[ { "path": ".coveragerc", "content": "# .coveragerc to control coverage.py\n[run]\nrelative_files = True\nbranch = True\nsource = ./tsfresh\nomit = tsfresh/utilities/profiling.py\n tsfresh/__init__.py\n tsfresh/feature_extraction/__init__.py\n tsfresh/example/__init__.py\n tsfresh/utilities/__init__.py\n tsfresh/transformers/__init__.py\n tsfresh/convenience/__init__.py\n tsfresh/examples/har_dataset.py\n tsfresh/examples/robot_execution_failures.py\n tsfresh/examples/driftbif_simulation.py\n tsfresh/examples/test_tsfresh_baseline_dataset.py\n tsfresh/scripts/test_timing.py\n tsfresh/scripts/measure_execution_time.py\n /opt/hostedtoolcache/Python/*\n\n[report]\n# Regexes for lines to exclude from consideration\nexclude_lines =\n # Have to re-enable the standard pragma\n pragma: no cover\n\n # Don't complain about missing debug-only code:\n def __repr__\n if self\\.debug\n\n # Don't complain if tests don't hit defensive assertion code:\n raise AssertionError\n raise NotImplementedError\n\n # Don't complain if non-runnable code isn't run:\n if 0:\n if __name__ == .__main__.:\n" }, { "path": ".github/ISSUE_TEMPLATE/bug-report.md", "content": "---\nname: Bug Report\nabout: Report a bug\nlabels:\n - bug\n---\n\n\n\n**The problem**:\n\n\n\n**Anything else we need to know?**:\n\n**Environment**:\n\n- Python version:\n- Operating System:\n- tsfresh version:\n- Install method (conda, pip, source):\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "blank_issues_enabled: false\ncontact_links:\n - name: \"Q&A and general Questions\"\n url: https://github.com/blue-yonder/tsfresh/discussions/categories/q-a\n about: \"If you have questions on how to use `tsfresh` for your use case, please ask on GitHub Discussions.\"\n - name: \"Discussion and feature requests\"\n url: https://github.com/blue-yonder/tsfresh/discussions\n about: \"For anything else, please use the general discussion forums.\"\n" }, { "path": ".github/workflows/benchmark_default_branch.yml", "content": "# Store benchmark results as an artifact\nname: Benchmark the default branch\non:\n # Only run on the default branch\n push:\n branches:\n - main\n\njobs:\n benchmark:\n name: Run pytest-benchmark\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - uses: actions/setup-python@v5\n with:\n python-version: \"3.10\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0 pip install \".[testing]\"\n - name: Run benchmark\n run: |\n cd tests\n pytest benchmark.py --benchmark-min-rounds=4 --benchmark-only -n 0 --no-cov --benchmark-json output.json\n - name: Upload the file\n uses: actions/upload-artifact@v4\n with:\n name: benchmark_results\n path: tests/output.json\n" }, { "path": ".github/workflows/deploy.yml", "content": "name: Upload Python Package\n\non:\n release:\n types: [created]\n\njobs:\n deploy:\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v4\n - name: Set up Python\n uses: actions/setup-python@v5\n with:\n python-version: \"3.8\"\n cache: 'pip' # caching pip dependencies\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install setuptools wheel twine\n - name: Build and publish\n env:\n TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}\n TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}\n run: |\n python setup.py sdist bdist_wheel\n twine upload dist/*\n" }, { "path": ".github/workflows/stylecheck.yml", "content": "---\nname: Python Style Check\non: [pull_request]\n\njobs:\n pre-commit:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - uses: actions/setup-python@v5\n - uses: pre-commit/action@v3.0.1\n" }, { "path": ".github/workflows/test.yml", "content": "---\nname: Test\non:\n pull_request:\n push:\n branches:\n - main\n\njobs:\n test:\n name: Test\n runs-on: ubuntu-latest\n strategy:\n fail-fast: false\n matrix:\n python-version: [3.9, \"3.10\", 3.11, 3.12]\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n cache: 'pip' # caching pip dependencies\n\n - name: Install dependencies\n run: |\n # Do all the installations\n python -m pip install --upgrade pip wheel setuptools\n\n SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0 pip install \".[testing]\"\n\n # Print out the pip versions for debugging\n pip list\n\n - name: Test with pytest\n run: |\n pytest --junitxml=junit/test-results-${{ matrix.python-version }}-${{ matrix.installation }}.xml --cov-report=xml tests\n\n - name: Upload pytest test results\n uses: actions/upload-artifact@v4\n with:\n name: pytest-results-${{ matrix.python-version }}-${{ matrix.installation }}\n path: junit/test-results-${{ matrix.python-version }}-${{ matrix.installation }}.xml\n # Use always() to always run this step to publish test results when there are test failures\n if: ${{ always() }}\n\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v3\n" }, { "path": ".github/workflows/test_all.yml", "content": "---\nname: Test Default Branch\non:\n push:\n branches:\n - main\n\njobs:\n test:\n name: Test\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python 3.9\n uses: actions/setup-python@v5\n with:\n python-version: 3.9\n cache: 'pip' # caching pip dependencies\n\n - name: Install dependencies\n run: |\n # Do all the installations\n python -m pip install --upgrade pip wheel setuptools\n SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0 pip install \".[testing]\"\n\n # Print out the pip versions for debugging\n pip list\n\n - name: Test with pytest\n run: |\n # Include notebook tests\n rm -rf .coverage.*\n export TEST_NOTEBOOKS=y\n pytest --junitxml=junit/test-results.xml --cov-report=xml tests\n\n - name: Upload pytest test results\n uses: actions/upload-artifact@v4\n with:\n name: pytest-results\n path: junit/test-results.xml\n # Use always() to always run this step to publish test results when there are test failures\n if: ${{ always() }}\n" }, { "path": ".gitignore", "content": "#others\n*.lock\n\n# Temporary and binary files\n*~\n*.py[cod]\n*.so\n*.cfg\n!setup.cfg\n*.orig\n*.log\n*.pot\n__pycache__/*\n.cache/*\n.*.swp\n\n# Project files\n.ropeproject\n.project\n.pydevproject\n.settings\n.idea\n.vscode\n\n# Package files\n*.egg\n*.eggs/\n.installed.cfg\n*.egg-info\n\n# Unittest and coverage\nhtmlcov/*\n.coverage\n.coverage.*\n.tox\njunit.xml\ncoverage.xml\n\n# Build and docs folder/files\nbuild/*\ndist/*\nsdist/*\ndocs/_build/*\ncover/*\nMANIFEST\ndocs/text/_generated/\n\n# Virtual environment\nvenv*\nactivate\n\n# IPython notebooks\n.ipynb_checkpoints\n\n# examples data directory\ntsfresh/examples/data/\ntsfresh/notebooks/data/\n# ds_store\n*.DS_Store\n\n# dask\ndask-worker-space\ndask-worker-space/\n\n# python version files\n.python_version\n\n# tox log files\n.tox\n" }, { "path": ".pre-commit-config.yaml", "content": "---\nrepos:\n - repo: https://github.com/psf/black\n rev: 22.12.0\n hooks:\n - id: black\n language_version: python3\n exclude: \"^docs/.*$\"\n - repo: https://github.com/pycqa/isort\n rev: 5.12.0\n hooks:\n - id: isort\n args:\n - \"--profile\"\n - \"black\"\n exclude: \"^docs/.*$\"\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v4.4.0\n hooks:\n - id: trailing-whitespace\n - id: end-of-file-fixer\n - id: check-yaml\n - id: check-added-large-files\n" }, { "path": ".readthedocs.yml", "content": "# .readthedocs.yaml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Set the version of Python and other tools you might need\nbuild:\n os: ubuntu-22.04\n tools:\n python: \"3.10\"\n\n# Build documentation in the docs/ directory with Sphinx\nsphinx:\n configuration: docs/conf.py\n\n# Optionally declare the Python requirements required to build your docs\npython:\n install:\n - method: pip\n path: .\n extra_requirements:\n - docs\n" }, { "path": "AUTHORS.rst", "content": "\n\nAuthors\n==========\n\n\nCore Development Team\n---------------------\n\n- Maximilian Christ (`maximilianchrist.com `_, `max.christ@me.com `_)\n- Nils Braun (`nilslennartbraun@gmail.com `_)\n- Julius Neuffer (`julius.neuffer@blue-yonder.com `_)\n- Andreas W. Kempa-Liehr (`a.kempa-liehr@auckland.ac.nz `_)\n\nContributions\n-------------\n\n- Alan Larson\n- Alex Broekhof\n- Alex F\n- Alex Kennedy\n- Alex Loosley\n- Andrew Van Benschoten\n- Ben Auffarth\n- Brian Sang\n- Brunno Vanelli\n- Chris Chow\n- Chris Holdgraf\n- Christoph Hösler\n- cnzero\n- CYHSM\n- Dan Mazur\n- Daniel Azevedo\n- Daniel Gomez\n- Daniel Naftalovich\n- Delyan\n- Denis Barbier\n- Derrick\n- Dhruv Srikanth\n- Dillon Wong\n- Dimitris Spathis\n- Dominic White\n- earthgecko\n- Emanuele Fumagalli\n- Erlend Aune\n- Evans Doe Ocansey\n- Ezekiel Kruglick\n- Filip Malkowski\n- filipj8\n- Florian Aspart\n- flyingdutchman23\n- Fujimoto Seiji\n- George Wambold\n- Greg Bodeker\n- Gregor Koehler\n- Gustavo Bertoli\n- Haris Sahovic\n- HaveF\n- He Kaisheng\n- Henry Swaffield\n- Igor Pechersky\n- J. Kleint\n- James Myatt\n- Jean-Francois Zinque\n- Jeroen Van Der Donckt\n- Justin Hong\n- Justin White\n- kartikey-vyas\n- koho\n- Lifepillar\n- lupupu\n- Mal Curtis\n- Mario Kahlhofer\n- Markus Frey\n- Marx\n- Matúš Tomlein\n- Maximilian Lohmann\n- meer1992\n- mendel5\n- Michele Tonutti\n- Moritz Gelb\n- Nigel Bosch\n- Niklas Haas\n- Oli\n- Omar Gutiérre\n- patrjon\n- Paul Fornia\n- Paul Voigt\n- Quan Gu\n- rhysimu\n- Ricardo Emanuel Vaz Vargas\n- Roman Yurchak\n- Roy Wedge\n- Sarius2009\n- Scott Simmons\n- Sean M. Law\n- Sergey Shepelev\n- Soledad Galli\n- Stephan Müller\n- supergitacc\n- Tan Tao-Lin\n- Teo Bucci\n- Thibault Blanc\n- Thibault de Boissiere\n- Timo Klerx\n- Vincent Tang\n- Will Koehrsen\n- Wojciech Indyk\n- yairst\n- YamaByte\n- yitao-yu\n" }, { "path": "CHANGES.rst", "content": "=========\nChangelog\n=========\n\ntsfresh uses `Semantic Versioning `_\n\nVersion 0.21.1\n==============\n- Bugfixes/Typos/Documentation:\n - Remove pkg_resources in preparation for its deprecation. (#1116)\n\nVersion 0.21.0\n==============\n- Breaking Change\n - Drop support for python 3.7 and 3.8 (#1100)\n- Bugfixes/Typos/Documentation:\n - Fix incompatibility with scipy versions 1.15 and higher by relying on the\n ``pywavelets`` package for cwt (#1097)\n - Improve code quality of feature extractors (#1103)\n - Improve developer experience with tox, bisect and docker (#1093, #994, #1102)\n\nVersion 0.20.3\n==============\n- Bugfixes/Typos/Documentation:\n * Fixes issue #1073: Updated setup.cfg to require newer scipy version (#1081)\n * extract_relevant_features now passes chunksize to extract_features (#1083)\n * Fix code and tests for numpy >= 2.0 (#1085)\n * Update tsfresh.feature_extraction.feature_calculators.skewness to make it consistent with the design principle of not ignoring nan (#1066)\n * Fix spelling/grammar in pipeline notebook (#1082)\n * Added recommendation to revert thread limitations (#1069)\n * Fix the 01 example notebook to not leak information between train and test set\n * Feature calculator return type documentation (#1070)\n\nVersion 0.20.2\n==============\n- Added Features\n - Make Dask and Distributed optional dependencies (#1061)\n - View and Set N Jobs (#1029)\n\n- Bugfixes/Typos/Documentation:\n - Extra notes on parallelization efficiencies (#1046)\n - Update doc extraction settings for clarity and formatting (#1033)\n - Typos (#1031, #1034, #1049, #1048)\n\nVersion 0.20.1\n==============\n\n- Added Features\n - Make tsfresh compatible with numpy 1.24 (#1018) and pandas 2.0 (#1028)\n\n- Bugfixes/Typos/Documentation:\n - Use pandas Index.equals in check_if_pandas_series (#963)\n - Updates to package layout, CI/CD and developer setup\n\n\nVersion 0.20.0\n==============\n\n- Breaking Change\n - The matrixprofile package becomes an optional dependency\n\n- Bugfixes/Typos/Documentation:\n - Fix feature extraction of Friedrich coefficients for pandas>1.3.5\n - Fix file paths after example notebooks were moved\n\nVersion 0.19.0\n==============\n\n- Breaking Change\n - Drop Python 3.6 support due to dependency on statsmodels 0.13\n\n- Added Features\n - Improve documentation (#831, #834, #851, #853, #870)\n - Add absolute_maximum and mean_n_absolute_max features (#833)\n - Make settings pickable (#845, #847, #910)\n - Disable multiprocessing for `n_jobs=1` (#852)\n - Add black, isort, and pre-commit (#876)\n\n- Bugfixes/Typos/Documentation:\n - Fix conversion of time-series into sequence for lempel_ziv_complexity (#806)\n - Fix range count config (#827)\n - Reword documentation (#893)\n - Fix statsmodels deprecation issues (#898, #912)\n - Fix typo in requirements (#903)\n - Bump statsmodels to v0.13 (#\n - Updated references\n\n\nVersion 0.18.0\n==============\n\n- Added Features\n - Allow arbitrary rolling sizes (#766)\n - Allow for multiclass significance tests (#762)\n - Add multiclass option to RelevantFeatureAugmenter (#782)\n - Addition of matrix_profile feature (#793)\n - Added new query similarity counter feature (#798)\n - Add root mean square feature (#813)\n- Bugfixes/Typos/Documentation:\n - Do not send coverage of notebook tests to codecov (#759)\n - Fix typos in notebook (#757, #780)\n - Fix output format of `make_forecasting_frame` (#758)\n - Fix badges and remove benchmark test\n - Fix BY notebook plot (#760)\n - Ts forecast example improvement (#763)\n - Also surpress warnings in dask (#769)\n - Update relevant_feature_augmenter.py (#779)\n - Fix column names in quick_start.rst (#778)\n - Improve relevance table function documentation (#781)\n - Fixed #789 Typo in \"how to add custom feature\" (#790)\n - Convert to the correct type on warnings (#799)\n - Fix minor typos in the docs (#802)\n - Add unwanted filetypes to gitignore (#819)\n - Fix build and test failures (#815)\n - Fix imputing docu (#800)\n - Bump the scikit-learn version (#822)\n\nVersion 0.17.0\n==============\n\nWe changed the default branch from \"master\" to \"main\".\n\n- Breaking Change\n - Changed constructed id in roll_time_series from string to tuple (#700)\n - Same for add_sub_time_series_index (#720)\n- Added Features\n - Implemented the Lempel-Ziv-Complexity and the Fourier Entropy (#688)\n - Prevent #524 by adding an assert for common identifiers (#690)\n - Added permutation entropy (#691)\n - Added a logo :-) (#694)\n - Implemented the benford distribution feature (#689)\n - Reworked the notebooks (#701, #704)\n - Speed up the result pivoting (#705)\n - Add a test for the dask bindings (#719)\n - Refactor input data iteration to need less memory (#707)\n - Added benchmark tests (#710)\n - Make dask a possible input format (#736)\n- Bugfixes:\n - Fixed a bug in the selection, that caused all regression tasks with un-ordered index to be wrong (#715)\n - Fixed readthedocs (#695, #696)\n - Fix spark and dask after #705 and for non-id named id columns (#712)\n - Fix in the forecasting notebook (#729)\n - Let tsfresh choose the value column if possible (#722)\n - Move from coveralls github action to codecov (#734)\n - Improve speed of data processing (#735)\n - Fix for newer, more strict pandas versions (#737)\n - Fix documentation for feature calculators (#743)\n\nVersion 0.16.0\n==============\n\n- Breaking Change\n - Fix the sorting of the parameters in the feature names (#656)\n The feature names consist of a sorted list of all parameters now.\n That used to be true for all non-combiner features, and is now also true for combiner features.\n If you relied on the actual feature name, this is a breaking change.\n - Change the id after the rolling (#668)\n Now, the old id of your data is still kept. Additionally, we improved the way\n dataframes without a time column are rolled and how the new sub-time series\n are named.\n Also, the documentation was improved a lot.\n- Added Features\n - Added variation coefficient (#654)\n - Added the datetimeindex explanation from the notebook to the docs (#661)\n - Optimize RelevantFeatureAugmenter to avoid re-extraction (#669)\n - Added a function `add_sub_time_series_index` (#666)\n - Added Dockerfile\n - Speed optimizations and speed testing script (#681)\n- Bugfixes\n - Increase the extracted `ar` coefficients to the full parameter range. (#662)\n - Documentation fixes (#663, #664, #665)\n - Rewrote the `sample_entropy` feature calculator (#681)\n It is now faster and (hopefully) more correct.\n But your results will change!\n\n\nVersion 0.15.1\n==============\n\n- Changelog and documentation fixes\n\nVersion 0.15.0\n==============\n\n- Added Features\n - Add count_above and count_below feature (#632)\n - Add convenience bindings for dask dataframes and pyspark dataframes (#651)\n- Bugfixes\n - Fix documentation build and feature table in sphinx (#637, #631, #627)\n - Add scripts to API documentation\n - Skip dask test for older python versions (#649)\n - Add missing distributor keyword (#648)\n - Fix tuple input for cwt (#645)\n\nVersion 0.14.1\n==============\n\n- Fix travis deployment\n\nVersion 0.14.0\n==============\n\n- Breaking Change\n - Replace Benjamini-Hochberg implementation with statsmodels implementation (#570)\n- Refactoring and Documentation\n - travis.yml (#605)\n - gitignore (#608)\n - Fix docstring of c3 (#590)\n - Feature/pep8 (#607)\n- Added Features\n - Improve test coverage (#609)\n - Add \"autolag\" parameter to augmented_dickey_fuller() (#612)\n- Bugfixes\n - Feature/pep8 (#607)\n - Fix filtering on warnings with multiprocessing on Windows (#610)\n - Remove outdated logging config (#621)\n - Replace Benjamini-Hochberg implementation with statsmodels implementation (#570)\n - Fix the kernel and the naming of a notebook (#626)\n\n\nVersion 0.13.0\n==============\n\n- Drop python 2.7 support (#568)\n- Fixed bugs\n - Fix cache in friedrich_coefficients and agg_linear_trend (#593)\n - Added a check for wrong column names and a test for this check (#586)\n - Make sure to not install the tests folder (#599)\n - Make sure there is at least a single column which we can use for data (#589)\n - Avoid division by zero in energy_ratio_by_chunks (#588)\n - Ensure that get_moment() uses float computations (#584)\n - Preserve index when column_value and column_kind not provided (#576)\n - Add @set_property(\"input\", \"pd.Series\") when needed (#582)\n - Fix off-by-one error in longest strike features (fixes #577) (#578)\n - Add `set_property` import (#572)\n - Fix typo (#571)\n - Fix indexing of melted normalized input (#563)\n - Fix travis (#569)\n- Remove warnings (#583)\n- Update to newest python version (#594)\n- Optimizations\n - Early return from change_quantiles if ql >= qh (#591)\n - Optimize mean_second_derivative_central (#587)\n - Improve performance with Numpy's sum function (#567)\n - Optimize mean_change (fixes issue #542) and correct documentation (#574)\n\n\nVersion 0.12.0\n==============\n\n- fixed bugs\n - wrong calculation of friedrich coefficients\n - feature selection selected too many features\n - an ignored max_timeshift parameter in roll_time_series\n- add deprecation warning for python 2\n- added support for index based features\n- new feature calculator\n - linear_trend_timewise\n- enable the RelevantFeatureAugmenter to be used in cross validated pipelines\n- increased scipy dependency to 1.2.0\n\n\nVersion 0.11.2\n==============\n- change chunking in energy_ratio_by_chunks to use all data points\n- fix warning for spkt_welch_density\n- adapt default settings for \"value_count\" and \"range_count\"\n- added\n - maxlag parameter to agg_autocorrelation function\n- now, the kind column of the input DataFrame is cast as str, old derived FC_Settings can become invalid\n- only set default_fc_parameters to ComprehensiveFCParameters() if also kind_to_fc_parameters is set None in `extract_features`\n- removed pyscaffold\n- use asymptotic algorithm to derive kendal tau\n\n\nVersion 0.11.1\n==============\n- general performance improvements\n- removed hard pinning of dependencies\n- fixed bugs\n - the stock price forecasting notebook\n - the multi classification notebook\n\nVersion 0.11.0\n==============\n- new feature calculators:\n - fft_aggregated\n - cid_ce\n- renamed mean_second_derivate_central to mean_second_derivative_central\n- add warning if no relevant features were found in feature selection\n- add columns_to_ignore parameter to from_columns method\n- add distribution module, contains support for distributed feature extraction on Dask\n\nVersion 0.10.1\n==============\n- split test suite into unit and integration tests\n- fixed the following bugs\n - use name of value column as time series kind\n - prevent the spawning of subprocesses which lead to high memory consumption\n - fix deployment from travis to pypi\n\nVersion 0.10.0\n==============\n- new feature calculators:\n - partial autocorrelation\n- added list of calculated features to documentation\n- added two ipython notebooks to\n - illustrate PCA on features\n - illustrate the Benjamini Yekutieli procedure\n- fixed the following bugs\n - improperly quotation of dickey fuller settings\n\nVersion 0.9.0\n=============\n- new feature calculators:\n - ratio_beyond_r_sigma\n - energy_ratio_by_chunks\n - number_crossing_m\n - c3\n - angle & abs for fft coefficients\n - agg_autocorrelation\n - p-Value and usedLag for augmented_dickey_fuller\n - change_quantiles\n- changed the calculation of the following features:\n - fft_coefficients\n - autocorrelation\n - time_reversal_asymmetry_statistic\n- removed the following feature calculators:\n - large_number_of_peak\n - mean_autocorrelation\n - mean_abs_change_quantiles\n- add support for multi classification in the feature selection\n- improved description of the rolling mechanism\n- added function make_forecasting_frame method for forecasting tasks\n- internally ditched the pandas representation of the time series, yielding drastic speed improvements\n- replaced feature calculator types from aggregate/aggregate with parameter/apply to simple/combiner\n- add test for the ipython notebooks\n- added notebook to inspect dft features\n- make sure that RelevantFeatureAugmentor always imputes\n- fixed the following bugs\n - impute was replacing whole columns by mean\n - fft coefficient were only calculated on truncated part\n - allow to suppress warnings from impute function\n - added missing lag in time_reversal_asymmetry_statistic\n\nVersion 0.8.1\n=============\n- new features:\n - linear trend\n - agg trend\n- new sklearn compatible transformers\n - PerColumnImputer\n- fixed bugs\n - make mannwhitneyu method compatible with scipy > v0.18.0\n- added caching to travis\n- internally, added serial calculation of features\n\nVersion 0.8.0\n=============\n- Breaking API changes:\n - removing of feature extraction settings object, replaced by keyword arguments and a plain dictionary (fc_parameters)\n - removing of feature selection settings object, replaced by keyword arguments\n- added notebook with examples of new API\n- added chapter in docs about the new API\n- adjusted old notebooks and documentation to new API\n\nVersion 0.7.1\n=============\n\n- added a maximum shift parameter to the rolling utility\n- added a FAQ entry about how to use tsfresh on windows\n- drastically decreased the runtime of the following features\n - cwt_coefficient\n - index_mass_quantile\n - number_peaks\n - large_standard_deviation\n - symmetry_looking\n- removed baseline unit tests\n- bugfixes:\n - per sample parallel imputing was done on chunks which gave non deterministic results\n - imputing on dtypes other that float32 did not work properly\n- several improvements to documentation\n\nVersion 0.7.0\n=============\n\n- new rolling utility to use tsfresh for time series forecasting tasks\n- bugfixes:\n - index_mass_quantile was using global index of time series container\n - an index with same name as id_column was breaking parallelization\n - friedrich_coefficients and max_langevin_fixed_point were occasionally stalling\n\nVersion 0.6.0\n=============\n\n- progress bar for feature selection\n- new feature: estimation of largest fixed point of deterministic dynamics\n- new notebook: demonstration how to use tsfresh in a pipeline with train and test datasets\n- remove no logging handler warning\n- fixed bug in the RelevantFeatureAugmenter regarding the evaluate_only_added_features parameters\n\nVersion 0.5.0\n=============\n\n- new example: driftbif simulation\n- further improvements of the parallelization\n- language improvements in the documentation\n- performance improvements for some features\n- performance improvements for the impute function\n- new feature and feature renaming: sum_of_recurring_values, sum_of_recurring_data_points\n\nVersion 0.4.0\n=============\n\n- fixed several bugs: checking of UCI dataset, out of index error for mean_abs_change_quantiles\n- added a progress bar denoting the progress of the extraction process\n- added parallelization per sample\n- added unit tests for comparing results of feature extraction to older snapshots\n- added \"high_comp_cost\" attribute\n- added ReasonableFeatureExtraction settings only calculating features without \"high_comp_cost\" attribute\n\nVersion 0.3.1\n=============\n\n- fixed several bugs: closing multiprocessing pools / index out of range cwt calculator / division by 0 in index_mass_quantile\n- now all warnings are disabled by default\n- for a singular type time series data, the name of value column is used as feature prefix\n\nVersion 0.3.0\n=============\n\n- fixed bug with parsing of \"NUMBER_OF_CPUS\" environment variable\n- now features are calculated in parallel for each type\n\nVersion 0.2.0\n=============\n\n- now p-values are calculated in parallel\n- fixed bugs for constant features\n- allow time series columns to be named 0\n- moved uci repository datasets to github mirror\n- added feature calculator sample_entropy\n- added MinimalFeatureExtraction settings\n- fixed bug in calculation of fourier coefficients\n\nVersion 0.1.2\n=============\n\n- added support for python 3.5.2\n- fixed bug with the naming of the features that made the naming of features non-deterministic\n\nVersion 0.1.1\n=============\n\n- mainly fixes for the read-the-docs documentation, the pypi readme and so on\n\nVersion 0.1.0\n=============\n\n- Initial version :)\n" }, { "path": "Dockerfile", "content": "# Define builder and base image\nFROM python:3.8-slim as base\nFROM python:3.8 as builder\n\nLABEL maintainer=\"nilslennartbraun@gmail.com\"\n\n# Install tsfresh from source into the builder image\nADD . /source\nWORKDIR /source\nRUN pip3 install --prefix=/install .\n\n# Copy the installed sources to the base image\nFROM base\nCOPY --from=builder /install /usr/local\n" }, { "path": "Dockerfile.testing", "content": "# Bakes the python versions which tsfresh targets into a testing env\nFROM ubuntu:22.04\n\nSHELL [\"/bin/bash\", \"-c\"]\n\n# These are required to build python from source\nRUN apt-get update && apt-get install -y \\\n python3-pip \\\n curl \\\n clang \\\n git \\\n build-essential \\\n libssl-dev \\\n libreadline-dev \\\n zlib1g-dev \\\n libbz2-dev \\\n libsqlite3-dev \\\n llvm \\\n libncurses5-dev \\\n libgdbm-dev \\\n libnss3-dev \\\n libffi-dev \\\n liblzma-dev \\\n libgmp-dev \\\n libmpfr-dev \\\n && apt-get clean\n\n\nRUN curl https://pyenv.run | bash\n\n# For interactive use (if any), this is an edge case.\nRUN echo 'export PYENV_ROOT=\"$HOME/.pyenv\"' >> ~/.bashrc && \\\n echo '[[ -d $PYENV_ROOT/bin ]] && export PATH=\"$PYENV_ROOT/bin:$PATH\"' >> ~/.bashrc && \\\n echo 'eval \"$(pyenv init -)\"' >> ~/.bashrc\n\nENV PYENV_ROOT=\"/root/.pyenv\"\nENV PATH=\"$PYENV_ROOT/bin:$PATH\"\nENV PATH=\"$PYENV_ROOT/shims:$PATH\"\n\nARG PYTHON_VERSIONS\nRUN for version in $PYTHON_VERSIONS; do \\\n echo Installing $version; \\\n # Band aid for https://github.com/pyenv/pyenv/issues/1738\n # since this also appears to apply to 3.7.X\n if [[ $version =~ ^3\\.7\\..*$ ]]; then \\\n echo Using clang to compile $version; \\\n CC=clang pyenv install $version || exit 1; \\\n else \\\n pyenv install $version || exit 1; \\\n fi; \\\n done\n\nRUN pyenv global $PYTHON_VERSIONS\n\nRUN pip install tox\n\nWORKDIR /tsfresh\n\n# Requires adding safe.directory so that tsfresh can build when the\n# repo is mounted.\n# Note cannot do this at build time as no git directory exists\nCMD [\"/bin/bash\", \"-c\", \"git config --global --add safe.directory /tsfresh && tox -r -p auto\"]\n" }, { "path": "LICENSE.txt", "content": "MIT LICENCE\n\nCopyright (c) 2016 Maximilian Christ, Blue Yonder GmbH\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\ndocumentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the\nrights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit\npersons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the\nSoftware.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE\nWARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\nCOPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR\nOTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n" }, { "path": "Makefile", "content": "WORKDIR := /tsfresh\nTEST_IMAGE := tsfresh-test-image\nTEST_DOCKERFILE := Dockerfile.testing\nTEST_CONTAINER := tsfresh-test-container\nPYTHON_VERSIONS := \"3.9 3.10 3.11 3.12\"\n\n# Tests `PYTHON_VERSIONS`, provided they are also\n# specified in setup.cfg `envlist`\ntest-all-testenv: build-docker-testenv run-docker-tests clean\n\nbuild-docker-testenv:\n\tdocker build \\\n\t\t\t-f $(TEST_DOCKERFILE) \\\n\t\t\t-t $(TEST_IMAGE) \\\n\t\t\t--build-arg PYTHON_VERSIONS=$(PYTHON_VERSIONS) \\\n\t\t\t.\n\nrun-docker-tests:\n\tdocker run --rm \\\n\t\t\t--name $(TEST_CONTAINER) \\\n\t\t\t-v .:$(WORKDIR) \\\n\t\t\t-v build_artifacts:$(WORKDIR)/build \\\n\t\t\t-v tox_artifacts:$(WORKDIR)/.tox \\\n\t\t\t-v egg_artifacts:$(WORKDIR)/tsfresh.egg-info \\\n\t\t\t$(TEST_IMAGE)\n\nclean:\n\trm -rf .tox build/ dist/ *.egg-info\n\nbisect:\n\t@if [ -z \"$(GOOD_COMMIT)\" ]; then \\\n\t\techo \"Error: GOOD_COMMIT is required. Usage: make bisect GOOD_COMMIT=.\"; \\\n\t\techo \"Assumes that the current checked-out commit is a known bad commit, and bisects from there.\"; \\\n\t\texit 1; \\\n\tfi\n\tgit bisect start\n\tgit bisect bad\n\tgit bisect good $(GOOD_COMMIT)\n\tgit bisect run pytest\n\tgit bisect reset\n\n.PHONY: build-docker-testenv clean run-docker-tests test-all-testenv bisect\n" }, { "path": "README.md", "content": "
\n \n
\n\n-----------------\n\n# tsfresh\n\n[![Documentation Status](https://readthedocs.org/projects/tsfresh/badge/?version=latest)](https://tsfresh.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://github.com/blue-yonder/tsfresh/workflows/Test%20Default%20Branch/badge.svg)](https://github.com/blue-yonder/tsfresh/actions)\n[![codecov](https://codecov.io/gh/blue-yonder/tsfresh/branch/main/graph/badge.svg)](https://codecov.io/gh/blue-yonder/tsfresh)\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/blue-yonder/tsfresh/blob/main/LICENSE.txt)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/blue-yonder/tsfresh/main?filepath=notebooks)\n[![Downloads](https://pepy.tech/badge/tsfresh)](https://pepy.tech/project/tsfresh)\n\nThis repository contains the *TSFRESH* python package. The abbreviation stands for\n\n*\"Time Series Feature extraction based on scalable hypothesis tests\"*.\n\nThe package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term *time-series* is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.\n\n## Spend less time on feature engineering\n\nData Scientists often spend most of their time either cleaning data or building features.\nWhile we cannot change the first thing, the second can be automated.\n*TSFRESH* frees your time spent on building features by extracting them automatically.\nHence, you have more time to study the newest deep learning paper, read hacker news or build better models.\n\n\n## Automatic extraction of 100s of features\n\n*TSFRESH* automatically extracts 100s of features from time series.\nThose features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic.\n\n![The features extracted from a exemplary time series](docs/images/introduction_ts_exa_features.png)\n\nThe set of features can then be used to construct statistical or machine learning models on the time series to be used for example in regression or\nclassification tasks.\n\n## Forget irrelevant features\n\nTime series often contain noise, redundancies or irrelevant information.\nAs a result most of the extracted features will not be useful for the machine learning task at hand.\n\nTo avoid extracting irrelevant features, the *TSFRESH* package has a built-in filtering procedure.\nThis filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.\n\nIt is based on the well developed theory of hypothesis testing and uses a multiple test procedure.\nAs a result the filtering process mathematically controls the percentage of irrelevant extracted features.\n\nThe *TSFRESH* package is described in the following open access paper:\n\n* Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018).\n _Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python package)._\n Neurocomputing 307, p. 72-77, [doi: 10.1016/j.neucom.2018.03.067](https://doi.org/10.1016/j.neucom.2018.03.067).\n\nThe FRESH algorithm is described in the following whitepaper:\n\n* Christ, M., Kempa-Liehr, A.W., and Feindt, M. (2017).\n _Distributed and parallel time series feature extraction for industrial big data applications._\n ArXiv e-print 1610.07717, [https://arxiv.org/abs/1610.07717](https://arxiv.org/abs/1610.07717).\n\nSystematic time-series feature extraction even works for unsupervised problems:\n\n* Teh, H.Y., Wang, K.I-K., Kempa-Liehr, A.W. (2021).\n _Expect the Unexpected: Unsupervised feature selection for automated sensor anomaly detection._\n IEEE Sensors Journal 15.16, p. 18033-18046, [doi: 10.1109/JSEN.2021.3084970](https://doi.org/10.1109/JSEN.2021.3084970).\n\n* Teh, H.Y., Wang, K.I-K., Kempa-Liehr, A.W. (2025).\n _Feature-based normality models for anomaly detection._\n Sensors 25.4757, p. 1-25, [doi: 10.3390/s25154757](https://doi.org/10.3390/s25154757).\n\nDue to the fact that tsfresh basically provides time-series feature extraction for free, you can now concentrate on engineering new time-series,\nlike e.g. differences of signals from synchronous measurements, which provide even better time-series features:\n\n* Kempa-Liehr, A.W., Oram, J., Wong, A., Finch, M., Besier, T. (2020).\n _Feature engineering workflow for activity recognition from synchronized inertial measurement units._\n In: Pattern Recognition. ACPR 2019. Ed. by M. Cree et al. Vol. 1180. Communications in Computer and Information Science (CCIS).\n Singapore: Springer, p. 223–231. [doi: 10.1007/978-981-15-3651-9_20](https://doi.org/10.1007/978-981-15-3651-9_20).\n\n* Simmons, S., Jarvis, L., Dempsey, D., Kempa-Liehr, A.W. (2021).\n _Data Mining on Extremely Long Time-Series._\n In: 2021 International Conference on Data Mining Workshops (ICDMW). Ed. by B. Xue et al.\n Los Alamitos: IEEE, p. 1057-1066. [doi: 10.1109/ICDMW53433.2021.00137](https://doi.org/10.1109/ICDMW53433.2021.00137).\n\nSystematic time-series features engineering allows to work with time-series samples of different lengths, because every time-series is projected\ninto a well-defined feature space. This approach allows the design of robust machine learning algorithms in applications with missing data.\n\n* Kennedy, A., Gemma, N., Rattenbury, N., Kempa-Liehr, A.W. (2021).\n _Modelling the projected separation of microlensing events using systematic time-series feature engineering._\n Astronomy and Computing 35.100460, p. 1–14, [doi: 10.1016/j.ascom.2021.100460](https://doi.org/10.1016/j.ascom.2021.100460)\n\nIs your time-series classification problem imbalanced? There is a good chance that undersampling of time-series feature matrices\nmight solve your problem:\n\n* Dempsey, D.E., Cronin, S.J., Mei, S., Kempa-Liehr, A.W. (2020).\n _Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand_.\n Nature Communications 11.3562, p. 1-8, [doi: 10.1038/s41467-020-17375-2](https://doi.org/10.1038/s41467-020-17375-2).\n\nYou are not working with time-series, but with 2D and 3D images? Spatial variation sequences (SVS) are an excellent application for tsfresh:\n\n* Jeune, H., Pechan, N., Reitsma, S., Kempa-Liehr, A.W. (2021).\n _Spatial Variation Sequences for Remote Sensing Applications with Small Sample Sizes._\n In: 2024 Image and Video Technology. 11th Pacific-Rim Symposium, Ed. by W.Q. Yan et al.,\n Lecture Notes in Computer Science (14403). Springer Nature: Singapore, p. 153-166. [doi: {10.1007/978-981-97-0376-0_12](https://doi.org/{10.1007/978-981-97-0376-0_12).\n\n* Koptev, I., Tian, J., Peel, E., Parker, R., Walker, C., Kempa-Liehr, A.W. (2025).\n _Interpretable Dimensionality Reduction in 3D Image Recognition with Small Sample Sizes_.\n Journal of Nondestructive Evaluation 44.44, p. 1-12, [doi: 10.1007/s10921-025-01183-z](https://doi.org/10.1007/s10921-025-01183-z).\n\nNatural language processing of written texts is an example of applying systematic time-series feature engineering to event sequences,\nwhich is described in the following open access paper:\n\n* Tang, Y., Blincoe, K., Kempa-Liehr, A.W. (2020).\n _Enriching Feature Engineering for Short Text Samples by Language Time Series Analysis._\n EPJ Data Science 9.26, p. 1–59. [doi: 10.1140/epjds/s13688-020-00244-9](https://doi.org/10.1140/epjds/s13688-020-00244-9)\n\n\n\n## Advantages of tsfresh\n\n*TSFRESH* has several selling points, for example\n\n1. it is field tested\n2. it is unit tested\n3. the filtering process is statistically/mathematically correct\n4. it has a comprehensive documentation\n5. it is compatible with sklearn, pandas and numpy\n6. it allows anyone to easily add their favorite features\n7. it both runs on your local machine or even on a cluster\n\n## Next steps\n\nIf you are interested in the technical workings, go to see our comprehensive Read-The-Docs documentation at [http://tsfresh.readthedocs.io](http://tsfresh.readthedocs.io).\n\nThe algorithm, especially the filtering part are also described in the paper mentioned above.\n\nWe appreciate any contributions, if you are interested in helping us to make *TSFRESH* the biggest archive of feature extraction methods in python, just head over to our [How-To-Contribute](http://tsfresh.readthedocs.io/en/latest/text/how_to_contribute.html) instructions.\n\nIf you want to try out `tsfresh` quickly or if you want to integrate it into your workflow, we also have a docker image available:\n\n docker pull nbraun/tsfresh\n\n\n## Backwards compatibility\n\nIf you need to reproduce or update time-series features, which were computed with the `matrixprofile` feature calculators, you need to create a Python 3.8 environment:\n\n conda create --name tsfresh__py_3.8 python=3.8\n conda activate tsfresh__py_3.8\n pip install tsfresh[matrixprofile]\n\n## Acknowledgements\n\nThe research and development of *TSFRESH* was funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004 (project iPRODICT).\n" }, { "path": "binder/requirements.txt", "content": "-e .[testing]\n" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/_static/.gitignore", "content": "# Empty directory\n" }, { "path": "docs/_static/theme_override.css", "content": "/* override table width restrictions. Taken from https://rackerlabs.github.io/docs-rackspace/tools/rtd-tables.html */\n@media screen and (min-width: 767px) {\n\n .wy-table-responsive table td {\n /* !important prevents the common CSS stylesheets from overriding\n this as on RTD they are loaded after this stylesheet */\n white-space: normal !important;\n }\n\n .wy-table-responsive {\n overflow: visible !important;\n }\n}\n" }, { "path": "docs/_templates/module_functions_template.rst", "content": ".. currentmodule:: {{ fullname }}\n\n{% block functions %}\n\n.. autosummary::\n{% for item in functions %}\n {{ item }}\n{%- endfor %}\n\n{% endblock %}\n" }, { "path": "docs/api/modules.rst", "content": "tsfresh\n=======\n\n.. toctree::\n :maxdepth: 4\n\n tsfresh\n" }, { "path": "docs/api/tsfresh.convenience.rst", "content": "tsfresh.convenience package\n===========================\n\nSubmodules\n----------\n\ntsfresh.convenience.bindings module\n-----------------------------------\n\n.. automodule:: tsfresh.convenience.bindings\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.convenience.relevant\\_extraction module\n-----------------------------------------------\n\n.. automodule:: tsfresh.convenience.relevant_extraction\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.convenience\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.examples.rst", "content": "tsfresh.examples package\n========================\n\nSubmodules\n----------\n\ntsfresh.examples.driftbif\\_simulation module\n--------------------------------------------\n\n.. automodule:: tsfresh.examples.driftbif_simulation\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.examples.har\\_dataset module\n------------------------------------\n\n.. automodule:: tsfresh.examples.har_dataset\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.examples.robot\\_execution\\_failures module\n--------------------------------------------------\n\n.. automodule:: tsfresh.examples.robot_execution_failures\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.examples\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.feature_extraction.rst", "content": "tsfresh.feature\\_extraction package\n===================================\n\nSubmodules\n----------\n\ntsfresh.feature\\_extraction.data module\n---------------------------------------\n\n.. automodule:: tsfresh.feature_extraction.data\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.feature\\_extraction.extraction module\n---------------------------------------------\n\n.. automodule:: tsfresh.feature_extraction.extraction\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.feature\\_extraction.feature\\_calculators module\n-------------------------------------------------------\n\n.. automodule:: tsfresh.feature_extraction.feature_calculators\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.feature\\_extraction.settings module\n-------------------------------------------\n\n.. automodule:: tsfresh.feature_extraction.settings\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.feature_extraction\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.feature_selection.rst", "content": "tsfresh.feature\\_selection package\n==================================\n\nSubmodules\n----------\n\ntsfresh.feature\\_selection.relevance module\n-------------------------------------------\n\n.. automodule:: tsfresh.feature_selection.relevance\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.feature\\_selection.selection module\n-------------------------------------------\n\n.. automodule:: tsfresh.feature_selection.selection\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.feature\\_selection.significance\\_tests module\n-----------------------------------------------------\n\n.. automodule:: tsfresh.feature_selection.significance_tests\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.feature_selection\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.rst", "content": "tsfresh package\n===============\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n tsfresh.convenience\n tsfresh.examples\n tsfresh.feature_extraction\n tsfresh.feature_selection\n tsfresh.scripts\n tsfresh.transformers\n tsfresh.utilities\n\nSubmodules\n----------\n\ntsfresh.defaults module\n-----------------------\n\n.. automodule:: tsfresh.defaults\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.scripts.rst", "content": "tsfresh.scripts package\n=======================\n\nSubmodules\n----------\n\ntsfresh.scripts.measure\\_execution\\_time module\n-----------------------------------------------\n\n.. automodule:: tsfresh.scripts.measure_execution_time\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.scripts.run\\_tsfresh module\n-----------------------------------\n\n.. automodule:: tsfresh.scripts.run_tsfresh\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.scripts.test\\_timing module\n-----------------------------------\n\n.. automodule:: tsfresh.scripts.test_timing\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.scripts\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.transformers.rst", "content": "tsfresh.transformers package\n============================\n\nSubmodules\n----------\n\ntsfresh.transformers.feature\\_augmenter module\n----------------------------------------------\n\n.. automodule:: tsfresh.transformers.feature_augmenter\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.transformers.feature\\_selector module\n---------------------------------------------\n\n.. automodule:: tsfresh.transformers.feature_selector\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.transformers.per\\_column\\_imputer module\n------------------------------------------------\n\n.. automodule:: tsfresh.transformers.per_column_imputer\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.transformers.relevant\\_feature\\_augmenter module\n--------------------------------------------------------\n\n.. automodule:: tsfresh.transformers.relevant_feature_augmenter\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.transformers\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/api/tsfresh.utilities.rst", "content": "tsfresh.utilities package\n=========================\n\nSubmodules\n----------\n\ntsfresh.utilities.dataframe\\_functions module\n---------------------------------------------\n\n.. automodule:: tsfresh.utilities.dataframe_functions\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.utilities.distribution module\n-------------------------------------\n\n.. automodule:: tsfresh.utilities.distribution\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.utilities.profiling module\n----------------------------------\n\n.. automodule:: tsfresh.utilities.profiling\n :members:\n :undoc-members:\n :show-inheritance:\n\ntsfresh.utilities.string\\_manipulation module\n---------------------------------------------\n\n.. automodule:: tsfresh.utilities.string_manipulation\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: tsfresh.utilities\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/authors.rst", "content": ".. _authors:\n.. include:: ../AUTHORS.rst\n" }, { "path": "docs/changes.rst", "content": ".. _changes:\n.. include:: ../CHANGES.rst\n" }, { "path": "docs/conf.py", "content": "# -*- coding: utf-8 -*-\n#\n# This file is execfile()d with the current directory set to its containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\nimport datetime\nimport sys\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n# sys.path.insert(0, os.path.abspath('.'))\n\n# -- Hack for ReadTheDocs ------------------------------------------------------\n# This hack is necessary since RTD does not issue `sphinx-apidoc` before running\n# `sphinx-build -b html . _build/html`. See Issue:\n# https://github.com/rtfd/readthedocs.org/issues/1139\n# DON'T FORGET: Check the box \"Install your project inside a virtualenv using\n# setup.py install\" in the RTD Advanced Settings.\nimport os\n\non_rtd = os.environ.get(\"READTHEDOCS\", None) == \"True\"\nif on_rtd:\n import inspect\n from sphinx.ext.apidoc import main\n\n __location__ = os.path.join(\n os.getcwd(), os.path.dirname(inspect.getfile(inspect.currentframe()))\n )\n\n output_dir = os.path.join(__location__, \"../docs/api\")\n module_dir = os.path.join(__location__, \"../tsfresh\")\n cmd_line_template = \"sphinx-apidoc -f -o {outputdir} {moduledir}\"\n cmd_line = cmd_line_template.format(outputdir=output_dir, moduledir=module_dir)\n main(cmd_line.split(\" \")[1:])\n\n# -- General configuration -----------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be extensions\n# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.doctest\",\n \"sphinx.ext.imgmath\",\n \"sphinx.ext.napoleon\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix of source filenames.\nsource_suffix = \".rst\"\n\n# The encoding of source files.\n# source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# General information about the project.\nnow = datetime.datetime.today()\nproject = \"tsfresh\"\ncopyright = \"2023-{}, Maximilian Christ et al./ Blue Yonder GmbH\".format(now.year)\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \"\" # Is set by calling `setup.py docs`\n# The full version, including alpha/beta/rc tags.\nrelease = \"\" # Is set by calling `setup.py docs`\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n# language = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n# today = ''\n# Else, today_fmt is used as the format for a strftime call.\n# today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = [\"_build\", \"api/tests*\"]\n\n# Boolean indicating whether to scan all found documents for autosummary\n# directives, and to generate stub pages for each\nautosummary_generate = True\n\n# -- Options for HTML output ---------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\"style_nav_header_background\": \"#51b63c\"}\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\ntry:\n from tsfresh import __version__ as version\nexcept ImportError:\n pass\nelse:\n release = version\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# From https://rackerlabs.github.io/docs-rackspace/tools/rtd-tables.html\nhtml_css_files = [\n 'theme_override.css',\n]\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"tsfresh-doc\"\n" }, { "path": "docs/images/rolling_mechanism_drawio_template.xml", "content": "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\n" }, { "path": "docs/index.rst", "content": ".. image:: images/tsfresh_logo.svg\n :width: 70 %\n :alt: some characteristics of the time series\n :align: center\n\n=======\ntsfresh\n=======\n\nThis is the documentation of **tsfresh**.\n\ntsfresh is a python package.\nIt automatically calculates a large number of time series characteristics, the so called features.\nFurther the package contains methods to evaluate the explaining power and importance of such characteristics for\nregression or classification tasks.\n\nYou can jump right into the package by looking into our :ref:`quick-start-label`.\n\nContents\n========\n\nThe following chapters will explain the tsfresh package in detail:\n\n.. toctree::\n :maxdepth: 1\n\n text/introduction\n text/quick_start\n text/data_formats\n text/sklearn_transformers\n text/list_of_features\n text/feature_extraction_settings\n text/feature_filtering\n text/how_to_add_custom_feature\n text/large_data\n text/tsfresh_on_a_cluster\n text/forecasting\n text/faq\n api/modules\n authors\n license\n changes\n text/how_to_contribute\n text/feature_calculation\n\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\n\nAcknowledgements\n================\n\nThe research and development of TSFRESH was funded in part by the German Federal Ministry of Education and Research\nunder grant number 01IS14004 (project iPRODICT).\n" }, { "path": "docs/license.rst", "content": ".. _license:\n\n=======\nLicense\n=======\n\n.. literalinclude:: ../LICENSE.txt\n" }, { "path": "docs/text/data_formats.rst", "content": ".. _data-formats-label:\n\nData Formats\n============\n\ntsfresh offers three different options to specify the format of the time series data to use with the function\n:func:`tsfresh.extract_features` (and all utility functions that expect a time series, for that\nmatter, like for example :func:`tsfresh.utilities.dataframe_functions.roll_time_series`).\n\nIrrespective of the input format, tsfresh will always return the calculated features in the same output format\ndescribed below.\n\nTypically, the input format options are :class:`pandas.DataFrame` objects, which we will discuss here, and also\nDask dataframes and PySpark computational graphs, which are discussed here :ref:`large-data-label`.\n\nThere are four important column types that\nmake up those DataFrames. Each will be described with an example from the robot failures dataset\n(see :ref:`quick-start-label`).\n\n:`column_id`: This column indicates which entities the time series belong to. Features will be extracted individually\n for each entity (id). The resulting feature matrix will contain one row per id.\n Each robot is a different entity, so each of the set of features has a different id.\n\n:`column_sort`: This column contains values which allows sorting of the time series (e.g. sorting by time stamps).\n In general, having equidistant time steps, or the same time scale for the different ids and/or kinds, is not a requirement.\n Some features, however, might only sense for equidistant time stamps.\n If you omit this column, the DataFrame is assumed to be already sorted in ascending order.\n Each of the robot sensor measurements has a time stamp which is used as the `column_sort`.\n\nNeed only to be specified on some data formats (see below):\n\n:`column_value`: This column contains the actual values of the time series.\n This corresponds to the measured values of different sensors on the robots.\n\n:`column_kind`: This column indicates the names of the different time series types (e.g. different sensors in an\n industrial application as in the robot dataset).\n For each kind of time series the features are calculated individually.\n\nImportant: None of these columns is allowed to contain ``NaN``, ``Inf`` or ``-Inf`` values.\n\nIn the following paragraphs, we describe the different input formats that are build based off those columns:\n\n* A flat DataFrame\n* A stacked DataFrame\n* A dictionary of flat DataFrames\n\nThe difference between a flat and a stacked DataFrame is indicated by specifying (or not) the parameters\n``column_value`` and ``column_kind`` in the :func:`tsfresh.extract_features` function.\n\nIf you are unsure which one to choose, try either the flat or stacked DataFrame.\n\nInput Option 1. Flat DataFrame or Wide DataFrame\n------------------------------------------------\n\nIf both ``column_value`` and ``column_kind`` are set to ``None``, the time series data is assumed to be in a flat\nDataFrame. This means that each different time series must be saved as its own column.\n\nExample: Imagine you record the values of time series x and y for different objects A and B for three different\ntimes t1, t2 and t3. Your resulting DataFrame may look like this:\n\n+----+------+----------+----------+\n| id | time | x | y |\n+====+======+==========+==========+\n| A | t1 | x(A, t1) | y(A, t1) |\n+----+------+----------+----------+\n| A | t2 | x(A, t2) | y(A, t2) |\n+----+------+----------+----------+\n| A | t3 | x(A, t3) | y(A, t3) |\n+----+------+----------+----------+\n| B | t1 | x(B, t1) | y(B, t1) |\n+----+------+----------+----------+\n| B | t2 | x(B, t2) | y(B, t2) |\n+----+------+----------+----------+\n| B | t3 | x(B, t3) | y(B, t3) |\n+----+------+----------+----------+\n\nNow, you want to calculate some features with tsfresh so you would pass:\n\n.. code:: python\n\n column_id=\"id\", column_sort=\"time\", column_kind=None, column_value=None\n\nto the extraction function, to extract features separately for all ids and separately for the x and y values.\nYou can also omit the ``column_kind=None, column_value=None`` as this is the default.\n\nInput Option 2. Stacked DataFrame or Long DataFrame\n---------------------------------------------------\n\nIf both ``column_value`` and ``column_kind`` are set, the time series data is assumed to be a stacked DataFrame.\nThis means that there are no different columns for the different types of time series.\nThis representation has several advantages over the flat Data Frame.\nFor example, the time stamps of the different time series do not have to align.\n\nIt does not contain different columns for the different types of time series but only one\nvalue column and a kind column. Following with our previous example, the dataframe would look like this:\n\n+----+------+------+----------+\n| id | time | kind | value |\n+====+======+======+==========+\n| A | t1 | x | x(A, t1) |\n+----+------+------+----------+\n| A | t2 | x | x(A, t2) |\n+----+------+------+----------+\n| A | t3 | x | x(A, t3) |\n+----+------+------+----------+\n| A | t1 | y | y(A, t1) |\n+----+------+------+----------+\n| A | t2 | y | y(A, t2) |\n+----+------+------+----------+\n| A | t3 | y | y(A, t3) |\n+----+------+------+----------+\n| B | t1 | x | x(B, t1) |\n+----+------+------+----------+\n| B | t2 | x | x(B, t2) |\n+----+------+------+----------+\n| B | t3 | x | x(B, t3) |\n+----+------+------+----------+\n| B | t1 | y | y(B, t1) |\n+----+------+------+----------+\n| B | t2 | y | y(B, t2) |\n+----+------+------+----------+\n| B | t3 | y | y(B, t3) |\n+----+------+------+----------+\n\nThen you would set:\n\n.. code:: python\n\n column_id=\"id\", column_sort=\"time\", column_kind=\"kind\", column_value=\"value\"\n\nto end up with the same extracted features.\nYou can also omit the value column and let ``tsfresh`` deduce it automatically.\n\n\nInput Option 3. Dictionary of flat DataFrames\n---------------------------------------------\n\nInstead of passing a DataFrame which must be split up by its different kinds by tsfresh, you can also give a\ndictionary mapping from the kind as string to a DataFrame containing only the time series data of that kind.\nSo essentially you are using a singular DataFrame for each kind of time series.\n\nThe data from the example can be split into two DataFrames resulting in the following dictionary:\n\n{ \"x\":\n\n +----+------+----------+\n | id | time | value |\n +====+======+==========+\n | A | t1 | x(A, t1) |\n +----+------+----------+\n | A | t2 | x(A, t2) |\n +----+------+----------+\n | A | t3 | x(A, t3) |\n +----+------+----------+\n | B | t1 | x(B, t1) |\n +----+------+----------+\n | B | t2 | x(B, t2) |\n +----+------+----------+\n | B | t3 | x(B, t3) |\n +----+------+----------+\n\n,\n\"y\":\n\n +----+------+----------+\n | id | time | value |\n +====+======+==========+\n | A | t1 | y(A, t1) |\n +----+------+----------+\n | A | t2 | y(A, t2) |\n +----+------+----------+\n | A | t3 | y(A, t3) |\n +----+------+----------+\n | B | t1 | y(B, t1) |\n +----+------+----------+\n | B | t2 | y(B, t2) |\n +----+------+----------+\n | B | t3 | y(B, t3) |\n +----+------+----------+\n\n}\n\nYou would pass this dictionary to tsfresh together with the following arguments:\n\n.. code:: python\n\n column_id=\"id\", column_sort=\"time\", column_kind=None, column_value=\"value\":\n\n\nIn this case we do not need to specify the kind column as the kind is the respective dictionary key.\n\nOutput Format\n-------------\n\nThe resulting feature matrix, containing the extracted features, is the same for all three input options.\nIt will always be a :class:`pandas.DataFrame` with the following layout:\n\n+----+-------------+-----+-------------+-------------+-----+-------------+\n| id | x_feature_1 | ... | x_feature_N | y_feature_1 | ... | y_feature_N |\n+====+=============+=====+=============+=============+=====+=============+\n| A | ... | ... | ... | ... | ... | ... |\n+----+-------------+-----+-------------+-------------+-----+-------------+\n| B | ... | ... | ... | ... | ... | ... |\n+----+-------------+-----+-------------+-------------+-----+-------------+\n\nwhere the x features are calculated using all x values (independently for A and B), the y features using all y values\n(independently for A and B), and so on.\n\nThis DataFrame is also the expected input format to the feature selection algorithms used by tsfresh (e.g. the\n:func:`tsfresh.select_features` function).\n" }, { "path": "docs/text/faq.rst", "content": "FAQ\n===\n\n\n 1. **Does tsfresh support different time series lengths?**\n\n Yes, it supports different time series lengths. However, some feature calculators can demand a minimal length\n of the time series. If a shorter time series is passed to the calculator, a NaN is returned for those\n features.\n\n\n\n 2. **Is it possible to extract features from rolling/shifted time series?**\n\n Yes, the :func:`tsfresh.dataframe_functions.roll_time_series` function allows to conveniently create a rolled\n time series dataframe from your data. You just have to transform your data into one of the supported tsfresh\n :ref:`data-formats-label`.