom .predictor import _Predictor\n\n# ------------------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass LogisticRegression(_Predictor):\n    \"\"\"Simple predictor for classification problems.\n\n    Learns a simple linear relationship using the sigmoid function:\n\n    $$\n    \\\\hat{y} = \\\\sigma(w_0 + w_1 * feature_1 + w_2 * feature_2 + ...)\n    $$\n\n    $\\\\sigma$ denotes the sigmoid function:\n\n    $$\n    \\\\sigma(z) = \\\\frac{1}{1 + exp(-z)}\n    $$\n\n    The weights are optimized by minimizing the cross entropy loss of\n    the predictions $\\\\hat{y}$ w.r.t. the [targets][annotating-data-target] $y$.\n\n    $$\n    L(\\\\hat{y},y) = - y*\\\\log \\\\hat{y} - (1 - y)*\\\\log(1 - \\\\hat{y})\n    $$\n\n    Logistic regressions are always trained numerically.\n\n    If you decide to pass categorical\n    features: `annotating_roles_categorical` to the\n    [`LogisticRegression`][getml.predictors.LogisticRegression], it will be trained\n    using the Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm.\n    Otherwise, it will be trained using adaptive moments (Adam). BFGS\n    is more accurate, but less scalable than Adam.\n\n    Args:\n        learning_rate:\n            The learning rate used for the Adaptive Moments algorithm\n            (only relevant when categorical features are\n            included). Range: (0, ∞]\n\n        reg_lambda:\n            L2 regularization parameter. Range: [0, ∞]\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    learning_rate: float = 0.9\n    reg_lambda: float = 1e-10\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[dict] = None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        Examples:\n            ```python\n            l = getml.predictors.LogisticRegression()\n            l.learning_rate = 20\n            l.validate()\n            ```\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        _validate_linear_model_parameters(params)\n\n\n# ------------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/predictors/predictor.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nBase class. Should not ever be directly initialized!\n\"\"\"\n\nimport numbers\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nfrom typing import ClassVar, Set\n\nimport numpy as np\n\nfrom getml.feature_learning.validation import Validator\nfrom getml.utilities.formatting import _SignatureFormatter\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass _Predictor(ABC):\n    \"\"\"\n    Base class. Should not ever be directly initialized!\n    \"\"\"\n\n    _supported_params: ClassVar[Set]\n\n    # ----------------------------------------------------------------\n\n    def __post_init__(self):\n        type(self)._supported_params = set(vars(self).keys())\n\n        self.validate()\n\n        for param in self._supported_params:\n            setattr(type(self), param, Validator(param))\n\n    # ----------------------------------------------------------------\n\n    def __eq__(self, other):\n        if not isinstance(other, _Predictor):\n            raise TypeError(\"A predictor can only compared to another predictor!\")\n\n        # ------------------------------------------------------------\n\n        # Check whether both objects have the same number of instance\n        # variables.\n        if len(set(self.__dict__.keys())) != len(set(other.__dict__.keys())):\n            return False\n\n        # ------------------------------------------------------------\n\n        for kkey in self.__dict__:\n            if kkey not in other.__dict__:\n                return False\n\n            # Take special care when comparing numbers.\n            if isinstance(self.__dict__[kkey], numbers.Real):\n                if not np.isclose(self.__dict__[kkey], other.__dict__[kkey]):\n                    return False\n\n            elif self.__dict__[kkey] != other.__dict__[kkey]:\n                return False\n\n        # ------------------------------------------------------------\n\n        return True\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self):\n        return str(self)\n\n    # ----------------------------------------------------------------\n\n    def __setattr__(self, name, value):\n        if name in self.__dataclass_fields__:  # pylint: disable=E1101\n            super().__setattr__(name, value)\n        else:\n            raise AttributeError(\n                f\"Instance variable '{name}' is not supported in {self.type}.\"\n            )\n\n    # ----------------------------------------------------------------\n\n    def __str__(self):\n        sig = _SignatureFormatter(self)\n        return sig._format()\n\n    # ----------------------------------------------------------------\n\n    def _getml_deserialize(self):\n        # To ensure the getML can handle all keys, we have to add\n        # a trailing underscore.\n        encoding_dict = dict()\n\n        for kkey in self.__dict__:\n            encoding_dict[kkey + \"_\"] = self.__dict__[kkey]\n\n        encoding_dict[\"type_\"] = self.type\n\n        return encoding_dict\n\n    # ----------------------------------------------------------------\n\n    @property\n    def type(self):\n        return type(self).__name__\n\n    # ----------------------------------------------------------------\n\n    @abstractmethod\n    def validate(self, params=None):\n        pass\n\n\n# ------------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/predictors/scale_gbm_classifier.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA gradient boosting model for predicting classification problems.\n\"\"\"\n\nimport numbers\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Literal, Optional\n\nimport numpy as np\n\nfrom getml.helpers import _check_parameter_bounds\n\nfrom .predictor import _Predictor\n\n\ndef _validate_scalegbm_parameters(parameters: Dict[str, Any]):\n    for kkey in parameters:\n        if kkey == \"colsample_bylevel\":\n            if not isinstance(parameters[\"colsample_bylevel\"], numbers.Real):\n                raise TypeError(\"'colsample_bylevel' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"colsample_bylevel\"],\n                \"colsample_bylevel\",\n                [np.finfo(np.float64).resolution, 1.0],  # pylint: disable=E1101\n            )\n\n        if kkey == \"colsample_bytree\":\n            if not isinstance(parameters[\"colsample_bytree\"], numbers.Real):\n                raise TypeError(\"'colsample_bytree' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"colsample_bytree\"],\n                \"colsample_bytree\",\n                [np.finfo(np.float64).resolution, 1.0],  # pylint: disable=E1101\n            )\n\n        if kkey == \"early_stopping_rounds\":\n            if not isinstance(parameters[kkey], numbers.Real):\n                raise TypeError(f\"'{kkey}' must be a real number\")\n            _check_parameter_bounds(parameters[kkey], kkey, [0, np.iinfo(np.int32).max])\n\n        if kkey == \"gamma\":\n            if not isinstance(parameters[\"gamma\"], numbers.Real):\n                raise TypeError(\"'gamma' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"gamma\"], \"gamma\", [0.0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"goss_a\":\n            if not isinstance(parameters[kkey], numbers.Real):\n                raise TypeError(f\"'{kkey}' must be a real number\")\n            _check_parameter_bounds(\n                parameters[kkey], kkey, [0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"goss_b\":\n            if not isinstance(parameters[kkey], numbers.Real):\n                raise TypeError(f\"'{kkey}' must be a real number\")\n            _check_parameter_bounds(\n                parameters[kkey], kkey, [0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"learning_rate\":\n            if not isinstance(parameters[\"learning_rate\"], numbers.Real):\n                raise TypeError(\"'learning_rate' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"learning_rate\"], \"learning_rate\", [0.0, 1.0]\n            )\n\n        if kkey == \"max_depth\":\n            if not isinstance(parameters[\"max_depth\"], numbers.Real):\n                raise TypeError(\"'max_depth' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"max_depth\"], \"max_depth\", [0.0, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"min_child_weights\":\n            if not isinstance(parameters[\"min_child_weights\"], numbers.Real):\n                raise TypeError(\"'min_child_weights' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"min_child_weights\"],\n                \"min_child_weights\",\n                [0.0, np.finfo(np.float64).max],\n            )\n\n        if kkey == \"n_estimators\":\n            if not isinstance(parameters[\"n_estimators\"], numbers.Real):\n                raise TypeError(\"'n_estimators' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"n_estimators\"], \"n_estimators\", [10, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"n_jobs\":\n            if not isinstance(parameters[\"n_jobs\"], numbers.Real):\n                raise TypeError(\"'n_jobs' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"n_jobs\"], \"n_jobs\", [0, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"reg_lambda\":\n            if not isinstance(parameters[\"reg_lambda\"], numbers.Real):\n                raise TypeError(\"'reg_lambda' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"reg_lambda\"], \"reg_lambda\", [0.0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"seed\":\n            if not isinstance(parameters[kkey], numbers.Real):\n                raise TypeError(f\"'{kkey}' must be a real number\")\n            _check_parameter_bounds(parameters[kkey], kkey, [0, np.iinfo(np.int32).max])\n\n\n@dataclass(repr=False)\nclass ScaleGBMClassifier(_Predictor):\n    \"\"\"Standard gradient boosting classifier that fully supports memory mapping\n       and can be used for datasets that do not fit into memory.\n\n\n    Gradient tree boosting trains an ensemble of decision trees by training\n    each tree to predict the *prediction error of all previous trees* in the\n    ensemble:\n\n    $$\n    \\\\min_{\\\\nabla f_{t,i}} \\\\sum_i L(f_{t-1,i} + \\\\nabla f_{t,i}; y_i),\n    $$\n\n    where $\\\\nabla f_{t,i}$ is the prediction generated by the\n    newest decision tree for sample $i$ and $f_{t-1,i}$ is\n    the prediction generated by all previous trees, $L(...)$ is\n    the loss function used and $y_i$ is the [target][annotating-data-target] we are trying to predict.\n\n    XGBoost implements this general approach by adding two specific components:\n\n    1. The loss function $L(...)$ is approximated using a Taylor series.\n\n    2. The leaves of the decision tree $\\\\nabla f_{t,i}$ contain weights\n       that can be regularized.\n\n    These weights are calculated as follows:\n\n    $$\n    w_l = -\\\\frac{\\\\sum_{i \\\\in l} g_i}{ \\\\sum_{i \\\\in l} h_i + \\\\lambda},\n    $$\n\n    where $g_i$ and $h_i$ are the first and second order derivative\n    of $L(...)$ w.r.t. $f_{t-1,i}$, $w_l$ denotes the weight\n    on leaf $l$ and $i \\\\in l$ denotes all samples on that leaf.\n\n    $\\\\lambda$ is the regularization parameter `reg_lambda`.\n    This hyperparameter can be set by the users or the hyperparameter\n    optimization algorithm to avoid overfitting.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Args:\n        colsample_bylevel:\n            Subsample ratio for the columns used, for each level\n            inside a tree.\n\n            Note that ScaleGBM grows its trees level-by-level, not\n            node-by-node.\n            At each level, a subselection of the features will be randomly\n            picked and the best\n            feature for each split will be chosen. This hyperparameter\n            determines the share of features randomly picked at each level.\n            When set to 1, then now such sampling takes place.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        colsample_bytree:\n            Subsample ratio for the columns used, for each tree.\n            This means that for each tree, a subselection\n            of the features will be randomly chosen. This hyperparameter\n            determines the share of features randomly picked for each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        early_stopping_rounds:\n            The number of early_stopping_rounds for which we see\n            no improvement on the validation set until we stop\n            the training process.\n\n            Range: (0, ∞]\n\n        gamma:\n            Minimum loss reduction required for any update\n            to the tree. This means that every potential update\n            will first be evaluated for its improvement to the loss\n            function. If the improvement exceeds gamma,\n            the update will be accepted.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        goss_a:\n            Share of the samples with the largest residuals\n            taken for each tree.\n\n            If `goss_a` is set to 1, then gradients one-sided\n            sampling is effectively turned off.\n\n            Range: [0, 1]\n\n        goss_b:\n            Share of the samples that are not in the `goss_a`\n            percentile of largest residuals randomly sampled\n            for each tree.\n\n            The sum of `goss_a` and `goss_b` cannot exceed\n            1.\n\n            Range: [0, 1]\n\n        learning_rate:\n            Learning rate for the gradient boosting algorithm.\n            When a new tree $\\\\nabla f_{t,i}$ is trained,\n            it will be added to the existing trees\n            $f_{t-1,i}$. Before doing so, it will be\n            multiplied by the *learning_rate*.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, 1]\n\n        max_depth:\n            Maximum allowed depth of the trees.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        min_child_weights:\n            Minimum sum of weights needed in each child node for a\n            split. The idea here is that any leaf should have\n            a minimum number of samples in order to avoid overfitting.\n            This very common form of regularizing decision trees is\n            slightly\n            modified to refer to weights instead of number of samples,\n            but the basic idea is the same.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        n_estimators:\n            Number of estimators (trees).\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [10, ∞]\n\n        n_jobs:\n            Number of parallel threads. When set to zero, then\n            the optimal number of threads will be inferred automatically.\n\n            Range: [0, ∞]\n\n        objective:\n            Specify the learning task and the corresponding\n            learning objective.\n\n            Possible values:\n\n            * `binary:logistic`\n\n        reg_lambda:\n            L2 regularization on the weights. Please refer to\n            the introductory remarks to understand how this\n            hyperparameter influences your weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        seed:\n            Seed used for random sampling and other random\n            factors.\n\n            Range: [0, ∞]\n\n    \"\"\"\n\n    colsample_bylevel: float = 1.0\n    colsample_bytree: float = 1.0\n    early_stopping_rounds: int = 10\n    gamma: float = 0.0\n    goss_a: float = 1.0\n    goss_b: float = 0.0\n    learning_rate: float = 0.1\n    max_depth: int = 3\n    min_child_weights: float = 1.0\n    n_estimators: int = 100\n    n_jobs: int = 1\n    objective: Literal[\"binary:logistic\"] = \"binary:logistic\"\n    reg_lambda: float = 1.0\n    seed: int = 5843\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[dict] = None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        unsupported_params = [\n            k for k in params if k not in type(self)._supported_params\n        ]\n\n        if unsupported_params:\n            raise KeyError(\n                \"The following instance variables are not supported \"\n                + f\"in {self.type}: {unsupported_params}\"\n            )\n\n        _validate_scalegbm_parameters(params)\n"
  },
  {
    "path": "src/python-api/getml/predictors/scale_gbm_regressor.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA gradient boosting model for predicting regression problems.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Literal\n\nfrom .predictor import _Predictor\nfrom .scale_gbm_classifier import _validate_scalegbm_parameters\n\n\n@dataclass(repr=False)\nclass ScaleGBMRegressor(_Predictor):\n    \"\"\"Standard gradient boosting regressor that fully supports memory mapping\n       and can be used for datasets that do not fit into memory.\n\n    Gradient tree boosting trains an ensemble of decision trees by training\n    each tree to predict the *prediction error of all previous trees* in the\n    ensemble:\n\n    $$\n    \\\\min_{\\\\nabla f_{t,i}} \\\\sum_i L(f_{t-1,i} + \\\\nabla f_{t,i}; y_i),\n    $$\n\n    where $\\\\nabla f_{t,i}$ is the prediction generated by the\n    newest decision tree for sample $i$ and $f_{t-1,i}$ is\n    the prediction generated by all previous trees, $L(...)$ is\n    the loss function used and $y_i$ is the [target][annotating-data-target] we are trying to predict.\n\n    The regressor implements this general approach by adding two specific components:\n\n    1. The loss function $L(...)$ is approximated using a Taylor series.\n\n    2. The leaves of the decision tree $\\\\nabla f_{t,i}$ contain weights\n       that can be regularized.\n\n    These weights are calculated as follows:\n\n    $$\n    w_l = -\\\\frac{\\\\sum_{i \\\\in l} g_i}{ \\\\sum_{i \\\\in l} h_i + \\\\lambda},\n    $$\n\n    where $g_i$ and $h_i$ are the first and second order derivative\n    of $L(...)$ w.r.t. $f_{t-1,i}$, $w_l$ denotes the weight\n    on leaf $l$ and $i \\\\in l$ denotes all samples on that leaf.\n\n    $\\\\lambda$ is the regularization parameter `reg_lambda`.\n    This hyperparameter can be set by the users or the hyperparameter\n    optimization algorithm to avoid overfitting.\n\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n\n    Args:\n        colsample_bylevel:\n            Subsample ratio for the columns used, for each level\n            inside a tree.\n\n            Note that ScaleGBM grows its trees level-by-level, not\n            node-by-node.\n            At each level, a subselection of the features will be randomly\n            picked and the best\n            feature for each split will be chosen. This hyperparameter\n            determines the share of features randomly picked at each level.\n            When set to 1, then now such sampling takes place.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        colsample_bytree:\n            Subsample ratio for the columns used, for each tree.\n            This means that for each tree, a subselection\n            of the features will be randomly chosen. This hyperparameter\n            determines the share of features randomly picked for each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        early_stopping_rounds:\n            The number of early_stopping_rounds for which we see\n            no improvement on the validation set until we stop\n            the training process.\n\n            Range: (0, ∞]\n\n        gamma:\n            Minimum loss reduction required for any update\n            to the tree. This means that every potential update\n            will first be evaluated for its improvement to the loss\n            function. If the improvement exceeds gamma,\n            the update will be accepted.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        goss_a:\n            Share of the samples with the largest residuals\n            taken for each tree.\n\n            If `goss_a` is set to 1, then gradients one-sided\n            sampling is effectively turned off.\n\n            Range: [0, 1]\n\n        goss_b:\n            Share of the samples that are not in the `goss_a`\n            percentile of largest residuals randomly sampled\n            for each tree.\n\n            The sum of `goss_a` and `goss_b` cannot exceed\n            1.\n\n            Range: [0, 1]\n\n        learning_rate:\n            Learning rate for the gradient boosting algorithm.\n            When a new tree $\\\\nabla f_{t,i}$ is trained,\n            it will be added to the existing trees\n            $f_{t-1,i}$. Before doing so, it will be\n            multiplied by the *learning_rate*.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, 1]\n\n        max_depth:\n            Maximum allowed depth of the trees.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        min_child_weights:\n            Minimum sum of weights needed in each child node for a\n            split. The idea here is that any leaf should have\n            a minimum number of samples in order to avoid overfitting.\n            This very common form of regularizing decision trees is\n            slightly\n            modified to refer to weights instead of number of samples,\n            but the basic idea is the same.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        n_estimators:\n            Number of estimators (trees).\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [10, ∞]\n\n        n_jobs:\n            Number of parallel threads. When set to zero, then\n            the optimal number of threads will be inferred automatically.\n\n            Range: [0, ∞]\n\n        objective:\n            Specify the learning task and the corresponding\n            learning objective.\n\n            Possible values:\n\n            * `reg:squarederror`\n\n        reg_lambda:\n            L2 regularization on the weights. Please refer to\n            the introductory remarks to understand how this\n            hyperparameter influences your weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        seed:\n            Seed used for random sampling and other random\n            factors.\n\n            Range: [0, ∞]\n    \"\"\"\n\n    colsample_bylevel: float = 1.0\n    colsample_bytree: float = 1.0\n    early_stopping_rounds: int = 10\n    gamma: float = 0.0\n    goss_a: float = 1.0\n    goss_b: float = 0.0\n    learning_rate: float = 0.1\n    max_depth: int = 3\n    min_child_weights: float = 1.0\n    n_estimators: int = 100\n    n_jobs: int = 1\n    objective: Literal[\"reg:squarederror\"] = \"reg:squarederror\"\n    reg_lambda: float = 1.0\n    seed: int = 5843\n\n    def validate(self, params=None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params (dict, optional): A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        unsupported_params = [\n            k for k in params if k not in type(self)._supported_params\n        ]\n\n        if unsupported_params:\n            raise KeyError(\n                \"The following instance variables are not supported \"\n                + f\"in {self.type}: {unsupported_params}\"\n            )\n\n        _validate_scalegbm_parameters(params)\n"
  },
  {
    "path": "src/python-api/getml/predictors/xgboost_classifier.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA gradient boosting model for predicting classification problems.\n\"\"\"\n\nimport numbers\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Literal, Optional\n\nimport numpy as np\n\nfrom getml.helpers import _check_parameter_bounds\n\nfrom .predictor import _Predictor\n\n# --------------------------------------------------------------------\n\n\ndef _validate_xgboost_parameters(parameters: Dict[str, Any]):\n    allowed_parameters = {\n        \"booster\",\n        \"colsample_bylevel\",\n        \"colsample_bytree\",\n        \"early_stopping_rounds\",\n        \"gamma\",\n        \"learning_rate\",\n        \"max_delta_step\",\n        \"max_depth\",\n        \"min_child_weights\",\n        \"n_estimators\",\n        \"n_jobs\",\n        \"external_memory\",\n        \"normalize_type\",\n        \"num_parallel_tree\",\n        \"objective\",\n        \"one_drop\",\n        \"rate_drop\",\n        \"reg_alpha\",\n        \"reg_lambda\",\n        \"sample_type\",\n        \"silent\",\n        \"skip_drop\",\n        \"subsample\",\n        \"type\",\n    }\n\n    # ----------------------------------------------------------------\n\n    for kkey in parameters:\n        if kkey not in allowed_parameters:\n            raise KeyError(\"'unknown XGBoost parameter: \" + kkey)\n\n        if kkey == \"booster\":\n            if not isinstance(parameters[\"booster\"], str):\n                raise TypeError(\"'booster' must be of type str\")\n            if parameters[\"booster\"] not in [\"gbtree\", \"gblinear\", \"dart\"]:\n                raise ValueError(\n                    \"'booster' must either be 'gbtree', 'gblinear', or 'dart'\"\n                )\n\n        if kkey == \"colsample_bylevel\":\n            if not isinstance(parameters[\"colsample_bylevel\"], numbers.Real):\n                raise TypeError(\"'colsample_bylevel' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"colsample_bylevel\"],\n                \"colsample_bylevel\",\n                [np.finfo(np.float64).resolution, 1.0],  # pylint: disable=E1101\n            )\n\n        if kkey == \"colsample_bytree\":\n            if not isinstance(parameters[\"colsample_bytree\"], numbers.Real):\n                raise TypeError(\"'colsample_bytree' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"colsample_bytree\"],\n                \"colsample_bytree\",\n                [np.finfo(np.float64).resolution, 1.0],  # pylint: disable=E1101\n            )\n\n        if kkey == \"gamma\":\n            if not isinstance(parameters[\"gamma\"], numbers.Real):\n                raise TypeError(\"'gamma' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"gamma\"], \"gamma\", [0.0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"learning_rate\":\n            if not isinstance(parameters[\"learning_rate\"], numbers.Real):\n                raise TypeError(\"'learning_rate' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"learning_rate\"], \"learning_rate\", [0.0, 1.0]\n            )\n\n        if kkey == \"max_delta_step\":\n            if not isinstance(parameters[\"max_delta_step\"], numbers.Real):\n                raise TypeError(\"'max_delta_step' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"max_delta_step\"],\n                \"max_delta_step\",\n                [0.0, np.finfo(np.float64).max],\n            )\n\n        if kkey == \"max_depth\":\n            if not isinstance(parameters[\"max_depth\"], numbers.Real):\n                raise TypeError(\"'max_depth' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"max_depth\"], \"max_depth\", [0.0, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"min_child_weights\":\n            if not isinstance(parameters[\"min_child_weights\"], numbers.Real):\n                raise TypeError(\"'min_child_weights' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"min_child_weights\"],\n                \"min_child_weights\",\n                [0.0, np.finfo(np.float64).max],\n            )\n\n        if kkey == \"n_estimators\":\n            if not isinstance(parameters[\"n_estimators\"], numbers.Real):\n                raise TypeError(\"'n_estimators' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"n_estimators\"], \"n_estimators\", [10, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"normalize_type\":\n            if not isinstance(parameters[\"normalize_type\"], str):\n                raise TypeError(\"'normalize_type' must be of type str\")\n\n            if \"booster\" in parameters and parameters[\"booster\"] == \"dart\":\n                if parameters[\"normalize_type\"] not in [\"forest\", \"tree\"]:\n                    raise ValueError(\n                        \"'normalize_type' must either be 'forest' or 'tree'\"\n                    )\n\n        if kkey == \"num_parallel_tree\":\n            if not isinstance(parameters[\"num_parallel_tree\"], numbers.Real):\n                raise TypeError(\"'num_parallel_tree' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"num_parallel_tree\"],\n                \"num_parallel_tree\",\n                [1, np.iinfo(np.int32).max],\n            )\n\n        if kkey == \"n_jobs\":\n            if not isinstance(parameters[\"n_jobs\"], numbers.Real):\n                raise TypeError(\"'n_jobs' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"n_jobs\"], \"n_jobs\", [0, np.iinfo(np.int32).max]\n            )\n\n        if kkey == \"objective\":\n            if not isinstance(parameters[\"objective\"], str):\n                raise TypeError(\"'objective' must be of type str\")\n            if parameters[\"objective\"] not in [\n                \"reg:squarederror\",\n                \"reg:tweedie\",\n                \"reg:linear\",\n                \"reg:logistic\",\n                \"binary:logistic\",\n                \"binary:logitraw\",\n            ]:\n                raise ValueError(\n                    \"\"\"'objective' must either be 'reg:squarederror', \"\"\"\n                    \"\"\"'reg:tweedie', 'reg:linear', 'reg:logistic', \"\"\"\n                    \"\"\"'binary:logistic', or 'binary:logitraw'\"\"\"\n                )\n\n        if kkey == \"external_memory\":\n            if not isinstance(parameters[\"external_memory\"], bool):\n                raise TypeError(\"'external_memory' must be a bool\")\n\n        if kkey == \"one_drop\":\n            if not isinstance(parameters[\"one_drop\"], bool):\n                raise TypeError(\"'one_drop' must be a bool\")\n\n        if kkey == \"rate_drop\":\n            if not isinstance(parameters[\"rate_drop\"], numbers.Real):\n                raise TypeError(\"'rate_drop' must be a real number\")\n            _check_parameter_bounds(parameters[\"rate_drop\"], \"rate_drop\", [0.0, 1.0])\n\n        if kkey == \"reg_alpha\":\n            if not isinstance(parameters[\"reg_alpha\"], numbers.Real):\n                raise TypeError(\"'reg_alpha' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"reg_alpha\"], \"reg_alpha\", [0.0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"reg_lambda\":\n            if not isinstance(parameters[\"reg_lambda\"], numbers.Real):\n                raise TypeError(\"'reg_lambda' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"reg_lambda\"], \"reg_lambda\", [0.0, np.finfo(np.float64).max]\n            )\n\n        if kkey == \"sample_type\":\n            if not isinstance(parameters[\"sample_type\"], str):\n                raise TypeError(\"'sample_type' must be of type str\")\n\n            if \"booster\" in parameters and parameters[\"booster\"] == \"dart\":\n                if parameters[\"sample_type\"] not in [\"uniform\", \"weighted\"]:\n                    raise ValueError(\n                        \"'sample_type' must either be 'uniform' or 'weighted'\"\n                    )\n\n        if kkey == \"silent\":\n            if not isinstance(parameters[\"silent\"], bool):\n                raise TypeError(\"'silent' must be of type bool\")\n\n        if kkey == \"skip_drop\":\n            if not isinstance(parameters[\"skip_drop\"], numbers.Real):\n                raise TypeError(\"'skip_drop' must be a real number\")\n            _check_parameter_bounds(parameters[\"skip_drop\"], \"skip_drop\", [0.0, 1.0])\n\n        if kkey == \"subsample\":\n            if not isinstance(parameters[\"subsample\"], numbers.Real):\n                raise TypeError(\"'subsample' must be a real number\")\n            _check_parameter_bounds(\n                parameters[\"subsample\"],\n                \"subsample\",\n                [np.finfo(np.float64).resolution, 1.0],  # pylint: disable=E1101\n            )\n\n\n# --------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass XGBoostClassifier(_Predictor):\n    \"\"\"Gradient boosting classifier based on\n    [xgboost ](https://xgboost.readthedocs.io/en/latest/).\n\n    XGBoost is an implementation of the gradient tree boosting algorithm that\n    is widely recognized for its efficiency and predictive accuracy.\n\n    Gradient tree boosting trains an ensemble of decision trees by training\n    each tree to predict the *prediction error of all previous trees* in the\n    ensemble:\n\n    $$\n    \\\\min_{\\\\nabla f_{t,i}} \\\\sum_i L(f_{t-1,i} + \\\\nabla f_{t,i}; y_i),\n    $$\n\n    where $\\\\nabla f_{t,i}$ is the prediction generated by the\n    newest decision tree for sample $i$ and $f_{t-1,i}$ is\n    the prediction generated by all previous trees, $L(...)$ is\n    the loss function used and $y_i$ is the [target][annotating-data-target] we are trying to predict.\n\n    XGBoost implements this general approach by adding two specific components:\n\n    1. The loss function $L(...)$ is approximated using a Taylor series.\n\n    2. The leaves of the decision tree $\\\\nabla f_{t,i}$ contain weights\n       that can be regularized.\n\n    These weights are calculated as follows:\n\n    $$\n    w_l = -\\\\frac{\\\\sum_{i \\\\in l} g_i}{ \\\\sum_{i \\\\in l} h_i + \\\\lambda},\n    $$\n\n    where $g_i$ and $h_i$ are the first and second order derivative\n    of $L(...)$ w.r.t. $f_{t-1,i}$, $w_l$ denotes the weight\n    on leaf $l$ and $i \\\\in l$ denotes all samples on that leaf.\n\n    $\\\\lambda$ is the regularization parameter `reg_lambda`.\n    This hyperparameter can be set by the users or the hyperparameter\n    optimization algorithm to avoid overfitting.\n\n    Args:\n        booster:\n            Which base classifier to use.\n\n            Possible values:\n\n            * `gbtree`: normal gradient boosted decision trees\n            * `gblinear`: uses a linear model instead of decision trees\n            * 'dart': adds dropout to the standard gradient boosting algorithm.\n              Please also refer to the remarks on *rate_drop* for further\n              explanation on 'dart'.\n\n        colsample_bylevel:\n            Subsample ratio for the columns used, for each level\n            inside a tree.\n\n            Note that XGBoost grows its trees level-by-level, not\n            node-by-node.\n            At each level, a subselection of the features will be randomly\n            picked and the best\n            feature for each split will be chosen. This hyperparameter\n            determines the share of features randomly picked at each level.\n            When set to 1, then now such sampling takes place.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        colsample_bytree:\n            Subsample ratio for the columns used, for each tree.\n            This means that for each tree, a subselection\n            of the features will be randomly chosen. This hyperparameter\n            determines the share of features randomly picked for each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        early_stopping_rounds:\n            The number of early_stopping_rounds for which we see\n            no improvement on the validation set until we stop\n            the training process.\n\n            Range: (0, ∞]\n\n        gamma:\n            Minimum loss reduction required for any update\n            to the tree. This means that every potential update\n            will first be evaluated for its improvement to the loss\n            function. If the improvement exceeds gamma,\n            the update will be accepted.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        learning_rate:\n            Learning rate for the gradient boosting algorithm.\n            When a new tree $\\\\nabla f_{t,i}$ is trained,\n            it will be added to the existing trees\n            $f_{t-1,i}$. Before doing so, it will be\n            multiplied by the *learning_rate*.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, 1]\n\n        max_delta_step:\n            The maximum delta step allowed for the weight estimation\n            of each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞)\n\n        max_depth:\n            Maximum allowed depth of the trees.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        min_child_weights:\n            Minimum sum of weights needed in each child node for a\n            split. The idea here is that any leaf should have\n            a minimum number of samples in order to avoid overfitting.\n            This very common form of regularizing decision trees is\n            slightly\n            modified to refer to weights instead of number of samples,\n            but the basic idea is the same.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        n_estimators:\n            Number of estimators (trees).\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [10, ∞]\n\n        external_memory:\n            When the in_memory flag of the Engine is set to False,\n            XGBoost can use the external memory functionality.\n            This reduces the memory consumption,\n            but can also affect the quality of the predictions.\n            External memory is deactivated by default and it\n            is recommended to only use external memory\n            for feature selection.\n            When the in_memory flag of the Engine is set to True,\n            (the default value), XGBoost will never use\n            external memory.\n\n        normalize_type:\n            This determines how to normalize trees during 'dart'.\n\n            Possible values:\n\n            * 'tree': a new tree has the same weight as a single\n              dropped tree.\n\n            * 'forest': a new tree has the same weight as a the sum of\n              all dropped trees.\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n        n_jobs:\n            Number of parallel threads. When set to zero, then\n            the optimal number of threads will be inferred automatically.\n\n            Range: [0, ∞]\n\n        objective:\n            Specify the learning task and the corresponding\n            learning objective.\n\n            Possible values:\n\n            * `reg:logistic`\n            * `binary:logistic`\n            * `binary:logitraw`\n\n        one_drop:\n            If set to True, then at least one tree will always be\n            dropped out. Setting this hyperparameter to *true* reduces\n            the likelihood of overfitting.\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n        rate_drop:\n            Dropout rate for trees - determines the probability\n            that a tree will be dropped out. Dropout is an\n            algorithm that enjoys considerable popularity in\n            the deep learning community. It means that every node can\n            be randomly removed during training.\n\n            This approach\n            can also be applied to gradient boosting, where it\n            means that every tree can be randomly removed with\n            a certain probability. Said probability is determined\n            by *rate_drop*. Dropout for gradient boosting is\n            referred to as the 'dart' algorithm.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n        reg_alpha:\n            L1 regularization on the weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        reg_lambda:\n            L2 regularization on the weights. Please refer to\n            the introductory remarks to understand how this\n            hyperparameter influences your weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        sample_type:\n            Possible values:\n\n            * `uniform`: every tree is equally likely to be dropped\n              out\n\n            * `weighted`: the dropout probability will be proportional\n              to a tree's weight\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to `dart`.\n\n        silent:\n            In silent mode, XGBoost will not print out information on\n            the training progress.\n\n        skip_drop:\n            Probability of skipping the dropout during a given\n            iteration. Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n            Range: [0, 1]\n\n        subsample:\n            Subsample ratio from the training set. This means\n            that for every tree a subselection of *samples*\n            from the training set will be included into training.\n            Please note that this samples *without* replacement -\n            the common approach for random forests is to sample\n            *with* replace.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n    \"\"\"\n\n    booster: str = \"gbtree\"\n    colsample_bylevel: float = 1.0\n    colsample_bytree: float = 1.0\n    early_stopping_rounds: int = 10\n    gamma: float = 0.0\n    learning_rate: float = 0.1\n    max_delta_step: float = 0.0\n    max_depth: int = 3\n    min_child_weights: float = 1.0\n    n_estimators: int = 100\n    external_memory: bool = False\n    normalize_type: str = \"tree\"\n    num_parallel_tree: int = 1\n    n_jobs: int = 1\n    objective: Literal[\"reg:logistic\", \"binary:logistic\", \"binary:logitraw\"] = (\n        \"binary:logistic\"\n    )\n    one_drop: bool = False\n    rate_drop: float = 0.0\n    reg_alpha: float = 0.0\n    reg_lambda: float = 1.0\n    sample_type: str = \"uniform\"\n    silent: bool = True\n    skip_drop: float = 0.0\n    subsample: float = 1.0\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[dict] = None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        ??? example\n            ```python\n            x = getml.predictors.XGBoostClassifier()\n            x.gamma = 200\n            x.validate()\n            ```\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        _validate_xgboost_parameters(params)\n\n        # ------------------------------------------------------------\n\n        if params[\"objective\"] not in [\n            \"reg:logistic\",\n            \"binary:logistic\",\n            \"binary:logitraw\",\n        ]:\n            raise ValueError(\n                \"\"\"'objective' supported in XGBoostClassifier\n                                 are 'reg:logistic', 'binary:logistic',\n                                 and 'binary:logitraw'\"\"\"\n            )\n"
  },
  {