\n Then, the :func:`tsfresh.dataframe_functions.roll_time_series` will return a DataFrame with the rolled time series,\n that you can pass to tsfresh.\n You can find more details here: :ref:`forecasting-label`.\n\n\n 3. **How can I use tsfresh with windows?**\n\n We recommend to use `Anaconda `_. After installation, open the\n Anaconda Prompt, create an environment and set up tsfresh\n (Please be aware that we're using multiprocessing, which can be `problematic `_.):\n\n .. code:: Bash\n\n conda create -n ENV_NAME python=VERSION\n conda install -n ENV_NAME pip requests numpy pandas scipy statsmodels patsy scikit-learn tqdm\n activate ENV_NAME\n pip install tsfresh\n\n\n 4. **Does tsfresh support different sampling rates in the time series?**\n\n Yes! The feature calculators in tsfresh do not care about the sampling frequency.\n You will have to use the second input format, the stacked DataFramed (see :ref:`data-formats-label`)\n\n\n 5. **Does tsfresh support irregularly spaced time series?**\n\n\tYes, but be careful. As its name suggests, the ``column_sort`` (i.e., timestamp) parameter is only used to sort observations.\n\tBeyond sorting, tsfresh does not use the timestamp in calculations.\n\tWhile many features do not need a timestamp (or only need it for ordering), others will assume that observations are evenly spaced in time (e.g., one second between each observation).\n\tSince tsfresh ignores spacing, care should be taken when selecting features to use with a highly irregular series.\n\n 6. **Even when just extracing the :class:`tsfresh.feature_extraction.settings.EfficientFCParameters`, tsfresh is taking a long time to run. Is there anything further I can do to speed up the processing?**\n\n\tIf you are using Parallelization (the default option), you may need to check you are not over-provisioning your avaiable cpu cores. Take a look at :ref:`notes-for-efficient-parallelization-label` for steps to eliminate this, which can speed up processing significantly.\n" }, { "path": "docs/text/feature_calculation.rst", "content": ".. _feature-naming-label:\n\nFeature Calculator Naming\n=========================\n\ntsfresh enforces a strict naming of the created features, which you have to follow whenever you create new feature\ncalculators.\nThis is due to the :func:`tsfresh.feature_extraction.settings.from_columns` method which needs to\ndeduce the following information from the feature name:\n\n * the time series that was used to calculate the feature\n * the feature calculator method that was used to derive the feature\n * all parameters that have been used to calculate the feature (optional)\n\nHence, to enable the :func:`tsfresh.feature_extraction.settings.from_columns` to deduce all the\nnecessary conditions, the features should be named in the following format:\n\n {time_series_name}__{feature_name}__{parameter name 1}_{parameter value 1}__[..]__{parameter name k}_{parameter value k}\n\nHere, we assumed that {feature_name} has k parameters.\n\nExamples of feature naming\n''''''''''''''''''''''''''\n\nSo for example the following feature name:\n\n temperature_1__quantile__q_0.6\n\nis the value of the feature :func:`tsfresh.feature_extraction.feature_calculators.quantile` for the time series\n```temperature_1``` and a parameter value of ``q=0.6``. On the other hand, the feature named:\n\n Pressure 5__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5\n\ndenotes the value of the feature :func:`tsfresh.feature_extraction.feature_calculators.cwt_coefficients` for\nthe time series ```Pressure 5``` under parameter values of ``widths=(2, 5, 10, 20)``, ``coeff=14`` and ``w=5``.\n" }, { "path": "docs/text/feature_extraction_settings.rst", "content": "Feature extraction settings\n===========================\n\nWhen starting a new data science project involving time series you probably want to start by extracting a\ncomprehensive set of features. Later you can identify which features are relevant for the task at hand.\nIn the final stages, you probably want to fine tune the parameter of the features to fine tune your models.\n\nYou can do all those things with tsfresh. So, you need to know how to control which features are calculated by tsfresh\nand how one can adjust the parameters. In this section, we will clarify this.\n\nFor the lazy: Just let me calculate some features!\n--------------------------------------------------\n\nTo calculate a comprehensive set of features, call the :func:`tsfresh.extract_features` method without\npassing a ``default_fc_parameters`` or ``kind_to_fc_parameters`` object. This way you will be using the default options,\nwhich will use all the feature calculators in this package, that we consider are OK to return by default.\n\nFor the advanced: How do I set the parameters for all kind of time series?\n----------------------------------------------------------------------------\n\nAfter digging deeper into your data, you maybe want to calculate more of a certain type of feature and less of another\ntype. So, you need to use custom settings for the feature extractors. To do that with tsfresh you will have to use a\ncustom settings object:\n\n>>> from tsfresh.feature_extraction import ComprehensiveFCParameters\n>>> settings = ComprehensiveFCParameters()\n>>> # Set here the options of the settings object as shown in the paragraphs below\n>>> # ...\n>>> from tsfresh.feature_extraction import extract_features\n>>> extract_features(df, default_fc_parameters=settings)\n\nThe ``default_fc_parameters`` is expected to be a dictionary which maps feature calculator names\n(the function names you can find in the :mod:`tsfresh.feature_extraction.feature_calculators` file) to a list\nof dictionaries, which are the parameters with which the function will be called (as key value pairs). Each\nfunction-parameter combination that is in this dict will be called during the extraction and will produce a feature.\nIf the function does not take any parameters, the value should be set to ``None``.\n\nFor example:\n\n.. code:: python\n\n fc_parameters = {\n \"length\": None,\n \"large_standard_deviation\": [{\"r\": 0.05}, {\"r\": 0.1}]\n }\n\nwill produce three features: one by calling the\n:func:`tsfresh.feature_extraction.feature_calculators.length` function without any parameters and two by calling\n:func:`tsfresh.feature_extraction.feature_calculators.large_standard_deviation` with ``r = 0.05`` and ``r = 0.1``.\n\nSo you can control which features will be extracted, by adding or removing either keys or parameters from this dict.\nIt is as easy as that.\nIf you decide not to calculate the length feature here, you delete it from the dictionary:\n\n.. code:: python\n\n del fc_parameters[\"length\"]\n\nAnd now, only the two other features are calculated.\n\nFor convenience, three dictionaries are predefined and can be used right away:\n\n* :class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters`: includes all features without parameters and\n all features with parameters, each with different parameter combinations. This is the default for :func:`tsfresh.extract_features`\n if you do not hand in a ``default_fc_parameters`` at all.\n* :class:`tsfresh.feature_extraction.settings.MinimalFCParameters`: includes only a handful of features\n and can be used for quick tests. The features which have the \"minimal\" attribute are used here.\n* :class:`tsfresh.feature_extraction.settings.EfficientFCParameters`: Mostly the same features as in the\n :class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters`, but without features which are marked with the\n \"high_comp_cost\" attribute. This can be used if runtime performance plays a major role.\n\nTheoretically, you could calculate an unlimited number of features with tsfresh by adding entry after entry to the\ndictionary.\n\n\nFor the ambitious: How do I set the parameters for different type of time series?\n---------------------------------------------------------------------------------\n\nIt is also possible to control the features to be extracted for the different kinds of time series individually.\nYou can do so by passing a ``kind_to_fc_parameters`` parameter to the :func:`tsfresh.extract_features` function.\nIt should be a dict mapping from kind names (as string) to ``fc_parameters`` objects,\nwhich you would normally pass as an argument to the ``default_fc_parameters`` parameter.\n\nSo, for example the following code snippet:\n\n.. code:: python\n\n kind_to_fc_parameters = {\n \"temperature\": {\"mean\": None},\n \"pressure\": {\"maximum\": None, \"minimum\": None}\n }\n\nwill extract the ``\"mean\"`` feature of the ``\"temperature\"`` time series and the ``\"minimum\"`` and ``\"maximum\"`` of the\n``\"pressure\"`` time series.\n\nThe ``kind_to_fc_parameters`` argument will partly override the ``default_fc_parameters``. So, if you include a kind\nname in the ``kind_to_fc_parameters`` parameter, its value will be used for that kind.\nOther kinds will still use the ``default_fc_parameters``.\n\n\nA handy trick: Do I really have to create the dictionary by hand?\n-----------------------------------------------------------------\n\nNot necessarily. Let's assume you have a DataFrame of tsfresh features.\nBy using feature selection algorithms you find out that only a subgroup of features is relevant.\n\n\nThen, we provide the :func:`tsfresh.feature_extraction.settings.from_columns` method that constructs the ``kind_to_fc_parameters``\ndictionary from the column names of this filtered feature matrix to make sure that only relevant features are extracted.\n\nThis can save a huge amount of time because you prevent the calculation of unnecessary features.\nLet's illustrate this with an example:\n\n.. code:: python\n\n # X_tsfresh contains the extracted tsfresh features\n X_tsfresh = extract_features(...)\n\n # which are now filtered to only contain relevant features\n X_tsfresh_filtered = some_feature_selection(X_tsfresh, y, ....)\n\n # we can easily construct the corresponding settings object\n kind_to_fc_parameters = tsfresh.feature_extraction.settings.from_columns(X_tsfresh_filtered)\n\nThe above code will construct for you the ``kind_to_fc_parameters`` dictionary that corresponds to the features and parameters (!) from\nthe tsfresh features that were filtered by the ``some_feature_selection`` feature selection algorithm.\n" }, { "path": "docs/text/feature_filtering.rst", "content": "Feature filtering\n=================\n\n\nThe all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes.\nThis problem is especially hard to solve for time series classification and regression in industrial applications such\nas predictive maintenance or production line optimization, for which each label or regression target is associated with\nseveral time series and meta-information simultaneously.\n\nTo limit the number of irrelevant features, tsfresh deploys the fresh algorithm (fresh stands for `FeatuRe Extraction\nbased on Scalable Hypothesis tests`) [1]_.\n\nThe algorithm is called by :func:`tsfresh.feature_selection.relevance.calculate_relevance_table`.\nIt is an efficient, scalable feature extraction algorithm, which filters the available features in an early stage of the\nmachine learning pipeline with respect to their significance for the classification or regression task, while\ncontrolling the expected percentage of selected but irrelevant features.\n\nThe filtering process consists of three phases depicted in the following figure:\n\n.. image:: ../images/feature_extraction_process_20160815_mc_1.png\n :scale: 70 %\n :alt: the time series\n :align: center\n\nPhase 1 - Feature extraction\n''''''''''''''''''''''''''''\n\nFirstly, the algorithm characterizes time series with comprehensive and well-established feature mappings and considers\nadditional features describing meta-information.\nThe feature calculators used to derive the features are those in\n:mod:`tsfresh.feature_extraction.feature_calculators`.\n\nIn the above figure, this corresponds to the change from raw time series to aggregated features.\n\nPhase 2 - Feature significance testing\n''''''''''''''''''''''''''''''''''''''\n\nIn a second step, each feature vector is individually and independently evaluated with respect to its significance for\npredicting the target under investigation.\nThose tests are located in the submodule :mod:`tsfresh.feature_selection.significance_tests`.\nThe result of these tests is a vector of p-values, quantifying the significance of each feature for predicting the\nlabel/target.\n\nIn the above figure, this corresponds to the change from aggregated features to p-values.\n\nPhase 3 - Multiple test procedure\n'''''''''''''''''''''''''''''''''\n\nThe vector of p-values is evaluated on the basis of the Benjamini-Yekutieli procedure [2]_ in order to decide which features\nto keep.\nThis multiple testing procedure is taken from the statsmodel package.\n\nIn the above figure, this corresponds to the change from p-values to selected features.\n\n\nReferences\n''''''''''\n\n .. [1] Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2016).\n Distributed and parallel time series feature extraction for industrial big data applications.\n ArXiv e-prints: 1610.07717 URL: http://adsabs.harvard.edu/abs/2016arXiv161007717C\n\n .. [2] Benjamini, Y. and Yekutieli, D. (2001).\n The control of the false discovery rate in multiple testing under dependency.\n Annals of statistics, 1165--1188\n" }, { "path": "docs/text/forecasting.rst", "content": ".. _forecasting-label:\n\nRolling/Time series forecasting\n===============================\n\nFeatures extracted with *tsfresh* can be used for many different tasks, such as time series classification,\ncompression or forecasting.\nThis section explains how we can use the features for time series forecasting.\n\nLet's say you have the price of a certain stock, e.g., Apple, for 100 time steps.\nNow, you want to build a feature-based model to forecast future prices of the Apple stock.\nYou could remove the last price value (of today) and extract features from the time series until today to predict the price of today.\nBut this would only give you a single example to train.\nInstead, you can repeat this process: for every day in your stock price time series, remove the current value, extract features for the time until this value and train to predict the value of the day (which you removed).\nYou can think of it as shifting a cut-out window over your sorted time series data: on each shift step you extract the data you see through your cut-out window to build a new, smaller time series and extract features only on this one.\nThen you continue shifting.\nIn ``tsfresh``, the process of shifting a cut-out window over your data to create smaller time series cut-outs is called *rolling*.\n\nRolling is a way to turn a single time series into multiple time series, each of them ending one (or n) time step later than the one before.\nThe rolling utilities implemented in `tsfresh` help you in this process of reshaping (and rolling) your data into a format on which you can apply the usual :func:`tsfresh.extract_features` method.\nThis means that the step of extracting the time series windows and the feature extraction are separated.\n\nPlease note that \"time\" does not necessarily mean clock time here.\nThe \"sort\" column of a DataFrame in the supported :ref:`data-formats-label` gives a sequential state to the\nindividual measurements.\nIn the case of time series this can be the *time* dimension while in other cases, this can be a location, a frequency. etc.\n\nThe following image illustrates the process:\n\n.. image:: ../images/rolling_mechanism_1.png\n :scale: 100 %\n :alt: The rolling mechanism\n :align: center\n\n\nAnother example can be found in streaming data, e.g., in Industry 4.0 applications.\nHere, you typically get one new data row at a time and use it to, for example, predict machine failures. To train your model,\nyou could act as if you would stream the data, by feeding your classifier the data after one time step,\nthe data after the first two time steps, etc.\n\nIn tsfresh, rolling is implemented via the helper function :func:`tsfresh.utilities.dataframe_functions.roll_time_series`.\nFurther, we provide the :func:`tsfresh.utilities.dataframe_functions.make_forecasting_frame` method as a convenient\nwrapper to quickly construct the container and target vector for a given sequence.\n\nLet's walk through an example to see how it works:\n\nThe rolling mechanism\n---------------------\n\nWe look into the following flat DataFrame example, which is a tsfresh suitable format (see :ref:`data-formats-label`).\nNote, that rolling also works for all other time series formats.\n\n+----+------+----+----+\n| id | time | x | y |\n+====+======+====+====+\n| 1 | 1 | 1 | 5 |\n+----+------+----+----+\n| 1 | 2 | 2 | 6 |\n+----+------+----+----+\n| 1 | 3 | 3 | 7 |\n+----+------+----+----+\n| 1 | 4 | 4 | 8 |\n+----+------+----+----+\n| 2 | 8 | 10 | 12 |\n+----+------+----+----+\n| 2 | 9 | 11 | 13 |\n+----+------+----+----+\n\nIn the above flat DataFrame, we measured the values from two sensors x and y for two different entities (id 1 and 2) in 4 or 2 time\nsteps (1, 2, 3, 4, 8, 9).\n\nIf you want to follow along, here is the python code to generate this data:\n\n.. code:: python\n\n import pandas as pd\n df = pd.DataFrame({\n \"id\": [1, 1, 1, 1, 2, 2],\n \"time\": [1, 2, 3, 4, 8, 9],\n \"x\": [1, 2, 3, 4, 10, 11],\n \"y\": [5, 6, 7, 8, 12, 13],\n })\n\nNow, we can use :func:`tsfresh.utilities.dataframe_functions.roll_time_series` to get consecutive sub-time series.\nYou could think of having a window sliding over your time series data and extracting out every data you can see through this window.\nThere are three parameters to tune for the window:\n\n* `max_timeshift` defines, how large the window is at maximum. The extracted time series will have at maximum length of `max_timeshift + 1`.\n (they can also be smaller, as time stamps in the beginning have less past values).\n* `min_timeshift` defines the minimal size of each window. Shorter time series (usually at the beginning) will be omitted.\n* Advanced: `rolling_direction`: if you want to slide in positive (increasing sort) or negative (decreasing sort) direction. You barely need negative direction, so you probably do not want to change the default. The absolute value of this parameter decides how much you want to shift per cut-out step.\n\nThe column parameters are the same as in the usual :ref:`data-formats-label`.\n\nLet's see what will happen with our data sample:\n\n.. code:: python\n\n from tsfresh.utilities.dataframe_functions import roll_time_series\n df_rolled = roll_time_series(df, column_id=\"id\", column_sort=\"time\")\n\nThe new data set consists only of values from the old data set, but with new ids.\nAlso the sort column values (in this case ``time``) is copied.\nIf you group by ``id``, you will end up with the following parts (or \"windows\"):\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (1,1) | 1 | 1 | 5 |\n+-------+-------+---+----+\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (1,2) | 1 | 1 | 5 |\n+-------+-------+---+----+\n| (1,2) | 2 | 2 | 6 |\n+-------+-------+---+----+\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (1,3) | 1 | 1 | 5 |\n+-------+-------+---+----+\n| (1,3) | 2 | 2 | 6 |\n+-------+-------+---+----+\n| (1,3) | 3 | 3 | 7 |\n+-------+-------+---+----+\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (1,4) | 1 | 1 | 5 |\n+-------+-------+---+----+\n| (1,4) | 2 | 2 | 6 |\n+-------+-------+---+----+\n| (1,4) | 3 | 3 | 7 |\n+-------+-------+---+----+\n| (1,4) | 4 | 4 | 8 |\n+-------+-------+---+----+\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (2,8) | 8 |10 | 12 |\n+-------+-------+---+----+\n\n+-------+-------+---+----+\n| id | time | x | y |\n+=======+=======+===+====+\n| (2,9) | 8 |10 | 12 |\n+-------+-------+---+----+\n| (2,9) | 9 |11 | 13 |\n+-------+-------+---+----+\n\nNow, you can run the usual feature extraction procedure on the rolled data:\n\n.. code:: python\n\n from tsfresh import extract_features\n df_features = extract_features(df_rolled, column_id=\"id\", column_sort=\"time\")\n\nYou will end up with features generated for each one of the parts above, which you can then use for training your forecasting model.\n\n+----------+----------------+-----------------------------+-----+\n| variable | x__abs_energy | x__absolute_sum_of_changes | ... |\n+==========+================+=============================+=====+\n| id | | | ... |\n+----------+----------------+-----------------------------+-----+\n| (1,1) | 1.0 | 0.0 | ... |\n+----------+----------------+-----------------------------+-----+\n| (1,2) | 5.0 | 1.0 | ... |\n+----------+----------------+-----------------------------+-----+\n| (1,3) | 14.0 | 2.0 | ... |\n+----------+----------------+-----------------------------+-----+\n| (1,4) | 30.0 | 3.0 | ... |\n+----------+----------------+-----------------------------+-----+\n| (2,8) | 100.0 | 0.0 | ... |\n+----------+----------------+-----------------------------+-----+\n| (2,9) | 221.0 | 1.0 | ... |\n+----------+----------------+-----------------------------+-----+\n\nThe features for example for id ``(1,3)`` are extracted using the data of ``id=1`` up to and including ``t=3`` (so ``t=1``, ``t=2`` and ``t=3``).\n\nIf you want to train a model for a forecasting, `tsfresh` also offers the function :func:`tsfresh.utilities.dataframe_functions.make_forecasting_frame`, which will help you match the target vector properly.\nThis process is visualized in the following figure.\nIt shows how the purple, rolled sub-timeseries are used as base for the construction of the feature matrix *X*\n(if *f* is the `extract_features` function).\nThe green data points need to be predicted by the model and are used as rows in the target vector *y*.\nBe aware that this only works for a one-dimensional time series of a single `id` and `kind`.\n\n.. image:: ../images/rolling_mechanism_2.png\n :scale: 100 %\n :alt: The rolling mechanism\n :align: center\n\nParameters and Implementation Notes\n-----------------------------------\n\nThe above example demonstrates the overall rolling mechanism, which creates new time series.\nNow, we discuss the naming convention for the new time series.\n\nFor identifying every subsequence, `tsfresh` uses the time stamp of the point that will be predicted together with the old identifier as \"id\".\nFor positive rolling, this `timeshift` is the last time stamp in the subsequence.\nFor negative rolling, it is the first one, for example the above dataframe rolled in negative direction gives us:\n\n+-------+------+----+----+\n| id | time | x | y |\n+=======+======+====+====+\n| (1,1) | 1 | 1 | 5 |\n+-------+------+----+----+\n| (1,1) | 2 | 2 | 6 |\n+-------+------+----+----+\n| (1,1) | 3 | 3 | 7 |\n+-------+------+----+----+\n| (1,1) | 4 | 4 | 8 |\n+-------+------+----+----+\n| (1,2) | 2 | 2 | 6 |\n+-------+------+----+----+\n| (1,2) | 3 | 3 | 7 |\n+-------+------+----+----+\n| (1,2) | 4 | 4 | 8 |\n+-------+------+----+----+\n| (1,3) | 3 | 3 | 7 |\n+-------+------+----+----+\n| (1,3) | 4 | 4 | 8 |\n+-------+------+----+----+\n| (1,4) | 4 | 4 | 8 |\n+-------+------+----+----+\n| (2,8) | 8 | 10 | 12 |\n+-------+------+----+----+\n| (2,8) | 9 | 11 | 13 |\n+-------+------+----+----+\n| (2,9) | 9 | 11 | 13 |\n+-------+------+----+----+\n\nwhich you could use to predict the current value using the future time series values (if that makes sense in your case).\n\nChoosing a non-default `max_timeshift` or `min_timeshift` would make the extracted sub-time-series smaller or even remove them completely (e.g. with `min_timeshift = 1` the ``(1,1)`` (i.e. ``id=1,timeshift=1``) of the positive rolling case would disappear).\nUsing a ``rolling_direction`` with a larger absolute value (e.g. -2 or 2) will skip some of the windows (in this case, every second).\n" }, { "path": "docs/text/how_to_add_custom_feature.rst", "content": "How to add a custom feature\n===========================\n\nIf you want to extract custom made features from your time series, tsfresh allows you to do so in a few\nsimple steps:\n\nStep 1. Decide which type of feature you want to implement\n----------------------------------------------------------\n\ntsfresh supports two types of feature calculation methods:\n\n *1.* simple\n\n *2.* combiner\n\nThe difference lays in the number of features calculated for a singular time series.\nThe feature_calculator is simple if it returns one (*1.*) feature, and it is a combiner and returns multiple features (*2.*).\nSo if you want to add a singular feature, you should select *1.*, the simple feature calculator class.\nIf it is however, better to calculate multiple features at the same time (e.g., to perform auxiliary calculations only\nonce for all features), then you should choose type *2.*.\n\n\nStep 2. Write the feature calculator\n------------------------------------\n\nDepending on which type of feature calculator you are implementing, you can use the following feature calculator skeletons:\n\n1. simple features\n~~~~~~~~~~~~~~~~~~\n\nYou can write a simple feature calculator that returns exactly one feature, without parameters as follows:\n\n.. code:: python\n\n from tsfresh.feature_extraction.feature_calculators import set_property\n\n\n @set_property(\"fctype\", \"simple\")\n def your_feature_calculator(x):\n \"\"\"\n The description of your feature\n\n :param x: the time series to calculate the feature of\n :type x: pandas.Series\n :return: the value of this feature\n :return type: bool, int or float\n \"\"\"\n # Calculation of feature as float, int or bool\n result = f(x)\n return result\n\nor with parameters:\n\n.. code:: python\n\n @set_property(\"fctype\", \"simple\"\")\n def your_feature_calculator(x, p1, p2, ...):\n \"\"\"\n Description of your feature\n\n :param x: the time series to calculate the feature of\n :type x: pandas.Series\n :param p1: description of your parameter p1\n :type p1: type of your parameter p1\n :param p2: description of your parameter p2\n :type p2: type of your parameter p2\n ...\n :return: the value of this feature\n :return type: bool, int or float\n \"\"\"\n # Calculation of feature as float, int or bool\n f = f(x)\n return f\n\n\n2. combiner features\n~~~~~~~~~~~~~~~~~~~~\n\nAlternatively, you can write a combiner feature calculator that returns multiple features as follows:\n\n.. code:: python\n\n from tsfresh.utilities.string_manipulation import convert_to_output_format\n\n\n @set_property(\"fctype\", \"combiner\")\n def your_feature_calculator(x, param):\n \"\"\"\n Short description of your feature (should be a one liner as we parse the first line of the description)\n\n Long detailed description, add somme equations, add some references, what kind of statistics is the feature\n capturing? When should you use it? When not?\n\n :param x: the time series to calculate the feature of\n :type x: pandas.Series\n :param c: the time series name\n :type c: str\n :param param: contains dictionaries {\"p1\": x, \"p2\": y, ...} with p1 float, p2 int ...\n :type param: list\n :return: list of tuples (s, f) where s are the parameters, serialized as a string,\n and f the respective feature value as bool, int or float\n :return type: pandas.Series\n \"\"\"\n # Do some pre-processing if needed for all parameters\n # f is a function that calculates the feature value for each single parameter combination\n return [(convert_to_output_format(config), f(x, config)) for config in param]\n\n\nWriting your own time-based feature calculators\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWriting your own time-based feature calculators is no different than usual. Only two new properties must be set using the `@set_property` decorator:\n\n* Adding ``@set_property(\"input\", \"pd.Series\")`` tells the function that the input of the function is a ``pd.Series`` rather than a ``numpy`` array.\n This allows the index to be used automatically.\n* Adding ``@set_property(\"index_type\", pd.DatetimeIndex)`` tells the function that the input is a `DatetimeIndex`,\n allowing it to perform calculations based on time data types.\n\nFor example, if we want to write a function that calculates the time between the first and last measurement, it could look something like this:\n\n.. code:: python\n\n @set_property(\"input\", \"pd.Series\")\n @set_property(\"index_type\", pd.DatetimeIndex)\n def timespan(x, param):\n ix = x.index\n\n # Get differences between the last timestamp and the first timestamp in seconds,\n # then convert to hours.\n times_seconds = (ix[-1] - ix[0]).total_seconds()\n return times_seconds / float(3600)\n\n\nStep 3. Add custom settings for your feature\n--------------------------------------------\n\nFinally, you need to add your new custom feature to the extraction settings, otherwise it is not used\nduring extraction.\nTo do this, create a new settings object (by default, ``tsfresh`` uses the\n:class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters`) and\nadd your function as a key to the dictionary.\nAs a value, either use ``None`` if your function does not need parameters or a list with the\nparameters you want to use (as dictionaries).\n\n.. code:: python\n\n settings = ComprehensiveFCParameters()\n settings[f] = [{\"n\": 1}, {\"n\": 2}]\n\nAfter that, make sure you pass your newly created settings in the call to ``extract_features``.\n\nStep 4. Make a pull request\n---------------------------\n\nWe would be very happy if you contribute your custom features to tsfresh.\n\nTo do this, add your feature into the ``feature_calculators.py`` file and append your\nfeature (as a name) with safe default parameters to the ``name_to_param`` dictionary inside the\n:class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters` constructor:\n\n.. code:: python\n\n name_to_param.update({\n # here are the existing settings\n ...\n # Now the settings of your feature calculator\n \"your_feature_calculator\" = [{\"p1\": x, \"p2\": y, ...} for x,y in ...],\n })\n\nMake sure, that the different feature extraction settings\n(e.g. :class:`tsfresh.feature_extraction.settings.EfficientFCParameters`,\n:class:`tsfresh.feature_extraction.settings.MinimalFCParameters` or\n:class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters`) do include different sets of\nfeature calculators to use. You can control, which feature extraction settings object will include your new\nfeature calculator by giving your function attributes like \"minimal\" or \"high_comp_cost\". See the\nclasses in :mod:`tsfresh.feature_extraction.settings` for more information.\n\nAfter that, add some tests and make a pull request to our `github repo `_.\nWe happily accept partly implemented feature calculators, which we can finalize together.\n" }, { "path": "docs/text/how_to_contribute.rst", "content": "How to contribute\n=================\n\nWe want tsfresh to become the biggest archive of feature extraction methods in python. To achieve this goal, we need\nyour help!\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. If you\nwant to add one or two interesting feature calculators, implement a new feature selection process or just fix 1-2 typos,\nyour help is appreciated.\n\nIf you want to help, just create a pull request on our github page. To the new user, working with Git can sometimes be\nconfusing and frustrating. If you are not familiar with Git you can also contact us by :ref:`email `.\n\n\nGuidelines\n''''''''''\n\nThere are three general coding paradigms that we believe in:\n\n 1. **Keep it simple**. We believe that *\"Programs should be written for people to read, and only incidentally for\n machines to execute.\"*.\n\n 2. **Keep it documented** by at least including a docstring for each method and class. Do not describe what you are\n doing but why you are doing it.\n\n 3. **Keep it tested**. We aim for a high test coverage.\n\n\nThere are two important copyright guidelines:\n\n 4. Please do not include any data sets for which a licence is not available or commercial use is even prohibited.\n Those can undermine the licence of the whole projects.\n\n 5. Do not use code snippets for which a licence is not available (e.g. from stackoverflow) or commercial use is\n even prohibited. Those can undermine the licence of the whole projects.\n\nFurther, there are some technical decisions we made:\n\n 6. Clear the Output of iPython notebooks. This improves the readability of related Git diffs.\n\n\nInstallation\n''''''''''''\n\nInstall all the relevant python packages with\n\n.. code::\n\n cd /path/to/tsfresh\n pip install -e \".[testing]\"\n pre-commit install\n\n\nThe last command will dynamically link the tsfresh package which means that changes to the code will directly show up\nfor example in your test run.\n\n\nTest framework\n''''''''''''''\n\nAfter making your changes, you probably want to test your changes locally. To run our comprehensive suite of unit tests\nyou have to:\n\n\n.. code::\n\n pytest\n\n\nTo test changes across multiple versions of Python, run:\n\n\n.. code::\n\n tox -r -p auto\n\n\nThis will execute tests for the Python versions specified in `setup.cfg `_ using the `envlist` variable. For example, if `envlist` is set to `py37, py38`, the test suite will run for Python 3.7 and 3.8 on the local development platform, assuming the binaries for those versions are available locally. The exact Python microversions (e.g. `3.7.1` vs `3.7.2`) depend on what is installed on the local development machine.\n\nA recommended way to manage multiple Python versions when testing locally is with `pyenv`, which enables organized installation and switching between versions.\n\nIn addition to running tests locally, you can also run them in a Dockerized testing environment:\n\n\n.. code::\n\n make test-all-testenv\n\n\nThis command will initially take some time. However subsequent invokations will be faster, and testing this way ensures a clean, consistent test environment, regardless of your local setup.\n\n\nDocumentation\n'''''''''''''\n\nBuild the documentation after installing as described above with\n\n\n.. code::\n\n pip install -e \".[docs]\"\n cd docs\n make html\n\nThe finished documentation can be found in the docs/_build/html folder.\n\n\nStyling\n'''''''\n\nWe use black and isort for styling. They are automatically triggered on every commit after having installed pre-commit\n(as described above).\n\n\nWe are looking forward to hear from you! =)\n\n\nPR Descriptions\n'''''''''''''''\n\nThe PR should have a clear and descriptive title, along with a detailed description of the changes made, the problem being addressed, and any relevant tips for reviewers. An example of what this might look like is `here. `_\n" }, { "path": "docs/text/introduction.rst", "content": "Introduction\n============\n\nWhy tsfresh?\n------------\n\ntsfresh is used for systematic feature engineering from time-series and other sequential data [1]_.\nThese data have in common that they are ordered by an independent variable.\nThe most common independent variable is time (time series).\nOther examples for sequential data are reflectance and absorption spectra,\nwhich have wavelength as their ordering dimension.\nIn order to keep things simple, we are simply referring to all different types of sequential data as time-series.\n\n.. image:: ../images/introduction_ts_exa.png\n :scale: 70 %\n :alt: the time series\n :align: center\n\n(and yes, it is pretty cold!)\n\nNow you want to calculate different characteristics such as the maximum or minimum temperature, the average temperature\nor the number of temporary temperature peaks:\n\n.. image:: ../images/introduction_ts_exa_features.png\n :scale: 70 %\n :alt: some characteristics of the time series\n :align: center\n\nWithout tsfresh, you would have to calculate all those characteristics manually; tsfresh automates this process\ncalculating and returning all those features automatically.\n\nIn addition, tsfresh is compatible with the Python libraries :mod:`pandas` and :mod:`scikit-learn`, so you can easily\nintegrate the feature extraction with your current routines.\n\nWhat can we do with these features?\n-----------------------------------\n\nThe extracted features can be used to describe the time series, i.e., often these features give new insights into the\ntime series and their dynamics. They can also be used to cluster time series and to train machine learning models that\nperform classification or regression tasks on time series.\n\nThe tsfresh package has been successfully used in the following projects:\n\n * prediction of steel billets quality during a continuous casting process [2]_,\n * activity recognition from synchronized sensors [3]_,\n * volcanic eruption forecasting [4]_,\n * authorship attribution from written text samples [5]_,\n * characterisation of extrasolar planetary systems from time-series with missing data [6]_,\n * sensor anomaly detection [7]_,\n * and `many many more `_.\n\nWhat can't we do with tsfresh?\n------------------------------\n\nCurrently, tsfresh is not suitable:\n\n * for streaming data (by streaming data we mean data that is usually used for online operations, while time series data is usually used for offline operations)\n * to train models on the extracted features (we do not want to reinvent the wheel, to train machine learning models check out the Python package\n `scikit-learn `_)\n * for usage with highly irregular time series; tsfresh uses timestamps only to order observations, while many features are interval-agnostic (e.g., number of peaks) and can be determined for any series, some otherfeatures (e.g., linear trend) assume equal spacing in time, and should be used with care when this assumption is not met.\n\nHowever, some of these use cases could be implemented, if you have an application in mind, open\nan issue at ``_, or feel free to contact us.\n\nWhat else is out there?\n-----------------------\n\nThere is a matlab package called `hctsa `_ which can be used to automatically\nextract features from time series.\nIt is also possible to use hctsa from within Python through the `pyopy `_\npackage.\nOther available packagers are `featuretools `_, `FATS `_ and `cesium `_.\n\nReferences\n----------\n\n .. [1] Christ, M., Braun, N., Neuffer, J. and Kempa-Liehr A.W. (2018).\n *Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)*.\n Neurocomputing 307 (2018) 72-77,\n `doi: 10.1016/j.neucom.2018.03.067 `_.\n .. [2] Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2016).\n *Distributed and parallel time series feature extraction for industrial big data applications*.\n Asian Conference on Machine Learning (ACML), Workshop on Learning on Big Data (WLBD).\n ``_.\n .. [3] Kempa-Liehr, A.W., Oram, J., Wong, A., Finch, M. and Besier, T. (2020).\n *Feature engineering workflow for activity recognition from synchronized inertial measurement units*.\n In: Pattern Recognition. ACPR 2019. Ed. by M. Cree et al. Vol. 1180.\n Communications in Computer and Information Science (CCIS).\n Singapore: Springer 2020, 223–231.\n `doi: 10.1007/978-981-15-3651-9_20 `_.\n .. [4] D. E. Dempsey, S. J. Cronin, S. Mei, and A. W. Kempa-Liehr (2020).\n *Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand*.\n Nature Communications 11.3562, pp. 1–8.\n `doi: 10.1038/s41467-020-17375-2 `_.\n .. [5] Tang, Y., Blincoe, K., Kempa-Liehr, A.W. (2020).\n *Enriching Feature Engineering for Short Text Samples by Language Time Series Analysis*.\n EPJ Data Science 9.26 (2020), 1–59.\n `doi: 10.1140/epjds/s13688-020-00244-9 `_.\n .. [6] Kennedy, A., Gemma, N., Rattenbury, N., Kempa-Liehr, A.W. (2021).\n *Modelling the projected separation of microlensing events using systematic time-series feature engineering*.\n Astronomy and Computing 35.100460 (2021), 1–14,\n `doi: 10.1016/j.ascom.2021.100460 `_.\n .. [7] Hui Yie Teh, Kevin I-Kai Wang, and Andreas W. Kempa-Liehr (2021).\n *Expect the Unexpected: Unsupervised feature selection for automated sensor anomaly detection*.\n IEEE Sensors Journal 15.16, pp. 18033–18046.\n `doi: 10.1109/JSEN.2021.3084970 `_.\n" }, { "path": "docs/text/large_data.rst", "content": ".. _large-data-label:\n\nLarge Input Data\n================\n\nIf you are working with large time series data, you are probably facing multiple problems.\nThe two most important ones are:\n\n* long execution times for feature extraction\n* large memory consumption, even beyond what a single machine can handle\n\nTo solve the first problem, you can parallelize the computation as described in :ref:`tsfresh-on-a-cluster-label`.\nNote, that parallelization on your local computer is already turned on by default.\n\nHowever, for larger data sets you need to handle both problems at the same time.\nYou have multiple options to do so, which we will discuss in the following paragraphs.\n\nDask - the simple way\n---------------------\n\n*tsfresh* accepts a `dask dataframe `_ instead of a\npandas dataframe as input for the :func:`tsfresh.extract_features` function.\nDask dataframes allow you to scale your computation beyond your local memory (via partitioning the data internally)\nand even to large clusters of machines.\nIts dataframe API is very similar to pandas dataframes and might even be a drop-in replacement.\n\nAll arguments discussed in :ref:`data-formats-label` are also valid for dask dataframes.\nThe input data will be transformed into the correct format for *tsfresh* using dask methods\nand the feature extraction will be added as additional computations to the computation graph.\nYou can then add additional computations to the result or trigger the computation as usual with ``.compute()``.\n\n.. NOTE::\n\n The last step of the feature extraction is to bring all features into a tabular format.\n Especially for very large data samples, this computation can be a large\n performance bottleneck.\n We therefore recommend to turn the pivoting off, if you do not really need it\n and work with the un-pivoted data as much as possible.\n\nFor example, to read in data from parquet and do the feature extraction:\n\n.. code::\n\n import dask.dataframe as dd\n from tsfresh import extract_features\n\n df = dd.read_parquet(...)\n\n X = extract_features(df,\n column_id=\"id\",\n column_sort=\"time\",\n pivot=False)\n\n result = X.compute()\n\nDask - more control\n-------------------\n\nThe feature extraction method needs to perform some data transformations before it\ncan call the actual feature calculators.\nIf you want to optimize your data flow, you might want to have more control on how\nexactly the feature calculation is added to you dask computation graph.\n\nTherefore, it is also possible to add the feature extraction directly:\n\n\n.. code::\n\n from tsfresh.convenience.bindings import dask_feature_extraction_on_chunk\n features = dask_feature_extraction_on_chunk(df_grouped,\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"time\",\n column_value=\"value\")\n\nIn this case however, ``df_grouped`` must already be in the correct format.\nCheck out the documentation of :func:`tsfresh.convenience.bindings.dask_feature_extraction_on_chunk`\nfor more information.\nNo pivoting will be performed in this case.\n\nPySpark\n-------\n\nSimilar to dask, it is also possible to pass the feature extraction into a Spark\ncomputation graph.\nYou can find more information in the documentation of :func:`tsfresh.convenience.bindings.spark_feature_extraction_on_chunk`.\n" }, { "path": "docs/text/list_of_features.rst", "content": "Overview on extracted features\n==============================\n\n*tsfresh* calculates a comprehensive number of features. All feature calculators are contained in the submodule:\n\n.. autosummary::\n :toctree: _generated\n :template: module_functions_template.rst\n\n tsfresh.feature_extraction.feature_calculators\n\n\nThe following list contains all the feature calculations supported in the current version of *tsfresh*:\n\n.. include:: _generated/tsfresh.feature_extraction.feature_calculators.rst\n" }, { "path": "docs/text/quick_start.rst", "content": ".. _quick-start-label:\n\nQuick Start\n===========\n\n\nInstall tsfresh\n---------------\n\nAs the compiled tsfresh package is hosted on the Python Package Index (PyPI) you can easily install it with pip\n\n.. code:: shell\n\n pip install tsfresh\n\nIf you need to work with large time series data that may not fit in memory, install tsfresh with\n`Dask `_:\n\n.. code:: shell\n\n pip install tsfresh[dask]\n\nSee also :ref:`large-data-label`.\n\n\nDive in\n-------\n\nBefore boring yourself by reading the docs in detail, you can dive right into tsfresh with the following example:\n\nWe are given a data set containing robot failures as discussed in [1]_.\nEach robot records time series from six different sensors.\nFor each sample denoted by a different id we are going to classify if the robot reports a failure or not.\nFrom a machine learning point of view, our goal is to classify each group of time series.\n\nTo start, we load the data into python\n\n.. code:: python\n\n from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, \\\n load_robot_execution_failures\n download_robot_execution_failures()\n timeseries, y = load_robot_execution_failures()\n\nand end up with a pandas.DataFrame `timeseries` having the following shape\n\n.. code:: python\n\n print(timeseries.head())\n\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| | id | time| F_x | F_y | F_z | T_x | T_y | T_z |\n+=====+=====+=====+=====+=====+=====+=====+=====+=====+\n| 0 | 1 | 0 | -1 | -1 | 63 | -3 | -1 | 0 |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| 1 | 1 | 1 | 0 | 0 | 62 | -3 | -1 | 0 |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| 2 | 1 | 2 | -1 | -1 | 61 | -3 | 0 | 0 |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| 3 | 1 | 3 | -1 | -1 | 63 | -2 | -1 | 0 |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| 4 | 1 | 4 | -1 | -1 | 63 | -3 | -1 | 0 |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n| ... | ... | ... | ... | ... | ... | ... | ... | ... |\n+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n\nThe first column is the DataFrame index and has no meaning here.\nThere are six different time series (`F_x`, `F_y`, `F_z`, `T_x`, `T_y`, `T_z`) for the different sensors. The different robots are denoted by the ids column.\n\nOn the other hand, ``y`` contains the information which robot `id` reported a failure and which not:\n\n+---+---+\n| 1 | 0 |\n+---+---+\n| 2 | 0 |\n+---+---+\n| 3 | 0 |\n+---+---+\n| 4 | 0 |\n+---+---+\n| 5 | 0 |\n+---+---+\n|...|...|\n+---+---+\n\nHere, for the samples with ids 1 to 5 no failure was reported.\n\nIn the following we illustrate the time series of the sample id 3 reporting no failure:\n\n.. code:: python\n\n import matplotlib.pyplot as plt\n timeseries[timeseries['id'] == 3].plot(subplots=True, sharex=True, figsize=(10,10))\n plt.show()\n\n.. image:: ../images/ts_example_robot_failures_nofail.png\n :alt: the time series for id 3 (no failure)\n :align: center\n\nAnd for id 20 reporting a failure:\n\n\n.. code:: python\n\n timeseries[timeseries['id'] == 20].plot(subplots=True, sharex=True, figsize=(10,10))\n plt.show()\n\n.. image:: ../images/ts_example_robot_failures_fail.png\n :alt: the time series for id 20 (failure)\n :align: center\n\nYou can already see some differences by eye - but for successful machine learning we have to put these differences into\nnumbers.\n\nFor this, tsfresh comes into place.\nIt allows us to automatically extract over 1200 features from those six different time series for each robot.\n\nFor extracting all features, we do:\n\n.. code:: python\n\n from tsfresh import extract_features\n extracted_features = extract_features(timeseries, column_id=\"id\", column_sort=\"time\")\n\nYou end up with the DataFrame `extracted_features` with more than 1200 different extracted features.\nWe will now first, remove all ``NaN`` values (which were created by feature calculators that can not be used on the\ngiven data, e.g., because the statistics are too low), and then select only the relevant features:\n\n.. code-block:: python\n\n from tsfresh import select_features\n from tsfresh.utilities.dataframe_functions import impute\n\n impute(extracted_features)\n features_filtered = select_features(extracted_features, y)\n\n\nOnly around 300 features were classified as relevant enough.\n\nFurther, you can even perform the extraction, imputing and filtering at the same time with the\n:func:`tsfresh.extract_relevant_features` function:\n\n.. code-block:: python\n\n from tsfresh import extract_relevant_features\n\n features_filtered_direct = extract_relevant_features(timeseries, y,\n column_id='id', column_sort='time')\n\n\nYou can now use the features in the DataFrame `features_filtered` (which is equal to\n`features_filtered_direct`) in conjunction with `y` to train your classification model.\nYou can find an example in the Jupyter notebook\n`01 Feature Extraction and Selection.ipynb `_\nwhere we train a RandomForestClassifier using the extracted features.\n\nReferences\n\n.. [1] http://archive.ics.uci.edu/ml/datasets/Robot+Execution+Failures\n" }, { "path": "docs/text/sklearn_transformers.rst", "content": ".. _sklearn-transformers-label:\n\nscikit-learn Transformers\n=========================\n\ntsfresh includes three scikit-learn compatible transformers, which allow you to easily incorporate feature extraction\nand feature selection from time series into your existing machine learning pipelines.\n\nThe scikit-learn pipeline allows you to assemble several pre-processing steps that will be executed in sequence and\nthus, can be cross-validated together while setting different parameters (for more details about the scikit-learn's\npipeline, take a look at the official documentation [1]_).\nOur tsfresh transformers allow you to extract and filter the time series features during these pre-processing sequence.\n\nThe first two estimators in tsfresh are the :class:`~tsfresh.transformers.feature_augmenter.FeatureAugmenter`,\nwhich extracts the features, and the :class:`~tsfresh.transformers.feature_selector.FeatureSelector`, which\nperforms the feature selection algorithm.\nIt is preferable to combine extracting and filtering of the features in a single step to avoid unnecessary feature\ncalculations.\nHence, the :class:`~tsfresh.transformers.feature_augmenter.RelevantFeatureAugmenter` combines both the\nextraction and filtering of the features in a single step.\n\nExample\n-------\n\nIn the following example you see how we combine tsfresh's\n:class:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter` and a\n:class:`~sklearn.ensemble.RandomForestClassifier` into a single pipeline. This pipeline can then fit both our\ntransformer and the classifier in one step.\n\n.. code-block:: python\n\n from sklearn.pipeline import Pipeline\n from sklearn.ensemble import RandomForestClassifier\n from tsfresh.examples import load_robot_execution_failures\n from tsfresh.transformers import RelevantFeatureAugmenter\n import pandas as pd\n\n # Download dataset\n from tsfresh.examples.robot_execution_failures import download_robot_execution_failures\n download_robot_execution_failures()\n\n pipeline = Pipeline([\n ('augmenter', RelevantFeatureAugmenter(column_id='id', column_sort='time')),\n ('classifier', RandomForestClassifier()),\n ])\n\n df_ts, y = load_robot_execution_failures()\n X = pd.DataFrame(index=y.index)\n\n pipeline.set_params(augmenter__timeseries_container=df_ts)\n pipeline.fit(X, y)\n\nThe parameters of the :class:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter` correspond to\nthe parameters of the top-level convenience function\n:func:`~tsfresh.convenience.relevant_extraction.extract_relevant_features`.\nIn the above example, we only set the names of two columns ``column_id='id'``, ``column_sort='time'``\n(see :ref:`data-formats-label` for more details on those parameters).\n\nBecause we cannot pass the time series container directly as a parameter to the augmenter step when calling fit or\ntransform on a :class:`sklearn.pipeline.Pipeline`, we have to set it manually by calling\n``pipeline.set_params(augmenter__timeseries_container=df_ts)``.\nIn general, you can change the time series container from which the features are extracted by calling either the\npipeline's :func:`~sklearn.pipeline.Pipeline.set_params` method or the transformers\n:func:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter.set_timeseries_container` method.\n\nFor further examples, visit the Jupyter Notebook 02 sklearn Pipeline.ipynb in the notebooks folder of the tsfresh\ngithub repository.\n\n\nReferences\n----------\n\n .. [1] http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html\n" }, { "path": "docs/text/tsfresh_on_a_cluster.rst", "content": ".. _tsfresh-on-a-cluster-label:\n\n.. role:: python(code)\n :language: python\n\nParallelization\n===============\n\nThe feature extraction, the feature selection, as well as the rolling, offer the possibility of parallelization.\nBy default, all of those tasks are parallelized by tsfresh.\nHere we discuss the different settings to control the parallelization.\nTo achieve the best results for your use-case you should experiment with the parameters.\n\n.. NOTE::\n This document describes parallelization to speed up processing time.\n If you are working with large amounts of data (which might not fit into memory),\n check :ref:`large-data-label`.\n\nPlease, let us know about your results tuning the below mentioned parameters! It will help improve the documentation as\nwell as the default settings.\n\nParallelization of Feature Selection\n------------------------------------\n\nWe use a :class:`multiprocessing.Pool` to parallelize the calculation of the p-values for each feature. On\ninstantiation we set the Pool's number of worker processes to\n`n_jobs`. This field defaults to\nthe number of processors on the current system. We recommend setting it to the maximum number of available (and\notherwise idle) processors.\n\nThe chunksize of the Pool's map function is another important parameter to consider. It can be set via the\n`chunksize` field. By default it is up to\n:class:`multiprocessing.Pool` is parallelisation parameter. One data chunk is\ndefined as a singular time series for one id and one kind. The chunksize is the\nnumber of chunks that are submitted as one task to one worker process. If you\nset the chunksize to 10, then it means that one worker task corresponds to\ncalculate all features for 10 id/kind time series combinations. If it is set\nto None, depending on distributor, heuristics are used to find the optimal\nchunksize. The chunksize can have a crucial influence on the optimal cluster\nperformance and should be optimised in benchmarks for the problem at hand.\n\nParallelization of Feature Extraction\n-------------------------------------\n\nFor the feature extraction tsfresh exposes the parameters\n`n_jobs` and `chunksize`. Both behave similarly to the parameters\nfor the feature selection.\n\nTo do performance studies and profiling, it is sometimes useful to turn off parallelization. This can be\ndone by setting the parameter `n_jobs` to 0.\n\nParallelization beyond a single machine\n---------------------------------------\n\nThe high volume of time series data can demand an analysis at scale.\nSo, time series need to be processed on a group of computational units instead of a singular machine.\n\nAccordingly, it may be necessary to distribute the extraction of time series features to a cluster.\nIt is possible to extract features with *tsfresh* in a distributed fashion.\nIn the following paragraphs we discuss how to setup a distributed *tsfresh*.\n\nTo distribute the calculation of features, we use a certain object, the Distributor class (located in the\n:mod:`tsfresh.utilities.distribution` module).\n\nEssentially, a Distributor organizes the application of feature calculators to data chunks.\nIt maps the feature calculators to the data chunks and then reduces them, meaning that it combines the results of the\nindividual mappings into one object, the feature matrix.\n\nSo, Distributor will, in the following order,\n\n 1. calculate an optimal :python:`chunk_size`, based on the characteristics of the time series data\n (by :func:`~tsfresh.utilities.distribution.DistributorBaseClass.calculate_best_chunk_size`)\n\n 2. split the time series data into chunks\n (by :func:`~tsfresh.utilities.distribution.DistributorBaseClass.partition`)\n\n 3. distribute the application of the feature calculators to the data chunks\n (by :func:`~tsfresh.utilities.distribution.DistributorBaseClass.distribute`)\n\n 4. combine the results into the feature matrix\n (by :func:`~tsfresh.utilities.distribution.DistributorBaseClass.map_reduce`)\n\n 5. close all connections, shutdown all resources and clean everything\n (by :func:`~tsfresh.utilities.distribution.DistributorBaseClass.close`)\n\nSo, how can you use the Distributor to extract features with *tsfresh*?\nYou will have to pass :python:`distributor` as an argument to the :func:`~tsfresh.feature_extraction.extract_features`\nmethod.\n\n\nThe following example shows how to define the MultiprocessingDistributor, which will distribute the calculations to a\nlocal pool of threads:\n\n.. code:: python\n\n from tsfresh.examples.robot_execution_failures import \\\n download_robot_execution_failures, \\\n load_robot_execution_failures\n from tsfresh.feature_extraction import extract_features\n from tsfresh.utilities.distribution import MultiprocessingDistributor\n\n # download and load some time series data\n download_robot_execution_failures()\n df, y = load_robot_execution_failures()\n\n # We construct a Distributor that will spawn the calculations\n # over four threads on the local machine\n Distributor = MultiprocessingDistributor(n_workers=4,\n disable_progressbar=False,\n progressbar_title=\"Feature Extraction\")\n\n # just to pass the Distributor object to\n # the feature extraction, along with the other parameters\n X = extract_features(timeseries_container=df,\n column_id='id',\n column_sort='time',\n distributor=Distributor)\n\nThe following example corresponds to the existing multiprocessing *tsfresh* API, where you just specify the number of\njobs, without the need to construct the Distributor:\n\n.. code:: python\n\n from tsfresh.examples.robot_execution_failures import \\\n download_robot_execution_failures, \\\n load_robot_execution_failures\n from tsfresh.feature_extraction import extract_features\n\n download_robot_execution_failures()\n df, y = load_robot_execution_failures()\n\n X = extract_features(timeseries_container=df,\n column_id='id',\n column_sort='time',\n n_jobs=4)\n\nUsing dask to distribute the calculations\n'''''''''''''''''''''''''''''''''''''''''\n\nWe provide a Distributor for the `dask framework `_, where\n*\"Dask is a flexible parallel computing library for analytic computing.\"*\n\n.. NOTE::\n This part of the documentation only handles parallelizing the computation using\n a dask cluster. The input and output are still pandas objects.\n If you want to use dask's capabilities to scale to data beyond your local\n memory, have a look at :ref:`large-data-label`.\n\nDask is a great framework to distribute analytic calculations into clusters.\nIt scales up and down, meaning that you can also use it on a singular machine.\nThe only thing that you will need to run *tsfresh* on a Dask cluster is the ip address and port number of the\n`dask-scheduler `_.\n\nLet's say that your dask scheduler is running at ``192.168.0.1:8786``, then we can construct a\n:class:`~sfresh.utilities.distribution.ClusterDaskDistributor` that connects to the scheduler and distributes the\ntime series data and the calculation to a cluster:\n\n.. code:: python\n\n from tsfresh.examples.robot_execution_failures import \\\n download_robot_execution_failures, \\\n load_robot_execution_failures\n from tsfresh.feature_extraction import extract_features\n from tsfresh.utilities.distribution import ClusterDaskDistributor\n\n download_robot_execution_failures()\n df, y = load_robot_execution_failures()\n\n Distributor = ClusterDaskDistributor(address=\"192.168.0.1:8786\")\n\n X = extract_features(timeseries_container=df,\n column_id='id',\n column_sort='time',\n distributor=Distributor)\n\nCompared to the :class:`~tsfresh.utilities.distribution.MultiprocessingDistributor` example from above, we only had to\nchange one line to switch from one machine to a whole cluster.\nIt is as easy as that.\nBy changing the Distributor you can easily deploy your application to run to a cluster instead of your workstation.\n\nYou can also use a local DaskCluster on your local machine to emulate a Dask network.\nThe following example shows how to setup a :class:`~tsfresh.utilities.distribution.LocalDaskDistributor` on a local cluster\nof 3 workers:\n\n.. code:: python\n\n from tsfresh.examples.robot_execution_failures import \\\n download_robot_execution_failures, \\\n load_robot_execution_failures\n from tsfresh.feature_extraction import extract_features\n from tsfresh.utilities.distribution import LocalDaskDistributor\n\n download_robot_execution_failures()\n df, y = load_robot_execution_failures()\n\n Distributor = LocalDaskDistributor(n_workers=3)\n\n X = extract_features(timeseries_container=df,\n column_id='id',\n column_sort='time',\n distributor=Distributor)\n\nWriting your own distributor\n''''''''''''''''''''''''''''\n\nIf you want to use other framework instead of Dask, you will have to write your own Distributor.\nTo construct your custom Distributor, you need to define an object that inherits from the abstract base class\n:class:`tsfresh.utilities.distribution.DistributorBaseClass`.\nThe :mod:`tsfresh.utilities.distribution` module contains more information about what you need to implement.\n\nNotes for efficient parallelization\n'''''''''''''''''''''''''''''''''''\n\nBy default tsfresh uses parallelization to distribute the single-threaded python code to the multiple cores available on the host machine.\n\nHowever, this can create an issue known as over-provisioning. Many of the underlying python libraries (e.g. numpy) used in the feature calculators have C code implementations for their low-level processing. Those `also` try to spread their workload between as many cores available - which is in conflict with the parallelization done by tsfresh.\n\nOver-provisioning is inefficient because of the overheads of repeated context switching.\n\nThis issue can be solved by constraining the C libraries to single threads, using the following environment variables:\n\n.. code:: python\n\n import os\n os.environ['OMP_NUM_THREADS'] = \"1\"\n os.environ['MKL_NUM_THREADS'] = \"1\"\n os.environ['OPENBLAS_NUM_THREADS'] = \"1\"\n\nPut these lines at the beginning of your notebook/python script - before you call any tsfresh code or import any other module.\n\nThe more cores your host computer has, the more improvement in processing speed will be gained by implementing these environment changes. Speed increases of between 6x and 26x have been observed depending on the type of the host machine.\n\n.. NOTE::\n If you intend to run machine learning pipelines after feature extraction, it is highly recommended to revert these changes.\n Settings have been tested on GPU-accelerated XGBoost classifier training task with 9x speed reduction.\n Many popular machine learning libraries like scikit-learn and Tensorflow/PyTorch rely on parallelization of your CPU cores to speed up CPU-dependent low-level computations.\n By forcing those libraries to only use a single thread, it creates a bottleneck for other downstream tasks even if you are accelerating them with GPUs.\n e.g. CPU-computation of loss function after every PyTorch neural network forward pass will only run on one thread, while the GPU idles and waits for its completion before starting on its backpropogation tasks.\n" }, { "path": "notebooks/01 Feature Extraction and Selection.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Extraction and Selection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This basic example shows how to use [tsfresh](https://tsfresh.readthedocs.io/) to extract useful features from multiple timeseries and use them to improve classification performance.\\n\",\n \"\\n\",\n \"We use the robot execution failure data set as an example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"\\n\",\n \"from tsfresh import extract_features, extract_relevant_features, select_features\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"from tsfresh.feature_extraction import ComprehensiveFCParameters\\n\",\n \"\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import classification_report\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load and visualize data\\n\",\n \"\\n\",\n \"The data set documents 88 robot executions (each has a unique `id` between 1 and 88), which is a subset of the [Robot Execution Failures Data Set](https://archive.ics.uci.edu/ml/datasets/Robot+Execution+Failures). \\n\",\n \"For the purpose of simplicity we are only differentiating between successfull and failed executions (`y`).\\n\",\n \"\\n\",\n \"For each execution 15 force (F) and torque (T) samples are given, which were measured at regular time intervals for the spatial dimensions x, y, and z. \\n\",\n \"Therefore each row of the data frame references a specific execution (`id`), a time index (`index`) and documents the respective measurements of 6 sensors (`F_x`, `F_y`, `F_z`, `T_x`, `T_y`, `T_z`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tsfresh.examples import robot_execution_failures\\n\",\n \"\\n\",\n \"robot_execution_failures.download_robot_execution_failures()\\n\",\n \"df, y = robot_execution_failures.load_robot_execution_failures()\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's draw some example executions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df[df.id == 3][['time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z']].plot(x='time', title='Success example (id 3)', figsize=(12, 6));\\n\",\n \"df[df.id == 20][['time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z']].plot(x='time', title='Failure example (id 20)', figsize=(12, 6));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extract Features\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use the data to extract time series features using `tsfresh`.\\n\",\n \"We want to extract features for each time series, that means for each robot execution (which is our `id`) and for each of the measured sensor values (`F_*` and `T_*`).\\n\",\n \"\\n\",\n \"You can think of it like this: tsfresh will result in a single row for each `id` and will calculate the features for each columns (we call them \\\"kind\\\") separately.\\n\",\n \"\\n\",\n \"The `time` column is our sorting column.\\n\",\n \"For an overview on the data formats of `tsfresh`, please have a look at [the documentation](https://tsfresh.readthedocs.io/en/latest/text/data_formats.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We are very explicit here and specify the `default_fc_parameters`. If you remove this argument,\\n\",\n \"# the ComprehensiveFCParameters (= all feature calculators) will also be used as default.\\n\",\n \"# Have a look into the documentation (https://tsfresh.readthedocs.io/en/latest/text/feature_extraction_settings.html)\\n\",\n \"# or one of the other notebooks to learn more about this.\\n\",\n \"extraction_settings = ComprehensiveFCParameters()\\n\",\n \"\\n\",\n \"X = extract_features(df, column_id='id', column_sort='time',\\n\",\n \" default_fc_parameters=extraction_settings,\\n\",\n \" # we impute = remove all NaN features automatically\\n\",\n \" impute_function=impute)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`X` now contains for each robot execution (= `id`) a single row, with all the features `tsfresh` calculated based on the measured times series values for this `id`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"Currently, 4674 non-NaN features are calculated. \\n\",\n \"This number varies with the version of `tsfresh` and with your data.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Select Features\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using the hypothesis tests implemented in `tsfresh` (see [here](https://tsfresh.readthedocs.io/en/latest/text/feature_filtering.html) for more information) it is now possible to select only the relevant features out of this large dataset.\\n\",\n \"\\n\",\n \"`tsfresh` will do a hypothesis test for each of the features to check, if it is relevant for your given target.\\n\",\n \"\\n\",\n \"To not leak information between the train and the test set, we will only perform the selection on the train set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_full_train, X_full_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_filtered_train = select_features(X_full_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X_filtered_train.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"Currently, 423 non-NaN features survive the feature selection given this target.\\n\",\n \"Again, this number will vary depending on your data, your target and the `tsfresh` version.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## Train and evaluate classifier\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's train a boosted decision tree on the filtered as well as the full set of extracted features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_filtered_train, X_filtered_test = X_full_train[X_filtered_train.columns], X_full_test[X_filtered_train.columns]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"classifier_full = DecisionTreeClassifier()\\n\",\n \"classifier_full.fit(X_full_train, y_train)\\n\",\n \"print(classification_report(y_test, classifier_full.predict(X_full_test)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"classifier_filtered = DecisionTreeClassifier()\\n\",\n \"classifier_filtered.fit(X_filtered_train, y_train)\\n\",\n \"print(classification_report(y_test, classifier_filtered.predict(X_filtered_test)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Compared to using all features (`classifier_full`), using only the relevant features (`classifier_filtered`) achieves similar or better classification performance with much less data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"Please remember that the hypothesis test in `tsfresh` is a statistical test.\\n\",\n \"You might get better performance with other feature selection methods (e.g. training a classifier with\\n\",\n \"all but one feature to find its importance) - but in general the feature selection implemented\\n\",\n \"in `tsfresh` will give you a very reasonable set of selected features.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extraction and Filtering is the same as filtered Extraction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Above, we performed the feature extraction and selection independently. \\n\",\n \"If you are only interested in the list of selected features, you can run this in one step:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"extract_relevant_features(df, y, column_id='id', column_sort='time',\\n\",\n \" default_fc_parameters=extraction_settings)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/02 sklearn Pipeline.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Selection in a sklearn pipeline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook is quite similar to [the first example](./01%20Feature%20Extraction%20and%20Selection.ipynb).\\n\",\n \"This time however, we use the `sklearn` pipeline API of `tsfresh`.\\n\",\n \"If you want to learn more, have a look at [the documentation](https://tsfresh.readthedocs.io/en/latest/text/sklearn_transformers.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from sklearn.pipeline import Pipeline\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.metrics import classification_report\\n\",\n \"\\n\",\n \"from tsfresh.examples import load_robot_execution_failures\\n\",\n \"from tsfresh.transformers import RelevantFeatureAugmenter\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load and Prepare the Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check out the first example notebook to learn more about the data and format.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tsfresh.examples.robot_execution_failures import download_robot_execution_failures\\n\",\n \"download_robot_execution_failures() \\n\",\n \"df_ts, y = load_robot_execution_failures()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to use the extracted features to predict for each of the robot executions, if it was a failure or not.\\n\",\n \"Therefore our basic \\\"entity\\\" is a single robot execution given by a distinct `id`.\\n\",\n \"\\n\",\n \"A dataframe with these identifiers as index needs to be prepared for the pipeline.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X = pd.DataFrame(index=y.index)\\n\",\n \"\\n\",\n \"# Split data into train and test set\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Build the pipeline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We build a sklearn pipeline that consists of a feature extraction step (`RelevantFeatureAugmenter`) with a subsequent `RandomForestClassifier`.\\n\",\n \"\\n\",\n \"The `RelevantFeatureAugmenter` takes roughly the same arguments as `extract_features` and `select_features` do.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl = Pipeline([\\n\",\n \" ('augmenter', RelevantFeatureAugmenter(column_id='id', column_sort='time')),\\n\",\n \" ('classifier', RandomForestClassifier())\\n\",\n \" ])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"Here comes the tricky part!\\n\",\n \" \\n\",\n \"The input to the pipeline will be our dataframe `X`, with one row per identifier.\\n\",\n \"It is currently empty.\\n\",\n \"But which time series data should the `RelevantFeatureAugmenter` use to actually extract the features from?\\n\",\n \"\\n\",\n \"We need to pass the time series data (stored in `df_ts`) to the transformer.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this case, df_ts contains the time series of both train and test set, if you have different dataframes for \\n\",\n \"train and test set, you have to call set_params two times \\n\",\n \"(see further below on how to deal with two independent data sets)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl.set_params(augmenter__timeseries_container=df_ts);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We are now ready to fit the pipeline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The augmenter has used the input time series data to extract time series features for each of the identifiers in the `X_train` and selected only the relevant ones using the passed `y_train` as target.\\n\",\n \"These features have been added to `X_train` as new columns.\\n\",\n \"The classifier can now use these features during trainings.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prediction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"During inference, the augmenter only extracts those features that it has found as being relevant in the training phase. The classifier predicts the target using these features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_pred = ppl.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"So, finally we inspect the performance:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(classification_report(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also find out which columns the augmenter has selected\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl.named_steps[\\\"augmenter\\\"].feature_selector.relevant_features\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"In this example we passed in an empty (except the index) `X_train` or `X_test` into the pipeline.\\n\",\n \"However, you can also fill the input with other features you have (e.g. features extracted from the metadata)\\n\",\n \"or even use other pipeline components before.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Separating the time series data containers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the example above we passed in a single `df_ts` into the `RelevantFeatureAugmenter`, which was used both for training and predicting.\\n\",\n \"During training, only the data with the `id`s from `X_train` were extracted. The rest of the data are extracted during prediction.\\n\",\n \"\\n\",\n \"However, it is perfectly fine to call `set_params` twice: once before training and once before prediction. \\n\",\n \"This can be handy if you for example dump the trained pipeline to disk and re-use it only later for prediction.\\n\",\n \"You only need to make sure that the `id`s of the entities you use during training/prediction are actually present in the passed time series data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_ts_train = df_ts[df_ts[\\\"id\\\"].isin(y_train.index)]\\n\",\n \"df_ts_test = df_ts[df_ts[\\\"id\\\"].isin(y_test.index)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl.set_params(augmenter__timeseries_container=df_ts_train);\\n\",\n \"ppl.fit(X_train, y_train);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pickle\\n\",\n \"with open(\\\"pipeline.pkl\\\", \\\"wb\\\") as f:\\n\",\n \" pickle.dump(ppl, f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Later: load the fitted model and do predictions on new, unseen data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pickle\\n\",\n \"with open(\\\"pipeline.pkl\\\", \\\"rb\\\") as f:\\n\",\n \" ppk = pickle.load(f)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ppl.set_params(augmenter__timeseries_container=df_ts_test);\\n\",\n \"y_pred = ppl.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(classification_report(y_test, y_pred))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/03 Feature Extraction Settings.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Calculator Settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default, all feature calculators are used when you call `extract_features`.\\n\",\n \"There could be multiple reasons why you do not want that:\\n\",\n \"* you are only interested on a certain feature (or features)\\n\",\n \"* you want to save time during extraction\\n\",\n \"* you have ran the feature selection before and already know, which features are relevant\\n\",\n \"\\n\",\n \"For more information on these settings, please have a look into [the documentation](http://tsfresh.readthedocs.io/en/latest/text/feature_extraction_settings.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tsfresh.feature_extraction import extract_features\\n\",\n \"from tsfresh.feature_extraction import settings\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Construct a time series container\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For testing, we construct the time series container that includes two sensor time series, \\\"temperature\\\" and \\\"pressure\\\", for two devices \\\"a\\\" and \\\"b\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.DataFrame({\\\"id\\\": [\\\"a\\\", \\\"a\\\", \\\"b\\\", \\\"b\\\"], \\\"temperature\\\": [1,2,3,1], \\\"pressure\\\": [-1, 2, -1, 7]})\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The `default_fc_parameters`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Which features are calculated by `tsfresh` is controlled by a dictionary that contains a mapping from feature calculator names to their parameters. \\n\",\n \"This dictionary is called `fc_parameters`. \\n\",\n \"It maps feature calculator names (= keys) to parameters (= values). \\n\",\n \"Every key in the dictionary will be looked up as a function in `tsfresh.feature_extraction.feature_calculators` and be used to extract features.\\n\",\n \"\\n\",\n \"`tsfresh` comes with some predefined sets of `fc_parameters` dictionaries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"settings.ComprehensiveFCParameters, settings.EfficientFCParameters, settings.MinimalFCParameters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example, to only calculate a very minimal set of features:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"settings_minimal = settings.MinimalFCParameters() \\n\",\n \"settings_minimal\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each key stands for one of the feature calculators. \\n\",\n \"The value are the parameters. If a feature calculator has no parameters, `None` is used as a value (and as these feature calculators are very simple, they all have no parameters).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This dictionary can passed to the extract method, resulting in a few basic time series beeing calculated:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_tsfresh = extract_features(df, column_id=\\\"id\\\", default_fc_parameters=settings_minimal)\\n\",\n \"X_tsfresh.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By using the settings_minimal as value of the default_fc_parameters parameter, those settings are used for all type of time series. \\n\",\n \"In this case, the `settings_minimal` dictionary is used for both \\\"temperature\\\" and \\\"pressure\\\" time series.\\n\",\n \"\\n\",\n \"Please note how the columns in the resulting dataframe depend both on the settings as well as the kinds of the data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, lets say we want to remove the length feature and prevent it from beeing calculated. We just delete it from the dictionary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"del settings_minimal[\\\"length\\\"]\\n\",\n \"settings_minimal\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, if we extract features for this reduced dictionary, the length feature will not be calculated\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_tsfresh = extract_features(df, column_id=\\\"id\\\", default_fc_parameters=settings_minimal)\\n\",\n \"X_tsfresh.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The `kind_to_fc_parameters`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, lets say we do not want to calculate the same features for both type of time series. Instead there should be different sets of features for each kind.\\n\",\n \"\\n\",\n \"To do that, we can use the `kind_to_fc_parameters` parameter, which lets us specifiy which `fc_parameters` we want to use for which kind of time series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fc_parameters_pressure = {\\\"length\\\": None, \\n\",\n \" \\\"sum_values\\\": None}\\n\",\n \"\\n\",\n \"fc_parameters_temperature = {\\\"maximum\\\": None, \\n\",\n \" \\\"minimum\\\": None}\\n\",\n \"\\n\",\n \"kind_to_fc_parameters = {\\n\",\n \" \\\"temperature\\\": fc_parameters_temperature,\\n\",\n \" \\\"pressure\\\": fc_parameters_pressure\\n\",\n \"}\\n\",\n \"\\n\",\n \"print(kind_to_fc_parameters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"So, in this case, for sensor \\\"pressure\\\" both \\\"max\\\" and \\\"min\\\" are calculated. \\n\",\n \"For the \\\"temperature\\\" signal, the length and sum\\\\_values features are extracted instead.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_tsfresh = extract_features(df, column_id=\\\"id\\\", kind_to_fc_parameters=kind_to_fc_parameters)\\n\",\n \"X_tsfresh.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extracting from data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After applying a feature selection algorithm to drop irrelevant feature columns you know which features are relevant and which are not.\\n\",\n \"You can also use this information to only extract these relevant features in the first place.\\n\",\n \"\\n\",\n \"The provided `from_columns` method can be used to infer a settings dictionary from the dataframe containing the features.\\n\",\n \"This dictionary can then for example be stored and be used in the next feature extraction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Assuming `X_tsfresh` contains only our relevant features\\n\",\n \"relevant_settings = settings.from_columns(X_tsfresh)\\n\",\n \"relevant_settings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## More complex dictionaries\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide `fc_parameters` dictionaries with larger sets of features.\\n\",\n \"\\n\",\n \"The `EfficientFCParameters` contain features and parameters that should be calculated quite fast:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"settings_efficient = settings.EfficientFCParameters()\\n\",\n \"settings_efficient\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `ComprehensiveFCParameters` are the biggest set of features. It will take the longest to calculate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"settings_comprehensive = settings.ComprehensiveFCParameters()\\n\",\n \"settings_comprehensive\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Feature Calculator Parameters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"More complex feature calculators have parameters that you can use to tune the extracted features.\\n\",\n \"The predefined settings (such as `ComprehensiveFCParameters`) already contain default values of these features.\\n\",\n \"\\n\",\n \"However for your own projects, you might want/need to tune them.\\n\",\n \"\\n\",\n \"In detail, the values in a `fc_parameters` dictionary contain a list of parameter dictionaries. \\n\",\n \"When calculating the feature, each entry in the list of parameters will be used to calculate one feature.\\n\",\n \"\\n\",\n \"For example, lets have a look into the feature `large_standard_deviation`, which depends on a single parameter called `r` (it basically defines how large \\\"large\\\" is).\\n\",\n \"The `ComprehensiveFCParameters` contains several default values for `r`. \\n\",\n \"Each of them will be used to calculate a single feature:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"settings_comprehensive['large_standard_deviation']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you use these settings in feature extraction, that would trigger the calculation of 20 different `large_standard_deviation` features, one for `r=0.05` up to `r=0.95`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"settings_tmp = {'large_standard_deviation': settings_comprehensive['large_standard_deviation']}\\n\",\n \"\\n\",\n \"X_tsfresh = extract_features(df, column_id=\\\"id\\\", default_fc_parameters=settings_tmp)\\n\",\n \"X_tsfresh.columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you now want to change the parameters for a specific feature calculator, all you need to do is to change the dictionary values.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/04 Multiclass Selection Example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Multiclass Example\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This example show shows how to use `tsfresh` to extract and select useful features from timeseries in a multiclass classification example. \\n\",\n \"The underlying control of the false discovery rate (FDR) has been introduced by [Tang et al. (2020, Sec. 3.2)](https://doi.org/10.1140/epjds/s13688-020-00244-9).\\n\",\n \"\\n\",\n \"We use an example dataset of human activity recognition for this.\\n\",\n \"The dataset consists of timeseries for 7352 accelerometer readings. \\n\",\n \"Each reading represents an accelerometer reading for 2.56 sec at 50hz (for a total of 128 samples per reading). Furthermore, each reading corresponds one of six activities (walking, walking upstairs, walking downstairs, sitting, standing and laying).\\n\",\n \"\\n\",\n \"For more information go to https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\\n\",\n \"\\n\",\n \"This notebook follows the example in [the first notebook](./01%20Feature%20Extraction%20and%20Selection.ipynb), so we will go quickly over the extraction and focus on the more interesting feature selection in this case.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"\\n\",\n \"from tsfresh import extract_features, extract_relevant_features, select_features\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import classification_report\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load and visualize data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tsfresh.examples.har_dataset import download_har_dataset, load_har_dataset, load_har_classes\\n\",\n \"\\n\",\n \"# fetch dataset from uci\\n\",\n \"download_har_dataset()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": \" 0 1 2 3 4 5 6 \\\\\\n0 0.000181 0.010139 0.009276 0.005066 0.010810 0.004045 0.004757 \\n1 0.001094 0.004550 0.002879 0.002247 0.003305 0.002416 0.001619 \\n2 0.003531 0.002285 -0.000420 -0.003738 -0.006706 -0.003148 0.000733 \\n3 -0.001772 -0.001311 0.000388 0.000408 -0.000355 0.000998 0.001109 \\n4 0.000087 -0.000272 0.001022 0.003126 0.002284 0.000885 0.001933 \\n\\n 7 8 9 ... 118 119 120 121 \\\\\\n0 0.006214 0.003307 0.007572 ... 0.001412 -0.001509 0.000060 0.000435 \\n1 0.000981 0.000009 -0.000363 ... -0.000104 -0.000141 0.001333 0.001541 \\n2 0.000668 0.002162 -0.000946 ... 0.000661 0.001853 -0.000268 -0.000394 \\n3 -0.003149 -0.008882 -0.010483 ... 0.000458 0.002103 0.001358 0.000820 \\n4 0.002270 0.002247 0.002175 ... 0.002529 0.003518 -0.000248 -0.002761 \\n\\n 122 123 124 125 126 127 \\n0 -0.000819 0.000228 -0.000300 -0.001147 -0.000222 0.001576 \\n1 0.001077 -0.000736 -0.003767 -0.004646 -0.002941 -0.001599 \\n2 -0.000279 -0.000316 0.000144 0.001246 0.003117 0.002178 \\n3 -0.000212 -0.001915 -0.001631 -0.000867 -0.001172 -0.000028 \\n4 0.000252 0.003752 0.001626 -0.000698 -0.001223 -0.003328 \\n\\n[5 rows x 128 columns]\",\n \"text/html\": \"
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\"\n },\n \"metadata\": {},\n \"execution_count\": 3\n }\n ],\n \"source\": [\n \"df = load_har_dataset()\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = load_har_classes()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The data is not in a typical time series format so far: \\n\",\n \"the columns are the time steps whereas each row is a measurement of a different person.\\n\",\n \"\\n\",\n \"Therefore we bring it to a format where the time series of different persons are identified by an `id` and are order by time vertically.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df[\\\"id\\\"] = df.index\\n\",\n \"df = df.melt(id_vars=\\\"id\\\", var_name=\\\"time\\\").sort_values([\\\"id\\\", \\\"time\\\"]).reset_index(drop=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": \" id time value\\n0 0 0 0.000181\\n1 0 1 0.010139\\n2 0 2 0.009276\\n3 0 3 0.005066\\n4 0 4 0.010810\",\n \"text/html\": \"
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},\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n }\n ],\n \"source\": [\n \"plt.title('accelerometer reading')\\n\",\n \"plt.plot(df[df[\\\"id\\\"] == 0].set_index(\\\"time\\\").value)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extract Features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": \"Feature Extraction: 100%|██████████| 20/20 [00:13<00:00, 1.43it/s]\\n\"\n }\n ],\n \"source\": [\n \"# only use the first 500 ids to speed up the processing\\n\",\n \"X = extract_features(df[df[\\\"id\\\"] < 500], column_id=\\\"id\\\", column_sort=\\\"time\\\", impute_function=impute)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": \" value__variance_larger_than_standard_deviation value__has_duplicate_max \\\\\\n0 0.0 0.0 \\n1 0.0 0.0 \\n2 0.0 0.0 \\n3 0.0 0.0 \\n4 0.0 0.0 \\n\\n value__has_duplicate_min value__has_duplicate value__sum_values \\\\\\n0 0.0 0.0 0.290392 \\n1 0.0 0.0 0.022239 \\n2 0.0 0.0 0.054796 \\n3 0.0 0.0 0.042157 \\n4 0.0 0.0 -0.024980 \\n\\n value__abs_energy value__mean_abs_change value__mean_change \\\\\\n0 0.001766 0.001435 0.000011 \\n1 0.000506 0.001323 -0.000021 \\n2 0.001106 0.001827 -0.000011 \\n3 0.000932 0.001470 0.000014 \\n4 0.000525 0.001215 -0.000027 \\n\\n value__mean_second_derivative_central value__median ... \\\\\\n0 -0.000032 0.002025 ... \\n1 -0.000008 0.000110 ... \\n2 0.000001 0.000627 ... \\n3 0.000003 0.000269 ... \\n4 -0.000007 -0.000144 ... \\n\\n value__fourier_entropy__bins_2 value__fourier_entropy__bins_3 \\\\\\n0 0.079487 0.079487 \\n1 0.187031 0.490294 \\n2 0.307845 0.582136 \\n3 0.187031 0.309682 \\n4 0.341663 0.506308 \\n\\n value__fourier_entropy__bins_5 value__fourier_entropy__bins_10 \\\\\\n0 0.079487 0.419383 \\n1 0.891060 1.336251 \\n2 0.948691 1.397948 \\n3 0.600197 1.022235 \\n4 0.866293 1.327997 \\n\\n value__fourier_entropy__bins_100 \\\\\\n0 2.000401 \\n1 3.019874 \\n2 2.576279 \\n3 2.654834 \\n4 2.760333 \\n\\n value__permutation_entropy__dimension_3__tau_1 \\\\\\n0 1.747535 \\n1 1.706146 \\n2 1.669552 \\n3 1.638109 \\n4 1.637482 \\n\\n value__permutation_entropy__dimension_4__tau_1 \\\\\\n0 3.015811 \\n1 2.903657 \\n2 2.772489 \\n3 2.654572 \\n4 2.680036 \\n\\n value__permutation_entropy__dimension_5__tau_1 \\\\\\n0 4.068979 \\n1 3.936212 \\n2 3.784472 \\n3 3.644889 \\n4 3.652981 \\n\\n value__permutation_entropy__dimension_6__tau_1 \\\\\\n0 4.555721 \\n1 4.520418 \\n2 4.409911 \\n3 4.335833 \\n4 4.350795 \\n\\n value__permutation_entropy__dimension_7__tau_1 \\n0 4.724480 \\n1 4.747206 \\n2 4.735843 \\n3 4.686101 \\n4 4.656301 \\n\\n[5 rows x 779 columns]\",\n \"text/html\": \"
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5 rows × 779 columns

\\n
\"\n },\n \"metadata\": {},\n \"execution_count\": 9\n }\n ],\n \"source\": [\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## Train and evaluate classifier\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For later comparison, we train a decision tree on all features (without selection):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train, X_test, y_train, y_test = train_test_split(X, y[:500], test_size=.2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"scrolled\": true,\n \"tags\": []\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": \" precision recall f1-score support\\n\\n 1 0.89 1.00 0.94 17\\n 2 1.00 0.91 0.95 11\\n 3 1.00 0.94 0.97 17\\n 4 0.35 0.43 0.39 14\\n 5 0.57 0.57 0.57 23\\n 6 0.33 0.28 0.30 18\\n\\n accuracy 0.67 100\\n macro avg 0.69 0.69 0.69 100\\nweighted avg 0.67 0.67 0.67 100\\n\\n\"\n }\n ],\n \"source\": [\n \"classifier_full = DecisionTreeClassifier()\\n\",\n \"classifier_full.fit(X_train, y_train)\\n\",\n \"print(classification_report(y_test, classifier_full.predict(X_test)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Multiclass feature selection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will now select a subset of relevant features using the `tsfresh` select features method.\\n\",\n \"However it only works for binary classification or regression tasks. \\n\",\n \"\\n\",\n \"For a 6 label multi classification we therefore split the selection problem into 6 binary one-versus all classification problems. \\n\",\n \"For each of them we can do a binary classification feature selection:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": \"Number of relevant features for class 5: 232/779\\nNumber of relevant features for class 4: 214/779\\nNumber of relevant features for class 6: 200/779\\nNumber of relevant features for class 1: 233/779\\nNumber of relevant features for class 3: 239/779\\nNumber of relevant features for class 2: 170/779\\n\"\n }\n ],\n \"source\": [\n \"relevant_features = set()\\n\",\n \"\\n\",\n \"for label in y.unique():\\n\",\n \" y_train_binary = y_train == label\\n\",\n \" X_train_filtered = select_features(X_train, y_train_binary)\\n\",\n \" print(\\\"Number of relevant features for class {}: {}/{}\\\".format(label, X_train_filtered.shape[1], X_train.shape[1]))\\n\",\n \" relevant_features = relevant_features.union(set(X_train_filtered.columns))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": \"289\"\n },\n \"metadata\": {},\n \"execution_count\": 45\n }\n ],\n \"source\": [\n \"len(relevant_features)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"we keep only those features that we selected above, for both the train and test set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train_filtered = X_train[list(relevant_features)]\\n\",\n \"X_test_filtered = X_test[list(relevant_features)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and train again:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": \" precision recall f1-score support\\n\\n 1 0.89 1.00 0.94 17\\n 2 1.00 0.91 0.95 11\\n 3 1.00 0.94 0.97 17\\n 4 0.32 0.50 0.39 14\\n 5 0.58 0.48 0.52 23\\n 6 0.43 0.33 0.38 18\\n\\n accuracy 0.67 100\\n macro avg 0.70 0.69 0.69 100\\nweighted avg 0.69 0.67 0.67 100\\n\\n\"\n }\n ],\n \"source\": [\n \"classifier_selected = DecisionTreeClassifier()\\n\",\n \"classifier_selected.fit(X_train_filtered, y_train)\\n\",\n \"print(classification_report(y_test, classifier_selected.predict(X_test_filtered)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It worked! The precision improved by removing irrelevant features.\"\n ]\n },\n {\n \"source\": [\n \"## Improved Multiclass feature selection\\n\",\n \"We can instead specify the number of classes for which a feature should be a relevant predictor in order to pass through the filtering process. This is as simple as setting the `multiclass` parameter to `True` and setting `n_significant` to the required number of classes. We will try with a requirement of being relevant for 5 classes.\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": \"(400, 189)\"\n },\n \"metadata\": {},\n \"execution_count\": 60\n }\n ],\n \"source\": [\n \"X_train_filtered_multi = select_features(X_train, y_train, multiclass=True, n_significant=5)\\n\",\n \"X_train_filtered_multi.shape\"\n ]\n },\n {\n \"source\": [\n \"We can see that the number of relevant features is lower than the previous implementation.\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": \" precision recall f1-score support\\n\\n 1 0.94 1.00 0.97 17\\n 2 1.00 1.00 1.00 11\\n 3 1.00 0.94 0.97 17\\n 4 0.35 0.43 0.39 14\\n 5 0.68 0.65 0.67 23\\n 6 0.56 0.50 0.53 18\\n\\n accuracy 0.74 100\\n macro avg 0.76 0.75 0.75 100\\nweighted avg 0.75 0.74 0.74 100\\n\\n\"\n }\n ],\n \"source\": [\n \"classifier_selected_multi = DecisionTreeClassifier()\\n\",\n \"classifier_selected_multi.fit(X_train_filtered_multi, y_train)\\n\",\n \"X_test_filtered_multi = X_test[X_train_filtered_multi.columns]\\n\",\n \"print(classification_report(y_test, classifier_selected_multi.predict(X_test_filtered_multi)))\"\n ]\n },\n {\n \"source\": [\n \"We now get slightly better classification performance, especially for classes where the previous classifier performed poorly. The parameter `n_significant` can be tuned for best results.\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.8.5 64-bit ('tsfresh_env': conda)\",\n \"language\": \"python\",\n \"name\": \"python_defaultSpec_1601507548243\"\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\": \"3.8.5-final\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/05 Timeseries Forecasting.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Timeseries Forecasting\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook explains how to use `tsfresh` in time series foreacasting.\\n\",\n \"Make sure you also read through the [documentation](https://tsfresh.readthedocs.io/en/latest/text/forecasting.html) to learn more on this feature.\\n\",\n \"\\n\",\n \"We will use the stock price of Apple for this.\\n\",\n \"In this notebook we will only showcase how to work with a single time series at a time (one stock).\\n\",\n \"There exist another notebook in the `advanced` folder, which treats several stocks at the same time.\\n\",\n \"Basically the same - but a bit more complex when it comes to pandas multi-indexing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"\\n\",\n \"from tsfresh import extract_features, select_features\\n\",\n \"from tsfresh.utilities.dataframe_functions import roll_time_series, make_forecasting_frame\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"\\n\",\n \"try:\\n\",\n \" import pandas_datareader.data as web\\n\",\n \"except ImportError:\\n\",\n \" print(\\\"You need to install the pandas_datareader. Run pip install pandas_datareader.\\\")\\n\",\n \"\\n\",\n \"from sklearn.linear_model import LinearRegression\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reading the data\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We download the data from \\\"stooq\\\" and only store the High value.\\n\",\n \"Please note: this notebook is for showcasing `tsfresh`s feature extraction - not to predict stock market prices :-)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = web.DataReader(\\\"AAPL\\\", 'stooq')[\\\"High\\\"]\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(15, 6))\\n\",\n \"df.plot(ax=plt.gca())\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to make the time dependency a bit clearer and add an identifier to each of the stock values (in this notebook we only have Google though).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_melted = pd.DataFrame({\\\"high\\\": df.copy()})\\n\",\n \"df_melted[\\\"date\\\"] = df_melted.index\\n\",\n \"df_melted[\\\"Symbols\\\"] = \\\"AAPL\\\"\\n\",\n \"\\n\",\n \"df_melted.head()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create training data sample\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Forecasting typically involves the following steps:\\n\",\n \"* take all data up to today\\n\",\n \"* do feature extraction (e.g. by running `extract_features`)\\n\",\n \"* run a prediction model (e.g. a regressor, see below)\\n\",\n \"* use the result as the forecast for tomorrow\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In training however, we need multiple examples to train.\\n\",\n \"If we would only use the time series until today (and wait for the value of tomorrow to have a target), we would only have a single training example.\\n\",\n \"Therefore we use a trick: we replay the history.\\n\",\n \"\\n\",\n \"Imagine you have a cut-out window sliding over your data.\\n\",\n \"At each time step $t$, you treat the data as it would be today. \\n\",\n \"You extract the features with everything you know until today (which is all data until and including $t$).\\n\",\n \"The target for the features until time $t$ is the time value of time $t + 1$ (which you already know, because everything has already happened).\\n\",\n \"\\n\",\n \"The process of window-sliding is implemented in the function `roll_time_series`.\\n\",\n \"Our window size will be 20 (we look at max 20 days in the past) and we disregard all windows which are shorter than 5 days.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled = roll_time_series(df_melted, column_id=\\\"Symbols\\\", column_sort=\\\"date\\\",\\n\",\n \" max_timeshift=20, min_timeshift=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled.head()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting dataframe now consists of these \\\"windows\\\" stamped out of the original dataframe.\\n\",\n \"For example all data with the `id = (AAPL, 2020-07-14 00:00:00)` comes from the original data of stock `AAPL` including the last 20 days until `2020-07-14`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled[df_rolled[\\\"id\\\"] == (\\\"AAPL\\\", pd.to_datetime(\\\"2020-07-14\\\"))]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_melted[(df_melted[\\\"date\\\"] <= pd.to_datetime(\\\"2020-07-14\\\")) & \\n\",\n \" (df_melted[\\\"date\\\"] >= pd.to_datetime(\\\"2020-06-15\\\")) & \\n\",\n \" (df_melted[\\\"Symbols\\\"] == \\\"AAPL\\\")]\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you now group by the new `id` column, each of the groups will be a certain stock symbol until and including the data until a certain day (and including the last 20 days in the past).\\n\",\n \"\\n\",\n \"Whereas we started with 1257 data samples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"len(df_melted)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"we now have 1253 unique windows (identified by stock symbol and ending date):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled[\\\"id\\\"].nunique()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We \\\"lost\\\" 5 windows, as we required to have a minimum history of more than 5 days.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled.groupby(\\\"id\\\").size().agg([np.min, np.max])\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The process is also shown in this image (please note that the window size is smaller for better visibility):\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extract Features\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The rolled (windowed) data sample is now in the correct format to use it for `tsfresh`s feature extraction.\\n\",\n \"As normal, features will be extracted using all data for a given `id`, which is in our case all data of a given window and a given id (one colored box in the graph above).\\n\",\n \"\\n\",\n \"If the feature extraction returns a row with the index `(AAPL, 2020-07-14 00:00:00)`, you know it has been calculated using the `AAPL` data up and including `2020-07-14` (and 20 days of history).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X = extract_features(df_rolled.drop(\\\"Symbols\\\", axis=1), \\n\",\n \" column_id=\\\"id\\\", column_sort=\\\"date\\\", column_value=\\\"high\\\", \\n\",\n \" impute_function=impute, show_warnings=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.head()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We make the data a bit easier to work with by removing the tuple-index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X = X.set_index(X.index.map(lambda x: x[1]), drop=True)\\n\",\n \"X.index.name = \\\"last_date\\\"\\n\",\n \"X.head()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our `(AAPL, 2020-07-14 00:00:00)` is also in the data again:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.loc['2020-07-14']\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2020-07-14` and the last 20 days.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prediction\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now use the extracted features to train a regressor.\\n\",\n \"But what will be our targets?\\n\",\n \"The target for the row `2020-07-13` is the value on the next timestep (that would be `2020-07-14` in this case).\\n\",\n \"\\n\",\n \"So all we need to do is go back to our original dataframe and take the stock value of tomorrow.\\n\",\n \"This is done with `shift`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = df_melted.set_index(\\\"date\\\").sort_index().high.shift(-1)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quick consistency test:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y[\\\"2020-07-13\\\"], df[\\\"2020-07-14\\\"]\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, we need to be a bit careful here: `X` is missing the first 5 dates (as our minimum window size was 5) and `y` is missing the last date (as there is nothing to predict on today).\\n\",\n \"So lets make sure we have a consistent view on the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = y[y.index.isin(X.index)]\\n\",\n \"X = X[X.index.isin(y.index)]\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now train normal AdaBoostRegressors to predict the next time step .\\n\",\n \"Let's split the data into a training and testing sample (but make sure to keep temporal consistency).\\n\",\n \"We take everything until 2020 as train data an the rest as test:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X[:\\\"2020\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train = X[:\\\"2020\\\"]\\n\",\n \"X_test = X[\\\"2021\\\":]\\n\",\n \"\\n\",\n \"y_train = y[:\\\"2020\\\"]\\n\",\n \"y_test = y[\\\"2021\\\":]\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and do feature selection before training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train_selected = select_features(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ada = LinearRegression()\\n\",\n \"\\n\",\n \"ada.fit(X_train_selected, y_train)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now lets check how good our prediction is:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_test_selected = X_test[X_train_selected.columns]\\n\",\n \"\\n\",\n \"y_pred = pd.Series(ada.predict(X_test_selected), index=X_test_selected.index)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The prediction is for the next day, so for drawing we need to shift 1 step back:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(15, 6))\\n\",\n \"\\n\",\n \"y.plot(ax=plt.gca())\\n\",\n \"y_pred.plot(ax=plt.gca(), legend=None, marker=\\\".\\\")\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Well, clearly not perfect ;-)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Timeseries Forecasting\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook explains how to use `tsfresh` in time series foreacasting.\\n\",\n \"Make sure you also read through the [documentation](https://tsfresh.readthedocs.io/en/latest/text/forecasting.html) to learn more on this feature.\\n\",\n \"\\n\",\n \"It is basically a copy of the other time series forecasting notebook, but this time using more than one \\n\",\n \"stock.\\n\",\n \"This is conceptionally not much different, but the pandas multi-index magic is a bit advanced :-)\\n\",\n \"\\n\",\n \"We will use the Google, Facebook and Alphabet stock.\\n\",\n \"Please find all documentation in the other notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"\\n\",\n \"from tsfresh import extract_features, select_features\\n\",\n \"from tsfresh.utilities.dataframe_functions import roll_time_series, make_forecasting_frame\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"\\n\",\n \"try:\\n\",\n \" import pandas_datareader.data as web\\n\",\n \"except ImportError:\\n\",\n \" print(\\\"You need to install the pandas_datareader. Run pip install pandas_datareader.\\\")\\n\",\n \"\\n\",\n \"from sklearn.ensemble import AdaBoostRegressor\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reading the data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = web.DataReader(['F', \\\"AAPL\\\", \\\"GOOGL\\\"], 'stooq')[\\\"High\\\"]\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(15, 6))\\n\",\n \"df.plot(ax=plt.gca())\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This time we need to make sure to preserve the stock symbol information while reordering:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_melted = df.copy()\\n\",\n \"df_melted[\\\"date\\\"] = df_melted.index\\n\",\n \"df_melted = df_melted.melt(id_vars=\\\"date\\\", value_name=\\\"high\\\").sort_values([\\\"Symbols\\\", \\\"date\\\"])\\n\",\n \"df_melted = df_melted[[\\\"Symbols\\\", \\\"date\\\", \\\"high\\\"]]\\n\",\n \"\\n\",\n \"df_melted.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create training data sample\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled = roll_time_series(df_melted, column_id=\\\"Symbols\\\", column_sort=\\\"date\\\",\\n\",\n \" max_timeshift=20, min_timeshift=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_rolled.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extract Features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X = extract_features(df_rolled.drop(\\\"Symbols\\\", axis=1), \\n\",\n \" column_id=\\\"id\\\", column_sort=\\\"date\\\", column_value=\\\"high\\\", \\n\",\n \" impute_function=impute, show_warnings=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We make the data a bit easier to work with by giving them a multi-index instead ot the tuple index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# split up the two parts of the index and give them proper names\\n\",\n \"X = X.set_index([X.index.map(lambda x: x[0]), X.index.map(lambda x: x[1])], drop=True)\\n\",\n \"X.index.names = [\\\"Symbols\\\", \\\"last_date\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our `(AAPL, 2020-07-14 00:00:00)` is also in the data again:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X.loc[\\\"AAPL\\\", pd.to_datetime('2020-07-14')]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just to repeat: the features in this row were only calculated using the time series values of `AAPL` up to and including `2020-07-14` and the last 20 days.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prediction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The next line might look like magic if you are not used to pandas transformations, but what it does is:\\n\",\n \"\\n\",\n \"for each stock symbol separately:\\n\",\n \"* sort by date\\n\",\n \"* take the high value\\n\",\n \"* shift 1 time step in the future\\n\",\n \"* bring into the same multi-index format as `X` above\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = df_melted.groupby(\\\"Symbols\\\").apply(lambda x: x.set_index(\\\"date\\\")[\\\"high\\\"].shift(-1)).T.unstack()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quick consistency test:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y[\\\"AAPL\\\", pd.to_datetime(\\\"2020-07-13\\\")], df.loc[pd.to_datetime(\\\"2020-07-14\\\"), \\\"AAPL\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = y[y.index.isin(X.index)]\\n\",\n \"X = X[X.index.isin(y.index)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The splitting into train and test samples workes in principle the same as with a single identifier, but this time we have a multi-index symbol-date, so the `loc` call looks a bit more complicated:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train = X.loc[(slice(None), slice(None, \\\"2020\\\")), :]\\n\",\n \"X_test = X.loc[(slice(None), slice(\\\"2021\\\", \\\"2022\\\")), :]\\n\",\n \"\\n\",\n \"y_train = y.loc[(slice(None), slice(None, \\\"2020\\\"))]\\n\",\n \"y_test = y.loc[(slice(None), slice(\\\"2021\\\", \\\"2022\\\"))]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train_selected = select_features(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We are training a regressor for each of the stocks separately\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adas = {stock: AdaBoostRegressor() for stock in [\\\"AAPL\\\", \\\"F\\\", \\\"GOOGL\\\"]}\\n\",\n \"\\n\",\n \"for stock, ada in adas.items():\\n\",\n \" ada.fit(X_train_selected.loc[stock], y_train.loc[stock])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now lets check again how good our prediction is:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_test_selected = X_test[X_train_selected.columns]\\n\",\n \"\\n\",\n \"y_pred = pd.concat({\\n\",\n \" stock: pd.Series(adas[stock].predict(X_test_selected.loc[stock]), index=X_test_selected.loc[stock].index)\\n\",\n \" for stock in adas.keys()\\n\",\n \"})\\n\",\n \"y_pred.index.names = [\\\"Symbols\\\", \\\"last_date\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(15, 6))\\n\",\n \"\\n\",\n \"y.unstack(\\\"Symbols\\\").plot(ax=plt.gca())\\n\",\n \"y_pred.unstack(\\\"Symbols\\\").plot(ax=plt.gca(), legend=None, marker=\\\".\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/advanced/compare-runtimes-of-feature-calculators.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/max/Documents/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\\n\",\n \" from pandas.core import datetools\\n\",\n \"/Users/max/Documents/tsfresh/venv/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\\n\",\n \" \\\"`IPython.html.widgets` has moved to `ipywidgets`.\\\", ShimWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tsfresh.examples import load_driftbif\\n\",\n \"from tsfresh.feature_extraction import ComprehensiveFCParameters, extract_features\\n\",\n \"from tsfresh.feature_extraction.settings import get_combiner_functions, get_simple_functions\\n\",\n \"import pandas as pd\\n\",\n \"import pprint\\n\",\n \"import timeit\\n\",\n \"from tqdm import tqdm\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" id time value\\n\",\n \"0 0 0.00 0.000000\\n\",\n \"1 0 0.05 0.000118\\n\",\n \"2 0 0.10 0.000184\\n\",\n \"3 0 0.15 0.000309\\n\",\n \"4 0 0.20 0.000373\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# load data\\n\",\n \"X, _ = load_driftbif(10, 1000)\\n\",\n \"X.drop(\\\"dimension\\\", axis=1, inplace=True)\\n\",\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# get all the parameters and respective functions\\n\",\n \"settings = ComprehensiveFCParameters()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 61/61 [10:37<00:00, 10.44s/it] \\n\"\n ]\n }\n ],\n \"source\": [\n \"# compare performance of tsfresh for simple feature calculator that are calculated individually\\n\",\n \"res = pd.DataFrame()\\n\",\n \"n_ts = 20\\n\",\n \"l_ts = 1000\\n\",\n \"n_ti = 3\\n\",\n \"\\n\",\n \"for f, param in tqdm(settings.items()):\\n\",\n \" res.loc[f, \\\"feature\\\"] = f\\n\",\n \" res.loc[f, \\\"n_samp\\\"] = n_ts\\n\",\n \" res.loc[f, \\\"length\\\"] = l_ts\\n\",\n \" \\n\",\n \" fc_dict = {f:param}\\n\",\n \" \\n\",\n \" t = timeit.timeit(lambda : extract_features(timeseries_container=X, \\n\",\n \" column_id=\\\"id\\\",\\n\",\n \" n_jobs=0, \\n\",\n \" default_fc_parameters=fc_dict, \\n\",\n \" disable_progressbar=True), \\n\",\n \" number=n_ti)\\n\",\n \" n_fs = len(param) if param is not None else 1\\n\",\n \" res.loc[f, \\\"n_fs\\\"] = n_fs\\n\",\n \" res.loc[f, \\\"t_abs\\\"] = t * 1.0/n_fs\\n\",\n \" res.loc[f, \\\"t_1ts\\\"] = t*1.0/(n_ts*n_fs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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featuren_samplengthn_fst_abst_1ts
agg_linear_trendagg_linear_trend20.01000.048.00.1654380.008272
binned_entropybinned_entropy20.01000.01.00.0409020.002045
medianmedian20.01000.01.00.0357620.001788
first_location_of_maximumfirst_location_of_maximum20.01000.01.00.0315730.001579
mean_second_derivate_centralmean_second_derivate_central20.01000.01.00.0366050.001830
absolute_sum_of_changesabsolute_sum_of_changes20.01000.01.00.0309320.001547
minimumminimum20.01000.01.00.0374290.001871
count_above_meancount_above_mean20.01000.01.00.0422200.002111
number_cwt_peaksnumber_cwt_peaks20.01000.02.03.1626680.158133
has_duplicate_minhas_duplicate_min20.01000.01.00.3041000.015205
has_duplicate_maxhas_duplicate_max20.01000.01.00.2950660.014753
count_below_meancount_below_mean20.01000.01.00.0339880.001699
augmented_dickey_fulleraugmented_dickey_fuller20.01000.03.00.8368130.041841
friedrich_coefficientsfriedrich_coefficients20.01000.04.00.1350990.006755
first_location_of_minimumfirst_location_of_minimum20.01000.01.00.0396190.001981
kurtosiskurtosis20.01000.01.00.0524590.002623
quantilequantile20.01000.08.00.0209930.001050
ratio_value_number_to_time_series_lengthratio_value_number_to_time_series_length20.01000.01.00.0469310.002347
approximate_entropyapproximate_entropy20.01000.05.019.7662240.988311
value_countvalue_count20.01000.05.00.0169280.000846
range_countrange_count20.01000.01.00.0531330.002657
cwt_coefficientscwt_coefficients20.01000.060.00.0018680.000093
sum_of_reoccurring_valuessum_of_reoccurring_values20.01000.01.00.0567060.002835
has_duplicatehas_duplicate20.01000.01.00.0558560.002793
sample_entropysample_entropy20.01000.01.0471.30989723.565495
skewnessskewness20.01000.01.00.0597930.002990
abs_energyabs_energy20.01000.01.00.0322250.001611
max_langevin_fixed_pointmax_langevin_fixed_point20.01000.01.00.5791130.028956
number_crossing_mnumber_crossing_m20.01000.03.00.0304560.001523
sum_of_reoccurring_data_pointssum_of_reoccurring_data_points20.01000.01.00.0776830.003884
.....................