    "path": "src/python-api/getml/predictors/xgboost_regressor.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA gradient boosting model for predicting regression problems.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Literal, Optional\n\nfrom .predictor import _Predictor\nfrom .xgboost_classifier import _validate_xgboost_parameters\n\n# ------------------------------------------------------------------------------\n\n\n@dataclass(repr=False)\nclass XGBoostRegressor(_Predictor):\n    \"\"\"Gradient boosting regressor based on [xgboost ](https://xgboost.readthedocs.io/en/latest/).\n\n    XGBoost is an implementation of the gradient tree boosting algorithm that\n    is widely recognized for its efficiency and predictive accuracy.\n\n    Gradient tree boosting trains an ensemble of decision trees by training\n    each tree to predict the *prediction error of all previous trees* in the\n    ensemble:\n\n    $$\n    \\\\min_{\\\\nabla f_{t,i}} \\\\sum_i L(f_{t-1,i} + \\\\nabla f_{t,i}; y_i),\n    $$\n\n    where $\\\\nabla f_{t,i}$ is the prediction generated by the\n    newest decision tree for sample $i$ and $f_{t-1,i}$ is\n    the prediction generated by all previous trees, $L(...)$ is\n    the loss function used and $y_i$ is the [target][annotating-data-target] we are trying to predict.\n\n    XGBoost implements this general approach by adding two specific components:\n\n    1. The loss function $L(...)$ is approximated using a Taylor series.\n\n    2. The leaves of the decision tree $\\\\nabla f_{t,i}$ contain weights\n       that can be regularized.\n\n    These weights are calculated as follows:\n\n    $$\n    w_l = -\\\\frac{\\\\sum_{i \\\\in l} g_i}{ \\\\sum_{i \\\\in l} h_i + \\\\lambda},\n    $$\n\n    where $g_i$ and $h_i$ are the first and second order derivative\n    of $L(...)$ w.r.t. $f_{t-1,i}$, $w_l$ denotes the weight\n    on leaf $l$ and $i \\\\in l$ denotes all samples on that leaf.\n\n    $\\\\lambda$ is the regularization parameter `reg_lambda`.\n    This hyperparameter can be set by the users or the hyperparameter\n    optimization algorithm to avoid overfitting.\n\n    Args:\n        booster:\n            Which base classifier to use.\n\n            Possible values:\n\n            * `gbtree`: normal gradient boosted decision trees\n            * `gblinear`: uses a linear model instead of decision trees\n            * 'dart': adds dropout to the standard gradient boosting algorithm.\n              Please also refer to the remarks on *rate_drop* for further\n              explanation on `dart`.\n\n        colsample_bylevel:\n            Subsample ratio for the columns used, for each level\n            inside a tree.\n\n            Note that XGBoost grows its trees level-by-level, not\n            node-by-node.\n            At each level, a subselection of the features will be randomly\n            picked and the best\n            feature for each split will be chosen. This hyperparameter\n            determines the share of features randomly picked at each level.\n            When set to 1, then now such sampling takes place.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        colsample_bytree:\n            Subsample ratio for the columns used, for each tree.\n            This means that for each tree, a subselection\n            of the features will be randomly chosen. This hyperparameter\n            determines the share of features randomly picked for each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n\n        early_stopping_rounds:\n            The number of early_stopping_rounds for which we see\n            no improvement on the validation set until we stop\n            the training process.\n\n            Range: (0, ∞]\n\n        external_memory:\n            When the in_memory flag of the Engine is set to False,\n            XGBoost can use the external memory functionality.\n            This reduces the memory consumption,\n            but can also affect the quality of the predictions.\n            External memory is deactivated by default and it\n            is recommended to only use external memory\n            for feature selection.\n            When the in_memory flag of the Engine is set to True,\n            (the default value), XGBoost will never use\n            external memory.\n\n        gamma:\n            Minimum loss reduction required for any update\n            to the tree. This means that every potential update\n            will first be evaluated for its improvement to the loss\n            function. If the improvement exceeds gamma,\n            the update will be accepted.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        learning_rate:\n            Learning rate for the gradient boosting algorithm.\n            When a new tree $\\\\nabla f_{t,i}$ is trained,\n            it will be added to the existing trees\n            $f_{t-1,i}$. Before doing so, it will be\n            multiplied by the *learning_rate*.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, 1]\n\n        max_delta_step:\n            The maximum delta step allowed for the weight estimation\n            of each tree.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞)\n\n        max_depth:\n            Maximum allowed depth of the trees.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        min_child_weights:\n            Minimum sum of weights needed in each child node for a\n            split. The idea here is that any leaf should have\n            a minimum number of samples in order to avoid overfitting.\n            This very common form of regularizing decision trees is\n            slightly\n            modified to refer to weights instead of number of samples,\n            but the basic idea is the same.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        n_estimators:\n            Number of estimators (trees).\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [10, ∞]\n\n        normalize_type:\n            This determines how to normalize trees during 'dart'.\n\n            Possible values:\n\n            * `tree`: a new tree has the same weight as a single\n              dropped tree.\n\n            * `forest`: a new tree has the same weight as the sum of\n              all dropped trees.\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to `dart`.\n\n        n_jobs:\n            Number of parallel threads. When set to zero, then\n            the optimal number of threads will be inferred automatically.\n\n            Range: [0, ∞]\n\n        objective:\n            Specify the learning task and the corresponding\n            learning objective.\n\n            Possible values:\n\n            * `reg:squarederror`\n            * `reg:tweedie`\n            * 'reg:linear'\n\n        one_drop:\n            If set to True, then at least one tree will always be\n            dropped out. Setting this hyperparameter to *true* reduces\n            the likelihood of overfitting.\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n        rate_drop:\n            Dropout rate for trees - determines the probability\n            that a tree will be dropped out. Dropout is an\n            algorithm that enjoys considerable popularity in\n            the deep learning community. It means that every node can\n            be randomly removed during training.\n\n            This approach\n            can also be applied to gradient boosting, where it\n            means that every tree can be randomly removed with\n            a certain probability. Said probability is determined\n            by *rate_drop*. Dropout for gradient boosting is\n            referred to as the 'dart' algorithm.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Will be ignored if `booster` is not set to `dart`.\n\n        reg_alpha:\n            L1 regularization on the weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        reg_lambda:\n            L2 regularization on the weights. Please refer to\n            the introductory remarks to understand how this\n            hyperparameter influences your weights.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: [0, ∞]\n\n        sample_type:\n            Possible values:\n\n            * `uniform`: every tree is equally likely to be dropped\n              out\n\n            * `weighted`: the dropout probability will be proportional\n              to a tree's weight\n\n            Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            Will be ignored if `booster` is not set to 'dart'.\n\n        silent:\n            In silent mode, XGBoost will not print out information on\n            the training progress.\n\n        skip_drop:\n            Probability of skipping the dropout during a given\n            iteration. Please also refer to the remarks on\n            *rate_drop* for further explanation.\n\n            *Increasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Will be ignored if `booster` is not set to `dart`.\n\n            Range: [0, 1]\n\n        subsample:\n            Subsample ratio from the training set. This means\n            that for every tree a subselection of *samples*\n            from the training set will be included into training.\n            Please note that this samples *without* replacement -\n            the common approach for random forests is to sample\n            *with* replace.\n\n            *Decreasing* this hyperparameter reduces the\n            likelihood of overfitting.\n\n            Range: (0, 1]\n    \"\"\"\n\n    booster: str = \"gbtree\"\n    colsample_bylevel: float = 1.0\n    colsample_bytree: float = 1.0\n    early_stopping_rounds: int = 10\n    external_memory: bool = False\n    gamma: float = 0.0\n    learning_rate: float = 0.1\n    max_delta_step: float = 0.0\n    max_depth: int = 3\n    min_child_weights: float = 1.0\n    n_estimators: int = 100\n    normalize_type: str = \"tree\"\n    num_parallel_tree: int = 1\n    n_jobs: int = 1\n    objective: Literal[\"reg:squarederror\", \"reg:tweedie\", \"reg:linear\"] = (\n        \"reg:squarederror\"\n    )\n    one_drop: bool = False\n    rate_drop: float = 0.0\n    reg_alpha: float = 0.0\n    reg_lambda: float = 1.0\n    sample_type: str = \"uniform\"\n    silent: bool = True\n    skip_drop: float = 0.0\n    subsample: float = 1.0\n\n    # ----------------------------------------------------------------\n\n    def validate(self, params: Optional[dict] = None):\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params: A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n\n        ??? example\n            ```python\n            x = getml.predictors.XGBoostRegressor()\n            x.gamma = 200\n            x.validate()\n            ```\n\n        Note:\n            This method is called at end of the `__init__` constructor\n            and every time before the predictor - or a class holding\n            it as an instance variable - is sent to the getML Engine.\n        \"\"\"\n\n        # ------------------------------------------------------------\n\n        if params is None:\n            params = self.__dict__\n        else:\n            params = {**self.__dict__, **params}\n\n        if not isinstance(params, dict):\n            raise ValueError(\"params must be None or a dictionary!\")\n\n        _validate_xgboost_parameters(params)\n\n        # ------------------------------------------------------------\n\n        if params[\"objective\"] not in [\"reg:squarederror\", \"reg:tweedie\", \"reg:linear\"]:\n            raise ValueError(\n                \"\"\"'objective' supported in XGBoostRegressor\n                                 are 'reg:squarederror', 'reg:tweedie',\n                                 and 'reg:linear'\"\"\"\n            )\n\n\n# ------------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom .category_trimmer import CategoryTrimmer\nfrom .email_domain import EmailDomain\nfrom .imputation import Imputation\nfrom .mapping import Mapping\nfrom .seasonal import Seasonal\nfrom .substring import Substring\nfrom .text_field_splitter import TextFieldSplitter\n\n__all__ = (\n    \"CategoryTrimmer\",\n    \"EmailDomain\",\n    \"Imputation\",\n    \"Mapping\",\n    \"Seasonal\",\n    \"Substring\",\n    \"TextFieldSplitter\",\n)\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/category_trimmer.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass CategoryTrimmer(_Preprocessor):\n    \"\"\"\n    Reduces the cardinality of high-cardinality categorical columns.\n\n    Args:\n        max_num_categories:\n            The maximum cardinality allowed. If the cardinality is\n            higher than that only the most frequent categories will\n            be kept, all others will be trimmed.\n\n        min_freq:\n            The minimum frequency required for a category to be\n            included.\n\n    ??? example\n        ```python\n        category_trimmer = getml.preprocessors.CategoryTrimmer()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[category_trimmer],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    max_num_categories: int = 999\n    min_freq: int = 30\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        params = _validate(self, params)\n\n        if not isinstance(params[\"max_num_categories\"], int):\n            raise TypeError(\"'max_num_categories' must be an int.\")\n\n        if not isinstance(params[\"min_freq\"], int):\n            raise TypeError(\"'min_freq' must be an int.\")\n\n        if params[\"max_num_categories\"] < 0:\n            raise ValueError(\"'max_num_categories' cannot be negative.\")\n\n        if params[\"min_freq\"] < 0:\n            raise ValueError(\"'min_freq' cannot be negative.\")\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/email_domain.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass EmailDomain(_Preprocessor):\n    \"\"\"\n    The EmailDomain preprocessor extracts the domain from e-mail addresses.\n\n    For instance, if the e-mail address is 'some.guy@domain.com',\n    the preprocessor will automatically extract '@domain.com'.\n\n    The preprocessor will be applied to all [`text`][getml.data.roles.text]\n    columns that were assigned one of the [`subroles`][getml.data.subroles]\n    [`include.email`][getml.data.subroles.include.email] or\n    [`only.email`][getml.data.subroles.only.email].\n\n    It is recommended that you assign [`only.email`][getml.data.subroles.only.email],\n    because it is unlikely that the e-mail address itself is interesting.\n\n    ??? example\n        ```python\n        my_data_frame.set_subroles(\"email\", getml.data.subroles.only.email)\n\n        domain = getml.preprocessors.EmailDomain()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[domain],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        _validate(self, params)\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/imputation.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass Imputation(_Preprocessor):\n    \"\"\"\n    The Imputation preprocessor replaces all NULL values in\n    numerical columns with the mean of the remaining\n    columns.\n\n    Optionally, it can additionally add a dummy column\n    that signifies whether the original value was imputed.\n\n    Args:\n        add_dummies:\n            Whether you want to add dummy variables\n            that signify whether the original value was imputed.\n\n    ??? example\n        ```python\n        imputation = getml.preprocessors.Imputation()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[imputation],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    add_dummies: bool = False\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        params = _validate(self, params)\n\n        if not isinstance(params[\"add_dummies\"], bool):\n            raise TypeError(\"'add_dummies' must be a bool.\")\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/mapping.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, ClassVar, Dict, Iterable, Optional\n\nfrom ..feature_learning.aggregations.sets import (\n    MAPPING,\n    MappingAggregationsSets,\n)\nfrom ..feature_learning.aggregations.types import MappingAggregations\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass Mapping(_Preprocessor):\n    \"\"\"\n    A mapping preprocessor maps categorical values, discrete values and individual\n    words in a text field to numerical values. These numerical values are retrieved\n    by aggregating targets in the relational neighbourhood.\n\n    You are particularly encouraged to use the mapping preprocessor in combination with\n    [`FastProp`][getml.feature_learning.FastProp].\n\n    Refer to the [User guide][preprocessing-mappings] for more information.\n\n    enterprise-adm: Enterprise edition\n        This feature is exclusive to the Enterprise edition and is not available in the Community edition. Discover the [benefits of the Enterprise edition][enterprise-benefits] and [compare their features][enterprise-feature-list].\n\n        For licensing information and technical support, please [contact us][contact-page].\n\n    Attributes:\n        agg_sets:\n            It is a class variable holding the available aggregation sets for the\n            mapping preprocessor.\n            Value: [`MAPPING`][getml.feature_learning.aggregations.sets.MAPPING].\n\n    Parameters:\n        aggregation:\n            The aggregation function to use over the targets.\n\n            Must be an aggregation supported by Mapping preprocessor\n            ([`MAPPING_AGGREGATIONS`][getml.feature_learning.aggregations.sets.MAPPING_AGGREGATIONS]).\n\n        min_freq:\n            The minimum number of targets required for a value to be included in\n            the mapping. Range: [0, ∞]\n\n        multithreading:\n            Whether you want to apply multithreading.\n\n    ??? example\n        ```python\n        mapping = getml.preprocessors.Mapping()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[mapping],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n\n    \"\"\"\n\n    agg_sets: ClassVar[MappingAggregationsSets] = MAPPING\n\n    aggregation: Iterable[MappingAggregations] = MAPPING.default\n    min_freq: int = 30\n    multithreading: bool = True\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        params = _validate(self, params)\n\n        if not all([agg in MAPPING.all for agg in params[\"aggregation\"]]):\n            raise ValueError(\n                \"'aggregation' must be from Mapping.agg_sets.all, \"\n                + \"meaning from the following set: \"\n                + str(MAPPING.all)\n                + \".\"\n            )\n\n        if not isinstance(params[\"min_freq\"], int):\n            raise TypeError(\"'min_freq' must be an int.\")\n\n        if params[\"min_freq\"] < 0:\n            raise TypeError(\"'min_freq' cannot be negative.\")\n\n        if not isinstance(params[\"multithreading\"], bool):\n            raise TypeError(\"'multithreading' must be a bool.\")\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/preprocessor.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nBase class - not meant for the end user.\n\"\"\"\n\nimport numbers\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass, fields\nfrom typing import List\n\nimport numpy as np\n\nfrom getml.feature_learning.validation import Validator\nfrom getml.utilities.formatting import _SignatureFormatter\n\n\n@dataclass(repr=False)\nclass _Preprocessor(ABC):\n    \"\"\"\n    Base class - not meant for the end user.\n    \"\"\"\n\n    def __post_init__(self: \"_Preprocessor\"):\n        self.validate()\n\n        for param in self._supported_params:\n            setattr(type(self), param, Validator(param))\n\n    # ------------------------------------------------------------------------\n\n    def __eq__(self, other):\n        if not isinstance(other, _Preprocessor):\n            raise TypeError(\"A preprocessor can only compared to another preprocessor!\")\n\n        if len(set(self.__dict__.keys())) != len(set(other.__dict__.keys())):\n            return False\n\n        for kkey in self.__dict__:\n            if kkey not in other.__dict__:\n                return False\n\n            # Take special care when comparing numbers.\n            if isinstance(self.__dict__[kkey], numbers.Real):\n                if not np.isclose(self.__dict__[kkey], other.__dict__[kkey]):\n                    return False\n\n            elif self.__dict__[kkey] != other.__dict__[kkey]:\n                return False\n\n        return True\n\n    # ------------------------------------------------------------------------\n\n    def _getml_deserialize(self):\n        encoding_dict = dict()\n\n        for kkey in self.__dict__:\n            encoding_dict[kkey + \"_\"] = self.__dict__[kkey]\n\n        encoding_dict[\"type_\"] = self.type\n\n        return encoding_dict\n\n    # ------------------------------------------------------------------------\n\n    def __repr__(self):\n        return str(self)\n\n    # ------------------------------------------------------------------------\n\n    def __setattr__(self, name, value):\n        if name in self.__dataclass_fields__:  # pylint: disable=E1101\n            super().__setattr__(name, value)\n        else:\n            raise AttributeError(\n                f\"Instance variable '{name}' is not supported in {self.type}.\"\n            )\n\n    # ------------------------------------------------------------------------\n\n    def __str__(self):\n        sig = _SignatureFormatter(self)\n        return sig._format()\n\n    # ------------------------------------------------------------------------\n\n    @property\n    def _supported_params(self) -> List[str]:\n        return [field.name for field in fields(self)]\n\n    # ------------------------------------------------------------------------\n\n    @property\n    def type(self):\n        return type(self).__name__\n\n    # ------------------------------------------------------------------------\n\n    @abstractmethod\n    def validate(self, params=None):\n        pass\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/seasonal.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass Seasonal(_Preprocessor):\n    \"\"\"\n    The Seasonal preprocessor extracts seasonal data from time stamps.\n\n    The preprocessor automatically iterates through\n    all time stamps in any data frame and extracts\n    seasonal parameters.\n\n    These include:\n\n    - year\n    - month\n    - weekday\n    - hour\n    - minute\n\n    The algorithm also evaluates the potential\n    usefulness of any extracted seasonal parameter.\n    Parameters that are unlikely to be useful are\n    not included.\n\n    Args:\n        disable_year:\n            Prevents the Seasonal preprocessor from\n            extracting the year from time stamps.\n\n        disable_month:\n            Prevents the Seasonal preprocessor from\n            extracting the month from time stamps.\n\n        disable_weekday:\n            Prevents the Seasonal preprocessor from\n            extracting the weekday from time stamps.\n\n        disable_hour:\n            Prevents the Seasonal preprocessor from\n            extracting the hour from time stamps.\n\n        disable_minute:\n            Prevents the Seasonal preprocessor from\n            extracting the minute from time stamps.\n\n    ??? example\n        ```python\n        seasonal = getml.preprocessors.Seasonal()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[seasonal],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    disable_year: bool = False\n    disable_month: bool = False\n    disable_weekday: bool = False\n    disable_hour: bool = False\n    disable_minute: bool = False\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        _validate(self, params)\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/substring.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n@dataclass(repr=False)\nclass Substring(_Preprocessor):\n    \"\"\"\n    The Substring preprocessor extracts substrings from\n    categorical columns and unused string columns.\n\n    The preprocessor will be applied to all\n    [`categorical`][getml.data.roles.categorical] and [`text`][getml.data.roles.text]\n    columns that were assigned one of the [`subroles`][getml.data.subroles]\n    [`include.substring`][getml.data.subroles.include.substring] or\n    [`only.substring`][getml.data.subroles.only.substring].\n\n    To further limit the scope of a substring preprocessor,\n    you can also assign a *unit*.\n\n    Args:\n        begin:\n            Index of the beginning of the substring (starting from 0).\n\n        length:\n            The length of the substring.\n\n        unit:\n            The unit of all columns to which the preprocessor\n            should be applied. These columns must also have the subrole\n            substring.\n\n            If it is left empty, then the preprocessor\n            will be applied to all columns with the subrole\n            [`include.substring`][getml.data.subroles.include.substring] or\n            [`only.substring`][getml.data.subroles.only.substring].\n\n    ??? example\n        ```python\n        my_df.set_subroles(\"col1\", getml.data.subroles.include.substring)\n\n        my_df.set_subroles(\"col2\", getml.data.subroles.include.substring)\n        my_df.set_unit(\"col2\", \"substr14\")\n\n        # Will be applied to col1 and col2\n        substr13 = getml.preprocessors.Substring(0, 3)\n\n        # Will only be applied to col2\n        substr14 = getml.preprocessors.Substring(0, 3, \"substr14\")\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[substr13],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    begin: int\n    length: int\n    unit: str = \"\"\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        params = _validate(self, params)\n\n        if not isinstance(params[\"begin\"], int):\n            raise TypeError(\"'begin' must be an integer!\")\n\n        if not isinstance(params[\"length\"], int):\n            raise TypeError(\"'length' must be an integer!\")\n\n        if not isinstance(params[\"unit\"], str):\n            raise TypeError(\"'unit' must be a string!\")\n\n        if params[\"begin\"] < 0:\n            raise ValueError(\"'begin' must be >= 0!\")\n\n        if params[\"length\"] <= 0:\n            raise ValueError(\"'length' must be > 0!\")\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/text_field_splitter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains routines for preprocessing data frames.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nfrom .preprocessor import _Preprocessor\nfrom .validate import _validate\n\n\n# NOTE: The r at the beginning of the docstring\n# is necessary to correctly display the characters.\n# https://docutils.sourceforge.io/docs/ref/rst/directives.html#code\n@dataclass(repr=False)\nclass TextFieldSplitter(_Preprocessor):\n    r\"\"\"\n    A TextFieldSplitter splits columns with role [`text`][getml.data.roles.text]\n    into relational bag-of-words representations to allow the\n    feature learners to learn patterns based on\n    the prescence of certain words within the text fields.\n\n    Text fields will be split on a whitespace or any of the\n    following characters:\n\n    ```python\n    ; , . ! ? - | \" \\t \\v \\f \\r \\n % ' ( ) [ ] { }\n    ```\n    Refer to the [User Guide][preprocessing-free-form-text] for more information.\n\n    ??? example\n        ```python\n        text_field_splitter = getml.preprocessors.TextFieldSplitter()\n\n        pipe = getml.Pipeline(\n            population=population_placeholder,\n            peripheral=[order_placeholder, trans_placeholder],\n            preprocessors=[text_field_splitter],\n            feature_learners=[feature_learner_1, feature_learner_2],\n            feature_selectors=feature_selector,\n            predictors=predictor,\n            share_selected_features=0.5\n        )\n        ```\n    \"\"\"\n\n    def validate(self, params: Optional[Dict[str, Any]] = None) -> None:\n        \"\"\"Checks both the types and the values of all instance\n        variables and raises an exception if something is off.\n\n        Args:\n            params:\n                A dictionary containing\n                the parameters to validate. If not is passed,\n                the own parameters will be validated.\n        \"\"\"\n        _validate(self, params)\n"
  },
  {
    "path": "src/python-api/getml/preprocessors/validate.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReduces boilerplate code for the validation.\n\"\"\"\n\n\ndef _validate(instance, params):\n    if params is None:\n        params = instance.__dict__\n    else:\n        params = {**instance.__dict__, **params}\n\n    if not isinstance(params, dict):\n        raise ValueError(\"params must be None or a dictionary!\")\n\n    for kkey in params:\n        if kkey not in instance._supported_params:\n            raise KeyError(\n                f\"Instance variable '{kkey}' is not supported in {instance.type}.\"\n            )\n\n    return params\n"
  },
  {
    "path": "src/python-api/getml/project/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThis module helps you handle your current project.\n\"\"\"\n\nfrom sys import modules\nfrom types import ModuleType\nfrom typing import TYPE_CHECKING\n\nfrom getml.engine import is_alive\n\nfrom .attrs import __getattr__, _all\n\nif TYPE_CHECKING:\n    from .attrs import delete, load, restart, save, suspend, switch  # noqa: F401\n    from .containers import DataFrames, Hyperopts, Pipelines\n\n    data_frames: DataFrames\n    hyperopts: Hyperopts\n    pipelines: Pipelines\n    name: str\n\n\ndef __dir__():\n    return _all\n\n\n# Fix mkdocs (griffe) warning about non-string item in _all\n_all = [str(item) for item in _all]\n\n__all__ = _all + [\"DataFrames\", \"Hyperopts\", \"Pipelines\"] + [\"__getattr__\"]  # noqa: PLE0605\n\n\nclass ProjectModule(ModuleType):\n    def __repr__(self):\n        output = \"No project set. To set: `getml.set_project(...)`\"\n        # resolving name has the side effect of reaching out to the Monitor and\n        # the Engine and therefore triggers a message if either is unreachable\n        project_name = self.name  # pylint: disable=E1101\n        if is_alive():\n            output = f\"Current project:\\n\\n{project_name}\"\n        return output\n\n\nmodules[__name__].__class__ = ProjectModule\n"
  },
  {
    "path": "src/python-api/getml/project/attrs.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHandles access to project-related information.\n\"\"\"\n\nimport warnings\nfrom os import PathLike\nfrom pathlib import Path\nfrom sys import modules\nfrom typing import List, Optional, Union\n\nfrom rich import print\n\nimport getml.communication as comm\nfrom getml.engine import is_engine_alive, is_monitor_alive, list_projects\n\nfrom .containers import DataFrames, Hyperopts, Pipelines\n\n_functions: List[str] = []\n\n_props: List[str] = []\n\n\ndef __getattr__(key):\n    if key in _functions:\n        return getattr(modules.get(__name__), key)\n\n    if key in _props:\n        if is_engine_alive():\n            return getattr(modules.get(__name__), \"_\" + key)()\n        else:\n            print(comm._make_error_msg())\n            return None\n\n    raise AttributeError(f\"module 'getml.project' has no attribute {key}\")\n\n\ndef module_function(func):\n    _functions.append(func.__name__)\n    return func\n\n\ndef module_prop(prop):\n    _props.append(prop.__name__[1:])\n    return prop\n\n\n@module_prop\ndef _data_frames():\n    return DataFrames()\n\n\n@module_prop\ndef _hyperopts():\n    return Hyperopts()\n\n\n@module_prop\ndef _pipelines():\n    return Pipelines()\n\n\n@module_prop\ndef _name():\n    return comm._get_project_name()\n\n\n@module_function\ndef load(bundle: str, name: Optional[str] = None) -> None:\n    \"\"\"\n    Loads a project from a bundle and connects to it.\n\n    Args:\n        bundle: The `.getml` bundle file to load.\n\n        name: A name for the project contained in the bundle.\n            If None, the name will be extracted from the bundle.\n    \"\"\"\n    return comm._load_project(bundle, name)\n\n\n@module_function\ndef delete() -> None:\n    \"\"\"\n    Deletes the currently connected project. All related pipelines,\n    data frames and hyperopts will be irretrievably deleted.\n    \"\"\"\n    comm._delete_project(_name())\n\n\n@module_function\ndef restart() -> None:\n    \"\"\"\n    Suspends and then relaunches the currently connected project.\n    This will kill all jobs currently running on that process.\n    \"\"\"\n    comm._set_project(_name(), restart=True)\n\n\n@module_function\ndef save(\n    filename: Optional[Union[PathLike, str]] = None,\n    target_dir: Optional[str] = None,\n    replace: bool = False,\n) -> None:\n    \"\"\"\n    Saves the currently connected project to disk.\n\n    Args:\n        filename: The name of the `.getml` bundle file\n\n        replace: Whether to replace an existing bundle.\n\n    Deprecated:\n        1.5: The `target_dir` argument is deprecated.\n    \"\"\"\n    if target_dir is not None:\n        warnings.warn(\n            \"The target_dir argument is deprecated. Use filename with a path instead.\",\n            DeprecationWarning,\n        )\n        bundle_name = filename if filename else f\"{_name()}.getml\"\n        filename = Path(target_dir) / bundle_name\n    return comm._save_project(_name(), filename, replace)\n\n\n@module_function\ndef suspend() -> None:\n    \"\"\"\n    Suspends the currently connected project.\n    \"\"\"\n    return comm._suspend_project(_name())\n\n\n@module_function\ndef switch(name: str) -> None:\n    \"\"\"Creates a new project or loads an existing one.\n\n    If there is no project called `name` present on the Engine, a new one will\n    be created. See the [User guide][project-management] for more\n    information.\n\n    Args:\n        name: Name of the new project.\n    \"\"\"\n    comm._set_project(name)\n\n\n_all = _functions + _props\n"
  },
  {
    "path": "src/python-api/getml/project/containers/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContainers for representing data of the current project.\n\"\"\"\n\nfrom .data_frames import DataFrames\nfrom .hyperopts import Hyperopts\nfrom .pipelines import Pipelines\n\n__all__ = (\n    \"DataFrames\",\n    \"Hyperopts\",\n    \"Pipelines\",\n)\n"
  },
  {
    "path": "src/python-api/getml/project/containers/data_frames.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContainer for data frames in memory.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Callable, List\n\nfrom getml.data import DataFrame\nfrom getml.data.helpers import list_data_frames\nfrom getml.data.helpers2 import load_data_frame\nfrom getml.utilities.formatting import _Formatter\n\n# --------------------------------------------------------------------\n\n\nclass DataFrames:\n    \"\"\"\n    Container which holds all data frames associated with the running\n    project that are currently stored in memory. The container supports\n    slicing and is sort- and filterable.\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(self, data=None):\n        self._in_memory = list_data_frames()[\"in_memory\"]\n        self._on_disk = list_data_frames()[\"on_disk\"]\n\n        if data is None:\n            self.data = [load_data_frame(name) for name in self._in_memory]\n        else:\n            self.data = data\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(self, key):\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            dfs_subset = self.data[key]\n            return DataFrames(data=dfs_subset)\n        if isinstance(key, str):\n            if key in self.in_memory:\n                return [df for df in self.data if df.name == key][0]\n            if key in self.on_disk:\n                raise AttributeError(f\"DataFrame {key} not loaded from disk.\")\n            raise AttributeError(f\"No DataFrame with name: {key}\")\n        raise TypeError(\n            f\"DataFrames can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self):\n        if len(self.in_memory) == 0:\n            output = \"No data frames in memory.\"\n        else:\n            output = self._format()._render_string()\n\n        if len(self.on_disk) > 0:\n            output += \"\\n\\nOn disk:\\n\"\n            output += \"\\n\".join(self.on_disk)\n\n        return output\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self):\n        if len(self.in_memory) == 0:\n            output = \"<p>No data frames in memory.</p>\"\n        else:\n            output = self._format()._render_html()\n\n        if len(self.on_disk) > 0:\n            output += \"<p>On disk:</p>\"\n            output += \"<br>\".join(self.on_disk)\n\n        return output\n\n    # ----------------------------------------------------------------\n\n    def _format(self):\n        headers = [[\"name\", \"rows\", \"columns\", \"memory usage\"]]\n\n        rows = [[df.name, df.nrows(), df.ncols(), df.memory_usage] for df in self.data]\n\n        formatted = _Formatter(headers, rows)\n\n        formatted[4].cell_template = \"{:{width}.2f} MB\"\n\n        return formatted\n\n    # ----------------------------------------------------------------\n\n    def delete(self) -> None:\n        \"\"\"\n        Deletes all data frames in the current project.\n        \"\"\"\n\n        for name in self.on_disk:\n            DataFrame(name).delete()\n\n    # ----------------------------------------------------------------\n\n    @property\n    def in_memory(self) -> List[str]:\n        \"\"\"\n        Returns the names of all data frames currently in memory.\n\n        Returns:\n                The names of all data frames currently in memory.\n        \"\"\"\n        return self._in_memory\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable) -> DataFrames:\n        \"\"\"\n        Filters the data frames container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered data frames.\n\n        ??? example\n            ```python\n            big_frames = getml.project.data_frames.filter(lambda frame: frame.memory_usage > 1000)\n            ```\n        \"\"\"\n\n        dfs_filtered = [df for df in self.data if conditional(df)]\n        return DataFrames(data=dfs_filtered)\n\n    # ----------------------------------------------------------------\n\n    def load(self) -> None:\n        \"\"\"\n        Loads all data frames stored in the project folder to memory.\n        \"\"\"\n\n        for df in self.on_disk:\n            if df not in self.in_memory:\n                self.data.append(load_data_frame(df))\n\n    # ----------------------------------------------------------------\n\n    @property\n    def on_disk(self) -> List[str]:\n        \"\"\"\n        Returns the names of all data frames stored in the project folder.\n        \"\"\"\n        return self._on_disk\n\n    # ----------------------------------------------------------------\n\n    def retrieve(self):\n        \"\"\"\n        Retrieve a dict of all data frames in memory.\n        \"\"\"\n\n        return {df.name: df for df in self.data}\n\n    # ----------------------------------------------------------------\n\n    def save(self) -> None:\n        \"\"\"\n        Saves all data frames currently in memory to disk.\n        \"\"\"\n\n        for df in self.data:\n            df.save()\n\n    # ----------------------------------------------------------------\n\n    def sort(self, key: Callable, descending: bool = False) -> DataFrames:\n        \"\"\"\n        Sorts the data frames container.\n\n        Args:\n            key:\n                A callable that evaluates to a sort key for a given item.\n\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted data frames.\n\n        ??? example\n            ```python\n            by_num_rows = getml.project.data_frames.sort(lambda frame: frame.nrows())\n            ```\n        \"\"\"\n\n        dfs_sorted = sorted(self.data, key=key, reverse=descending)\n        return DataFrames(data=dfs_sorted)\n\n    # ----------------------------------------------------------------\n\n    def unload(self) -> None:\n        \"\"\"\n        Unloads all data frames in the current project from memory.\n        \"\"\"\n\n        for name in self.on_disk:\n            DataFrame(name).unload()\n"
  },
  {
    "path": "src/python-api/getml/project/containers/hyperopts.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContainer which holds a project's hyperopts.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Callable\n\nfrom getml.hyperopt.helpers import list_hyperopts\nfrom getml.hyperopt.load_hyperopt import load_hyperopt\nfrom getml.utilities.formatting import _Formatter\n\n# --------------------------------------------------------------------\n\n\nclass Hyperopts:\n    \"\"\"\n    Container which holds all hyperopts associated with the currently running\n    project. The container supports slicing and is sort- and filterable.\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(self, data=None):\n        self.ids = list_hyperopts()\n\n        if data is None:\n            self.data = [load_hyperopt(id) for id in self.ids]\n        else:\n            self.data = data\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(self, key):\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            hyperopts_subset = self.data[key]\n            return Hyperopts(data=hyperopts_subset)\n        if isinstance(key, str):\n            if key in self.ids:\n                return [hyperopt for hyperopt in self.data if hyperopt.name == key][0]\n            raise AttributeError(f\"No Hyperopt with id: {key}\")\n        raise TypeError(\n            f\"Hyperopts can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self):\n        if len(list_hyperopts()) == 0:\n            return \"No hyperopt in memory.\"\n\n        return self._format()._render_string()\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self):\n        if len(list_hyperopts()) == 0:\n            return \"<p>No hyperopt in memory.</p>\"\n\n        return self._format()._render_html()\n\n    # ----------------------------------------------------------------\n\n    def _format(self):\n        headers = [[\"id\", \"type\", \"best pipeline\"]]\n\n        rows = [\n            [self.ids[index], hyperopt.type, hyperopt.best_pipeline.name]\n            for index, hyperopt in enumerate(self.data)\n        ]\n\n        return _Formatter(headers, rows)\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable) -> Hyperopts:\n        \"\"\"\n        Filters the hyperopts container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered hyperopts.\n\n        ??? example\n            ```python\n            gaussian_hyperopts = getml.project.hyperopts.filter(lamda hyp: \"Gaussian\" in hyp.type)\n            ```\n        \"\"\"\n        hyperopts_filtered = [\n            hyperopt for hyperopt in self.data if conditional(hyperopt)\n        ]\n        return Hyperopts(data=hyperopts_filtered)\n\n    # ----------------------------------------------------------------\n\n    def sort(self, key: Callable, descending: bool = False) -> Hyperopts:\n        \"\"\"\n        Sorts the hyperopts container.\n\n        Args:\n            key:\n                A callable that evaluates to a sort key for a given item.\n\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted hyperopts.\n\n        ??? example\n            ```python\n            by_type = getml.project.hyperopt.sort(lambda hyp: hyp.type)\n            ```\n        \"\"\"\n        hyperopts_sorted = sorted(self.data, key=key, reverse=descending)\n        return Hyperopts(data=hyperopts_sorted)\n"
  },
  {
    "path": "src/python-api/getml/project/containers/pipelines.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContainer which holds all of a project's pipelines.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Any, Callable, List\n\nfrom getml.pipeline.helpers2 import _refresh_all, list_pipelines\nfrom getml.pipeline.metrics import accuracy, auc, cross_entropy, mae, rmse, rsquared\nfrom getml.utilities.formatting import _Formatter\n\n# --------------------------------------------------------------------\n\n\nclass Pipelines:\n    \"\"\"\n    Container which holds all pipelines associated with the currently running\n    project. The container supports slicing and is sort- and filterable.\n\n    ??? example\n        Show the first 10 pipelines belonging to the current project:\n        ```python\n        getml.project.pipelines[:10]\n        ```\n        You can use nested list comprehensions to retrieve a scoring history\n        for your project:\n        ```python\n        import matplotlib.pyplot as plt\n\n        hyperopt_scores = [(score.date_time, score.mae) for pipe in getml.project.pipelines\n                              for score in pipe.scores[\"data_test\"]\n                              if \"hyperopt\" in pipe.tags]\n\n        fig, ax = plt.subplots()\n        ax.bar(*zip(*hyperopt_scores))\n        ```\n    \"\"\"\n\n    # ----------------------------------------------------------------\n\n    def __init__(self, data=None):\n        self.ids = list_pipelines()\n\n        if data is None:\n            self.data = _refresh_all()\n        else:\n            self.data = data\n\n    # ----------------------------------------------------------------\n\n    def __getitem__(self, key):\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            pipelines_subset = self.data[key]\n            return Pipelines(data=pipelines_subset)\n        if isinstance(key, str):\n            if key in self.ids:\n                return [pipeline for pipeline in self.data if pipeline.id == key][0]\n            raise AttributeError(f\"No Pipeline with id: {key}\")\n        raise TypeError(\n            f\"Pipelines can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ----------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ----------------------------------------------------------------\n\n    def __repr__(self):\n        if len(self.ids) == 0:\n            return \"No pipelines in memory.\"\n\n        return self._format()._render_string()\n\n    # ----------------------------------------------------------------\n\n    def _repr_html_(self):\n        if len(self.ids) == 0:\n            return \"<p>No pipelines in memory.</p>\"\n        return self._format()._render_html()\n\n    # ----------------------------------------------------------------\n\n    @property\n    def _contains_regresion_pipelines(self):\n        return any(pipe.is_regression for pipe in self.data)\n\n    # ----------------------------------------------------------------\n\n    @property\n    def _contains_classification_pipelines(self):\n        return any(pipe.is_classification for pipe in self.data)\n\n    # ----------------------------------------------------------------\n\n    def _format(self):\n        scores: List[Any] = []\n        scores_headers: List[Any] = []\n\n        if self._contains_classification_pipelines:\n            scores.extend(\n                [\n                    pipeline._scores.get(accuracy, []),\n                    pipeline._scores.get(auc, []),\n                    pipeline._scores.get(cross_entropy, []),\n                ]\n                for pipeline in self.data\n            )\n\n            scores_headers.extend([accuracy, auc, cross_entropy])\n\n        if self._contains_regresion_pipelines:\n            scores.extend(\n                [\n                    pipeline._scores.get(mae, []),\n                    pipeline._scores.get(rmse, []),\n                    pipeline._scores.get(rsquared, []),\n                ]\n                for pipeline in self.data\n            )\n\n            scores_headers.extend([mae, rmse, rsquared])\n\n        if (\n            self._contains_classification_pipelines\n            and self._contains_regresion_pipelines\n        ):\n            scores = [\n                [*classf, *reg]\n                for classf, reg in zip(\n                    scores[: len(self.data)], scores[len(self.data) :]\n                )\n            ]\n\n        sets_used = [pipeline._scores.get(\"set_used\", \"\") for pipeline in self.data]\n\n        targets = [pipeline.targets for pipeline in self.data]\n\n        feature_learners = [\n            [feature_learner.type for feature_learner in pipeline.feature_learners]\n            for pipeline in self.data\n        ]\n\n        tags = [pipeline.tags for pipeline in self.data]\n\n        headers = [\n            [\n                \"id\",\n                \"tags\",\n                \"feature learners\",\n                \"targets\",\n                *scores_headers,\n                \"set used\",\n            ]\n        ]\n\n        rows = [\n            [\n                pipeline.id,\n                tags[index],\n                feature_learners[index],\n                targets[index],\n                *scores[index],\n                sets_used[index],\n            ]\n            for index, pipeline in enumerate(self.data)\n        ]\n\n        # ------------------------------------------------------------\n\n        return _Formatter(headers, rows)\n\n    # ----------------------------------------------------------------\n\n    def sort(self, key: Callable, descending: bool = False) -> Pipelines:\n        \"\"\"\n        Sorts the pipelines container.\n\n        Args:\n            key:\n                A callable that evaluates to a sort key for a given item.\n\n            descending:\n                Whether to sort in descending order.\n\n        Returns:\n                A container of sorted pipelines.\n\n        ??? example\n            ```python\n            by_auc = getml.project.pipelines.sort(key=lambda pipe: pipe.auc)\n            by_fl = getml.project.pipelines.sort(key=lambda pipe: pipe.feature_learners[0].type)\n            ```\n        \"\"\"\n        pipelines_sorted = sorted(self.data, key=key, reverse=descending)\n        return Pipelines(data=pipelines_sorted)\n\n    # ----------------------------------------------------------------\n\n    def filter(self, conditional: Callable) -> Pipelines:\n        \"\"\"\n        Filters the pipelines container.\n\n        Args:\n            conditional:\n                A callable that evaluates to a boolean for a given item.\n\n        Returns:\n                A container of filtered pipelines.\n\n        ??? example\n            ```python\n            pipelines_with_tags = getml.project.pipelines.filter(lambda pipe: len(pipe.tags) > 0)\n            accurate_pipes = getml.project.pipelines.filter(lambda pipe: all(acc > 0.9 for acc in pipe.accuracy))\n            ```\n        \"\"\"\n        pipelines_filtered = [\n            pipeline for pipeline in self.data if conditional(pipeline)\n        ]\n\n        return Pipelines(data=pipelines_filtered)\n"
  },
  {
    "path": "src/python-api/getml/spark.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThis module is useful for productionizing pipelines\non Apache Spark.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nfrom typing import TYPE_CHECKING\n\nfrom getml.sqlite3.execute import _retrieve_scripts\nfrom getml.sqlite3.helpers import _log\n\nif TYPE_CHECKING:\n    import pyspark.sql.session\n\n\ndef execute(spark: pyspark.sql.session.SparkSession, fname: str) -> None:\n    \"\"\"\n    Executes an SQL script or several SQL scripts on Spark.\n\n    Args:\n        spark:\n            The spark session.\n\n        fname:\n            The names of the SQL script or a folder containing SQL scripts.\n            If you decide to pass a folder, the SQL scripts must have the ending '.sql'.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(fname, str):\n        raise TypeError(\"'fname' must be of type str\")\n\n    # ------------------------------------------------------------\n\n    if os.path.isdir(fname):\n        scripts = _retrieve_scripts(fname, \".sql\")\n        for script in scripts:\n            execute(spark, script)\n        return\n\n    # ------------------------------------------------------------\n\n    _log(\"Executing \" + fname + \"...\")\n\n    with open(fname, encoding=\"utf-8\") as sql_files:\n        queries = sql_files.read().split(\";\")\n\n    for query in queries:\n        if query.strip():\n            spark.sql(query)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nThis module contains wrappers around sqlite3 and related utility\nfunctions, which enable you to productionize pipelines using\nonly sqlite3 and Python, fully based on open-source code.\n\nThis requires SQLite version 3.33.0 or above. To check the sqlite3\nversion of your Python distribution, do the following:\n\n```python\nimport sqlite3\nsqlite3.sqlite_version\n```\n\n??? example\n    For our example we will assume that you want to productionize\n    the [CORA project](https://github.com/getml/getml-demo/blob/master/cora.ipynb).\n\n    First, we want to transpile the features into SQL code,\n    like this:\n\n    ```python\n    # Set targets to False, if you want an inference pipeline.\n    pipe1.features.to_sql(targets=True).save(\"cora\")\n    ```\n    This transpiles the features learned by pipe1 into a set of\n    SQLite3 scripts ready to be executed. These scripts are contained\n    in a folder called \"cora\".\n\n    We also assume that you have the three tables needed for\n    the CORA project in the form of pandas.DataFrames (other data\n    sources are possible).\n\n    We want to create a new sqlite3 connection and then read in the\n    data:\n    ```python\n    conn = getml.sqlite3.connect(\"cora.db\")\n\n    getml.sqlite3.read_pandas(\n        conn, table_name=\"cites\", data_frame=cites, if_exists=\"replace\")\n\n    getml.sqlite3.read_pandas(\n        conn, table_name=\"content\", data_frame=content, if_exists=\"replace\")\n\n    getml.sqlite3.read_pandas(\n        conn, table_name=\"paper\", data_frame=paper, if_exists=\"replace\")\n    ```\n    Now we can execute the scripts we have just created:\n    ```python\n    conn = getml.sqlite3.execute(conn, \"cora\")\n    ```\n    The transpiled pipeline will always create a table called \"FEATURES\", which\n    contain the features. Here is how we retrieve them:\n    ```python\n    features = getml.sqlite3.to_pandas(conn, \"FEATURES\")\n    ```\n    Now you have created your features in a pandas DataFrame ready to be inserted\n    into your favorite machine learning library.\n\n    To build stable data science pipelines, it is often a good idea to ensure\n    type safety by hard-coding your table schema. You can use the sniff...\n    methods to do that:\n    ```python\n    getml.sqlite3.sniff_pandas(\"cites\", cites)\n    ```\n    This will generate SQLite3 code that creates the \"cites\" table. You can\n    hard-code that into your pipeline. This will ensure that the data always have\n    the correct types, avoiding awkward problems in the future.\n\"\"\"\n\nfrom .connect import connect\nfrom .execute import execute\nfrom .read_csv import read_csv\nfrom .read_list import read_list\nfrom .read_pandas import read_pandas\nfrom .sniff_csv import sniff_csv\nfrom .sniff_pandas import sniff_pandas\nfrom .to_list import to_list\nfrom .to_pandas import to_pandas\n\n__all__ = [\n    \"connect\",\n    \"execute\",\n    \"read_csv\",\n    \"read_list\",\n    \"read_pandas\",\n    \"sniff_csv\",\n    \"sniff_pandas\",\n    \"to_list\",\n    \"to_pandas\",\n]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/connect.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains a wrapper around connect.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport sqlite3\n\nfrom .contains import _contains\nfrom .count_above_mean import _CountAboveMean\nfrom .count_below_mean import _CountBelowMean\nfrom .count_distinct_over_count import _CountDistinctOverCount\nfrom .email_domain import _email_domain\nfrom .ewma import (\n    _EWMA1D,\n    _EWMA1H,\n    _EWMA1M,\n    _EWMA1S,\n    _EWMA7D,\n    _EWMA30D,\n    _EWMA90D,\n    _EWMA365D,\n)\nfrom .ewma_trend import (\n    _EWMATrend1D,\n    _EWMATrend1H,\n    _EWMATrend1M,\n    _EWMATrend1S,\n    _EWMATrend7D,\n    _EWMATrend30D,\n    _EWMATrend90D,\n    _EWMATrend365D,\n)\nfrom .first import _First\nfrom .get_word import _get_word\nfrom .helpers import _try_import_scipy\nfrom .kurtosis import _Kurtosis\nfrom .last import _Last\nfrom .median import _Median\nfrom .mode import _Mode\nfrom .num_max import _NumMax\nfrom .num_min import _NumMin\nfrom .num_words import _num_words\nfrom .quantiles import (\n    _Q1,\n    _Q5,\n    _Q10,\n    _Q25,\n    _Q75,\n    _Q90,\n    _Q95,\n    _Q99,\n)\nfrom .skew import _Skew\nfrom .stddev import _Stddev\nfrom .time_since_first_maximum import _TimeSinceFirstMaximum\nfrom .time_since_first_minimum import _TimeSinceFirstMinimum\nfrom .time_since_last_maximum import _TimeSinceLastMaximum\nfrom .time_since_last_minimum import _TimeSinceLastMinimum\nfrom .trend import _Trend\nfrom .var import _Var\nfrom .variation_coefficient import _VariationCoefficient\n\n\ndef connect(database: str) -> sqlite3.Connection:\n    \"\"\"\n    Generates a new sqlite3 connection.\n\n    This connection contains all customized aggregations\n    and transformation functions needed to execute the\n    SQL pipeline generated by getML. Other than that\n    it behaves just like a normal sqlite3 connection from\n    the Python standard library.\n\n    Args:\n        database:\n            Filename of the database. Use ':memory:' to\n            create an in-memory database.\n\n    Returns:\n        A new sqlite3 connection with all custom\n            functions and aggregations registered.\n    \"\"\"\n\n    if not isinstance(database, str):\n        raise TypeError(\"'database' must be of type str\")\n\n    if sqlite3.sqlite_version < \"3.33.0\":\n        raise ValueError(\n            \"getML requires SQLite version 3.33.0 or above. Found version \"\n            + sqlite3.sqlite_version\n            + \". Please upgrade Python and/or the Python sqlite3 package.\"\n        )\n\n    # Lazily check if scipy is installed\n    _try_import_scipy()\n\n    conn = sqlite3.connect(database)\n\n    conn.create_function(\"contains\", 2, _contains)\n    conn.create_function(\"email_domain\", 1, _email_domain)\n    conn.create_function(\"get_word\", 2, _get_word)\n    conn.create_function(\"num_words\", 1, _num_words)\n\n    conn.create_aggregate(\"COUNT_ABOVE_MEAN\", 1, _CountAboveMean)  # type: ignore\n    conn.create_aggregate(\"COUNT_BELOW_MEAN\", 1, _CountBelowMean)  # type: ignore\n    conn.create_aggregate(\"COUNT_DISTINCT_OVER_COUNT\", 1, _CountDistinctOverCount)  # type: ignore\n    conn.create_aggregate(\"EWMA_1S\", 2, _EWMA1S)  # type: ignore\n    conn.create_aggregate(\"EWMA_1M\", 2, _EWMA1M)  # type: ignore\n    conn.create_aggregate(\"EWMA_1H\", 2, _EWMA1H)  # type: ignore\n    conn.create_aggregate(\"EWMA_1D\", 2, _EWMA1D)  # type: ignore\n    conn.create_aggregate(\"EWMA_7D\", 2, _EWMA7D)  # type: ignore\n    conn.create_aggregate(\"EWMA_30D\", 2, _EWMA30D)  # type: ignore\n    conn.create_aggregate(\"EWMA_90D\", 2, _EWMA90D)  # type: ignore\n    conn.create_aggregate(\"EWMA_365D\", 2, _EWMA365D)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_1S\", 2, _EWMATrend1S)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_1M\", 2, _EWMATrend1M)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_1H\", 2, _EWMATrend1H)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_1D\", 2, _EWMATrend1D)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_7D\", 2, _EWMATrend7D)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_30D\", 2, _EWMATrend30D)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_90D\", 2, _EWMATrend90D)  # type: ignore\n    conn.create_aggregate(\"EWMA_TREND_365D\", 2, _EWMATrend365D)  # type: ignore\n    conn.create_aggregate(\"FIRST\", 2, _First)  # type: ignore\n    conn.create_aggregate(\"KURTOSIS\", 1, _Kurtosis)\n    conn.create_aggregate(\"LAST\", 2, _Last)  # type: ignore\n    conn.create_aggregate(\"MEDIAN\", 1, _Median)\n    conn.create_aggregate(\"MODE\", 1, _Mode)\n    conn.create_aggregate(\"NUM_MAX\", 1, _NumMax)  # type: ignore\n    conn.create_aggregate(\"NUM_MIN\", 1, _NumMin)  # type: ignore\n    conn.create_aggregate(\"Q1\", 1, _Q1)  # type: ignore\n    conn.create_aggregate(\"Q5\", 1, _Q5)  # type: ignore\n    conn.create_aggregate(\"Q10\", 1, _Q10)  # type: ignore\n    conn.create_aggregate(\"Q25\", 1, _Q25)  # type: ignore\n    conn.create_aggregate(\"Q75\", 1, _Q75)  # type: ignore\n    conn.create_aggregate(\"Q90\", 1, _Q90)  # type: ignore\n    conn.create_aggregate(\"Q95\", 1, _Q95)  # type: ignore\n    conn.create_aggregate(\"Q99\", 1, _Q99)  # type: ignore\n    conn.create_aggregate(\"SKEW\", 1, _Skew)\n    conn.create_aggregate(\"STDDEV\", 1, _Stddev)\n    conn.create_aggregate(\"TIME_SINCE_FIRST_MAXIMUM\", 2, _TimeSinceFirstMaximum)  # type: ignore\n    conn.create_aggregate(\"TIME_SINCE_FIRST_MINIMUM\", 2, _TimeSinceFirstMinimum)  # type: ignore\n    conn.create_aggregate(\"TIME_SINCE_LAST_MAXIMUM\", 2, _TimeSinceLastMaximum)  # type: ignore\n    conn.create_aggregate(\"TIME_SINCE_LAST_MINIMUM\", 2, _TimeSinceLastMinimum)  # type: ignore\n    conn.create_aggregate(\"TREND\", 2, _Trend)  # type: ignore\n    conn.create_aggregate(\"VAR\", 1, _Var)\n    conn.create_aggregate(\"VARIATION_COEFFICIENT\", 1, _VariationCoefficient)\n\n    return conn\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/contains.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nChecks whether a textfield contains a certain keyword.\n\"\"\"\n\nfrom .split_text_field import _split_text_field\n\n\ndef _contains(textfield, keyword):\n    splitted = _split_text_field(textfield)\n    k = keyword.lower()\n    return len([\"\" for s in splitted if s == k])\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/count_above_mean.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCOUNT_ABOVE_MEAN aggregation.\n\"\"\"\n\nfrom typing import List, Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _CountAboveMean:\n    def __init__(self):\n        self.values: List[float] = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> float:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return 0.0\n\n        # Needed to catch numerical instability\n        # issues\n        if np.unique(self.values).shape[0] == 1:\n            return 0.0\n\n        mean = np.mean(self.values)\n        filtered = [0.0 for v in self.values if v > mean]\n        return float(len(filtered))\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/count_below_mean.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCOUNT_BELOW_MEAN aggregation.\n\"\"\"\n\nfrom typing import List, Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _CountBelowMean:\n    def __init__(self):\n        self.values: List[float] = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> float:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return 0.0\n\n        # Needed to catch numerical instability\n        # issues\n        if np.unique(self.values).shape[0] == 1:\n            return 0.0\n\n        mean = np.mean(self.values)\n        filtered = [0.0 for v in self.values if v < mean]\n        return float(len(filtered))\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/count_distinct_over_count.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCOUNT_DISTINCT_OVER_COUNT aggregation.\n\"\"\"\n\nfrom typing import List, Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _CountDistinctOverCount:\n    def __init__(self):\n        self.count = 0.0\n        self.values: List[float] = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        self.count += 1.0\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> Optional[float]:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if self.count == 0:\n            return None\n        count_distinct = float(len(np.unique(self.values)))\n        return count_distinct / self.count\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/email_domain.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nExtracts the email domain.\n\"\"\"\n\n\ndef _email_domain(textfield):\n    splitted = textfield.split(\"@\")\n\n    if len(splitted) != 2:\n        return \"\"\n\n    if \".\" not in splitted[1]:\n        return \"\"\n\n    return splitted[1]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/ewma.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains the exponentially weighted moving average aggregations.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA:\n    log05 = np.log(0.5)\n\n    t1s = 1.0\n    t1m = t1s * 60.0\n    t1h = t1m * 60.0\n    t1d = t1h * 24.0\n    t7d = t1d * 7.0\n    t30d = t1d * 30.0\n    t90d = t1d * 90.0\n    t365d = t1d * 365.0\n\n    def __init__(self, half_life):\n        self.half_life = half_life\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        weights = [np.exp(_EWMA.log05 * v[0] / self.half_life) for v in self.values]\n        sum_weights = np.sum(weights)\n        if sum_weights == 0.0:\n            return None\n        sum_all = np.dot([v[1] for v in self.values], weights)\n        return sum_all / sum_weights\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA1S(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t1s)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA1M(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t1m)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA1H(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t1h)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA1D(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t1d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA7D(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t7d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA30D(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t30d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA90D(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t90d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMA365D(_EWMA):\n    def __init__(self):\n        super().__init__(_EWMA.t365d)\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/ewma_trend.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains the exponentially weighted moving average aggregations.\n\"\"\"\n\nfrom typing import Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend:\n    log05 = np.log(0.5)\n\n    t1s = 1.0\n    t1m = t1s * 60.0\n    t1h = t1m * 60.0\n    t1d = t1h * 24.0\n    t7d = t1d * 7.0\n    t30d = t1d * 30.0\n    t90d = t1d * 90.0\n    t365d = t1d * 365.0\n\n    def __init__(self, half_life):\n        self.half_life = half_life\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self) -> Optional[float]:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        weights = [\n            np.exp(_EWMATrend.log05 * v[0] / self.half_life) for v in self.values\n        ]\n        sum_weights = np.sum(weights)\n        if sum_weights == 0.0:\n            return None\n        mean_x = sum((v[0] * w for v, w in zip(self.values, weights))) / sum_weights\n        mean_y = sum((v[1] * w for v, w in zip(self.values, weights))) / sum_weights\n        sum_xx = sum(\n            (\n                (v[0] - mean_x) * (v[0] - mean_x) * w\n                for v, w in zip(self.values, weights)\n            )\n        )\n        if sum_xx == 0.0:\n            return mean_y\n        sum_xy = sum(\n            (\n                (v[0] - mean_x) * (v[1] - mean_y) * w\n                for v, w in zip(self.values, weights)\n            )\n        )\n        beta = sum_xy / sum_xx\n        return mean_y - beta * mean_x\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend1S(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t1s)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend1M(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t1m)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend1H(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t1h)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend1D(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t1d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend7D(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t7d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend30D(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t30d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend90D(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t90d)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _EWMATrend365D(_EWMATrend):\n    def __init__(self):\n        super().__init__(_EWMATrend.t365d)\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/execute.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nExecutes SQL scripts on SQLite3\n\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nimport sqlite3\nfrom typing import List\n\nfrom .helpers import _log\n\n# ----------------------------------------------------------------------------\n\n\ndef _retrieve_scripts(folder: str, file_type: str) -> List[str]:\n    if folder[-1] != \"/\":\n        folder = folder + \"/\"\n    scripts = os.listdir(folder)\n    scripts = [script for script in scripts if script[-len(file_type) :] == file_type]\n    scripts = [folder + script for script in scripts]\n    scripts.sort()\n    return scripts\n\n\n# ----------------------------------------------------------------------------\n\n\ndef execute(conn: sqlite3.Connection, fname: str) -> None:\n    \"\"\"\n    Executes an SQL script or several SQL scripts on SQLite3.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        fname:\n            The names of the SQL script or a folder containing SQL scripts.\n            If you decide to pass a folder, the SQL scripts must have the ending '.sql'.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not isinstance(fname, str):\n        raise TypeError(\"'fname' must be of type str\")\n\n    # ------------------------------------------------------------\n\n    # Store temporary object in-memory.\n    conn.execute(\"PRAGMA temp_store=2;\")\n\n    if os.path.isdir(fname):\n        scripts = _retrieve_scripts(fname, \".sql\")\n        for script in scripts:\n            execute(conn, script)\n        return\n\n    _log(\"Executing \" + fname + \"...\")\n\n    with open(fname, encoding=\"utf-8\") as sqlfile:\n        queries = sqlfile.read().split(\";\")\n\n    for query in queries:\n        conn.execute(query + \";\")\n\n    conn.commit()\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/first.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nFIRST aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_earlier(tup1, tup2):\n    if tup2[0] < tup1[0]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _First:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_earlier, self.values, self.values[0])[1]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/get_word.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns the word indicated by i from the textfield.\nNote that this begins with 1, following the SQLite\nconvention.\n\"\"\"\n\nfrom .split_text_field import _split_text_field\n\n\ndef _get_word(textfield, i):\n    splitted = _split_text_field(textfield)\n    if i > 0 and i - 1 < len(splitted):\n        return splitted[i - 1]\n    return None\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains simple helper functions for the sqlite3 module\n\"\"\"\n\nimport importlib\nimport logging\nfrom inspect import cleandoc\nfrom typing import Optional\n\nimport numpy as np\n\n# ----------------------------------------------------------------------------\n\n\ndef _try_import_scipy():\n    try:\n        return importlib.import_module(\"scipy\")\n    except ImportError:\n        raise ImportError(\n            \"The 'scipy' package is required for custom sqlite3 aggregations.\"\n        )\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _create_table(conn, table_name, schema, if_exists=\"append\"):\n    if _table_exists(conn, table_name):\n        if if_exists == \"fail\":\n            raise ValueError(f\"Table {table_name} already exists!\")\n        if if_exists == \"replace\":\n            conn.executescript(schema)\n        elif if_exists == \"append\":\n            _log(\"Appending...\")\n        else:\n            raise TypeError(\n                \"`if_exists` has to be one of: 'append', 'replace', or 'fail'.\"\n            )\n    else:\n        conn.executescript(schema)\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _generate_schema(name, sql_types):\n    cols = []\n\n    max_width = max(\n        len(str(cname)) for cnames in sql_types.values() for cname in cnames\n    )\n\n    for type_, colnames in sql_types.items():\n        colnames_ = [f'\"{name}\"' for name in colnames]\n        cols.extend([f\"{name:{max_width+2}} {type_}\" for name in colnames_])\n\n    col_lines = \",\\n    \".join(cols)\n\n    template = cleandoc(\n        \"\"\"\n        DROP TABLE IF EXISTS \"{name}\";\n\n        CREATE TABLE \"{name}\" (\n            {col_lines}\n        );\n        \"\"\"\n    )\n\n    return template.format(name=name, col_lines=col_lines)\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _get_colnames(conn, table_name):\n    cursor = conn.execute('SELECT * FROM \"' + table_name + '\" LIMIT 0')\n    names = [description[0] for description in cursor.description]\n    return names\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _get_num_columns(conn, table_name):\n    return len(_get_colnames(conn, table_name))\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_int_type(coltype):\n    return coltype in [\n        int,\n        np.int_,\n        np.int8,\n        np.int16,\n        np.int32,\n        np.int64,\n        np.uint8,\n        np.uint16,\n        np.uint32,\n        np.uint64,\n    ]\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _log(msg):\n    logging.basicConfig(format=\"%(asctime)s %(message)s\", level=logging.INFO)\n    logging.info(msg)\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _not_null(value: Optional[float]):\n    return value is not None and not np.isnan(value) and not np.isinf(value)\n\n\n# ----------------------------------------------------------------------------\n\n\ndef _table_exists(conn, table_name):\n    query = (\n        f\"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}';\"\n    )\n\n    cursor = conn.execute(query)\n\n    if cursor.fetchone():\n        return True\n    return False\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/kurtosis.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nKURTOSIS aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null, _try_import_scipy\n\n\nclass _Kurtosis:\n    def __init__(self):\n        self._scipy = _try_import_scipy()\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n\n        # There appear to be numerical instability\n        # issues with the scipy implementation.\n        if np.unique(self.values).shape[0] == 1:\n            return 0.0\n\n        return self._scipy.stats.kurtosis(self.values, fisher=False)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/last.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nLAST aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_later(tup1, tup2):\n    if tup2[0] > tup1[0]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Last:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_later, self.values, self.values[0])[1]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/median.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nMEDIAN aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _Median:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        return np.median(self.values)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/mode.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nMODE aggregation.\n\"\"\"\n\nfrom .helpers import _not_null, _try_import_scipy\n\n\nclass _Mode:\n    def __init__(self):\n        self._scipy = _try_import_scipy()\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        result = self._scipy.stats.mode(self.values, keepdims=True)[0][0]\n        try:\n            return float(result)\n        except ValueError:\n            return None\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/num_max.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nNUM_MAX aggregation.\n\"\"\"\n\nfrom typing import Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _NumMax:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> Optional[float]:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        maximum = np.max(self.values)\n        filtered = [0.0 for v in self.values if v == maximum]\n        return float(len(filtered))\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/num_min.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nNUM_MIN aggregation.\n\"\"\"\n\nfrom typing import Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _NumMin:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> Optional[float]:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        minimum = np.min(self.values)\n        filtered = [0.0 for v in self.values if v == minimum]\n        return float(len(filtered))\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/num_words.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCounts the number of words in a textfield.\n\"\"\"\n\nfrom .split_text_field import _split_text_field\n\n\ndef _num_words(textfield):\n    splitted = _split_text_field(textfield)\n    return len(splitted)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/quantiles.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains the quantile aggregations.\n\"\"\"\n\nfrom typing import List, Optional\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\nclass _Quantile:\n    def __init__(self, quantile: float):\n        self.quantile: float = quantile\n        self.values: List[float] = []\n\n    def step(self, value: Optional[float]):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and value is not None:\n            self.values.append(value)\n\n    def finalize(self) -> float:\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        return np.quantile(self.values, self.quantile, interpolation=\"linear\")  # type:ignore\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q1(_Quantile):\n    def __init__(self):\n        super().__init__(0.01)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q5(_Quantile):\n    def __init__(self):\n        super().__init__(0.05)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q10(_Quantile):\n    def __init__(self):\n        super().__init__(0.1)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q25(_Quantile):\n    def __init__(self):\n        super().__init__(0.25)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q75(_Quantile):\n    def __init__(self):\n        super().__init__(0.75)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q90(_Quantile):\n    def __init__(self):\n        super().__init__(0.90)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q95(_Quantile):\n    def __init__(self):\n        super().__init__(0.95)\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _Q99(_Quantile):\n    def __init__(self):\n        super().__init__(0.99)\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/read_csv.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains utility functions for reading CSV files\ninto sqlite3.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport csv\nimport sqlite3\nfrom typing import List, Optional, Union\n\nfrom getml.data.helpers import _is_non_empty_typed_list, _is_typed_list\n\nfrom .helpers import _create_table, _log\nfrom .read_list import read_list\nfrom .sniff_csv import sniff_csv\n\n# ----------------------------------------------------------------------------\n\n\ndef _read_csv_file(fname, sep, quotechar, header, skip=0):\n    with open(fname, newline=\"\\n\") as csvfile:\n        reader = csv.reader(csvfile, delimiter=sep, quotechar=quotechar)\n        if header:\n            return list(reader)[skip + 1 :]\n        return list(reader)[skip:]\n\n\n# ----------------------------------------------------------------------------\n\n\ndef read_csv(\n    conn: sqlite3.Connection,\n    fnames: Union[str, List[str]],\n    table_name: str,\n    header: bool = True,\n    if_exists: str = \"append\",\n    quotechar: str = '\"',\n    sep: str = \",\",\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n) -> None:\n    \"\"\"\n    Reads a list of CSV files and writes them into an sqlite3 table.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        fnames:\n            The names of the CSV files.\n\n        table_name:\n            The name of the table to write to.\n\n        header:\n            Whether the csv file contains a header. If True, the first line\n            is skipped and column names are inferred accordingly.\n\n        quotechar:\n            The string escape character.\n\n        if_exists:\n            How to behave if the table already exists:\n\n            - 'fail': Raise a ValueError.\n            - 'replace': Drop the table before inserting new values.\n            - 'append': Insert new values to the existing table.\n\n        sep:\n            The field separator.\n\n        skip:\n            The number of lines to skip (before a possible header)\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you can\n            explicitly pass them. If you pass colnames, it is assumed that the\n            CSV files do not contain a header, thus overriding the 'header' variable.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(fnames, list):\n        fnames = [fnames]\n\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not _is_non_empty_typed_list(fnames, str):\n        raise TypeError(\"'fnames' must be a string or a non-empty list of strings\")\n\n    if not isinstance(table_name, str):\n        raise TypeError(\"'table_name' must be a string\")\n\n    if not isinstance(header, bool):\n        raise TypeError(\"'header' must be a bool\")\n\n    if not isinstance(quotechar, str):\n        raise TypeError(\"'quotechar' must be a str\")\n\n    if not isinstance(if_exists, str):\n        raise TypeError(\"'if_exists' must be a str\")\n\n    if not isinstance(sep, str):\n        raise TypeError(\"'sep' must be a str\")\n\n    if not isinstance(skip, int):\n        raise TypeError(\"'skip' must be an int\")\n\n    if colnames is not None and not _is_typed_list(colnames, str):\n        raise TypeError(\"'colnames' must be a list of strings or None\")\n\n    # ------------------------------------------------------------\n\n    schema = sniff_csv(\n        fnames=fnames,\n        table_name=table_name,\n        header=header,\n        quotechar=quotechar,\n        sep=sep,\n        skip=skip,\n        colnames=colnames,\n    )\n\n    _create_table(conn, table_name, schema, if_exists)\n\n    for fname in fnames:\n        _log(\"Loading '\" + fname + \"' into '\" + table_name + \"'...\")\n        data = _read_csv_file(fname, sep, quotechar, header and not colnames, skip)\n        read_list(conn, table_name, data)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/read_list.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReads data into an sqlite3 table.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport sqlite3\nfrom typing import Any, List\n\nfrom .helpers import _get_num_columns, _log\n\n\ndef read_list(conn: sqlite3.Connection, table_name: str, data: List[List[Any]]) -> None:\n    \"\"\"\n    Reads data into an sqlite3 table.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        table_name:\n            The name of the table to write to.\n\n        data:\n            The data to insert into the table.\n            Every list represents one row to be read into the table.\n    \"\"\"\n\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not isinstance(table_name, str):\n        raise TypeError(\"'table_name' must be a string\")\n\n    if not isinstance(data, list):\n        raise TypeError(\"'data' must be a list of lists\")\n\n    # ------------------------------------------------------------\n\n    ncols = _get_num_columns(conn, table_name)\n    old_length = len(data)\n    data = [line for line in data if len(line) == ncols]\n    placeholders = \"(\" + \",\".join([\"?\"] * ncols) + \")\"\n    query = 'INSERT INTO \"' + table_name + '\" VALUES ' + placeholders\n    conn.executemany(query, data)\n    conn.commit()\n    _log(\n        \"Read \"\n        + str(len(data))\n        + \" lines. \"\n        + str(old_length - len(data))\n        + \" invalid lines.\"\n    )\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/read_pandas.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains utility functions for reading a pandas DataFrame\ninto sqlite3.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport numbers\nimport sqlite3\nfrom typing import Literal\n\nimport pandas as pd\n\nfrom .helpers import _create_table, _get_colnames, _log\nfrom .read_list import read_list\nfrom .sniff_pandas import sniff_pandas\n\n\ndef read_pandas(\n    conn: sqlite3.Connection,\n    table_name: str,\n    data_frame: pd.DataFrame,\n    if_exists: Literal[\"fail\", \"replace\", \"append\"] = \"append\",\n) -> None:\n    \"\"\"\n    Loads a pandas.DataFrame into SQLite3.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        table_name:\n            The name of the table to write to.\n\n        data_frame:\n            The pandas.DataFrame to read\n            into the table. The column names must match the column\n            names of the target table in the SQLite3 database, but\n            their order is not important.\n\n        if_exists:\n            How to behave if the table already exists:\n\n            - 'fail': Raise a ValueError.\n            - 'replace': Drop the table before inserting new values.\n            - 'append': Insert new values into the existing table.\n    \"\"\"\n\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not isinstance(table_name, str):\n        raise TypeError(\"'table_name' must be a str\")\n\n    if not isinstance(data_frame, pd.DataFrame):\n        raise TypeError(\"'data_frame' must be a pandas.DataFrame\")\n\n    if not isinstance(if_exists, str):\n        raise TypeError(\"'if_exists' must be a str\")\n\n    # ------------------------------------------------------------\n\n    _log(\"Loading pandas.DataFrame into '\" + table_name + \"'...\")\n\n    schema = sniff_pandas(table_name, data_frame)\n\n    _create_table(conn, table_name, schema, if_exists)\n\n    colnames = _get_colnames(conn, table_name)\n    data = data_frame[colnames].values.tolist()\n    data = [\n        [\n            field\n            if isinstance(field, (numbers.Number, str)) or field is None\n            else str(field)\n            for field in row\n        ]\n        for row in data\n    ]\n    read_list(conn, table_name, data)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/skew.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSKEW aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null, _try_import_scipy\n\n\nclass _Skew:\n    def __init__(self):\n        self._scipy = _try_import_scipy()\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n\n        # There appear to be numerical instability\n        # issues with the scipy implementation.\n        if np.unique(self.values).shape[0] == 1:\n            return 0.0\n\n        return self._scipy.stats.skew(self.values)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/sniff_csv.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains utility functions for sniffing sqlite data types from CSV files.\n\"\"\"\n\nfrom typing import List, Optional, Union\n\nimport pandas as pd\n\nfrom getml.data.helpers import _is_non_empty_typed_list, _is_typed_list\n\nfrom .sniff_pandas import sniff_pandas\n\n# ----------------------------------------------------------------------------\n\n\ndef sniff_csv(\n    fnames: Union[str, List[str]],\n    table_name: str,\n    header: bool = True,\n    num_lines_sniffed: int = 1000,\n    quotechar: str = '\"',\n    sep: str = \",\",\n    skip: int = 0,\n    colnames: Optional[List[str]] = None,\n) -> str:\n    \"\"\"\n    Sniffs a list of csv files.\n\n    Args:\n        fnames:\n            The list of CSV file names to be read.\n\n        table_name:\n            Name of the table in which the data is to be inserted.\n\n        header:\n            Whether the csv file contains a header. If True, the first line\n            is skipped and column names are inferred accordingly.\n\n        num_lines_sniffed:\n            Number of lines analyzed by the sniffer.\n\n        quotechar:\n            The character used to wrap strings.\n\n        sep:\n            The separator used for separating fields.\n\n        skip:\n            Number of lines to skip at the beginning of each\n            file.