longest_strike_below_meanlongest_strike_below_mean20.01000.01.00.0432510.002163
number_peaksnumber_peaks20.01000.05.00.3859180.019296
sum_valuessum_values20.01000.01.00.0576410.002882
mean_abs_changemean_abs_change20.01000.01.00.0568310.002842
last_location_of_maximumlast_location_of_maximum20.01000.01.00.0597280.002986
index_mass_quantileindex_mass_quantile20.01000.08.00.0080160.000401
variance_larger_than_standard_deviationvariance_larger_than_standard_deviation20.01000.01.00.0437850.002189
c3c320.01000.03.00.0147650.000738
linear_trendlinear_trend20.01000.05.00.0350710.001754
percentage_of_reoccurring_values_to_all_valuespercentage_of_reoccurring_values_to_all_values20.01000.01.00.1070760.005354
ratio_beyond_r_sigmaratio_beyond_r_sigma20.01000.010.00.3510780.017554
large_standard_deviationlarge_standard_deviation20.01000.019.00.0241750.001209
change_quantileschange_quantiles20.01000.0100.00.3920620.019603
mean_changemean_change20.01000.01.00.0365540.001828
last_location_of_minimumlast_location_of_minimum20.01000.01.00.0323290.001616
symmetry_lookingsymmetry_looking20.01000.020.00.0026630.000133
maximummaximum20.01000.01.00.0404530.002023
longest_strike_above_meanlongest_strike_above_mean20.01000.01.00.0418070.002090
percentage_of_reoccurring_datapoints_to_all_datapointspercentage_of_reoccurring_datapoints_to_all_da...20.01000.01.00.0424220.002121
lengthlength20.01000.01.00.0355420.001777
energy_ratio_by_chunksenergy_ratio_by_chunks20.01000.010.00.0043190.000216
fft_coefficientfft_coefficient20.01000.0400.00.0005430.000027
agg_autocorrelationagg_autocorrelation20.01000.03.00.0214510.001073
standard_deviationstandard_deviation20.01000.01.00.0353920.001770
autocorrelationautocorrelation20.01000.010.00.0073870.000369
variancevariance20.01000.01.00.0340870.001704
partial_autocorrelationpartial_autocorrelation20.01000.010.00.0076920.000385
spkt_welch_densityspkt_welch_density20.01000.03.00.0172740.000864
time_reversal_asymmetry_statistictime_reversal_asymmetry_statistic20.01000.03.00.0186460.000932
meanmean20.01000.01.00.0333230.001666
\\n\",\n \"

61 rows × 6 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" feature \\\\\\n\",\n \"agg_linear_trend agg_linear_trend \\n\",\n \"binned_entropy binned_entropy \\n\",\n \"median median \\n\",\n \"first_location_of_maximum first_location_of_maximum \\n\",\n \"mean_second_derivate_central mean_second_derivate_central \\n\",\n \"absolute_sum_of_changes absolute_sum_of_changes \\n\",\n \"minimum minimum \\n\",\n \"count_above_mean count_above_mean \\n\",\n \"number_cwt_peaks number_cwt_peaks \\n\",\n \"has_duplicate_min has_duplicate_min \\n\",\n \"has_duplicate_max has_duplicate_max \\n\",\n \"count_below_mean count_below_mean \\n\",\n \"augmented_dickey_fuller augmented_dickey_fuller \\n\",\n \"friedrich_coefficients friedrich_coefficients \\n\",\n \"first_location_of_minimum first_location_of_minimum \\n\",\n \"kurtosis kurtosis \\n\",\n \"quantile quantile \\n\",\n \"ratio_value_number_to_time_series_length ratio_value_number_to_time_series_length \\n\",\n \"approximate_entropy approximate_entropy \\n\",\n \"value_count value_count \\n\",\n \"range_count range_count \\n\",\n \"cwt_coefficients cwt_coefficients \\n\",\n \"sum_of_reoccurring_values sum_of_reoccurring_values \\n\",\n \"has_duplicate has_duplicate \\n\",\n \"sample_entropy sample_entropy \\n\",\n \"skewness skewness \\n\",\n \"abs_energy abs_energy \\n\",\n \"max_langevin_fixed_point max_langevin_fixed_point \\n\",\n \"number_crossing_m number_crossing_m \\n\",\n \"sum_of_reoccurring_data_points sum_of_reoccurring_data_points \\n\",\n \"... ... \\n\",\n \"longest_strike_below_mean longest_strike_below_mean \\n\",\n \"number_peaks number_peaks \\n\",\n \"sum_values sum_values \\n\",\n \"mean_abs_change mean_abs_change \\n\",\n \"last_location_of_maximum last_location_of_maximum \\n\",\n \"index_mass_quantile index_mass_quantile \\n\",\n \"variance_larger_than_standard_deviation variance_larger_than_standard_deviation \\n\",\n \"c3 c3 \\n\",\n \"linear_trend linear_trend \\n\",\n \"percentage_of_reoccurring_values_to_all_values percentage_of_reoccurring_values_to_all_values \\n\",\n \"ratio_beyond_r_sigma ratio_beyond_r_sigma \\n\",\n \"large_standard_deviation large_standard_deviation \\n\",\n \"change_quantiles change_quantiles \\n\",\n \"mean_change mean_change \\n\",\n \"last_location_of_minimum last_location_of_minimum \\n\",\n \"symmetry_looking symmetry_looking \\n\",\n \"maximum maximum \\n\",\n \"longest_strike_above_mean longest_strike_above_mean \\n\",\n \"percentage_of_reoccurring_datapoints_to_all_dat... percentage_of_reoccurring_datapoints_to_all_da... \\n\",\n \"length length \\n\",\n \"energy_ratio_by_chunks energy_ratio_by_chunks \\n\",\n \"fft_coefficient fft_coefficient \\n\",\n \"agg_autocorrelation agg_autocorrelation \\n\",\n \"standard_deviation standard_deviation \\n\",\n \"autocorrelation autocorrelation \\n\",\n \"variance variance \\n\",\n \"partial_autocorrelation partial_autocorrelation \\n\",\n \"spkt_welch_density spkt_welch_density \\n\",\n \"time_reversal_asymmetry_statistic time_reversal_asymmetry_statistic \\n\",\n \"mean mean \\n\",\n \"\\n\",\n \" n_samp length n_fs \\\\\\n\",\n \"agg_linear_trend 20.0 1000.0 48.0 \\n\",\n \"binned_entropy 20.0 1000.0 1.0 \\n\",\n \"median 20.0 1000.0 1.0 \\n\",\n \"first_location_of_maximum 20.0 1000.0 1.0 \\n\",\n \"mean_second_derivate_central 20.0 1000.0 1.0 \\n\",\n \"absolute_sum_of_changes 20.0 1000.0 1.0 \\n\",\n \"minimum 20.0 1000.0 1.0 \\n\",\n \"count_above_mean 20.0 1000.0 1.0 \\n\",\n \"number_cwt_peaks 20.0 1000.0 2.0 \\n\",\n \"has_duplicate_min 20.0 1000.0 1.0 \\n\",\n \"has_duplicate_max 20.0 1000.0 1.0 \\n\",\n \"count_below_mean 20.0 1000.0 1.0 \\n\",\n \"augmented_dickey_fuller 20.0 1000.0 3.0 \\n\",\n \"friedrich_coefficients 20.0 1000.0 4.0 \\n\",\n \"first_location_of_minimum 20.0 1000.0 1.0 \\n\",\n \"kurtosis 20.0 1000.0 1.0 \\n\",\n \"quantile 20.0 1000.0 8.0 \\n\",\n \"ratio_value_number_to_time_series_length 20.0 1000.0 1.0 \\n\",\n \"approximate_entropy 20.0 1000.0 5.0 \\n\",\n \"value_count 20.0 1000.0 5.0 \\n\",\n \"range_count 20.0 1000.0 1.0 \\n\",\n \"cwt_coefficients 20.0 1000.0 60.0 \\n\",\n \"sum_of_reoccurring_values 20.0 1000.0 1.0 \\n\",\n \"has_duplicate 20.0 1000.0 1.0 \\n\",\n \"sample_entropy 20.0 1000.0 1.0 \\n\",\n \"skewness 20.0 1000.0 1.0 \\n\",\n \"abs_energy 20.0 1000.0 1.0 \\n\",\n \"max_langevin_fixed_point 20.0 1000.0 1.0 \\n\",\n \"number_crossing_m 20.0 1000.0 3.0 \\n\",\n \"sum_of_reoccurring_data_points 20.0 1000.0 1.0 \\n\",\n \"... ... ... ... \\n\",\n \"longest_strike_below_mean 20.0 1000.0 1.0 \\n\",\n \"number_peaks 20.0 1000.0 5.0 \\n\",\n \"sum_values 20.0 1000.0 1.0 \\n\",\n \"mean_abs_change 20.0 1000.0 1.0 \\n\",\n \"last_location_of_maximum 20.0 1000.0 1.0 \\n\",\n \"index_mass_quantile 20.0 1000.0 8.0 \\n\",\n \"variance_larger_than_standard_deviation 20.0 1000.0 1.0 \\n\",\n \"c3 20.0 1000.0 3.0 \\n\",\n \"linear_trend 20.0 1000.0 5.0 \\n\",\n \"percentage_of_reoccurring_values_to_all_values 20.0 1000.0 1.0 \\n\",\n \"ratio_beyond_r_sigma 20.0 1000.0 10.0 \\n\",\n \"large_standard_deviation 20.0 1000.0 19.0 \\n\",\n \"change_quantiles 20.0 1000.0 100.0 \\n\",\n \"mean_change 20.0 1000.0 1.0 \\n\",\n \"last_location_of_minimum 20.0 1000.0 1.0 \\n\",\n \"symmetry_looking 20.0 1000.0 20.0 \\n\",\n \"maximum 20.0 1000.0 1.0 \\n\",\n \"longest_strike_above_mean 20.0 1000.0 1.0 \\n\",\n \"percentage_of_reoccurring_datapoints_to_all_dat... 20.0 1000.0 1.0 \\n\",\n \"length 20.0 1000.0 1.0 \\n\",\n \"energy_ratio_by_chunks 20.0 1000.0 10.0 \\n\",\n \"fft_coefficient 20.0 1000.0 400.0 \\n\",\n \"agg_autocorrelation 20.0 1000.0 3.0 \\n\",\n \"standard_deviation 20.0 1000.0 1.0 \\n\",\n \"autocorrelation 20.0 1000.0 10.0 \\n\",\n \"variance 20.0 1000.0 1.0 \\n\",\n \"partial_autocorrelation 20.0 1000.0 10.0 \\n\",\n \"spkt_welch_density 20.0 1000.0 3.0 \\n\",\n \"time_reversal_asymmetry_statistic 20.0 1000.0 3.0 \\n\",\n \"mean 20.0 1000.0 1.0 \\n\",\n \"\\n\",\n \" t_abs t_1ts \\n\",\n \"agg_linear_trend 0.165438 0.008272 \\n\",\n \"binned_entropy 0.040902 0.002045 \\n\",\n \"median 0.035762 0.001788 \\n\",\n \"first_location_of_maximum 0.031573 0.001579 \\n\",\n \"mean_second_derivate_central 0.036605 0.001830 \\n\",\n \"absolute_sum_of_changes 0.030932 0.001547 \\n\",\n \"minimum 0.037429 0.001871 \\n\",\n \"count_above_mean 0.042220 0.002111 \\n\",\n \"number_cwt_peaks 3.162668 0.158133 \\n\",\n \"has_duplicate_min 0.304100 0.015205 \\n\",\n \"has_duplicate_max 0.295066 0.014753 \\n\",\n \"count_below_mean 0.033988 0.001699 \\n\",\n \"augmented_dickey_fuller 0.836813 0.041841 \\n\",\n \"friedrich_coefficients 0.135099 0.006755 \\n\",\n \"first_location_of_minimum 0.039619 0.001981 \\n\",\n \"kurtosis 0.052459 0.002623 \\n\",\n \"quantile 0.020993 0.001050 \\n\",\n \"ratio_value_number_to_time_series_length 0.046931 0.002347 \\n\",\n \"approximate_entropy 19.766224 0.988311 \\n\",\n \"value_count 0.016928 0.000846 \\n\",\n \"range_count 0.053133 0.002657 \\n\",\n \"cwt_coefficients 0.001868 0.000093 \\n\",\n \"sum_of_reoccurring_values 0.056706 0.002835 \\n\",\n \"has_duplicate 0.055856 0.002793 \\n\",\n \"sample_entropy 471.309897 23.565495 \\n\",\n \"skewness 0.059793 0.002990 \\n\",\n \"abs_energy 0.032225 0.001611 \\n\",\n \"max_langevin_fixed_point 0.579113 0.028956 \\n\",\n \"number_crossing_m 0.030456 0.001523 \\n\",\n \"sum_of_reoccurring_data_points 0.077683 0.003884 \\n\",\n \"... ... ... \\n\",\n \"longest_strike_below_mean 0.043251 0.002163 \\n\",\n \"number_peaks 0.385918 0.019296 \\n\",\n \"sum_values 0.057641 0.002882 \\n\",\n \"mean_abs_change 0.056831 0.002842 \\n\",\n \"last_location_of_maximum 0.059728 0.002986 \\n\",\n \"index_mass_quantile 0.008016 0.000401 \\n\",\n \"variance_larger_than_standard_deviation 0.043785 0.002189 \\n\",\n \"c3 0.014765 0.000738 \\n\",\n \"linear_trend 0.035071 0.001754 \\n\",\n \"percentage_of_reoccurring_values_to_all_values 0.107076 0.005354 \\n\",\n \"ratio_beyond_r_sigma 0.351078 0.017554 \\n\",\n \"large_standard_deviation 0.024175 0.001209 \\n\",\n \"change_quantiles 0.392062 0.019603 \\n\",\n \"mean_change 0.036554 0.001828 \\n\",\n \"last_location_of_minimum 0.032329 0.001616 \\n\",\n \"symmetry_looking 0.002663 0.000133 \\n\",\n \"maximum 0.040453 0.002023 \\n\",\n \"longest_strike_above_mean 0.041807 0.002090 \\n\",\n \"percentage_of_reoccurring_datapoints_to_all_dat... 0.042422 0.002121 \\n\",\n \"length 0.035542 0.001777 \\n\",\n \"energy_ratio_by_chunks 0.004319 0.000216 \\n\",\n \"fft_coefficient 0.000543 0.000027 \\n\",\n \"agg_autocorrelation 0.021451 0.001073 \\n\",\n \"standard_deviation 0.035392 0.001770 \\n\",\n \"autocorrelation 0.007387 0.000369 \\n\",\n \"variance 0.034087 0.001704 \\n\",\n \"partial_autocorrelation 0.007692 0.000385 \\n\",\n \"spkt_welch_density 0.017274 0.000864 \\n\",\n \"time_reversal_asymmetry_statistic 0.018646 0.000932 \\n\",\n \"mean 0.033323 0.001666 \\n\",\n \"\\n\",\n \"[61 rows x 6 columns]\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"res[\\\"feature\\\"] = res.feature.astype(str)\\n\",\n \"res = res.sort_values(by=\\\"feature\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/xR/18XFxdsbGxo06bNff3PslevXkyYMIGQkJAHHt/KyorFixcTFhZG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\\n8vPzKSgoICYmhhYtWtCn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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(6, 20))\\n\",\n \"sns.barplot(y=\\\"feature\\\", x=\\\"t_abs\\\", data=res)\\n\",\n \"plt.title(\\\"Runtime of 1 apply features for 1 time series of length 1000\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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7RaLa6uruTl5eHt7c2MGTNITU1Fq9WW+Xopz5Pm+M0332T79u36u0E3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\\nRxL+V8XExLBp0yYCAgKs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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(6,20))\\n\",\n \"sns.barplot(y=\\\"feature\\\", x=\\\"t_1ts\\\", data=res)\\n\",\n \"plt.title(\\\"Runtime of aggregate features for 1 time series of length 1000\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"sample_entropy and approximate_entropy make up for most of the runtime, we create the following plots without them\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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vZhOp2OLl26kJubi4+PD9OnTyclJQWdTlfs66UkpR3jN998k+3btxvu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\\nRZyIiIiIHfr/ggaCKW02EaoAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(6,20))\\n\",\n \"sns.barplot(y=\\\"feature\\\", x=\\\"t_1ts\\\", data=res.drop([\\\"sample_entropy\\\", \\\"approximate_entropy\\\"]))\\n\",\n \"plt.title(\\\"Runtime of aggregate features for 1 time series of length 1000\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"fft_coefficient 0.000027\\n\",\n \"cwt_coefficients 0.000093\\n\",\n \"symmetry_looking 0.000133\\n\",\n \"energy_ratio_by_chunks 0.000216\\n\",\n \"autocorrelation 0.000369\\n\",\n \"partial_autocorrelation 0.000385\\n\",\n \"index_mass_quantile 0.000401\\n\",\n \"c3 0.000738\\n\",\n \"value_count 0.000846\\n\",\n \"spkt_welch_density 0.000864\\n\",\n \"time_reversal_asymmetry_statistic 0.000932\\n\",\n \"quantile 0.001050\\n\",\n \"agg_autocorrelation 0.001073\\n\",\n \"large_standard_deviation 0.001209\\n\",\n \"number_crossing_m 0.001523\\n\",\n \"absolute_sum_of_changes 0.001547\\n\",\n \"first_location_of_maximum 0.001579\\n\",\n \"abs_energy 0.001611\\n\",\n \"last_location_of_minimum 0.001616\\n\",\n \"mean 0.001666\\n\",\n \"count_below_mean 0.001699\\n\",\n \"variance 0.001704\\n\",\n \"linear_trend 0.001754\\n\",\n \"standard_deviation 0.001770\\n\",\n \"length 0.001777\\n\",\n \"median 0.001788\\n\",\n \"mean_change 0.001828\\n\",\n \"mean_second_derivate_central 0.001830\\n\",\n \"minimum 0.001871\\n\",\n \"first_location_of_minimum 0.001981\\n\",\n \" ... \\n\",\n \"binned_entropy 0.002045\\n\",\n \"longest_strike_above_mean 0.002090\\n\",\n \"count_above_mean 0.002111\\n\",\n \"percentage_of_reoccurring_datapoints_to_all_datapoints 0.002121\\n\",\n \"longest_strike_below_mean 0.002163\\n\",\n \"variance_larger_than_standard_deviation 0.002189\\n\",\n \"ar_coefficient 0.002235\\n\",\n \"ratio_value_number_to_time_series_length 0.002347\\n\",\n \"kurtosis 0.002623\\n\",\n \"range_count 0.002657\\n\",\n \"has_duplicate 0.002793\\n\",\n \"sum_of_reoccurring_values 0.002835\\n\",\n \"mean_abs_change 0.002842\\n\",\n \"sum_values 0.002882\\n\",\n \"last_location_of_maximum 0.002986\\n\",\n \"skewness 0.002990\\n\",\n \"sum_of_reoccurring_data_points 0.003884\\n\",\n \"percentage_of_reoccurring_values_to_all_values 0.005354\\n\",\n \"friedrich_coefficients 0.006755\\n\",\n \"agg_linear_trend 0.008272\\n\",\n \"has_duplicate_max 0.014753\\n\",\n \"has_duplicate_min 0.015205\\n\",\n \"ratio_beyond_r_sigma 0.017554\\n\",\n \"number_peaks 0.019296\\n\",\n \"change_quantiles 0.019603\\n\",\n \"max_langevin_fixed_point 0.028956\\n\",\n \"augmented_dickey_fuller 0.041841\\n\",\n \"number_cwt_peaks 0.158133\\n\",\n \"approximate_entropy 0.988311\\n\",\n \"sample_entropy 23.565495\\n\",\n \"Name: t_1ts, Length: 61, dtype: float64\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# we calculate how much many features cause 90% of the runtime \\n\",\n \"r = res.t_1ts.copy()\\n\",\n \"r.sort_values(ascending=True, inplace=True)\\n\",\n \"r\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"fft_coefficient 0.000109\\n\",\n \"cwt_coefficients 0.000374\\n\",\n \"symmetry_looking 0.000533\\n\",\n \"energy_ratio_by_chunks 0.000865\\n\",\n \"autocorrelation 0.001479\\n\",\n \"partial_autocorrelation 0.001540\\n\",\n \"index_mass_quantile 0.001605\\n\",\n \"c3 0.002956\\n\",\n \"value_count 0.003390\\n\",\n \"spkt_welch_density 0.003459\\n\",\n \"time_reversal_asymmetry_statistic 0.003733\\n\",\n \"quantile 0.004203\\n\",\n \"agg_autocorrelation 0.004295\\n\",\n \"large_standard_deviation 0.004841\\n\",\n \"number_crossing_m 0.006098\\n\",\n \"absolute_sum_of_changes 0.006194\\n\",\n \"first_location_of_maximum 0.006322\\n\",\n \"abs_energy 0.006452\\n\",\n \"last_location_of_minimum 0.006473\\n\",\n \"mean 0.006672\\n\",\n \"count_below_mean 0.006805\\n\",\n \"variance 0.006825\\n\",\n \"linear_trend 0.007022\\n\",\n \"standard_deviation 0.007087\\n\",\n \"length 0.007117\\n\",\n \"median 0.007161\\n\",\n \"mean_change 0.007319\\n\",\n \"mean_second_derivate_central 0.007329\\n\",\n \"minimum 0.007494\\n\",\n \"first_location_of_minimum 0.007933\\n\",\n \" ... \\n\",\n \"binned_entropy 0.008190\\n\",\n \"longest_strike_above_mean 0.008371\\n\",\n \"count_above_mean 0.008454\\n\",\n \"percentage_of_reoccurring_datapoints_to_all_datapoints 0.008494\\n\",\n \"longest_strike_below_mean 0.008660\\n\",\n \"variance_larger_than_standard_deviation 0.008767\\n\",\n \"ar_coefficient 0.008950\\n\",\n \"ratio_value_number_to_time_series_length 0.009397\\n\",\n \"kurtosis 0.010504\\n\",\n \"range_count 0.010639\\n\",\n \"has_duplicate 0.011184\\n\",\n \"sum_of_reoccurring_values 0.011354\\n\",\n \"mean_abs_change 0.011379\\n\",\n \"sum_values 0.011541\\n\",\n \"last_location_of_maximum 0.011959\\n\",\n \"skewness 0.011972\\n\",\n \"sum_of_reoccurring_data_points 0.015554\\n\",\n \"percentage_of_reoccurring_values_to_all_values 0.021440\\n\",\n \"friedrich_coefficients 0.027051\\n\",\n \"agg_linear_trend 0.033126\\n\",\n \"has_duplicate_max 0.059081\\n\",\n \"has_duplicate_min 0.060890\\n\",\n \"ratio_beyond_r_sigma 0.070297\\n\",\n \"number_peaks 0.077273\\n\",\n \"change_quantiles 0.078503\\n\",\n \"max_langevin_fixed_point 0.115956\\n\",\n \"augmented_dickey_fuller 0.167555\\n\",\n \"number_cwt_peaks 0.633263\\n\",\n \"approximate_entropy 3.957801\\n\",\n \"sample_entropy 94.370607\\n\",\n \"Name: t_1ts, Length: 61, dtype: float64\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# in percentage\\n\",\n \"r / r.sum()*100\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "notebooks/advanced/feature_extraction_with_datetime_index.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Example of extracting features from dataframes with Datetime indices\\n\",\n \"\\n\",\n \"Assuming that time-varying measurements are taken at regular intervals can be sufficient for many situations. However, for a large number of tasks it is important to take into account **when** a measurement is made. An example can be healthcare, where the interval between measurements of vital signs contains crucial information. \\n\",\n \"\\n\",\n \"Tsfresh now supports calculator functions that use the index of the timeseries container in order to calculate the features. The only requirements for these function is that the index of the input dataframe is of type `pd.DatetimeIndex`. These functions are contained in the new class TimeBasedFCParameters.\\n\",\n \"\\n\",\n \"Note that the behaviour of all other functions is unaffected. The settings parameter of `extract_features()` can contain both index-dependent functions and 'regular' functions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2019-03-22T10:19:26.504073Z\",\n \"start_time\": \"2019-03-22T10:19:26.397323Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from tsfresh.feature_extraction import extract_features\\n\",\n \"# TimeBasedFCParameters contains all functions that use the Datetime index of the timeseries container\\n\",\n \"from tsfresh.feature_extraction.settings import TimeBasedFCParameters \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Build a time series container with Datetime indices\\n\",\n \"\\n\",\n \"Let's build a dataframe with a datetime index. The format must be with a `value` and a `kind` column, since each measurement has its own timestamp - i.e. measurements are not assumed to be simultaneous.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2019-03-22T10:09:14.175077Z\",\n \"start_time\": \"2019-03-22T10:09:14.119751Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idvaluekind
2019-03-01 10:04:00a1temperature
2019-03-01 10:50:00a2temperature
2019-03-02 00:00:00a3pressure
2019-03-02 09:04:59a1pressure
2019-03-02 23:54:12b3temperature
2019-03-03 08:13:04b1temperature
2019-03-04 08:00:00b0pressure
2019-03-04 08:01:00b8pressure
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\"\n ],\n \"text/plain\": [\n \" id value kind\\n\",\n \"2019-03-01 10:04:00 a 1 temperature\\n\",\n \"2019-03-01 10:50:00 a 2 temperature\\n\",\n \"2019-03-02 00:00:00 a 3 pressure\\n\",\n \"2019-03-02 09:04:59 a 1 pressure\\n\",\n \"2019-03-02 23:54:12 b 3 temperature\\n\",\n \"2019-03-03 08:13:04 b 1 temperature\\n\",\n \"2019-03-04 08:00:00 b 0 pressure\\n\",\n \"2019-03-04 08:01:00 b 8 pressure\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame({\\\"id\\\": [\\\"a\\\", \\\"a\\\", \\\"a\\\", \\\"a\\\", \\\"b\\\", \\\"b\\\", \\\"b\\\", \\\"b\\\"], \\n\",\n \" \\\"value\\\": [1, 2, 3, 1, 3, 1, 0, 8],\\n\",\n \" \\\"kind\\\": [\\\"temperature\\\", \\\"temperature\\\", \\\"pressure\\\", \\\"pressure\\\",\\n\",\n \" \\\"temperature\\\", \\\"temperature\\\", \\\"pressure\\\", \\\"pressure\\\"]},\\n\",\n \" index=pd.DatetimeIndex(\\n\",\n \" ['2019-03-01 10:04:00', '2019-03-01 10:50:00', '2019-03-02 00:00:00', '2019-03-02 09:04:59',\\n\",\n \" '2019-03-02 23:54:12', '2019-03-03 08:13:04', '2019-03-04 08:00:00', '2019-03-04 08:01:00']\\n\",\n \" ))\\n\",\n \"df = df.sort_index()\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Right now `TimeBasedFCParameters` only contains `linear_trend_timewise`, which performs a calculation of a linear trend, but using the time difference in hours between measurements in order to perform the linear regression. As always, you can add your own functions in `tsfresh/feature_extraction/feature_calculators.py`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2019-03-22T10:09:23.054729Z\",\n \"start_time\": \"2019-03-22T10:09:23.006889Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'linear_trend_timewise': [{'attr': 'pvalue'},\\n\",\n \" {'attr': 'rvalue'},\\n\",\n \" {'attr': 'intercept'},\\n\",\n \" {'attr': 'slope'},\\n\",\n \" {'attr': 'stderr'}]}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"settings_time = TimeBasedFCParameters()\\n\",\n \"settings_time\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We extract the features as usual, specifying the column value, kind, and id.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2019-03-22T10:09:35.217431Z\",\n \"start_time\": \"2019-03-22T10:09:35.009287Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Extraction: 100%|██████████| 4/4 [00:00<00:00, 591.10it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"variable pressure__linear_trend__attr_\\\"intercept\\\" \\\\\\n\",\n \"id \\n\",\n \"a 3.0 \\n\",\n \"b 0.0 \\n\",\n \"\\n\",\n \"variable pressure__linear_trend__attr_\\\"pvalue\\\" \\\\\\n\",\n \"id \\n\",\n \"a 0.0 \\n\",\n \"b 0.0 \\n\",\n \"\\n\",\n \"variable pressure__linear_trend__attr_\\\"rvalue\\\" \\\\\\n\",\n \"id \\n\",\n \"a -1.0 \\n\",\n \"b 1.0 \\n\",\n \"\\n\",\n \"variable pressure__linear_trend__attr_\\\"slope\\\" \\\\\\n\",\n \"id \\n\",\n \"a -2.0 \\n\",\n \"b 8.0 \\n\",\n \"\\n\",\n \"variable pressure__linear_trend__attr_\\\"stderr\\\" \\\\\\n\",\n \"id \\n\",\n \"a 0.0 \\n\",\n \"b 0.0 \\n\",\n \"\\n\",\n \"variable temperature__linear_trend__attr_\\\"intercept\\\" \\\\\\n\",\n \"id \\n\",\n \"a 1.0 \\n\",\n \"b 3.0 \\n\",\n \"\\n\",\n \"variable temperature__linear_trend__attr_\\\"pvalue\\\" \\\\\\n\",\n \"id \\n\",\n \"a 0.0 \\n\",\n \"b 0.0 \\n\",\n \"\\n\",\n \"variable temperature__linear_trend__attr_\\\"rvalue\\\" \\\\\\n\",\n \"id \\n\",\n \"a 1.0 \\n\",\n \"b -1.0 \\n\",\n \"\\n\",\n \"variable temperature__linear_trend__attr_\\\"slope\\\" \\\\\\n\",\n \"id \\n\",\n \"a 1.0 \\n\",\n \"b -2.0 \\n\",\n \"\\n\",\n \"variable temperature__linear_trend__attr_\\\"stderr\\\" \\n\",\n \"id \\n\",\n \"a 0.0 \\n\",\n \"b 0.0 \"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_tsfresh = extract_features(df, column_id=\\\"id\\\", column_value='value', column_kind='kind',\\n\",\n \" default_fc_parameters=settings_regular)\\n\",\n \"X_tsfresh.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Writing your own time-based feature calculators\\n\",\n \"\\n\",\n \"Writing your own time-based feature calculators is no different from usual. Only two new properties must be set using the `@set_property` decorator:\\n\",\n \"\\n\",\n \"1) `@set_property(\\\"input\\\", \\\"pd.Series\\\")` tells the function that the input of the function is a `pd.Series` rather than a numpy array. This allows the index to be used.\\n\",\n \"2) `@set_property(\\\"index_type\\\", pd.DatetimeIndex)` tells the function that the input is a DatetimeIndex, allowing it to perform calculations based on time datatypes.\\n\",\n \"\\n\",\n \"For example, if we want to write a function that calculates the time between the first and last measurement, it could look something like this:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"@set_property(\\\"input\\\", \\\"pd.Series\\\")\\n\",\n \"@set_property(\\\"index_type\\\", pd.DatetimeIndex)\\n\",\n \"def timespan(x, param):\\n\",\n \" ix = x.index\\n\",\n \"\\n\",\n \" # Get differences between the last timestamp and the first timestamp in seconds, then convert to hours.\\n\",\n \" times_seconds = (ix[-1] - ix[0]).total_seconds()\\n\",\n \" return times_seconds / float(3600)\\n\",\n \"```\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.6.7\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\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\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/advanced/friedrich_coefficients.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

Estimating Friedrich's coefficients describing the deterministic dynamics of Langevin model

\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Andreas W. Kempa-Liehr (Department of Engineering Science, University of Auckland)
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebooks explains the friedrich_coefficient features, which has been inspired by the paper of Friedrich et al. (2000): *Extracting model equations from experimental data*. Physics Letters A 271, p. 217-222\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The general idea is to assume a Langevin model for the dynamics of the time series $x(t)$ \\n\",\n \"$$\\\\dot{x}(t) = h(x(t)) + \\\\mathcal{N}(0,R)$$\\n\",\n \"with $\\\\dot{x}(t)$ denoting the temporal derivative, $h(x(t))$ the deterministic dynamics, and $\\\\mathcal{N}(0,R)$ a Langevin force modelled as Gaussian white noise with standard deviation $R$.\\n\",\n \"Now, an estimate $\\\\tilde{h}(x)$ of the deterministic dynamics can be computed by averaging $\\\\dot{x}(t)$ for a specific interval $x(t)\\\\in[x-\\\\epsilon,x+\\\\epsilon]$ with $|\\\\epsilon|\\\\ll 1$:\\n\",\n \"$$\\\\left.\\\\tilde{h}(x)\\\\right|_{x\\\\in[x-\\\\epsilon,x+\\\\epsilon]} \\\\approx \\\\frac{\\\\sum\\\\limits_{x(t)\\\\in[x-\\\\epsilon,x+\\\\epsilon]} x(t+\\\\Delta_t)-x(t)}{\\\\Delta_t \\\\sum\\\\limits_{x(t)\\\\in[x-\\\\epsilon,x+\\\\epsilon]} 1}.$$\\n\",\n \"Having a set of estimations $\\\\{\\\\tilde{h}(x_1),\\\\tilde{h}(x_2),\\\\ldots,\\\\tilde{h}(x_n)\\\\}$ with $x_10:\\n\",\n \" active='Active'\\n\",\n \" else:\\n\",\n \" active=''\\n\",\n \" \\n\",\n \" plt.title('{} Brownian Motion (largest equilibrium velocity in red)'.format(active))\\n\",\n \" plt.subplot(2,1,2)\\n\",\n \" ax = plt.errorbar(result.v_mean,result.a_mean,\\n\",\n \" xerr=result.v_std,fmt='o')\\n\",\n \" x = np.linspace(-0.004, 0.004, 201)\\n\",\n \" print(dynamics)\\n\",\n \" plt.plot(x, np.poly1d(dynamics)(x), label='estimated dynamics')\\n\",\n \" plt.plot(v0,0.,'ro')\\n\",\n \" plt.axvline(x=v0, color='r')\\n\",\n \" plt.xlabel('mean velocity')\\n\",\n \" plt.ylabel('mean acceleration')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Beyond drift-bifurcation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)\\n\",\n \"v = ds.simulate(1000000, v0=np.zeros(1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-343.32787387478163, 0.03745305712005136, 0.002256967951186641, -1.0236536382784505e-07]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"friedrich_method(v, default)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Before drift-bifurcation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = velocity(tau=2./0.3-3.8, delta_t=0.05, R=3e-4, seed=0)\\n\",\n \"v = ds.simulate(1000000, v0=np.zeros(1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-430.6354499011763, 0.08347639306912537, -0.002023472165970792, 4.3026801890003886e-08]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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dmzZrB2dlZ/HeVKlUAAO3atZM6z0TLVZ2jAwcOxKdPn6RqZjds2AB7e3vxsX7y5AkePHgg/iwkP7f27dsjKSmJtRYVEJ6XiYmJ6NOnj1STfrFixdCtWzdcvHgROTk5yMnJQWxsLL755hu95ZhZWVlh5MiROHToEF6+fAkAePr0KaKiojB8+HC1vpdsdL0Oyn53jx07hsLCQvTt21fq2Do4OKBp06bi74ToWPbq1Uuq7P7+/ggPD2fdl4eHBwDgzZs3ar8/Y6GAqgjy9fVF7dq1Ubt2bURERGDSpEmYPHky/v33Xxw7dkxqXbZeQWlpaazLfXx8xM8Dwi9F+fLlceLECXGgJQqoRDf5EydOwNHRUe7L4+TkBAcHB6ll9vb2yM3NVfreFi5ciGHDhqFevXrYvXs3Ll68iCtXrqBt27asCfXu7u5y+wA0T74fNGgQDh48iIULF8LR0VFcZS8rLS0NNjY2chdYHo8HLy8v8bHTxIcPH8AwjFqfiYi27/vTp09yn4si48ePx+TJk9GlSxccPHgQly5dwpUrV1CjRg3W/ciWX1RmT09PuXVll719+xbp6emws7ODra2t1CM5ORmpqakAID4fPTw8MGLECJQvXx7ly5fHX3/9pdZ7YvP27Vvcvn1bbr8uLi5gGEa8b9FnL3vsvby8tN63rtiOrag8sueMvnsIipQsWVLqb1HvLUXLVV0Dqlatijp16oib/fh8PrZu3YrOnTuLtynK4Zw4caLc5zZ8+HAAEH9uskTHRdH3TSAQ4MOHD/jw4QP4fL7eO80MHDgQjo6O4jyiZcuWwdHRUeGPOHXoeh2UPRai41unTh2547tr1y6p7wTA/h1Q9L0QXX9MrYMUQMMmkM9Ev0hu3bqFNm3aiJez/eJxd3dHUlKS3HJRUnOpUqXEy0Q5ULGxsRAIBIiIiICLiwt8fHxw/PhxnDhxAo0bN9Zb76OtW7ciIiICK1askFqelZWll+0r0rVrV4wYMQJz5szBkCFD4OjoyLqeu7s7CgsL8e7dO6mgimEYJCcno06dOhrvW5Tor+5nootSpUrh+vXraq27detW9O3bF7NmzZJanpqaKpUvJyJ7roku8pIdGESSk5PlyuXu7i7Of5Ll4uIi/n/jxo3RuHFj8Pl8XL16FUuXLsXYsWPh6emJnj17qvXeZPft6Ogo1TFB9nnR+xHlLUrewGTfCyC8ach2wAAU3+S1pezYyt5k1a39MFbZlRkwYACGDx+O+/fv49mzZ0hKSpLKaRR9JpMmTULXrl1Zt1G5cmXW5aLjouj7ZmVlhRIlSoDH48Ha2hqvX7/W9e1IcXNzQ79+/bB27VpMnDgRGzZsQK9evVi/U8Yie26Iju9///0Hf39/ha8THUu27wDbMuBLnqy+rmn6RDVUBABw8+ZNAF+qU5Vp0aIFTp06JdcrbPPmzXByckL9+vXFy1q2bIm3b99i8eLFqF+/vvjG1qJFC+zduxdXrlyRa+7TBY/HkwvObt++zdr7Rp8cHR0xZcoUdOrUCcOGDVO4XosWLQAIgw1Ju3fvRnZ2tvh5QPgrUZ1fYc7OzqhXrx727Nkjtb5AIMDWrVvh6+sr12yrraCgIKSlpamVrMr2WRw+fFjtqvrKlSvDy8tLrtfVy5cvceHCBallHTt2RFpaGvh8vrj2VfLBdnO0trZGvXr1xD2qRIGipr/OO3bsiKdPn8Ld3Z1136Ieb6JE/m3btkm9fvv27XLbLFeuHFJSUqQCnvz8fLkaZF1lZWXhwIEDcuWxsrJCkyZNtNqmscquzLfffgsHBwds3LgRGzduRJkyZdC6dWvx85UrV0bFihVx69Yt1s+sdu3aUkG4pMqVK6NMmTLYvn27VIJ8dnY2du/eLe755+joiKZNm+Lff//VOJhUdQ6OHj0aqamp6N69O9LT0zFy5EiNtm9obdq0gY2NDZ4+farw+ALCY+nt7Y0dO3ZIHcsXL17IfcdFRM30hh7+QRtUQ1UEvXz5UtwlNjs7G3FxcZg9ezb8/f0V/lqTNHXqVBw6dAjNmjXDlClTULJkSWzbtg2HDx/GvHnz4ObmJl5XNFp4dHS0eMgFQBho9evXT/x/fenYsSN+//13TJ06FU2bNsXDhw8xY8YMBAQEGHxco/Hjx2P8+PFK12nVqhXatGmDn3/+GZmZmWjYsCFu376NqVOnIjQ0VKqnZUhICHbu3Ildu3YhMDAQDg4OCAkJYd3u7Nmz0apVKzRr1gwTJ06EnZ0dli9fjrt372LHjh1a51bIioiIAMMwuHTpktQNik3Hjh2xceNGBAUFoXr16rh27Rrmz5+vdhOIlZUVpk+fjh9++AHdu3fHwIEDkZ6ejunTp8Pb21sqf6Vnz57Ytm0b2rdvjzFjxqBu3bqwtbXF69evcfr0aXTu3BlfffUVVq5ciVOnTqFDhw4oW7YscnNzxTVLovPQxcUF/v7+2L9/P1q0aIGSJUuiVKlSCke8Hzt2LHbv3o0mTZpg3LhxqF69OgQCAV6+fIno6GhMmDAB9erVQ+vWrdGkSRP89NNPyM7ORu3atXH+/Hls2bJFbps9evTAlClT0LNnT/z444/Izc3FkiVL9D6bgbu7O4YNG4aXL1+iUqVKOHLkCNasWYNhw4ahbNmyWm3TWGVXpnjx4vjqq6+wceNGpKenY+LEiXJDmKxatQrt2rVDmzZt0L9/f5QpUwbv37/H/fv3cf36dfz777+s27ayssK8efPQu3dvdOzYET/88APy8vIwf/58pKenY86cOeJ1Fy5ciEaNGqFevXr45ZdfUKFCBbx9+xYHDhzAqlWrFAZtou/53Llz0a5dO1hbW6N69eriZs9KlSqhbdu2OHr0KBo1aoQaNWro47DpTbly5TBjxgz8+uuvePbsGdq2bYsSJUrg7du3uHz5MpydnTF9+nRYWVnh999/x+DBg/HVV19hyJAhSE9Px7Rp0xQ2+V28eBHu7u4Kr4Wc4jIjnhgXWy8/BwcHplKlSszYsWPlenj5+/szHTp0YN3WnTt3mE6dOjFubm6MnZ0dU6NGDYU9vEJDQxkAzPnz58XL3rx5wwBg3N3d5Xqm9OvXj3F2dpbbjqJeQpK9o/Ly8piJEycyZcqUYRwcHJhatWox+/btk+t5JDoW8+fPl9uP7DbZSPbyU0a2lx/DMMynT5+Yn3/+mfH392dsbW0Zb29vZtiwYcyHDx+k1ktISGBat27NuLi4MADE21HUo+7s2bNM8+bNGWdnZ8bR0ZGpX78+c/DgQal1RL1yZHuksfU0YsPn85ly5crJ9RJkK9OHDx+YQYMGMR4eHoyTkxPTqFEj5uzZs3I9jVQdy9WrVzMVKlRg7OzsmEqVKjHr169nOnfuzISGhkqtV1BQwCxYsICpUaMG4+DgwBQrVowJCgpifvjhB+bx48cMwzBMXFwc89VXXzH+/v6Mvb094+7uzjRt2pQ5cOCA1LZOnDjBhIaGMvb29gwAqV50bD5+/Mj89ttvTOXKlRk7OzvGzc2NCQkJYcaNG8ckJyeL10tPT2cGDhzIFC9enHFycmJatWrFPHjwgPWcO3LkCFOzZk3G0dGRCQwMZP7++2+99/KrWrUqExMTw9SuXZuxt7dnvL29mf/9739SPd+UfVcUnYvqlh0AM2LECNZtyu5P3e+cSHR0tPg69+jRI9Z1bt26xXzzzTeMh4cHY2try3h5eTHNmzdnVq5cKbdf2e/Gvn37mHr16jEODg6Ms7Mz06JFC6lrnEh8fDzz9ddfM+7u7oydnR1TtmxZpn///kxubq7C7efl5TGDBw9mSpcuzfB4PLnPjmEYZuPGjQwAZufOnWodD4ZR3MtP2+ugouuJyL59+5hmzZoxrq6ujL29PePv7890796dOXHihNR6a9euZSpWrCj1HWfrLSoQCBh/f3+53oOmgscwBhrSlBBikf7880/88ccfePPmjcJcMUNKT09HpUqV0KVLF6xevdro+zcEHo+HqVOncjIvJTFPoh6FCQkJBul9aYpOnjyJ1q1b4969e3obB02fKIeKEKKRESNGwM3NTZx7ZEjJyckYNWoU9uzZg9jYWGzevBnNmjVDVlYW6+CThFiyvLw8xMXF4a+//sLevXvx448/FplgCgBmzpyJgQMHmmQwBVAOFSFEQw4ODtiyZQvrFBL6Zm9vj4SEBAwfPhzv378Xd3pYuXIlqlatavD9E2JKkpKSEB4eDldXV/zwww8YNWoU10Uymg8fPqBp06biYS1METX5EUIIIYToiJr8CCGEEEJ0RAEVIYQQQoiOKKAihBBCCNERJaUbiUAgQGJiIlxcXPQ2yCIhhBBCDIthGGRlZcHHx0dugFhJFFAZSWJiIvz8/LguBiGEEEK08OrVK6UzPVBAZSSiKQZevXoFV1dXjktDCCGEEHVkZmbCz89P4VRBIhRQGYmomc/V1ZUCKkIIIcTMqErXoaR0QgghhBAdUUBFCCGEEKIjCqgIIYQQQnREARUhhBA5WbkFSEjN5roYhJgNCqgIIYTIqTfrJCIWxODR2yyui0KIWaCAihBCiJycfD4A4MyjdxyXhBDzQAEVIYSokJVbgIxPBVwXo0hIzsjF//bewcNk060Z23LxBS4+S+O6GMTEUEBFzMbrDzmYffQ+kjI+cV0UUoQIBAxCpkWjxvRo5BbwuS6OScnKLcCD5Ey9bnPUjuvYfuklOiw5K17GMAzmHH2AXVde6nVf2rjwNBWT991Fz9UXuS4KMTEUUBGz0XvtJayKfYZBG69yXRRShOQWfgmi3mXlcVgS09NsQQzaLj6Ly8/f622b9xKFAVqhgBEvu/4yHStjn+Ln3Xf0th9tvUzL0Wj9V+9zcOd1hoFKQ0wJBVQWLD0nH83/jMGi44+4LopevPh8IYtP0u8vYqIZgYDB8fi3SMnM5boohGOpH/MBAMfjk/W2TYaRX5ZpQs2tLMVTqvG80+j09zm8eq9ZIFbUpGTlgmH78M0IBVQWbM3ZZ3j2Lht/nXzMdVHMxqQ9t9Fv/WUIBOb9xTak3ddfY8jmq2gy/zTXRVHLm/RP2HrxhV6a68z8eq8VVdNtSFpy8jF2X3ut0/4YjUMW4xJoeRKYck4Y1/bffIO6f5zE5P13uS6KTiigsmCFFBRobMflV4h99I5qwZSI+dzrK7dAwHFJ1NN6YSx+23cXi05oV1PLg/oBhS5iH73Ta9OZsd19k4GFxx9hwr+3dNoOa7xinI9ALdoG1ddfftBvQRSIupuEBccemlVtz7yohwCArRe5z5HTBQVUFupDdj5WxT7juhhmi0/BqEImdG9TS/bn7v8XnujeK0udypojd5Kw78Ybjbab+jEP/dZfxjer4szqRijCMMCHnHz9bEvD9QUCBv3WX8ZUI9VuaPvpLI95ik/5hu/UMHTrdfx9+on4h485sLKQSMRC3gaRteasZQVTWbnGzaEwv1ua8ShqAhIIGAzfds1kc/Y0aLlSSFWsk18owPBt1zF21018yFY/wEj7qF0wkpVboPImnZyRa/ARz/X6fWHZGJ+veA/XXn5A7KN32BT3Qp+lUEyHgDcnv1CPBVHOnDpQWGnx5WQYBmkfTes9UkBloUyhue9JSha+33wVd9/o3sNl/rGHeigREUn7mKd1TYiiS1/cszQcuZPMmrPHFzA61bykfczDn9EPNe5hJUmfNWuXnqVh2oF7yM6TvkEWCr40g2ZrcPPUJm8ot4CPkGnRqDIlSumxrT9bOOJ5up5qkAyNLUfpn6uvFK5fwDdu0zP3V1bLo01A9eu+uwibeQJRd5MMUCLtUEBFDKbPusuIjn+LzsvO67ytB0mU0KkvF56kImzmCYzccUOv21WU9F3AF6DZghh8sypO622P++cWlp56gq+Wq38unXn0Tvpiq48qqs96rL6IjRcSsOTUY7000Qm0iAleSASX6iSOv/6g3fhtVxPUy+u6+0Y/eYdsRzNZQY/SV+9z0H/9Fb3sV12GapFlGAbLTj/B+SepetmeoZvm0z7miYPZV+9zMPNQPBLTtTvHtCnr9kvCfKsf/72t1T4NgQIqAgC4/Todt16l63WbSRnCi6AoH+nO6wykallFq2vPn7Vnn2Hj+ec6bcNU5BbwMWrHDey/qVmejsiK2KcAgMO39fvLTtGN5kFSFl6+z8GVBO2Tci99HpU6TYNmtL7rL2Po1uviv1VdtE89eIvj8W81Kteq2GdotiBGr0056t6wjdUb7uhd1UMiMAzwJl0/wwKwBaiKPrsJ/95CvrFrqAwUUUXdTcb8Yw/Re+0lvWzPkGfH89RshM08gU5LzwEAeq29iLXnnmPgRmFwu+PyS7WnLMr4VIBnOjRJZ+UZrxlVFQqoLFBGTgEeaNBFN7eAj8i/z6PzsvMGS5q8/Todnf4+h9ozT2DmoXhE/n1OYY1GbgEf366+iNVnnqq9/dMPUtB28RncS5RvXnyfnY+Zh+9j2sF4tW98mlw0P2TnY/2551oHi5radCEBB28lYszOm0bZnyzZyhC+gEHbxWcweLN6A66qynt49DYLvddexLUXX2pG2D6NT/l8jYZCUFaJk1vAx8CNVzFk81VkyuTrqar8SUjLwZE7wqBD23utJsFRTn4hGIbReF96rKCTY+jgTjTYpywuxkKTfKf6nI7opR7GqZpxMF4PJVHt8O1EABDfZ169/yT+++6bDEzacwd9119WuZ0P2fmYG/XAcAU1MgqoLFCDOSc1mtA0K/dLkKFJ3ocmLjz90sNq7bnnuP06A0cVtH3vuvIKcc/SMOuI+l+0ARuv4EFyFoZskr+pSx4LdXvvaXJ7GLnjOmYcisegjcZpenivQS2NIUjel99n5+Ps43dKA3jJG/mOyy8RNvMEFijJift6ZRzOP0lDtxWKmwgL+AIET41C9WnRcp/pucepOKGgpunMo3esybqStRw5edJBmuT2C7Rpm1ODusFRfGImgqccw8R/b2seULHU8+QXCrD4xCO9jORto6euWmxvyxRyQkUkj7vkeXztxQcM3HgFz959BCDsDPBGpgksMd1wAeCLtGysl6iF56o3rqhlQpX0nHyE/n5c3HRnCSigskA5GtYySf661CY5UK19sFwPCxT03PnEUuug7s0jM1c+IBy766b4/5oMUqiu85+7499SclP6mFeoUa1Xdl4hRu+4gai7yWAYBrOP3Nd5wEQRfbZYPEn5iNsa3IynfO7a/vfpJwrXUedXf0pWHhhGGAhJ1jrm5Bfiu3WXWGvLbrxMR9/1l9FsQYza5QWkk57zNBh7yxDnmqi5dvd19c6F5xJNKWzDGkw/eA+LTzxGp7/P6Vy2RhVK6bwNQLPzk4swS3KfkjlD3VZcwKkHKfh+yzUU8gWoP/skGs45JfVavpI3p+p0efU+B9suvUBeIfv1Pa9Q+tw0xLHJLeDjflKm0s9I3evcDT2nmJgCG64LQLgnef4b6lcNa5OAgu8dWxmuvpDOv/nrxGOMaVlRbr2PKtrTufjV9uzdRzT/MxbNgzywvn8dtV6zKvYpDtxKxIFbidg+uB5WnREOg9EtzFfnN6Fr88z7HOmAR9uRo9kUqpkPIzmSvRWPh1fvc/D9lmvoGlpG5WvZzpHLzxQnXtvZfPnd6eLAfsnk61hzJXkI1T2a6nyOksEj29Qn2/RUO8AwgLW18b5d77PzEZ+YKZWY/6UsjM7B7NvMXFxN+IC21bxgbSW9Lckgiu3cf5LyEcFTj+m0fzYtF8Yir1CAxPRP+LFNkN63r44eq+Jw63UGgrxcFK5jOnWJxkc1VETqYm7MGipFNwS2IpQqZi/196ITj7RKojdkHokioirtUw9SWJ+PvpeMmIfSzz2XuFF8kAlgjDVytyKSTagMw0CT1hhVZV8eo17enNSPAJ6w5ut+Uib+OHJf/cJIOP2Q/bNR15lH8j2zNMnvUjfIlRzbStM4VtV325hzzZ19/A4NZmuWmiDCMAxq/X4c362TT94+cicJNaZHa7VdSc0XxGDE9uvYEpcg95zkmHiKPoL8QvYAW8Awcs2AIqq+G6IaqPMKBqiVfbUuV4kzj96Jmy4liWrhNcnRVcTcBghWBwVUROpizjPiGaHoRsx2YfF3d5Jb9v5zE4Ym8+6pG4wYa7DqD9n5+H7LNfTfcEWqaengrcQvZTHx33zxChKGV8Q8RbMFMXgnmYSu4vDvvKygxkTmEDxO+XJBt+LxxKOhc0X02Ul2TGjxZ6zaYz+xnW+f8vn4M/qhVH7TOQVd6hXdwKWoOPaN553WKclakxtkn3WXkZSRq1bisixleZDDt11HZm6hVtuVJDqfYlkCM8nPStPUrhHbrqPhnFM4ekf7HraGvhrcfi1sGm/+Z6xWr2c7lx+/zULnv88pDCYtBQVUBAWFX74BhUpGJNYFW7u6oqCF7Yc068WaEW438H9H1C6HujVUsjVGulB2RCU7BBxR8yKray2bPoPFAj6DE/fZE8DnRj3A89RsTD9wT7xM8safk18od4FVt2hyeXY6vKc/DsdLNX2lZGmfOPxU5le97A25gC/AD1uuyg1UyVb8JaceY+mpJ2rlN22/pHqUcHVqn0890GzYCC5w9fMiPScfUXeTpX74aDqJuihhe+UZ7WeyeJ9t2N7EinpUKvJcbsgD+WPSatEZ3HqdIZVT9iRFvgbM3FFARZCV9+VXqaFGU85iSRZ/kMz+xVW3J56AYRQ2oymibjCijyptNqIu72zlUXcYBG3jqczcAjx991GvAdVJNW7ACQpGN2845xQazjkl1bSgqGyyYw1V8CimfiGVSMr4hDVnpccnm3lI82ZDUc2RshrQT/l8VPz1KI7de4uf/pMejFDyVaLz477MBN2P3kqfk5LHKlWNqWusWIrmaGst9ffzd4adokYfuJrqsMeqixi69Rr23fxSe6zP/EF1iYYokMVFOgMAuU4e6hwShmEw87B2zfOmjAIqIvUFiI5/i/G7bup9PKrNLPNssS0DpJs1RDcXtu8owwCDWIZJUMbQTX5swaDkHoOnHMPQrde027hoe1peOOv9cRIt/oxVGMiqcj8pU26qFU1/oUsS5YZJ5WSpWf9gZy196dK2WZStqUx2HCp1zgVRz1rZz+baiw/i/Ky1SubXVHR+S2q96IzMayQS89miJRmishXwBfhu7SXMi3rA2qMW0Cz/S1hW3RPB1d6XEeuoJN/Tw7fyP7JStJwvj+1I6XL43mbmYrYGw8woY4xPUdtR+02d2QVUy5cvR0BAABwcHBAWFoazZ88qXT82NhZhYWFwcHBAYGAgVq5cKbfO7t27ERwcDHt7ewQHB2Pv3r1Sz8+ePRt16tSBi4sLPDw80KVLFzx8aF5zyy0+8UjhwJ2SF9U5Rx9gz403Si/+2ihU0Auq99qLmHlIejA6yV99MQ/f4dX7HFyT6eUnu566JC9ar97nKBlkUn83aNktHbv3pVZHnRuh5NtMzsjFstPyidtnH7/D3hvKu9KLPmfZJHd1xDxMQbu/zqLdX9LfN33c2iS3oW58po/9ZuYWsI68rkvHjCsy07RsjnuBARuuIDH9E+vArzEPUzA36gFrIK7q/JbuTPJl/+ces+dZiX5MnHqQgnNPUlk7AIg2qWxsoHuJGWgw+6TUMB7GrKcxdKWQZE9TVUMAaNtspc/YMyUrF/VmncRJmdp6bQNcyZdN/PcW1p97rtGQL+qsyUXNnjGYVUC1a9cujB07Fr/++itu3LiBxo0bo127dnj5kv3L//z5c7Rv3x6NGzfGjRs38L///Q+jR4/G7t27xevExcWhR48e6NOnD27duoU+ffrgm2++waVLX3qQxMbGYsSIEbh48SKOHz+OwsJCtG7dGtnZpl89LrL4xGPcepXOOskoW96UKJH4xssPOk1IK6LoRnn+SRrWnvvS5MIwjFQtUlp2PrquuMD6Wm2+kv9efY036Z+Q9jEPjeedRtjME+zb1sP3PSE1W66ZRpaiS17dgJKsy0fvvMG6vM+6yxi36xZrzxxA9wlkD94S5nfJjuasj+MkleSrRY2XtmWoPi0aXZfLn1uy9yFNNq9oTK6UrDzWZrn+G65gRcxT7GEZV0oyyGJripcs15lH7yAQMPh6ZRy+W3dJqjegiOh9yY5XJLXNzxtNV5KcPnbnTSRl5GLCv7cUrmNIup5zm+MS0GjuKbxIY79+X36u3tyFpuLWK/ZzTh9T5Px37TVmHIrHj/+pP1+ehcZKajGrgGrhwoUYNGgQBg8ejCpVqmDx4sXw8/PDihUrWNdfuXIlypYti8WLF6NKlSoYPHgwBg4ciAULFojXWbx4MVq1aoVJkyYhKCgIkyZNQosWLbB48WLxOlFRUejfvz+qVq2KGjVqYMOGDXj58iWuXdOt6YYL6t5YeQBepuXgq+UX0GT+abVewzCMwqZCdfKinqdmo/7sk3I9mdhGthbtj42ysYz+t/cOWv4Zy1p9L7kfbecHk7wZRyyIQetFZ5CpRc+pMsUdWZc/SFLeXKeoCaLz36onFV52+gmWxygecNPQLj9/L1dj1HnZebz+IB3ECQSMzs0Syr4H6ubwaeqwkk4HkmMbifYuObsAW+2W5ECd11+mSyX4sw3iqU6NBQMG77PzcUHJBL26BufKyNbwsl1PdK3dmLL/Hl5/+IRpEp0lJEmOym6sZkxT95+eBhUGgIvP0pT2Su299qLGTc6mwmwCqvz8fFy7dg2tW7eWWt66dWtcuMBegxEXFye3fps2bXD16lUUFBQoXUfRNgEgI0P4i6BkSfZaBADIy8tDZmam1MMUqDseFI/Hk6tdScr4pPRXz8gdN1BlShQStJzocsbBe3ibqX5OgqLr+j9X1Wj6kngbogBMMj9In70d3yrJs1DUvKTtZVzRxxOvIhBLz8nH/GMPMS/qIevAl4ruK/rMZxnLUvt261U6Gs2VDuhrTI/GMx2TpzddSFD4nGyHBENNhqurApmbkqrcPDVal8EwQPM/Y+QG0lUlr0CA23oY+fqmzDaqTo2S+4Gkr+/m2c9NoymZuZgb9cCo43DpM0x7q2A+Q0VTwPAFin/46oOqa0LP1RfRSiYfUNL5J2kaB3BN559GlBqTeBua2QRUqamp4PP58PT0lFru6emJ5GT2A5mcnMy6fmFhIVJTU5Wuo2ibDMNg/PjxaNSoEapVq6awvLNnz4abm5v44efnp/I9GgPbyX6RZZRoHk+6SWHn5ZdoMPsUpuxn/1UHAIdvC3+Bb72ougs3G43HdNl+nXX5H4fj0XnZeTx+m6XWr+kN5xMASAcN2gYKom2oexNWd7JmEdljJNtsISr37muvsU6iKVUVyRo5tho+RTcAfVTmaFrjkZVXiNlHpXsIaRrzaNuLU1WFhcKhQLTam4p9yfytqrs7Dzw8T83GFRVNWuksOXbTDtzD/ptvFL5m19VX+PP4I6Xb1YaAkW9+/OvkY71sW1QT9cPWa1gR8xQ9VgnnjjRG+Mx2jLU1W8FgtqJR5I/Hv5XqMRr59zlUmRKFDAVl0HXgYH0EvJrWUL1Iy9G5s48+mE1AJSJbBauqdwnb+rLLNdnmyJEjcfv2bezYsUNpOSdNmoSMjAzx49Ur+dwlLrBd8Nl+md17I31xnvN5RvAtagZL2lTZss1Rpc1XOzufj1uv0tFq0Rl8tZy9qUvyMIhG15a8kOgaKKg7nc/1l+nsr1ewvmztUdP5MVLLJu25g7SPeZjw7y38fige+2++kWpOkiVq3pJ872zniMIaKj3efUy1eUWTt6goEFfVTKXNe1e2SbbtWfGEXdyVfYePK5hUeuOFBIzZeVOu+dUYjsrUPGxiGb1cFzc+fwcT1ZzUVx+esdTia3v+KxvU9tardAzZfBXt/jqLtI95uJLwXhx4KxokVtEFS7b3qyJsHYiKCrOZy69UqVKwtraWqzlKSUmRq2ES8fLyYl3fxsYG7u7uStdh2+aoUaNw4MABnDlzBr6+vkrLa29vD3t7e6XrcIHtGsx2sb+c8B5DmgR+eZ0Gd5W1556jhl9xjcvGVlOm6/367hv1m1qTJarOtW3mEQUmkq9WFlyq0wyj6BeoiOTYQS/ScjBVIjdE1dhWVaZEYU3f2qjq4ypeJir7y7QcbI5LwKDGAQp/tSpKgtcEj6ddAC5iiG706Tn5KO5kp7ftxT1jny6ETW4BH7ayw0KwNdVreI6qM73OYxW91hrNPQ0HW+P+DpfNQTRUjpsxlXTW37mlzEOJmtjG806Lh/dQhGEYhRfd3w/Gsz8huw0Tn9nBkMymhsrOzg5hYWE4fvy41PLjx48jPDyc9TUNGjSQWz86Ohq1a9eGra2t0nUkt8kwDEaOHIk9e/bg1KlTCAgI0MdbMghVFxu2a7Cil0hesCWnpFCnmWrUDvbeaKbiPUsvKMlpTxQdx/NPUpXOE8b2I/OdwqEZIDfxqng7Ev/X9JfzJQ16KeUXCjBo4xXWgSW/XXMRa889R4PZp7CLpXeopvtSZNaRBwiaHGVS01JINitpErc42FizLlc1NYzkdy1kWjSWqNGspaxYbGeVqtxCdeUW6JaUrmkzty75PrkFfHyzKg7LTnPX2YIN249Ybetn7W3Yb+M8nnQgLxtMsQU+32+5hp92s/fo+1fNvCbJH1/3Etl7IGrq/JNUs+h9aTYBFQCMHz8ea9euxfr163H//n2MGzcOL1++xNChQwEIm9n69u0rXn/o0KF48eIFxo8fj/v372P9+vVYt24dJk6cKF5nzJgxiI6Oxty5c/HgwQPMnTsXJ06cwNixY8XrjBgxAlu3bsX27dvh4uKC5ORkJCcn49Mn07kBiFxS8UuY7UukaVf1vTcU51Lom6Eagf4+JX+BvSGREMt2SHIL+Oi99hL6rr+MrNwC5BXy5aq3ReVVt/ZAH81cd2UuWop6RSoug3Q5RCU3pQBH1tN3srlj+vVRYmT/26/T1X6dus0iqiyUyUfaxjIu1BslgyNy3Xr6LisPCanZaP5nDHZdkS57rzXSkxrPP6afASnZ/HftNS4/f4/5x9QfN9AYnRD0mUOl6LPmQRiIKMI2e4WiJl9tdViietokVdJz8tF77SV8sypOaQ9uU2BWAVWPHj2wePFizJgxAzVr1sSZM2dw5MgR+Pv7AwCSkpKkxqQKCAjAkSNHEBMTg5o1a+L333/HkiVL0K1bN/E64eHh2LlzJzZs2IDq1atj48aN2LVrF+rVqydeZ8WKFcjIyEBERAS8vb3Fj127dhnvzatJVXf/XJZfe5pW0bIFG4b6Bajpr1l1KRohWoTtiEiO35OVW4jIpefRTWaMLFFNnrpHVB/3vUl77uj0etnmPBPt1GZUkr0vNQlQrySw54/o2mS3kaVX4h4j/rDRFMMwmLz/Lp69y8bPu6XPT9mefGwD1eqLZFOyus3Tkp/K1YT3LHPVGYayIFhZk7i2SeR5BhqaQN+Ddkq2Jph6a6/Z5FCJDB8+HMOHD2d9buPGjXLLmjZtiuvX2XuDiXTv3h3du3dX+Lypdptmo6qkh24nYXzrylLLFDVvKduWbOK+Jr8ANSE7z5q+sA4VIfH/WyxdwKV7AbJPRXHhaRo61fBROyldl1G59YYnM5ecmeVAMIzm31FVq0t+LOP/0X0AS331TJMkO9efpAwtxj7TtzyJpsG3mbnwdHXQajv6+oq8eJ+DwNLK54CUPY8ycwvRbEEMEuZ00E8hJEzdfxeBpYvBzsYKe6+/QYPy7grX1TZvjItvsj6T0k89eCtVk2YKl0tlzC6gIiqo+AbxWe4kmn5ZYx6kYGH0Qyz8piaaBXlo9FpNGerG8ClfuibvrxOPVX5Z2fKMZIlGtJYMSpQ161mbQB0xD8BTyV/v5hVPGcSnAj7iEzPlekhO/PcWdn7fQC/7kDyFXuh5DKTIv88bJAjQiMRpP2nPHazvX0erzWj7ezY7r1B6Al41tmPM386bZOYyvfZScSCirFiKatv/vfZaafK7oXrV6mvg18dvszBwo/RcrSYeT1FAZSkK+QJce/FBZW8ptgtGoYYBlWjOqAEbr+DmlFZITDdcd2ND1Q7Kjjy96ITqMXQkL0APktjHMbr47D36NCinQUm4v0TweNKBK8VTwP6bidh/M1FuOVtPVG1JNo3oOlCpqWEgfWafepCCBcceIjreeIMvsn1+qnB57mtTC6UqYZ+t842IZDz19N1H1pwqrggYRq9DSxgLBVQWYtGJR1h2+qnKbvhszTm6DMRW548TKNDjqOKyDPWL0beEo8Yznkse2llHlQ9loG6542USypvOP40JMk2yhsYDT6onoRm1cGvNFJo1uS+BYcneEP/muKfdpwK+wlHFpZjgB5Ms09OXYRj8e+21TsONpOcUoOPSs+hSs4x0TZ6O9BH07L2RiDEtKspvW+ctGxYFVBZi/bkEAKqT9thulooGeFM0a70kfQVTinoaZrFMg6IP6gRTOy+/RLsQb7g5CofYkLxOKKpROB7/Fq/e56C0y5cxyJRdBJbI9DZ8kZaD0TtuoGtoGZXl05dPBXxM3ndX/LcpBBuaMFT+nqHJTjStb1zOh8Ywmvc2VUQ2iV1d+TIDBQ/fpjyXFgDuvMnA9svyPSq5Jtvj9vyTNPykwYTFbEQ9STUZq89YhPmBLHmuJh5RmUAGBzEmUUCVW8DHmJ03MFLB9C2A+qOi64Mp3sJ/2XMH43fdFP+tTgJ5Pl+ArisuSAWuqgZLJLrZeCFB8/PHBE44Qw9QqWpAWHOhcERvFdZ/nlJKE12WnWcdOoDLjkkMw8gF30/1MKCuoegr5tFkzERTQQGVhdA0ct9wPgH7bybi0Of597h2+oHqUZy5cFKLcr3LytO5lidGyeChhqaPX6yi5HxT9YqDKVRkGfoeHXWPu8liTaGWU581gP8oGNjWGFadeSZVgwzodxwrU8V2Bu3WcNJkY6OAykKoG0+9Sf+EMTtv4OR9/Q7gpqvBm6+qXoljmtwAdb1ZKksmNbQhm6/iSYp2kweLROt5gEBVND3eisaL0oVa+TkSFkQbtqlS10ludWFpeXiyY2kZ2qHbieI0iDlH5Qc+VacTjbljO4cUjeJuKiigKoL230zE1SI8gaW24p5p1/Rgjo7d0y0gOqBFDytzV2/WSY3W1/eo1LK4rCXSds+yvW+LqpHbb5j0wK3GkJbNfi6Y8riQFFARoiZNekOa7lfeOLTNe9GWtonLluxtJnfBibY3PUPX2hlC1N0klXM1auOiBhNqmxJ9JY4r6vCUrGFNsDFRQGUhTH18DkvwIk39nAxVNxT6JU4smbaVCBlmmBs0dOt1/HVS/01wAgFj0rUxhqbojsZlU7YqFFBZCNM9xSwH2yjziqhac9Am084Zo/icGNOADZcBSM+XaU4O3tJ/5549N96g9swTet+uuZgTxT5pdqHAdM8RGoeKEDVcSXiPLXHqDyOhKvZimyvQlBThH8ZED26/zlC9koTTD4W9Ws01kDfUmGJpHHZO4ZqiZtRGc0+jnLuTkUujHgqozNytV+nYdumFwQbAJEJfr4zT7AVmHpAYeowkYtk07fEoYuzpT7Yacay9okK2Se7263S97yNBg/QLY6KAysx1Xnae6yIQFqYwDo8uou5yN4YRMX8CM6ni/E1mfCeif5F/F517FOVQEWIAhpww2hhy8qnGk2hPX9POEPOTU1B0rx0UUBFiAO2XnOW6CMRAMj6ZX080Y1t15pnGr+Fy7kGiP6/eazbpvCWhgIoQFQwxxgygeEJoYtraLDrDdREsUtDkKK6LQIhOKKAiRIXfD8UbZLvnn5ruyOummvRpCkx5YEFCirKsXG5rjymgIkSFLQbqCfQpn5o4CCFEX0JnHOd0/xRQEcIRavAjhBD9KeQ4jULjgCoiIgKbN2/Gp09FN/GMEH2Yp2AkYEIIIeZH44AqLCwMP/30E7y8vDBkyBBcvHjREOUixOI9fZfNdREIIYToicYB1Z9//ok3b95g8+bNePfuHZo0aYLg4GAsWLAAb9++NUQZCSGEEEJMmlY5VNbW1ujcuTP27duHN2/eoFevXpg8eTL8/PzQpUsXnDp1St/lJIQQQggxWTolpV++fBlTpkzBggUL4OHhgUmTJsHDwwOdOnXCxIkT9VVGQgghhBCTpvFcfikpKdiyZQs2bNiAx48fo1OnTti5cyfatGkD3uepwr/55ht06dIFCxYs0HuBCSGEEEJMjcYBla+vL8qXL4+BAweif//+KF26tNw6devWRZ06dfRSQEIIIYQQU6dxQHXy5Ek0btxY6Tqurq44ffq01oUihBBCCDEnGudQTZ06Fenp6XLLMzMz0bx5c32UiRBCCCHErGgcUMXGxiI/P19ueW5uLs6ePauXQhFCCCGEmBO1m/xu374NAGAYBvHx8UhOThY/x+fzERUVhTJlyui/hIQQQgghasgvFMDOhptZ9dQOqGrWrAkejwcej8fatOfo6IilS5fqtXCEEEIIIep6kJyJ6r7FOdm32gHV8+fPwTAMAgMDcfnyZanefXZ2dvDw8IC1tbVBCkkIIYQQokpKZh5n+1Y7oPL39wcACAQCgxWGEEIIIURbDIf7ViugOnDgANq1awdbW1scOHBA6bqRkZF6KRghhBBCiCYYhruQSq2AqkuXLkhOToaHhwe6dOmicD0ejwc+n6+vshFCCCGEmAW1AirJZj5q8iOEEEIIkcZN30JCCCGEED3jModK44Bq9OjRWLJkidzyv//+G2PHjtVHmQghhBBCNMZhCpXmAdXu3bvRsGFDueXh4eH477//9FIoQgghhBBzovHkyGlpaXBzc5Nb7urqitTUVL0USpnly5dj/vz5SEpKQtWqVbF48WKlkzXHxsZi/PjxuHfvHnx8fPDTTz9h6NChUuvs3r0bkydPxtOnT1G+fHn88ccf+Oqrr3Tar0LZ2YAex+tyzM/V27YIIYQQc2adky28z+qTuttjNFS1alVm6dKlcsuXLFnCVKlSRdPNaWTnzp2Mra0ts2bNGiY+Pp4ZM2YM4+zszLx48YJ1/WfPnjFOTk7MmDFjmPj4eGbNmjWMra0t899//4nXuXDhAmNtbc3MmjWLuX//PjNr1izGxsaGuXjxotb7ZZORkcEAYDKENZL0oAc96EEPetDDDB4ZAAOAycjIUHqfh9oRwWfr1q1jHB0dmSlTpjAxMTFMTEwMM3nyZMbJyYlZvXq1ppvTSN26dZmhQ4dKLQsKCmJ++eUX1vV/+uknJigoSGrZDz/8wNSvX1/89zfffMO0bdtWap02bdowPXv21Hq/bCigogc96EEPetDD/B7qBlQaN/kNHDgQeXl5+OOPP/D7778DAMqVK4cVK1agb9++mm5Obfn5+bh27Rp++eUXqeWtW7fGhQsXWF8TFxeH1q1bSy1r06YN1q1bh4KCAtja2iIuLg7jxo2TW2fx4sVa7xcA8vLykJf3ZQj8zMxM4X8SEwFXV6XvVRNVJkfpbVuEEEKIOetWqwxmfhWi341mZgI+PipX0zigAoBhw4Zh2LBhePfuHRwdHVGsWDFtNqOR1NRU8Pl8eHp6Si339PREcnIy62uSk5NZ1y8sLERqaiq8vb0VriPapjb7BYDZs2dj+vTp8k84OwsfevLJzkFv2yKEEELM2dMc6PUeCwBQc8ByrcehevfuHR4+fIhbt24ZJRldhMfjSf3NMIzcMlXryy5XZ5ua7nfSpEnIyMgQP169eqVwXUIIIYToTslt2eA0rqHKzs7GqFGjsHnzZvGo6dbW1ujbty+WLl0KJycnvRcSAEqVKgVra2u5WqGUlBS52iMRLy8v1vVtbGzg7u6udB3RNrXZLwDY29vD3t5evTdHCCGEEJ1xGVBpXEM1fvx4xMbG4uDBg0hPT0d6ejr279+P2NhYTJgwwRBlBADY2dkhLCwMx48fl1p+/PhxhIeHs76mQYMGcutHR0ejdu3asLW1VbqOaJva7JcQQgghRYvGNVS7d+/Gf//9h4iICPGy9u3bw9HREd988w1WrFihz/JJGT9+PPr06YPatWujQYMGWL16NV6+fCkeV2rSpEl48+YNNm/eDAAYOnQo/v77b4wfPx5DhgxBXFwc1q1bhx07doi3OWbMGDRp0gRz585F586dsX//fpw4cQLnzp1Te7+EEEII4R4P3FVRaRxQ5eTksDZ1eXh4ICcnRy+FUqRHjx5IS0vDjBkzkJSUhGrVquHIkSPw9/cHACQlJeHly5fi9QMCAnDkyBGMGzcOy5Ytg4+PD5YsWYJu3bqJ1wkPD8fOnTvx22+/YfLkyShfvjx27dqFevXqqb1fQgghhHCPyyY/HiPK0lZTixYt4O7ujs2bN8PBQdjD7NOnT+jXrx/ev3+PEydOGKSg5i4zMxNubm7IyMiAqx6HTSj3y2G9bYsQQggxZ40rlsKWQfVUr6gBde/fGtdQ/fXXX2jbti18fX1Ro0YN8Hg83Lx5Ew4ODjh27JhOhSaEEEII0Zay3veGpnFAVa1aNTx+/Bhbt27FgwcPwDAMevbsid69e8PR0dEQZSSEEEIIMWlaDezp6OiIIUOG6LsshBBCCCFa4zCFSr2A6sCBA2pvMDIyUuvCEM0FebngQXIW18UghBBCijS1AqouXbqotTEejwe+mkO0E0IIIYTo07UXHzjbt1oBlWhEdEIIIYQQU/Uxr5CzfWs9lx8A5Obm6qschBBCCCFmS+OAis/n4/fff0eZMmVQrFgxPHv2DAAwefJkrFu3Tu8FJIQQQghRh5ujLWf71jig+uOPP7Bx40bMmzcPdnZ24uUhISFYu3atXgtHCCGEEKKu2v4lONu3xgHV5s2bsXr1avTu3RvW1tbi5dWrV8eDBw/0WjhC2EzuGMx1EQghhJggLqee0TigevPmDSpUqCC3XCAQoKCgQC+FIkSRZpVLo394Oa6LQQghxCRxF1FpHFBVrVoVZ8+elVv+77//IjQ0VC+FIkQZaysuh24jmvJ3d+K6CISQIkOj6Yn1SuOR0qdOnYo+ffrgzZs3EAgE2LNnDx4+fIjNmzfj0KFDhigjIcSMRY9rgsq/RXFdDEJIkWBGNVSdOnXCrl27cOTIEfB4PEyZMgX379/HwYMH0apVK0OUkRAxLie+JNqxt7FWvRIhhJg5jWuoBgwYgO+++w4xMTF0czMBNO0MIYQQImRWwyakpaWhQ4cO8PX1xcSJE3Hz5k0DFIsQdj7FHbguAiGEEBPFZc6mxgHVgQMHkJycjKlTp+Lq1asICwtDcHAwZs2ahYSEBAMUkZAvfmwdxHURCCFFxLJetbguAtEQw11OunZTzxQvXhzff/89YmJi8OLFCwwYMABbtmxhHU6BEH1yc+KuOpcQU1QvoCTXRbBYHap7c10EoiGGw15+Os3lV1BQgKtXr+LSpUtISEiAp6envspFCCFEDTSMCCFfmF0N1enTpzFkyBB4enqiX79+cHFxwcGDB/Hq1St9l48QQogSXWqW4boIhBBo0cvP19cXaWlpaNOmDVatWoVOnTrBwYEShYlidtZWyOcLuC4GIRbnv6ENEOZfAj/tvs11UQgp8jQOqKZMmYKvv/4aJUpwNwEhMR93prXGyfspGLvrJtdFIRxytLXGpwI+18WwOLXLUf4UEfJwsUdKVh7XxTCamn7FcfNVOtfFkKJxk9/3339PwRRRm4uDLdqFeHFdDMKxWv7FuS4CIVq5Pa0110VQKaSMGy7/2pLrYhhVroIfaBymUOmWlE6IOuxtrLF9SD2ui2FxgrxcuC6C2rhMFCVEF64Opt+zuCj2SzDFgcUpoCLETEWNbcJ1EdRGARUhhlPMQePsHbPnbKdgSisOLzYUUBFCCCEGUtcIeW4965Q1+D5MTXiFUqzLqcmPWDwehzOAE+5xOdgeIVz6Z2gDg+/DwbboTUBuis2cFFARQgyOmvwIMRxTDC64YnYDexKiKRPMHyRG1KC8O9dFIMRiGWu0/EqexYyyH12Y7dQzhHsBpZy5LoJaKJ4q2oZFlOe6CIRYLGMFVJ6upj+IN5fpJRRQmTlXPffu8HS11+n1+i4PMU0tgjw0Wt/epujleBAiwlZD37KK/ua+tTJSE4CA2u6VooDKzOn79L70v5ZoEKh984zk2CBtqxpuQM9VfcIMtm2iGl1WCVFfrbLyg2GPblFBb9s3VkD1NCXbKPvRBTX5EZMQPU44rpG+qo871/QR/18y0Lo/o63O225T1QvtaQR2Yob2j2gIAKjq48pxSYixsNXsVPctrrftG6vJLzkz1yj70QUlpROT4O0mbB8Pr6BLDZXq5Y521oge1wR7hodrvR9NBHu74s+va+DCL82Nsj9JB0c2Mvo+RdqHeGF+9+oG2TYPQElnO5Xr9ajtZ5D9mxvJXMcafsWRMKcDDo9uzGGJiKFU93WTWya6tsoq7aJbioWItRp38g4h3ljbtza2DbaMWSt44KFNVf01m+oDBVRmzhDRuK2V4U+LSp4urNXgmlD3vdvaWKFbmC98ijvqtD9tPHmXZfR9ivB4PHxtwIDm69q+KtfpVa/oDTjIxtC9HG1MpN98wpwOcsvKcPC9U8bQP3IqlJbvCTeuZSWD7pNtGpZBjQJk1gFaBnvqdB30K2lan+VYluNqrNo6NhRQETm6NMfbSARjkvGOlxa9Q/o28Jf6W9RUojEO64DzCwWc7duQqQSOdtaY0KqyyvVq+BU3XCHMSBU9zbuoaNTtJ7Pa62X76lJ2jfCRqY1xdTStufBCWGqQ9IntR0RFTxfW2lp93fpLF5Ov6ZrcMZh1XTsb7W/75jBAs28J7oI+CqiImOhXjoeWXWMHNgzA6r7syeJ+JZ2w8rsw7Py+vtrbmx5ZVfz/6r5ucjfnZhr2NOOCoWK5mmoEKsp65FTx1i1/57cOwTpdmLnEyaTSekoaXta7ll62Y0ztqhWtXMcQXzeULekEACjn7iRePpel+V1fueTqNL+Lru+67FLf33ldv4s5+YVyy2jYBGISnD5PX+Cg5pemdbB0+/WUTsFKm/HaVvNCfQ16EEpWY7PFBv4lneQXsvBTcz1DMFQl0ZDGgSrXEQVUbDkdjAaR3uIeNeWWeSnICdGlh6ixdAktY/R9li6m+oan1nb0lHNjTP7uTvi9c1XVK342XMMxy37rUEXTIqklUMsx/vgCBtHjmmBxj5r4b5jyPFF93fwVBWaxP0aI/2+K+Yzf1fdXvZISb9JNK0meAiozp88uoqItqfsrxNle9zGndKmeZcsbkPTPDw3QNbSMVE2XsTkaaI6t7Dz5X2ayRDHTru8bgMeT/jWoyXgyYf6Kg+QuEj05T0+MwOZBddXerjGwjZfFRYpF62AvDGwYgCXfhmq9DXOtEexU3QdOdupdK6r6uOL7Jqp/LEgarMaPC21INt3V0KCZUMAI59brEloGpVia4gxBUWDm7+6Mx3+0w9mfmqFRRfbJhDWhyQ8xdVT3dUP50toFrgwY2FnLv29FP/aMwTy/oUQsJTNPrfWmKGhPZ9NIwSzesvTx5SrupH1+harq8roBJbGwR024G+mixoatdkgfzj5JVbmO6NNxtLPG89kdMKZFxS/PafDROdkpDgo7S9T2BJRyhq063Y30YLmaTV9uLPk7+qgVqOHrBgdb9d+rlRUPUzoFI7KGj+qVFSimhx8w+qLu+VPVxxVWVjy1m7YOj26M4k52iJkYAWcl5522NLkOSt6YNfnxqOytGmrgY2XH19baSmEtPVvtszL6rnG3sbLC8XFNpZZ1VbMGmQeeVM6uSGM9BI7aMpuA6sOHD+jTpw/c3Nzg5uaGPn36ID09XelrGIbBtGnT4OPjA0dHR0RERODevXtS6+Tl5WHUqFEoVaoUnJ2dERkZidevX4ufT0hIwKBBgxAQEABHR0eUL18eU6dORX5+viHepsZSstQLqGxZInlFbNS8KQr08O1S98LM1tRh+umRqmvRRBTNkeXvrn1zpbKAV5OPTtnNJKJSaWwZVBcXJ7XQYIuaKcXSXNY+xNtg+1OHv7sz7k5rI9fsbUgN1fyho0/9w8shonJprV//1eebo6KBJ7+rz94LtFwpZ8z/uobccslcofDy7tg0ULMaUVsNavkkz3tNapqVJuzL9LDTVw6VnQY/ZCT3qfH+JS4cmjbNsm+OgZVMlbGDTCCtrKfwoxTpXtQ1/Yqrfc01BLMJqHr16oWbN28iKioKUVFRuHnzJvr06aP0NfPmzcPChQvx999/48qVK/Dy8kKrVq2QlfXlQxg7diz27t2LnTt34ty5c/j48SM6duwIPp8PAHjw4AEEAgFWrVqFe/fuYdGiRVi5ciX+97//GfT9WgJ1giXJdQ6NaoSZXaoh2NtV7lcG282kpl9xVCsjnVz9g4bNBaaiWhn2mizJLsCbJW4e9QLYe3tJUnb89VV1z+Px0LhiadZq9hI61D5KMsXZLqr7uqn9w0Nf/viqmlH3BwDTIqti44C6Gk1JJXlDE52/ipq+KnsqTkpuV81Lrgu85J/bh9RH00oaBnsanEySe54WWVXtiYGNmRR9a2prxM9oIxeUqEtZ8MGWMC559Ca0Vt3LV1dVfVw1ChYVjfdlLGYRUN2/fx9RUVFYu3YtGjRogAYNGmDNmjU4dOgQHj58yPoahmGwePFi/Prrr+jatSuqVauGTZs2IScnB9u3bwcAZGRkYN26dfjzzz/RsmVLhIaGYuvWrbhz5w5OnDgBAGjbti02bNiA1q1bIzAwEJGRkZg4cSL27