\n\n        colnames:\n            The first line of a CSV file\n            usually contains the column names. When this is not the case, you can\n            explicitly pass them. If you pass colnames, it is assumed that the\n            CSV files do not contain a header, thus overriding the 'header' variable.\n\n    Returns:\n            Appropriate `CREATE TABLE` statement.\n    \"\"\"\n\n    # ------------------------------------------------------------\n\n    if not isinstance(fnames, list):\n        fnames = [fnames]\n\n    # ------------------------------------------------------------\n\n    if not _is_non_empty_typed_list(fnames, str):\n        raise TypeError(\"'fnames' must be a string or a non-empty list of strings\")\n\n    if not isinstance(table_name, str):\n        raise TypeError(\"'table_name' must be a string\")\n\n    if not isinstance(header, bool):\n        raise TypeError(\"'header' must be a bool\")\n\n    if not isinstance(num_lines_sniffed, int):\n        raise TypeError(\"'num_lines_sniffed' must be a int\")\n\n    if not isinstance(quotechar, str):\n        raise TypeError(\"'quotechar' must be a str\")\n\n    if not isinstance(sep, str):\n        raise TypeError(\"'sep' must be a str\")\n\n    if not isinstance(skip, int):\n        raise TypeError(\"'skip' must be an int\")\n\n    if colnames is not None and not _is_typed_list(colnames, str):\n        raise TypeError(\"'colnames' must be a list of strings or None\")\n\n    # ------------------------------------------------------------\n\n    header_lines = 0 if header and not colnames else None\n\n    def read(fname):\n        return pd.read_csv(\n            fname,\n            nrows=num_lines_sniffed,\n            header=header_lines,\n            sep=sep,\n            quotechar=quotechar,\n            skiprows=skip,\n            names=colnames,\n        )\n\n    data_frames = [read(fname) for fname in fnames]\n\n    merged = pd.concat(data_frames, join=\"inner\")\n\n    return sniff_pandas(table_name, merged)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/sniff_pandas.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nContains utility functions for siffing sqlite data types from pandas DataFrames.\n\"\"\"\n\nfrom typing import Dict, List\n\nimport pandas as pd\n\nfrom getml.data.helpers import _is_numerical_type_numpy\n\nfrom .helpers import _generate_schema, _is_int_type\n\n# ----------------------------------------------------------------------------\n\n\ndef sniff_pandas(table_name: str, data_frame: pd.DataFrame) -> str:\n    \"\"\"\n    Sniffs a pandas data frame.\n\n    Args:\n        table_name:\n            Name of the table in which the data is to be inserted.\n\n        data_frame:\n            The pandas.DataFrame to read into the table.\n\n    Returns:\n            Appropriate `CREATE TABLE` statement.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(table_name, str):\n        raise TypeError(\"'table_name' must be a str\")\n\n    if not isinstance(data_frame, pd.DataFrame):\n        raise TypeError(\"'data_frame' must be a pandas.DataFrame\")\n\n    # ------------------------------------------------------------\n\n    colnames = data_frame.columns\n    coltypes = data_frame.dtypes\n\n    sql_types: Dict[str, List[str]] = {\"INTEGER\": [], \"REAL\": [], \"TEXT\": []}\n\n    for cname, ctype in zip(colnames, coltypes):\n        if _is_int_type(ctype):\n            sql_types[\"INTEGER\"].append(cname)\n            continue\n        if _is_numerical_type_numpy(ctype):\n            sql_types[\"REAL\"].append(cname)\n        else:\n            sql_types[\"TEXT\"].append(cname)\n\n    return _generate_schema(table_name, sql_types)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/split_text_field.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSplits a text field into its individual components.\nThis can not be called directly from SQLite3. It is\na helper function.\n\"\"\"\n\nSEPARATORS = \";,.!?-|\\t\\\"\\v\\f\\r\\n%'()[]}{\"\n\n\ndef _split_text_field(textfield):\n    lower = textfield.lower()\n    for sep in SEPARATORS:\n        lower = lower.replace(sep, \" \")\n    splitted = lower.split(\" \")\n    return [s for s in splitted if s]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/stddev.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nSTDDEV aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _Stddev:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        return np.sqrt(np.var(self.values))\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/time_since_first_maximum.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTIME_SINCE_FIRST_MAXIMUM aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_better(tup1, tup2):\n    if tup2[1] == tup1[1]:\n        if tup2[0] > tup1[0]:\n            return tup2\n        return tup1\n    if tup2[1] > tup1[1]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _TimeSinceFirstMaximum:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_better, self.values, self.values[0])[0]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/time_since_first_minimum.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTIME_SINCE_FIRST_MINIMUM aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_better(tup1, tup2):\n    if tup2[1] == tup1[1]:\n        if tup2[0] > tup1[0]:\n            return tup2\n        return tup1\n    if tup2[1] < tup1[1]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _TimeSinceFirstMinimum:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_better, self.values, self.values[0])[0]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/time_since_last_maximum.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTIME_SINCE_LAST_MAXIMUM aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_better(tup1, tup2):\n    if tup2[1] == tup1[1]:\n        if tup2[0] < tup1[0]:\n            return tup2\n        return tup1\n    if tup2[1] > tup1[1]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _TimeSinceLastMaximum:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_better, self.values, self.values[0])[0]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/time_since_last_minimum.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nTIME_SINCE_LAST_MINIMUM aggregation.\n\"\"\"\n\nimport functools\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\ndef _is_better(tup1, tup2):\n    if tup2[1] == tup1[1]:\n        if tup2[0] < tup1[0]:\n            return tup2\n        return tup1\n    if tup2[1] < tup1[1]:\n        return tup2\n    return tup1\n\n\n# ----------------------------------------------------------------------------\n\n\nclass _TimeSinceLastMinimum:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value) and _not_null(time_stamp):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n\n        if not self.values:\n            return None\n\n        return functools.reduce(_is_better, self.values, self.values[0])[0]\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/to_list.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns a table as a list of lists.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport sqlite3\nfrom typing import List, Tuple\n\n# ----------------------------------------------------------------------------\n\n\ndef _handle_query(query):\n    if \"SELECT\" in query:\n        return query\n    return 'SELECT * FROM \"' + query + '\";'\n\n\n# ----------------------------------------------------------------------------\n\n\ndef to_list(conn: sqlite3.Connection, query: str) -> Tuple[List[str], List[list]]:\n    \"\"\"\n    Transforms a query or table into a list of lists. Returns\n    a tuple which contains the column names and the actual data.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        query:\n            The query used to get the table. You can also\n            pass the name of the table, in which case the entire\n            table will be imported.\n\n    Returns:\n            The column names and the data as a list of lists.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not isinstance(query, str):\n        raise TypeError(\"'query' must be a str\")\n\n    # ------------------------------------------------------------\n\n    query = _handle_query(query)\n    cursor = conn.execute(query)\n    colnames = [description[0] for description in cursor.description]\n    data = cursor.fetchall()\n    return colnames, data\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/to_pandas.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nReturns a table as a pandas.DataFrame.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport sqlite3\n\nimport pandas as pd\n\nfrom .to_list import to_list\n\n\ndef to_pandas(conn: sqlite3.Connection, query: str) -> pd.DataFrame:\n    \"\"\"\n    Returns a table as a pandas.DataFrame.\n\n    Args:\n        conn:\n            A sqlite3 connection created by [`connect`][getml.sqlite3.connect.connect].\n\n        query:\n            The query used to get the table. You can also\n            pass the name of the table, in which case the entire\n            table will be imported.\n\n    Returns:\n            The table as a pandas.DataFrame.\n    \"\"\"\n    # ------------------------------------------------------------\n\n    if not isinstance(conn, sqlite3.Connection):\n        raise TypeError(\"'conn' must be an sqlite3.Connection object\")\n\n    if not isinstance(query, str):\n        raise TypeError(\"'query' must be a str\")\n\n    # ------------------------------------------------------------\n\n    colnames, data = to_list(conn, query)\n    data_frame = pd.DataFrame(data)\n    data_frame.columns = colnames\n    return data_frame\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/trend.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nExtracts linear trends over time.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n# ----------------------------------------------------------------------------\n\n\nclass _Trend:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value, time_stamp):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append((time_stamp, value))\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        mean_x = np.mean([v[0] for v in self.values])\n        mean_y = np.mean([v[1] for v in self.values])\n        sum_xx = np.sum([(v[0] - mean_x) * (v[0] - mean_x) for v in self.values])\n        if sum_xx == 0.0:\n            return mean_y\n        sum_xy = np.sum([(v[0] - mean_x) * (v[1] - mean_y) for v in self.values])\n        beta = sum_xy / sum_xx\n        return mean_y - beta * mean_x\n\n\n# ----------------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/var.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nVAR aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _Var:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        if not self.values:\n            return None\n        return np.var(self.values)\n"
  },
  {
    "path": "src/python-api/getml/sqlite3/variation_coefficient.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nVARIATION_COEFFICIENT aggregation.\n\"\"\"\n\nimport numpy as np\n\nfrom .helpers import _not_null\n\n\nclass _VariationCoefficient:\n    def __init__(self):\n        self.values = []\n\n    def step(self, value):\n        \"\"\"\n        Executed every time the function is called.\n        \"\"\"\n        if _not_null(value):\n            self.values.append(value)\n\n    def finalize(self):\n        \"\"\"\n        Executed after all values are inserted.\n        \"\"\"\n        mean = np.mean(self.values)\n        if mean == 0.0:\n            return None\n        return np.var(self.values) / mean\n"
  },
  {
    "path": "src/python-api/getml/utilities/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nfrom .cell_formatter import CellFormatter\nfrom .column_formatter import _ColumnFormatter\nfrom .data_frame_formatter import _DataFrameFormatter\nfrom .ellipsis import _Ellipsis\nfrom .formatter import _Formatter\nfrom .signature_formatter import _SignatureFormatter\nfrom .view_formatter import _ViewFormatter\n\n__all__ = [\n    \"CellFormatter\",\n    \"_ColumnFormatter\",\n    \"_DataFrameFormatter\",\n    \"_Ellipsis\",\n    \"_Formatter\",\n    \"_SignatureFormatter\",\n    \"_ViewFormatter\",\n]\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/cell_formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nimport string\n\n\nclass CellFormatter(string.Formatter):\n    \"\"\"\n     Custom formatter for cells in output columns. Supports all python-native format specs\n     [https://docs.python.org/3/library/string.html#formatspec](https://docs.python.org/3/library/string.html#formatspec) plus the following custom\n     format specs:\n\n    - `<w_idth>.<p_recision>fd` (float): like `<w>.<p>f`, but the values of a column are\n      decimal point aligned\n\n     - `<w_idth>.<p_recision>d` (str): formats strings (holdings numbers) in a column on\n       the decimal point by taking into account the precision\n    \"\"\"\n\n    integer_overhang = 1\n\n    # ------------------------------------------------------------\n\n    def format_field(self, value, format_spec):\n        if format_spec.endswith(\"fd\"):\n            return self._format_float_decimal_point(value, format_spec)\n\n        if format_spec.endswith(\"d\"):\n            return self._format_string_decimal_point(value, format_spec)\n\n        return super().format_field(value, format_spec)\n\n    # ------------------------------------------------------------\n\n    def _format_float_decimal_point(self, value, format_spec):\n        width = int(format_spec.split(\".\")[0].strip())\n        precision = int(format_spec.split(\".\")[1][0])\n        padding = precision - self.integer_overhang\n\n        if value.is_integer():\n            formatted = f\"{value:{width-padding}.{self.integer_overhang}f}\"\n            # fix misalignment due to missing decimal point\n            if self.integer_overhang == 0:\n                formatted = formatted[1:]\n            return f\"{formatted:{width}}\"\n\n        formatted = super().format_field(value, format_spec[:-1])\n        stripped = formatted.rstrip(\"0\")\n        return f\"{stripped:{width}}\"\n\n    # ------------------------------------------------------------\n\n    def _format_string_decimal_point(self, value, format_spec):\n        width = int(format_spec.split(\".\")[0].strip())\n        precision = int(format_spec.split(\".\")[1][0])\n\n        if value.strip(\"-\").isdigit() or value == \"nan\":\n            formatted = f\"{value:>{width-precision-1}}\"\n            return f\"{formatted:{width}}\"\n\n        if \".\" in value:\n            digits = len(value.split(\".\")[1])\n        else:\n            digits = 0\n        formatted = f\"{value:>{width-precision+digits}}\"\n        return f\"{formatted:{width}}\"\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/column_formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDisplays a column\n\"\"\"\n\nfrom inspect import cleandoc\n\nfrom ..templates import environment\nfrom .data_frame_formatter import (\n    _DataFrameFormatColumn,\n    _DataFrameFormatter,\n    _get_subroles,\n)\nfrom .helpers import _get_column_content\n\n# -----------------------------------------------------------------------------\n\n\nclass _ColumnFormatter(_DataFrameFormatter):\n    max_rows = _DataFrameFormatter.max_rows\n\n    template = environment.get_template(\"column.jinja2\")\n\n    # ------------------------------------------------------------\n\n    def __init__(self, col, num_head=max_rows // 2, num_tail=max_rows // 2):\n        self.coltype = type(col).__name__\n\n        self.name = getattr(col, \"name\", None)\n        self.role = getattr(col, \"role\", None)\n\n        self.unit = getattr(col, \"unit\", None)\n        self.subroles = {\n            cat: subroles for cat, subroles in _get_subroles(col).items() if subroles\n        }\n\n        nrows_is_known = not isinstance(col.length, str)\n\n        self.n_rows = int(col.length) if nrows_is_known else None\n\n        num_head = min(num_head, self.n_rows or self.max_rows // 2 + 1)\n        num_tail = min(num_tail, self.n_rows or self.max_rows // 2)\n\n        head = _get_column_content(\n            col=col.cmd,\n            coltype=self.coltype.replace(\"View\", \"\"),\n            start=0,\n            length=num_head,\n        )[\"data\"]\n\n        if num_tail > 0 and \"View\" not in self.coltype:\n            tail = _get_column_content(\n                col=col.cmd,\n                coltype=self.coltype.replace(\"View\", \"\"),\n                start=int(self.n_rows - num_tail),\n                length=num_tail,\n            )[\"data\"]\n        else:\n            tail = []\n\n        cells = [cell[0] for cell in head + tail]\n\n        header = [self.name or \"\"]\n\n        if self.role:\n            header += [self.role]\n\n        if self.unit:\n            header += [self.unit]\n\n        if self.subroles:\n            header += [\"\"] + [\n                subroles\n                for subroles in self.subroles.values()\n                if any(subrole for subrole in subroles)\n            ]\n\n        self.data = [\n            _DataFrameFormatColumn(\n                headers=list(header),\n                cells=cells,\n                max_cells=self.max_rows,\n                n_cells=self.n_rows,\n                role=self.role,\n            )\n        ]\n\n        index_headers = [\"name\" if self.name else \"\"]\n        if self.role:\n            index_headers += [\"role\"]\n        if self.unit:\n            index_headers += [\"unit\"]\n        if self.subroles:\n            index_headers += [\"subroles:\"] + [f\"- {cat:7}\" for cat in self.subroles]\n\n        self._add_index(index_headers)\n\n        if \"View\" in self.coltype:\n            self.max_rows = num_head\n\n    # ------------------------------------------------------------\n\n    def _render_footer_lines(self, footer):\n        footer_lines = cleandoc(\n            f\"\"\"\n            {footer.n_rows} rows\n            type: {footer.type}\n            \"\"\"\n        )\n\n        if footer.url is not None:\n            footer_lines += f\"\\nurl: {footer.url}\"\n\n        return footer_lines\n\n    # ------------------------------------------------------------\n\n    def _render_body(self, as_html=False):\n        return super()._render_body(as_html)\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/data_frame_formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHolds the DataFrameFormatter.\n\"\"\"\n\nimport re\nfrom collections import namedtuple\nfrom copy import deepcopy\nfrom inspect import cleandoc\nfrom typing import Any, Dict, List, Union\n\nimport numpy as np\n\nfrom getml.data.roles import (\n    join_key,\n    numerical,\n    target,\n    time_stamp,\n    unused_float,\n)\n\nfrom ..templates import environment\nfrom .formatter import (\n    MAX_CELL_WIDTH,\n    _clip_cell,\n    _FormatColumn,\n    _Formatter,\n    _IndexColumn,\n    _infer_precision,\n)\nfrom .helpers import _get_data_frame_content\n\nUNITS = [\"D\", \"h\", \"m\", \"s\", \"ms\"]\n\"\"\"\nNumpy datetime units.\n\"\"\"\n\nUNITS_FORMATS = dict(\n    D=\"{:%Y-%m-%d}\",\n    h=\"{:%Y-%m-%d %H}\",\n    m=\"{:%Y-%m-%d %H:%M}\",\n    s=\"{:%Y-%m-%d %H:%M:%S}\",\n    ms=\"{:%Y-%m-%d %H:%M:%S.%f}\",\n)\n\"\"\"\nA mapping of numpy datetime units to strftime format specifiers.\n\"\"\"\n\n# --------------------------------------------------------------------\n\n\ndef _get_subroles(col):\n    subroles = {}\n\n    subroles[\"exclude\"] = [\n        _parse_subrole(subrole)\n        for subrole in getattr(col, \"subroles\", [])\n        if subrole.startswith(\"exclude \")\n    ]\n    subroles[\"include\"] = [\n        _parse_subrole(subrole)\n        for subrole in getattr(col, \"subroles\", [])\n        if subrole.startswith(\"include \")\n    ]\n    subroles[\"only\"] = [\n        _parse_subrole(subrole)\n        for subrole in getattr(col, \"subroles\", [])\n        if subrole.startswith(\"only \")\n    ]\n\n    return subroles\n\n\n# --------------------------------------------------------------------\n\n\ndef _parse_subrole(subrole):\n    prefix = re.compile(r\"^(exclude |include |only )\")\n    return re.sub(prefix, \"\", subrole)\n\n\n# --------------------------------------------------------------------\n\n\ndef _structure_subroles(df):\n    subroles: Dict[str, List[str]] = {\"exclude\": [], \"include\": [], \"only\": []}\n    df_subroles = [_get_subroles(df[col]) for col in df.colnames]\n\n    for col_subroles in df_subroles:\n        for cat, values in subroles.items():\n            values.append(col_subroles[cat])\n\n    return subroles\n\n\n# --------------------------------------------------------------------\n\n\ndef _infer_resolution(cells):\n    cells = np.array(cells, dtype=\"datetime64\")\n    deltas = np.unique(cells[1:] - cells[:-1])\n\n    for unit in UNITS:\n        normalized_deltas = np.array(\n            [np.timedelta64(delta, \"ms\") for delta in deltas]\n        ) / np.timedelta64(1, unit)\n        if all(np.mod(normalized_deltas, 1) == 0):\n            return unit\n\n\n# --------------------------------------------------------------------\n\n\ndef _max_digits(cells):\n    cells = [cell for cell in cells if \".\" in cell]\n    if len(cells) > 0:\n        return max(len(cell.split(\".\")[1]) for cell in cells)\n    return 0\n\n\n# --------------------------------------------------------------------\n\n\nclass _DataFrameFormatter(_Formatter):\n    \"\"\"\n    A Formatter for DataFrames.\n    \"\"\"\n\n    max_rows = 10\n\n    template = environment.get_template(\"data_frame.jinja2\")\n\n    # ------------------------------------------------------------\n\n    def __init__(self, df, num_head=max_rows // 2, num_tail=max_rows // 2):\n        self.colnames = df.colnames\n        self.n_rows = len(df)\n        self.name = df.name\n        self.memory_usage = df.memory_usage\n\n        num_head = min(num_head, self.n_rows // 2 + 1)\n        num_tail = min(num_tail, self.n_rows // 2)\n\n        roles = [df[colname].role for colname in df.colnames]\n\n        units = [df[colname].unit for colname in df.colnames]\n\n        self.units = None\n\n        self.subroles = {\n            cat: subroles\n            for cat, subroles in _structure_subroles(df).items()\n            if any(subrole for subrole in subroles)\n        }\n\n        head = _get_data_frame_content(name=df.name, start=0, length=num_head)[\"data\"]\n        if num_tail > 0:\n            tail = _get_data_frame_content(\n                name=df.name, start=int(self.n_rows - num_tail), length=num_tail\n            )[\"data\"]\n        else:\n            tail = []\n\n        rows = head + tail\n\n        columns = [list(col) for col in zip(*rows)]\n\n        headers = [self.colnames] + [roles]\n\n        if any(unit != \"\" for unit in units):\n            self.units = units\n            headers += [units]\n\n        if self.subroles:\n            headers += [[\"\"] * len(columns)] + [\n                subroles\n                for subroles in self.subroles.values()\n                if any(subrole for subrole in subroles)\n            ]\n\n        headers = list(zip(*headers))\n\n        self.data = [\n            _DataFrameFormatColumn(\n                headers=list(header),\n                cells=cells,\n                max_cells=self.max_rows,\n                n_cells=self.n_rows,\n                role=role,\n            )\n            for header, role, cells in zip(headers, roles, columns)\n        ]\n\n        index_headers = [\"name\"]\n        if self.roles:\n            index_headers += [\"role\"]\n        if self.units:\n            index_headers += [\"unit\"]\n        if self.subroles:\n            index_headers += [\"subroles:\"] + [f\"- {cat:7}\" for cat in self.subroles]\n\n        self._add_index(index_headers)\n\n    # ------------------------------------------------------------\n\n    def __len__(self):\n        return self.n_rows\n\n    # ------------------------------------------------------------\n\n    def _render_footer_lines(self, footer):\n        if footer.url is None or footer.name is None:\n            return cleandoc(\n                f\"\"\"\n                {footer.n_rows} rows x {footer.n_cols} columns\n                memory usage: {footer.memory_usage:.2f} MB\n                type: {footer.type}\n                \"\"\"\n            )\n        return cleandoc(\n            f\"\"\"\n            {footer.n_rows} rows x {footer.n_cols} columns\n            memory usage: {footer.memory_usage:.2f} MB\n            name: {footer.name}\n            type: {footer.type}\n            url: {footer.url}\n            \"\"\"\n        )\n\n    # ------------------------------------------------------------\n\n    def _add_index(self, headers):\n        index_column = _IndexColumn(\n            headers=headers,\n            n_cells=self.n_rows or self.max_rows + 1,\n            length=self.max_rows,\n        )\n\n        self.data = [index_column] + self.data\n\n    # ------------------------------------------------------------\n\n    def _render_html(self, footer=None):\n        headers = self._render_head()\n        rows = self._render_body(as_html=True)\n\n        precisions = [getattr(column, \"precision\", None) for column in self.data]\n\n        cell = namedtuple(\"cell\", [\"role\", \"value\"])\n        headers = [\n            [cell(role, header) for role, header in zip(self.roles, headers)]\n            for headers in headers\n        ]\n        rows = [\n            [cell(role, value) for role, value in zip(self.roles, row)] for row in rows\n        ]\n\n        return self.template.render(\n            headers=headers, rows=rows, precisions=precisions, footer=footer\n        )\n\n    # ------------------------------------------------------------\n\n    def _render_body(self, as_html=False):\n        for col in self.data:\n            if col._instances_are(str):\n                col.data = [_clip_cell(cell, MAX_CELL_WIDTH) for cell in col.data]\n\n        columns_formatted = [column._format_cells() for column in self.data]\n\n        columns_clipped = [column._clip() for column in columns_formatted]\n\n        if as_html:\n            cells = [column._unnest_and_strip_html() for column in columns_clipped]\n        else:\n            cells = [column._expand_cells() for column in columns_clipped]\n\n        rows = [list(row) for row in zip(*cells)]\n\n        return rows\n\n    # ------------------------------------------------------------\n\n    def _render_string(self, footer=None):\n        lines = super()._render_string()\n\n        footer_lines = self._render_footer_lines(footer) if footer else \"\"\n\n        return lines + \"\\n\\n\\n\" + footer_lines\n\n    @property\n    def roles(self):\n        return [getattr(column, \"role\", None) for column in self.data]\n\n\n# --------------------------------------------------------------------\n\n\nclass _DataFrameFormatColumn(_FormatColumn):\n    \"\"\"\n    A Formatter for columns of a DataFrame.\n\n    The cell format is set based on the column's role.\n    \"\"\"\n\n    # ------------------------------------------------------------\n\n    def __init__(self, *args, n_cells, role=None, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.n_cells = n_cells\n        self.resolution = None\n        self.role = role\n\n        # self.parse_cells()\n        if self.role in [numerical, target, time_stamp, join_key, unused_float]:\n            self.precision = _max_digits(self.data)\n\n        self.cell_template = self.default_cell_template\n        self.header_template = self.default_header_template\n\n    # ------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ------------------------------------------------------------\n\n    def _format_cells(self):\n        \"\"\"\n        A patch to justify time stamps. This is necessary because afaik we are not able\n        to specify justification within strftime format strings.\n        \"\"\"\n        cells_formatted = super()._format_cells()\n\n        if self.role == \"time_stamp\":\n            cells_formatted.data = [\n                cell.ljust(self.width) for cell in cells_formatted.data\n            ]\n\n        return cells_formatted\n\n    # ------------------------------------------------------------\n\n    def _clip(self):\n        ellipses: Union[str, List[Any]] = \"...\" + getattr(self, \"precision\", 0) * \" \"\n        column_clipped = deepcopy(self)\n\n        if self.role is None:\n            ellipses = [self.header_template.format(ellipses)]\n            if self.nested:\n                ellipses = [ellipses]\n            column_clipped.data += ellipses\n            return column_clipped\n\n        return super()._clip(ellipses=ellipses)\n\n    # ------------------------------------------------------------\n\n    def _parse_cells(self):\n        # some fun parsing\n        if self.role == time_stamp:\n            if all(cell.isdigit() for cell in self.data):\n                self.data = np.array(self.data, dtype=\"float\")\n                self.data = np.array(self.data, dtype=\"datetime64[s]\").tolist()\n            else:\n                self.data = np.array(self.data, dtype=\"datetime64[ms]\").tolist()\n\n            resolutions = list(\n                set(\n                    [\n                        _infer_resolution(self.data[: len(self.data) // 2]),\n                        _infer_resolution(self.data[len(self.data) // 2 :]),\n                    ]\n                )\n            )\n\n            # sort resolutions in ascending order and pick the smallest one\n            self.resolution = sorted(\n                resolutions, key=lambda res: UNITS.index(res), reverse=True\n            )[0]\n\n        if self.role in [numerical, target, join_key, unused_float]:\n            # this logic might be too simplistic, but data comes from the Engine\n            # in a standardized format anyway, so...¯\\_(ツ)_/¯\n            if any(\".\" in cell for cell in self.data):\n                self.data = [float(cell) for cell in self.data]\n                self.precision = _infer_precision(self.data)\n            elif all(cell.isdigit() for cell in self.data):\n                self.data = [int(cell) for cell in self.data]\n\n    # ------------------------------------------------------------\n\n    @property\n    def default_cell_template(self):\n        if getattr(self, \"role\", None) in [\n            join_key,\n            numerical,\n            target,\n            time_stamp,\n            unused_float,\n        ]:\n            if all(str(cell).isdigit() for cell in self.data):\n                return \"{:\" f\">{self.width}\" \"}\"\n            return \"{:\" f\" {self.width}.{self.precision}d\" \"}\"\n\n        return super().default_cell_template\n\n    # ------------------------------------------------------------\n\n    @property\n    def default_header_template(self):\n        if getattr(self, \"role\", None) in [\n            join_key,\n            numerical,\n            target,\n            time_stamp,\n            unused_float,\n        ]:\n            return \"{:\" f\">{self.width}\" \"}\"\n\n        return super().default_header_template\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/ellipsis.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nA class with a nice repr for suppressing parameter values.\n\"\"\"\n\n\nclass _Ellipsis(str):\n    \"\"\"\n    A class with a nice repr for suppressing parameter values.\n    \"\"\"\n\n    def __repr__(self):\n        return \"...\"\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHolds Formatter and FormatColumn classes which are used for formatting tabular output\nwithin the getML python API.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport datetime\nimport html\nimport itertools as it\nimport math\nfrom collections import namedtuple\nfrom copy import deepcopy\nfrom numbers import Number\nfrom typing import Optional\n\nimport numpy as np\n\nfrom ..templates import environment\nfrom .cell_formatter import CellFormatter\n\nBASE_PRECISION = 4\n\"\"\"\nThe baseline (minimum) number of significant digits when formatting floats.\n\"\"\"\nMAX_CELL_WIDTH = 32\n\nMIN_CELL_WIDTH = 16\n\n# --------------------------------------------------------------------\n\n\ndef _clip_lines(lines, max_cols):\n    if len(lines[0]) > max_cols:\n        threshold = max_cols // 2\n        lines_clipped = []\n        ellipses = [\"...\" if row[0].strip() else \"   \" for row in lines]\n        for line, ellipsis in zip(lines, ellipses):\n            line_ = list(line)\n            lines_clipped.append(line_[:threshold] + [ellipsis] + line_[-threshold:])\n        return lines_clipped\n    return lines\n\n\n# --------------------------------------------------------------------\n\n\ndef _expand_cells(cells, depths, width=4):\n    cells_expanded = []\n    pad_cell = \" \" * width\n\n    for cell, row_depth in zip(cells, depths):\n        cell_expanded = [cell] if not isinstance(cell, list) else list(cell)\n        depth = len(cell)\n        if depth < row_depth:\n            remaining = row_depth - depth\n            cell_expanded += [pad_cell] * remaining\n        cells_expanded.extend(cell_expanded)\n\n    return cells_expanded\n\n\n# --------------------------------------------------------------------\n\n\ndef _clip_cell(cell, width):\n    if len(cell) > width:\n        return f\"{cell:.{width}}...\"\n    return cell\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_depth(cell):\n    return len(cell) if isinstance(cell, list) else 1\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_width(cell, decimal_places=None):\n    if isinstance(cell, list):\n        if len(cell) > 0:\n            return max(_get_width(elem, decimal_places) for elem in cell)\n        return 0\n    if isinstance(cell, float) and decimal_places is not None:\n        return len(f\"{cell: .{decimal_places}f}\")\n    return min(len(str(cell)), MAX_CELL_WIDTH)\n\n\n# --------------------------------------------------------------------\n\n\ndef _infer_precision(cells):\n    cells = np.array(cells)\n    cells = cells[~np.isnan(cells)]\n\n    if len(cells) == 0:\n        return 0\n\n    max_frac = max(len(str(cell).split(\".\")[1].rstrip(\"0\")) for cell in cells)\n\n    if all(cells > 1):\n        return min(max_frac, BASE_PRECISION)\n\n    if all(cells == 0):\n        return 1\n\n    for i in range(10):\n        if all(cells // 10**-i >= 1):\n            return min(max_frac, i - 1 + BASE_PRECISION)\n\n    return BASE_PRECISION\n\n\n# --------------------------------------------------------------------\n\n\ndef _split_lines(lines, margin, max_width):\n    lines_split = []\n    lines_remaining = []\n\n    for line in lines:\n        truncated = list(_truncate_line(line, margin, max_width))\n        remaining = line[len(truncated) :]\n\n        lines_split.append(truncated)\n\n        if remaining:\n            index = [line[0]]\n            lines_remaining.append(index + remaining)\n\n    if lines_remaining:\n        lines_split.append([\"\"])\n        lines_split.extend(_split_lines(lines_remaining, margin, max_width))\n\n    return lines_split\n\n\n# --------------------------------------------------------------------\n\n\ndef _truncate_line(line, margin, max_width, template=\"{}\"):\n    used_width = 0\n\n    for cell in line:\n        cell_width = len(template.format(cell)) + margin\n        available_width = max_width - used_width\n        if cell_width + used_width <= max_width:\n            yield cell\n        elif cell_width > max_width and available_width >= MIN_CELL_WIDTH:\n            clipped_cell = _clip_cell(cell, available_width)\n            cell_width = len(template.format(clipped_cell)) + margin\n            yield clipped_cell\n        used_width += cell_width\n\n\n# --------------------------------------------------------------------\n\n\nclass _Formatter:\n    \"\"\"A formatter for tabular output of data (usually a container of some kind).\"\"\"\n\n    # the margin between columns (in chars)\n    margin = 3\n    # maximum tolerable width of an output line (in chars)\n    max_width = 130\n    # maximum number of output rows\n    max_rows = 10\n    # maximum number of output columns\n    max_cols = 10\n\n    template = environment.get_template(\"container.jinja2\")\n\n    # ------------------------------------------------------------\n\n    def __init__(self, headers, rows):\n        self.data = []\n\n        depths = [max(_get_depth(cell) for cell in row) for row in rows]\n\n        columns = zip(*rows)\n        headers = zip(*headers)\n\n        for header, cells in it.zip_longest(headers, columns, fillvalue=[]):  # type: ignore\n            self.data.append(\n                _FormatColumn(\n                    list(header),\n                    list(cells),\n                    self.max_rows,\n                    depths,\n                )\n            )\n\n        self.n_rows: Optional[int] = self.data[0].n_cells\n\n        self._add_index()\n\n    # ------------------------------------------------------------\n\n    def __getitem__(self, key):\n        if isinstance(key, int):\n            return self.data[key]\n        if isinstance(key, slice):\n            fmt = deepcopy(self)\n            cols = self.data[key]\n            fmt.data = cols\n            return fmt\n        if isinstance(key, str):\n            try:\n                return [column for column in self.data if column._name == key][0]  # type: ignore\n            except IndexError as exc:\n                raise KeyError(f\"No FormatColumn with name: {key}\") from exc\n        raise TypeError(\n            f\"Formatters can only be indexed by: int, slices, or str, not {type(key).__name__}\"\n        )\n\n    # ------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ------------------------------------------------------------\n\n    def __repr__(self):\n        output = [f\"{'column':20}{'cell template':25}{'header template':15}\"]\n        for column in self.data:\n            output.append(\n                f\"{column.name:20}{column.cell_template!r:25}{column.header_template!r:15}\"\n            )\n        return \"\\n\".join(output)\n\n    # ------------------------------------------------------------\n\n    def _pivot(self):\n        columns = [column.data for column in self.data]\n        return list(zip(*columns))\n\n    # ------------------------------------------------------------\n\n    def _add_index(self):\n        index_column = _IndexColumn(\n            headers=[\"\"],\n            n_cells=self.n_rows,\n            length=self.max_rows,\n        )\n\n        self.data = [index_column] + self.data\n\n    # ------------------------------------------------------------\n\n    def _remove_index(self):\n        if self.index:\n            self.data.pop(0)\n\n    # ------------------------------------------------------------\n\n    def _render_head(self):\n        columns_formatted = [column._format_cells() for column in self.data]\n\n        depths = [\n            [len(cell) if isinstance(cell, list) else 1 for cell in column.sub_headers]\n            for column in self.data\n        ]\n\n        max_depths = [\n            max(row_depth) for row_depth in it.zip_longest(*depths, fillvalue=0)\n        ]\n\n        headers = [column.header for column in columns_formatted]\n        sub_headers_cols = [\n            _expand_cells(column.sub_headers, max_depths, column.width)\n            for column in columns_formatted\n        ]\n        sub_headers = [list(sub_header) for sub_header in zip(*sub_headers_cols)]\n\n        return [headers] + sub_headers\n\n    # ------------------------------------------------------------\n\n    def _render_body(self, as_html=False):\n        if not as_html:\n            for col in self.data:\n                if col._instances_are(str):\n                    col.data = [_clip_cell(cell, MAX_CELL_WIDTH) for cell in col.data]\n\n        columns_formatted = [column._format_cells() for column in self.data]\n\n        columns_clipped = [column._clip() for column in columns_formatted]\n\n        if as_html:\n            cells = [column._unnest_and_strip_html() for column in columns_clipped]\n        else:\n            cells = [column._expand_cells() for column in columns_clipped]\n\n        rows = [list(row) for row in zip(*cells)]\n\n        return rows\n\n    # ------------------------------------------------------------\n\n    def _render_string(self):\n        headers = self._render_head()\n        rows = self._render_body()\n\n        lines = headers + rows\n        lines_clipped = _clip_lines(lines, self.max_cols)\n        lines_split = _split_lines(lines_clipped, self.margin, self.max_width)\n\n        margin = \" \" * self.margin\n        lines_rendered = [margin.join(line) for line in lines_split]\n\n        return \"\\n\".join(lines_rendered)\n\n    # ------------------------------------------------------------\n\n    def _render_html(self):\n        headers = self._render_head()\n        rows = self._render_body(as_html=True)\n\n        # fall back to str for columns holding multiple types\n        dtypes = [\n            column.dtype.__name__ if isinstance(column.dtype, type) else \"str\"\n            for column in self.data\n        ]\n\n        cell = namedtuple(\"cell\", [\"dtype\", \"value\"])\n        headers = [\n            [cell(role, header) for role, header in zip(dtypes, headers)]\n            for headers in headers\n        ]\n        rows = [\n            [cell(dtype, value) for dtype, value in zip(dtypes, row)] for row in rows\n        ]\n\n        return self.template.render(headers=headers, rows=rows)\n\n    # ------------------------------------------------------------\n\n    @property\n    def index(self):\n        \"\"\"\n        Returns the IndexColumn of a Formatter, if any is present.\n        \"\"\"\n        if isinstance(self.data[0], _IndexColumn):\n            return self.data[0]\n\n\n# --------------------------------------------------------------------\n\n\nclass _FormatColumn:\n    \"\"\"A formatable column of an output table.\"\"\"\n\n    # ------------------------------------------------------------\n\n    def __init__(self, headers, cells, max_cells, depths=None, index=None):\n        self.data = cells\n        if self._instances_are(float):\n            self.precision = _infer_precision(self._unnest())\n        self.depths = depths or [1] * (len(cells) + 1)\n        self.formatted = False\n        self.header = headers[0]\n        self.max_cells = max_cells\n        self.n_cells = len(self.data)  # just an alias here\n        self.sub_headers = []\n        if len(headers) > 1:\n            self.sub_headers = headers[1:]\n\n        self.cell_template = self.default_cell_template\n        self.header_template = self.default_header_template\n        self.index = index\n\n    # ------------------------------------------------------------\n\n    def __len__(self):\n        return len(self.data)\n\n    # ------------------------------------------------------------\n\n    def __repr__(self):\n        max_length = 10\n        if self.formatted:\n            cells = self._unnest()[:max_length]\n        else:\n            cells = [str(cell) for cell in self._unnest()[:max_length]]\n\n        output = \"\\n\".join(\n            [self.header] + [f\"{self.cell_template:^{self.width}}\"] + cells\n        )\n\n        if len(self) > max_length:\n            output += f\"\\n{'...':^{self.width}}\"\n\n        return output\n\n    # ------------------------------------------------------------\n\n    def _expand_cells(self):\n        return _expand_cells(self.data, self.depths, self.width)\n\n    # ------------------------------------------------------------\n\n    def _format_cells(self):\n        # make format-relevant params accessible as local vars\n        fmt_params = {\"width\": self.width}\n\n        fmt = CellFormatter()\n\n        column_formatted = deepcopy(self)\n\n        def fmt_cell(cell, template):\n            if isinstance(cell, list):\n                if not cell:\n                    yield cell\n                for sub_cell in cell:\n                    yield from fmt_cell(sub_cell, template)\n            else:\n                yield fmt.format(template, cell, cell=cell, **fmt_params)\n\n        if self.nested:\n            cells_formatted = [\n                [\n                    fmt.format(self.cell_template, elem, cell=elem, **fmt_params)\n                    for elem in cell\n                ]\n                for cell in self.data\n            ]\n\n        else:\n            cells_formatted = [\n                fmt.format(self.cell_template, cell, cell=cell, **fmt_params)  # type: ignore\n                for cell in self.data\n            ]\n\n        column_formatted.data = cells_formatted\n\n        column_formatted.header = fmt.format(\n            self.header_template, self.header, cell=self.header, **fmt_params\n        )\n\n        column_formatted.sub_headers = list(\n            fmt_cell(self.sub_headers, self.header_template)\n        )\n\n        column_formatted.formatted = True\n\n        return column_formatted\n\n    # ------------------------------------------------------------\n\n    def _instances_are(self, type_):\n        if self.nested:\n            return all(isinstance(elem, type_) for cell in self.data for elem in cell)\n        return all(isinstance(cell, type_) for cell in self.data)\n\n    # ------------------------------------------------------------\n\n    def _unnest(self):\n        if self.nested:\n            return [elem for cell in self.data for elem in cell]\n        return self.data\n\n    # ------------------------------------------------------------\n\n    def _unnest_and_strip_html(self):\n        if self.nested:\n            return [\n                \"<br>\".join([html.escape(elem).strip() for elem in cell])\n                for cell in self.data\n            ]\n        return [html.escape(cell.strip()) for cell in self.data]\n\n    # ------------------------------------------------------------\n\n    @property\n    def default_cell_template(self):\n        \"\"\"\n        The default cell template. Will be set based on the type\n        of the column's cells.\n        \"\"\"\n        if self._instances_are(float):\n            return \"{:\" f\" {self.width}.{self.precision}fd\" \"}\"\n        if self._instances_are(Number):\n            return \"{:\" f\">{self.width}\" \"}\"\n        if self._instances_are(datetime.datetime):\n            return \"{:\" \"%Y-%m-%d %H:%M:%S\" \"}\"\n        return \"{:\" f\"{self.width}\" \"}\"\n\n    # ------------------------------------------------------------\n\n    @property\n    def default_header_template(self):\n        \"\"\"\n        The default header template. Will be set based on the type\n        of the column's cells.\n        \"\"\"\n        if self._instances_are(float):\n            return \"{:\" f\">{self.width}\" \"}\"\n        if self._instances_are(datetime.datetime):\n            # the default datetime format has a fixed\n            # width of 19 chars\n            return \"{:\" f\"{self.width}\" \"}\"\n        return self.default_cell_template\n\n    # ------------------------------------------------------------\n\n    def _clip(self, ellipses=\"...\") -> _FormatColumn:\n        column_clipped = deepcopy(self)\n        if self.n_cells > self.max_cells:\n            head = self.data[: self.max_cells // 2]\n            tail = self.data[-self.max_cells // 2 :]\n            ellipses = [self.header_template.format(ellipses, cell=ellipses)]\n            if self.nested:\n                ellipses = [ellipses]\n            column_clipped.data = head + ellipses + tail\n            return column_clipped\n\n        return column_clipped\n\n    # ------------------------------------------------------------\n\n    @property\n    def dtype(self):\n        \"\"\"\n        Returns the type of a column's cells. If a column contains cells of more than\n        one type, returns the set of the types it holds. Returns str if a column\n        contains only empty lists or Nones.\n        \"\"\"\n        # TODO Implement more sanity checks\n        if self.nested:\n            dtypes = set(type(elem) for cell in self.data for elem in cell)\n        else:\n            dtypes = set(type(cell) for cell in self.data)\n\n        if len(dtypes) == 1:\n            return dtypes.pop()\n\n        if len(dtypes) > 1:\n            return dtypes\n\n        # fallback to str for empty lists or None\n        return str\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self):\n        \"\"\"\n        The name of the column (usually the header).\n        \"\"\"\n        return self.header\n\n    # ------------------------------------------------------------\n\n    @property\n    def nested(self):\n        \"\"\"\n        If a column contains only lists, it is regarded as \"nested\". Nested\n        columns can be formatted like normal columns. For rendering, nested columns can\n        be unnested, while the unnesting strategy depends on the rendering target. See:\n        _expand_cells, _unnest and _unnest_and_strip_html.\n        \"\"\"\n        return all(isinstance(cell, list) for cell in self.data)\n\n    # ------------------------------------------------------------\n\n    @property\n    def width(self):\n        \"\"\"\n        The width of a column (in chars). With parsing enabled the final width of column\n        of floats depends on the precision specified.\n        \"\"\"\n        return max(\n            _get_width(cell, getattr(self, \"precision\", None))\n            for cell in [self.header] + self.sub_headers + self.data\n        )\n\n\n# --------------------------------------------------------------------\n\n\nclass _IndexColumn(_FormatColumn):\n    def __init__(self, headers, n_cells, length):\n        length = min(length, n_cells)\n        head = list(range(math.ceil(length / 2)))\n        tail = list(range(n_cells - length // 2, n_cells))\n        cells = head + tail\n        super().__init__(cells=cells, headers=headers, max_cells=length)\n        self.n_cells = n_cells\n\n    # ------------------------------------------------------------\n\n    def _clip(self):\n        return super()._clip(ellipses=\"\")\n\n    # ------------------------------------------------------------\n\n    @property\n    def name(self):\n        \"\"\"\n        The name of an IndexColumn is always \"index\".\n        \"\"\"\n        return \"index\"\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/helpers.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nCollection of helper functions that are not intended to be used by\nthe end-user.\n\"\"\"\n\nimport json\nfrom typing import Any, Dict\n\nimport getml.communication as comm\n\n\ndef _get_column_content(col, coltype, start, length):\n    \"\"\"\n    Returns the contents of a data frame in a format that is\n    compatible with jquery.data.tables.\n\n    Args:\n        col (dict): The cmd describing the dict.\n\n        coltype (str): The type of the column\n            (FloatColumn, StringColumn or BooleanColumn).\n\n        start (int): The number of the first line to retrieve.\n\n        length (int): The number of lines to retrieve.\n    \"\"\"\n\n    if not isinstance(start, int):\n        raise TypeError(\"'start' must be an int.\")\n\n    if not isinstance(length, int):\n        raise TypeError(\"'length' must be an int.\")\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = coltype + \".get_content\"\n    cmd[\"name_\"] = \"\"\n\n    cmd[\"col_\"] = col\n    cmd[\"draw_\"] = 1\n\n    cmd[\"start_\"] = start\n    cmd[\"length_\"] = length\n\n    with comm.send_and_get_socket(cmd) as sock:\n        json_str = comm.recv_string(sock)\n        if json_str[0] != \"{\":\n            comm.handle_engine_exception(json_str)\n\n    return json.loads(json_str)\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_data_frame_content(name, start, length):\n    \"\"\"\n    Returns the contents of a data frame in a format that is\n    compatible with jquery.data.tables.\n\n    Args:\n        name (str): The name of the data frame.\n\n        start (int): The number of the first line to retrieve.\n\n        length (int): The number of lines to retrieve.\n    \"\"\"\n\n    if not isinstance(name, str):\n        raise TypeError(\"'name' must be a str.\")\n\n    if not isinstance(start, int):\n        raise TypeError(\"'start' must be an int.\")\n\n    if not isinstance(start, int):\n        raise TypeError(\"'length' must be an int.\")\n\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"DataFrame.get_content\"\n    cmd[\"name_\"] = name\n\n    cmd[\"start_\"] = start\n    cmd[\"length_\"] = length\n\n    cmd[\"draw_\"] = 1\n\n    with comm.send_and_get_socket(cmd) as sock:\n        json_str = comm.recv_string(sock)\n\n    if json_str[0] != \"{\":\n        comm.handle_engine_exception(json_str)\n\n    return json.loads(json_str)\n\n\n# --------------------------------------------------------------------\n\n\ndef _get_view_content(start, length, cols):\n    cmd: Dict[str, Any] = {}\n\n    cmd[\"type_\"] = \"View.get_content\"\n    cmd[\"name_\"] = \"\"\n\n    cmd[\"start_\"] = start\n    cmd[\"length_\"] = length\n    cmd[\"cols_\"] = cols\n\n    cmd[\"draw_\"] = 1\n\n    with comm.send_and_get_socket(cmd) as sock:\n        json_str = comm.recv_string(sock)\n        if json_str[0] != \"{\":\n            comm.handle_engine_exception(json_str)\n\n    return json.loads(json_str)\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/signature_formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nHolds the SignatureFormatter class that allows for the proper formatting of function and\nclass signatures.\n\"\"\"\n\nfrom copy import deepcopy\n\nfrom .formatter import _clip_cell, _truncate_line\n\n# --------------------------------------------------------------------\n\nSTYLE = \"pep8\"\n\n# --------------------------------------------------------------------\n\n\ndef _split_value(value, indent, max_width, initial_indent=None):\n    split = []\n\n    if isinstance(value, list):\n        truncated = list(\n            _truncate_line(\n                value,\n                2,\n                max_width - (initial_indent or indent),\n                template=\"{!r}\",\n            )\n        )\n        remaining = value[len(truncated) :]\n        split.append(truncated)\n\n        if remaining:\n            split.extend(_split_value(remaining, indent, max_width))\n\n        return split\n\n    return value\n\n\n# --------------------------------------------------------------------\n\n\ndef _format_list(values, indent, value_indent, template):\n    values_formatted = [\", \".join(f\"{elem!r}\" for elem in line) for line in values]\n    indentation = \" \" * (indent + value_indent)\n    lines_formatted = f\",\\n{indentation}\".join(values_formatted)\n    return template.format(lines_formatted)\n\n\n# --------------------------------------------------------------------\n\n\ndef _remove_private(dict):\n    return {key: val for key, val in dict.items() if key[0] != \"_\"}\n\n\n# --------------------------------------------------------------------\n\n\nclass _SignatureFormatter:\n    \"\"\"\n    A formatter for function and class signatures. Used for generating appropriate\n    __repr__ s.\n\n    Outputs a PEP8-compliant string representation of a signature with a maximum of one\n    argument per line. Lists of parameter values that exceed a given width are split\n    to span multiple lines, while preserving proper indentation.\n    \"\"\"\n\n    max_width = 88\n    value_indent = 4\n\n    # ------------------------------------------------------------\n\n    def __init__(self, obj=None, data=None):\n        if obj and not data:\n            self.data = vars(obj)\n            self.type = type(obj).__name__\n        elif data and not obj:\n            data = deepcopy(data)\n            self.type = data.pop(\"type\")\n            self.data = data\n        else:\n            raise TypeError(\"Can construct signatures only from one of object or dict.\")\n\n        self.data = _remove_private(self.data)\n\n        self.template = f\"{self.type}\" \"({})\"\n        self.indent = len(self.template[:-3])\n\n    # ------------------------------------------------------------\n\n    def _format_pep8(self, suppress_none=False):\n        params_formatted = []\n\n        for name, value in self.data.items():\n            if suppress_none and value is None:\n                continue\n\n            if (\n                isinstance(value, list)\n                and self.indent + len(f\"{name}={value!r}\") > self.max_width\n            ):\n                list_split = _split_value(\n                    value,\n                    initial_indent=self.indent + len(f\"{name}=\"),\n                    indent=self.indent + self.value_indent,\n                    max_width=self.max_width,\n                )\n\n                list_template = \"[{}]\"\n\n                value_formatted = _format_list(\n                    list_split,\n                    self.indent,\n                    len(name) + 2,\n                    list_template,\n                )\n            elif isinstance(value, str):\n                value_formatted = (\n                    f\"{_clip_cell(value, self.max_width - self.indent - 2)!r}\"\n                )\n            else:\n                value_formatted = f\"{value!r}\"\n\n            params_formatted.append(f\"{name}={value_formatted}\")\n\n        return self.template.format(f\",\\n{self.indentation}\".join(params_formatted))\n\n    # ------------------------------------------------------------\n\n    def _format_black(self, suppress_none=False):\n        if len(self.params_formatted) + self.value_indent < self.max_width:\n            return self.template.format(\n                f\"\\n{self.value_indentation}\" + self.params_formatted + \"\\n\"\n            )\n\n        params_formatted = []\n\n        for name, value in self.data.items():\n            if suppress_none and value is None:\n                continue\n            if len(f\"{name}={value}\") > self.max_width:\n                list_split = [[elem] for elem in value]\n\n                list_template = (\n                    f\"[\\n{self.value_indentation * 2}{{}},\\n{self.value_indentation}]\"\n                )\n\n                value_formatted = _format_list(\n                    list_split, self.value_indent, self.value_indent, list_template\n                )\n\n            else:\n                value_formatted = f\"{value!r}\"\n\n            params_formatted.append(f\"{name}={value_formatted}\")\n\n        return self.template.format(\n            f\"\\n{self.value_indentation}\"\n            + f\",\\n{self.value_indentation}\".join(params_formatted)\n            + \"\\n\"\n        )\n\n    # ------------------------------------------------------------\n\n    def _format_compact(self, suppress_none=False):\n        # tokenize params regardless of parameter values' depth by splitting\n        # params formatted as a single line string on \", \"\n        params_tokenized = self.params_formatted.split(\", \", suppress_none)\n\n        params_split = _split_value(\n            params_tokenized, start=self.indent, max_width=self.max_width\n        )\n\n        params_formatted = f\",\\n{self.indentation}\".join(\n            \", \".join(elem for elem in line) for line in params_split\n        )\n\n        return self.template.format(params_formatted)\n\n    # ------------------------------------------------------------\n\n    def _format(self, suppress_none=False, style=None):\n        style = style or STYLE\n\n        one_line = self.template.format(self.params_formatted, suppress_none)\n        if len(one_line) < self.max_width:\n            return one_line\n\n        styles = [attr[8:] for attr in vars(type(self)) if attr.startswith(\"_format_\")]\n\n        if style in styles:\n            return getattr(self, \"_format_\" + style)(suppress_none)\n\n        raise AttributeError(\n            f\"No format style: {style}. Choose one of: {', '.join(styles)}\"\n        )\n\n    # ------------------------------------------------------------\n\n    @property\n    def indentation(self):\n        return \" \" * self.indent\n\n    # ------------------------------------------------------------\n\n    @property\n    def value_indentation(self):\n        return \" \" * self.value_indent\n\n    # ------------------------------------------------------------\n\n    @property\n    def params_formatted(self):\n        return \", \".join(\n            f\"{name}={value!r}\"\n            for name, value in self.data.items()\n            if value is not None\n        )\n"
  },
  {
    "path": "src/python-api/getml/utilities/formatting/view_formatter.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nDisplays a View.\n\"\"\"\n\nfrom copy import deepcopy\nfrom inspect import cleandoc\nfrom typing import Any, List, Optional, Union\n\nfrom ..templates import environment\nfrom .data_frame_formatter import (\n    _DataFrameFormatColumn,\n    _DataFrameFormatter,\n    _structure_subroles,\n)\nfrom .formatter import _FormatColumn\nfrom .helpers import _get_view_content\n\n# -----------------------------------------------------------------------------\n\n\nclass _ViewFormatter(_DataFrameFormatter):\n    max_rows = _DataFrameFormatter.max_rows // 2\n\n    template = environment.get_template(\"column.jinja2\")\n\n    # ------------------------------------------------------------\n\n    def __init__(self, view, num_head=max_rows):\n        self.colnames = view.colnames\n\n        nrows_is_known = not isinstance(view.nrows(), str)\n\n        self.n_rows: Optional[int] = int(view.nrows()) if nrows_is_known else None\n\n        if self.n_rows is not None:\n            num_head = min(num_head, self.n_rows)\n\n        cols = [view[colname].cmd for colname in view.colnames]\n        roles = [view.roles.column(colname) for colname in view.colnames]\n        units = [view[colname].unit for colname in view.colnames]\n\n        self.units = None\n\n        self.subroles = {\n            cat: subroles\n            for cat, subroles in _structure_subroles(view).items()\n            if any(subrole for subrole in subroles)\n        }\n\n        rows = _get_view_content(start=0, length=num_head, cols=cols)[\"data\"]\n\n        columns = [list(col) for col in zip(*rows)]\n\n        headers = [self.colnames] + [roles]\n\n        if any(unit != \"\" for unit in units):\n            self.units = units\n            headers += [units]\n\n        if self.subroles:\n            headers += [[\"\"] * len(columns)] + [\n                subroles\n                for subroles in self.subroles.values()\n                if any(subrole for subrole in subroles)\n            ]\n\n        headers = list(zip(*headers))\n\n        self.data = [\n            _ViewFormatColumn(\n                headers=list(header),\n                cells=cells,\n                max_cells=self.max_rows,\n                n_cells=self.n_rows,\n                role=role,\n            )\n            for header, role, cells in zip(headers, roles, columns)\n        ]\n\n        index_headers = [\"name\"]\n        if self.roles:\n            index_headers += [\"role\"]\n        if self.units:\n            index_headers += [\"unit\"]\n        if self.subroles:\n            index_headers += [\"subroles:\"] + [f\"- {cat:7}\" for cat in self.subroles]\n\n        self._add_index(index_headers)\n\n    # ------------------------------------------------------------\n\n    def _add_index(self, headers):\n        if not self.data:\n            return _ViewFormatColumn(headers=headers, cells=[], max_cells=0, n_cells=0)\n        index_column = _ViewFormatColumn(\n            headers=headers,\n            cells=list(range(len(self.data[0]))),\n            max_cells=self.max_rows,\n            n_cells=self.n_rows,\n        )\n\n        self.data = [index_column] + self.data\n\n    # ------------------------------------------------------------\n\n    def _render_footer_lines(self, footer):\n        footer_lines = cleandoc(\n            f\"\"\"\n            {footer.n_rows} rows x {footer.n_cols} columns\n            type: {footer.type}\n            \"\"\"\n        )\n\n        return footer_lines\n\n\n# -----------------------------------------------------------------------------\n\n\nclass _ViewFormatColumn(_DataFrameFormatColumn):\n    \"\"\"\"\"\"\n\n    def _clip(self, placeholder=\"...\") -> _FormatColumn:\n        ellipses: Union[str, List[Any]] = \"...\" + getattr(self, \"precision\", 0) * \" \"\n        column_clipped = deepcopy(self)\n        if not self.n_cells or self.n_cells > self.max_cells:\n            ellipses = [self.header_template.format(ellipses)]\n            if self.nested:\n                ellipses = [ellipses]\n            column_clipped.data += ellipses\n            return column_clipped\n        return column_clipped\n"
  },
  {
    "path": "src/python-api/getml/utilities/progress.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"Displays progress in bar form.\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nimport warnings\nfrom collections.abc import Iterator\nfrom contextlib import contextmanager\nfrom enum import Enum\nfrom pathlib import Path\nfrom types import TracebackType\nfrom typing import Optional, Type\n\nfrom rich.console import Console\nfrom rich.live import Live\nfrom rich.progress import (\n    BarColumn,\n    DownloadColumn,\n    SpinnerColumn,\n    TaskID,\n    TextColumn,\n    TimeRemainingColumn,\n)\nfrom rich.progress import Progress as RichProgress\nfrom rich.style import Style\n\nfrom getml.events.handlers import engine_event_handler, monitor_event_handler\nfrom getml.events.types import Event, EventType, EventTypeState\n\n\ndef _is_emacs_kernel() -> bool:\n    try:\n        from IPython.core.getipython import get_ipython  # type: ignore[name-defined]\n    except ImportError:\n        return False\n\n    ip = get_ipython()\n    if ip is None:\n        return False\n    if ip_kernel_app := ip.config.get(\"IPKernelApp\"):\n        connection_file = Path(ip_kernel_app.get(\"connection_file\", \"\"))\n        return connection_file.name.startswith(\"emacs\")\n    return False\n\n\ndef _is_jupyter() -> bool:\n    return Console().is_jupyter\n\n\ndef _is_vscode() -> bool:\n    return os.getenv(\"VSCODE_PID\") is not None\n\n\ndef _is_jupyter_without_ipywidgets() -> bool:\n    if not _is_jupyter():\n        return False\n    try:\n        import ipywidgets  # type: ignore[name-defined]\n    except ImportError:\n        return not _is_emacs_kernel() and not _is_vscode()\n    return False\n\n\n_ENV_VAR_TRUTHY_VALUES = (\"1\", \"t\", \"true\", \"True\")\n\n\ndef _should_enforce_textual_output() -> bool:\n    if env_value := os.getenv(\"GETML_PROGRESS_FORCE_TEXTUAL_OUTPUT\"):\n        return env_value in _ENV_VAR_TRUTHY_VALUES\n    if not _is_jupyter():\n        return False\n    if _is_jupyter_without_ipywidgets():\n        return True\n    if _is_emacs_kernel():\n        return True\n    if _is_vscode():\n        return True\n    return False\n\n\ndef _should_enforce_monochrome_output() -> bool:\n    if env_value := os.getenv(\"GETML_PROGRESS_FORCE_MONOCHROME_OUTPUT\"):\n        return env_value in _ENV_VAR_TRUTHY_VALUES\n    if _is_vscode():\n        return True\n    return False\n\n\ndef _should_show_ipywidgets_warning() -> bool:\n    if env_value := os.getenv(\"GETML_SHOW_IPYWIDGETS_WARNING\"):\n        return env_value in _ENV_VAR_TRUTHY_VALUES\n    return True\n\n\ndef _should_disable_progress_bars() -> bool:\n    \"\"\"\n    Returns True if progress bars should be disabled.\n    This is the case if the environment variable\n    GETML_DISABLE_PROGRESS_BARS is set to a truthy value.\n    \"\"\"\n    if env_value := os.getenv(\"GETML_PROGRESS_DISABLE\"):\n        return env_value in _ENV_VAR_TRUTHY_VALUES\n    return False\n\n\nSPEED_ESTIMATE_PERIOD = 300\nTASK_FAILED_DESCRIPTION = \"[danger]Failed[/danger]\"\nDESCRIPTION_COLUMN_WIDTH = 40\nBAR_COLUMN_WIDTH = 15\nFORCE_TEXTUAL_OUTPUT = _should_enforce_textual_output()\n\"\"\"\nIf set to True, forces the progress bar to be displayed in textual form.\nNeccessary because terminal-based environments communicating with jupyter\nkernels over zmq are falsely detected as jupyter environments with ipywidget\nsupport by rich.\n\"\"\"\nFORCE_MONOCHROME_OUTPUT = _should_enforce_monochrome_output()\n\"\"\"\nIf set to True, forces the progress bar to be displayed in monochrome form.\n\"\"\"\nSHOW_IPYWIDGETS_WARNING = _should_show_ipywidgets_warning()\n\"\"\"\nIf set to True, shows a warning when ipywidgets are not available in a jupyter\nenvironment.\n\"\"\"\n\nDISABLE = _should_disable_progress_bars()\n\"\"\"\nIf set to True, disables the progress bar completely.\n\"\"\"\n\nif SHOW_IPYWIDGETS_WARNING and _is_jupyter_without_ipywidgets():\n    warnings.warn(\n        \"\"\"\n        'ipywidgets' are currently not installed in your environment. For\n        an optimal output experience, install 'ipywidgets'. To suppress\n        this warning in the future, set the environment variable\n        GETML_SHOW_IPYWIDGETS_WARNING to 'f'.\n        \"\"\",\n        UserWarning,\n    )\n\n\ndef _pad_description(description: str) -> str:\n    \"\"\"\n    Pads the description to a fixed width for consistent display in the\n    progress bar.\n    \"\"\"\n    return f\"{description:<{DESCRIPTION_COLUMN_WIDTH}}\"\n\n\nclass ProgressBarStyle(Enum):\n    \"\"\"\n    Enum mapping progress bar styles to rich style specifications.\n    Member values are the valid style specs for rich.\n\n    See:\n    https://github.com/Textualize/rich/blob/master/rich/default_styles.py\n    \"\"\"\n\n    COMPLETE = Style(color=\"red\")\n    FINISHED = Style(color=\"green\")\n    PULSE = \"bar.pulse\"\n\n\nclass ProgressBarColumns(Enum):\n    BASE = (\n        SpinnerColumn(),\n        TextColumn(\"[progress.description]{task.description}\"),\n        BarColumn(\n            complete_style=ProgressBarStyle.COMPLETE.value,\n            finished_style=ProgressBarStyle.FINISHED.value,\n            pulse_style=ProgressBarStyle.PULSE.value,\n            bar_width=BAR_COLUMN_WIDTH,\n        ),\n        TextColumn(\"[progress.percentage]{task.percentage:>3.0f}%\"),\n    )\n    TIME = (TimeRemainingColumn(elapsed_when_finished=True, compact=True),)\n    DEFAULT = (\n        *BASE,\n        TextColumn(\"•\"),\n        *TIME,\n    )\n    DOWNLOAD = (\n        *BASE,\n        TextColumn(\"•\"),\n        DownloadColumn(),\n        TextColumn(\"•\"),\n        *TIME,\n    )\n\n\nclass ProgressType(Enum):\n    \"\"\"\n    Enum representing different types of progress bars.\n    \"\"\"\n\n    DEFAULT = \"Default\"\n    DOWNLOAD = \"Download\"\n\n    @property\n    def columns(self):\n        return ProgressBarColumns[self.name].value\n\n\nclass Progress:\n    \"\"\"\n    Displays progress in bar form.\n\n    Thin wrapper around rich.progress.Progress.\n    \"\"\"\n\n    def __init__(\n        self,\n        *,\n        progress_type: ProgressType = ProgressType.DEFAULT,\n        finish_all_tasks_on_stop: bool = True,\n    ):\n        \"\"\"\n        Handles multiple progress bars and updates them.\n\n        Args:\n            progress_type: Type of progress bar to display. Defaults to\n              ProgressType.DEFAULT.\n            finish_all_tasks_on_exit: If True, all tasks are finished when\n              __exit__ is called. Defaults to True.\n        \"\"\"\n        self.finish_all_tasks_on_stop = finish_all_tasks_on_stop\n        self.progress_type = progress_type\n\n        console = Console(\n            color_system=\"standard\" if FORCE_TEXTUAL_OUTPUT else \"auto\",\n            force_jupyter=False if FORCE_TEXTUAL_OUTPUT else None,\n            force_terminal=FORCE_TEXTUAL_OUTPUT or None,\n            force_interactive=FORCE_TEXTUAL_OUTPUT or None,\n            no_color=FORCE_MONOCHROME_OUTPUT,\n        )\n\n        self._progress = RichProgress(\n            *progress_type.columns,\n            speed_estimate_period=SPEED_ESTIMATE_PERIOD,\n            console=console,\n        )\n        self.descriptions = {}\n\n    def add_task(\n        self,\n        description: str,\n        *,\n        total: int = 100,\n        completed: int = 0,\n        visible: bool = True,\n    ) -> TaskID:\n        if not self.live.is_started:\n            self.start()\n\n        description = _pad_description(description)\n\n        task_id = self._progress.add_task(\n            description=description,\n            total=total,\n            completed=completed,\n            visible=visible,\n        )\n        self.descriptions[task_id] = description\n        return task_id\n\n    def advance(self, task_id: TaskID, *, steps: float = 1):\n        if task_id in self.task_ids:\n            self._progress.advance(task_id, advance=steps)\n\n    @property\n    def console(self) -> Console:\n        return self._progress.console\n\n    def finish(self, task_id: TaskID, refresh: bool = True):\n        if task_id in self.task_ids:\n            self.update(task_id, completed=self.tasks[task_id].total, refresh=refresh)\n        if task_id == len(self.task_ids) - 1:\n            self._set_finished_time(task_id)\n\n    def finish_all(self):\n        for task in self.tasks:\n            self.finish(task.id)\n\n    @property\n    def finished(self):\n        return self._progress.finished\n\n    @property\n    def live(self):\n        return self._progress.live\n\n    def new_task(\n        self,\n        description: str,\n        *,\n        total: int = 100,\n        completed: int = 0,\n        visible: bool = True,\n    ) -> TaskID:\n        \"\"\"\n        Adds a new task and finishes all other tasks.\n        \"\"\"\n        self.finish_all()\n        task_id = self.add_task(\n            description, total=total, completed=completed, visible=visible\n        )\n        return task_id\n\n    def new_task_and_reconstruct_renderable_maybe(\n        self,\n        description: str,\n        *,\n        total: int = 100,\n        completed: int = 0,\n        visible: bool = True,\n    ) -> TaskID:\n        \"\"\"\n        Adds a new task and finishes all other tasks. Reconstructs the\n        renderable to ensure that the progress bar is displayed correctly\n        with interleaved `print` statements.\n        \"\"\"\n        if _should_enforce_textual_output():\n            self._reconstruct_renderable()\n\n        task_id = self.new_task(\n            description, total=total, completed=completed, visible=visible\n        )\n        return task_id\n\n    def refresh(self):\n        self._progress.refresh()\n\n    def _reconstruct_renderable(self):\n        self.stop()\n        self._progress = RichProgress(\n            *self.progress_type.columns,\n            speed_estimate_period=SPEED_ESTIMATE_PERIOD,\n            console=self.console,\n        )\n        self.start()\n\n    def _reset_description(self, task_id: TaskID):\n        if (\n            task_id in self.task_ids\n            and self.tasks[task_id].description != self.descriptions[task_id]\n            and self.tasks[task_id].description != TASK_FAILED_DESCRIPTION\n        ):\n            self.update(task_id, description=self.descriptions[task_id])\n\n    def _set_finished_time(self, task_id: TaskID):\n        if task_id in self.task_ids:\n            self.tasks[task_id].finished_time = self.tasks[task_id].elapsed\n            self.refresh()\n\n    def set_all_failed(self):\n        for task in self.tasks:\n            if not task.finished:\n                self.update(task.id, description=TASK_FAILED_DESCRIPTION)\n\n    @property\n    def tasks(self):\n        return self._progress.tasks\n\n    @property\n    def task_ids(self):\n        return self._progress.task_ids\n\n    def start(self):\n        self._progress.start()\n\n    def stop(self, failed: bool = False):\n        if failed:\n            self.set_all_failed()\n        elif self.finish_all_tasks_on_stop:\n            self.finish_all()\n        self._progress.stop()\n\n    def update(\n        self,\n        task_id: TaskID,\n        *,\n        completed: Optional[float] = None,\n        description: Optional[str] = None,\n        refresh: bool = True,\n    ):\n        if description is not None:\n            description = _pad_description(description)\n\n        if task_id in self.task_ids:\n            self._progress.update(\n                task_id,\n                description=description,\n                completed=completed,\n                refresh=refresh,\n            )\n            if self.tasks[task_id].finished:\n                self._reset_description(task_id)\n\n    def __enter__(self) -> Progress:\n        # Nop because we start the live renderable lazily upon adding the first\n        # task to avoid the display of control chars when forcefully outputting\n        # text inside jupyter\n        return self\n\n    def __exit__(\n        self,\n        exc_type: Optional[Type[BaseException]] = None,\n        exc_val: Optional[BaseException] = None,\n        exc_tb: Optional[TracebackType] = None,\n    ):\n        failed = exc_type is not None\n\n        self.stop(failed=failed)\n\n\nclass ProgressEventHandler:\n    def __init__(self, progress: Progress):\n        self.progress = progress\n\n    @contextmanager\n    def live_renderable(self, event: Event) -> Iterator[Live]:\n        yield self.progress.live\n        if event.type is EventType.COMMAND_FINISHED:\n            self.progress.stop()\n            self.progress = Progress()\n\n    def create_or_update_progress(self, event: Event):\n        \"\"\"\n        Create a new progress task or update an existing one.\n\n        A new Progress is created lazily if all previous tasks have finished. Progresses\n        cannot be created eagerly, because construction a live renderable in a\n        textual environment results in control characters being printed to the output.\n        \"\"\"\n\n        with self.live_renderable(event):\n            if event.type.state is EventTypeState.START:\n                task_id = self.progress.new_task_and_reconstruct_renderable_maybe(\n                    event.attributes[\"description\"]\n                )\n            elif event.type.state is EventTypeState.PROGRESS and self.progress.tasks:\n                *_, task_id = self.progress.task_ids\n                self.progress.update(\n                    task_id,\n                    description=event.attributes[\"description\"],\n                    completed=event.attributes[\"progress\"],\n                )\n\n\nprogress_event_handler = ProgressEventHandler(progress=Progress())\n\n\n@monitor_event_handler(sync=True)\n@engine_event_handler(sync=True)\ndef show_progress(event: Event):\n    if DISABLE:\n        return\n    try:\n        progress_event_handler.create_or_update_progress(event)\n    except Exception as e:\n        progress_event_handler.progress.set_all_failed()\n        message = f\"An error occurred while displaying the progress bar: {e!r}\"\n        raise OSError(message) from e\n"
  },
  {
    "path": "src/python-api/getml/utilities/templates/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\n\"\"\"\nManages jinja2 templates.\n\"\"\"\n\nfrom jinja2 import Environment, PackageLoader\n\nloader = PackageLoader(\"getml\", \"utilities/templates\")\nenvironment = Environment(loader=loader)\n"
  },
  {
    "path": "src/python-api/getml/utilities/templates/column.jinja2",
    "content": "<style>\n  th {\n    text-align: left !important;\n  }\n  th.sub-header {\n    font-weight: normal;\n    font-style: italic;\n    text-align: left !important;\n  }\n  td {\n    text-align: left !important;\n  }\n  th:nth-child(1) {\n    text-align: right !important;\n    border-right: 1px solid LightGray;\n  }\n  th.numerical {\n    text-align: right !important;\n  }\n  td.numerical {\n    text-align: right !important;\n  }\n\n  td.char-align {\n    text-align: right !important;\n  }\n  span.left {\n    text-align: right;\n    width: 3em;\n  }\n  span.right {\n    float: right;\n    text-align: left;\n  }\n</style>\n\n<table class=\"dataframe\">\n  <thead>\n    <tr>\n      {% for cell in headers[0] %}\n        {% if cell.role %}\n          <th class=\"{{ cell.role }}\">{{ cell.value }}</th>\n        {% else %}\n          <th>{{ cell.value }}</th>\n        {% endif %}\n      {% endfor %}\n    </tr>\n    {% for header in headers[1:] %}\n      <tr>\n        {% for cell in header %}\n          {% if cell.role %}\n            <th class=\"sub-header {{ cell.role }}\">{{ cell.value }}</th>\n          {% else %}\n            <th class=\"sub-header\">{{ cell.value }}</th>\n          {% endif %}\n        {% endfor %}\n      </tr>\n    {% endfor %}\n  </thead>\n  <tbody>\n    {% for row in rows %}\n      <tr>\n        <th>{{ row[0].value }}</th>\n        {% for cell in row[1:] %}\n          {% if cell.role in [\"numerical\", \"target\", \"unused_float\"] %}\n            {% if \".\" in cell.value and cell.value != \"...\"  %}\n              {% set int, frac = cell.value.split(\".\") %}\n              {% set sep = \".\" %}\n            {% else %}\n              {% set int, frac = (cell.value, \"&nbsp;\") %}\n              {% set sep = \"\" %}\n            {% endif %}\n          <td class=\"char-align numerical\">\n            <span class=\"left\">{{ int }}</span\n            ><span class=\"right\" style=\"width: {{ precisions[loop.index] }}ch\"\n              >{{ sep + frac }}</span\n            >\n          </td>\n          {% elif cell.role %}\n            <td class=\"{{ cell.role }}\">{{ cell.value }}</td>\n          {% else %}\n            <td>{{ cell.value }}</td>\n          {% endif %}\n        {% endfor %}\n      </tr>\n    {% endfor %}\n  </tbody>\n</table>\n{% if footer %}\n  <p>\n    {{ footer.n_rows }} rows<br />\n    {% if footer.data_frame %}\n      data frame: {{ footer.data_frame }}<br />\n    {% endif %}\n    type: {{ footer.type }}<br />\n    {% if footer.url %}\n      url: <a href=\"{{ footer.url }}\" target=\"_blank\"> {{ footer.url }}</a>\n    {% endif %}\n  </p>\n{% endif %}\n"
  },
  {
    "path": "src/python-api/getml/utilities/templates/container.jinja2",
    "content": "<style>\n  th {\n    text-align: left !important;\n  }\n  td {\n    text-align: left !important;\n  }\n  th:nth-child(1) {\n    text-align: right;\n    border-right: 1px solid LightGray;\n  }\n  th.float {\n    text-align: right !important;\n  }\n  td.float {\n    text-align: right !important;\n  }\n  th.int {\n    text-align: right !important;\n  }\n  td.int {\n    text-align: right !important;\n  }\n</style>\n\n<table class=\"dataframe\">\n  <thead>\n    <tr>\n      {% for cell in headers[0] %}\n        {% if cell.dtype %}\n          <th class=\"{{ cell.dtype }}\">{{ cell.value }}</th>\n        {% else %}\n          <th>{{ cell.value }}</th>\n        {% endif %}\n      {% endfor %}\n    </tr>\n    {% for header in headers[1:] %}\n      <tr>\n        {% for cell in header %}\n          {% if cell.role %}\n            <th class=\"sub-header {{ cell.dtype }}\">{{ cell.value }}</th>\n          {% else %}\n            <th class=\"sub-header\">{{ cell.value }}</th>\n          {% endif %}\n        {% endfor %}\n      </tr>\n    {% endfor %}\n  </thead>\n  <tbody>\n    {% for row in rows %}\n      <tr>\n        <th>{{ row[0].value }}</th>\n          {% for cell in row[1:] %}\n            {% if cell.dtype %}\n              <td class=\"{{ cell.dtype }}\">{{ cell.value }}</td>\n            {% else %}\n              <td>{{ cell.value }}</td>\n            {% endif %}\n          {% endfor %}\n      </tr>\n    {% endfor %}\n  </tbody>\n</table>\n"
  },
  {
    "path": "src/python-api/getml/utilities/templates/data_frame.jinja2",
    "content": "<style>\n  table {\n    font-family: Helvetica, sans-serif;\n  }\n\n  th {\n    text-align: left !important;\n  }\n  td {\n    text-align: left !important;\n  }\n  th:nth-child(1) {\n    text-align: right !important;\n    border-right: 1px solid LightGray;\n  }\n  th.sub-header {\n    font-weight: normal;\n    font-style: italic;\n  }\n  .join_key,\n  .numerical,\n  .target,\n  .unused_float {\n    text-align: right !important;\n  }\n\n  .char-align {\n    text-align: right !important;\n  }\n  span.left {\n    text-align: right;\n    width: 3em;\n  }\n  span.right {\n    float: right;\n    text-align: left;\n  }\n</style>\n\n<table class=\"dataframe\">\n  <thead>\n    <tr>\n      {% for cell in headers[0] %}\n        {% if cell.role %}\n          <th class=\"{{ cell.role }}\">{{ cell.value }}</th>\n        {% else %}\n          <th>{{ cell.value }}</th>\n        {% endif %}\n      {% endfor %}\n    </tr>\n    {% for header in headers[1:] %}\n      <tr>\n        {% for cell in header %}\n          {% if cell.role %}\n            <th class=\"sub-header {{ cell.role }}\">{{ cell.value }}</th>\n          {% else %}\n            <th class=\"sub-header\">{{ cell.value }}</th>\n          {% endif %}\n        {% endfor %}\n      </tr>\n    {% endfor %}\n  </thead>\n  <tbody>\n    {% for row in rows %}\n      <tr>\n        <th>{{ row[0].value }}</th>\n        {% for cell in row[1:] %}\n          {% if cell.role in [\"numerical\", \"target\", \"unused_float\"] %}\n            {% if \".\" in cell.value and cell.value != \"...\"  %}\n              {% set int, frac = cell.value.split(\".\") %}\n              {% set sep = \".\" %}\n            {% else %}\n              {% set int, frac = (cell.value, \"&nbsp;\") %}\n              {% set sep = \"\" %}\n          {% endif %}\n            <td class=\"char-align {{ cell.role }}\">\n              <span class=\"left\">{{ int }}</span\n              ><span class=\"right\" style=\"width: {{ precisions[loop.index] }}ch\"\n                >{{ sep + frac }}</span\n              >\n            </td>\n          {% elif cell.role %}\n            <td class=\"{{ cell.role }}\">{{ cell.value }}</td>\n          {% else %}\n            <td>{{ cell.value }}</td>\n          {% endif %}\n        {% endfor %}\n      </tr>\n    {% endfor %}\n  </tbody>\n</table>\n{% if footer %}\n  <p>\n    {{ footer.n_rows }} rows x {{ footer.n_cols }} columns<br />\n    memory usage: {{ \"{:.2f}\".format(footer.memory_usage) }} MB<br />\n    name: {{ footer.name }}<br />\n    type: {{ footer.type }}<br />\n    {% if footer.url %}\n        url: <a href=\"{{ footer.url }}\" target=\"_blank\"> {{ footer.url }}</a>\n    {% endif %}\n  </p>\n{% endif %}\n"
  },
  {
    "path": "src/python-api/getml/version.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport os\nfrom pathlib import Path\n\nDEFAULT_VERSION = \"0.0.0\"\nVERSION_FILE = Path(__file__).parent / \"VERSION\"\n\ntry:\n    __version__ = VERSION_FILE.read_text().strip()\nexcept FileNotFoundError:\n    __version__ = os.getenv(\"GETML_VERSION\", DEFAULT_VERSION)\n"
  },
  {
    "path": "src/python-api/hatch_build.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nimport os\nfrom pathlib import Path\n\nfrom hatchling.builders.hooks.plugin.interface import BuildHookInterface\nfrom wheel.bdist_wheel import get_platform\n\ngetml_bin_path = Path(__file__).parent / \"getml\" / \".getML\"\n\n\nclass PlatformTag(BuildHookInterface):\n    def initialize(self, version: str, build_data: dict) -> None:\n        prefix = \"py3-none-\"\n        if getml_bin_path.exists():\n            if platform := os.getenv(\"WHEEL_PLATFORM\"):\n                build_data[\"tag\"] = prefix + platform\n            else:\n                build_data[\"tag\"] = prefix + get_platform(archive_root=None)\n"
  },
  {
    "path": "src/python-api/pyproject.toml",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# this file is licensed under the elastic license 2.0 (elv2).\n# refer to the license.txt file in the root of the repository\n# for details.\n#\n\n[build-system]\nrequires = [\"wheel~=0.43.0\", \"hatchling\", \"hatch-vcs\"]\nbuild-backend = \"hatchling.build\"\n\n[project]\nname = \"getml\"\ndescription = \"Python API for getML\"\ndynamic = [\"version\"]\nreadme = \"README.md\"\nrequires-python = \">=3.8\"\nlicense = {text = \"Elastic License 2.0 (ELv2)\"}\nauthors = [{name=\"getML\", email=\"getml@getml.com\"},]\nkeywords = [\"AutoML\", \"feature learning\", \"feature engineering\"]\nclassifiers = [\n    \"Programming Language :: Python\",\n    \"Programming Language :: Python :: 3\",\n    \"Programming Language :: Python :: 3.8\",\n    \"Programming Language :: Python :: 3.9\",\n    \"Programming Language :: Python :: 3.10\",\n    \"Programming Language :: Python :: 3.11\",\n    \"Programming Language :: Python :: 3.12\",\n    \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n    \"Intended Audience :: Education\",\n    \"Intended Audience :: Science/Research\",\n    \"Intended Audience :: Financial and Insurance Industry\",\n    \"Intended Audience :: Telecommunications Industry\",\n    \"Intended Audience :: Information Technology\",\n    \"Intended Audience :: Manufacturing\",\n    \"Intended Audience :: Healthcare Industry\",\n    \"Operating System :: OS Independent\",\n]\ndependencies = [\n    \"pandas>=2.0.3,<2.3\",\n    \"pyarrow>=16.0,<20.0\",\n    \"numpy>=1.24.4,<2.3\",\n    \"jinja2~=3.1.0\",\n    \"rich~=13.0\",\n    \"typing-extensions~=4.0\",\n]\n\n[project.urls]\n\"Hompage\" = \"https://getml.com\"\n\"Documentation\" = \"https://getml.com/latest\"\n\"Bug Tracker\" = \"https://github.com/getml/getml-community/issues\"\n\"Download\" = \"https://getml.com/latest/install\"\n\"Source Code\" = \"https://github.com/getml/getml-community\"\n\n[project.optional-dependencies]\nscipy = [\"scipy\"]\npyspark = [\"pyspark\"]\njupyter = [\"jupyterlab\", \"ipywidgets\"]\n\n[dependency-groups]\ndev = [\n  \"hatchling\",\n  \"keyring\",\n  \"keyrings.google-artifactregistry-auth\",\n  \"pyright\",\n  \"pytest\",\n  \"ruff\",\n]\n\n[tool.hatch.version]\nsource = \"code\"\npath = \"getml/version.py\"\nexpression = \"__version__\"\n\n[tool.hatch.envs.test]\ninstaller = \"uv\"\ndependencies = [\n  \"pytest\",\n  \"pytest-xdist\",\n]\n\n[[tool.hatch.envs.test.matrix]]\npython = [\"3.8\", \"3.9\", \"3.10\", \"3.11\", \"3.12\", \"3.13\"]\n\n[tool.hatch.build]\ninclude = [\n    \"getml\",\n    \"VERSION\",\n    \"getml/utilities/templates/*.jinja2\",\n]\nexclude = [\"tests\"]\n\n[tool.hatch.build.target.wheel]\ninclude = [\".getML/**/*\"]\n\n[tool.hatch.build.targets.wheel.hooks.custom]\npath = \"hatch_build.py\"\n\n[tool.ruff.lint]\npreview = true\nexplicit-preview-rules = true\nselect = [\"PL\", \"UP\", \"CPY001\"]\nignore = [\n  # Allow over\n  \"A003\",\n  # Allow non-abstract empty methods in abstract base classes\n  \"B027\",\n  # Allow for string literals in exceptions\n  \"EM\",\n  # Allow boolean positional values in function calls, like `dict.get(... True)`\n  \"FBT003\",\n  # Ignore checks for possible passwords\n  \"S105\", \"S106\", \"S107\",\n  # Ignore complexity\n  \"C901\", \"PLR0911\", \"PLR0912\", \"PLR0913\", \"PLR0915\", \"PLR2004\", \"PLR0124\"\n]\nexclude = [\"tests/**/*\",]\nextend-select = [\"I\"]\n\n[tool.ruff.lint.pyupgrade]\n# Preserve types, even if a file imports `from __future__ import annotations`.\nkeep-runtime-typing = true\n\n\n[tool.pytest.ini_options]\nmarkers = [\n    \"getml_engine: tests that rely on a running getML process (processes can automatically be launched and shutdown on linux)\",\n    \"slow: integration tests deemed time-intensive to execute.\"\n]\n"
  },
  {
    "path": "src/python-api/tests/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n"
  },
  {
    "path": "src/python-api/tests/conftest.py",
    "content": "import decimal\nimport os\nimport platform\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom inspect import cleandoc\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile, TemporaryDirectory\n\nimport pyarrow as pa\nimport pytest\n\nimport getml\nfrom getml.data._io.arrow import MAX_IEEE754_COMPATIBLE_INT\n\n\ndef pytest_addoption(parser):\n    parser.addoption(\n        \"--workdir\",\n        action=\"store\",\n        default=None,\n        help=\"Specify a working directory for tests\",\n    )\n\n\ndef pytest_collection_modifyitems(config, items):\n    for item in items:\n        try:\n            if \"getml_engine\" in item.fixturenames:\n                item.add_marker(pytest.mark.getml_engine)\n                if platform.system() != \"Linux\" and not getml.engine.is_monitor_alive():\n                    engine_skip_mark = pytest.mark.skip(\n                        reason=\"Engine is not running. Please start it manually.\"\n                    )\n                    item.add_marker(engine_skip_mark)\n        except:\n            pass\n\n\n@pytest.fixture(autouse=True)\ndef change_workdir(request, monkeypatch):\n    workdir = request.config.getoption(\"--workdir\")\n\n    if workdir:\n        workdir_path = Path(workdir)\n\n        if not workdir_path.is_dir():\n            raise ValueError(f\"{workdir} does not exist.\")\n\n        monkeypatch.chdir(workdir_path)\n\n\n@contextmanager\ndef workdir(dir: str):\n    cwd = Path.cwd()\n    os.chdir(dir)\n    try:\n        yield\n    finally:\n        os.chdir(cwd)\n\n\n@pytest.fixture(scope=\"module\")\ndef getml_engine():\n    if platform.system() != \"Linux\":\n        yield getml\n        return\n\n    print(\"Starting Engine\")\n    with TemporaryDirectory() as tmpdir:\n        getml.engine.launch(project_directory=tmpdir, log=True)\n\n        yield getml\n\n        print(\"Shutting down Engine\")\n        getml.engine.shutdown()\n\n\n@pytest.fixture(scope=\"function\")\ndef getml_project(request, getml_engine):\n    project_name = Path(request.node.name).stem\n    print(f\"Using project: {project_name}\")\n    getml_engine.set_project(project_name)\n\n    yield getml.project\n\n    print(f\"Deleting project: {project_name}\")\n    getml_engine.engine.delete_project(project_name)\n\n\n@pytest.fixture\ndef csv_file_with_changing_type_in_row_2():\n    data = cleandoc(\n        \"\"\"\n        time_stamp,join_key,targets,column_01\n        1970-01-01 00:00:00.202923,0,0,-0.9284\n        1970-01-01 00:00:00.134847,1,1.000001,-0.4343\n        \"\"\"\n    )\n\n    with NamedTemporaryFile(mode=\"w\", delete=False) as csv_file:\n        csv_file.write(data)\n        csv_file.flush()\n        yield csv_file.name\n        os.remove(csv_file.name)\n\n\n@pytest.fixture\ndef df_or_view(request, df):\n    \"\"\"\n    Example data:\n\n        >>> df\n\n        name  time_stamp  join_key  targets  column_01\n        role  time_stamp  join_key  target  numerical\n        unit  time stamp\n        0  1970-01-01 00:00:00.202923  0  0   -0.9284\n        1  1970-01-01 00:00:00.134847  1  0   -0.4343\n        2  1970-01-01 00:00:00.592543  2  0   -0.7653\n        3  1970-01-01 00:00:00.272449  3  0   0.6704\n        4  1970-01-01 00:00:00.918001  4  0   0.3387\n        5  1970-01-01 00:00:00.825230  5  0   0.728\n        6  1970-01-01 00:00:00.541397  6  0   0.56\n        7  1970-01-01 00:00:00.688549  7  0   0.8729\n        8  1970-01-01 00:00:00.150777  8  0   0.9636\n        9  1970-01-01 00:00:00.101308  9  0   -0.8872\n\n        10 rows x 4 columns\n    \"\"\"\n\n    if getattr(request, \"param\", None) == \"view\":\n        return df[:]\n\n    if getattr(request, \"param\", \"df\") == \"df\":\n        return df\n\n    return df\n\n\n@pytest.fixture\ndef df():\n    \"\"\"\n    Example data:\n\n        >>> df\n\n        name  time_stamp  join_key  targets  column_01\n        role  time_stamp  join_key  target  numerical\n        unit  time stamp\n        0  1970-01-01 00:00:00.202923  0  0   -0.9284\n        1  1970-01-01 00:00:00.134847  1  0   -0.4343\n        2  1970-01-01 00:00:00.592543  2  0   -0.7653\n        3  1970-01-01 00:00:00.272449  3  0   0.6704\n        4  1970-01-01 00:00:00.918001  4  0   0.3387\n        5  1970-01-01 00:00:00.825230  5  0   0.728\n        6  1970-01-01 00:00:00.541397  6  0   0.56\n        7  1970-01-01 00:00:00.688549  7  0   0.8729\n        8  1970-01-01 00:00:00.150777  8  0   0.9636\n        9  1970-01-01 00:00:00.101308  9  0   -0.8872\n\n        10 rows x 4 columns\n    \"\"\"\n    df, _ = getml.datasets.make_numerical(n_rows_population=10, n_rows_peripheral=0)\n    yield df\n\n\n@pytest.fixture\ndef view(df):\n    \"\"\"\n    Example data:\n\n        >>> df\n\n        name  time_stamp  join_key  targets  column_01\n        role  time_stamp  join_key  target  numerical\n        unit  time stamp\n        0  1970-01-01 00:00:00.202923  0  0   -0.9284\n        1  1970-01-01 00:00:00.134847  1  0   -0.4343\n        2  1970-01-01 00:00:00.592543  2  0   -0.7653\n        3  1970-01-01 00:00:00.272449  3  0   0.6704\n        4  1970-01-01 00:00:00.918001  4  0   0.3387\n        5  1970-01-01 00:00:00.825230  5  0   0.728\n        6  1970-01-01 00:00:00.541397  6  0   0.56\n        7  1970-01-01 00:00:00.688549  7  0   0.8729\n        8  1970-01-01 00:00:00.150777  8  0   0.9636\n        9  1970-01-01 00:00:00.101308  9  0   -0.8872\n\n        10 rows x 4 columns\n    \"\"\"\n    return df[:]\n\n\n@pytest.fixture\ndef tmpdir():\n    with TemporaryDirectory() as tmpdir:\n        with workdir(tmpdir):\n            yield tmpdir\n\n\n@pytest.fixture\ndef timestamp_with_utc_tz_batch():\n    return pa.RecordBatch.from_arrays(\n        [\n            pa.array(\n                [datetime(2021, 1, 1, tzinfo=timezone.utc)],\n                type=pa.timestamp(\"ns\", tz=\"UTC\"),\n            )\n        ],\n        [\"utc_time\"],\n    )\n\n\n@pytest.fixture\ndef timestamp_with_non_utc_tz_batch():\n    return pa.RecordBatch.from_arrays(\n        [\n            pa.array(\n                [datetime(2021, 1, 1).astimezone(timezone.utc)],\n                type=pa.timestamp(\"ns\", tz=\"PST8PDT\"),\n            )\n        ],\n        [\"non_utc_time\"],\n    )\n\n\n@pytest.fixture\ndef timestamp_without_tz_batch():\n    return pa.RecordBatch.from_arrays(\n        [pa.array([datetime(2021, 1, 1)], type=pa.timestamp(\"ns\"))],\n        [\"no_tz_time\"],\n    )\n\n\n@pytest.fixture\ndef int_batch():\n    return pa.RecordBatch.from_arrays(\n        [\n            pa.array([1]),\n            pa.array([MAX_IEEE754_COMPATIBLE_INT]),\n            pa.array([MAX_IEEE754_COMPATIBLE_INT + 1]),\n        ],\n        schema=pa.schema(\n            [\n                pa.field(\"ints\", pa.int64()),\n                pa.field(\"int64_hits_float64_upper_bound\", pa.int64()),\n                pa.field(\"int64_exceeds_float64_upper_bound\", pa.int64()),\n            ],\n        ),\n    )\n\n\n@pytest.fixture\ndef decimal_batch():\n    return pa.RecordBatch.from_arrays(\n        [pa.array([decimal.Decimal(1.0)])],\n        [\"decimals\"],\n    )\n\n\n@pytest.fixture\ndef timestamp_batch(\n    timestamp_with_utc_tz_batch,\n    timestamp_without_tz_batch,\n):\n    return pa.RecordBatch.from_arrays(\n        [\n            timestamp_with_utc_tz_batch[0],\n            timestamp_without_tz_batch[0],\n        ],\n        [\"utc_time\", \"no_tz_time\"],\n    )\n"
  },
  {
    "path": "src/python-api/tests/data/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n"
  },
  {
    "path": "src/python-api/tests/data/conftest.py",
    "content": "import decimal\nimport os\nfrom datetime import datetime, timezone\nfrom inspect import cleandoc\nfrom tempfile import NamedTemporaryFile\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pytest\n\nimport getml\n\n\n@pytest.fixture\ndef parquet_file():\n    table = pa.table({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n    with NamedTemporaryFile() as f:\n        pq.write_table(table, f.name)\n        yield f.name\n\n\n@pytest.fixture\ndef csv_file():\n    data = cleandoc(\n        \"\"\"\n        name,column_01,join_key,time_stamp\n        patrick,0,22,2019-01-01\n        alex,1,23,2019-01-02\n        phil,2, 24,2019-01-03\n        ulrike,3,25,2019-01-04\n        \"\"\"\n    )\n\n    with NamedTemporaryFile(mode=\"w\", delete=False) as csv_file:\n        csv_file.write(data)\n        csv_file.flush()\n        yield csv_file.name\n        os.remove(csv_file.name)\n\n\n@pytest.fixture\ndef csv_file_custom_ts():\n    data = cleandoc(\n        \"\"\"\n        name,time_stamp\n        patrick,date: 2019-01-01; time: 00:00:00\n        alex,date: 2019-01-02; time: 00:00:00\n        phil,date: 2019-01-03; time: 00:00:00\n        ulrike,date: 2019-01-04; time: 00:00:00\n        \"\"\"\n    )\n\n    with NamedTemporaryFile(mode=\"w\", delete=False) as csv_file:\n        csv_file.write(data)\n        csv_file.flush()\n        yield csv_file.name\n        os.remove(csv_file.name)\n\n\n@pytest.fixture\ndef json_payload():\n    payload = cleandoc(\n        \"\"\"\n        {\n            \"colors\": [\"blue\", \"green\", \"red\", \"yellow\"],\n            \"numbers\": [2.4, 3.0, 1.2, 1.4]\n        }\n        \"\"\"\n    )\n    return payload\n\n\n@pytest.fixture\ndef pandas_df():\n    random = np.random.RandomState(8290)\n    pandas_df = pd.DataFrame()\n    pandas_df[\"col\"] = random.rand(100) * 2.0 - 1.0\n    pandas_df[\"join_key\"] = random.randint(1, 200, 100)\n    pandas_df[\"time_stamp\"] = [pd.Timestamp(ii) for ii in random.rand(100) * 1e15]\n    return pandas_df\n\n\n@pytest.fixture\ndef arrow_table(pandas_df):\n    return pa.Table.from_pandas(pandas_df).cast(\n        pa.schema(\n            {\n                \"col\": pa.float64(),\n                \"join_key\": pa.string(),\n                \"time_stamp\": pa.string(),\n            }\n        )\n    )\n\n\n@pytest.fixture\ndef df1():\n    json_str1 = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4],\n        \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n    }\"\"\"\n    my_df1 = getml.DataFrame(\n        \"MY DF 1\",\n        roles={\n            \"categorical\": [\"names\"],\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_json(json_str1)\n    return my_df1\n\n\n@pytest.fixture\ndef df2():\n    json_str2 = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"johannes\", \"ulrike\", \"adil\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4, 3.4, 2.2],\n        \"join_key\": [\"0\", \"1\", \"2\", \"2\", \"3\", \"4\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\", \"2019-01-05\", \"2019-01-06\"]\n    }\"\"\"\n\n    my_df2 = getml.DataFrame(\n        \"MY DF 2\",\n        roles={\n            \"categorical\": [\"names\"],\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_json(json_str2)\n\n    return my_df2\n\n\n@pytest.fixture\ndef df3():\n    json_str = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\", \"patrick\", \"alex\", \"phil\", \"ulrike\", \"NULL\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4, 3.4, 2.2, 10.2, 13.5, 11.0],\n        \"join_key\": [\"0\", \"1\", \"2\", \"2\", \"3\", \"3\", \"4\", \"4\", \"4\"]\n    }\"\"\"\n\n    my_df3 = getml.DataFrame(\n        \"MY DF3\",\n        roles={\n            \"categorical\": [\"names\"],\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n        },\n    ).read_json(json_str)\n    return my_df3\n\n\n@pytest.fixture\ndef duckdb_conn():\n    conn = getml.database.connect_duckdb()\n    yield conn\n\n\n@pytest.fixture\ndef prepopulated_duckdb_conn(duckdb_conn):\n    conn = duckdb_conn\n    queries = (\n        cleandoc(\n            \"\"\"\n            CREATE TABLE Product (\n                ProductID integer,\n                MakeFlag integer,\n                FinishedGoodsFlag integer,\n                Name text,\n                ProductNumber text\n            );\n\n            INSERT INTO Product\n                VALUES (1, 0, 1, 'Adjust Headset', 'AD-100');\n\n            INSERT INTO Product\n                VALUES (2, 1, 0, 'Bearing Ball', 'BA-832');\n\n            INSERT INTO Product\n                VALUES (3, 0, 1, 'BB Ball Bearing', 'BE-234');\n\n            INSERT INTO Product\n                VALUES (4, 1, 0, 'Headset Ball Bearings', 'BE-290');\n\n            INSERT INTO Product\n                VALUES (5, 0, 1, 'Blade', 'BL-2036');\n            \"\"\"\n        ),\n        cleandoc(\n            \"\"\"\n            CREATE TABLE SalesOrderDetail (\n                SalesOrderID integer,\n                SalesOrderDetailID integer,\n                OrderQty integer,\n                ProductID integer,\n                UnitPrice text,\n                UnitPriceDiscount text\n            );\n\n            INSERT INTO SalesOrderDetail\n                VALUES (43659, 1, 1, 776, '2024.9940', '0.0000');\n\n            INSERT INTO SalesOrderDetail\n                VALUES (43659, 2, 3, 777, '2024.9940', '0.0000');\n\n            INSERT INTO SalesOrderDetail\n                VALUES (43659, 3, 1, 778, '2024.9940', '0.0000');\n\n            INSERT INTO SalesOrderDetail\n                VALUES (43659, 4, 1, 771, '2039.9940', '0.0000');\n\n            INSERT INTO SalesOrderDetail\n                VALUES (43660, 1, 1, 776, '2024.9940', '0.0000');\n            \"\"\"\n        ),\n        cleandoc(\n            \"\"\"\n            CREATE TABLE Store (\n                BusinessEntityID integer,\n                Name text,\n                Demographics text,\n                rowguid text,\n                ModifiedDate text\n            );\n\n            INSERT INTO Store\n                VALUES (292, 'Store 292', 'demographics', 'guid', '2014-09-12 11:15:07.263');\n\n            INSERT INTO Store\n                VALUES (293, 'Store 293', 'demographics', 'guid', '2014-09-12 11:15:07.263');\n\n            INSERT INTO Store\n                VALUES (294, 'Store 294', 'demographics', 'guid', '2014-09-12 11:15:07.263');\n\n            INSERT INTO Store\n                VALUES (295, 'Store 295', 'demographics', 'guid', '2014-09-12 11:15:07.263');\n\n            INSERT INTO Store\n                VALUES (296, 'Store 296', 'demographics', 'guid', '2014-09-12 11:15:07.263');\n            \"\"\"\n        ),\n    )\n\n    getml.database.execute(\"\\n\".join(queries), conn=conn)\n\n    yield conn\n\n    for table in getml.database.get(\"SHOW TABLES;\", conn=conn).name:\n        getml.database.execute(f\"DROP TABLE {table};\", conn=conn)\n"
  },
  {
    "path": "src/python-api/tests/data/test_arrow_type_casts.py",
    "content": "import pyarrow as pa\nimport pytest\n\nimport getml\nfrom getml.data._io.arrow import (\n    INT64_EXCEEDS_FLOAT64_BOUNDS_ERROR_TEMPLATE,\n    TIMEZONE_NON_UTC_ERROR_TEMPLATE,\n    TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE,\n)\n\n\ndef test_from_arrow_utc_timestamp(getml_project, timestamp_with_utc_tz_batch):\n    with pytest.warns(UserWarning) as warnings:\n        df = getml.data.DataFrame.from_arrow(\n            timestamp_with_utc_tz_batch,\n            name=\"tz_utc\",\n            roles={\"time_stamp\": [\"utc_time\"]},\n        )\n    assert str(warnings[0].message) == TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE.format(\n        column_name=timestamp_with_utc_tz_batch.schema[0].name,\n    )\n    assert \"utc_time\" in df.roles.time_stamp\n    assert (\n        df.to_arrow()\n        .to_batches()[0][0]\n        .equals(timestamp_with_utc_tz_batch[0].cast(pa.timestamp(\"ns\", tz=None)))\n    )\n    assert (\n        df.to_arrow()[\"utc_time\"].type.unit\n        == timestamp_with_utc_tz_batch.schema[0].type.unit\n    )\n    assert df.to_arrow()[\"utc_time\"].type == pa.timestamp(\"ns\", tz=None)\n    assert df.to_arrow()[\"utc_time\"].type.tz is None\n\n\ndef test_from_arrow_non_utc_timestamp(getml_project, timestamp_with_non_utc_tz_batch):\n    with pytest.raises(TypeError) as exc_info:\n        getml.data.DataFrame.from_arrow(\n            timestamp_with_non_utc_tz_batch,\n            name=\"tz_non_utc\",\n            roles={\"time_stamp\": [\"non_utc_time\"]},\n        )\n    assert str(exc_info.value) == TIMEZONE_NON_UTC_ERROR_TEMPLATE.format(\n        column_name=timestamp_with_non_utc_tz_batch.schema[0].name,\n        timezone=timestamp_with_non_utc_tz_batch.schema[0].type.tz,\n    )\n\n\ndef test_from_arrow_no_tz_timestamp(getml_project, timestamp_without_tz_batch):\n    df = getml.data.DataFrame.from_arrow(\n        timestamp_without_tz_batch, name=\"tz_none\", roles={\"time_stamp\": [\"no_tz_time\"]}\n    )\n    assert \"no_tz_time\" in df.roles.time_stamp\n    assert df.to_arrow().to_batches()[0].equals(timestamp_without_tz_batch)\n    assert (\n        df.to_arrow()[\"no_tz_time\"].type.unit\n        == timestamp_without_tz_batch.schema[0].type.unit\n    )\n    assert df.to_arrow()[\"no_tz_time\"].type == pa.timestamp(\"ns\", tz=None)\n    assert df.to_arrow()[\"no_tz_time\"].type.tz is None\n\n\ndef test_from_arrow_int(getml_project, int_batch):\n    with pytest.raises(OverflowError) as exc_info:\n        getml.data.DataFrame.from_arrow(int_batch, name=\"int\")\n    assert str(exc_info.value) == INT64_EXCEEDS_FLOAT64_BOUNDS_ERROR_TEMPLATE.format(\n        column_name=\"int64_exceeds_float64_upper_bound\",\n    )\n"
  },
  {
    "path": "src/python-api/tests/data/test_columns.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport pathlib\n\nimport numpy as np\nimport pytest\n\nimport getml.data as data\nimport getml.data.roles as roles\nfrom getml.data.columns.columns import TIME_STAMP_PARSING_LIKELY_FAILED_WARNING_TEMPLATE\n\n\n@pytest.mark.parametrize(\"df_or_view\", [\"df\", \"view\"], indirect=True)\ndef test_column_eq_missmatch(getml_project, df_or_view):\n    with pytest.raises(TypeError):\n        df_or_view.column_01 == \"string\"\n        df_or_view.time_stamp == \"string\"\n        df_or_view.join_key == 1  # join_keys are encoded strings\n        df_or_view.time_stamp == \"2019-01-01\"\n        df_or_view.time_stamp.as_str() == 1\n\n    df_or_view.column_01 > 2.4\n    df_or_view.time_stamp == 123456789\n    df_or_view.join_key == \"0\"\n    df_or_view.targets >= 0\n\n\ndef test_column_operators(getml_project):\n    json_str = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4],\n        \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n    }\"\"\"\n\n    my_df = data.DataFrame(\n        \"MY DF\",\n        roles={\n            \"unused_string\": [\"names\", \"join_key\", \"time_stamp\"],\n            \"unused_float\": [\"column_01\"],\n        },\n    ).read_json(json_str)\n\n    json_str = \"\"\"{\n        \"column_01\": [3.4, 2.1, 1.6, 0.8],\n        \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n    }\"\"\"\n\n    my_df2 = data.DataFrame(\n        \"MY DF2\",\n        roles={\n            \"unused_string\": [\"join_key\", \"time_stamp\"],\n            \"unused_float\": [\"column_01\"],\n        },\n    ).read_json(json_str)\n\n    np_arr = np.ones(4)\n\n    my_df3 = data.DataFrame(\"MY DF3\")\n    my_df3.add(np_arr, \"np_arr\")\n    my_df3.add(np_arr.astype(str), \"str_arr\")\n\n    my_df4 = data.DataFrame(\"MY DF4\")\n    my_df4.add(np_arr.astype(str), \"str_arr\")\n    my_df4.add(np_arr, \"np_arr\")\n\n    getting_it_back = my_df3[\"np_arr\"].to_numpy()\n    getting_it_back = my_df3[\"str_arr\"].to_numpy()\n\n    my_df.set_role(\"join_key\", roles.join_key)\n\n    # ----------------\n\n    col1 = my_df[\"column_01\"]\n\n    # ----------------\n\n    col2 = 2.0 - col1\n\n    my_df.add(col2, \"name\", roles.numerical)\n\n    # If you do not explicitly set a role,\n    # the assigned role will either be\n    # roles.unused_float or roles.unused_string.\n\n    col3 = (col1 + 2.0 * col2) / 3.0\n\n    my_df[\"column_03\"] = col3\n\n    my_df5 = data.DataFrame(\"MY DF5\")\n    my_df5[\"column_03\"] = col3\n\n    my_df6 = data.DataFrame(\"MY DF6\")\n    my_df6[\"column_06\"] = col3.as_str()\n\n    col4 = col1**-col3\n\n    my_df.add(col4, \"column_04\", roles.numerical)\n\n    col5 = col1**2.0\n\n    my_df.add(col5, \"column_05\", roles.numerical)\n\n    col6 = 2.0**col1\n\n    my_df.add(col6, \"column_06\", roles.numerical)\n\n    col7 = (col1 * 100000.0).sqrt()\n\n    my_df.add(\n        col7, name=\"column_07\", role=roles.numerical, unit=\"time stamp, comparison only\"\n    )\n\n    col8 = col1 % 0.5\n\n    my_df.add(col8, \"column_08\", roles.numerical)\n\n    # Operations across different data frames are allowed, as\n    # long as these data frames have the same number of rows.\n\n    col9 = (my_df[\"column_01\"] * col8 + my_df2[\"column_01\"] * 3.0) / 5.0\n\n    my_df.add(col9, \"column_09\", roles.numerical)\n\n    col10 = col1.ceil()\n\n    my_df.add(col10, \"column_10\", roles.time_stamp)\n\n    col11 = col7.year()\n\n    my_df.add(col11, \"year\", roles.numerical, unit=\"year, comparison only\")\n\n    col12 = col7.month()\n\n    my_df.add(col12, \"month\", roles.numerical)\n\n    col13 = col7.day()\n\n    my_df.add(col13, \"day\", roles.numerical)\n\n    col14 = col7.hour()\n\n    my_df.add(col14, \"hour\", roles.numerical)\n\n    col15 = col7.minute()\n\n    my_df.add(col15, \"minute\", roles.numerical)\n\n    col16 = col7.second()\n\n    my_df.add(col16, \"second\", roles.numerical)\n\n    col17 = col7.weekday()\n\n    my_df.add(col17, \"weekday\", roles.numerical)\n\n    col18 = col7.yearday()\n\n    my_df.add(col18, \"yearday\", roles.numerical)\n\n    col19 = my_df[\"names\"]\n\n    col20 = col19.substr(4, 3)\n\n    my_df.add(col20, \"short_names\", roles.categorical)\n\n    # If you do not explicitly set a role,\n    # the assigned role will either be\n    # roles.unused_float or roles.unused_string.\n\n    col21 = \"user-\" + col19 + \"-\" + col20\n\n    my_df[\"new_names\"] = col21\n\n    col22 = col17.as_str()\n\n    my_df.add(col22, \"weekday\", roles.categorical)\n\n    col23 = my_df[\"time_stamp\"].as_ts(\n        time_formats=[\"%Y-%m-%dT%H:%M:%s%z\", \"%Y-%m-%d %H:%M:%S\", \"%Y-%m-%d\"]\n    )\n\n    my_df.add(col23, \"ts\", roles.time_stamp)\n\n    # If you get things wrong, you will get a warning\n    with pytest.warns(UserWarning) as warnings:\n        my_df.add(col21, \"ts_null\", roles.time_stamp)\n\n    assert str(\n        warnings[0].message\n    ) == TIME_STAMP_PARSING_LIKELY_FAILED_WARNING_TEMPLATE.format(column=\"names\")\n\n    col24 = col19.contains(\"rick\").as_str()\n\n    my_df.add(col24, \"contains 'rick'?\", roles.categorical)\n\n    col25 = col19.update(col19.contains(\"rick\"), \"Patrick U\")\n\n    col25 = col25.update(col19.contains(\"lex\"), \"Alex U\")\n\n    my_df.add(col25, \"update names\", roles.categorical)\n\n    home_folder = str(pathlib.Path.home()) + \"/\"\n\n    my_df.to_csv(home_folder + \"MYDF.csv\")\n\n    # You can write data frames to the data base - but be\n    # careful. Your data base might have stricter naming\n    # conventions for your columns than the getml.DataFrames.\n    #\n    # You can remove any column using DataFrame.drop(...) or del\n\n    my_df.drop([\"column_04\", \"column_05\"])\n\n    del my_df[\"update names\"]\n\n    my_df.to_db(\"MYDF\")\n\n    my_view = my_df.where((col1 > 1.3) | (col19 == \"alex\") | ~(col1 <= 1.3))\n\n    my_other_view = my_df.where(data.random(seed=100) > 0.5)  # type: ignore\n\n    deep_copy = my_df.copy(\"DEEPCOPY\")\n\n\ndef test_as_ts(getml_project, df):\n    # StringColumnView.as_ts\n    df[\"ts_parsed\"] = df.time_stamp.as_str().as_ts()\n\n    df[\"ts_str\"] = df.time_stamp.as_str()\n    # StringColumn.as_ts\n    df[\"ts_str_parsed\"] = df.ts_str.as_ts()\n\n    df.set_role([\"ts_parsed\", \"ts_str_parsed\"], roles.time_stamp)\n\n    assert \"ts_parsed\" in df.roles.time_stamp\n    assert \"ts_str_parsed\" in df.roles.time_stamp\n    assert df.ts_parsed == df.time_stamp\n    assert df.ts_str_parsed == df.time_stamp\n"
  },
  {
    "path": "src/python-api/tests/data/test_concat.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport getml\n\n# --------------------------------------------------------------------\n\n\ndef make_df1():\n    json_str1 = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"ulrike\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4],\n        \"join_key\": [\"0\", \"1\", \"2\", \"3\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\"]\n    }\"\"\"\n    my_df1 = getml.DataFrame(\n        \"MY DF 1\",\n        roles={\n            \"categorical\": [\"names\"],\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_json(json_str1)\n    return my_df1\n\n\n# --------------------------------------------------------------------\n\n\ndef make_df2():\n    json_str2 = \"\"\"{\n        \"names\": [\"patrick\", \"alex\", \"phil\", \"johannes\", \"ulrike\", \"adil\"],\n        \"column_01\": [2.4, 3.0, 1.2, 1.4, 3.4, 2.2],\n        \"join_key\": [\"0\", \"1\", \"2\", \"2\", \"3\", \"4\"],\n        \"time_stamp\": [\"2019-01-01\", \"2019-01-02\", \"2019-01-03\", \"2019-01-04\", \"2019-01-05\", \"2019-01-06\"]\n    }\"\"\"\n\n    my_df2 = getml.DataFrame(\n        \"MY DF 2\",\n        roles={\n            \"categorical\": [\"names\"],\n            \"join_key\": [\"join_key\"],\n            \"numerical\": [\"column_01\"],\n            \"time_stamp\": [\"time_stamp\"],\n        },\n    ).read_json(json_str2)\n\n    return my_df2\n\n\n# --------------------------------------------------------------------\n\n\ndef test_concat(getml_project):\n    df1 = make_df1()\n    df2 = make_df2()\n    df3 = getml.data.concat(\"CONCAT1\", [df1, df2])\n    assert df3.shape[0] == df1.shape[0] + df2.shape[0]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_concat_views(getml_project):\n    df1 = make_df1()\n    df2 = make_df2()\n    df4 = getml.data.concat(\"CONCAT2\", [df1, df2[:3]])\n    assert df4.shape[0] == df1.shape[0] + 3\n"
  },
  {
    "path": "src/python-api/tests/data/test_data_frame.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport json\nfrom typing import Any, Dict\n\nimport numpy as np\nimport pyarrow as pa\nimport pytest\n\nimport getml\n\n# --------------------------------------------------------------------\n\n\ndef test_from_pandas(getml_project, pandas_df):\n    df = getml.DataFrame.from_pandas(name=\"databert\", pandas_df=pandas_df)\n\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\"databert\"]\n    assert df._unused_names == [\"col\", \"join_key\", \"time_stamp\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_pandas_with_roles(getml_project, pandas_df):\n    roles = {\n        \"join_key\": [\"join_key\"],\n        \"time_stamp\": [\"time_stamp\"],\n        \"numerical\": [\"col\"],\n    }\n\n    df = getml.DataFrame.from_pandas(name=\"databert\", pandas_df=pandas_df, roles=roles)\n\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\"databert\"]\n    assert df._time_stamp_names == [\"time_stamp\"]\n    assert df._numerical_names == [\"col\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_pandas_round_trip_roles(getml_project, pandas_df):\n    roles = {\n        \"join_key\": [\"join_key\"],\n        \"time_stamp\": [\"time_stamp\"],\n        \"numerical\": [\"col\"],\n    }\n\n    df = getml.DataFrame.from_pandas(name=\"databert\", pandas_df=pandas_df, roles=roles)\n    pandas_df = df.to_pandas()\n    df_reload = getml.DataFrame.from_pandas(name=\"databert_reload\", pandas_df=pandas_df)\n    assert df_reload.roles == df.roles\n\n\n# --------------------------------------------------------------------\n\n\ndef test_to_pandas(getml_project, pandas_df):\n    df = getml.DataFrame.from_pandas(name=\"databert\", pandas_df=pandas_df)\n\n    pandas_df_reload = df.to_pandas()\n    assert all(pandas_df[\"join_key\"] == pandas_df_reload[\"join_key\"])\n    assert all(pandas_df[\"col\"].astype(int) == pandas_df_reload[\"col\"].astype(int))\n    assert pandas_df.shape == pandas_df_reload.shape\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.parametrize(\"df\", [\"df1\", \"df2\", \"df3\"], indirect=True)\ndef test_to_pandas_metadata(getml_project, df):\n    pandas_df = df.to_pandas()\n    metadata = pandas_df.attrs[\"getml\"]\n    assert metadata[\"name\"] == df.name\n    assert metadata[\"last_change\"] == df.last_change\n    assert metadata[\"roles\"] == df.roles.to_dict()\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_arrow(getml_project, arrow_table):\n    df = getml.DataFrame.from_arrow(arrow_table, name=\"databert\")\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\"databert\"]\n    assert set(df.roles.unused) == set(arrow_table.column_names)\n    assert df.shape == arrow_table.shape\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_arrow_with_roles(getml_project, arrow_table):\n    roles = {\n        \"join_key\": [\"join_key\"],\n        \"time_stamp\": [\"time_stamp\"],\n        \"numerical\": [\"col\"],\n    }\n\n    df = getml.DataFrame.from_arrow(arrow_table, name=\"databert\", roles=roles)\n\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\"databert\"]\n    assert df._time_stamp_names == [\"time_stamp\"]\n    assert df._numerical_names == [\"col\"]\n    assert df.shape == arrow_table.shape\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_arrow_round_trip_roles(getml_project, arrow_table):\n    roles = {\n        \"join_key\": [\"join_key\"],\n        \"time_stamp\": [\"time_stamp\"],\n        \"numerical\": [\"col\"],\n    }\n\n    df = getml.DataFrame.from_arrow(arrow_table, name=\"databert\", roles=roles)\n    arrow_table = df.to_arrow()\n    df_reload = getml.DataFrame.from_arrow(arrow_table, name=\"databert_reload\")\n    assert df_reload.roles == df.roles\n\n\n# --------------------------------------------------------------------\n\n\ndef test_to_arrow(getml_project, arrow_table):\n    df = getml.DataFrame.from_arrow(arrow_table, name=\"databert\")\n\n    arrow_table_reload = df.to_arrow()\n    assert arrow_table.shape == arrow_table_reload.shape\n    assert set(arrow_table.column_names) == set(arrow_table_reload.column_names)\n    assert arrow_table.equals(arrow_table_reload)\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.parametrize(\"df\", [\"df1\", \"df2\", \"df3\"], indirect=True)\ndef test_to_arrow_metadata(getml_project, df):\n    arrow_table = df.to_arrow()\n    metadata = arrow_table.schema.metadata[b\"getml\"]\n    metadata_unmarshaled = json.loads(metadata)\n    assert metadata_unmarshaled[\"name\"] == df.name\n    assert metadata_unmarshaled[\"last_change\"] == df.last_change\n    assert metadata_unmarshaled[\"roles\"] == df.roles.to_dict()\n\n\n# --------------------------------------------------------------------\n\n\ndef test_read_pandas(getml_project, pandas_df):\n    df = getml.DataFrame(name=\"testbert\")\n\n    with pytest.raises(Exception):\n        df.read_pandas(pandas_df)\n\n    df = getml.DataFrame(name=\"testbert\", roles={\"numerical\": [\"col\"]})\n\n    df.read_pandas(pandas_df)\n\n    assert df._numerical_names == [\"col\"]\n    assert len(df[\"col\"].to_numpy()) == 100\n\n    df.read_pandas(pandas_df, append=True)\n\n    assert len(df[\"col\"].to_numpy()) == 200\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.skip(\"Not implemented yet\")\ndef test_from_db():\n    pass\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_json(getml_project, json_payload):\n    df = getml.DataFrame.from_json(json_payload, name=\"jason\")\n    assert df._unused_float_names == [\"numbers\"]\n    assert df._unused_string_names == [\"colors\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_dict(getml_project):\n    data: Dict[str, Any] = dict(\n        animals=[\"dog\", \"cat\", \"mouse\"], weight=[12.2, 125.2, 12], number=[1, 2, 3]\n    )\n\n    df = getml.DataFrame.from_dict(data, name=\"Zoo\")\n\n    assert df._unused_string_names == [\"animals\"]\n    assert df._unused_float_names == [\"weight\", \"number\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_from_csv(getml_project, csv_file):\n    df = getml.DataFrame.from_csv([csv_file], name=\"Testiana\", sep=\",\")\n    assert set(df.roles.unused) == {\"column_01\", \"join_key\", \"name\", \"time_stamp\"}\n    assert (df[\"column_01\"].to_numpy() == np.array([0, 1, 2, 3])).all()\n\n\n# --------------------------------------------------------------------\n\n\ndef test_list_data_frames(getml_project, pandas_df):\n    d = getml.DataFrame.from_pandas(pandas_df, \"schnippi\")\n    assert (\n        json.dumps(getml.data.list_data_frames())\n        == '{\"in_memory\": [\"schnippi\"], \"on_disk\": []}'\n    )\n    d.save()\n    assert (\n        json.dumps(getml.data.list_data_frames())\n        == '{\"in_memory\": [\"schnippi\"], \"on_disk\": [\"schnippi\"]}'\n    )\n\n\n# --------------------------------------------------------------------\n\n\ndef test_load_data_frames(getml_project, pandas_df):\n    d = getml.DataFrame.from_pandas(pandas_df, \"schnippi\")\n    d2 = getml.data.load_data_frame(\"schnippi\")\n    assert d == d2\n\n\n# --------------------------------------------------------------------\n\n\ndef test_set_roles(getml_project):\n    d_num, _ = getml.datasets.make_numerical(n_rows_population=10, n_rows_peripheral=20)\n    d_cat, d_cat_2 = getml.datasets.make_categorical(\n        n_rows_population=10, n_rows_peripheral=20\n    )\n\n    assert d_num._categorical_names == []\n    assert d_num._numerical_names == [\"column_01\"]\n    assert d_cat._categorical_names == [\"column_01\"]\n    assert d_cat._numerical_names == []\n    assert d_cat_2._categorical_names == [\"column_01\"]\n    assert d_cat_2._numerical_names == []\n    assert d_cat_2._time_stamp_names == [\"time_stamp\"]\n\n    d_num.set_role(\"column_01\", getml.data.roles.categorical)\n    d_cat.set_role(\"column_01\", getml.data.roles.numerical)\n    d_cat_2.set_role(\"column_01\", getml.data.roles.time_stamp)\n\n    assert d_num._categorical_names == [\"column_01\"]\n    assert d_num._numerical_names == []\n    assert d_cat._categorical_names == []\n    assert d_cat._numerical_names == [\"column_01\"]\n    assert d_cat_2._categorical_names == []\n    assert d_cat_2._numerical_names == []\n    assert d_cat_2._time_stamp_names == [\"time_stamp\", \"column_01\"]\n\n\n# --------------------------------------------------------------------\n\n\ndef test_compare(getml_project):\n    databert_pop_1, databert_peri_1 = getml.datasets.make_numerical(\n        random_state=829034, n_rows_population=10, n_rows_peripheral=20\n    )\n    databert_pop_2, databert_peri_2 = getml.datasets.make_numerical(\n        random_state=829034, n_rows_population=10, n_rows_peripheral=20\n    )\n    databert_pop_3, databert_peri_3 = getml.datasets.make_numerical(\n        random_state=829, n_rows_population=10, n_rows_peripheral=20\n    )\n\n    assert databert_pop_1 == databert_pop_1\n    assert databert_peri_1 == databert_peri_1\n    assert databert_pop_1 == databert_pop_2\n    assert databert_peri_1 == databert_peri_2\n    assert databert_pop_3 != databert_pop_2\n    assert databert_peri_3 != databert_peri_2\n\n\n# --------------------------------------------------------------------\n\n\ndef test_print(getml_project):\n    databert, _ = getml.datasets.make_numerical()\n    assert \"join_key\" in str(databert)\n\n\n# --------------------------------------------------------------------\n\n\ndef test_delete(getml_project):\n    databert, _ = getml.datasets.make_numerical(random_state=123123)\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\n        \"numerical_peripheral_123123\",\n        \"numerical_population_123123\",\n    ]\n    databert.delete()\n    assert getml.data.list_data_frames()[\"in_memory\"] == [\"numerical_peripheral_123123\"]\n"
  },
  {
    "path": "src/python-api/tests/data/test_data_frames.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nTests the data frames container.\n\"\"\"\n\nimport numpy as np\nimport getml\nfrom getml import data, datasets\n\n\ndef make_populations(base, percentiles):\n    population_tables = []\n\n    targets = base[\"targets\"].to_numpy()\n\n    for p in percentiles:\n        targets_ = np.clip(targets, 0, np.percentile(targets, p))\n        pop_ = base.copy(\"pop_0\" + str(p))\n        pop_[\"targets\"] = targets_\n        pop_.set_role(\"targets\", data.roles.target)\n        population_tables += [pop_]\n\n    return population_tables\n\n\ndef test_data_frames(getml_project):\n    \"\"\"\n    Tests the data frames cotainer.\n    \"\"\"\n\n    # Generate artificial datasets\n    pop, _ = datasets.make_numerical()\n    pop = pop.copy(\"pop_100\")\n\n    # ----------------\n\n    pops = make_populations(pop, range(55, 95, 5))\n\n    # ----------------\n\n    assert (\n        len(getml_project.data_frames) == len(pops) + 3\n    ), \"Expected length of data frames container to equal the length of all populations + 2\"\n\n    getml_project.data_frames.save()\n\n    names_in_memory = sorted(df.name for df in getml_project.data_frames)\n    names_on_disk = sorted(getml_project.data_frames.on_disk)\n\n    assert (\n        names_on_disk == names_in_memory\n    ), \"Expected names for data frames in memory and in project folder to be the same\"\n\n    dfs = getml_project.data_frames.retrieve()\n\n    assert names_in_memory == sorted(\n        list(dfs.keys())\n    ), \"Expected keys of handler to be the same as the names in memory\"\n\n    assert all(\n        handle == df.name for handle, df in zip(dfs.keys(), dfs.values())\n    ), \"Expected handle to correspond to the name on Engine\"\n\n    getml_project.data_frames.unload()\n\n    assert (\n        len(getml_project.data_frames.on_disk) > 1\n    ), \"Expected data frames in project folder\"\n\n    assert (\n        len(getml_project.data_frames.in_memory) == 0\n    ), \"Expected no data frames in memory\"\n\n    getml_project.data_frames.load()\n\n    assert len(getml_project.data_frames.in_memory) == len(\n        names_in_memory\n    ), \"Expected all data frames to reappear in memory\"\n\n    getml_project.data_frames.delete()\n\n    assert (\n        len(getml_project.data_frames.on_disk) == 0\n    ), \"Expected no data frames in project folder\"\n\n    # ----------------\n"
  },
  {
    "path": "src/python-api/tests/data/test_from_query.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport pytest\n\nimport getml\n\n\n@pytest.mark.parametrize(\n    (\"table_name\", \"columns\", \"expected_roles\"),\n    [\n        (\n            \"Product\",\n            [\"ProductID\", \"MakeFlag\", \"FinishedGoodsFlag\", \"Name\", \"ProductNumber\"],\n            [\n                \"unused_float\",\n                \"unused_float\",\n                \"unused_float\",\n                \"unused_string\",\n                \"unused_string\",\n            ],\n        ),\n        (\n            \"SalesOrderDetail\",\n            [\n                \"SalesOrderID\",\n                \"SalesOrderDetailID\",\n                \"OrderQty\",\n                \"ProductID\",\n                \"UnitPrice\",\n                \"UnitPriceDiscount\",\n            ],\n            [\n                \"unused_float\",\n                \"unused_float\",\n                \"unused_float\",\n                \"unused_float\",\n                \"unused_string\",\n                \"unused_string\",\n            ],\n        ),\n        (\n            \"Store\",\n            [\"BusinessEntityID\", \"Name\", \"Demographics\", \"rowguid\", \"ModifiedDate\"],\n            [\n                \"unused_float\",\n                \"unused_string\",\n                \"unused_string\",\n                \"unused_string\",\n                \"unused_string\",\n            ],\n        ),\n    ],\n)\ndef test_from_query(\n    getml_project, prepopulated_duckdb_conn, table_name, columns, expected_roles\n):\n    conn = prepopulated_duckdb_conn\n    df = getml.DataFrame.from_query(\n        name=f\"{table_name}\",\n        query=f\"SELECT {', '.join(columns)} FROM {table_name}\",\n        conn=conn,\n    )\n    expected_length = getml.database.get(\n        f\"SELECT COUNT(*) FROM {table_name}\", conn=conn\n    ).iloc[0, 0]\n    assert df.name == table_name\n    assert len(df) == expected_length\n    assert len(df.columns) == len(columns)\n    assert set(df.columns) == set(columns)\n    assert [df[col].role for col in columns] == expected_roles\n"
  },
  {
    "path": "src/python-api/tests/data/test_io_read.py",
    "content": "from tempfile import NamedTemporaryFile\n\nimport pytest\n\nimport getml\n\nfrom ..unit.data.test_arrow_schema_processors import _get_inferrence_blob_block_size\n\n\ndef test_read_parquet(getml_project, parquet_file):\n    df = getml.DataFrame.from_parquet(parquet_file, name=\"df\")\n    assert df.shape == (3, 2)\n    assert df.columns == [\"a\", \"b\"]\n    assert df[\"a\"].to_numpy().tolist() == [1, 2, 3]  # type: ignore\n    assert df[\"b\"].to_numpy().tolist() == [4, 5, 6]  # type: ignore\n\n\n@pytest.mark.parametrize(\"df\", [\"df1\", \"df2\", \"df3\"], indirect=True)\ndef test_read_parquet_round_trip_roles(getml_project, df):\n    with NamedTemporaryFile() as f:\n        df.to_parquet(f\"{f.name}.parquet\")\n        df_read = getml.DataFrame.from_parquet(f\"{f.name}.parquet\", name=\"_\")\n        assert df.roles == df_read.roles\n\n\ndef test_read_csv(getml_project, csv_file):\n    df = getml.DataFrame.from_csv(csv_file, name=\"df\")\n    assert df.shape == (4, 4)\n    assert sorted(df.columns) == sorted([\"name\", \"column_01\", \"join_key\", \"time_stamp\"])\n    assert df[\"column_01\"].to_numpy().tolist() == list(range(4))  # type: ignore\n    assert df[\"join_key\"].to_numpy().tolist() == list(range(22, 26))  # type: ignore\n    assert df[\"time_stamp\"].to_numpy().tolist() == [  # type: ignore\n        \"2019-01-01\",\n        \"2019-01-02\",\n        \"2019-01-03\",\n        \"2019-01-04\",\n    ]\n\n\ndef test_read_csv_num_lines_sniffed_deprecation_warning(getml_project, csv_file):\n    with pytest.warns(DeprecationWarning):\n        getml.DataFrame.from_csv(csv_file, num_lines_sniffed=10, name=\"df\")\n\n\ndef test_read_csv_custom_ts(getml_project, csv_file_custom_ts):\n    df = getml.DataFrame(name=\"Testiana\", roles={\"time_stamp\": [\"time_stamp\"]})\n    df.read_csv(csv_file_custom_ts, time_formats=[\"date: %Y-%m-%d; time: %H:%M:%S\"])\n    assert df.roles.time_stamp == [\n        \"time_stamp\",\n    ]\n\n\ndef test_read_csv_changing_type_in_row_2(\n    getml_project, csv_file_with_changing_type_in_row_2\n):\n    source = csv_file_with_changing_type_in_row_2\n    with open(source, \"r\") as f:\n        inferrence_blob_block_size = _get_inferrence_blob_block_size(\n            f.read(), n_lines_inferred=1\n        )\n    df_read = getml.DataFrame.from_csv(\n        csv_file_with_changing_type_in_row_2,\n        block_size=inferrence_blob_block_size,\n        name=\"df_read\",\n    )\n\n    df_stream = getml.DataFrame.from_csv(\n        csv_file_with_changing_type_in_row_2,\n        block_size=inferrence_blob_block_size,\n        name=\"df_stream\",\n        in_batches=True,\n    )\n\n    assert df_read.roles == df_stream.roles\n    assert df_read.columns == df_stream.columns\n    assert df_read.to_arrow().equals(df_stream.to_arrow())\n"
  },
  {
    "path": "src/python-api/tests/data/test_io_stream.py",
    "content": "import pytest\n\n\n@pytest.mark.parametrize(\"df\", [\"df1\", \"df2\", \"df3\"], indirect=True)\ndef test_read_arrow_stream(getml_project, df):\n    with df.to_arrow_stream() as stream:\n        assert stream is not None\n        for batch in stream:\n            assert batch is not None\n            assert len(batch) > 0\n            assert len(batch.column_names) > 0\n            assert set(batch.column_names) == set(df.columns)\n"
  },
  {
    "path": "src/python-api/tests/data/test_io_write.py",
    "content": "import json\n\nimport pyarrow.parquet as pq\nimport pytest\n\nimport getml\nfrom tests.conftest import workdir\n\n\ndef test_write_csv(getml_project, tmpdir):\n    with workdir(tmpdir):\n        df = getml.DataFrame.from_dict(\n            {\n                \"a\": [1, 2, 3],\n                \"b\": [4, 5, 6],\n            },\n            name=\"df\",\n        )\n        df.to_csv(\"test.csv\")\n        df2 = getml.DataFrame.from_csv(\"test.csv\", name=\"df2\")\n        assert df2.shape == (3, 2)\n        assert df2.columns == [\"a\", \"b\"]\n        assert df2[\"a\"].to_numpy().tolist() == [1, 2, 3]  # type: ignore\n\n\ndef test_write_parquet(getml_project, tmpdir):\n    with workdir(tmpdir):\n        df = getml.DataFrame.from_dict(\n            {\n                \"a\": [1, 2, 3],\n                \"b\": [4, 5, 6],\n            },\n            name=\"df\",\n        )\n        df.to_parquet(\"test.parquet\")\n        df2 = getml.DataFrame.from_parquet(\"test.parquet\", name=\"df2\")\n        assert df2.shape == (3, 2)\n        assert df2.columns == [\"a\", \"b\"]\n        assert df2[\"a\"].to_numpy().tolist() == [1, 2, 3]  # type: ignore\n\n\n@pytest.mark.parametrize(\"df\", [\"df1\", \"df2\", \"df3\"], indirect=True)\ndef test_to_parquet_metadata(getml_project, df, tmpdir):\n    with workdir(tmpdir):\n        df.to_parquet(\"test.parquet\")\n        schema = pq.read_schema(\"test.parquet\")\n        metadata = schema.metadata[b\"getml\"]\n        metadata_unmarshaled = json.loads(metadata)\n        assert metadata_unmarshaled[\"name\"] == df.name\n        assert metadata_unmarshaled[\"last_change\"] == df.last_change\n        assert metadata_unmarshaled[\"roles\"] == df.roles.to_dict()\n"
  },
  {
    "path": "src/python-api/tests/data/test_save_and_load.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport getml\n\n\ndef load_if_needed(name):\n    \"\"\"\n    Loads the data from the relational learning\n    repository, if the data frame has not already\n    been loaded.\n    \"\"\"\n    if not getml.data.exists(name):\n        data_frame = getml.DataFrame.from_db(name=name, table_name=name)\n        data_frame.save()\n    else:\n        data_frame = getml.data.load_data_frame(name)\n    return data_frame\n\n\ndef make_target_columns(data_frame, class_label):\n    for label in class_label:\n        name = \"class_label=\" + label\n        data_frame[name] = data_frame.class_label == label\n        data_frame.set_role(name, getml.data.roles.target)\n    data_frame.set_role(\"class_label\", getml.data.roles.unused_string)\n\n\ndef test_save_and_load(getml_project):\n    getml.database.connect_mysql(\n        host=\"relational.fel.cvut.cz\",\n        dbname=\"CORA\",\n        port=3306,\n        user=\"guest\",\n        password=\"ctu-relational\",\n    )\n\n    paper = load_if_needed(\"paper\")\n    cites = load_if_needed(\"cites\")\n    content = load_if_needed(\"content\")\n\n    paper.set_role(\"paper_id\", getml.data.roles.join_key)\n    paper.set_role(\"class_label\", getml.data.roles.categorical)\n\n    cites.set_role([\"cited_paper_id\", \"citing_paper_id\"], getml.data.roles.join_key)\n\n    content.set_role(\"paper_id\", getml.data.roles.join_key)\n    content.set_role(\"word_cited_id\", getml.data.roles.categorical)\n\n    split = getml.data.split.random(train=0.7, test=0.3, validation=0.0)\n\n    data_train = paper[split == \"train\"].to_df(\"data_train\")\n    data_test = paper[split == \"test\"].to_df(\"data_test\")\n\n    class_label = paper.class_label.unique()\n\n    make_target_columns(data_train, class_label)\n    make_target_columns(data_test, class_label)\n\n    container = getml.data.Container(train=data_train, test=data_test[:800])\n    container.add(paper=paper, cites=cites, content=content)\n    container.freeze()\n\n    container.save()\n\n    container2 = getml.data.load_container(container.id)\n\n    container2.train\n    container2.test\n    container2.split\n"
  },
  {
    "path": "src/python-api/tests/data/test_subsetting.py",
    "content": "from dataclasses import dataclass\n\nimport pytest\nimport getml\n\n\n@pytest.mark.parametrize(\"df_or_view\", [\"df\", \"view\"], indirect=True)\ndef test_boolean_subsetting(getml_project, df_or_view):\n    unique_values = df_or_view.column_01.unique()\n    assert len(df_or_view[df_or_view.column_01 > df_or_view.column_01.max()]) == 0\n    assert len(df_or_view[df_or_view.column_01 < df_or_view.column_01.min()]) == 0\n    assert len(df_or_view[df_or_view.column_01 == unique_values[0]]) == 1\n    assert (\n        df_or_view[df_or_view.column_01 == unique_values[0]].column_01[0]\n        == unique_values[0]\n    )\n    assert (\n        len(df_or_view[df_or_view.column_01 != unique_values[0]])\n        == len(unique_values) - 1\n    )\n    assert df_or_view[df_or_view.column_01 != unique_values[0]].nrows() == \"unknown\"\n\n\n@pytest.mark.parametrize(\"df_or_view\", [\"df\", \"view\"], indirect=True)\ndef test_scalar_subsetting(getml_project, df_or_view):\n    assert len(df_or_view[0]) == 1\n    assert len(df_or_view[len(df_or_view) - 1]) == 1\n    assert len(df_or_view[-1]) == 1\n    assert len(df_or_view[-len(df_or_view)]) == 1\n    assert df_or_view[-len(df_or_view)].to_arrow() == df_or_view[0].to_arrow()\n    assert df_or_view[len(df_or_view) - 1].to_arrow() == df_or_view[-1].to_arrow()\n\n\n@pytest.mark.parametrize(\"df_or_view\", [\"df\", \"view\"], indirect=True)\ndef test_slicing(getml_project, df_or_view):\n    assert len(df_or_view[:]) == len(df_or_view)\n    assert len(df_or_view[: len(df_or_view)]) == len(df_or_view)\n    assert len(df_or_view[len(df_or_view) :]) == 0\n    assert len(df_or_view[-len(df_or_view) :]) == len(df_or_view)\n    assert len(df_or_view[: -len(df_or_view)]) == 0\n    assert len(df_or_view[-len(df_or_view) : len(df_or_view)]) == len(df_or_view)\n    assert len(df_or_view[-6:-4]) == 2\n    assert len(df_or_view[-4:-6]) == 0\n    assert df_or_view[:].to_arrow() == df_or_view.to_arrow()\n    assert df_or_view[4:6].to_arrow() == df_or_view.to_arrow()[4:6]\n    assert df_or_view[-4:-6].to_arrow() == df_or_view.to_arrow()[-4:-6]\n    assert df_or_view[-6:-4].to_arrow() == df_or_view.to_arrow()[-6:-4]\n    assert df_or_view[-6:-4:-1].to_arrow() == df_or_view.to_arrow()[-6:-4:-1]\n\n\n@pytest.mark.parametrize(\"df_or_view\", [\"df\", \"view\"], indirect=True)\ndef test_by_column(getml_project, df_or_view):\n    ix = getml.data.columns.arange(0, 10, 1)\n    reverse_ix = getml.data.columns.arange(9, -1, -1)\n\n    assert len(ix) == 10\n    assert len(reverse_ix) == 10\n\n    assert len(ix[ix]) == 10\n    assert len(reverse_ix[ix]) == 10\n\n    assert len(ix[reverse_ix]) == 10\n    assert len(reverse_ix[reverse_ix]) == 10\n\n    assert len(df_or_view[ix]) == 10\n    assert len(df_or_view[reverse_ix]) == 10\n"
  },
  {
    "path": "src/python-api/tests/datasets/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n"
  },
  {
    "path": "src/python-api/tests/datasets/test_base.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nimport getml\nimport pytest\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.slow\ndef test_load_loans(getml_project):\n    dfs = getml.datasets.load_loans()\n\n    assert [df.name for df in dfs] == [  # type: ignore\n        \"population_train\",\n        \"population_test\",\n        \"order\",\n        \"trans\",\n        \"meta\",\n    ]\n\n    assert isinstance(dfs[0], getml.DataFrame)  # type: ignore\n\n    assert getml_project.data_frames.in_memory == [\n        \"meta\",\n        \"order\",\n        \"population_test\",\n        \"population_train\",\n        \"trans\",\n    ]\n\n    assert dfs[0][\"default\"].role == getml.data.roles.target  # type: ignore\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.slow\ndef test_load_occupancy(getml_project):\n    dfs = getml.datasets.load_occupancy()\n\n    assert getml_project.data_frames.in_memory == [\n        \"population_test\",\n        \"population_train\",\n        \"population_validation\",\n    ]\n\n    assert dfs[0][\"Occupancy\"].role == getml.data.roles.target  # type: ignore\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.slow\ndef test_load_interstate94(getml_project):\n    traffic = getml.datasets.load_interstate94()\n\n    assert getml_project.data_frames.in_memory == [\n        \"traffic\",\n    ]\n\n\n# --------------------------------------------------------------------\n\n\n@pytest.mark.slow\ndef test_load_air_pollution(getml_project):\n    population = getml.datasets.load_air_pollution()\n\n    assert getml_project.data_frames.in_memory == [\"population\"]\n\n    assert population[\"pm2.5\"].role == getml.data.roles.target\n\n\n# --------------------------------------------------------------------\n"
  },
  {
    "path": "src/python-api/tests/engine/test_launch.py",
    "content": "import platform\nfrom unittest.mock import patch\n\nimport pytest\n\nimport getml\nfrom getml.engine._launch import (\n    COMPOSE_FILE_URL,\n    DOCKER_DOCS_URL,\n    OS_NOT_SUPPORTED_NATIVELY_ERROR_MSG_TEMPLATE,\n)\n\n\n@patch(\"getml.communication.is_monitor_alive\")\n@patch(\"platform.system\")\n@pytest.mark.parametrize(\"system\", [\"Windows\", \"Darwin\"])\ndef test_launch_non_native(mock_platform_system, mock_getml_is_monitor_alive, system):\n    mock_platform_system.return_value = system\n    mock_getml_is_monitor_alive.return_value = False\n    with pytest.raises(OSError) as exc_info:\n        getml.engine.launch()\n    assert str(exc_info.value) == OS_NOT_SUPPORTED_NATIVELY_ERROR_MSG_TEMPLATE.format(\n        os=system.lower(),\n        docker_docs_url=DOCKER_DOCS_URL,\n        compose_file_url=COMPOSE_FILE_URL,\n    )\n\n\n@pytest.mark.skipif(\n    platform.system() != \"Linux\",\n    reason=(\n        \"Testing 'launch' is only sypported on Linux \"\n        \"(as launching getML from python is only supported with native binaries.\"\n    ),\n)\n@pytest.mark.skipif(\n    getml.engine.is_monitor_alive(),\n    reason=(\n        \"getML is already running. \"\n        \"To test 'launch', please save your current session \"\n        \"and stop the running getML instance.\"\n    ),\n)\ndef test_launch():\n    getml.engine.launch()\n    assert getml.engine.is_monitor_alive()\n    getml.engine.shutdown()\n    assert not getml.engine.is_monitor_alive()\n\n\n@pytest.mark.skipif(\n    platform.system() != \"Linux\",\n    reason=(\n        \"Testing 'launch' is only sypported on Linux \"\n        \"(as launching getML from python is only supported with native binaries.\"\n    ),\n)\n@pytest.mark.skipif(\n    getml.engine.is_monitor_alive(),\n    reason=(\n        \"getML is already running. \"\n        \"To test 'launch', please save your current session \"\n        \"and stop the running getML instance.\"\n    ),\n)\ndef test_launch_quiet(capsys):\n    getml.engine.launch(quiet=True)\n    captured = capsys.readouterr()\n    assert captured.out == \"\"\n    assert getml.engine.is_monitor_alive()\n    getml.engine.shutdown()\n    assert not getml.engine.is_monitor_alive()\n"
  },
  {
    "path": "src/python-api/tests/engine/test_set_project.py",
    "content": "import platform\n\nimport pytest\n\nimport getml\n\n\n@pytest.mark.skipif(\n    platform.system() != \"Linux\",\n    reason=(\n        \"Testing 'set_project_implicit_launch' is only supported on Linux \"\n        \"(as launching getML from python is only supported with native binaries.\"\n    ),\n)\n@pytest.mark.skipif(\n    getml.engine.is_monitor_alive(),\n    reason=(\n        \"getML is already running. \"\n        \"To test 'set_project_implicit_launch', please save your current session \"\n        \"and stop the running getML instance.\"\n    ),\n)\ndef test_set_project_implicit_launch(request):\n    assert not getml.communication.is_monitor_alive()\n    getml.set_project(request.node.name)\n    assert getml.communication.is_monitor_alive()\n    getml.engine.shutdown()\n"
  },
  {
    "path": "src/python-api/tests/engine/test_shutdown.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\nimport platform\nfrom tempfile import TemporaryDirectory\n\nimport pytest\n\nimport getml\n\n\n@pytest.mark.skipif(\n    getml.engine.is_monitor_alive(),\n    reason=(\n        \"getML is already running. \"\n        \"To test 'shutdown', please save your current session \"\n        \"and stop the running getML instance.\"\n    ),\n)\n@pytest.mark.skipif(\n    platform.system() != \"Linux\",\n    reason=(\n        \"Testing 'shutdown' is only sypported on Linux \"\n        \"(as launching getML from python is only supported with native binaries.\"\n    ),\n)\n@pytest.mark.parametrize(\"loop\", range(5))\ndef test_shutdown(loop):\n    with TemporaryDirectory() as tmpdir:\n        getml.engine.launch(project_directory=tmpdir)\n        project_name = f\"test_shutdown_{loop}\"\n        getml.engine.set_project(project_name)\n        getml.engine.delete_project(project_name)\n        getml.engine.shutdown()\n"
  },
  {
    "path": "src/python-api/tests/pipeline/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n"
  },
  {
    "path": "src/python-api/tests/pipeline/test_adventure_works.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis is an integration test based on\nthe Adventure Works dataset.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\nimport getml\n\n\ndef test_adventure_works(getml_project):\n    \"\"\"\n    This is an integration test based on\n    the Adventure Works dataset.\n    \"\"\"\n\n    conn = getml.database.connect_mysql(\n        host=\"relational.fel.cvut.cz\",\n        dbname=\"AdventureWorks2014\",\n        port=3306,\n        user=\"guest\",\n        password=\"ctu-relational\",\n    )\n\n    container = _make_container(conn)\n\n    dm = _make_data_model(container)\n\n    trimmer = getml.preprocessors.CategoryTrimmer()\n\n    fast_prop = getml.feature_learning.FastProp(  # pylint: disable=unexpected-keyword-arg\n        loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n        num_threads=1,\n        num_features=400,\n    )\n\n    predictor = getml.predictors.XGBoostClassifier(n_jobs=1)\n\n    pipe1 = getml.Pipeline(\n        tags=[\"fast_prop\"],\n        data_model=dm,\n        preprocessors=[trimmer],\n        feature_learners=[fast_prop],\n        predictors=[predictor],\n        include_categorical=True,\n    )\n\n    pipe1.check(container.train)\n    pipe1.fit(container.train)\n    scores = pipe1.score(container.test)\n\n    assert scores.auc > 0.969, \"Expected an AUC greater 0.969, got \" + str(scores.auc)  # type: ignore\n\n\ndef _load_if_needed(name: str, conn: getml.database.Connection):\n    \"\"\"\n    Loads the data from the relational learning\n    repository, if the data frame has not already\n    been loaded.\n    \"\"\"\n    if not getml.data.exists(name):\n        data_frame = getml.DataFrame.from_db(name=name, table_name=name, conn=conn)\n        data_frame.save()\n    else:\n        data_frame = getml.data.load_data_frame(name)\n    return data_frame\n\n\ndef _make_product(conn: getml.database.Connection) -> getml.DataFrame:\n    product = _load_if_needed(\"Product\", conn)\n    product.set_role(\"ProductID\", getml.data.roles.join_key)\n    product.set_role(\n        [\"MakeFlag\", \"ProductSubcategoryID\", \"ProductModelID\"],\n        getml.data.roles.categorical,\n    )\n    product.set_role(\n        [\"SafetyStockLevel\", \"ReorderPoint\", \"StandardCost\", \"ListPrice\"],\n        getml.data.roles.numerical,\n    )\n    return product\n\n\ndef _make_sales_order_detail(conn: getml.database.Connection) -> getml.DataFrame:\n    sales_order_detail = _load_if_needed(\"SalesOrderDetail\", conn)\n    sales_order_detail.set_role(\n        [\"SalesOrderID\", \"SalesOrderDetailID\", \"ProductID\", \"SpecialOfferID\"],\n        getml.data.roles.join_key,\n    )\n    sales_order_detail.set_role(\n        [\"OrderQty\", \"UnitPrice\", \"UnitPriceDiscount\", \"LineTotal\"],\n        getml.data.roles.numerical,\n    )\n    sales_order_detail.set_role(\"ModifiedDate\", getml.data.roles.time_stamp)\n    return sales_order_detail\n\n\ndef _make_sales_order_header(conn: getml.database.Connection) -> getml.DataFrame:\n    sales_order_header = _load_if_needed(\"SalesOrderHeader\", conn)\n\n    sales_order_header[\"SalesPersonIDCat\"] = sales_order_header[\"SalesPersonID\"]\n    sales_order_header[\"TerritoryIDCat\"] = sales_order_header[\"TerritoryID\"]\n\n    sales_order_header.set_role(\n        [\"CustomerID\", \"SalesOrderID\", \"SalesPersonID\", \"TerritoryID\"],\n        getml.data.roles.join_key,\n    )\n    sales_order_header.set_role(\n        [\n            \"RevisionNumber\",\n            \"OnlineOrderFlag\",\n            \"SalesPersonIDCat\",\n            \"TerritoryIDCat\",\n            \"ShipMethodID\",\n        ],\n        getml.data.roles.categorical,\n    )\n    sales_order_header.set_role(\n        [\"SubTotal\", \"TaxAmt\", \"Freight\", \"TotalDue\"], getml.data.roles.numerical\n    )\n    sales_order_header.set_role(\n        [\"OrderDate\", \"DueDate\", \"ShipDate\", \"ModifiedDate\"],\n        getml.data.roles.time_stamp,\n    )\n\n    sales_order_header[\"churn\"] = _calculate_churn(sales_order_header)\n\n    sales_order_header = sales_order_header[~sales_order_header.churn.is_nan()].to_df(\n        \"SalesOrderHeaderRefined\"\n    )\n\n    sales_order_header.set_role(\"churn\", getml.data.roles.target)\n\n    return sales_order_header\n\n\ndef _calculate_churn(sales_order_header: getml.DataFrame) -> np.ndarray:\n    sales_order_header_pd = sales_order_header[\n        [\"OrderDate\", \"CustomerID\", \"SalesOrderID\"]\n    ].to_pandas()\n    repeat_purchases = sales_order_header_pd.merge(\n        sales_order_header_pd[[\"OrderDate\", \"CustomerID\"]],\n        on=\"CustomerID\",\n        how=\"left\",\n    )\n    repeat_purchases = repeat_purchases[\n        repeat_purchases[\"OrderDate_y\"] > repeat_purchases[\"OrderDate_x\"]\n    ]\n    repeat_purchases = repeat_purchases[\n        repeat_purchases[\"OrderDate_y\"] - repeat_purchases[\"OrderDate_x\"]\n        > pd.Timedelta(\"180 days\")\n    ]\n    repeat_purchases = repeat_purchases.groupby(\n        \"SalesOrderID\", as_index=False\n    ).aggregate({\"CustomerID\": \"max\"})\n    repeat_purchase_ids = {sid: True for sid in repeat_purchases[\"SalesOrderID\"]}\n    cut_off_date = max(sales_order_header_pd[\"OrderDate\"]) - pd.Timedelta(\"180 days\")\n    return np.asarray(\n        [\n            np.nan\n            if order_date >= cut_off_date\n            else 0\n            if order_id in repeat_purchase_ids\n            else 1\n            for (order_date, order_id) in zip(\n                sales_order_header_pd[\"OrderDate\"],\n                sales_order_header_pd[\"SalesOrderID\"],\n            )\n        ]\n    )\n\n\ndef _make_sales_order_reason(conn: getml.database.Connection) -> getml.DataFrame:\n    sales_order_reason = _load_if_needed(\"SalesOrderHeaderSalesReason\", conn)\n    sales_order_reason.set_role(\"SalesOrderID\", getml.data.roles.join_key)\n    sales_order_reason.set_role(\"SalesReasonID\", getml.data.roles.categorical)\n    return sales_order_reason\n\n\ndef _make_special_offer(conn: getml.database.Connection) -> getml.DataFrame:\n    special_offer = _load_if_needed(\"SpecialOffer\", conn)\n    special_offer.set_role([\"SpecialOfferID\"], getml.data.roles.join_key)\n    special_offer.set_role([\"MinQty\", \"DiscountPct\"], getml.data.roles.numerical)\n    special_offer.set_role(\n        [\"Category\", \"Description\", \"Type\"], getml.data.roles.categorical\n    )\n    special_offer.set_role([\"StartDate\", \"EndDate\"], getml.data.roles.time_stamp)\n    return special_offer\n\n\ndef _make_store(conn: getml.database.Connection) -> getml.DataFrame:\n    store = _load_if_needed(\"Store\", conn)\n    store.set_role([\"SalesPersonID\"], getml.data.roles.join_key)\n    store.set_role([\"SalesPersonID\"], getml.data.roles.join_key)\n    return store\n\n\ndef _make_container(conn: getml.database.Connection) -> getml.data.Container:\n    product = _make_product(conn)\n    sales_order_detail = _make_sales_order_detail(conn)\n    sales_order_header = _make_sales_order_header(conn)\n    sales_order_reason = _make_sales_order_reason(conn)\n    special_offer = _make_special_offer(conn)\n    store = _make_store(conn)\n\n    split = getml.data.split.random(train=0.8, test=0.2)\n\n    container = getml.data.Container(population=sales_order_header, split=split)\n    container.add(\n        product=product,\n        sales_order_detail=sales_order_detail,\n        sales_order_header=sales_order_header,\n        sales_order_reason=sales_order_reason,\n        special_offer=special_offer,\n        store=store,\n    )\n\n    return container\n\n\ndef _make_data_model(container: getml.data.Container) -> getml.data.DataModel:\n    dm = getml.data.DataModel(container.sales_order_header.to_placeholder(\"population\"))\n\n    dm.add(\n        getml.data.to_placeholder(\n            product=container.product,\n            sales_order_detail=container.sales_order_detail,\n            sales_order_header=container.sales_order_header,\n            sales_order_reason=container.sales_order_reason,\n            special_offer=container.special_offer,\n            store=container.store,\n        )\n    )\n\n    dm.population.join(\n        dm.sales_order_header,\n        on=\"CustomerID\",\n        time_stamps=\"OrderDate\",\n        lagged_targets=True,\n        horizon=getml.data.time.days(1),\n    )\n\n    dm.population.join(\n        dm.sales_order_detail,\n        on=\"SalesOrderID\",\n    )\n\n    dm.population.join(\n        dm.sales_order_reason,\n        on=\"SalesOrderID\",\n    )\n\n    dm.population.join(\n        dm.store,\n        on=\"SalesPersonID\",\n    )\n\n    dm.sales_order_detail.join(\n        dm.product,\n        on=\"ProductID\",\n        relationship=getml.data.relationship.many_to_one,\n    )\n\n    dm.sales_order_detail.join(\n        dm.special_offer,\n        on=\"SpecialOfferID\",\n        relationship=getml.data.relationship.many_to_one,\n    )\n\n    return dm\n"
  },
  {
    "path": "src/python-api/tests/pipeline/test_interstate94.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis is an integration test based on\nthe Interstate94 dataset.\n\"\"\"\n\nimport getml as getml\n\n\ndef test_interstate94(getml_project):\n    \"\"\"\n    This is an integration test based on\n    the Interstate94 dataset.\n    \"\"\"\n\n    traffic = getml.datasets.load_interstate94(roles=False, units=False)\n\n    traffic.set_role(\"ds\", getml.data.roles.time_stamp)\n    traffic.set_role(\"traffic_volume\", getml.data.roles.target)\n    traffic.set_role(\"holiday\", getml.data.roles.categorical)\n\n    split = getml.data.split.time(\n        traffic, \"ds\", test=getml.data.time.datetime(2018, 3, 15)\n    )\n\n    seasonal = getml.preprocessors.Seasonal()\n\n    time_series = getml.data.TimeSeries(\n        population=traffic,\n        split=split,\n        time_stamps=\"ds\",\n        horizon=getml.data.time.hours(1),\n        memory=getml.data.time.days(7),\n        lagged_targets=True,\n    )\n\n    fast_prop = getml.feature_learning.FastProp(  # pylint: disable=unexpected-keyword-arg\n        loss_function=getml.feature_learning.loss_functions.SquareLoss,\n        num_threads=1,\n        num_features=20,\n    )\n\n    predictor = getml.predictors.XGBoostRegressor()\n\n    pipe = getml.pipeline.Pipeline(\n        tags=[\"memory: 7d\", \"horizon: 1h\", \"fast_prop\"],\n        data_model=time_series.data_model,\n        preprocessors=[seasonal],\n        feature_learners=[fast_prop],\n        predictors=[predictor],\n    )\n\n    pipe.fit(time_series.train)\n\n    scores = pipe.score(time_series.test)\n\n    assert scores.rsquared > 0.981, \"Expected an r-squared greater 0.981, got \" + str(  # type: ignore\n        scores.rsquared\n    )\n"
  },
  {
    "path": "src/python-api/tests/pipeline/test_loans.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis is an integration test based on\nthe loans dataset.\n\"\"\"\n\nimport getml as getml\n\n\ndef test_loans(getml_project):\n    \"\"\"\n    This is an integration test based on\n    the loans dataset.\n    \"\"\"\n    star_schema = _make_star_schema()\n\n    fast_prop = getml.feature_learning.FastProp(  # pylint: disable=unexpected-keyword-arg\n        aggregation=getml.feature_learning.FastProp.agg_sets.All,\n        loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n        num_threads=1,\n    )\n\n    feature_selector = getml.predictors.XGBoostClassifier(n_jobs=1)\n\n    predictor = getml.predictors.XGBoostClassifier(\n        gamma=2,\n        n_jobs=1,\n    )\n\n    pipe = getml.pipeline.Pipeline(\n        data_model=star_schema.data_model,\n        feature_learners=[fast_prop],\n        feature_selectors=[feature_selector],\n        predictors=predictor,\n    )\n\n    pipe.fit(star_schema.train)\n\n    scores = pipe.score(star_schema.test)\n\n    assert scores.auc > 0.92, \"Expected an AUC greater 0.92, got \" + str(scores.auc)  # type: ignore\n\n\ndef _make_star_schema() -> getml.data.StarSchema:\n    population_train, population_test, order, trans, meta = getml.datasets.load_loans(\n        roles=True, units=True\n    )\n\n    star_schema = getml.data.StarSchema(\n        train=population_train, test=population_test, alias=\"population\"\n    )\n\n    star_schema.join(\n        trans,\n        on=\"account_id\",\n        time_stamps=(\"date_loan\", \"date\"),\n    )\n\n    star_schema.join(\n        order,\n        on=\"account_id\",\n    )\n\n    star_schema.join(\n        meta,\n        on=\"account_id\",\n    )\n\n    return star_schema\n"
  },
  {
    "path": "src/python-api/tests/pipeline/test_pipelines_save_load.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis is an integration test based on\nthe loans dataset. The purpose is\nto make sure that saving and loading pipelines\nworks as expected.\n\"\"\"\n\nimport getml as getml\nimport numpy as np\n\n\ndef test_save_load(getml_project):\n    \"\"\"\n    This is an integration test based on\n    the loans dataset. The purpose is\n    to make sure that saving and loading pipelines\n    works as expected.\n    \"\"\"\n    star_schema = _make_star_schema()\n\n    fast_prop = getml.feature_learning.FastProp(  # pylint: disable=unexpected-keyword-arg\n        aggregation=getml.feature_learning.FastProp.agg_sets.All,\n        loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n        num_threads=1,\n    )\n\n    feature_selector = getml.predictors.XGBoostClassifier(n_jobs=1)\n\n    predictor = getml.predictors.XGBoostClassifier(\n        gamma=2,\n        n_jobs=1,\n    )\n\n    pipe1 = getml.pipeline.Pipeline(\n        data_model=star_schema.data_model,\n        feature_learners=[fast_prop],\n        feature_selectors=[feature_selector],\n        predictors=predictor,\n    )\n\n    pipe1.fit(star_schema.train)\n\n    predictions1 = pipe1.predict(star_schema.test)\n    sql_code1 = pipe1.features.to_sql()\n\n    project_name = getml_project.name\n    getml.engine.suspend_project(project_name)\n    getml.engine.set_project(project_name)\n\n    star_schema = _make_star_schema()\n\n    pipe2 = getml.pipeline.load(pipe1.id)\n    predictions2 = pipe2.predict(star_schema.test)\n    sql_code2 = pipe2.features.to_sql()\n\n    assert np.allclose(predictions1, predictions2), \"Must be the same\"  # type: ignore\n    assert sql_code1.to_str() == sql_code2.to_str(), \"Must be the same\"\n\n\ndef _make_star_schema() -> getml.data.StarSchema:\n    population_train, population_test, order, trans, meta = getml.datasets.load_loans(\n        roles=True, units=True\n    )\n\n    star_schema = getml.data.StarSchema(\n        train=population_train, test=population_test, alias=\"population\"\n    )\n\n    star_schema.join(\n        trans,\n        on=\"account_id\",\n        time_stamps=(\"date_loan\", \"date\"),\n    )\n\n    star_schema.join(\n        order,\n        on=\"account_id\",\n    )\n\n    star_schema.join(\n        meta,\n        on=\"account_id\",\n    )\n\n    return star_schema\n"
  },
  {
    "path": "src/python-api/tests/pipeline/test_robot.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n\"\"\"\nThis is an integration test based on\nthe robot dataset.\n\"\"\"\n\nimport getml as getml\n\n\ndef test_robot(getml_project):\n    \"\"\"\n    This is an integration test based on\n    the robot dataset.\n    \"\"\"\n\n    data_all = getml.data.DataFrame.from_csv(\n        \"https://static.getml.com/datasets/robotarm/robot-demo.csv\", \"data_all\"\n    )\n\n    data_all.set_role([\"f_x\", \"f_y\", \"f_z\"], getml.data.roles.target)\n    data_all.set_role(data_all.roles.unused, getml.data.roles.numerical)\n\n    split = getml.data.split.time(data_all, \"rowid\", test=10500)\n\n    time_series = getml.data.TimeSeries(\n        population=data_all,\n        split=split,\n        time_stamps=\"rowid\",\n        lagged_targets=False,\n        memory=30,\n    )\n\n    fast_prop = getml.feature_learning.FastProp(  # pylint: disable=unexpected-keyword-arg\n        loss_function=getml.feature_learning.loss_functions.SquareLoss,\n        num_features=10,\n    )\n\n    xgboost = getml.predictors.XGBoostRegressor()\n\n    pipe1 = getml.pipeline.Pipeline(\n        data_model=time_series.data_model,\n        feature_learners=[fast_prop],\n        predictors=xgboost,\n    )\n\n    pipe1.check(time_series.train)\n    pipe1.fit(time_series.train)\n\n    scores = pipe1.score(time_series.test)\n\n    assert all(\n        (r > 0.986 for r in scores.rsquared)  # type: ignore\n    ), \"Expected all r-squared values to be greater than 0.986, got \" + str(\n        scores.rsquared\n    )\n"
  },
  {
    "path": "src/python-api/tests/project/test_project_save_load.py",
    "content": "import os\nimport zipfile\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile, TemporaryDirectory\n\nimport pytest\n\nimport getml\n\n\n@pytest.fixture\ndef bundle(getml_project, df, tmpdir):\n    df.save()\n    bundle = Path(f\"{getml_project.name}.getml\")\n    getml_project.save(bundle)\n    getml_project.delete()\n    yield bundle\n\n\ndef test_save_project_bundle(getml_project, bundle, df):\n    assert bundle.exists()\n    assert bundle.stat().st_size > 0\n    assert bundle.suffix == \".getml\"\n    assert zipfile.is_zipfile(bundle)\n\n    with zipfile.ZipFile(bundle, \"r\") as zf:\n        zf.extractall()\n        output_dir = Path(bundle.stem)\n        assert output_dir.exists()\n        extracted_files = output_dir.rglob(\"*\")\n        assert len(list(extracted_files)) > 0\n        assert (output_dir / f\"data/{df.name}\").exists()\n\n\ndef test_save_project_bundle_default_name(getml_project, df, tmpdir):\n    getml_project.save()\n    assert Path(f\"{getml_project.name}.getml\").exists()\n\n\ndef test_load_project(getml_project, bundle, df):\n    getml_project.load(bundle)\n    getml_project.data_frames.load()\n    assert df.name in [df.name for df in getml_project.data_frames.data]\n    assert getml_project.name == bundle.stem\n    assert [df.name for df in getml_project.data_frames.data]\n"
  },
  {
    "path": "src/python-api/tests/unit/__init__.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\n"
  },
  {
    "path": "src/python-api/tests/unit/conftest.py",
    "content": "from __future__ import annotations\n\nimport fnmatch\nimport itertools as it\nimport struct\nfrom typing import Iterable, List, Tuple, Type\n\nimport pytest\n\nfrom getml.events.types import EventContext, EventSource\n\n\nclass FakePath:\n    valid_paths: List[str] = []\n    valid_files: List[str] = []\n\n    def __init__(self, path: str):\n        self._path = path\n\n    def __eq__(self, other: FakePath) -> bool:\n        return self._path == other._path\n\n    def __str__(self) -> str:\n        return self._path\n\n    def __repr__(self) -> str:\n        return f\"FakePath({self._path})\"\n\n    def __fspath__(self) -> str:\n        return self._path\n\n    def exists(self) -> bool:\n        return self._path in FakePath.valid_paths or self._path in FakePath.valid_files\n\n    def glob(self, pattern: str) -> List[FakePath]:\n        pattern_abs = f\"{self._path}/{pattern}\"\n        return [\n            FakePath(path)\n            for path in it.chain(self.valid_paths, self.valid_files)\n            if fnmatch.fnmatch(path, pattern_abs)\n        ]\n\n    def is_file(self) -> bool:\n        return self._path in FakePath.valid_files\n\n    def is_dir(self) -> bool:\n        return self._path in FakePath.valid_paths\n\n    @property\n    def name(self) -> str:\n        return next(reversed(self._path.split(\"/\")), self._path)\n\n    def read_text(self) -> str:\n        if self._path in FakePath.valid_files:\n            return \"fake file content for \" + self._path\n        else:\n            raise FileNotFoundError(f\"No such file: '{self._path}'\")\n\n    def write_text(self, content) -> None:\n        if self._path in FakePath.valid_files:\n            print(f\"Writing '{content}' to fake path {self._path}\")\n        else:\n            FakePath.valid_files.append(self._path)\n            print(f\"Creating and writing '{content}' to fake path {self._path}\")\n\n\n@pytest.fixture\ndef fake_path(request) -> Type[FakePath]:\n    FakePath.valid_paths = request.param.get(\"valid_paths\", [])\n    FakePath.valid_files = request.param.get(\"valid_files\", [])\n    return FakePath\n\n\nclass MockSocket:\n    \"\"\"\n    A mock socket that can be used to simulate a connection to the engine.\n\n    The socket will return a stream of messages that are encoded in the same way\n    as the engine would send them (prefixing each message with its length in bytes).\n    \"\"\"\n\n    def __init__(self, messages: Iterable[str]):\n        self.messages = messages\n        self.buffer = b\"\"\n        self._buffer_messages()\n\n    def _buffer_messages(self):\n        for message in self.messages:\n            self.buffer += struct.pack(\">I\", len(message))\n            self.buffer += message.encode()\n\n    def recv(self, size: int) -> bytes:\n        result = self.buffer[:size]\n        self.buffer = self.buffer[size:]\n        return result\n\n    def getpeername(self):\n        return (\"localhost\", 1709)\n\n\ndef pipeline_fit_context_messages() -> Tuple[EventContext, List[str]]:\n    return EventContext(source=EventSource.ENGINE, cmd={\"type_\": \"Pipeline.fit\"}), [\n        \"log: Staging...\",\n        \"log: Progress: 100%.\",\n        \"log: Preprocessing...\",\n        \"log: Progress: 100%.\",\n        \"log: Checking...\",\n        \"log: Progress: 12%.\",\n        \"log: Progress: 25%.\",\n        \"log: Progress: 37%.\",\n        \"log: Progress: 50%.\",\n        \"log: Progress: 57%.\",\n        \"log: Progress: 64%.\",\n        \"log: Progress: 71%.\",\n        \"log: Progress: 78%.\",\n        \"log: Progress: 85%.\",\n        \"log: Progress: 92%.\",\n        \"log: Progress: 100%.\",\n        \"Success!\",\n        \"log: Staging...\",\n        \"log: Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Training features...\",\n        \"log: Trained new features. Progress: 60%.\",\n        \"log: Trained new features. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. 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Progress: 57%.\",\n        \"log: XGBoost: Trained tree 58. Progress: 58%.\",\n        \"log: XGBoost: Trained tree 59. Progress: 59%.\",\n        \"log: XGBoost: Trained tree 60. Progress: 60%.\",\n        \"log: XGBoost: Trained tree 61. Progress: 61%.\",\n        \"log: XGBoost: Trained tree 62. Progress: 62%.\",\n        \"log: XGBoost: Trained tree 63. Progress: 63%.\",\n        \"log: XGBoost: Trained tree 64. Progress: 64%.\",\n        \"log: XGBoost: Trained tree 65. Progress: 65%.\",\n        \"log: XGBoost: Trained tree 66. Progress: 66%.\",\n        \"log: XGBoost: Trained tree 67. Progress: 67%.\",\n        \"log: XGBoost: Trained tree 68. Progress: 68%.\",\n        \"log: XGBoost: Trained tree 69. Progress: 69%.\",\n        \"log: XGBoost: Trained tree 70. Progress: 70%.\",\n        \"log: XGBoost: Trained tree 71. Progress: 71%.\",\n        \"log: XGBoost: Trained tree 72. Progress: 72%.\",\n        \"log: XGBoost: Trained tree 73. Progress: 73%.\",\n        \"log: XGBoost: Trained tree 74. Progress: 74%.\",\n        \"log: XGBoost: Trained tree 75. Progress: 75%.\",\n        \"log: XGBoost: Trained tree 76. Progress: 76%.\",\n        \"log: XGBoost: Trained tree 77. Progress: 77%.\",\n        \"log: XGBoost: Trained tree 78. Progress: 78%.\",\n        \"log: XGBoost: Trained tree 79. Progress: 79%.\",\n        \"log: XGBoost: Trained tree 80. Progress: 80%.\",\n        \"log: XGBoost: Trained tree 81. Progress: 81%.\",\n        \"log: XGBoost: Trained tree 82. Progress: 82%.\",\n        \"log: XGBoost: Trained tree 83. Progress: 83%.\",\n        \"log: XGBoost: Trained tree 84. Progress: 84%.\",\n        \"log: XGBoost: Trained tree 85. Progress: 85%.\",\n        \"log: XGBoost: Trained tree 86. Progress: 86%.\",\n        \"log: XGBoost: Trained tree 87. Progress: 87%.\",\n        \"log: XGBoost: Trained tree 88. Progress: 88%.\",\n        \"log: XGBoost: Trained tree 89. Progress: 89%.\",\n        \"log: XGBoost: Trained tree 90. Progress: 90%.\",\n        \"log: XGBoost: Trained tree 91. Progress: 91%.\",\n        \"log: XGBoost: Trained tree 92. Progress: 92%.\",\n        \"log: XGBoost: Trained tree 93. Progress: 93%.\",\n        \"log: XGBoost: Trained tree 94. Progress: 94%.\",\n        \"log: XGBoost: Trained tree 95. Progress: 95%.\",\n        \"log: XGBoost: Trained tree 96. Progress: 96%.\",\n        \"log: XGBoost: Trained tree 97. Progress: 97%.\",\n        \"log: XGBoost: Trained tree 98. Progress: 98%.\",\n        \"log: XGBoost: Trained tree 99. Progress: 99%.\",\n        \"log: XGBoost: Trained tree 100. Progress: 100%.\",\n        \"log: XGBoost: Training as predictor...\",\n        \"log: XGBoost: Trained tree 1. Progress: 1%.\",\n        \"log: XGBoost: Trained tree 2. Progress: 2%.\",\n        \"log: XGBoost: Trained tree 3. Progress: 3%.\",\n        \"log: XGBoost: Trained tree 4. Progress: 4%.\",\n        \"log: XGBoost: Trained tree 5. Progress: 5%.\",\n        \"log: XGBoost: Trained tree 6. Progress: 6%.\",\n        \"log: XGBoost: Trained tree 7. Progress: 7%.\",\n        \"log: XGBoost: Trained tree 8. Progress: 8%.\",\n        \"log: XGBoost: Trained tree 9. Progress: 9%.\",\n        \"log: XGBoost: Trained tree 10. Progress: 10%.\",\n        \"log: XGBoost: Trained tree 11. Progress: 11%.\",\n        \"log: XGBoost: Trained tree 12. Progress: 12%.\",\n        \"log: XGBoost: Trained tree 13. Progress: 13%.\",\n        \"log: XGBoost: Trained tree 14. Progress: 14%.\",\n        \"log: XGBoost: Trained tree 15. Progress: 15%.\",\n        \"log: XGBoost: Trained tree 16. Progress: 16%.\",\n        \"log: XGBoost: Trained tree 17. Progress: 17%.\",\n        \"log: XGBoost: Trained tree 18. Progress: 18%.\",\n        \"log: XGBoost: Trained tree 19. Progress: 19%.\",\n        \"log: XGBoost: Trained tree 20. Progress: 20%.\",\n        \"log: XGBoost: Trained tree 21. Progress: 21%.\",\n        \"log: XGBoost: Trained tree 22. Progress: 22%.\",\n        \"log: XGBoost: Trained tree 23. Progress: 23%.\",\n        \"log: XGBoost: Trained tree 24. Progress: 24%.\",\n        \"log: XGBoost: Trained tree 25. Progress: 25%.\",\n        \"log: XGBoost: Trained tree 26. Progress: 26%.\",\n        \"log: XGBoost: Trained tree 27. Progress: 27%.\",\n        \"log: XGBoost: Trained tree 28. Progress: 28%.\",\n        \"log: XGBoost: Trained tree 29. Progress: 29%.\",\n        \"log: XGBoost: Trained tree 30. Progress: 30%.\",\n        \"log: XGBoost: Trained tree 31. Progress: 31%.\",\n        \"log: XGBoost: Trained tree 32. Progress: 32%.\",\n        \"log: XGBoost: Trained tree 33. Progress: 33%.\",\n        \"log: XGBoost: Trained tree 34. Progress: 34%.\",\n        \"log: XGBoost: Trained tree 35. Progress: 35%.\",\n        \"log: XGBoost: Trained tree 36. Progress: 36%.\",\n        \"log: XGBoost: Trained tree 37. Progress: 37%.\",\n        \"log: XGBoost: Trained tree 38. Progress: 38%.\",\n        \"log: XGBoost: Trained tree 39. Progress: 39%.\",\n        \"log: XGBoost: Trained tree 40. Progress: 40%.\",\n        \"log: XGBoost: Trained tree 41. Progress: 41%.\",\n        \"log: XGBoost: Trained tree 42. Progress: 42%.\",\n        \"log: XGBoost: Trained tree 43. Progress: 43%.\",\n        \"log: XGBoost: Trained tree 44. Progress: 44%.\",\n        \"log: XGBoost: Trained tree 45. Progress: 45%.\",\n        \"log: XGBoost: Trained tree 46. Progress: 46%.\",\n        \"log: XGBoost: Trained tree 47. Progress: 47%.\",\n        \"log: XGBoost: Trained tree 48. Progress: 48%.\",\n        \"log: XGBoost: Trained tree 49. Progress: 49%.\",\n        \"log: XGBoost: Trained tree 50. Progress: 50%.\",\n        \"log: XGBoost: Trained tree 51. Progress: 51%.\",\n        \"log: XGBoost: Trained tree 52. Progress: 52%.\",\n        \"log: XGBoost: Trained tree 53. Progress: 53%.\",\n        \"log: XGBoost: Trained tree 54. Progress: 54%.\",\n        \"log: XGBoost: Trained tree 55. Progress: 55%.\",\n        \"log: XGBoost: Trained tree 56. Progress: 56%.\",\n        \"log: XGBoost: Trained tree 57. Progress: 57%.\",\n        \"log: XGBoost: Trained tree 58. Progress: 58%.\",\n        \"log: XGBoost: Trained tree 59. Progress: 59%.\",\n        \"log: XGBoost: Trained tree 60. Progress: 60%.\",\n        \"log: XGBoost: Trained tree 61. Progress: 61%.\",\n        \"log: XGBoost: Trained tree 62. Progress: 62%.\",\n        \"log: XGBoost: Trained tree 63. Progress: 63%.\",\n        \"log: XGBoost: Trained tree 64. Progress: 64%.\",\n        \"log: XGBoost: Trained tree 65. Progress: 65%.\",\n        \"log: XGBoost: Trained tree 66. Progress: 66%.\",\n        \"log: XGBoost: Trained tree 67. Progress: 67%.\",\n        \"log: XGBoost: Trained tree 68. Progress: 68%.\",\n        \"log: XGBoost: Trained tree 69. Progress: 69%.\",\n        \"log: XGBoost: Trained tree 70. Progress: 70%.\",\n        \"log: XGBoost: Trained tree 71. Progress: 71%.\",\n        \"log: XGBoost: Trained tree 72. Progress: 72%.\",\n        \"log: XGBoost: Trained tree 73. Progress: 73%.\",\n        \"log: XGBoost: Trained tree 74. Progress: 74%.\",\n        \"log: XGBoost: Trained tree 75. Progress: 75%.\",\n        \"log: XGBoost: Trained tree 76. Progress: 76%.\",\n        \"log: XGBoost: Trained tree 77. Progress: 77%.\",\n        \"log: XGBoost: Trained tree 78. Progress: 78%.\",\n        \"log: XGBoost: Trained tree 79. Progress: 79%.\",\n        \"log: XGBoost: Trained tree 80. Progress: 80%.\",\n        \"log: XGBoost: Trained tree 81. Progress: 81%.\",\n        \"log: XGBoost: Trained tree 82. Progress: 82%.\",\n        \"log: XGBoost: Trained tree 83. Progress: 83%.\",\n        \"log: XGBoost: Trained tree 84. Progress: 84%.\",\n        \"log: XGBoost: Trained tree 85. Progress: 85%.\",\n        \"log: XGBoost: Trained tree 86. Progress: 86%.\",\n        \"log: XGBoost: Trained tree 87. Progress: 87%.\",\n        \"log: XGBoost: Trained tree 88. Progress: 88%.\",\n        \"log: XGBoost: Trained tree 89. Progress: 89%.\",\n        \"log: XGBoost: Trained tree 90. Progress: 90%.\",\n        \"log: XGBoost: Trained tree 91. Progress: 91%.\",\n        \"log: XGBoost: Trained tree 92. Progress: 92%.\",\n        \"log: XGBoost: Trained tree 93. Progress: 93%.\",\n        \"log: XGBoost: Trained tree 94. Progress: 94%.\",\n        \"log: XGBoost: Trained tree 95. Progress: 95%.\",\n        \"log: XGBoost: Trained tree 96. Progress: 96%.\",\n        \"log: XGBoost: Trained tree 97. Progress: 97%.\",\n        \"log: XGBoost: Trained tree 98. Progress: 98%.\",\n        \"log: XGBoost: Trained tree 99. Progress: 99%.\",\n        \"log: XGBoost: Trained tree 100. Progress: 100%.\",\n        \"log: XGBoost: Training as predictor...\",\n        \"log: XGBoost: Trained tree 1. Progress: 1%.\",\n        \"log: XGBoost: Trained tree 2. Progress: 2%.\",\n        \"log: XGBoost: Trained tree 3. Progress: 3%.\",\n        \"log: XGBoost: Trained tree 4. Progress: 4%.\",\n        \"log: XGBoost: Trained tree 5. Progress: 5%.\",\n        \"log: XGBoost: Trained tree 6. Progress: 6%.\",\n        \"log: XGBoost: Trained tree 7. Progress: 7%.\",\n        \"log: XGBoost: Trained tree 8. Progress: 8%.\",\n        \"log: XGBoost: Trained tree 9. Progress: 9%.\",\n        \"log: XGBoost: Trained tree 10. Progress: 10%.\",\n        \"log: XGBoost: Trained tree 11. Progress: 11%.\",\n        \"log: XGBoost: Trained tree 12. Progress: 12%.\",\n        \"log: XGBoost: Trained tree 13. Progress: 13%.\",\n        \"log: XGBoost: Trained tree 14. Progress: 14%.\",\n        \"log: XGBoost: Trained tree 15. Progress: 15%.\",\n        \"log: XGBoost: Trained tree 16. Progress: 16%.\",\n        \"log: XGBoost: Trained tree 17. Progress: 17%.\",\n        \"log: XGBoost: Trained tree 18. Progress: 18%.\",\n        \"log: XGBoost: Trained tree 19. Progress: 19%.\",\n        \"log: XGBoost: Trained tree 20. Progress: 20%.\",\n        \"log: XGBoost: Trained tree 21. Progress: 21%.\",\n        \"log: XGBoost: Trained tree 22. Progress: 22%.\",\n        \"log: XGBoost: Trained tree 23. Progress: 23%.\",\n        \"log: XGBoost: Trained tree 24. Progress: 24%.\",\n        \"log: XGBoost: Trained tree 25. Progress: 25%.\",\n        \"log: XGBoost: Trained tree 26. Progress: 26%.\",\n        \"log: XGBoost: Trained tree 27. Progress: 27%.\",\n        \"log: XGBoost: Trained tree 28. Progress: 28%.\",\n        \"log: XGBoost: Trained tree 29. Progress: 29%.\",\n        \"log: XGBoost: Trained tree 30. Progress: 30%.\",\n        \"log: XGBoost: Trained tree 31. Progress: 31%.\",\n        \"log: XGBoost: Trained tree 32. Progress: 32%.\",\n        \"log: XGBoost: Trained tree 33. Progress: 33%.\",\n        \"log: XGBoost: Trained tree 34. Progress: 34%.\",\n        \"log: XGBoost: Trained tree 35. Progress: 35%.\",\n        \"log: XGBoost: Trained tree 36. Progress: 36%.\",\n        \"log: XGBoost: Trained tree 37. Progress: 37%.\",\n        \"log: XGBoost: Trained tree 38. Progress: 38%.\",\n        \"log: XGBoost: Trained tree 39. Progress: 39%.\",\n        \"log: XGBoost: Trained tree 40. Progress: 40%.\",\n        \"log: XGBoost: Trained tree 41. Progress: 41%.\",\n        \"log: XGBoost: Trained tree 42. Progress: 42%.\",\n        \"log: XGBoost: Trained tree 43. Progress: 43%.\",\n        \"log: XGBoost: Trained tree 44. Progress: 44%.\",\n        \"log: XGBoost: Trained tree 45. Progress: 45%.\",\n        \"log: XGBoost: Trained tree 46. Progress: 46%.\",\n        \"log: XGBoost: Trained tree 47. Progress: 47%.\",\n        \"log: XGBoost: Trained tree 48. Progress: 48%.\",\n        \"log: XGBoost: Trained tree 49. Progress: 49%.\",\n        \"log: XGBoost: Trained tree 50. Progress: 50%.\",\n        \"log: XGBoost: Trained tree 51. Progress: 51%.\",\n        \"log: XGBoost: Trained tree 52. Progress: 52%.\",\n        \"log: XGBoost: Trained tree 53. Progress: 53%.\",\n        \"log: XGBoost: Trained tree 54. Progress: 54%.\",\n        \"log: XGBoost: Trained tree 55. Progress: 55%.\",\n        \"log: XGBoost: Trained tree 56. Progress: 56%.\",\n        \"log: XGBoost: Trained tree 57. Progress: 57%.\",\n        \"log: XGBoost: Trained tree 58. Progress: 58%.\",\n        \"log: XGBoost: Trained tree 59. Progress: 59%.\",\n        \"log: XGBoost: Trained tree 60. Progress: 60%.\",\n        \"log: XGBoost: Trained tree 61. Progress: 61%.\",\n        \"log: XGBoost: Trained tree 62. Progress: 62%.\",\n        \"log: XGBoost: Trained tree 63. Progress: 63%.\",\n        \"log: XGBoost: Trained tree 64. Progress: 64%.\",\n        \"log: XGBoost: Trained tree 65. Progress: 65%.\",\n        \"log: XGBoost: Trained tree 66. Progress: 66%.\",\n        \"log: XGBoost: Trained tree 67. Progress: 67%.\",\n        \"log: XGBoost: Trained tree 68. Progress: 68%.\",\n        \"log: XGBoost: Trained tree 69. Progress: 69%.\",\n        \"log: XGBoost: Trained tree 70. Progress: 70%.\",\n        \"log: XGBoost: Trained tree 71. Progress: 71%.\",\n        \"log: XGBoost: Trained tree 72. Progress: 72%.\",\n        \"log: XGBoost: Trained tree 73. Progress: 73%.\",\n        \"log: XGBoost: Trained tree 74. Progress: 74%.\",\n        \"log: XGBoost: Trained tree 75. Progress: 75%.\",\n        \"log: XGBoost: Trained tree 76. Progress: 76%.\",\n        \"log: XGBoost: Trained tree 77. Progress: 77%.\",\n        \"log: XGBoost: Trained tree 78. Progress: 78%.\",\n        \"log: XGBoost: Trained tree 79. Progress: 79%.\",\n        \"log: XGBoost: Trained tree 80. Progress: 80%.\",\n        \"log: XGBoost: Trained tree 81. Progress: 81%.\",\n        \"log: XGBoost: Trained tree 82. Progress: 82%.\",\n        \"log: XGBoost: Trained tree 83. Progress: 83%.\",\n        \"log: XGBoost: Trained tree 84. Progress: 84%.\",\n        \"log: XGBoost: Trained tree 85. Progress: 85%.\",\n        \"log: XGBoost: Trained tree 86. Progress: 86%.\",\n        \"log: XGBoost: Trained tree 87. Progress: 87%.\",\n        \"log: XGBoost: Trained tree 88. Progress: 88%.\",\n        \"log: XGBoost: Trained tree 89. Progress: 89%.\",\n        \"log: XGBoost: Trained tree 90. Progress: 90%.\",\n        \"log: XGBoost: Trained tree 91. Progress: 91%.\",\n        \"log: XGBoost: Trained tree 92. Progress: 92%.\",\n        \"log: XGBoost: Trained tree 93. Progress: 93%.\",\n        \"log: XGBoost: Trained tree 94. Progress: 94%.\",\n        \"log: XGBoost: Trained tree 95. Progress: 95%.\",\n        \"log: XGBoost: Trained tree 96. Progress: 96%.\",\n        \"log: XGBoost: Trained tree 97. Progress: 97%.\",\n        \"log: XGBoost: Trained tree 98. Progress: 98%.\",\n        \"log: XGBoost: Trained tree 99. Progress: 99%.\",\n        \"log: XGBoost: Trained tree 100. Progress: 100%.\",\n        \"log: XGBoost: Training as predictor...\",\n        \"log: XGBoost: Trained tree 1. Progress: 1%.\",\n        \"log: XGBoost: Trained tree 2. Progress: 2%.\",\n        \"log: XGBoost: Trained tree 3. Progress: 3%.\",\n        \"log: XGBoost: Trained tree 4. Progress: 4%.\",\n        \"log: XGBoost: Trained tree 5. Progress: 5%.\",\n        \"log: XGBoost: Trained tree 6. Progress: 6%.\",\n        \"log: XGBoost: Trained tree 7. Progress: 7%.\",\n        \"log: XGBoost: Trained tree 8. Progress: 8%.\",\n        \"log: XGBoost: Trained tree 9. Progress: 9%.\",\n        \"log: XGBoost: Trained tree 10. Progress: 10%.\",\n        \"log: XGBoost: Trained tree 11. Progress: 11%.\",\n        \"log: XGBoost: Trained tree 12. Progress: 12%.\",\n        \"log: XGBoost: Trained tree 13. Progress: 13%.\",\n        \"log: XGBoost: Trained tree 14. Progress: 14%.\",\n        \"log: XGBoost: Trained tree 15. Progress: 15%.\",\n        \"log: XGBoost: Trained tree 16. Progress: 16%.\",\n        \"log: XGBoost: Trained tree 17. Progress: 17%.\",\n        \"log: XGBoost: Trained tree 18. Progress: 18%.\",\n        \"log: XGBoost: Trained tree 19. Progress: 19%.\",\n        \"log: XGBoost: Trained tree 20. Progress: 20%.\",\n        \"log: XGBoost: Trained tree 21. Progress: 21%.\",\n        \"log: XGBoost: Trained tree 22. Progress: 22%.\",\n        \"log: XGBoost: Trained tree 23. Progress: 23%.\",\n        \"log: XGBoost: Trained tree 24. Progress: 24%.\",\n        \"log: XGBoost: Trained tree 25. Progress: 25%.\",\n        \"log: XGBoost: Trained tree 26. Progress: 26%.\",\n        \"log: XGBoost: Trained tree 27. Progress: 27%.\",\n        \"log: XGBoost: Trained tree 28. Progress: 28%.\",\n        \"log: XGBoost: Trained tree 29. Progress: 29%.\",\n        \"log: XGBoost: Trained tree 30. Progress: 30%.\",\n        \"log: XGBoost: Trained tree 31. Progress: 31%.\",\n        \"log: XGBoost: Trained tree 32. Progress: 32%.\",\n        \"log: XGBoost: Trained tree 33. Progress: 33%.\",\n        \"log: XGBoost: Trained tree 34. Progress: 34%.\",\n        \"log: XGBoost: Trained tree 35. Progress: 35%.\",\n        \"log: XGBoost: Trained tree 36. Progress: 36%.\",\n        \"log: XGBoost: Trained tree 37. Progress: 37%.\",\n        \"log: XGBoost: Trained tree 38. Progress: 38%.\",\n        \"log: XGBoost: Trained tree 39. Progress: 39%.\",\n        \"log: XGBoost: Trained tree 40. Progress: 40%.\",\n        \"log: XGBoost: Trained tree 41. Progress: 41%.\",\n        \"log: XGBoost: Trained tree 42. Progress: 42%.\",\n        \"log: XGBoost: Trained tree 43. Progress: 43%.\",\n        \"log: XGBoost: Trained tree 44. Progress: 44%.\",\n        \"log: XGBoost: Trained tree 45. Progress: 45%.\",\n        \"log: XGBoost: Trained tree 46. Progress: 46%.\",\n        \"log: XGBoost: Trained tree 47. Progress: 47%.\",\n        \"log: XGBoost: Trained tree 48. Progress: 48%.\",\n        \"log: XGBoost: Trained tree 49. Progress: 49%.\",\n        \"log: XGBoost: Trained tree 50. Progress: 50%.\",\n        \"log: XGBoost: Trained tree 51. Progress: 51%.\",\n        \"log: XGBoost: Trained tree 52. Progress: 52%.\",\n        \"log: XGBoost: Trained tree 53. Progress: 53%.\",\n        \"log: XGBoost: Trained tree 54. Progress: 54%.\",\n        \"log: XGBoost: Trained tree 55. Progress: 55%.\",\n        \"log: XGBoost: Trained tree 56. Progress: 56%.\",\n        \"log: XGBoost: Trained tree 57. Progress: 57%.\",\n        \"log: XGBoost: Trained tree 58. Progress: 58%.\",\n        \"log: XGBoost: Trained tree 59. Progress: 59%.\",\n        \"log: XGBoost: Trained tree 60. Progress: 60%.\",\n        \"log: XGBoost: Trained tree 61. Progress: 61%.\",\n        \"log: XGBoost: Trained tree 62. Progress: 62%.\",\n        \"log: XGBoost: Trained tree 63. Progress: 63%.\",\n        \"log: XGBoost: Trained tree 64. Progress: 64%.\",\n        \"log: XGBoost: Trained tree 65. Progress: 65%.\",\n        \"log: XGBoost: Trained tree 66. Progress: 66%.\",\n        \"log: XGBoost: Trained tree 67. Progress: 67%.\",\n        \"log: XGBoost: Trained tree 68. Progress: 68%.\",\n        \"log: XGBoost: Trained tree 69. Progress: 69%.\",\n        \"log: XGBoost: Trained tree 70. Progress: 70%.\",\n        \"log: XGBoost: Trained tree 71. Progress: 71%.\",\n        \"log: XGBoost: Trained tree 72. Progress: 72%.\",\n        \"log: XGBoost: Trained tree 73. Progress: 73%.\",\n        \"log: XGBoost: Trained tree 74. Progress: 74%.\",\n        \"log: XGBoost: Trained tree 75. Progress: 75%.\",\n        \"log: XGBoost: Trained tree 76. Progress: 76%.\",\n        \"log: XGBoost: Trained tree 77. Progress: 77%.\",\n        \"log: XGBoost: Trained tree 78. Progress: 78%.\",\n        \"log: XGBoost: Trained tree 79. Progress: 79%.\",\n        \"log: XGBoost: Trained tree 80. Progress: 80%.\",\n        \"log: XGBoost: Trained tree 81. Progress: 81%.\",\n        \"log: XGBoost: Trained tree 82. Progress: 82%.\",\n        \"log: XGBoost: Trained tree 83. Progress: 83%.\",\n        \"log: XGBoost: Trained tree 84. Progress: 84%.\",\n        \"log: XGBoost: Trained tree 85. Progress: 85%.\",\n        \"log: XGBoost: Trained tree 86. Progress: 86%.\",\n        \"log: XGBoost: Trained tree 87. Progress: 87%.\",\n        \"log: XGBoost: Trained tree 88. Progress: 88%.\",\n        \"log: XGBoost: Trained tree 89. Progress: 89%.\",\n        \"log: XGBoost: Trained tree 90. Progress: 90%.\",\n        \"log: XGBoost: Trained tree 91. Progress: 91%.\",\n        \"log: XGBoost: Trained tree 92. Progress: 92%.\",\n        \"log: XGBoost: Trained tree 93. Progress: 93%.\",\n        \"log: XGBoost: Trained tree 94. Progress: 94%.\",\n        \"log: XGBoost: Trained tree 95. Progress: 95%.\",\n        \"log: XGBoost: Trained tree 96. Progress: 96%.\",\n        \"log: XGBoost: Trained tree 97. Progress: 97%.\",\n        \"log: XGBoost: Trained tree 98. Progress: 98%.\",\n        \"log: XGBoost: Trained tree 99. Progress: 99%.\",\n        \"log: XGBoost: Trained tree 100. Progress: 100%.\",\n        \"log: XGBoost: Training as predictor...\",\n        \"log: XGBoost: Trained tree 1. Progress: 1%.\",\n        \"log: XGBoost: Trained tree 2. Progress: 2%.\",\n        \"log: XGBoost: Trained tree 3. Progress: 3%.\",\n        \"log: XGBoost: Trained tree 4. Progress: 4%.\",\n        \"log: XGBoost: Trained tree 5. Progress: 5%.\",\n        \"log: XGBoost: Trained tree 6. Progress: 6%.\",\n        \"log: XGBoost: Trained tree 7. Progress: 7%.\",\n        \"log: XGBoost: Trained tree 8. Progress: 8%.\",\n        \"log: XGBoost: Trained tree 9. Progress: 9%.\",\n        \"log: XGBoost: Trained tree 10. Progress: 10%.\",\n        \"log: XGBoost: Trained tree 11. Progress: 11%.\",\n        \"log: XGBoost: Trained tree 12. Progress: 12%.\",\n        \"log: XGBoost: Trained tree 13. Progress: 13%.\",\n        \"log: XGBoost: Trained tree 14. Progress: 14%.\",\n        \"log: XGBoost: Trained tree 15. Progress: 15%.\",\n        \"log: XGBoost: Trained tree 16. Progress: 16%.\",\n        \"log: XGBoost: Trained tree 17. Progress: 17%.\",\n        \"log: XGBoost: Trained tree 18. Progress: 18%.\",\n        \"log: XGBoost: Trained tree 19. Progress: 19%.\",\n        \"log: XGBoost: Trained tree 20. Progress: 20%.\",\n        \"log: XGBoost: Trained tree 21. Progress: 21%.\",\n        \"log: XGBoost: Trained tree 22. Progress: 22%.\",\n        \"log: XGBoost: Trained tree 23. Progress: 23%.\",\n        \"log: XGBoost: Trained tree 24. 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Progress: 83%.\",\n        \"log: XGBoost: Trained tree 84. Progress: 84%.\",\n        \"log: XGBoost: Trained tree 85. Progress: 85%.\",\n        \"log: XGBoost: Trained tree 86. Progress: 86%.\",\n        \"log: XGBoost: Trained tree 87. Progress: 87%.\",\n        \"log: XGBoost: Trained tree 88. Progress: 88%.\",\n        \"log: XGBoost: Trained tree 89. Progress: 89%.\",\n        \"log: XGBoost: Trained tree 90. Progress: 90%.\",\n        \"log: XGBoost: Trained tree 91. Progress: 91%.\",\n        \"log: XGBoost: Trained tree 92. Progress: 92%.\",\n        \"log: XGBoost: Trained tree 93. Progress: 93%.\",\n        \"log: XGBoost: Trained tree 94. Progress: 94%.\",\n        \"log: XGBoost: Trained tree 95. Progress: 95%.\",\n        \"log: XGBoost: Trained tree 96. Progress: 96%.\",\n        \"log: XGBoost: Trained tree 97. Progress: 97%.\",\n        \"log: XGBoost: Trained tree 98. Progress: 98%.\",\n        \"log: XGBoost: Trained tree 99. Progress: 99%.\",\n        \"log: XGBoost: Trained tree 100. Progress: 100%.\",\n        \"^D\",\n    ]\n\n\ndef pipeline_transform_context_messages() -> Tuple[EventContext, List[str]]:\n    return EventContext(\n        source=EventSource.ENGINE, cmd={\"type_\": \"Pipeline.transform\"}\n    ), [\n        \"log: Staging...\",\n        \"log: Progress: 100%.\",\n        \"log: Preprocessing...\",\n        \"log: Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: FastProp: Building subfeatures...\",\n        \"log: Built 100 rows. Progress: 100%.\",\n        \"log: Relboost: Building features...\",\n        \"log: Built FEATURE_1. Progress: 10%.\",\n        \"log: Built FEATURE_2. Progress: 20%.\",\n        \"log: Built FEATURE_3. Progress: 30%.\",\n        \"log: Built FEATURE_4. Progress: 40%.\",\n        \"log: Built FEATURE_5. Progress: 50%.\",\n        \"log: Built FEATURE_6. Progress: 60%.\",\n        \"log: Built FEATURE_7. Progress: 70%.\",\n        \"log: Built FEATURE_8. Progress: 80%.\",\n        \"log: Built FEATURE_9. Progress: 90%.\",\n        \"log: Built FEATURE_10. Progress: 100%.\",\n        \"^D\",\n    ]\n\n\n@pytest.fixture(\n    params=[pipeline_fit_context_messages(), pipeline_transform_context_messages()]\n)\ndef pipeline_context(request):\n    context, messages = request.param\n    return context, MockSocket(messages)\n"
  },
  {
    "path": "src/python-api/tests/unit/data/test_arrow_schema_processors.py",
    "content": "import pyarrow as pa\nimport pyarrow.csv as pa_csv\nimport pytest\nfrom getml.data._io.arrow import (\n    DECIMAL_CONVERSION_LOST_PRECISION_WARNING_TEMPLATE,\n    MAX_SUPPORTED_TIMESTAMP_RESOLUTION,\n    TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE,\n    postprocess_arrow_schema,\n    postprocess_timestamp_drop_timezone,\n    preprocess_arrow_schema,\n    preprocess_parameterized_type,\n    preprocess_timestamp_cast_to_microseconds,\n    preprocess_unparameterized_type,\n)\nfrom getml.data.roles.container import Roles\n\n\n@pytest.fixture\ndef schema() -> pa.Schema:\n    return pa.schema(\n        [\n            pa.field(\"time_stamp\", pa.timestamp(\"ns\", tz=\"UTC\")),\n            pa.field(\"join_key\", pa.int32()),\n            pa.field(\"target\", pa.int32()),\n            pa.field(\"column_01\", pa.float64()),\n        ]\n    )\n\n\ndef _get_inferrence_blob_block_size(csv_blob: str, n_lines_inferred: int = 1) -> int:\n    lines = csv_blob.splitlines()\n    inferrence_blob = \"\\n\".join(lines[: 1 + n_lines_inferred]).encode()\n    return len(inferrence_blob) + 1  # 1 byte for the trailing newline character\n\n\ndef test_process_timestamp(timestamp_batch):\n    preprocessed_fields = [\n        preprocess_parameterized_type(field) for field in timestamp_batch.schema\n    ]\n    assert all(pa.types.is_timestamp(field.type) for field in preprocessed_fields)\n    preprocessed_timestamps_microseconds = [\n        preprocess_timestamp_cast_to_microseconds(field)\n        for field in timestamp_batch.schema\n    ]\n    assert all(\n        pa.types.is_timestamp(field.type)\n        for field in preprocessed_timestamps_microseconds\n    )\n    assert all(\n        field.type.unit == MAX_SUPPORTED_TIMESTAMP_RESOLUTION\n        for field in preprocessed_timestamps_microseconds\n    )\n    with pytest.warns(UserWarning) as warnings:\n        postprocessed_fields_dropped_tz = [\n            postprocess_timestamp_drop_timezone(field)\n            for field in timestamp_batch.schema\n        ]\n    assert str(warnings[0].message) == TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE.format(\n        column_name=timestamp_batch.schema[0].name,\n    )\n    assert all(\n        pa.types.is_timestamp(field.type) for field in postprocessed_fields_dropped_tz\n    )\n\n    ts_roles = Roles(time_stamp=[\"utc_time\", \"no_tz_time\"])\n\n    preprocessed_schema = preprocess_arrow_schema(timestamp_batch.schema, ts_roles)\n\n    assert all(pa.types.is_timestamp(field.type) for field in preprocessed_schema)\n    assert all(\n        field.type.unit == MAX_SUPPORTED_TIMESTAMP_RESOLUTION\n        for field in preprocessed_schema\n    )\n\n    with pytest.warns(UserWarning) as warnings:\n        postprocessed_schema_target_timestamp = postprocess_arrow_schema(\n            preprocessed_schema, ts_roles\n        )\n    assert str(warnings[0].message) == TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE.format(\n        column_name=\"utc_time\",\n    )\n    assert all(\n        pa.types.is_timestamp(field.type)\n        for field in postprocessed_schema_target_timestamp\n    )\n    assert all(\n        field.type.unit == MAX_SUPPORTED_TIMESTAMP_RESOLUTION\n        for field in postprocessed_schema_target_timestamp\n    )\n    assert all(field.type.tz is None for field in postprocessed_schema_target_timestamp)\n\n    string_roles = Roles(unused_string=[\"utc_time\", \"no_tz_time\"])\n    postprocessed_schema_target_string = postprocess_arrow_schema(\n        preprocessed_schema, string_roles\n    )\n    assert all(\n        pa.types.is_string(field.type) for field in postprocessed_schema_target_string\n    )\n\n\ndef test_process_int(int_batch):\n    processed_fields = [\n        preprocess_unparameterized_type(field) for field in int_batch.schema\n    ]\n    assert all(pa.types.is_float64(field.type) for field in processed_fields)\n\n\ndef test_process_decimal(decimal_batch):\n    with pytest.warns(UserWarning) as warnings:\n        processed_fields = [\n            preprocess_parameterized_type(field) for field in decimal_batch.schema\n        ]\n    assert str(\n        warnings[0].message\n    ) == DECIMAL_CONVERSION_LOST_PRECISION_WARNING_TEMPLATE.format(\n        column_name=decimal_batch.schema[0].name,\n        precision=decimal_batch.schema[0].type.precision,\n    )\n    assert all(pa.types.is_float64(field.type) for field in processed_fields)\n\n\ndef test_schema_preprocessing(schema):\n    specific_roles = Roles(\n        time_stamp=[\"time_stamp\"],\n        join_key=[\"join_key\"],\n        target=[\"target\"],\n        numerical=[\"column_01\"],\n    )\n    preprocessed_schema = preprocess_arrow_schema(schema, specific_roles)\n    # time stamp: target role is timestamp, timezone is dropped, resolution is\n    # cast to microseconds\n    assert preprocessed_schema[0].type == pa.timestamp(\n        MAX_SUPPORTED_TIMESTAMP_RESOLUTION, preprocessed_schema[0].type.tz\n    )\n    # join_key: target role is categorical like, int32 is left untouched\n    assert preprocessed_schema[1].type == pa.int32()\n    # target: target role is neither numerical like not categorical like, type\n    # is untouched\n    assert preprocessed_schema[2].type == pa.float64()\n    # target role is numerical like, type is already compliant with getml\n    assert preprocessed_schema[3].type == pa.float64()\n\n\ndef test_schema_postprocessing(schema):\n    specific_roles = Roles(\n        time_stamp=[\"time_stamp\"],\n        join_key=[\"join_key\"],\n        target=[\"target\"],\n        numerical=[\"column_01\"],\n    )\n    with pytest.warns(UserWarning) as warnings:\n        postprocessed_schema = postprocess_arrow_schema(schema, specific_roles)\n    assert str(warnings[0].message) == TIMEZONE_UTC_DROPPED_WARNING_TEMPLATE.format(\n        column_name=\"time_stamp\",\n    )\n    # time stamp: target role is timestamp, timezone is dropped, resolution is\n    # untouched at postprocessing\n    assert postprocessed_schema[0].type.tz is None\n    assert postprocessed_schema[0].type.unit != MAX_SUPPORTED_TIMESTAMP_RESOLUTION\n    assert postprocessed_schema[0].type.unit == \"ns\"\n    assert [field.type for field in list(postprocessed_schema)[1:]] == [\n        field.type for field in list(schema)[1:]\n    ]\n\n    string_roles = Roles(\n        unused_string=[\"time_stamp\", \"join_key\", \"target\", \"column_01\"]\n    )\n    postprocessed_schema = postprocess_arrow_schema(schema, string_roles)\n    # target role is unused_string: all fields (except the timestamp) are cast\n    # to string\n    assert all(\n        pa.types.is_string(field.type) for field in list(postprocessed_schema)[:1]\n    )\n\n\ndef test_arrow_inference_csv_changing_type(csv_file_with_changing_type_in_row_2):\n    source = csv_file_with_changing_type_in_row_2\n    with open(source, \"r\") as f:\n        inferrence_blob_block_size = _get_inferrence_blob_block_size(\n            f.read(), n_lines_inferred=1\n        )\n    ro = pa_csv.ReadOptions(block_size=inferrence_blob_block_size)\n\n    batches = pa_csv.open_csv(source, read_options=ro)\n\n    next(batches)\n\n    # The second batch has a different type for the column \"targets\"\n    # s.t. it is not compliant with the schema of the first batch\n    with pytest.raises(pa.ArrowInvalid):\n        next(batches)\n\n    # when reading the csv in full, pyarrow inferrs the correct schema\n    table = pa_csv.read_csv(source, read_options=ro)\n\n    assert table.schema != batches.schema\n"
  },
  {
    "path": "src/python-api/tests/unit/data/test_data_frame_init.py",
    "content": "import pytest\n\nimport getml\n\n\ndef test_init():\n    df = getml.DataFrame(name=\"initbert\")\n    assert df.name == \"initbert\"\n\n    roles = dict(\n        dummbert=[\"dumm\", \"bert\"],  # invalid role\n        numerical=[1, 2],  # invalid list\n        join_key=[\"join_key\"],\n        time_stamp=[\"date_1\", \"date_2\"],\n        categorical=[\"cat_1\", \"cat_2\", \"cat_3\"],\n    )\n\n    with pytest.raises(ValueError):\n        df = getml.DataFrame(name=\"failbert\", roles=roles)\n    roles.pop(\"dummbert\")\n\n    with pytest.raises(TypeError):\n        df = getml.DataFrame(name=\"failbert\", roles=roles)\n    roles[\"numerical\"] = [\"num_1\", \"num_2\"]\n\n    df = getml.DataFrame(name=\"rolebert\", roles=roles)\n    assert df._categorical_names == [\"cat_1\", \"cat_2\", \"cat_3\"]\n    assert df._numerical_names == [\"num_1\", \"num_2\"]\n    assert df._join_key_names == [\"join_key\"]\n    assert df._time_stamp_names == [\"date_1\", \"date_2\"]\n"
  },
  {
    "path": "src/python-api/tests/unit/data/test_subsetting.py",
    "content": "from dataclasses import dataclass\n\nimport pytest\n\nfrom getml.data.helpers import _infer_arange_args_from_slice as infer\n\n\n@dataclass\nclass FakeDFHasLen:\n    length: int\n\n    def __len__(self) -> int:\n        return self.length\n\n\n@pytest.fixture\ndef fake_df_has_len_10():\n    return FakeDFHasLen(10)\n\n\ntestdata_infer_arange_args_from_slice = [\n    (slice(None, None, None), (0, 10, 1)),\n    (slice(0, None, None), (0, 10, 1)),\n    (slice(0, 10, None), (0, 10, 1)),\n    (slice(0, 10, 1), (0, 10, 1)),\n    (slice(0, 10, 2), (0, 10, 2)),\n    (slice(0, 10, -1), (0, 0, 1)),\n    (slice(0, 10, -2), (0, 0, 1)),\n    (slice(10, 0, -1), (10, 0, -1)),\n    (slice(10, 0, -2), (10, 0, -2)),\n    (slice(10, 0, 1), (0, 0, 1)),\n    (slice(-10, None, None), (0, 10, 1)),\n    (slice(-10, 10, None), (0, 10, 1)),\n    (slice(-10, 10, 1), (0, 10, 1)),\n    (slice(-10, 10, 2), (0, 10, 2)),\n    (slice(-10, 10, -1), (0, 0, 1)),\n    (slice(-10, 10, -2), (0, 0, 1)),\n    (slice(-6, -4, 1), (4, 6, 1)),\n    (slice(-6, -4, -1), (0, 0, 1)),\n    (slice(-6, -4, 2), (4, 6, 2)),\n    (slice(-4, -6, 1), (0, 0, 1)),\n    (slice(None, None, 1), (0, 10, 1)),\n    (slice(None, None, -1), (9, -1, -1)),\n    (slice(5, None, 1), (5, 10, 1)),\n    (slice(5, None, -1), (5, -1, -1)),\n    (slice(None, 5, 1), (0, 5, 1)),\n    (slice(None, 5, -1), (9, 5, -1)),\n    # we don't care about upper bounds on the python side and let the Engine\n    # handle it lazily\n    (slice(0, 100, 1), (0, 100, 1)),\n    (slice(0, 100, -1), (0, 0, 1)),\n    (slice(0, 100, 2), (0, 100, 2)),\n    # as we have a natural zero, we can immediately handle lower bounds, though\n    (slice(100, 0, -1), (100, 0, -1)),\n    (slice(100, 0, 1), (0, 0, 1)),\n    (slice(-100, 0, 1), (0, 0, 1)),\n]\n\n\n@pytest.mark.parametrize(\"given_slice, expected\", testdata_infer_arange_args_from_slice)\ndef test_infer_arange_args_from_slice(fake_df_has_len_10, given_slice, expected):\n    assert infer(given_slice, fake_df_has_len_10) == expected\n"
  },
  {
    "path": "src/python-api/tests/unit/test_events.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport itertools as it\n\nimport pytest\nfrom rich import print\n\nfrom getml.communication import LogStreamListener\nfrom getml.events import (\n    DispatcherEventEmitter,\n    LogMessageEventParser,\n    PoolEventDispatcher,\n    engine_event_handler,\n)\nfrom getml.events.handlers import EventHandlerRegistry\nfrom getml.events.types import Event, EventSource, EventType\n\n\n@pytest.fixture\ndef tracked_events():\n    emitted_events = []\n\n    orginal_handlers = EventHandlerRegistry.handlers\n    EventHandlerRegistry.handlers = {source: [] for source in EventSource}\n\n    @engine_event_handler\n    def track_event(event: Event):\n        print(event)\n        emitted_events.append(event)\n\n    yield emitted_events\n    EventHandlerRegistry.unregister_handler(EventSource.ENGINE, track_event)\n    EventHandlerRegistry.handlers = orginal_handlers\n\n\ndef test_log_stream_listener(monkeypatch, pipeline_context, tracked_events):\n    monkeypatch.setattr(\n        \"getml.communication._list_projects_impl\", lambda running_only: []\n    )\n    monkeypatch.setattr(\"getml.communication.tcp_port\", 1709)\n    context, socketlike = pipeline_context\n    parser = LogMessageEventParser(context=context)\n    dispatcher = PoolEventDispatcher()\n    emitter = DispatcherEventEmitter(dispatcher)\n\n    parsed_events = list(\n        it.chain.from_iterable(parser.parse(msg) for msg in socketlike.messages)\n    )\n\n    with LogStreamListener(\n        parser=parser,\n        emitter=emitter,\n    ) as listener:\n        listener.listen(socket=socketlike, exit_on=lambda msg: msg == \"^D\")\n\n    prefix = context.cmd[\"type_\"].replace(\".\", \"_\").upper()\n\n    # at least one event for each log message\n    assert len(tracked_events) >= len(\n        [msg for msg in socketlike.messages if msg.startswith(\"log: \")]\n    )\n\n    # Account for the extra COMMAND_FINISHED event added by the listener\n    num_handlers = sum(\n        len(handlers) for handlers in EventHandlerRegistry.handlers.values()\n    )\n    assert len(tracked_events) == (len(parsed_events) + 1) * num_handlers\n\n    # Check that we have the expected event types plus COMMAND_FINISHED\n    expected_types = set(type for type in EventType if type.name.startswith(prefix))\n    expected_types.add(EventType.COMMAND_FINISHED)\n    assert set(event.type for event in tracked_events) == expected_types\n\n\n@pytest.fixture\ndef failing_event_handler():\n    orginal_handlers = EventHandlerRegistry.handlers\n    EventHandlerRegistry.handlers = {source: [] for source in EventSource}\n\n    @engine_event_handler\n    def failing_handler(event: Event):\n        raise Exception(\"This is a test exception.\")\n\n    yield failing_handler\n    EventHandlerRegistry.unregister_handler(EventSource.ENGINE, failing_handler)\n    EventHandlerRegistry.handlers = orginal_handlers\n\n\ndef test_event_handler_exception(failing_event_handler):\n    event = Event(\n        type=EventType.PIPELINE_FIT_STAGE_START,\n        source=EventSource.ENGINE,\n        attributes={},\n    )\n    dispatcher = PoolEventDispatcher()\n\n    emitter = DispatcherEventEmitter(dispatcher)\n\n    with pytest.warns(RuntimeWarning) as warnings:\n        with dispatcher:\n            emitter.emit([event])\n\n    assert str(warnings[0].message).startswith(\n        \"An exception occurred while dispatching event\"\n    )\n    assert failing_event_handler.__name__ in str(warnings[0].message)\n    assert str(event) in str(warnings[0].message)\n\n\ndef test_sync_handler_execution():\n    \"\"\"Test that handlers with sync=True execute synchronously in the calling thread.\"\"\"\n    import threading\n\n    orginal_handlers = EventHandlerRegistry.handlers\n    EventHandlerRegistry.handlers = {source: [] for source in EventSource}\n\n    sync_thread_id = None\n    async_thread_id = None\n    main_thread_id = threading.current_thread().ident\n\n    @engine_event_handler(sync=True)\n    def sync_handler(event: Event):\n        nonlocal sync_thread_id\n        sync_thread_id = threading.current_thread().ident\n\n    @engine_event_handler(sync=False)\n    def async_handler(event: Event):\n        nonlocal async_thread_id\n        async_thread_id = threading.current_thread().ident\n\n    event = Event(\n        type=EventType.PIPELINE_FIT_STAGE_START,\n        source=EventSource.ENGINE,\n        attributes={},\n    )\n\n    dispatcher = PoolEventDispatcher()\n    emitter = DispatcherEventEmitter(dispatcher)\n\n    with dispatcher:\n        emitter.emit([event])\n\n    # Sync handler should execute in main thread\n    assert sync_thread_id == main_thread_id\n\n    # Async handler should execute in a worker thread\n    assert async_thread_id != main_thread_id\n\n    EventHandlerRegistry.unregister_handler(EventSource.ENGINE, sync_handler)\n    EventHandlerRegistry.unregister_handler(EventSource.ENGINE, async_handler)\n    EventHandlerRegistry.handlers = orginal_handlers\n"
  },
  {
    "path": "src/python-api/tests/unit/test_exceptions.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nimport pytest\n\nfrom getml.exceptions import (\n    ENTERPRISE_DOCS_URL,\n    ENTERPRISE_FEATURE_NOT_AVAILABLE_ERROR_MSG_TEMPLATE,\n    handle_engine_exception,\n)\n\n\ndef test_handle_enterprise_feature_not_available_error():\n    engine_error_message = (\n        \"The \"\n        \"__name__\"\n        \" \"\n        \"__type__\"\n        \" is not supported in the community edition. Please \"\n        \"upgrade to getML enterprise to use this. An overview of what is \"\n        \"supported in the community edition can be found in the official \"\n        \"getML documentation.\"\n    )\n\n    with pytest.raises(OSError) as excinfo:\n        handle_engine_exception(engine_error_message)\n\n    assert str(\n        excinfo.value\n    ) == ENTERPRISE_FEATURE_NOT_AVAILABLE_ERROR_MSG_TEMPLATE.format(\n        missing_feature_name=\"__name__\",\n        missing_feature_type=\"__type__\",\n        enterprise_docs_url=ENTERPRISE_DOCS_URL,\n    )\n"
  },
  {
    "path": "src/python-api/tests/unit/test_import_all.py",
    "content": "# Copyright 2025 Code17 GmbH\n#\n# This file is licensed under the Elastic License 2.0 (ELv2).\n# Refer to the LICENSE.txt file in the root of the repository\n# for details.\n#\n\nfrom importlib import import_module\nfrom pathlib import Path\n\n\ndef test_import_all():\n    root = Path(__file__).parent.parent.parent / \"getml\"\n    for path in root.rglob(\"*.py\"):\n        rel_path = path.relative_to(root)\n        module = (\n            str(rel_path).replace(\"/\", \".\").replace(\".py\", \"\").replace(\".__init__\", \"\")\n        )\n        import_module(f\"getml.{module}\")\n"
  },
  {
    "path": "src/python-api/tests/unit/test_locate.py",
    "content": "from unittest.mock import patch\n\nimport pytest\n\nfrom getml.engine import Edition\nfrom getml.engine._launch import locate_executable, locate_installed_packages_by_edition\n\nfrom .conftest import FakePath\n\n\ndef access(path: str, mode: str):\n    return True\n\n\n@pytest.mark.parametrize(\n    \"fake_path, install_locations, expected\",\n    [\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux\",\n                    \"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [],\n            },\n            [FakePath(\"/fake/home/.getML\"), FakePath(\"/fake/usr/local/getML\")],\n            {\n                Edition.COMMUNITY: [\n                    FakePath(\"/fake/home/.getML/getml-community-1.2.3-amd64-linux\")\n                ],\n                Edition.ENTERPRISE: [\n                    FakePath(\"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux\")\n                ],\n            },\n        ),\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/home/.getML/getml-community-1.2.2-amd64-linux\",\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [],\n            },\n            [FakePath(\"/fake/home/.getML\")],\n            {\n                Edition.COMMUNITY: [\n                    FakePath(\"/fake/home/.getML/getml-community-1.2.3-amd64-linux\")\n                ],\n                Edition.ENTERPRISE: [],\n            },\n        ),\n    ],\n    indirect=[\"fake_path\"],\n)\ndef test_locate_installed_packages_by_edition(fake_path, install_locations, expected):\n    # fmt: off\n    with patch(\"getml.engine._launch.INSTALL_LOCATIONS\", install_locations),\\\n         patch(\"getml.engine._launch.__version__\", \"1.2.3\"),\\\n         patch(\"os.access\", access):\n    # fmt: on\n            assert locate_installed_packages_by_edition() == expected\n\n\n@pytest.mark.parametrize(\n    \"fake_path, install_locations, expected\",\n    [\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux\",\n                    \"/fake/usr/local/getML/getml-community-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux/getML\",\n                    \"/fake/usr/local/getML/getml-community-1.2.3-amd64-linux/getML\",\n                ],\n            },\n            [FakePath(\"/fake/home/.getML\"), FakePath(\"/fake/usr/local/getML\")],\n            FakePath(\"/fake/home/.getML/getml-community-1.2.3-amd64-linux/getML\"),\n        ),\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/home/.getML/getml-enterprise-1.2.3-amd64-linux\",\n                    \"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [\n                    \"/fake/home/.getML/getml-enterprise-1.2.3-amd64-linux/getML\",\n                    \"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux/getML\",\n                ],\n            },\n            [FakePath(\"/fake/home/.getML\"), FakePath(\"/fake/usr/local/getML\")],\n            FakePath(\"/fake/home/.getML/getml-enterprise-1.2.3-amd64-linux/getML\"),\n        ),\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux\",\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [\n                    \"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux/getML\",\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux/getML\",\n                ],\n            },\n            [FakePath(\"/fake/home/.getML\"), FakePath(\"/fake/usr/local/getML\")],\n            FakePath(\"/fake/usr/local/getML/getml-enterprise-1.2.3-amd64-linux/getML\"),\n        ),\n        (\n            {\n                \"valid_paths\": [\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux\",\n                ],\n                \"valid_files\": [\n                    \"/fake/home/.getML/getml-community-1.2.3-amd64-linux/getML\",\n                ],\n            },\n            [FakePath(\"/fake/home/.getML\")],\n            FakePath(\"/fake/home/.getML/getml-community-1.2.3-amd64-linux/getML\"),\n        ),\n        (\n            {\n                \"valid_paths\": [],\n                \"valid_files\": [],\n            },\n            [],\n            None,\n        ),\n    ],\n    indirect=[\"fake_path\"],\n)\ndef test_locate_executable(fake_path, install_locations, expected):\n    # fmt: off\n    with patch(\"getml.engine._launch.INSTALL_LOCATIONS\", install_locations),\\\n         patch(\"getml.engine._launch.__version__\", \"1.2.3\"),\\\n         patch(\"os.access\", access):\n    # fmt: on\n        assert locate_executable() == expected\n"
  },
  {
    "path": "src/python-api/tests/unit/test_progress_bar.py",
    "content": "import pytest\n\nfrom getml.utilities.progress import Progress\n\n\ndef test_exception_in_progress_bar(capsys):\n    with pytest.raises(ValueError):\n        with Progress() as progress:\n            progress.add_task(\"Test\")\n            raise ValueError(\"Test\")\n\n    captured = capsys.readouterr()\n    assert \"Failed\" in captured.out\n\n\n@pytest.mark.parametrize(\n    \"finish_all_tasks_on_stop, expected\",\n    [(True, \"100%\"), (False, \"0%\")],\n)\ndef test_finished_on_stop(finish_all_tasks_on_stop: bool, expected: str, capsys):\n    with Progress(finish_all_tasks_on_stop=finish_all_tasks_on_stop) as progress:\n        progress.add_task(\"Test\")\n        assert all(not task.finished for task in progress.tasks)\n\n    captured = capsys.readouterr()\n    assert all(task.finished == finish_all_tasks_on_stop for task in progress.tasks)\n    assert expected in captured.out\n\n\n@pytest.mark.parametrize(\n    \"finish_all_tasks_on_stop, expected_final_description\",\n    [(True, \"Test\"), (False, \"Toast\")],\n)\ndef test_progress_reset_description(\n    capsys, finish_all_tasks_on_stop: bool, expected_final_description: str\n):\n    with Progress(finish_all_tasks_on_stop=finish_all_tasks_on_stop) as progress:\n        task_id = progress.add_task(\"Test\")\n        progress.update(task_id, description=\"Toast\")\n        assert progress.tasks[task_id].description.lstrip().startswith(\"Toast\")\n        assert progress.descriptions[task_id].lstrip().startswith(\"Test\")\n    assert (\n        progress.tasks[task_id]\n        .description.lstrip()\n        .startswith(expected_final_description)\n    )\n    captured = capsys.readouterr()\n    assert expected_final_description in captured.out\n"
  }
]