NljtPevFzye0qabRT1qaDx+h7O9jfhCW1bLxG/JL2i1Mm74rr4/Do9uJBU8KGJjbYWDIxuhocRApJree5tUKi3VLMRWG6ILySOq7OahzkW4hNOXsqmTm6HsWBgjRimjp+7LfC0iqqaVSuPaby1ZqzH18QO2X3g5tbZVq2xxjZKB2ZooRVwduBt+IGpME6nvmbpEH13DCu7sNzslB5DH46GPTNKyrrUPmtSqS+7Lr6QTosc1VauJTFkRK3hIB2W6noou9jZq56ex0biCSuK7qI/xnkSbG9gwQG6Z6P+SP9ZOTvjSPMiWP8z15M1mEVDFxcXBzc0N9ep9GeG1fv36cHNzw4ULF1hf8/z5cyQnJ6N169biZfb29mjatKn4NdeuXUNBQYHUOj4+PqhWrZrC7QLCQKxkSdU1BKaEB2DP8HB0qM7eVPJVqOpBGmVNaF0JO79vgG/r+mGLlsnIbL+CeDye2hdOHo+n0y/Caj6uUj1h1FXZ0wW7ZIaAYJu7UPJtyF5MJXWtxZ43IPnOQnzdxMdLnYEiZRPPJcuiSYyiyS9EAFjXrzZq+hXHXz3VT8D2d3fChv51xH9L9rBKzylgfc1eJSPtB5Ryhnsxe3xb1zADi4pyxVRNH7JneEPW7vKK6CthXtHN7va01qzLVSnhbIeZXUI0fp3oHOTxeBjfSn4QRiue8sEiJ7aRrgXRtuedvqjTQ1TZd2tG52r4tq6f+NzVNUDU5uWS10tNX1/OQMd/SqcvuW3F7L/88BcwDPqHl8M3tX2x8rswqWsR23XfQ4OaVEMwi4AqOTkZHh7yvXU8PDyQnJys8DUA4OkpnePg6ekpfi45ORl2dnYoUaKEwnVkPX36FEuXLsXQoUOVljkvLw+ZmZlSD64FebliWS/9jWNTqpg9Ako5Y3bX6vB31+6LNrVTMPqHl9Np9GLJi4KyX/iKFHfSvFbq2LgmqCczRMBKLSds3j64HhpWKIVTE5pigsxNR/aCe3h0Y9yf0Vat96mvJj8rKx6uT26l9votqnhi34iGKM8yYrQiK78LkxpXjAGwY0h9LP02VGEgGqpkiA5R4FmnXEm5/C62m1j3MOEPipZVNMuJGtw4AL+0C8LRMfqZRqaEFueiLAdbK4U/MRxstE/0lpw+R1ng96NEEKSqNykPPHSsrjhRv5i9jdT4c4t71kTnmj7Yp8Egv2NbSnbGUP+817ZZ3F5JnlZJZzvM7lpd6blraJLlY0vqVsbDxR6HRjXS6kcoG8mv4uyuIahTrgRGNJOeNNrB1hrzutdAW5bxzGRz8xpX0D7fTx84DaimTZsmro1Q9Lh69SoA9osgwzAqI3zZ59V5jaJ1EhMT0bZtW3z99dcYPHiw0m3Mnj1bnEDv5uYGPz/TGwNEU5I1AuoMJKdOxUZxJztMi6yqt9GLBzQsh5ZVPPU2h13zzzd52VwtWdqMBC8S/jk/LLB0Mdir6DlmbcWDo0TTreSUFhNbV8Ll/6mXHK7pxKwlne3grsZnrkz/z81k/WRGwAfkBxplGAYNyrujUw0frQb/k8wpkc3vYhu/LLKGD2J/jMAqDYNiextrDG1aXueBUgFhTqC2QbmknnUU18pJXtb8SjoaZILxOhL5fVLNNwrK01zFAL2Sn5e3myP+6hmq1sC2IpLTk2jb5KcJbfOZACC0bHG11pvaKRjzu1fXqowlJL7HysY1YxhgRe9aaCXR8ULACFMztP0BzbYPkW/rlsW/Q8OlfuAqC8hlm/x+61DF4KPgq8JpP9yRI0eiZ8+eStcpV64cbt++jbdv38o99+7dO7kaKBEvL+GFIjk5Gd7eX5q5UlJSxK/x8vJCfn4+Pnz4IFVLlZKSgvBw6eaExMRENGvWDA0aNMDq1atVvrdJkyZh/Pjx4r8zMzMNHlRFVC6NmIfvpJbZWPFQKGD00kNIeqBNxSd6//ByuPEqHc2D9NcDStn+JMvlZGeDtf1qa7z9b+v6YcflVxjfqjL+t/eOePmiHjWx78YbdKjujdozTyh5PftNTNcEVVWvHtm8AuoHlkSIr5tcLoWyi5E2+U265h5N7RSMgQ0DwOMBm+JeAAAmtKrEOj2Nrj1IFeWrLf02VOH70NdNQhsBpZyV1rpoMoSAqs+pXkBJXHr+Huv71UFFTxd0XX4e11+mq7194T54CqtAJb+rUmuwrF6qmL3RhtowB+rOyThAIudIG5PaBSEhLQe1JAK4yBo+aB/ijaFbr4mXtQvxRrsQb5T75TAAzcav00X3MF/8d+01RksM9cJG8mhpEmQbCqdncqlSpRAUFKT04eDggAYNGiAjIwOXL18Wv/bSpUvIyMiQC3xEAgIC4OXlhePHj4uX5efnIzY2VvyasLAw2NraSq2TlJSEu3fvSm33zZs3iIiIQK1atbBhwwZYqVFNam9vD1dXV6mHoUnmoIhc/rUlzv7UTKq6Xlvq3k+nRVbF/hENjTYQYfPP3bpd1BjjZeV3YVLriW4+s74Kwbmfm8l10XVztEW/8HIKk8B71PaDq4ONwu7fupBMwFTE2oqHeoHuOiWmSnr8Rzslz+qa78FDWXcnqRv+gEYBaMLSU0vXi6OiOQQZsAcchQJu5lzcOzwcrYM9Wb+7In9+XQPHx6s+FyQpuu3xIGxKvTmlFSp+DjoN2c1c0Q14ybehGNI4AC2reOh9sEhlFO1JVQ20oZSXac7W9VCUU3OYlR+alsfsriFSn32wjytrs5ok2fIpG1dNdtBmdcc3BID53avj0v9aKG0OBnQcAsIAzOKnQZUqVdC2bVsMGTIEFy9exMWLFzFkyBB07NgRlSt/aa8PCgrC3r17AQgvEmPHjsWsWbOwd+9e3L17F/3794eTkxN69eoFAHBzc8OgQYMwYcIEnDx5Ejdu3MB3332HkJAQtGzZEoCwZioiIgJ+fn5YsGAB3r17h+TkZIU5VlxiuzA621trNfWKqnGrTKkne58G5bCidy2cVOOm07aaF25N+ZKYK7pA8Hg8+JbQ/DjN7V4dN6a0Vjh4qGQTnqZf+PKli+l0kZCvlVQ+lY8VT5hsragr+orvasHZzhpzumqenKypcImke33e8BU151969l7nbcdMjMChUZrlAoaWLYHVfWsrTfbtFuYrN36Rtng8HqyseFrlDUptR831FAUIkTV88GuHYPB4PJXXEn1eaxQFb39+XRNlZI6xMQK9Bd2ro1utLx2CjFUDxEadXcuW76+eNdXevjo9t0V4PJ5aPfakWwC4j6jMIqACgG3btiEkJAStW7dG69atUb16dWzZskVqnYcPHyIjI0P8908//YSxY8di+PDhqF27Nt68eYPo6Gi4uHxpDli0aBG6dOmCb775Bg0bNoSTkxMOHjwIa2thFXt0dDSePHmCU6dOwdfXF97e3uKHJft3qHzNn7a9xAzN2oqHdiHeak/qrCrHQVFeiWhcJdkRq5V1H5a8KNirkRAse1x1aTKUHQdKstxsNwvXz4nuigZLrFOuJO5Ma4OeOvack9y1Nu+umQ4DTSrap6IbWWBpZ/zQJBBHRqtOOi9XylnheGKmQtdbjmgqnz4seXBsJGslbBT8SDPmtYStSc3R1hqVvVxw/pfmGNzoS1OaMQaI9HB1wJ/f1BDPGBFRmbsJ32W/A2zDEsg2xWtyjHTJLWMjd63kPp7iNodKEyVLlsTWrVuVriN7k+DxeJg2bRqmTZum8DUODg5YunQpli5dyvp8//790b9/f02La/Zq+hXH7mENUMzeFm0WnwHA7SzeippwdMX2JVR0gd/5fQMsiH7I2v3bFMm+j/Dy7uge5osgLxe8kxhhf9PAuph95D7mqZHIr++LoiKSvaBkv9e65jqxJeQ7Kmgy3dC/Dqe5VdrggadR7cr0yKrouuICxrasiHlR7OP6ifzdqxauvniPegHu2HA+gXUdyV1XlBgMs0N1b2y79BJ1ZQalVVVWfdQUjW1ZEacfvsM3deTzWA+O+pK79lPbIKw99xyAZrNL6OrY2Ca48DQVHUJ8sPD4I6XrDtQxf0oXAn1Mj6FHphBESTKbgIqwq+hRDI9TPip83lqHMy7Mn/uxtmImRuB5WjbqlNNvWUq72ONdVh5aBbN0xVUQNFT2csGavponvIuo81H0rFsWGy8kaNx9Xx1WVjws+Dydx5E7SeLlTSuV1nzEaQO59L8WeJuZi8oKevZ9W9cPE1rrFtCWdLbDlV9bYtjWa7j64gMAoCPL+GyLetQwu2AKUD5hOts5WK2MG+7PaAtrKx6epHzEnutvFL7e0c4ajSuqf65I/gizt7HG7mHyNd/GuEWPbVlJqrefiJujLSp4fDnX7GysUKqYHVI/5qOGhj1hdeHp6qByLMAyxR2xqk+YwWpBZYMltjhW2bkly9jBjrJx/ozFbJr8iOb+HdpAr9NjcPFroFwpZzQzQDV4zMQInJzQlDX5+Ze2QfBxc8Cv7avoZV8VP3/RG0iMW1WnHPs4NG6OtrjwS3P83kU4zYgux1zZa9tV88JfPWvihIbJzobm6eogV4Mk2awwu2t1uOhhxPDSLvZSc7VVYukVKFsju2d4OCp4FNN6EFtjWtWHPfBX1EQjarZe+E1NnSYs14aqCihths1Qf9/yO4+b1ALxM9poNCGyMZz/pbnKYEqX4FSdgETTvhuLetSAvY0VayAN6NbcKztItbLxv4zFtM4Yolf6rtUBgJldquG3fXex9Fv1R8E2Rc72NgoHnvQr6YTzvzTXWw5F1NgmyCvk4+KzNPGynd83wKHbiajFMsCfvvar7GLF4/HQuaZ6s7pbgt71yuJKwnu0kRjNXtPDXKtsCZMLQNnwwJMaO0jz1+uxLGpsTFWT3pAmgeALgGZBimvGyhR3xJv0T5oWjzUAsbW2KlJDOewb0RC3X6fL9fBjm59Tk6R5Hnj4KtRXq1k41OFXwklmKB+D7EYjReesIXrxXX1/PPmjHWtXd0ui9aB+Ei8TDWRpbcWDk52NVI2HtZUwoNGmBybR3B9fhSB6XFM42H7pGKDoE67tLwxyI3RMftfWWh2alQHd51A0RjK2pLIquvrb21hjTMuKSgej7VhDs05CoprpLnr4UfF3L8P/uNw4QPGwGrqq6VccfRuUE3/u2wfXQ8sqnqw5lbIdcpRhG1z56m8tcUpiOBhd5gPk8UyhX580qqEyc8MiymP8P7cMMuKxLNF1Vp/NiJYm9sdmiH30DtV93VDNh9seX6aWsMmGyzIqChz++aEB8vkCqeDLmFpqWbu0eWBdxDx8JzehMABM7his1jhtgP7mE1SXt5sjdg9roNW0USKKOswo6hG6aUBdnH3yTi+5it5ujrg/oy1WxD5Fax1qBpUxZu+/8AqlxLM3yGoXol7g2rRSabmpuQDhQK6litmjf3g5JGfkooq3bs25kmNdmUINFQVUZq5rLV/U9i+p1a/SUsXskfoxT/WKRG1+JZ3wHcsNDYDCwUGVMXZtgSkaHlEeB28lokdt40zfZGXFg4MVN8GULppUKq2w5niQxHAAqul2zmlzY9O1A4yir4mi74+bk63KQSM14WhnbZDevzV83TQalNnQQYWqYLumX3HcfJUuN0CyrGmRVXUuC8NIT1mlScK8oVBAZQFUVZkrsvK7WsjnCxBYSr3eEVwOm2AJQnzd8L/2QVoNIMqVb2obJv9BE1W8XfHg97Z6rzEy9NmsatoMU2WOMbyiIhvirSzuURO7rrxCnEROpD6FlHHDnTfC8RT36zBpvCGoCth2/VAfr97nSPWcNBZTGNGB2m6KMB4PCC9fijX5UNH6RDffNymP9mpWmwNAt1rCHI+qPtxMjWGI4E+bsaxUBVOyPX7UYajz+fDoRvitQxWMbl7BMDtQk7bBsK5NflzUFCiuodL/vrqElsGO7+uL/9b3eFUDG5XT6/b0SdUna29jbbRgiscz3rh46qIaqiLMw0W9QIpwZ0DDAAR7u3I+i7o++bg5oF01LzjaWettDsJ21bzwQ5NARP59XoNXGeZiXNXHDVV1zJ/TtteapBJaTi9jpWUUMrKZfABprB9himrPDdlk/kPTQLz58Akheh4XqkvNMvBxc0SQt+Y/ogwdX7D1yHSxt0FWXqFhd6yA5Ns15pyQilBAZWFaBXviePxbtFMyyeW2wfXwISdf4x5mVENlfNZWPIUJouaKx+NhxXdhet1m9TLFUdbdCXO7hVhEz8l+4f6YdeSBTr1phzergMcpH9G5pma5Qpp+zUsVs8fxcU1QwlkYwElOim6sNAGFNVQG3OekdvoZp04Wj8djTehWx5JvQzFgwxX81Lay6pW1wBaytKjigX03Ew2yP1mSY4PJBv6m0ORHAZWFWdyjJmIevlPa5Vt+0lz1UA6Vefi2bllceJqKTkpmgleXqX/il39tgQ/ZBeI8wh511J9n0JR/IAxuFIg65UpKJd1qys3RFuv7a97dfmhEeUzZf0/pjzJZomAKEPZ6G9gwAE521lLBlSEp+ihNfW5FfavuWxxXf2tpsJo5riuBSjrb4c+va8DOxgp2NlbSQRUFVETfnO1ttMonUcQUqlGJZmZ3DQHDMEWih6CHi4PWTdemfHSsrHhS8xkaU5/6/qgf6I5ANXuXsZ1mUzoF67lUyvUNL4ctF1+gUw0fxD56hxdpOQCEzXJFjWG/99zfD7qFfckNlI6nuC8bBVREKcl4qgjcny2Gvi6q9JkXPTwej3UqHlNWqpg9rv3WClZWPDSdf1q83N7G/Ia/MGWm0KwmSbIJ0BQ+awqoiNro3lr0GKvJhgsULFoWU+vxZYnYJ0zmTjF7G2wZVBdWPB4c7SigImaELlhFx49tKuPE/bfoXY99kFJLQDmBlok+VcMxhWY1WY0rms40aBRQEaWsrHjoUdsPGZ8K1M6pIOZvRLMKGMHSDd6SUA2VfpjaYSwKuYNcYauhoqP9BQVURKW5LJNcEmLu3ItpN04TIUUVW/0UBbBfWG6CBCGEKDGxdWW0CPLA8t61uC6KRjYNrAtPV3tsGliX66IAML2aPhMrjkVxsZevgylrAeO+6QvVUBFCiqTiTnZYp8U4TVxrWqk0Lv2vJdfFgLebA5IyctFUh8FHDeGvnqHos/4SfmxjmMEti6KZXarhXmIm62c9tGl5ZHwqQOtgTw5KZlp4DA00ZBSZmZlwc3NDRkYGXF25mZeNEEL0JTkjF8fuJaN7mK9U93VTUFTGYSPGoe7927S+BYQQQsyCl5sD+oWX47oYrCiYIlygHCpCCCGEEB1RQEUIIYQQoiMKqAghhBBCdEQBFSGEEEKIjigp3UhEnSkzMzM5LgkhhBBC1CW6b6saFIECKiPJysoCAPj5+XFcEkIIIYRoKisrC25ubgqfp3GojEQgECAxMREuLi567dKbmZkJPz8/vHr1isa3MjA61sZBx9k46DgbBx1n4zDkcWYYBllZWfDx8YGVleJMKaqhMhIrKyv4+voabPuurq70ZTUSOtbGQcfZOOg4GwcdZ+Mw1HFWVjMlQknphBBCCCE6ooCKEEIIIURHFFCZOXt7e0ydOhX29vZcF8Xi0bE2DjrOxkHH2TjoOBuHKRxnSkonhBBCCNER1VARQgghhOiIAipCCCGEEB1RQEUIIYQQoiMKqAghhBBCdEQBlRlYvnw5AgIC4ODggLCwMJw9e1bp+rGxsQgLC4ODgwMCAwOxcuVKI5XUvGlynPfs2YNWrVqhdOnScHV1RYMGDXDs2DEjltZ8aXo+i5w/fx42NjaoWbOmYQtoQTQ91nl5efj111/h7+8Pe3t7lC9fHuvXrzdSac2Xpsd527ZtqFGjBpycnODt7Y0BAwYgLS3NSKU1T2fOnEGnTp3g4+MDHo+Hffv2qXyN0e+FDDFpO3fuZGxtbZk1a9Yw8fHxzJgxYxhnZ2fmxYsXrOs/e/aMcXJyYsaMGcPEx8cza9asYWxtbZn//vvPyCU3L5oe5zFjxjBz585lLl++zDx69IiZNGkSY2try1y/ft3IJTcvmh5nkfT0dCYwMJBp3bo1U6NGDeMU1sxpc6wjIyOZevXqMcePH2eeP3/OXLp0iTl//rwRS21+ND3OZ8+eZaysrJi//vqLefbsGXP27FmmatWqTJcuXYxccvNy5MgR5tdff2V2797NAGD27t2rdH0u7oUUUJm4unXrMkOHDpVaFhQUxPzyyy+s6//0009MUFCQ1LIffviBqV+/vsHKaAk0Pc5sgoODmenTp+u7aBZF2+Pco0cP5rfffmOmTp1KAZWaND3WR48eZdzc3Ji0tDRjFM9iaHqc58+fzwQGBkotW7JkCePr62uwMloadQIqLu6F1ORnwvLz83Ht2jW0bt1aannr1q1x4cIF1tfExcXJrd+mTRtcvXoVBQUFBiurOdPmOMsSCATIyspCyZIlDVFEi6Dtcd6wYQOePn2KqVOnGrqIFkObY33gwAHUrl0b8+bNQ5kyZVCpUiVMnDgRnz59MkaRzZI2xzk8PByvX7/GkSNHwDAM3r59i//++w8dOnQwRpGLDC7uhTQ5sglLTU0Fn8+Hp6en1HJPT08kJyezviY5OZl1/cLCQqSmpsLb29tg5TVX2hxnWX/++Seys7PxzTffGKKIFkGb4/z48WP88ssvOHv2LGxs6HKlLm2O9bNnz3Du3Dk4ODhg7969SE1NxfDhw/H+/XvKo1JAm+McHh6Obdu2oUePHsjNzUVhYSEiIyOxdOlSYxS5yODiXkg1VGaAx+NJ/c0wjNwyVeuzLSfSND3OIjt27MC0adOwa9cueHh4GKp4FkPd48zn89GrVy9Mnz4dlSpVMlbxLIom57RAIACPx8O2bdtQt25dtG/fHgsXLsTGjRuplkoFTY5zfHw8Ro8ejSlTpuDatWuIiorC8+fPMXToUGMUtUgx9r2QfvKZsFKlSsHa2lrul05KSopc5C3i5eXFur6NjQ3c3d0NVlZzps1xFtm1axcGDRqEf//9Fy1btjRkMc2epsc5KysLV69exY0bNzBy5EgAwps+wzCwsbFBdHQ0mjdvbpSymxttzmlvb2+UKVMGbm5u4mVVqlQBwzB4/fo1KlasaNAymyNtjvPs2bPRsGFD/PjjjwCA6tWrw9nZGY0bN8bMmTOpFUFPuLgXUg2VCbOzs0NYWBiOHz8utfz48eMIDw9nfU2DBg3k1o+Ojkbt2rVha2trsLKaM22OMyCsmerfvz+2b99O+Q9q0PQ4u7q64s6dO7h586b4MXToUFSuXBk3b95EvXr1jFV0s6PNOd2wYUMkJibi48eP4mWPHj2ClZUVfH19DVpec6XNcc7JyYGVlfSt19raGsCXGhSiO07uhQZLdyd6IeqSu27dOiY+Pp4ZO3Ys4+zszCQkJDAMwzC//PIL06dPH/H6oq6i48aNY+Lj45l169bRsAlq0PQ4b9++nbGxsWGWLVvGJCUliR/p6elcvQWzoOlxlkW9/NSn6bHOyspifH19me7duzP37t1jYmNjmYoVKzKDBw/m6i2YBU2P84YNGxgbGxtm+fLlzNOnT5lz584xtWvXZurWrcvVWzALWVlZzI0bN5gbN24wAJiFCxcyN27cEA9PYQr3QgqojCw2Npbp2LEj4+3trVbXT4ZhmGXLljH+/v6MnZ0dU6tWLSY2Nlb8XL9+/ZimTZtKrR8TE8OEhoYydnZ2jJubGwNA6uHp6annd2UZNDnOTZs2lTuuAJh+/foZv+BmRtPzWRIFVJrR9Fjfv3+fadmyJePo6Mj4+voy48ePZ3JycoxcavOj6XFesmQJExwczDg6OjLe3t5M7969mdevXxu51Obl9OnTSq+5qu6F5cqVY1asWGHQMvIYhuoYjeno0aM4f/48atWqhW7dumHv3r3o0qWLwfY3bdo0/Pfffzhx4oR4mbW1NUqXLm2wfRJCCCFFDSWlG1m7du3Qrl07hc/n5+fjt99+w7Zt25Ceno5q1aph7ty5iIiI0HqfNjY28PLy0vr1hBBCCFGOktJNzIABA3D+/Hns3LkTt2/fxtdff422bdvi8ePHWm/z8ePH8PHxQUBAAHr27Ilnz57pscSEEEIIoSY/DvF4PKkmv6dPn6JixYp4/fo1fHx8xOu1bNkSdevWxaxZszTex9GjR5GTk4NKlSrh7du3mDlzJh48eIB79+7RMAqEEEKInlANlQm5fv06GIZBpUqVUKxYMfEjNjYWT58+BQAkJCSAx+MpfYjG7AGETYzdunVDSEgIWrZsicOHDwMANm3axMl7JIQQQiwR5VCZEIFAAGtra1y7dk08LolIsWLFAABlypTB/fv3lW6nRIkSCp9zdnZGSEiITk2IhBBCCJFGAZUJCQ0NBZ/PR0pKCho3bsy6jq2tLYKCgrTeR15eHu7fv69w+4QQQgjRHAVURvbx40c8efJE/Pfz589x8+ZNlCxZEpUqVULv3r3Rt29f/PnnnwgNDUVqaipOnTqFkJAQtG/fXuP9TZw4EZ06dULZsmWRkpKCmTNnIjMzE/369dPn2yKEEEKKNMqhMrKrV68iNDQUoaGhAIDx48cjNDQUU6ZMAQBs2LABffv2xYQJE1C5cmVERkbi0qVL8PPz02p/r1+/xrfffovKlSuja9eusLOzw8WLF+Hv76+390QIIYRw5cyZM+jUqRN8fHzA4/Gwb98+jbfBMAwWLFiASpUqwd7eHn5+fhp3BKNefoQQokBMTAyaNWuGDx8+oHjx4lwXhxDCQh8DZo8ePRrR0dGYN28eQkJCkJGRgdTUVI0mvacaKi3Mnj0bPB4PY8eO5boohBA9ioiIkPpeh4eHIykpCW5ubtwVihCiVLt27TBz5kx07dqV9fn8/Hz89NNPKFOmDJydnVGvXj3ExMSIn79//z5WrFiB/fv3IzIyEgEBAahZs6ZGwRRAAZXGrly5gtWrV6N69epcF4UQYmB2dnbw8vICj8fjuiiEEC2pGjD74MGDCAwMxKFDhxAQEIBy5cph8ODBeP/+vUb7oSY/DXz8+BG1atXC8uXLMXPmTNSsWROLFy9W67UCgQCJiYlwcXGhizMhJmjo0KHYsWOH1LLly5dj+PDhePHiBYoXL45t27bhl19+wZo1a/C///0Pb968QevWrbFy5Urs378fs2bNQmZmJnr06IE5c+aIhz/Jz8/H77//jn/++QcZGRkIDg7G9OnTqbctIXrm5uaGbdu2oWPHjgCAZ8+eITQ0FA8ePIC3t7d4vcjISISFhWHq1KkYO3Ystm3bhpCQEMycORN8Ph+TJk1C8eLFcejQITAMg6ysLPj4+MDKSnE9FAVUGujXrx9KliyJRYsWISIiQmlAlZeXh7y8PPHfb968QXBwsJFKSgghhBB9evXqFXx9fRU+T8MmqGnnzp24fv06rly5otb6s2fPxvTp0+WWv3r1Cq6urvouHiHElGRnA6LpoxITAWdnbstDCNFaZmYm/Pz84OLionQ9CqjU8OrVK4wZMwbR0dFwcHBQ6zWTJk3C+PHjxX+LPhBXV1cKqAixdJIzHbi6UkBFiAVQla5DAZUarl27hpSUFISFhYmX8fl8nDlzBn///Tfy8vLkpoqxt7eHvb29sYtKCCGEEA5QQKWGFi1a4M6dO1LLBgwYgKCgIPz8889ywRQhhBBCihYKqNTg4uKCatWqSS1zdnaGu7u73HJO8AsBa/ooCSGEEK7QOFTmLn4/sKYZkPGG65IQQgghRRZVa2hJcpRVzhTmA9GTgfQXwNoWQK9/AG8acJQQQggxNqqhMmc2dkD/Q0DpKkBWErChHfAomutSEUIIIUUOBVTmrnhZYGAUEBgB5H8EdvQArqzlulSEEEJIkUIBlSVwLA70/g8I/Q5gBMDhCcCxXwGBgOuSEUIIIUUCBVSWwtoWiPwbaP6b8O+4v4F/+wL5OdyWixBCCCkCKKCyJDwe0ORHoNs6wNoOuH8Q2NQRyErmumSEEEKIRaOAyhKFdAf67gccSwBvrgGrmwn/JYQQQohBUEBlqfzDgcEngdJBQFYisL4dcPsfrktFCCGEWCQKqCyZe3lg0HGgUjuAnwfsGQIcnwII+FyXjBBCCLEoFFBZOgdXoOd2oPEE4d/n/wJ29ARyM7gtFyGEEGJBKKAqCqysgBZThMnqNo7A42hgTQsg9QnXJSOEEEIsAgVURUlId+EgoK5lgLTHwjkA7x/iulSEEEKI2aOAqqjxqQl8HwOUbQDkZQK7egvzqviFXJeMEEIIMVsUUBVFxTyAfgeBBiOFf5//C9jcGch6y225CCGEEDNlw3UBNJWeno7Lly8jJSUFApmpVfr27ctRqcyQtS3Q5g/Atw6wfyTw4hywqjHw9UbhkAuEEEIIURuPYRiG60Ko6+DBg+jduzeys7Ph4uICHo8nfo7H4+H9+/cclk65zMxMuLm5ISMjA66urlwXR1rqY2BXH+DdfYBnDbSaATQYIRx5nRCiuexsoFgx4f8/fgScnbktDyFEa+rev82qyW/ChAkYOHAgsrKykJ6ejg8fPogfphxMmbxSFYEhJ4GQbwCGD0T/Cuz6DsihY0oIIYSow6wCqjdv3mD06NFwcnLiuiiWx84Z6LoaaL9AOA/gg0PAykZAwnmuS0YIIYSYPLMKqNq0aYOrV69yXQzLxeMBdYcAg08A7hWAzDfCyZVPz6ZegIQQQogSZpWU3qFDB/z444+Ij49HSEgIbG1tpZ6PjIzkqGQWxrsG8H0scPQn4OY2IHYO8PyMsAaruB/XpSOEEEJMjlklpVtZKa5Q4/F44PNNd446k05KV+b2v8ChcUB+FuBQHOj8N1ClE9elIsS0UVI6IRbDIpPSBQKBwocpB1NmrfrXwNAzgE8tIDddmKy+fySQm8l1yQghhBCTYVYBFeFIyUBg4DGg4RgAPODGFmBlQyDhHNclI4QQQkyC2QVUsbGx6NSpEypUqICKFSsiMjISZ8+e5bpYls/GTjg+Vf9DQPGyQPpLYGNH4NivQEEu16UjhBBCOGVWAdXWrVvRsmVLODk5YfTo0Rg5ciQcHR3RokULbN++neviFQ3lGgHDLgC1+gJggLi/gdVNgcSbXJeMEEII4YxZJaVXqVIF33//PcaNGye1fOHChVizZg3u37/PUclUM9ukdGUeRgEHRgHZKYCVDdDkJ6DxeOG0NoQUZZSUTojFsMik9GfPnqFTJ/keZpGRkXj+/DkHJSriKrcFhl8EqkQCgkIgZhawOgJ4c53rkhFCCCFGZVYBlZ+fH06ePCm3/OTJk/Dzo/GROOHsDnyzGei6FnAsCby9C6xtAURPBvJzuC4dIYQQYhRmNbDnhAkTMHr0aNy8eRPh4eHg8Xg4d+4cNm7ciL/++ovr4hVdPJ5weIXACCDqF+Duf8CFJcD9g0DkEiCgCdclJIQQQgzKrHKoAGDv3r34888/xflSVapUwY8//ojOnTtzXDLlLDKHSpGHUcLBQLMShX/X6gu0+h1wLM5psQgxGsqhIsRiqHv/NruAylwVqYAKEA78eWIacHWd8O9inkDrmUDI18IaLUIsGQVUhFgMi0xKJ2bEwRXouBAYcBRwrwh8fAvsGQJs6gSkPOC6dIQQQohemXxAVbJkSaSmpgIASpQogZIlSyp8EBPkHw4MOw80nwzYOAIJZ4WjrB+fAuR95Lp0hBBCiF6YfFL6okWL4OLiIv4/j4PmotmzZ2PPnj148OABHB0dER4ejrlz56Jy5cpGL4tZsrEHmkwUNvdF/QI8PAKc/wu4sxtoO0s47AI1AxJCCDFjlEOlhrZt26Jnz56oU6cOCgsL8euvv+LOnTuIj4+Hs5q5EUUuh0qZh0eBoz8Jp68BgMBmQJtZgGcwt+UiRF8oh4oQi2GRSenW1tZISkqCh4eH1PK0tDR4eHiAz+cbpRzv3r2Dh4cHYmNj0aSJekMCUEAlIz8HOLdQWFPFzwd4VkBYf6DZr4BzKa5LR4huKKAixGJYZFK6otgvLy8PdnZ2RitHRkYGACjN28rLy0NmZqbUg0iwcwKa/waMuCxs8mMEwNX1wJJQYZBVmMd1CQkhhBC1mXwOFQAsWbIEAMDj8bB27VoUE/3yA8Dn83HmzBkEBQUZpSwMw2D8+PFo1KgRqlWrpnC92bNnY/r06UYpk1krGQD02AIknBfmVyXfFiasX10vHLuqSifKryKEEGLyzKLJLyAgAADw4sUL+Pr6wtraWvycnZ0dypUrhxkzZqBevXoGL8uIESNw+PBhnDt3Dr6+vgrXy8vLQ17el1qWzMxM+Pn5UZOfMgIBcGsHcHK6cJgFAPCrB7ScJuwtSIi5oCY/QiyGReZQNWvWDHv27EGJEiU42f+oUaOwb98+nDlzRhzkqYtyqDSQ9xE4twiIWwYUfhIuq9gaaDEV8FJcK0iIyaCAihCLYZEBFVcYhsGoUaOwd+9exMTEoGLFihpvgwIqLWQmAWfmAdc2AQwfAE849EKz/wmbCgkxVRRQEWIxLDagev36NQ4cOICXL18iPz9f6rmFCxcaZJ/Dhw/H9u3bsX//fqmxp9zc3ODo6KjWNiig0kHaU+DUTODeHuHfVrZAWD+g8QTA1YfbshHChgIqQiyGRQZUJ0+eRGRkJAICAvDw4UNUq1YNCQkJYBgGtWrVwqlTpwyyX0WDiW7YsAH9+/dXaxsUUOlB4k3g5Azg6Unh39Z2QK1+QKNxgFsZTotGiBQKqAixGBYZUNWtWxdt27bFjBkz4OLiglu3bsHDwwO9e/dG27ZtMWzYMK6LqBAFVHr0/CwQMxt4cV74t7UdENpHGFgV9+O2bIQAFFARYkEschyq+/fvo1+/fgAAGxsbfPr0CcWKFcOMGTMwd+5cjktHjCagMTDgCNDvEODfSDgw6NV1wjGsDo4FPrzguoSEEEKKGLMKqJydncVDEfj4+ODp06fi50QTKJMiJKAxMOAw0P8wUK4xICgArm0QBlZ7vgeS73JdQkIIIUWEWQVU9evXx/nzwmaeDh06YMKECfjjjz8wcOBA1K9fn+PSEc6UawT0PwQMOAoERgh7BN7eBaxsCGztLmwiNJ+WbUIIIWbIrHKonj17ho8fP6J69erIycnBxIkTce7cOVSoUAGLFi2Cv78/10VUiHKojOjNdeDCEiB+v3BKGwAoEwY0HAMEdQSsrJW/nhBdUQ4VIRbD4pLS+Xw+zp07h+rVq3M2sKcuKKDiQNpT4eCgN7cBhbnCZSXKAXV/AEJ7Aw5unBaPWDAKqAixGBYXUAGAg4MD7t+/r/Eo5aaAAioOfXwHXF4FXF4D5KYLl9k6AzV7AXW/B0pX4rR4xAJRQEWIxbDIXn4hISF49uwZ18Ug5qZYaaD5b8D4eKDjYqB0FaAgG7iyBlhWB9jSFXgULZxLkBBCCNGCWdVQRUdH4+eff8bvv/+OsLAwOMv86jPlmh+qoTIhDAM8jwUurQIeHgXw+StQvKxwoNDQ7wAXL06LSMwc1VARYjEsssnPyupLhZrk6OUMw4DH44HP53NRLLVQQGWi3j8HrqwFrm8B8jKEy6xsgMrtgLD+QGBzwMqsKnKJKaCAihCLYZEBVWxsrNLnmzZtaqSSaI4CKhOXnwPE7wOubQReXfqyvHhZoFZfoMa3gJsvV6Uj5oYCKkIshkUGVOaMAioz8jZeGFjd2vml1go8IKCJMJG9SifAjm6QRAkKqAixGBaZlA4AZ8+exXfffYfw8HC8efMGALBlyxacO3eO45IRi+EZDLSfB0x4AHRZIZzeBp/zrvb+AMyvCOwdBjw/Q4nshBBCAJhZQLV79260adMGjo6OuH79ungamqysLMyaNYvj0hGLY+ckrJEacBgYcwuI+B9QIkDYQ/DWdmBTJ2BxNeDYr8LBRKmylxBCiiyzavILDQ3FuHHj0LdvX7i4uODWrVsIDAzEzZs30bZtWyQnJ3NdRIWoyc9CMIwwx+rmduDeXiAv88tzJcoB1boBVbsCnlUBiY4TpIihJj9CLIa6928bI5ZJZw8fPkSTJk3klru6uiI9Pd34BSJFD48HlK0vfLSbBzw5DtzdIxx+4UMCcPZP4aNUZSC4MxDUAfCuQcEVIYRYOLMKqLy9vfHkyROUK1dOavm5c+cQGBjITaFI0WXrIExQr9IJyPsIPIoS1lo9jgZSHwJn5gkfrr5AUHthcOXfELC25brkhBBC9MysAqoffvgBY8aMwfr168Hj8ZCYmIi4uDhMnDgRU6ZM4bp4pCizLwaEdBc+cjOENVYPDgFPTgKZr4HLq4UPBzegYhthcFWhBWDvonLTfAGDy8/fIyUrFx4uDqgbUBLWVlTjRQghpsSscqgA4Ndff8WiRYuQmyuc7Nbe3h4TJ07E77//znHJlKMcqiKq4BPwLAZ4cFgYZOWkfnnO2g4o1wio0BIo3wIoXVmuaTDqbhKmH4xHUkaueJm3mwOmdgpG22reRnoTRGOUQ0WIxbDocahycnIQHx8PgUCA4OBgFBNduEwYBVQEAj7w6jLw8DBw/xDw4bn0865lgPLNhTVXgRGIepqLYVuvQ/YLKgq5VnxXi4IqU0UBFSEWw6IDKnNEARWRlJNXAF7qQ1g/OwXrZ6dg9fICePw88fOFsEajguVI5hfDlxDqCx4AT1cHHB/fRGXzn5OdWbXsWwYKqAixGBbTy69r165qr7tnzx4DloQQ/QmeGv35f+UBlIcD+qKe1QM0sbqNJla3kcq4IpmvOL+KAZCcmYuQadEK1xFJmNNBL2UmhBCimMkHVG5ublwXgRCDy4U9YgU1ECuoAQAojkwVrxBqa3UJGSiGG4IKyIW9IYtICCFECZMPqDZs2MB1EQjRu/gZbZQ+f/n5e/TfcEXldvpZR6OB9X0wVjYQeFSFoEztz486YIqXU2v8K+pFSAghujP5gEpWYWEhYmJi8PTpU/Tq1QsuLi5ITEyEq6urWSSnEwKozmtqXLE0vN0ckJyRK5eUDghzqLwc+agbHAy8+ADex2RYJ9+CdfIt4Nq6zztxB3zrAL61Ad+6gE8o4CDd/k+9CAkhRD/MKin9xYsXaNu2LV6+fIm8vDw8evQIgYGBGDt2LHJzc7Fy5Uqui6gQJaUTTUXdTcKwrdcBQCqokuvlxzBAxivg9RXg9VXhv0m3AH6+/EZLBgJe1QHvGojKrYZhJws06kVItVlqoqR0QiyGRfby69KlC1xcXLBu3Tq4u7uL5/KLjY3F4MGD8fjxY66LqBAFVEQbWtcgFeYByXeEwdWry8JAK+Ol+Gk+w0OjvCVIQkko6kXo5eaAcz83FwdMxqjNspiAjQIqQiyGRQZUpUqVwvnz51G5cmWpyZETEhIQHByMnJwcrouoEAVURFt6CzJy3gtrrpJu4c+jd7CUr7oH7Y6g82hQwQNROUEYdoqvVm2WqvIqet6imh8poCLEYljMsAmSBAIB+Hy+3PLXr1/DxUX1FB6EmCNrKx4alHfXfUNOJYHyzYDyzbD00GG1XpLy9Dr4zy9iet4SMCy1WcznJdMP3EOrYC8cj09WGhQpCpoia3hj9ZnncgFbcoZwcNNlvUJRwtne/GuuCCEWy6xqqHr06AE3NzesXr0aLi4uuH37NkqXLo3OnTujbNmyJt0jkGqoiCmJeZiiVi/CHfUSgA8v8O2jpirXHed4FIs/tf0cFH0JdkT/+75JAGvQpA4rHiCQeKG3mwMmd6jCGmSx1YABMG5TItVQEWIxLLLJLzExEc2aNYO1tTUeP36M2rVr4/HjxyhVqhTOnDkDDw8ProuoEAVUxJTwBQwazT2lvBfh5xyqQ7cTMWbnTZXbLI4spIN9ZHeAgRUYCMBT8LzuRDVdB24lSdWAFXeyBQCk5xSIl3m52uPbumVRrpSzTkGXwuZNCqgIsRgWGVABwKdPn7Bz505cu3YNAoEAtWrVQu/eveHo6Mh10ZSigIqYGnV7EcY9TcO3ay4avXzGpE3QlZCagx2XXyI5k6V5M8CVAipCLITFBlRcWr58OebPn4+kpCRUrVoVixcvRuPGjdV6LQVUxBSpkwiuTm2Wm6Mt0j8VsDxrOdiCLlk8CIPTcY3LotyPI+Hx8QPqrpwD6zZtAGtr8XqyNVth/iVw7cUHyhEjxARZZEA1e/ZseHp6YuDAgVLL169fj3fv3uHnn3822L537dqFPn36YPny5WjYsCFWrVqFtWvXIj4+HmXLllX5egqoiKlSpxehqtqssS0rYtEJ0x22xLhEqfpC3pnvMPXmbrSdOADo2pU1iGXLEWPr3Wgxw0oQYkYsMqAqV64ctm/fjvDwcKnlly5dQs+ePfH8+XOD7btevXqoVasWVqxYIV5WpUoVdOnSBbNnz1b5egqoiLlTVpvVKthLaS0WIAwaGAZKk9JFNTwWhREA4GHQ1f1w7dIBi9/YqnyPbMNRaDKshKLAiwIyQjRnkQGVg4MD7t+/j4CAAKnlz549Q3BwMHJzcxW8Ujf5+flwcnLCv//+i6+++kq8fMyYMbh58yZiY2NVboMCKmIJlN2QVdViiXr5KXteNqFctuamqCnpbIvJHaviZVoOFp94xDoOGANgXMuK4lyvD9n5+P0w+9AUssfXbMf5IsSILHIcKj8/P5w/f14uoDp//jx8fHwMtt/U1FTw+Xx4enpKLff09ERycjLra/Ly8pCXlyf+OzMz02DlI8RYlI2J1baaN1Z8V0uuFsVL4qYdWraE0ud/altFKmD7kJ2PEdvlg7Si4n12AcbtuqnwedExUdXcmpSRi1Vn5GvwaZwvQvTHrAKqwYMHY+zYsSgoKEDz5s0BACdPnsRPP/2ECRMmGHz/PJ7MoIYMI7dMZPbs2Zg+fbrBy0SIKWlbzRutgr0U1mKpep4tYFthJR+kEf0QBWQjd9xgzeFS9lkRQqSZVZMfwzD45ZdfsGTJEuTnCyd+dXBwwM8//4zJkycrDG50pU2TH1sNlZ+fHzX5EaIF2aZGTZq11OmdR6SJmhKLO9lKHTe2HqAUcBFLZ5E5VCIfP37E/fv34ejoiIoVK8Le3t7g+6xXrx7CwsKwfPly8bLg4GB07tyZktIJ4YAmideA8vGj9BZ0MQxgoB92pkAyWR6AWknyFHQRc2eRAVVGRgb4fD5Kliwptfz9+/ewsbExaKAiGjZh5cqVaNCgAVavXo01a9bg3r178Pf3V/l6CqgIMR36CrokB/9MSM3B4hMPAYYBw7My7hsyIh4ANydbZOQUqJwsW5sJrykAI6bGIgOqdu3aoVOnThg+fLjU8pUrV+LAgQM4cuSIQfe/fPlyzJs3D0lJSahWrRoWLVqEJk2aqPVaCqgIMS/azAkYdTcJ0/+5hqR8zQIA8+zNKD3eloho2qLJHYIxYvt1lUGXJG0CMEIMzSIDqpIlS+L8+fOoUqWK1PIHDx6gYcOGSEtL46hkqlFARUjRwBcwuHzvFY6PnYH1tbuAx+PJDRMhO9SBaKT05IxP+P3wfXzIzjf7Xo0lrXLwXuAIZUHXuZ+byw27oUkARogxWOSwCXl5eSgsLJRbXlBQgE+fPnFQIkIIkWZtxUODQHc0OLUOdV/HY3rfGUjK+tJBxUtBjYuod6OjnTWGbb0uN8ipbCAmbGJ8BED1kBKKEvYNWTP2XuCk8DkGwqEcLkfvQIPgQPBLVsTUA/dY34eoHmzagXi0Cvai5j9issyqhioiIgIhISFYunSp1PIRI0bg9u3bOHv2LEclU41qqAgpQrKzxZMj8zOzcDklT6OcIHWbvhStN7lDFdZxpdh6S3I5ztdftkvR2ToOcfwq+LZgssr1dwypr3AcNEIMxSJrqP744w+0bNkSt27dQosWLQAIx6G6cuUKoqOjOS4dIYTIUzYYqiKqxuvSdD1lZWEb50s0XIKiWrLiCpLSReuUcLbF+2zVPSY9ygQCn14j5X1xlesCQMrDi4B7CODmZ9G9KYl5MqsaKgC4efMm5s+fj5s3b8LR0RHVq1fHpEmTULFiRa6LphTVUBFShEjUUOHjR8DZmdvyqMCWgH88PllhLRkApdMMLesVit8P31c4t6NsDlXcw9f4dsMtleXcYfs7GljfB5xKAWVqAT6hgM/nf108Vb5e0XulZkSijEUmpZszCqgIKULMLKBSRNXcjcqaJVXN7SiZZM4XMEon1+aBgZfdJ5zzWgLr1HhAIJ9LC9cynwOsUKBMGOBbG7B3kVqFehESbVh8QPXp0ycUFEhXKZtyoEIBFSFFiIUEVKqoqu3RJIBROwAr+AS8vQe8uQ4k3hA+3j2AXBYYzwrwCgH86gNl6yEqJwjD9iRQL0KiMYsMqHJycvDTTz/hn3/+YR0igc/nc1Aq9VBARUgRUkQCKnVo0sSmbQ1Szsd0WCXfgVXSDeHjzRVYZbz6UgaGh0Z5S5CEklA0jIOnqwOOj2+itPnPyc6s0o6JnlhkUvqPP/6I06dPY/ny5ejbty+WLVuGN2/eYNWqVZgzZw7XxSOEECJDk6R8TZPsRYJnnv/8v/KfH93hhTTUtnqEMKtHsEc+kqC4DAyA5MxchExT3rkpYU4Htd4HKZrMKqA6ePAgNm/ejIiICAwcOBCNGzdGhQoV4O/vj23btqF3795cF5EQQogOtOkVySYZ7jgkaIBDggZqv2aq9SY48vJwQVAVcYKqeIfiOpeDFB1mFVC9f/8eAQEBAIT5Uu/fvwcANGrUCMOGDeOyaIQQQjgSP6ON0ucvP3+P/huuqNxOkNVLNLC+j56IAQAISgeDX6El+OVbQ+BbRx9FJRbMrAKqwMBAJCQkwN/fH8HBwfjnn39Qt25dHDx4EMWLF+e6eIQQQjigKrepccXS8HZzUD6Mg6s96nabASTEAs/PAMl3YPUuHlbv4mEbtwRwKA5UaAFUbANUaAk40wCjRJpZJaUvWrQI1tbWGD16NE6fPo0OHTqAz+ejsLAQCxcuxJgxY7guokKUlE5IEUJJ6SZHk2EcAADZacDTU8DjY8CTE8CnD9Kv8q0DVGwNVG4LeFajgUYtmEX28pP18uVLXL16FeXLl0eNGjW4Lo5SFFARUoRQQGWStB6HSsAHXl8VBlePooG3d6SfL1EOqNIJqNJZOAaWlZVh3gDhRJEIqMwJBVSEFCEUUJksvYyUnpkIPI4GHh0Dnp4GCj99ec7FGwjqCARHAmXDAWuzyqwhLCigMjEUUBFShFBAVXTkZwubBOMPCAOs/KwvzzmXBqp+BYR8LWwipGZBs0QBlYmhgIqQIoQCqqKpMA94FgPcPwA8OCydd1W8LFCtmzC48qzKWRGJ5iigMjEUUBFShFBARfgFwuDqzr/C4Cr/45fnSlcBavQUPly8OCsiUQ8FVCaGAipCihAKqIik/BzgURRwd7cw94qfL1zOsxIOwVCzN1C5HWBjz205CSuLDagEAgGePHmClJQUCAQCqeeaNGnCUalUo4CKkCKEAiqiyKd0IH4/cHMb8OrSl+WOJYTNgTV7Az41uSodYWGRAdXFixfRq1cvvHjxArLF5vF4NDkyIcQ0UEBF1JH6RBhY3doJZCV+We4TCtQeKMy5sqNzh2sWGVDVrFkTlSpVwvTp0+Ht7Q2eTI8JNzc3jkqmGgVUhBQhFFARTQj4wuEXbm4V5luJmgTtXYV5VrUHAh5VuC1jEWaRAZWzszNu3bqFChUqcF0UjVFARUgRQgEV0VZ2qrDW6uoG4MPzL8vLhgN1BgHBnQFrW+7KVwSpe/82q+Fc69WrhydPnnBdDEIIIcQwnEsBDccAo64D3+0RDhLKswZeXgB2DwIWhwBn/wRy3nNdUiLDrIZwHTVqFCZMmIDk5GSEhITA1lY6Sq9evTpHJSOEEEL0yMpKOBlzhRbCkdmvbwaurAOykoCTM4DY+cLmwPrDgNKVuS4tgZk1+VmxzI/E4/HAMAwlpRNCTAc1+RFDKMwD7u0F4pYBybe/LK/QEmgwAghsRqOxG4C692+zqqF6/vy56pUIIYQQS2RjL6yVqt4DeHEBuLhcmMT+5ITw4V0TaDROOFGzlTXXpS1yzKqGypxRDRUhRQjVUBFjef8cuLRS2CRYkCNc5l5BmIdVvSdgY8dt+SyARfbyE4mPj8fLly+Rn58vtTwyMpKjEqlGARUhRQgFVMTYstOAy6uAS6uA3HThMhcfYVNg7QE0npUOLDKgevbsGb766ivcuXNHnDsFQDweFeVQEUJMAgVUhCt5WcC1TUDc38IEdgBwKgU0GgvUHgTYOXFaPHNkkcMmjBkzBgEBAXj79i2cnJxw7949nDlzBrVr10ZMTAzXxSOEEEK4Ze8ChI8ExtwCOi0BSgQAOalA9G/AXzWECe0Fn7gupUUyqxqqUqVK4dSpU6hevTrc3Nxw+fJlVK5cGadOncKECRNw48YNrouoENVQEVKEUA0VMRX8QuD2TiB2HpD+QrismJcweT2sP2DrwGnxzIFF1lDx+XwU+3yRKlWqFBIThXMf+fv74+HDh1wWjRBCCDE91jZA6HfAqGvCGiu3ssDHZCDqZ2BJKHBtozDoIjozq4CqWrVquH1bOPZGvXr1MG/ePJw/fx4zZsxAYGCgQfaZkJCAQYMGISAgAI6OjihfvjymTp0qlxBPCCGEmCxrWyCsnzCw6rgYcPUVTsh8cAywogFw/xBgPg1WJsmsxqH67bffkJ2dDQCYOXMmOnbsiMaNG8Pd3R27du0yyD4fPHgAgUCAVatWoUKFCrh79y6GDBmC7OxsLFiwwCD7JIQQQgzCxk7Y669mL+DqemFTYOojYFdvwK8e0GoGULY+16U0S2aVQ8Xm/fv3KFGihLinnzHMnz8fK1aswLNnz9R+DeVQEVKEUA4VMRe5GcD5JcJk9cLPyeqV2wMtpgIeQdyWzURYZA6VyJMnT3Ds2DF8+vQJJUuWNPr+MzIyONkvIYQQolcObkCLycDoG0DYAOFEzA+PACvCgSM/0iTMGjCrgCotLQ0tWrRApUqV0L59eyQlCcfYGDx4MCZMmGCUMjx9+hRLly7F0KFDla6Xl5eHzMxMqQchhBBikly9gU6LgeEXgaCOAMMHLq8GltYCLq+hxHU1mFVANW7cONja2uLly5dwcvoyOFmPHj0QFRWl0bamTZsGHo+n9HH16lWp1yQmJqJt27b4+uuvMXjwYKXbnz17Ntzc3MQPPz8/jcpHCCGEGF3pSkDPbUC/g4BHVeDTB+DIRGBlI+BZDNelM2lmlUPl5eWFY8eOoUaNGnBxccGtW7cQGBiI58+fIyQkBB8/flR7W6mpqUhNTVW6Trly5eDgIByjIzExEc2aNUO9evWwceNGWFkpj0Xz8vKQl5cn/jszMxN+fn6UQ0VIUUA5VMQS8AuB65uAUzOBT5+b/oI6Aq1/B0oapme9KVI3h8qsevllZ2dL1UyJpKamwt7eXqNtlSpVCqVKlVJr3Tdv3qBZs2YICwvDhg0bVAZTAGBvb69xmQghhBCTYW0D1BkEVP0KiJ0rbPp7cAh4fBxoMlE4AbMN3edEzKrJr0mTJti8ebP4bx6PB4FAgPnz56NZs2YG2WdiYiIiIiLg5+eHBQsW4N27d0hOTkZycrJB9kcIIYSYFKeSQLu5wLALQGAEwM8DTv8BrGgIPIvlunQmw6xqqObPn4+IiAhcvXoV+fn5+Omnn3Dv3j28f/8e58+fN8g+o6Oj8eTJEzx58gS+vr5Sz5lRaykhhBCiG48goM8+4O5u4Nj/gLTHwOZIoHoPoPVMoJgH1yXklFnlUAFAcnIyVqxYgWvXrkEgEKBWrVoYMWIEvL29uS6aUjQOFSFFCOVQEUv3KV2YW3VlLQDm8/ALU4VDL6iRFmNO1L1/m11AZa4ooCKkCKGAihQVb64Bh8YBSbeEf/vVAyL/FvYWtBAWG1Dl5ubi9u3bSElJgUAgkHouMjKSo1KpRgEVIUUIBVSkKBHwhTVVJ38H8rMAa3ug2SSgwShhYruZs8iAKioqCn379mUd7oDH44HP53NQKvVQQEVIEUIBFSmK0l8Bh8YCT04I//auCXReBnhV47JUOrPIqWdGjhyJr7/+GklJSRAIBFIPUw6mCCGEEItX3A/o/R/QZYUwpyrpJrC6KXB6NlCYz3XpDM6sAqqUlBSMHz8enp6eXBeFEEIIIbJ4PKBmL2DEZeEgoIJCIHYOsDriS56VhTKrgKp79+6IiYnhuhiEEEIIUcbFC+ixFei+AXByB1LuAWtaAGcXCnOuLJBZ5VDl5OTg66+/RunSpRESEgJbW1up50ePHs1RyVSjHCpCihDKoSLki+xU4OAY4SjrAOBXH+i6CihRjtNiqcsik9LXrl2LoUOHwtHREe7u7uDxeOLneDwenj17xmHplKOAipAihAIqQqQxDHBzO3D0Z2FPQLtiwtHXa/YWNhOaMIsMqLy8vDB69Gj88ssvas2nZ0oooCKkCKGAihB2HxKAvUOBl3HCv4M6Ap3+ApzVm1uXCxbZyy8/Px89evQwu2CKEEIIIRA28/U/DLScBljZCpsBlzcAnp7mumQ6M6vIpF+/fti1axfXxSCEEEKItqysgUbjgCGngNJVgOwUYMtXwoFB+YVcl05rZjWEKZ/Px7x583Ds2DFUr15dLil94cKFHJWMEEIIIRrxrg58fxqImgRc2wCcXQC8OA90Wwu4+XJdOo2ZVUB1584dhIaGAgDu3r0r9RzPxJPaCCGEECLD1hHotBgIaCLsCfgyDljZSDg4aOV2XJdOI2aVlG7OKCmdkCKEktIJ0dz758B/A4DEG8K/6w8HWk4HbOw4LZZFJqUTQgghxEKVDAAGRgP1Rwj/vrgcWN9aOEegGaCAihBCCCGmwcYOaDsL+HYX4FhCWFu1qolZ9AKkgIoQQgghpqVyW+CHM4B3TeDTe2BrV+G0NSacpUQBFSGEEEJMT/GywMBjQOh3ACMATk4Hdn0H5GZyXTJWFFARQgghxDTZOgCdlwlHU7e2Ew4EuqYZkPKA65LJoYCKEEIIIaYtrD8wIApwLQOkPQHWNAfu7eO6VFIooCKEEEKI6fMNE+ZVBTQBCrKBf/sBp2cBAgHXJQNAARUhhBBCzIVzKeC7vUCDkcK/Y+cC//QB8j5yWy5QQEUIIYQQc2JtA7T5A+i8/Ete1brWwIcXnBaLAipCCCGEmJ/Q3kD/w4CzB5ByT5isnnCes+JQQEUIIYQQ8+RXF/g+5vN4VemAoJCzopjV5MiEEEIIIVLcygADjgIvLwCBTTkrBtVQEUIIIcS82TkBFVpyWgQKqAghhBBCdEQBFSGEEEKIjiigIoQQQgjREQVUhBBCCCE6ol5+RsIwDAAgM9M0Z8kmhOhRdvaX/2dmAnw+d2UhhOhEdN8W3ccVoYDKSLKysgAAfn5+HJeEEGJUPj5cl4AQogdZWVlwc3NT+DyPURVyEb0QCARITEyEi4sLeDye3rabmZkJPz8/vHr1Cq6urnrbrimx9PdI78/8Wfp7tPT3B1j+e6T3pz2GYZCVlQUfHx9YWSnOlKIaKiOxsrKCr6+vwbbv6upqkV8SSZb+Hun9mT9Lf4+W/v4Ay3+P9P60o6xmSoSS0gkhhBBCdEQBFSGEEEKIjiigMnP29vaYOnUq7O3tuS6KwVj6e6T3Z/4s/T1a+vsDLP890vszPEpKJ4QQQgjREdVQEUIIIYToiAIqQgghhBAdUUBFCCGEEKIjCqgIIYQQQnREAZWJ+fDhA/r06QM3Nze4ubmhT58+SE9PV/oahmEwbdo0+Pj4wNHREREREbh3757UOnl5eRg1ahRKlSoFZ2dnREZG4vXr16zby8vLQ82aNcHj8XDz5k09vTMhLt9fZGQkypYtCwcHB3h7e6NPnz5ITEzU6/sDuHuPCQkJGDRoEAICAuDo6Ijy5ctj6tSpyM/Pt4j3BwB//PEHwsPD4eTkhOLFi+vtPS1fvhwBAQFwcHBAWFgYzp49q3T92NhYhIWFweH/7d17UJTVGwfw73Jbrq4IclN+rhdkIS6TlxSkEFOBlJlmEgXJUNMRUgM1TW0ik5lAm5xKSwWdLc0Ri0toiooJMsGiCRgoBEUiKOCmKSDFRXh+fzRsrLugsKuL+HxmdnTPnvec890Vfdz3vLvGxhgzZgz27Nmj0iclJQWurq4QCoVwdXVFWlqaxvP2ly7y5eTkICgoCA4ODhAIBPj++++1GUmJLvLFxcVh8uTJsLCwgI2NDV599VWUl5drNVd3usi4e/dueHh4KD4s08vLCxkZGVrN1UVXP4Nd4uLiIBAIEB0d3f8QxAaUgIAAcnNzo7y8PMrLyyM3NzeaO3dur8fEx8eThYUFpaSkUElJCS1YsIDs7e2psbFR0SciIoJGjBhBmZmZVFhYSH5+fuTp6Un3799XGe/tt9+mwMBAAkBFRUWDJt+OHTtIJpNRVVUV5ebmkpeXF3l5eWk1ny4zZmRk0OLFi+nUqVNUWVlJ6enpZGNjQ+vWrRsU+YiIYmJiaMeOHbR27VoSiURayZOUlESGhoaUmJhIpaWlFBUVRWZmZnTt2jW1/f/44w8yNTWlqKgoKi0tpcTERDI0NKTk5GRFn7y8PNLX16ePPvqIysrK6KOPPiIDAwPKz8/v97xPW74TJ07Qe++9RykpKQSA0tLStJpL1/n8/f1JKpXS5cuX6dKlSzRnzhz63//+R/fu3Rs0GY8ePUrHjx+n8vJyKi8vp82bN5OhoSFdvnx5UOTrcuHCBRKLxeTh4UFRUVH9zsEF1QBSWlpKAJRecJlMRgDo119/VXtMZ2cn2dnZUXx8vKKtpaWFRCIR7dmzh4iI7t69S4aGhpSUlKToc+PGDdLT06OTJ08qjXfixAmSSCR05coVrRdUAyFfd+np6SQQCKitrU3TaAoDLeP27dtp9OjRmsZSGCj5pFKp1gqqF154gSIiIpTaJBIJbdy4UW3/DRs2kEQiUWpbsWIFTZ06VXF//vz5FBAQoNTH39+fQkJC+j1vf+kqX3ePs6AaCPmIiORyOQGgc+fO9TXCQw2UjERElpaWtG/fvr4s/6F0ma+pqYmcnJwoMzOTfH19NSqo+JTfACKTySASiTBlyhRF29SpUyESiZCXl6f2mKtXr6K+vh6zZ89WtAmFQvj6+iqOKSgoQHt7u1IfBwcHuLm5KY178+ZNLF++HAcPHoSpqam24+k8X3d//fUXDh06BG9vbxgaGmojHoCBlREAGhoaMGzYME1jKQy0fJpqa2tDQUGB0rwAMHv27B7nlclkKv39/f1x8eJFtLe399qna8z+zNsfusr3pAykfA0NDQCg1Z83YOBk7OjoQFJSEpqbm+Hl5dXfOCp0nW/lypWYM2cOZs6cqWkU3kM1kNTX18PGxkal3cbGBvX19T0eAwC2trZK7ba2torH6uvrYWRkBEtLyx77EBEWL16MiIgITJo0SeMsPa1VV/m6vPvuuzAzM4OVlRWqq6uRnp7e7zw9rVfXGbtUVlZi586diIiI6HOOngykfNpw69YtdHR09Lq2B9XX16vtf//+fdy6davXPl1j9mfe/tBVvidloOQjIqxduxY+Pj5wc3Prbxy1dJ2xpKQE5ubmEAqFiIiIQFpaGlxdXTWNpaDLfElJSSgsLERcXJw2onBB9SRs2bIFAoGg19vFixcBAAKBQOV4IlLb3t2Djz/KMd377Ny5E42Njdi0aVNfogF4OvJ1Wb9+PYqKinD69Gno6+vjjTfeAD3ClwU8TRkBoLa2FgEBAQgODsayZcseFu+py6dtfV2buv4Ptj/KmP15TvpDV/meFF3nW7VqFYqLi3H48OE+rbsvdJXR2dkZly5dQn5+PiIjIxEeHo7S0tJ+ZejNk85XU1ODqKgofPPNNzA2NtZo7V0MtDIK69WqVasQEhLSax+xWIzi4mLcvHlT5bE///xTpdLuYmdnB+Dfatze3l7RLpfLFcfY2dmhra0Nd+7cUXoHQC6Xw9vbGwBw9uxZ5Ofnq3wP0qRJkxAWFoavv/76qc7XxdraGtbW1hg/fjxcXFzg6OiI/Pz8h76F/TRlrK2thZ+fH7y8vJCQkNDrmp/GfNpkbW0NfX19lf8Jd1/bg+zs7NT2NzAwgJWVVa99usbsz7z9oat8T8pAyLd69WocPXoUOTk5GDlypCZx1NJ1RiMjI4wbNw7Av/8e/Pzzz/jss8+wd+9ejXJ10VW+goICyOVyTJw4UfF4R0cHcnJysGvXLrS2tkJfX79vYfq9+4ppXdeG3/Pnzyva8vPzH2nD77Zt2xRtra2tajf8HjlyRNGntrZWacPvtWvXqKSkRHE7deoUAaDk5GSqqal56vOpU11dTQAoKytLw2T/0XXG69evk5OTE4WEhKi9glNTus7XRdub0iMjI5XaXFxcet0Q6+LiotQWERGhsiE2MDBQqU9AQIDKpvS+zNtfusrXHR7zpnRd5Ovs7KSVK1eSg4MDVVRUaBqjVwPhNewyY8YMCg8P78PqH04X+RobG5X+zSspKaFJkybR66+/TiUlJf3KwQXVABMQEEAeHh4kk8lIJpORu7u7yiXpzs7OlJqaqrgfHx9PIpGIUlNTqaSkhEJDQ9Vekj5y5Eg6c+YMFRYW0owZM3r82AQioqtXrz62j03QRb7z58/Tzp07qaioiKqqqujs2bPk4+NDY8eOpZaWlkGR8caNGzRu3DiaMWMGXb9+nerq6hS3wZCP6N/Cv6ioiD788EMyNzenoqIiKioqoqampn7n6bpke//+/VRaWkrR0dFkZmZGVVVVRES0ceNGWrRokaJ/1yXba9asodLSUtq/f7/KJdu5ubmkr69P8fHxVFZWRvHx8T1+bEJP82qLrvI1NTUpXh8AtGPHDioqKnpsHwvxpPNFRkaSSCSi7OxspZ+1v//+W6v5dJlx06ZNlJOTQ1evXqXi4mLavHkz6enp0enTpwdFvgdpepUfF1QDzO3btyksLIwsLCzIwsKCwsLC6M6dO0p9AJBUKlXc7+zspA8++IDs7OxIKBTSSy+9pFJh//PPP7Rq1SoaNmwYmZiY0Ny5c6m6urrHdTyugkpX+YqLi8nPz4+GDRtGQqGQxGIxRURE0PXr17WaT5cZpVIpAVB7Gwz5iIjCw8PV5tP0XcYvvviCRo0aRUZGRjRhwgSlS9/Dw8PJ19dXqX92djY9//zzZGRkRGKxmHbv3q0y5nfffUfOzs5kaGhIEomEUlJS+jSvNukiX1ZWltrXStvvbugqX08/a93/3GuTLjIuXbpUMefw4cPp5Zdf1noxpct8D9K0oBIQPcKOXMYYY4wx1iO+yo8xxhhjTENcUDHGGGOMaYgLKsYYY4wxDXFBxRhjjDGmIS6oGGOMMcY0xAUVY4wxxpiGuKBijDHGGNMQF1SMMaYD2dnZEAgEuHv37oAcjzHWN1xQMcbYIODt7Y26ujqIRCIAwFdffYWhQ4fqdlGMPUMMdL0AxhhjmjMyMoKdnZ2ul8HYM4vfoWKMDQjTp0/H6tWrER0dDUtLS9ja2iIhIQHNzc1YsmQJLCwsMHbsWGRkZCgdV1paildeeQXm5uawtbXFokWLcOvWLcXjJ0+ehI+PD4YOHQorKyvMnTsXlZWViserqqogEAiQmpoKPz8/mJqawtPTEzKZrMe1hoaGIiQkRKmtvb0d1tbWkEqlAAAiwvbt2zFmzBiYmJjA09MTycnJvT4HKSkpeO655yAUCiEWi/HJJ58oPd7a2ooNGzbA0dERQqEQTk5O2L9/PwDlU37Z2dlYsmQJGhoaIBAIIBAIsGXLFmzduhXu7u4q806cOBExMTG9ro0x9hD9/hZAxhjTIl9fX7KwsKDY2FiqqKig2NhY0tPTo8DAQEpISKCKigqKjIwkKysram5uJiKi2tpasra2pk2bNlFZWRkVFhbSrFmzyM/PTzFucnIypaSkUEVFBRUVFVFQUBC5u7tTR0cHEf33ReASiYR++OEHKi8vp3nz5tGoUaOovb1d7VqPHTtGJiYm1NTUpNRmbGxMDQ0NRES0efNmkkgkdPLkSaqsrCSpVEpCoZCys7OJ6L8vD+76YumLFy+Snp4ebd26lcrLy0kqlZKJiYnSl+3Onz+fHB0dKTU1lSorK+nMmTOUlJSkMl5rayt9+umnNGTIEKqrq6O6ujpqamqimpoa0tPTowsXLijG/OWXX0ggEFBlZaWGryBjzzYuqBhjA4Kvry/5+Pgo7t+/f5/MzMxo0aJFira6ujoCQDKZjIiI3n//fZo9e7bSODU1NQSAysvL1c4jl8sJAJWUlBDRfwXVvn37FH2uXLlCAKisrEztGG1tbWRtbU0HDhxQtIWGhlJwcDAREd27d4+MjY0pLy9P6bg333yTQkNDiUi1oFq4cCHNmjVLqf/69evJ1dWViIjKy8sJAGVmZqpd04PjSaVSEolEKv0CAwMpMjJScT86OpqmT5+udkzG2KPjU36MsQHDw8ND8Xt9fX1YWVkpnaKytbUFAMjlcgBAQUEBsrKyYG5urrhJJBIAUJzWq6ysxMKFCzFmzBgMGTIEo0ePBgBUV1f3OLe9vb3SPA8yNDREcHAwDh06BABobm5Geno6wsLCAPx7GrKlpQWzZs1SWtuBAweUTjd2V1ZWhmnTpim1TZs2Db/99hs6Ojpw6dIl6Ovrw9fXt8fn71EsX74chw8fRktLC9rb23Ho0CEsXbpUozEZY7wpnTE2gBgaGirdFwgESm0CgQAA0NnZqfg1KCgI27ZtUxmrqygKCgqCo6MjEhMT4eDggM7OTri5uaGtra3HuR+cR52wsDD4+vpCLpcjMzMTxsbGCAwMVDru+PHjGDFihNJxQqFQ7XhEpJi3e1sXExOTHtfSF0FBQRAKhUhLS4NQKERraytee+01rYzN2LOMCyrG2FNrwoQJSElJgVgshoGB6l9nt2/fRllZGfbu3YsXX3wRAPDTTz9pZW5vb284OjriyJEjyMjIQHBwMIyMjAAArq6uEAqFqK6ufuR3lFxdXVXWlpeXh/Hjx0NfXx/u7u7o7OzEuXPnMHPmzIeOZ2RkhI6ODpV2AwMDhIeHQyqVQigUIiQkBKampo+0RsZYz7igYow9tVauXInExESEhoZi/fr1sLa2xu+//46kpCQkJibC0tISVlZWSEhIgL29Paqrq7Fx40atzC0QCLBw4ULs2bMHFRUVyMrKUjxmYWGBd955B2vWrEFnZyd8fHzQ2NiIvLw8mJubIzw8XGW8devWYfLkyYiNjcWCBQsgk8mwa9cufPnllwAAsViM8PBwLF26FJ9//jk8PT1x7do1yOVyzJ8/X2U8sViMe/fu4ccff4SnpydMTU0VhdOyZcvg4uICAMjNzdXK88HYs473UDHGnloODg7Izc1FR0cH/P394ebmhqioKIhEIujp6UFPTw9JSUkoKCiAm5sb1qxZg48//lhr84eFhaG0tBQjRoxQ2f8UGxuLmJgYxMXFwcXFBf7+/jh27JhiD9eDJkyYgG+//RZJSUlwc3NDTEwMtm7disWLFyv67N69G/PmzcNbb70FiUSC5cuXo7m5We143t7eiIiIwIIFCzB8+HBs375d8ZiTkxO8vb3h7OyMKVOmaP5EMMYgoO4n6RljjA16RASJRIIVK1Zg7dq1ul4OY4MCn/JjjLFniFwux8GDB3Hjxg0sWbJE18thbNDggooxxp4htra2sLa2RkJCAiwtLXW9HMYGDS6oGGPsGcK7PBh7PHhTOmOMMcaYhrigYowxxhjTEBdUjDHGGGMa4oKKMcYYY0xDXFAxxhhjjGmICyrGGGOMMQ1xQcUYY4wxpiEuqBhjjDHGNMQFFWOMMcaYhv4PCYqzA3hldHoAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"friedrich_method(v, default)\"\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\": \"3.8.15\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/advanced/inspect_dft_features.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook illustrates the Discrete Fourier Transformation (DFT) for different time series. \\n\",\n \"\\n\",\n \"The coefficients of the DFT are one of the many features in *tsfresh*.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Some theoretical background\\n\",\n \"\\n\",\n \"If we see a series of length $n$ - without timestamps - we don't have any idea about the time domain: In what time was the series recorded? What is the timedelta between 2 observations? The only thing we know (or, more realistic, assume) is that the series is uniformly sampled through time. Of course, with the knowledge of the sampling frequency, one can draw conclusions about the real frequencies in the time domain. But `tsfresh` does not incorporate this information.\\n\",\n \"\\n\",\n \"Therefore, in Discrete Fourier Transform (DFT), \\\"frequencies\\\" are expressed in terms of \\\"fractions of series length\\\", $2\\\\pi k/n$, $k=0, ..., n-1$. One can read it as \\\"$2\\\\pi k$ divided on $n$ points\\\" or \\\"$k$ periods of length $2\\\\pi$ divided on $n$ points\\\" or \\\"$k$ cycles among $n$ points\\\". At \\\"time\\\" $m \\\\in \\\\lbrace0, ..., n-1\\\\rbrace$, the value $a_m$ is observed. \\n\",\n \"\\n\",\n \"The definition used in the implementation in `np.fft` is (picture from numpy docs):\\n\",\n \"![Definition in implementation np.fft](https://docs.scipy.org/doc/numpy/_images/math/60627d7b56cbf58e87ccb5c3ee92a36ebccc62f5.png)\\n\",\n \"\\n\",\n \"with $a_m$ being the (possibly complex) value of the series at position $m$ and the series length $n$. $a_m$ is multiplied with the complex number on the angle $2\\\\pi k m/n$ (in rad not deg measure) on the unit circle. The following picture shows how the complex exponential function rotates among the unit circle / shows \\\"Euler's Theorem\\\" (figure taken from Wikipedia):\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Euler's Theorem on the unit circle](https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Euler%27s_formula.svg/180px-Euler%27s_formula.svg.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting coefficients $A_k$ can be drawn on a complex pane, too (figure from Wikipedia):\\n\",\n \"![The resulting FFT coefficient is complex](https://upload.wikimedia.org/wikipedia/commons/thumb/7/7a/Complex_number_illustration_modarg.svg/220px-Complex_number_illustration_modarg.svg.png)\\n\",\n \"\\n\",\n \"\\n\",\n \"The absolute value $r$ of the complex DFT coefficient corresponds to the amplitude of the oscillation, and the angle $\\\\phi$ corresponds to the phase.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is stated in [Wikipedia](https://en.wikipedia.org/wiki/Discrete_Fourier_transform#Definition) (Notation is modified):\\n\",\n \"> It's amplitude and phase are \\n\",\n \">\\n\",\n \"> $|A_k|$ / $n $ and $arctan(Im(A_k), Re(A_k))$\\n\",\n \"\\n\",\n \"Note the **normalization of the Amplitude with the series length $n$**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import pandas as pd\\n\",\n \"from numpy.fft import rfft\\n\",\n \"sns.set_style(\\\"whitegrid\\\")\\n\",\n \"sns.set_palette('colorblind')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_x_and_rfft(x, fft_coeffs):\\n\",\n \" fft = fft_coeffs\\n\",\n \" plt.figure(figsize=(12, 4))\\n\",\n \" \\n\",\n \" plt.subplot(211)\\n\",\n \" plt.title(\\\"Time Series\\\")\\n\",\n \" plt.plot(x)\\n\",\n \" \\n\",\n \" plt.subplot(212)\\n\",\n \" plt.plot(fft.real, label=\\\"real\\\", marker=\\\"o\\\")\\n\",\n \" plt.plot(fft.imag, label=\\\"imag\\\", marker=\\\"o\\\")\\n\",\n \" plt.plot(np.abs(fft), label=\\\"abs\\\", marker=\\\"o\\\")\\n\",\n \" plt.title(\\\"Real, imaginary and absolute part of DFT coefficients A(k)\\\")\\n\",\n \" plt.legend()\\n\",\n \" \\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Compose a Signal With up to 14 Periods of Cos Included\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's compose a series as the sum of some cosine waves with different frequencies.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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UwIIYQQQgghhBBC/EYaRUIIIYQQQgghhBACAJ3SAW4mPj5e6QhCCKFa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\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ZzaMrRWqKwo06OZH5+bUG2hKApZOp6eSqE+Q1lWu+FSxD0zRdFAPItu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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n = 200 # no of samples\\n\",\n \"m = np.linspace(0, 1, n, endpoint=False) # the results differ with the defaul endpoint=True\\n\",\n \"x = np.zeros(n)\\n\",\n \"plt.figure(figsize=(20,20))\\n\",\n \"for i in range(15):\\n\",\n \" plt.subplot(5, 3, i+1)\\n\",\n \" # the results differ with the defaul endpoint=True\\n\",\n \" cos_wave = np.cos(np.linspace(0, i*2*np.pi, n, endpoint=False))\\n\",\n \" plt.title(\\\"Signal no {}: {} Cos-Waves\\\".format(i,i))\\n\",\n \" plt.plot(cos_wave)\\n\",\n \" x = x + cos_wave\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The analytical solution of the DFT is:\\n\",\n \"\\n\",\n \"- Amplitude (i.e. absolute value) is 1 for the first 15 coefficients\\n\",\n \"- angle, i.e. phase, should be 0 deg for the first 15 coefficients\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No \\t Abs.Value \\t Abs.Value/N \\t angle in deg\\n\",\n \"0 \\t 200.000000 \\t 1.000000 \\t 0.00\\n\",\n \"1 \\t 100.000000 \\t 0.500000 \\t 0.00\\n\",\n \"2 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"3 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"4 \\t 100.000000 \\t 0.500000 \\t 0.00\\n\",\n \"5 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"6 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"7 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"8 \\t 100.000000 \\t 0.500000 \\t 0.00\\n\",\n \"9 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"10 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"11 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"12 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"13 \\t 100.000000 \\t 0.500000 \\t 0.00\\n\",\n \"14 \\t 100.000000 \\t 0.500000 \\t -0.00\\n\",\n \"15 \\t 0.000000 \\t 0.000000 \\t -50.74\\n\",\n \"16 \\t 0.000000 \\t 0.000000 \\t -120.06\\n\",\n \"17 \\t 0.000000 \\t 0.000000 \\t -47.79\\n\",\n \"18 \\t 0.000000 \\t 0.000000 \\t -57.17\\n\",\n \"19 \\t 0.000000 \\t 0.000000 \\t -4.00\\n\"\n ]\n }\n ],\n \"source\": [\n \"fft_coeffs = rfft(x)\\n\",\n \"amp_deg = tuple(zip(range(20), np.abs(fft_coeffs)[:20], np.angle(fft_coeffs, deg=True)[:20]))\\n\",\n \"print(\\\"No \\\\t Abs.Value \\\\t Abs.Value/N \\\\t angle in deg\\\")\\n\",\n \"for no, amp, deg in amp_deg:\\n\",\n \" print(\\\"{:d} \\\\t {:f} \\\\t {:f} \\\\t {:4.2f}\\\".format(no, amp, amp / n, deg))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you use `np.linspace` with `endpoint=True` in the signal generation, you won't get the correct results.\\n\",\n \"\\n\",\n \"The amplitude for the 0th Fourier coefficient - which corresponds to the constant 1 - is correct. But obviously, all the other amplitudes are wrong. Moreover, they are consistently half of the correct amplitude. Why is this the case?\\n\",\n \"\\n\",\n \"I won't go into detail here, but it is because the [output of the DFT - in case of a real valued elements $a_m$ - is symmetric](https://en.wikipedia.org/wiki/Discrete_Fourier_transform#The_real-input_DFT). This means that an investigated \\\"frequency\\\" is splitted among two values of $k$, and so is the amplitude (not for $k=0$).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Use Normalization to get Amplitude Information\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There is a difference in the implementations in `np.fft` and `scipy.fftpack`: NumPy provides the normalization flag, whereas `fftpack` does not.\\n\",\n \"But be aware: **The normalization does not yield true amplitudes**, because the normalization is done by $1 / \\\\sqrt{n}$. Correct amplitudes can be calculated by multiplication with $1/n$. However, this normalization is sufficient to compare timeseries with different length.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unnormalized\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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Whie0o+Xb2d6ObjbOtwmo0kVBYUEeRLnVHh57QCW4cihBBCCCGETe07\\nU0JJtYGINtw7BZJQWdS5rkwZ9ieEEEIIIa5118JwP5CEyqKGdvbEWadhiyxMIYQQQgghrnFbknJR\\nqyC8h4+tQ2lWklBZkL1Ww+hu3hzNKedMcZWtwxFCCCGEEMImSqvr2H26mMGBnng42tk6nGYlCZWF\\nyfLpQgghhBDiWvdDagFGk9Lmh/uBJFQWd26NfZlHJYQQQgghrlXm+VNt+PlT50hCZWE99S60d3dg\\nW0o+JpNi63CEEEIIIYSwuq3Jebjaaxkc6GHrUJqdRROq2NhYhgwZQnR0NNHR0Tz//POW3H2roFKp\\nmBDkS35FLQczS2wdjhBCCCGEEFZ1oqCSlPwKwrt7Y6dp+/03WkvvcPDgwbz55puW3m2rMiHYl4/2\\nphOTlMeADm0/KxdCCCGEEOKcc2sJTAjW2zgS62j7KaMNjO/hg0oFMTKPSgghhBBCXGM2J+UC18b8\\nKWiGhCo1NZV7772XOXPm8Ntvv1l6962Cj4s9gzp68OuJQkqq6mwdjhBCCCGEEFZRazCxNTmP7j7O\\ndPdxtnU4VqFSFMViKyfk5OQQFxfHxIkTSU9PZ/78+WzZsgWdTtfg5+Pi4ix16BbnvfhSViaU89II\\nT8YFOto6HCGEEEIIIZrdvpwa7t1ewC1Bzjw20N3W4VhUWFhYg69bdA6Vn58fkyZNAiAwMBAfHx9y\\ncnLo2LHjFQdmK3FxcRaJaYFPESsTfiW51pUlYX0tEFnbZakyF40nZW59UubWJ2VufVLm1idlbn1S\\n5pe35n9HgAJuG9WLsBA/i+yzJZT55TqCLDrkb/369XzwwQcA5OXlUVBQgJ+fZQqytRnY0QNvJzs2\\nJ+ViwU5AIYQQQgghWqxNx3Jx0KoZ093H1qFYjUUTqvDwcPbu3cvcuXNZuHAhy5Ytu+Rwv7ZOo1YR\\nGawno6Saw1lltg5HCCGEEEKIZpVeVEVCdhljunvjaKexdThWY9Ehfy4uLrz77ruW3GWrNjFEz+cH\\nMth0LJc+AW62DkcIIYQQQohmc251v4k9r43l0s+RZdObUWSwLypVfdenEEIIIYQQbdm5Nu8kC82d\\nai0koWpGvmeXT//tRCGl1bJ8uhBCCCGEaJtqDSa2JedfU8ulnyMJVTOb2FOPwaSwLTnf1qEIIYQQ\\nQgjRLHaeLKSsxnDNDfcDSaia3blKJcP+hBBCCCFEW3WurTuxp6+NI7E+Saia2bnl0zcdk+XThRBC\\nCCFE23QtLpd+jiRUzUyWTxdCCCGEEG3ZmeIqDmdde8uln2PRZdNFw6b28uPzAxl8czirRS2fbjIp\\nJGSX8UtaAb+cKCS9uIrrAz0Z292bUV29cXe0s3WIQgghhBDXpLJqA7+eKODH4wXsPFmEn6s9I7t4\\nMaKLF30D3NBqWk6/yDeHswCYco2t7neOJFRWMOU6Pxy0ar48lMkzE4JQqVS2Don/Hshg8boE8spr\\nza+pVLDzZBGv/5yGWgURQb68NSP0mlupRQghhBDCVtKLqnjguwTWJ+ZgNNVPF1GpQFHg6/j6xMXD\\n0Y6Xp4Rw1/WBLaJd+eXBTNQqmNmnna1DsQlJqKzAxV7L5Ov8+Do+i8TsMnq3s10vVWWtgQe/S2Tl\\n7tM46zTcNrADo7p6M7KrFx08HNl9qogfUwvYkpxHTFIeoa/8yLLIYB4Z3bVF3QkRQgghhGhLTCaF\\nf+88ydKNRymvMdK/vRtRPfWM7ebDsM6e5JbX8suJAn5JK2RtfBZ3fxXPjpR83ru5D24OthtVdKa4\\nit9OFjG2uzf+bg42i8OWJKGykll9A/g6PosvD2XaLKE6mlPGrFVxJGSX0TfAjS/nhxHk63LBZ8Z2\\n92Fsdx+WRQaxNj6L+745zNLvj7LmYAar5vS3aTIohBBCCNEWHc+v4LYvDvDbySI8HO348Jbe3D6o\\n4wW9T13stXTxdmL+wI78PaIHsz/dz38PZrI3vZg10WGEdfSwSexrz/aazeobYJPjtwSSUFnJ5BA9\\njnZqvjyYybORwVbvnk3IKmX4W79RWm1g4bDOvHbDdThcZtKgSqXi5r4BjOvhwyPrj/Dx3nSGrfiN\\nr28bSERwy1sOMyWvnDUHM0nNryC9uJozJVUUV9XR3ceZXv6uXOfnyvWBHgzp5NkiusaFEEIIYRmK\\nonAgo4TfThRxJKeMIzllJOdV4GKvpYO7Ax09HOnq7cTNfQPo5e9q63AvsutkITd8uJf8ilpu6tOO\\nFdN7/2lPT6CnEz8tGsbTm5N4cUcqI976jR1/HcrQzl5Wivp354b7zQi9Nof7gSRUVuNsr2VyiB9r\\n47M4nFVm1cUpskqrmfR+LKXVBj6e3Y/bBnVs9LZeTjo+mt2PqGBf5n9xkEnvx7Ly5r7cPrjx+2gu\\nFTUG1sZn8cGe0/ySVnjBez7OOtwctMSeLmbnySLz6z31LvxlSCDzwzrg42Jv7ZAbraSqjt2nisgt\\nr6Gsxkh5jYFao4lOno4E610I8nXBQxYNEUIIcRVKq+tIzqsgOa+cE4WVaNVqXO21uNhr8HHWMaST\\nJ15OOluHeUnFVXV8FneGlbGnOZRZan5drYJOnk6UVtfxY36F+fVntyQzONCDOwd3ZHa/9i1i8a1v\\n4rO49bP91JkU3rupD3cP7dTobe00al6YHMKQTp7M/GQf0z7ay677R9DNinPfTxdVsutUEeHdfdC7\\nttx2VXOThMqKZvULYO3ZYX/WSqgqagxM/WAP6cXVLJ/U84qSqfPd0r89Ae4OTPtwL3esOcipokqe\\nttECGyaTwkd703li41Hzohrjevhw+6CODOnkSQd3B3PvW43BSEpeBQnZZaxPzOHr+CweWX+EJ74/\\nxryw9jwdEUQnLyerf4c/MpkUdqTm8/3RHH5OK+RgRgmmP3lsWUcPB6Ze58+Nvf0Z3c0bnbZlzXGr\\nNZhIzC4jIbuUxOxyEnPKKK2uw6SA0aSgUkGgx7kEsb4nMdTfDbW65fYg1hiMpBdXc7KwkpOFlVTV\\nmbDTqNCqVei0ajp5OtJT74reRdcqekIVRaG02kBOeQ3ZpTUYTApOOg3OOg0uOi3t3R1aXL26HKNJ\\nobS6jqKqOooq6zhSUItHfgUejnZ4ONqhacF1648MRhNnSqoprzFQUWukotaIRg1+Lvb4udrj4WjX\\nKuoYQH55DcdyyzlZVEV1nRGDSaHOqGB/9m+ms5cTgZ6OLXqpZUVROJJTTkJWKUlnE5CThZWYlPrG\\nu0atwsVeS4jehd7+bvRu50pvf9fLjgSxBYPRxK8nCvkuMZv1iTmkFVT+6Tah7VwZ3dWbqJ56IoN9\\nW8R86rzyGp7fmsL7saeoqjOhVauYHurPzNB29G7nSpCvi7k+1RiMZJRUE3emhI/3prP5WC57Thfz\\n+IajPBcVzMJhnW32nd74OY2H1ifiZKfhm9sHMrGJK+RN6+3P2zN6c+/aw0x+P5adi0dYLRE2D/fr\\nd+32ToEkVFY1qaceJ52GLw9m8nxU8w/7M5oU5qzeT9yZEhYMDmRpePer2t/Irt7svH84k97fw7It\\nyWSWVvPOzD5WbaTsOV3Efd8ksDe9GGedhr+N78GCwYF08W44KbLXaujdzo3e7dyY3b89BRW1fBp3\\nhnd3nuTDPemsjsvgnqGd+Nv4HvjZ4M5KVmk1H+1J5/3Y05worL+w6TRqhnfxYlRXbzp7Op69W6hF\\no1ZxorCS5LxyknIriD1dxDs7T/LOzpO4O2iZ2acd9w7tzKBA24yhhvqG08ZjufwvMYeYpDzKagwX\\nfUatArVKhQIX9B5Cfc9iRJAvE4J8ierpa/PJrZkl1fycVsBPxwv4Ka2AY7nlNOb53O4OWnr5uzK6\\nmzfjuvswrIv1h2D80bnHJOw5XcTe9GL2phdzNKecaoPpktto1Sp6+DrTy6++cTiyqzdDO3u2iIZv\\nSVUdP6cVcDCzlENn/x0vqLj49xOzw/y/Xbyc6NPOlb4B7vRr78aort54O9v+7rvBaGJvejE7UvOJ\\nz/x9uFKt8dK/G51GTbDemUEdPRjU0YPBgR70DXC3edKoKAoHM0rYeDSXLcl5HM4qpbCyrlHb9vBx\\nZlRXb0Z382J0N28CPW17s6ugopbNx+q/x5akPLLLai54/1wiZTQp5htgG4/mmt930mmI6OHD1F7+\\nTA7R2/R8Fp9Zyru7TrLmYKb59+HmoGVCkK/5plY3bycU6pfqLq81kl5cxS9phew6VcjhrDLe+u0k\\n7d0dWDA4kAXXd7TJ76ey1sD//XyCF3ekUlZjoLOXI/cM6cTtgzpesnzttRq6ejvT1duZm/sGkFFS\\nxcd703n1xzQeWJfI+7GnWTG9N6O7We9htIqisPT7o7z8w3H8Xe35/q7BDOhwddfue4Z25nh+Ja/8\\neJzpH+1lyz1DsNc2/7lahvvVUylKY5oHzSMuLo6wsDBbHb5BzR3TLavi+PJQJgceHkW/9u7NdhyA\\nR9cn8tpPaUQE+fD9XddjZ6E7MDllNUxcuZsDGaXc1Kcdq2/tf1V/tI0p84oaA49/f5R3dp5EUWBO\\n//a8MjWE9u6OTTqm0aTwxYEMnolJIq2gEiedhgdHduHRMd3wtMJdnYMZJby0I5Wv4rMwnu0VmN0v\\ngFsHdGh0g/XcncZ1Cdl8m5DN6aIqAMI6uPPXYZ2Z3S8AZ/uG75lYsp5X1hpYl5DNJ3vPsC0lz9yw\\n6OrtxPgePvRpV3+3tpefK97Ov/fcmEwK6cVV5uEm+86UsCUpj8zSaqB+idgx3by5pV8AM0PbWWWI\\npqIoHM0p55vDWXybkM3+MyXm95x1GsI6uNPV25nOno508nTC1UFDnVHBYFKoqjOSVlBJUl45x3LL\\nSc6rMC93q9Oo6eujZe6QHtzQy4+u3tYZjlFSVcfW5Dw2Hs1l07HcCxqE9lo1vfxdCXBzONvrocNe\\nq6Gitr5HpLTaQGp+xdmeRcMF2w3t5Mm4Hj5EBusJ6+BulV5Fo0lhX3px/Qqkx3LZfbrYXL4AXk52\\nhOhd8HHW4emkw8NRS25OLvZuXhRV1VFQWcux3PKLHhUR1sGdiCBfIoJ8GdbZ0yoNEIC0ggq2nm2o\\nb0/Jp+S8Mnax13Cdnys9fJxxd7DD+WyvocGkkFNeQ05ZDZml1SRml1FV93vS5eVkx/ge9TcjIoP1\\nBLhbpwFfUlXHtpT6erb+cAb5VfUxqVT1SVKwrws99S5093HGSadBq67v1a2qM3KqqIoThZWcKKwk\\n7kzJBXWtt78rM0LbMT3Un74BblbpkSuuquPbw1msOZjJtpR8cx3Tu9Tf7Bkc6EGwb/2w60BPR3MC\\nqygKxVV1JGaXkZhTRnxmGdtT8kjK+3242ehu3tw2sAM39QnA1cFy97MvdT6vrjOyNj6Lf+88ab55\\n1c7Nnht7149sGNPNp1E90LUGE3tOF/HFgUxW7z9DabUBlQqm9fLn8fDuDOnkabHvcilGk8Ine9P5\\n++YkMkur8XHW8XREEPcM7dTkXvTcshqe3HiMD/eeRlFg/sAOvHlj70YNA7yaa6jBaOKetfF8uCed\\nIF9nttw9xGIjZUwmhVs+jWNtfBa3D+rIR7P7WWS/l3KysJIu/9zO+B4+bL13aLMeqyXkDJeLweIJ\\n1fLlyzl06BAqlYonn3ySPn36NCkwW2numL6Oz+SmT+J4Ylx3lk8KabbjbDyaw+T399BT78LuxSMs\\nPk64pKqOaR/t5afjBYzr4cO3tw9q8gXiz8o89lQR0Z8fICW/guv8XHhnZqjF7iTVGkx8uOc0z21N\\nJqu0Bg9HO5aM7cbiEV0umYw0laIo/Hi8gJd2pBKTlAfUD6X467DOzO1/dWO5TSaFrcl5/HvnSf53\\nJAeTUt9Lctugjtw7tBMhfhdOwr3aem4yKfx6opBP9qXz1aEsc0/U9YEeTA9tx9Tr/Ajxc7niBtC5\\nYTWbj+XyzeEscyNAo1YxvocPt/QLYHpoO4vOH1MUhX3pJXxzOItvDmeRfLYBZKdRMbabD+ODfBjV\\n1ZsBHdyv6KbEuQcy7kgtYHtKHgcyfh/ff52fCzf08mfqdX5c38nTYr0K58pv49EcNh7N5dcThRjO\\naxBG9dQzvLMXgzp60Luda6O+j6IoZJZWs/9MCT8eL2BHSj6HskrNPUE+zjomBPkyMUTPhCBfi46h\\nTy+qYuvZRzhsS8kz31lXq2BwoCcRQT5cH+hJ3wA32rs7XFTf/ljPFUUhp6yGQ5ml7E0vZltKPjtP\\nFlJnrP84gPfnAAAgAElEQVQyTjoNo7p6mROs3v6uFmvEl1TV8UNqvrm34/h5Q626eDkRGVx/zIEd\\n3eno4dio4xqMJo7klLM3vZhdJ4vYkpxLenG1+f3e/q7mYVojunhZbPiZotT3dm46msvGY7n8dl49\\n87BXM6V3Oyb19GNCsO8V9QAaTQqHMkv4Oa2Q7Sn5bE3Oo+ZsD2oXLyemh/ozI7QdQzt5WjSJL6s2\\nsD4xmzUHM4lJyjP3DA7s6M7M0HZE9dTTp13ThiOn5JWz4UgO3yZkm+f6OtqpmRHajtsGdiS8h89V\\n//3/sZ4fz6/gvV2n+HDPaQrO/s1EBvuycFhnJoXor2p4W0WNgS8PZfLvnafYm14M1CeKj4/tRlRP\\nvcWTXkVR2HQsl8c3HCUhuwxHOzUPj+7GkrHdLLZM+N7Txfz163jizpQQ6OnIJ7P7Mab75dsYTb2G\\nVtcZmbN6P+sSshnY0Z2Nd12Pr4VvFlbVGRn19m/sSy/h07n9mRfWwaL7P9+rPxznsQ1H+M/NffjL\\nkMbP/WqKlpAzWC2h2rNnDx988AHvvfcex48f58knn2TNmjVNCsxWmjumyloD+me24OdqT+oT4c1y\\nxy2rtJq+r/1ESZWBPQ+OoG9A8/SEnX9iCOvgzre3D6Kj55X3GF2qzOuMJv65LYV/bEvBpCg8PKor\\n/5jYs1nGpFfVGXn715O8sCOFwso69C46nhofxN1DA6/6jrXJpLAuIZuXfkhlz+n6C9CYbt48Ht6d\\nyGBfi9eB9KIqVsaeYuXu0+YeidHdvLl1QHtmhLbD21nX5HqenFfO5/szWLXvjHmIYkcPB+YP7Mj8\\ngR0uWob/ap0uquTLg1msOZTBvvT63iI7jaq+Ad9TT0SwLz18nK+4DIsqa/khtYBtKXn8LzGHMyX1\\njVAnnYaJPfXMCPVncoifRW9ExPy6h9NaP/6XmMO2lDxzz4Kvi45JPfWMOjucLtjXpdENN0VROFVU\\nxQ+p+fyQWsCO1HwySn5vUA/q6MHkED2TQvws2pNUUFHLjtR8Nh/LZfOx33sVAQZ0cCcy2JehnTwZ\\n0MGdALeLE52GGE0KxwsqiEsv4Yfj9d8n9bzJ5B3cHYgM1hPZ05dxPXwaNT+gMfW8vMbAz2kFbEnK\\nY2tyHkdyys3v+bvan02ufBjY0YMePs6Nboxml1ZzIKOE2NPFbE3OI/a8HjU3By3h3X2YEFw/vNVS\\nE8jP9bDGJOUSk5THT8cLzEM6He3UjO3uQ2Rwfe9VkG/j/24URSE1v4Jdp4r49UQhm47mmv9mVKr6\\nejapp56JIXpUuccZNHCgRb5PeY2BTcdy+fZwNhuO5Jhv3Pi52jMlxI+IIB/Ce/g0qUF6oqDybLKe\\ny8ajueZy6tPOjVv6BTCrX4DFH2p/oqCST+POsGpfujmh7uDuwLywDtw6oD29mpjAx8XF0f26PnyX\\nmM1ncRlsSa6/YeftZMedgwO5Z2gniy9SoCgKPx0v4KUfUtl8rP54fdq58Xh4N2b1DbjqOUmKovBD\\nagHPb03mx+MFqFVw+6COPBcV3OSRKZdTZzSxfFsKz29LwWhSeHh0V/55mfZGU66huWU13LxqHz+n\\nFV71jeg/czy/gv7/+hkFhYMPj262RSoG/d/PHMgoJfuZiGYfRdIScgarJVRvvPEGAQEB3HzzzQBE\\nRUWxdu1aXFwabmS1hML5I2vEdNsXB1i17wzf3zWYSU2cgHgpJpNC1MrdbE3O540be7F4ZFeL7v+P\\nDEYTf/36MO/HnsbXRcea6DDG/smdnT9qqMwPnCnhzjUHOZhZ2ug7RpZQWl3Hv35K47WfjlNeYyTQ\\n05FlE4KIDutwxReIwspavjyYyes/p5GcV4FKBTf29ufxsd253gpDJOqMJr5LyObfO0+xIzUfqJ8T\\nMyHYl36uNdw8oi+9/C/fS1FcVcee00VsPFrf6Eg528B11mmY2af+DuuYbt5WGfJ1PL+CLw9lsuZg\\n5gWrOQV6OjKqqxfBvi708HGmh2/9MCmF+otynVEhrbCSpNxykvPK2Z9RQtyZEnMPi4ejHTf08mNG\\naDsignxw0jXPBe78el5Za2B7Sj7/O5LD/xJzLhiK5+loR//27gR6OtLe3aF+kRWthhqjkeo6E5V1\\nRlLzKziaU87R3HKKq36fn+LjrCO8uw+TQvRE9dRbZV7g+b0Vm5Pqe8XO9fhAfc9Yb3839C46fJzr\\n/2k1KkqrDZTVGCiuMpCUV86R7LIL5nO5OWgZ3dWb8B71SUBP/ZX3eDblfJ5RUsW25PrekW0p+eSc\\n97vRadSE+NUPX/N0tMPVXoubgxYFyK+oJb+8ltzyGhJzysgq/X27cz1qE4J8mRBcP2zMUkOwL6eq\\nzsjPxwuISapPHM5PFt0ctPTUuxCid6GHrzMuOi32WrW5rmWUVJNRUk16cRUHMkrJr/h9qKSXkx2R\\nwXomhdT3fp2f0DTXNbTGYGR7Sj7fHs5mXUL2BfH0DXBjYAcPgvXOBPm60M3bCXutGpVKhQoorzWQ\\nkldBSn4FSbnl/Hqi8IIewp56F2b3C+CWfgH09Gv+JbUVRWHnySI+2ZfOmoOZ5mGOnTwdmXT2Bsiw\\nzpdfXc9gNHE0t5y9p4tZvTOJndm15t68YZ09+euwztzUp51VFsU4mFHCyz8cZ83BDEwKdPZy5MGR\\nXZndv/0Vn4NMJoX/Hcnhhe0pxJ69ATkpRM+Lk0MItcJzMPecrh8Rk5xXQbCvMx/c0o/hDcyBvdJ6\\nvud0ETM/3seZkmpu7tuOT+de3VSJxlgdd4bozw8wONCDX+8bbvFzzu5TRQx981cmh+jZcNf1Ft13\\nQ1pCzmC1hOrvf/87o0ePZvz48QDMnTuXf/7zn3Tp0uWSgV2LkovqmLspjzC9jvfGWzZJ+PRoOW8c\\nKGVEgD2vj/ayyphzRVH4KqWS1+JKUID7+7kxr+eV9xoA1BoV3k8o45Mj5RgVmNbNiYf6u+Gis+4K\\nPMXVRj4+Us6XyRXUmsDXUc2I9g6Mau/AID97HLQXfzdFUSioNrEnu4Ytp6rYlVWDUQGtGiZ3cSI6\\nxIXObrZZByarwsDWU9VsOVXFsaLfG+A6NfTwtMPbQYNOA3bq+gZIZoWR02UGCqt/b+A6aVVc72/P\\n6A4OhHd0wMnOdis9ZZQbiM2uITarhr05NZTWNv40plVDHx8dg/zsGexvTy9vO7Q2nMhvUhRSig3E\\n59USn1/L4fxazpQb/3Q7jQo6uGrp6q5lgK+OQf72dHXXorbxym8VdSYO5NZyrKiOpMI6korqyKy4\\n/PfRqaGLux3dPbR097Cjv6+Onl62/b3A2Z6ZYgP7cmpILq7jeLGBtBID1cbL1zc/JzXBnjqCPbX0\\n9NLRX6/DzcrnsIZkVxjZnVVNbHYNx0sMnC4zcJk1Scz8nTT08dXRx8eOUB8dPT1tu2Ki0aRwrKiO\\nPdk17M2u4WBeLbWN+B7nONupGOhnz/X+9f8CXTU2WzGx2qDw05lqfjxTf80or/u9brnbq+nkqqG9\\nixYVUGdSqDVBUbWR5KIL62E3dy0RnRyZEOhIoI2uMxnlBlYfLWd9WiU1xvobCQP9dEzo5MgQfwf8\\nnNQNlnOtUSEup4afM6r5JaOG7Mr688XYDg7c3suFXt7WXTim2mDi7UNl/Dep/gbi7GBnFvZ1xbEJ\\nc7UUReHb45W8sq8EowJ/7ePKbde5WO08/fedRWw6WcUd17mwqJ9lE9LHfinkh/Rq3h3nzUC/a2e5\\ndJskVHPmzGH58uWXTahsnW3+kbVimvDeLrYm57P3wZEMtNCTrePSixm64le8nXTEPzra4uNy/8zO\\nE4XctGofWaU1RAb7snxSz0atWhMXF0fffv3578FMnt+aTHJeBZ08HVl5c1+bP0T4THEVL2xPZc3B\\nDPNYdAetmi7eTng71d9td9CqOV5QybHc8gtWtevf3o3Z/dpza1j7Zhmi0FQpeeWs2rGfPI0H+9KL\\nic8qvaBHAernLHX2dCTI14Xe/q5EBvsysmvLW5od6htXaQX1d5/P3YWurDWiUoEKFRo1dPZyIsi3\\nfnJ8dx9nmyxlfCXnlqq6cz0EVZwprqbWaMJBq8Feq8bRTk1nLye6eTu3yN9HQ2oMxvoenIpa8spr\\nMSkKbg52uJ3t4Wnn5tAsDfTmOJ+bTApZZdWUVhvMvWyKUj9008dZh7ezrkWsgtgYBqOJtMJK0goq\\nqaqr7wGtMZjQqKGDR30PaXs3hyuaT2qL63qN4eyCMGcXgzlRWEmdUUFBQVHAwU5Nd5/63qsePvWr\\n2bWEpb//yGA0setUEZuO5XI4q4zkvHLSCirN89PO0ahV9PZ3ZWAHDwZ2dMerKptZ44bYKOqL5ZXX\\n8Pn+DL44kGHuZYL6kQ3B+vrfQa3RRMHZc8KpoioqauuTKHcHLdND2/HomG42fwDvzhOF3LnmIEln\\n2yR/G9+D+QM7YK/VNKqeH8ku46nNx/j2cDbeTnZ8MS/M6m2a0uo6+v/rZ04UVrLtnqGE97DMTfyU\\nvHKCX/qBAe3d2fvgSKvckGgJOcPlYrDobQy9Xk9+fr7559zcXHx9bdsgbqkeG9Odrcn5vPLDcdbM\\nv/oKUl5jYM7q/dQZFVbN6W/1ZApgWBcv9j80ivlfHDg7xCSP6aH+PBsZfMmu+uo6I9+kVjAr5gfS\\nCirRqFXcN7wzL0wOwcXCi0I0RQcPR96eGcqb03uz+1QR/0vMYXNSLhkl1STllptXtNNp1PTwdSbY\\n15n+7d25uW8AwXrLzieylB6+LtzY3ZmwsPoFY+qMJipqjdQYTNQYjBhNtKrnD2nUKnr4utDD1wWa\\nb50Xq3K009Ddx9niczhsxV6rob27Y4u6sdBUarXq7HexdSRXT6tRE3R2xbrWzF6rIcTP9aLFd1ob\\nrUbNyK7ejOzqbX6tzmgio6QaFfUrbNpr1TjrtBecn+PiCmwQ7aX5utjzwKiuPDCqKycKKvnqUCb7\\nzhSTlFvBkewy8+qpKlX9EOcuXk5EBPkytZcfI7p4WWU4bGMM6+LFwUdG8+yWZP71Uxp3fxXPs1uS\\neWxMNwboLt0lmpxXzrMxyXxxMANFgaGdPPli3gCbPPPSzcGOL+YNYPiK34j+/ACHHhllkblO//op\\nDUWBx8Z0azXPw2tuFm2xDh8+nBUrVjB79mwSExPR6/WXnD91rRsf5EO/ADfWxmeSVtDzqpdRXvxt\\nAin5FTw6pptNe3X83RyIuXsI21Py+fvmJL49nM23h7Pp6u3EdX6uhOhd0LvYk5BdyoGMUo7klGEw\\n1T/g8a/DOrFkbHc6t4AH7f6RRq1ieBcvhnfx4sUp9a12k0mhuLqOihojAe7Nc4fdGuw0ajwcW8YF\\nTAghxO/sNOoWeU1srC7eTiw57xmYJlP9qqEOWjWeTroWf910sNPwwuQQ7hvRmX/9lMa7u07x4HeJ\\naFTQ85cf6d/ejT7t3CisrONoThlHcspJPfs8vH4BbjwXFcyU6/xsmnQMDvTk+ahgnth4jAVfHmLd\\nHYOuKp7csho+3ptOZy9HZva5tp89dT6LJlQDBgygV69ezJ49G5VKxTPPPGPJ3bcpKpWKR8d0Y97n\\nB3j9pzRWzAht8r7WHMjgo73phHVw558Te1owyqZRqVSMD6pfiWvTsVz+7+c0DmWWsuFIDhuO5Jg/\\n52inJqyDOyEuBpbfPJR2Nn6I65VSq1V4Oeloxdc6IYQQwmrUahUdPFpfT3V7d0deu6EXT4R3563f\\nTrLuwEmOF1WSmF0GZJg/5+1kx+iu3tw3ojPTe7ezyoJNjbFkbP2oqPWJOby76xR/Hda5yft6+7eT\\nVBtMPDyqW4scOmsrFh9T9eijj1p6l23WrH4BPLHxKB/uTWdZZPAVPa/jnJOFldy9Nh5nnYYv5g1o\\nUcO0VCoVk0L8zCsZFlTUcjSnjJzyGq7zcyXI1wWNWkVcXFyrS6aEEEIIcW3xcbFnWWQwU33K6d9/\\nAMcLKjicVYa3sx3X+bnaZLpFY6jVKj6d258+r/7Iw98lMrKLF72bsGpiZa2Bt387gZeTHXcO7tgM\\nkbZeLaf1fQ2y06h5aFRXKmuNvPnLiSvevtZg4tbP9lNabeCt6aH1c0haMG9nHSO6ejOzTwAhfq4t\\nvqtfCCGEEKIh6rPzd2f0acfobk17Hpo1Bbg78NHsflQbTMxZvZ+K8xbRaqz3Y+sfFr1wWOcrWqzm\\nWiAJlY3ddX0n9C46lm9P4de0xk8sNZoUoj8/wM6TRczuF8Btg5rvSdhCCCGEEKJ1m9rLn/uGdyYh\\nu4yZn+yjtjHPTTgrPrOUJzYew9Vey30jGl69+1omCZWNuTpo+W90GApw86o4Ms8+ff5yFEXh3rXx\\nfHkok5Fdvfjglr6yyooQQgghhLisf03rxZTr/IhJymPe5/sxmv786UmFlbVM/3gvlbVGVs3pZ5WH\\nxrc2klC1AGO7+/DKlBCyy2q46ZN91Bgu/RBMRVFYsuEo78eeZkAHd/5352CcdNLtKoQQQgghLs9O\\no+bL+WGM7ubNV4eyuOereC73SFqjSWHu6v2kFVTy1Pge3BgqK/s1RBKqFuLBUV2Z2789u04V8cC6\\nxAY/U1Zt4PENR3n1x+P01Luw+S/X4+5oZ+VIhRBCCCFEa+Vop2H9nYMY2NGdD/ac5q9fH6aosrbB\\nz/598zFikvKYFKJnWWSwlSNtPaRro4VQqVSsnNWHxJwy3tt1iiM5ZdzUpx0zQtvhrNOw4teTvPFL\\nGoWVdXT2cmTrPUNa/ARIIYQQQgjR8rg52LHprusZ/c5O3tt1is/3Z3DfiM48NKorJgW+PZzF1/FZ\\nbEvJp5u3E6vn9pfFxC5DEqoWxEmnZd0dg7j9vwf5Oa2AX9IKeWBdIo52aqrqTHg52fF8VDD3jeiC\\nh/RMCSGEEEKIJvJxsSf2gZG8t+sUr/x4nBe2p/Kvn9KoNZo4Nwrw+kAPPrylH55OV/5on2uJJFQt\\nTGcvJ35cOIys0mq+PZzN2vhMMkqq+cv1nbh3WCdcZJlKIYQQQghhAS72Wh4Z042FwzvzYexp3tl5\\nEk9HO27qG8CMUH8CPZ1sHWKrIK3zFqqdmwMLh3dm4fDOtg5FCCGEEEK0YY52GhaN6MIiWRK9SWRR\\nCiGEEEIIIYRoIkmohBBCCCGEEKKJVMrlFp9vZnFxcbY6tBBCCCGEEEI0WlhYWIOv2zShEkIIIYQQ\\nQojWTIb8CSGEEEIIIUQTSUIlhBBCCCGEEE0kCZUQQgghhBBCNJEkVEIIIYQQQgjRRJJQCSGEEEII\\nIUQTSUIlhBBCCCGEEE2ktXUALcny5cs5dOgQKpWKJ598kj59+tg6pDbp5ZdfJi4uDoPBwD333MOO\\nHTtITEzEw8MDgAULFjBmzBjbBtmGxMbG8sADD9CjRw8AgoKCuOuuu1iyZAlGoxFfX19eeeUVdDqd\\njSNtO7766ivWr19v/jkhIYHevXtTWVmJk5MTAI8//ji9e/e2VYhtRnJyMgsXLuT2229n3rx5ZGVl\\nNVi3169fzyeffIJarWbWrFncfPPNtg691WqozJ944gkMBgNarZZXXnkFX19fevXqxYABA8zbffzx\\nx2g0GhtG3nr9scyXLl3a4HVT6rnl/LHMFy9eTFFREQDFxcX069ePe+65h6lTp5rP5Z6enrz55pu2\\nDLtV+2P7MDQ0tPWczxWhKIqixMbGKnfffbeiKIqSmpqqzJo1y8YRtU27du1S7rrrLkVRFKWwsFAZ\\nPXq08vjjjys7duywcWRt1+7du5X777//gteWLl2qbNy4UVEURXnttdeUzz77zBahXRNiY2OVZcuW\\nKfPmzVOSkpJsHU6bUlFRocybN0956qmnlE8//VRRlIbrdkVFhTJhwgSltLRUqaqqUiZPnqwUFRXZ\\nMvRWq6EyX7JkifL9998riqIoq1evVl566SVFURRl8ODBNouzLWmozBu6bko9t5yGyvx8S5cuVQ4d\\nOqSkp6cr06dPt0GEbU9D7cPWdD6XIX9n7dq1i/HjxwPQrVs3SkpKKC8vt3FUbc+gQYN44403AHBz\\nc6Oqqgqj0WjjqK49sbGxjBs3DoCxY8eya9cuG0fUdr399tssXLjQ1mG0STqdjpUrV6LX682vNVS3\\nDx06RGhoKK6urjg4ODBgwAD2799vq7BbtYbK/JlnniEyMhKov0NfXFxsq/DapIbKvCFSzy3ncmWe\\nlpZGWVmZjGKysIbah63pfC4J1Vn5+fl4enqaf/by8iIvL8+GEbVNGo3GPORp7dq1jBo1Co1Gw+rV\\nq5k/fz4PPfQQhYWFNo6y7UlNTeXee+9lzpw5/Pbbb1RVVZmH+Hl7e0tdbybx8fG0a9cOX19fAN58\\n801uvfVWnn76aaqrq20cXeun1WpxcHC44LWG6nZ+fj5eXl7mz8j5vekaKnMnJyc0Gg1Go5HPP/+c\\nqVOnAlBbW8sjjzzC7Nmz+eijj2wRbpvQUJkDF103pZ5bzqXKHGDVqlXMmzfP/HN+fj6LFy9m9uzZ\\nFwz1FlemofZhazqfyxyqS1AUxdYhtGnbtm1j7dq1fPjhhyQkJODh4UFISAj/+c9/eOutt3j66adt\\nHWKb0blzZ+677z4mTpxIeno68+fPv6BXUOp681m7di3Tp08HYP78+QQHBxMYGMgzzzzDZ599xoIF\\nC2wcYdt2qbotdd7yjEYjS5YsYciQIQwdOhSAJUuWcMMNN6BSqZg3bx4DBw4kNDTUxpG2DdOmTbvo\\nutm/f/8LPiP13PJqa2uJi4tj2bJlAHh4ePDAAw9www03UFZWxs0338yQIUP+tDdRXNr57cMJEyaY\\nX2/p53PpoTpLr9eTn59v/jk3N9d8V1lY1i+//MK7777LypUrcXV1ZejQoYSEhAAQHh5OcnKyjSNs\\nW/z8/Jg0aRIqlYrAwEB8fHwoKSkx95Dk5OTIyb+ZxMbGmhs5ERERBAYGAlLPm5OTk9NFdbuh87vU\\nect64okn6NSpE/fdd5/5tTlz5uDs7IyTkxNDhgyROm9BDV03pZ43v717914w1M/FxYWZM2diZ2eH\\nl5cXvXv3Ji0tzYYRtm5/bB+2pvO5JFRnDR8+nJiYGAASExPR6/W4uLjYOKq2p6ysjJdffpn33nvP\\nvDrR/fffT3p6OlDfAD23Gp2wjPXr1/PBBx8AkJeXR0FBATNmzDDX9y1btjBy5Ehbhtgm5eTk4Ozs\\njE6nQ1EUbr/9dkpLSwGp581p2LBhF9Xtvn37cvjwYUpLS6moqGD//v0MHDjQxpG2HevXr8fOzo7F\\nixebX0tLS+ORRx5BURQMBgP79++XOm9BDV03pZ43v8OHD9OzZ0/zz7t37+aFF14AoLKykmPHjtGl\\nSxdbhdeqNdQ+bE3ncxnyd9aAAQPo1asXs2fPRqVS8cwzz9g6pDZp48aNFBUV8eCDD5pfmzFjBg8+\\n+CCOjo44OTmZT07CMsLDw3n00UfZvn07dXV1LFu2jJCQEB5//HHWrFlDQEAAN954o63DbHPy8vLM\\n47xVKhWzZs3i9ttvx9HRET8/P+6//34bR9j6JSQk8NJLL5GRkYFWqyUmJoZXX32VpUuXXlC37ezs\\neOSRR1iwYAEqlYpFixbh6upq6/BbpYbKvKCgAHt7e6Kjo4H6hZ2WLVuGv78/N910E2q1mvDwcJnE\\n30QNlfm8efMuum46ODhIPbeQhsp8xYoV5OXlmUcaAAwcOJB169Zxyy23YDQaufvuu/Hz87Nh5K1X\\nQ+3DF198kaeeeqpVnM9VSksZfCiEEEIIIYQQrYwM+RNCCCGEEEKIJpKESgghhBBCCCGaSBIqIYQQ\\nQgghhGgiSaiEEEIIIYQQookkoRJCCCGEEEKIJpKESgghhBBCCCGaSBIqIYQQQgghhGgiSaiEEEII\\nIYQQookkoRJCCCGEEEKIJpKESgghhBBCCCGaSBIqIYQQQgghhGgiSaiEEEIIIYQQookkoRJCCCGE\\nEEKIJpKESgghhBBCCCGaSBIqIYRVBQcHExERQVRUFFFRUURERPDkk09SWVnZ5H3GxsYSERHxp5+L\\niooiPz+/ycdpLGsdpzlEREQQGxvb6M+fOXOG6667zmLHDw8PZ9++fZf9TH5+Ptu3b7fYMa9EWloa\\ne/fuvaJtjEYj8+fPJzw8nKSkpAve++abb+jXrx9RUVGMHz+e8ePHs3z5ckpLS82fiY6OZsSIEea/\\nmXP/8vPzzf8/ZswYevfubf75ueees8j3vZT09HQiIiKYNm0aAI8++iijR4/ml19+4bbbbiMxMfGS\\n28bHx7NgwYKrOv6XX37ZpO2Ki4sZNmwYTz31VIPvP/jgg3z99desWLGCv/3tbxe8ZzKZmDt3Ljt3\\n7mzSsYUQbZfW1gEIIa49n376Kf7+/gDU1tby0EMP8d577/HQQw8163E3b97crPu39nGuVbGxsezc\\nuZNx48ZZ/djbtm3DYDAwaNCgRm+Tm5vL3r17iY+Px87O7qL3+/Xrx8cffwxAWVkZr776KtHR0Xz5\\n5ZfY29sD8Nhjj5mTl/Odq2uxsbE89dRTVqt7cXFx+Pr68vnnnwPw/fffExMTQ2BgICNHjrzstn36\\n9OGDDz5o8rGNRiMvv/wys2bNuuJtN2zYQHR0NGvXrqWmpsZcvgAbN26krKyMmTNnsmLFiou2VavV\\nLF++nDvuuINNmzbh4ODQ5O8ghGhbpIdKCGFTOp2OkSNHcvToUaA+wfrHP/5BZGQk4eHhvPvuu+bP\\nHjhwgBkzZhAVFcWkSZOu+E5xcHAw2dnZxMbGcsstt/DPf/6TcePGMWPGDA4dOkR0dDTDhw/nzTff\\nNG/z9ttvExkZyfjx47nnnnvMPQfFxcXMnz+fMWPGsHjxYv72t7+ZG2F/PM5rr73GxIkTCQ8PZ8+e\\nPQBUVVXx4IMPmr/nSy+9ZD5mdHQ0r7/+OhMnTuTf//43U6ZMueB7zJgxg23btl30/S4V64oVK3ju\\nuedYtGgR48aN46abbiI3NxeAhIQEJk+eTGRkJMuXL79k2aWlpTFnzhwmTpxIREQEGzZsuOD9jz76\\nyJgd6S4AACAASURBVPwdz8WWk5PDbbfdxqRJkxg/fjyvv/46UH+n//XXXzf3pixduvSiHso/9jqe\\n+zkxMZHnnnuOmJgYcwK+bds2pk6dyrhx47jzzjspLCy8KP7Y2FimTp3Kiy++aC7zgwcPXtHv4q23\\n3uK9995j1apVvPjiixcd49ixY8yePZuoqCimTZvGL7/8gtFoJDo6GpPJxNSpUzl27NglyxjA1dWV\\nZ599FmdnZ9atW3fZz16p9PR0br31ViIiIpg5c6a5FykzM5MFCxYQGRnJlClTLjhuQ2V74MABXn31\\nVY4cOcINN9xg/n4LFizgp59+uqCXcd26dURGRhIZGcljjz1GbW3tBb/by/29h4eH89///pebbrqJ\\nESNGmMv8jjvuoKysjKioKNLT09m0aRNTpkxh4sSJTJ069bI9rOvWrWPKlCkMHz78ol7Ot99+m7/8\\n5S8XbZOdnU14eDj79++nc+fO9O3bl6+++qqJvwUhRJukCCGEFQUFBSlZWVnmn4uLi5Vbb71Veeed\\ndxRFUZS33npLue2225SamhqloqJCufHGG5UdO3YoiqIoU6ZMUTZs2KAoiqJ8++23yvjx4xVFUZTd\\nu3eb/78xx969e7fSq1cvZffu3YrJZFJmzpypzJgxQ6msrFSSkv6/vfsOiOrK2wf+zFBC6EVAIMEW\\nu6CIDTtiATUJalREY0tiVtmU1U2ixMTE3t+owbLEvPHdFDAkttWoMdGoWRtlBUtsEQUEZKRXmZnz\\n+4N1fiIzlGHInQnP5y/mnnvufe7MAefrvffca6JLly6ivLxcpKSkiICAAFFUVCRUKpWYOXOmiIqK\\nEkIIsXr1avHWW28JIYRISUkR3bt3F5s3b66xn27duokff/xRCCFEdHS0mDlzphBCiJ07d4pXX31V\\nqNVqkZ+fL/r06SMuXLgghBBi2rRpYvbs2UKlUomHDx+KPn36iKtXrwohhMjIyBD+/v6ioqKi2rHV\\nlnXz5s0iICBApKenC7VaLebMmaN5vydMmCBiYmKEEEIcOnRIdOrUSZw9e7bGe/f666+LHTt2CCGE\\nOH/+vPD19RUPHz4UaWlpokOHDiI6OloIIcTp06dFv379xMOHD8Xq1avFli1bhBBClJaWir/97W8i\\nOztb/Otf/xKhoaGipKREKJVKMXfuXE3WwMBAceHChRqf6eOvN2/eLCIjI4UQQty9e1f4+fmJa9eu\\nCSGE2L59u3jjjTdq5D979qzo3LmzOHjwoBBCiN27d4sXX3yxQZ+FEEK89957mqyPU6lUIiQkRBw4\\ncEAIIURycrLo3bu3KCoqEmlpaaJz5841+gghxHfffSdmzJhRY/k//vEP8be//U2TYe/evVr7a3t/\\ndJkxY4b46quvhBBC/Pjjj2L06NFCCCFmz54ttm/fLoQQIj09Xfj7+4u0tLRa39sncz/+e/3oM0xL\\nSxP9+vUTWVlZQq1Wi4iICBEdHV0ta22/74GBgWL+/PlCqVSKrKws0bVrV5GZmVnj/ezbt69IT08X\\nQghx4cIFsXLlSq3Hf/36dREWFqZZ77XXXtO03bhxQ/Tu3VvzOT8aY2VlZWLcuHFi//79mnUPHjwo\\npkyZUut7TUTNC89QEdEf7uWXX0ZwcDCCgoIQFBSEfv36af5n+Pjx4wgPD4elpSWsra3x4osv4ujR\\nowCq/nc5JCQEAODv74+0tDS9M9jb26Nv376QyWRo3749+vTpg6effhrt27eHSqVCbm4uunXrhhMn\\nTsDW1hZyuRx+fn6afcbHx2vOHHXr1g2+vr5a92NjY4Phw4cDALp27Yp79+4BAGbPno2tW7dCJpPB\\nwcEB7du3R3p6uqbfkCFDIJfLYWFhgVGjRuHgwYMAqs4YBAUFwdLSstp+assKAL169YKXlxdkMhk6\\nd+6MzMxMVFRUICUlBaNHjwZQde/X008/rfU4tm7dqrnvxd/fHxUVFcjJydG0jxs3DgAwYMAAKJVK\\n3L17Fy4uLjh9+jTi4+NhaWmJjRs3ws3NDSdOnEBoaCisra1hZmaG8ePH49dff63Px1bDyZMn0adP\\nH3To0AEAEBYWhp9//hkqlarGutbW1prxM3LkSFy9ehVlZWX1/ixqk56eDoVCgTFjxgAAfHx84Onp\\niZSUFL2Oy9bWFkVFRZrX69atq3b/1AsvvNCg7VVUVODcuXOaMRsUFITdu3ejsrIS//73vxEeHg4A\\n8PLyQt++fXH27NkGvbfa/Prrr/Dz84O7uztkMhk2bNiAmTNnVluntt93AHj++edhZmYGd3d3uLi4\\nIDMzs8Z+XFxcEBMTg4yMDPTq1QuLFi3SmmfPnj2a983f3x+pqamaex2Tk5PRtWvXGp9zZGQkhg0b\\nhueff16zzNfXF8nJyRBC1Ot9IKI/P95DRUR/uEf3UOXm5mou3zM3r/pzVFRUhFWrVmHjxo0Aqi4J\\nelSsHDhwAP/3f/+HkpISqNXqRn2hsbGx0fwsl8thbW0NAJDJZJDL5VCpVCgrK8OqVas0lxAVFBRg\\n6NChAIDCwkI4ODhotuHu7q51P3Z2dtX2o1arAQCpqalYvXo1fv/9d8jlcmRlZWH8+PGadR/f9pgx\\nY7Bo0SIsWLAAx44d03pDf21Zn8xhZmYGlUqF/Px8AFVf3h8du729vdbjOHXqFLZt24a8vDzIZDII\\nITTHAgBOTk7V9lVYWIiZM2dCrVbj448/xv379zF16lS88cYbyM3NrXZ8Dg4OePDggdb91qWoqAjx\\n8fEIDg7WLLO1tUV+fj5cXFyqrWtvbw+ZTKb5Gaj6HLOzs+v9WeiSm5sLOzs7zfYf7SM3NxfPPvts\\ng48rIyOjWn5d91DVV35+PtRqtWYcyGQy2NjYICcnB0KIauPjUW61Wq3zva2PvLy8auPp8fuVHqnt\\n9/3R/h55NG6ftG3bNmzbtg3jx4+Hh4cHIiMj0adPn2rrqFQqHDhwAKWlpdiwYQOAqiLzwIEDmDVr\\nFh48eABnZ+dqfY4ePYqHDx8iICCg2nIXFxdUVlaioKAAjo6O9XoviOjPjQUVEUnG2dkZL7/8Mtat\\nW4dt27YBANzc3DB79mwEBgZWWzc7OxuLFy/Gt99+i86dOyM1NRWjRo1q0ny7du1Camoqvv/+e9jY\\n2OB//ud/kJ2dDaCqIHv8vp+cnBx4e3vXe9tLly5F165dERUVBTMzM4SFhelct3fv3lAqlTh+/Dhu\\n3LiB/v37NyirLo8KheLiYtjZ2UGtVqOgoKDGepWVlXj77bfxySefYMiQITW+9AJVBdyjoqqgoAAO\\nDg4wNzfHnDlzMGfOHNy+fRuvvfYa/P390aJFi2pfyvPz89GiRYtq23vyy/Pjs949zs3NDf379692\\n35suj+/z0XE6Ojpi0aJF9f4sdHFxcUFBQQGEEJqiSltRVx8qlQrHjh1DREREg/vq4uTkBJlMhry8\\nPDg7O0MIgbt378LT0xNyuVzzmT2eWyaT1fu91bXPpKQkzevi4mKUl5dXW0fX73tDeHt7Y9WqVVCr\\n1di7dy8WLFiAU6dOVVvn9OnT6NChQ7XJMK5cuYJFixZh1qxZWv9zpkuXLli4cCFmzZqF/v37w8vL\\nS++MRPTnxkv+iEhSs2bNQlJSkmayhqCgIHz77bdQqVQQQmDr1q04efIkcnNzYW1tjbZt20KpVCI2\\nNhYAUFJS0mTZHjx4gLZt28LGxgYZGRn45ZdfNEWUr6+vZka1q1evIjk5ucHb7ty5M8zMzPDrr7/i\\nzp07OqeOl8vlGD16NJYtW4Zhw4ZpnSmutqy6WFlZoVOnTvjxxx8BVM3UVlFRUWO9srIylJaWolu3\\nbgCqijcLC4tq2z9w4ACAqsu8nn76aXh7e+PDDz/UXMrn7e2NFi1aQCaTYejQodi/fz/KysqgVCoR\\nFxeHIUOGVNunq6srcnJy8ODBA83ZhUfMzc01l8MNHDgQ8fHxmssbk5OTsXz5cq3HW15erpkw48iR\\nI+jWrRueeuqpBn0Wj+/7cc888wxatmyJQ4cOAQASExOhUCh0XgqqS2lpKT744AM4ODhoLk80BEtL\\nSwwYMAB79uwBUHXGcc6cObCwsMDAgQM1v093795FfHw8+vfv36D3VpshQ4YgMTER6enpEEJgyZIl\\niIuLq7aOrt/32lhYWECtVqO4uBi5ubmYNWsWiouLIZfL0b1792pnCR/Zs2eP5tLbR7p06YKioiJc\\nu3YNLi4uyMvLq9b+zDPPoHPnzpgxYwYiIyM1RVdubi4sLCx0ns0louaHBRURScrW1hZz5szBmjVr\\nIIRAeHg4PD09MWbMGAQHB+PWrVvw9/dHp06dMHjwYIwaNQqTJ0/GsGHD0KNHD7z88svVtmeIZ9w8\\nEhYWhgsXLmDUqFFYs2YNFi5ciDNnzuCLL77A3Llzcfv2bYwYMQKff/45goKCtH6R02Xu3LlYs2YN\\nxo4di/Pnz+Ovf/0rtmzZgoSEBK3rjxkzBhkZGZr7nRqStTYfffQRoqOjMWrUKCQnJ6Ndu3Y11rG3\\nt8err76K0NBQhIaGwtvbG8OHD8df/vIXlJWVwdraGmq1GmPHjsWSJUuwYsUKmJubIywsTDOb3+jR\\no+Hn54eAgAAEBwdj8ODBGD9+PMaOHYuWLVti+vTp1fbZqlUrTJgwAaGhoQgPD0e/fv00bQMGDMDZ\\ns2cxYcIEuLm5YdmyZYiIiEBISAiWLl2q8z3y8vJCQkICRo0ahR07dmDJkiUN/iwCAwMRExODN998\\ns9pymUyGjRs34ssvv0RISAiWL1+OTZs2aS4lrc1//vMfBAcHY+TIkQgODsZTTz2FnTt3ai6DNZQV\\nK1bg+PHjCAoKwieffIL169cDAD7++GOcO3cOwcHBiIiIwPLly+Hh4dGg91abli1bYunSpZgxY4bm\\nbPKsWbOqraPr9702rq6u8Pf3R2BgIFJTUzFo0CBMmDABo0ePxvz587FixYpq6xcWFmqO+0lBQUHY\\nu3cvfH19cfnyZa1nqubMmYOSkhJ8+eWXAICLFy/Cx8enzvvqiKj5kAneVUlEfzLz58/X3JPRlB6/\\nvOvNN9+Ev78/ZsyY0ST7UigUGDduHE6cOAEzM7Mm2cef2aPnND06G0f0pEdFY13PGFuwYAF8fHxq\\nTLBBRM0X/3uFiP5U8vLyEBoa2uT7+fLLLzF37lyo1Wo8ePAA58+fh5+fX5Ptb/PmzZgyZQqLKaIm\\nMnfuXERHR9e6TlpaGuLj4/V6qDAR/XmxoCKiPxUnJycMHjy4yfczbtw4WFpaYuTIkZgyZQpmz57d\\n4Ptl6kOhUCAoKAgKhQKzZ882+PaJqMoLL7wAKysrzX1mT1Kr1Vi0aBGWL19er0s5iaj54CV/RERE\\nREREeuIZKiIiIiIiIj1J+hwqXbNZERERERERGRNds5DWq6Bau3YtEhISoFQq8frrr8PHxwfvvvsu\\nVCoVXF1dsW7dOlhaWmL//v3YtWsX5HI5Jk2ahIkTJ+od7I/2Q8xW4PgGeJWnIsOqNRC4ACFh86SO\\nRSYuISHBaMY4mT6OJzIkjicyJI4nMiRjHE+1nQiqs6A6e/Ysbty4gdjYWOTl5WHcuHEICAhAeHg4\\nQkJCsHHjRsTFxSE0NBRRUVGIi4uDhYUFXnrpJYwYMQKOjo4GPZim8EPMVrT64f8/kb5V+e/ADxH4\\nAWBRRUREREREOtV5D1Xv3r2xadMmAFUPdywrK8O5c+c0D8gLDAzEmTNnNA+6s7Ozg5WVFXr27InE\\nxMSmTW8oxzfoWN70z7EhIiIiIiLTVecZKjMzM830oHFxcRg8eDBOnz4NS0tLAICLiwtycnKgUCjg\\n7Oys6efs7IycnJw6AxjDfVRe5alal3uWpxpFPjJtHENkSBxPZEgcT2RIHE9kSKY0nuo9KcWxY8cQ\\nFxeHzz//HCNHjtQs1zXren1nYzeG6yN/sGpddZnfE+5ZtUaIEeQj02WM1wCT6eJ4IkPieCJD4ngi\\nQzLG8VRbgVevadNPnTqF7du3Izo6GnZ2drC2tkZ5eTkAIDs7G25ubnBzc4NCodD0uX//Ptzc3BoZ\\n/Q8SuEDH8vl/bA4iIiIiIjIpdRZURUVFWLt2LXbs2KGZYKJ///44cuQIAODo0aMYNGgQunfvjpSU\\nFBQWFqKkpASJiYno1atX06Y3kJCwebgTEoUscw8AQK7cEXdCojghBRERERER1arOS/4OHTqEvLw8\\nvP3225plq1evxuLFixEbGwtPT0+EhobCwsICCxYswCuvvAKZTIaIiAjY2dk1aXhDCgmbh3sDRyP/\\n/TbIcu6Ol1hMERERERFRHeosqCZPnozJkyfXWP6///u/NZYFBwcjODjYMMkk0NLTG7fl9nAquCF1\\nFCIiIiIiMgH1uoequZDL5ch8ug3cKzNRVFggdRwiIiIiIjKAvn37Ntm2WVA9oci+LeQQuHbpgtRR\\niIiIiIhMWkxSBnzXn4D5O/+C7/oTiEnKkDqSwdV72vRmo8VzQCaQeS0R6D9c6jRERERERCYpJikD\\nU75M1LxOySzSvA7z89Jrm99//z1OnjyJ+/fvY9CgQfjll18gl8sxfPhwzJ49G1lZWXjnnXcAAEql\\nEmvWrIG3t3fjD6YWLKieYOfRAUgBStNSpI5CRERERGS03jlwBd9evKez/V5hudbl079JwsKDV7W2\\nTezuiTDP2vebmZmJ9evXIzIyEt988w0AYMqUKQgODoZCoUBERAT69euHuLg4fP3111i4cGH9DkhP\\nLKie4O7dHgBgmaP9QyYiIiIiorpVqkSDlteXj48PUlJScOfOHUyfPh0AUFJSgoyMDDzzzDNYvnw5\\ntmzZgsLCQnTt2rVR+6oPFlRPsLa2RaaFB9yLb0KtVkMu521mRERERERPWvd8F6x7vovOdt/1J5CS\\nWVRzuYc9Lv59iM5+CQkJte7XwsICFhYWGDp0KJYuXVqtbdGiRRg4cCCmTJmCw4cP48SJE7UfhAGw\\nWtAiz749HNUFyLp3V+ooREREREQmKTKovdbli4Kea/S2u3btinPnzqGsrAxCCCxfvhzl5eXIy8uD\\nt7c3hBD46aefUFlZ2eh91YUFlRZq96pK+yZn+iMiIiIi0kuYnxe+mdYTvh72MJfL4Othj2+m9dR7\\nQorHeXp6Yvr06Zg6dSomTZoEV1dXWFlZYfLkyVi2bBleffVVjBkzBufPn8fp06cNcDS68ZI/Lexb\\ndweuAIpbSQAmSh2HiIiIiMgkhfl5GaSAemT8+PGan6dOnYqpU6dWaw8MDERgYKDm9alTpwAA586d\\nM1iGJ/EMlRbenXsBACozLkuchIiIiIiIjBkLKi3ad/JFBSxgm3tN6ihERERERGTEWFBpYWFpiUyr\\nVvAouw2lUil1HCIiIiIiMlIsqHQodu4IKzzEret8wC8REREREWnHgkoHM8+qh4DduRIvcRIiIiIi\\nIjJWLKh0cGnrBwAouH1R4iRERERERGSsWFDp0K5b1Ux/yL4ibRAiIiIiIgIAnDx5El9//bXUMarh\\nc6h08Hq2Lc7LbeGYf13qKEREREREJinm9ySsTP4ZV/Kz0cXRHZG+wxD23yvB9DF48GADpjMMFlQ6\\nyOVyZNm0x3NF/0FxUSFs7eyljkREREREZDJifk/ClF++0rxOycvUvNa3qPr+++9x4sQJKBQKeHt7\\nIykpCVOmTMG1a9dw8eJFzcN+9+/fjy+//BJyuRzt27fHsmXLUFRUhDfffBPl5eUYMmQIdu/ejZ9/\\n/rnRx8mCqhYVLTpBXpSE65cT0LNfYN0diIiIiIiaiXcuHMC3qck62++VFmpdPv1UDBYmHNLaNrG1\\nL8LknnXu++rVq4iKikJBQQHGjh2Ln376CRUVFXjjjTcwdepUlJWV4bPPPoO9vT2mTp2Ka9eu4fz5\\n82jXrh0WL16Mr776qs591BfvoaqF1bM+AICMawkSJyEiIiIiMi2ValWDljeEt7c3nJyc4OrqCmdn\\nZ7i7u8PFxQVFRUUAAAcHB8ybNw/Tpk3DrVu3kJ+fj1u3bqFnz54AgKCgoEZneIRnqGrh0aEncBIo\\nvctnURERERERPW5d7+exrvfzOtt9925ASl5mzeVOHrgYukBnv4SEuk9mmJmZaX42N69e0jx8+BBL\\nly7Fvn374Orqitdffx0AIISAXF51Pkkmk9W5j/riGapadPTpAwCwzLkqcRIiIiIiItMS6TtM6/JF\\nOpYbSklJCczMzODq6orMzExcunQJlZWV8Pb2xqVLlwBUzRZoKCyoauHg6IQs85ZwLb4pdRQiIiIi\\nIpMS1tYP3wyZCl8nD5jL5PB18sA3Q6Y2apa/+nBycsKAAQMwYcIEfPrpp3j11VexatUqjBs3DvHx\\n8Xj55ZehUCg0Z6sai5f81SHXvj265J5CdmY63D2ekToOEREREZHJCGvrZ9ACavz48Rg/frzmtY2N\\njWamvsd/Xr16dbV+s2bNQkZGBiIiIjBo0CAkJSXhwoULBsnEgqoOavfOQO4p3Ei5wIKKiIiIiMhE\\n2dnZ4YsvvkBUVBQA4P333zfIdllQ1cGudXfgKnD/ZiIwcpzUcYiIiIiISA/29vbYuXOnwbfLe6jq\\n4N2pFwCgMuOyxEmIiIiIiMjYsKCqQ/vO3VEJc1jn/iZ1FCIiIiIiMjIsqOpg+dRTuGfVCh5lt6FS\\nNv4hZERERERE9OfBgqoeipw6wFqU4/bNK1JHISIiIiIiI8KCqh7kHl0BALevGGZqRSIiIiIiMoxh\\nw4ahpKREsv2zoKoHl3Y9AAD5ty9KnISIiIiIyHQUnI3Brfd9cWWWOW6974uCszFSRzI4TpteD227\\n9EEJAGTxkj8iIiIiovooOBuDjG1TNK8r0lM0rx36hem1zeLiYixYsAClpaUoLy/HBx98AADYsWMH\\n4uPjYWZmhqioKBQXF+Odd96BXC6HSqXCunXr4OXl1fiD0qJeBdX169cxb948zJw5E9OmTUNmZibe\\nffddqFQquLq6Yt26dbC0tMT+/fuxa9cuyOVyTJo0CRMnTmyS0H+0Z1u3wwWZDRzyr0sdhYiIiIjI\\nKGTHvIPCC9/qbK/Mu6d1ecY/puP+twu1ttn3ngi0111s5eTkYOLEiRg+fDjOnDmD6OhoAEDHjh0x\\nf/58rFmzBvv27YNSqUT//v0RERGBy5cvIycnR7qCqrS0FMuWLUNAQIBm2ebNmxEeHo6QkBBs3LgR\\ncXFxCA0NRVRUFOLi4mBhYYGXXnoJI0aMgKOjY5ME/yMd2b0dbkIJr4d3cXhOW4hhf0dI2DxN+w8x\\nW4HjG+BVnooMq9ZA4AJNe21tjenbVNs1xeMhIiIiIiOkqmzY8npo0aIFtm7dip07d+Lhw4ewtrYG\\nAPTt2xcA4OPjg/j4eISFheGvf/0rioqKMGrUKPj5+em9z7rIhBCithWUSiWUSiWio6Ph5OSEadOm\\nYdiwYTh8+DAsLS2RlJSEzz//HOHh4fjuu++wfv16AMCHH36IoUOHYtiwYTq3nZCQAH9/f8MeUSM9\\nmemHmK1o9UNEjfWSW4fBvkN/FF7/N3xTa14Lmty6qrLW1daYvk21XVM7njshUUZfVBnjGCfTxfFE\\nhsTxRIbE8UTa3HrfFxXpKTWWP/WsL9ot1z03QW3j6dNPP0V5eTn+/ve/IyUlBWvXrkVGRga+/fZb\\nuLi44NChQ0hISMAHH3yAsrIy/Prrr9i1axcmTJiA0NBQvY+ltkx1nqEyNzeHuXn11crKymBpaQkA\\ncHFxQU5ODhQKBZydnTXrODs7IycnR+/QRuP4Bq2LfVNjAC1f8qu119bWmL5NtV0TOx4c3wgYeUFF\\nRERE1Fy1eD6y2j1UmuVjF+m9zby8PHTs2BEAcOzYMVRWVp3tio+Px6hRo3Dx4kW0bdsWBw8exLPP\\nPovhw4fD0dERhw8fblRBVZtGT0qh6wRXHSe+NBISEhobweAez+RVnqp1HRXkiO82H70ubYAZah6r\\nCnIAQmdbY/o21XZN7Xg8y1ONcvw8yRQykungeCJD4ngiQ+J4ohos2sMseAXM47+ALPd3COe2UPaa\\niZsW7YE6xouu8dSxY0ds27YNu3fvxsiRI5Geng4hBE6ePIkdO3YAAAYOHIjs7Gy89957sLKyglwu\\nx4wZM5psjOpVUFlbW6O8vBxWVlbIzs6Gm5sb3NzcoFAoNOvcv38fPXr0qHNbxnZ6uMYlf1at0ar8\\n9xrrpVu1wax31uGH17/X2Q6IJunbVNs1teO5Z9UaIUY2fp7ESyDIkDieyJA4nsiQOJ5IJ39/YEpk\\ng7rUNp78/f0xadIkzes5c+bo3M5LL73UoP3WlUkXvZ5D1b9/fxw5cgQAcPToUQwaNAjdu3dHSkoK\\nCgsLUVJSgsTERPTq1Uu/xMYkcIGO5fPrbm+qvsaYqTF9G7NdIiIiIiIJ1XmG6tKlS1izZg0yMjJg\\nbm6OI0eOYP369Vi4cCFiY2Ph6emJ0NBQWFhYYMGCBXjllVcgk8kQEREBOzu7P+IYmlRI2Dz8AADH\\nN8KzPBX3rFoDgfM1kyHU1d5UfY0xU1Mfj+3RxXBR5SHL3B0VIz40+gkpiIiIiOjPr85Z/pqSMZ4e\\nNsZMVOXY3l3w3DMTV/zm46W3tU8WYmw4nsiQOJ7IkDieyJA4nsiQjHE81ZZJr0v+iKTQpktvAIA6\\n87LESYiIiIiIqrCgIpPRum1HlMiehn3edamjEBEREREBYEFFJsTM3AxZ1m3hUXEXFeXlUschIiIi\\nImJBRaal1KUjLKDC9atJUkchIiIiImJBRabF0ssHAJB2NV7iJERERERELKjIxLg95wcAKE5NljgJ\\nERERERELKjIxHXz6AADM7l+ROAkREREREQsqMjGu7h5QmDnDpeiG1FGIiIiIiFhQkelR2LWHmzIH\\nuQ9ypI5CRERERM0cCyoyOZWunQAA11POS5yEiIiIiJo7FlRkcmy8fQEAWTc4dToRERERSYsFFZkc\\nr47+AICK9BSJkxARERFRc8eCikxOh27+UEEOK8VvUkchIiIiomaOBRWZHBsbW2RaeqFlyS2orHme\\n/AAACmpJREFU1Wqp4xARERFRM8aCikxSgUMH2IkSpKXekjoKERERETVjLKjINHl0AQD8foUz/RER\\nERGRdFhQkUlybNMDAPDg1n8kTkJEREREzRkLKjJJrbv0AgCoMy9LnISIiIiImjMWVGSS2j7XFWWy\\np2CXd13qKERERETUjLGgIpNkZm6GzKfbwLP8Dh5WVEgdh4iIiIiaKRZUZLJKnDvCAkrcuHpR6ihE\\nRERE1EyxoCKTZeHlAwC4+1u8xEmIiIiIqLliQUUmy+25ngCAolSeoSIiIiIiabCgIpPV3qc3AECe\\nfVXiJERERETUXLGgIpPl7vEMcs2c4FLImf6IiIiISBosqMik5dg+B3dlNvLzHkgdhYiIiIiaIRZU\\nZNIeunYGAFxLviBxEiIiIiJqjlhQkUmz9q6a6S/rZqLESYiIiIioOWJBRSbNq6M/AKA8LUXiJERE\\nRETUHLGgIpPWoas/1JDhKcVvUkchIiIiomaIBRWZNFs7e2RaeKJlyU2o1Wqp4xARERFRM8OCikxe\\nvmMH2KuLkZH2u9RRiIiIiKiZYUFFps+9CwDg1qV4iYMQERERUXNj8IJq5cqVmDx5MsLCwpCcnGzo\\nzRPV4NCmOwDgwe9JEichIiIioubG3JAbO3/+PO7cuYPY2FjcunULkZGRiI2NNeQumkzM70lYmfwz\\nruRloUvaCUT6DkNYW7/qbfnZ6OLoXq2trvam6muMmaQ6nt/uXsAzADrGr8W+uV8gtddreOuV5Zq+\\nm3YuRuv4aLQpzcFta9dq7fq2NaTvvkb0bapMhuzLTBxPxvg+MRPHEzNxPBnr+8RM+o8nYyUTQghD\\nbWzTpk3w9PTExIkTAQDBwcGIi4uDra2t1vUTEhLg7+9vqN3rLeb3JEz55asay78ZMhUAdLaFtfWT\\npK8xZpLqeLKPf4cRJ1fUaDv+3AR07fkSLifGIfDmd1rbAejV1pjtMhMzMRMzMRMzMRMzMVP9+/44\\n+H2jKKpqq1sMWlB98MEHGDJkCIYPHw4ACA8Px4oVK9CmTZsGB/sj+e7dgJS8zBrLLeRmAIBKtUpr\\nm6e1Pe6VFupsb6q+xphJquOJPfc5OpYoarQRERERkem7bu2O0G1ZUseotW4x6CV/T6pPrZaQkNCU\\nEerlSp72D0nbl/jH2x5WPNS5TlP2NcZMUh1Pu5IHWttUkGF7m6GYe/u41hsFVZBBBqFXW2O2y0zM\\nxEzMxEzMxEzMxEz179umLMco6oXaGPQM1ZYtW+Dq6oqwsDAAQFBQEPbt22f0l/zpOkPl6+QBAehs\\nuxi6QJK+xphJquNZemQt2pfer9H26H8z9s1119kug9CrrTHbZSZmYiZmYiZmYiZmYqaG95VabXWL\\nQWf5GzBgAI4cOQIAuHz5Mtzc3HQWU8Yk0neY1uWLfIfV2iZVX2PM1Ji+jdluaq/XtLbd6fUqANTa\\nrm9bY7bLTMzETMzETMzETMzETA3va8zMPvroo48MtTEPDw/cvHkTmzdvxqlTp7BkyRK0aNFC5/qZ\\nmZnw9PQ01O711s3JA50cXHGjUAFFeQl8nDzwSd8XEdbWr1rbg4pSdHNqqWl7su+T7U3V1xgzSXU8\\n/XoOw9e5SqhyrsNeWYZb1m5IDHhbc/Nibe36tjVmu8zETMzETMzETMzETMzU8L5Sq61uMeglfw1l\\n7NdDEhERERERAfhjZvkjIiIiIiJqTgx6DxUREREREVFzwoKKiIiIiIhITyyoiIiIiIiI9MSCioiI\\niIiISE8sqIiIiIiIiPRkLnUAY7Jy5UpcvHgRMpkMkZGR8PX1lToSmZi1a9ciISEBSqUSr7/+Onx8\\nfPDuu+9CpVLB1dUV69atg6WlpdQxyYSUl5dj7NixmDdvHgICAjieSG/79+/HZ599BnNzc7z55pvo\\n2LEjxxPppaSkBO+99x4KCgpQWVmJiIgIuLq64tGjTTt27IiPP/5Y2pBkEq5fv4558+Zh5syZmDZt\\nGjIzM7X+Xdq/fz927doFuVyOSZMmYeLEiVJHr4ZnqP7r/PnzuHPnDmJjY7FixQqsWLFC6khkYs6e\\nPYsbN24gNjYWn332GVauXInNmzcjPDwcX3/9NVq1aoW4uDipY5KJ2bZtGxwcHACA44n0lpeXh6io\\nKHz99dfYvn07fvrpJ44n0tuePXvQpk0b/POf/8SmTZs035siIyMRExOD4uJi/PLLL1LHJCNXWlqK\\nZcuWISAgQLNM29+l0tJSREVF4YsvvsA///lP7Nq1C/n5+RImr4kF1X+dOXMGw4cPBwC0a9cOBQUF\\nKC4uljgVmZLevXtj06ZNAAB7e3uUlZXh3LlzCAoKAgAEBgbizJkzUkYkE3Pr1i3cvHkTQ4cOBQCO\\nJ9LbmTNnEBAQAFtbW7i5uWHZsmUcT6Q3JycnzRfawsJCODo6IiMjQ3NlD8cT1YelpSWio6Ph5uam\\nWabt79LFixfh4+MDOzs7WFlZoWfPnkhMTJQqtlYsqP5LoVDAyclJ89rZ2Rk5OTkSJiJTY2ZmBmtr\\nawBAXFwcBg8ejLKyMs0lNC4uLhxT1CBr1qzBwoULNa85nkhf6enpKC8vx1/+8heEh4fjzJkzHE+k\\ntzFjxuDevXsYMWIEpk2bhnfffRf29vaado4nqg9zc3NYWVlVW6bt75JCoYCzs7NmHWP8js57qHQQ\\nQkgdgUzUsWPHEBcXh88//xwjR47ULOeYoobYu3cvevTogWeffVZrO8cTNVR+fj4+/fRT3Lt3D9On\\nT682hjieqCH27dsHT09P7Ny5E7/99hsiIiJgZ2enaed4IkPQNY6McXyxoPovNzc3KBQKzev79+/D\\n1dVVwkRkik6dOoXt27fjs88+g52dHaytrVFeXg4rKytkZ2dXO61NVJsTJ04gLS0NJ06cQFZWFiwt\\nLTmeSG8uLi7w8/ODubk5vL29YWNjAzMzM44n0ktiYiIGDhwIAOjUqRMqKiqgVCo17RxPpC9t/85p\\n+47eo0cPCVPWxEv+/mvAgAE4cuQIAODy5ctwc3ODra2txKnIlBQVFWHt2rXYsWMHHB0dAQD9+/fX\\njKujR49i0KBBUkYkE/LJJ5/gu+++w+7duzFx4kTMmzeP44n0NnDgQJw9exZqtRp5eXkoLS3leCK9\\ntWrVChcvXgQAZGRkwMbGBu3atUN8fDwAjifSn7a/S927d0dKSgoKCwtRUlKCxMRE9OrVS+Kk1cmE\\nMZ43k8j69esRHx8PmUyGJUuWoFOnTlJHIhMSGxuLLVu2oE2bNpplq1evxuLFi1FRUQFPT0+sWrUK\\nFhYWEqYkU7RlyxZ4eXlh4MCBeO+99zieSC8xMTGamfzmzp0LHx8fjifSS0lJCSIjI/HgwQMolUq8\\n9dZbcHV1xYcffgi1Wo3u3btj0aJFUsckI3fp0iWsWbMGGRkZMDc3h7u7O9avX4+FCxfW+Lt0+PBh\\n7Ny5EzKZDNOmTcMLL7wgdfxqWFARERERERHpiZf8ERERERER6YkFFRERERERkZ5YUBEREREREemJ\\nBRUREREREZGeWFARERERERHpiQUVERERERGRnlhQERERERER6YkFFRERERERkZ7+HyvQ5W9NLndd\\nAAAAAElFTkSuQmCC\\n\",\n 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AbF1BjRWklCZQZPRzsGdHQn9mQBxZU6a4cjhBBCCCFEs1Pb+BAVIgmV\\nqMe4EB8MRoXfUvKsHYoQQgghhBDNzobkHJzsbBjSycPaoTQqSajMFHV2ppLagXZCCCGEEEKIGmkF\\nFRzOKmV0Vy/sNTbWDqdRSUJlpoGB7rhqNTKOSgghhBBCiH+onbxtXCvv7geSUJnN1kZNxFXeHMsr\\n51humbXDEUIIIYQQotmobXRo7RNSgCRUV6Q2494o06cLIYQQQggBgMGosOloDoEeDoT4Ols7nEYn\\nCdUVkOdRCSGEEEIIUdee9CLyy3WMC/ZBpVJZO5xGpzFnpbi4OB544AG6desGQHBwMM8++6xFA2sJ\\nuno70cXLkc1Hc9EbjGhsJD8VQgghhBBt24bkmknb2sL4KTAzoQIYOHAg7777riVjaZHGBfvwUexJ\\n4k4VMqyzp7XDEUIIIYQQwqpiknJQqWBMN29rh9IkpEnlCtU+qGzdEZk+XQghhBBCtG2FFTq2nyhg\\nYEd3PB3trB1OkzA7oUpJSeHuu+9m1qxZ/PXXX5aMqUUZ080HWxuVJFRCCCGEEKLN25Scg8GoMCHU\\nz9qhNBmVoihKQ1fKysoiPj6e6Oho0tLSmDt3Lhs2bMDOrv4sND4+/ooDbc7u2ZzLzqxq1k/1w9uh\\ndT+4TAghhBBCiAt5fkcBv6RW8GWUN2FerauFKjw8vN7lZo2h8vPzY8KECQAEBgbi7e1NVlYWHTt2\\nbHAA1hQfH2+RuGaUHGPnL4fIsGtHVPiFy0BYrsxFw0i5W4eUe9OTMrcOKfemJ2VuHVLuF6coCrt+\\n2YiPsx2zIwejVltmhr/mUO4XayAyq8vf6tWr+eyzzwDIyckhLy8PP7+206z3TxNCfQFYeyTLypEI\\nIYQQQghhHftOF5NZUsX4EF+LJVMtgVktVBERETz66KNs3rwZnU7H4sWLL9jdry3o7utMkIcDG5Jy\\nZPp0IYQQQgjRJtXOKVDb2NBWmJVQOTs789FHH1k6lhZLpVIxIdSXD7efJPZkASO6eFk7JCGEEEII\\nIZrU2sNZqFVt5/lTtaQpxUKiu9dk4jLbnxBCCCGEaGsKyquJPVnA4CCPNjNdei1JqCwk4ipv7GzU\\nrD0sCZUQQgghhGhbNiTlYFT+bmRoSyShshAnew2ju3qx/0wxp4sqrB2OEEIIIYQQTWZtGx0/BZJQ\\nWVT02Qq0/kiOlSMRQgghhBCiaRiNCuuPZOPnYk/fADdrh9PkJKGyINP06Ydl+nQhhBBCCNE27Dld\\nRHZpNdHd29Z06bUkobKgbt5OdPFyZGNyLtV6o7XDEUIIIYQQotHVziHQFsdPgZnTpov6qVQqJof5\\n8e8/jrPpaA4TQpvPw46NRoXD2aX8eTyPP4/ncyy3nPAOblxzlTcju3ji7Wxv7RCFEEIIIQRgMCrs\\nPV3E7yl5/H4sl3KdgaGdPBnR2ZMhnTxw1dpaO8Q6fjiQgZ2Nus1Nl15LEioLm9EngH//cZwV+840\\nm4Tqp4MZ3PPDQTJLqkzLVCqIPVnAe3+dAGBYJw+WTu3F1R3aXr9XIYQQQojmoFJnYMnmo7y77ThF\\nlXrTcpUKfkvJA0Ctgpl92/PB9F64OVg/sTqSVcKBjGIm9fDDvRnEYw2SUFnYoEAPOrprWZWQSZXe\\ngL3GxmqxVOoMPPrLId7/6wQOtmpmh7dnRGcvRnTxpLOnI7vTCvn9WB6bj+by+7E8BryzjUdHdWVR\\nVDAOttaLWwghhBCirdmWmsed3+0nKacMfxd7ZvQNYHRXL0Z19cLZTsP2E/lsO57PmkNZfLv3NLEn\\nC/jfnH4MDPSwatzf7c8AYEbfAKvGYU2SUFmYWq1iRp8A3tqaSsyRHCb39LdKHEnZpcz8Kp59Z4oJ\\n83dhxZxwwvxd6rxneBcvhnfx4pmxwWxMyuGulQd47bcUVh44wxcz+zK8i5dVYhdCCCGEaCsqdAYe\\n/jmRj2JPolLB/cM783J0d1y0dS/To0P9iA7144WoEBZvSGbJ5qMMW/oXr0wI5eFRXaw2GcSKfaex\\n16iZHNY8emZZgyRUjWBG35qE6rv9Z6ySUB3KLGHYe39RWKFj/uBA3p4ShqPdxb/qsSE+HHx0FIti\\nknj7j1QiPorlsxl9mNO/YxNFffnKqvT8cDCDnacKSSusIL2oktNFlXg42NLDz5kwfxfC/FyI6u7b\\nZpuehRBCiLZAURR2niok7lQBiZklHMoq5WhuGS72Gjq6a+ng5kAXL0eu792Onu1crR3uebJLqpj8\\nn53EnSokzN+FT2f0YXDQxVucNDZqXoruzjVdvZj97V4eW3OIU4UVvHNdGCpV0yZVtWU+tZd/sxvX\\n1ZQkoWoEAzq608nTgZ8TM6nQGZq0+1xmcSUTPo2jsELHpzP6MG9Q4GWv62Sv4c3JYUzs4cfUL3Yz\\n97/7OFFQwTOR3Zr8B/pPiqKw/UQBn+9MY8X+05RWGUyvaTVq2rtpySiu5Eh2KT8ezATAwVbNDX0C\\nuHNQIMM6e1p9Hy7mdFEFu9OKKCjXUVqtp6Sqpt/0Vd5OBPs40c3b6ZJJsRBCCHE5jEaFM8WVJGWX\\nkpxbRk5pNc72NjjbaXCx19DZy5HwDm7Y2jTfyaBzS6tYHp/Op3GnOJxValquVkEnT0eKK3X8llJm\\nWv78hmQGdHTntoEdmXV1+2ZxwzUpu5QJn8aRmlfOnPAOLJvRu0FDRcYE+7DvkVFEfhTL0j+P09nT\\ngYdGdW3EiM+3Yt9poGYOgbZMrtAagUpV0+3v9d+Osf5INlN7tWuS7ZZW6Zn42U5OFlTwUnRIg5Kp\\nc42+ypu/7hvGhE/jeG59Eql55Xx8fW/sNNY5sO4/U8SCnxL4IzUfgEAPBx4e2ZHJYX508nTE09EW\\nlUqFoihkFFdxKKuEHScL+GJXGst3p7N8dzqhfs48GxnMjX0DmsXzEcqq9PyUkMnmo7n8kZpHal75\\nJdfp4efM5DB/ruvpz4CO7s1iP86lNxg5kl1KQmYJiZklJGaVkF+uw2BUMBgVFKC9m9aUIHb3dSa8\\ng7vV6tXlUBSFnNJqThSUc7KggsziKtQqsLVRo1GrcHewJdjHiau8ndC2kHGHRqNCQYWOrJIqskpr\\nJqpxsrPByU6Ds50NHdwdsGlmdetiFEWhpEpPQbmOggodCbnVOGeX4uFgi7uDbbOuX/UpKK8mp6ya\\nsioDZdV6KnRG3B1s8XOxw8/F3qrjchtCURQyS6pIyi7lVGEF1XojOqOC3qDgYKsmyMORTp4OBHo4\\nNPt9SiuoYM/pIpJzalo+juaUUak3YqOq6eZvZ6PmKm8nwvxc6NnOhd7tXPFysrN22OfJKa1izaEs\\nVp0995RVGy76fkc7G4YEeTCyixfTevk3m9ad9MIKFsckszw+DZ1Bwc5Gzcy+AUwO86dnOxe6nXM8\\nrtQZOFNcSXx6EV/sSmP9kWx2pRXyxJrDLI4K5v7hna2WNG5LzeO6z3eRX67jubHBLI4KNuvGr5+L\\nPb/eMZDB7/7JI78cItDDgem9mya5URSF7/adwcFWzcQebbe7H0hC1Whu7FuTUK3Yd6ZJEiq9wcis\\nr/cQn17EvIGBPDWm2xV9Xg9/F3YsGM6k/+zki11pZBRX8sMt/XGyb7oqk19ezbPrkvgo9gRGBSb2\\n8OOBEZ2JuMq73mRCpVIR4KYlwE1LZLAPT43pxtbUPJbtOMX3+89w0zd7eGXLUV4c353JYX5WabHa\\nk17Ish2n+HbvaYrPzt7j7mDLpB5+DO3kgZ+LPc72NXcIdQYjKbllHM0t40h2KTtOFvDqlhRe3ZJC\\nO1d7Zl3dnruGBBHs49zk+1GrsEJHzJFsfjmUxboj2eSX6857j0oFNmfLesdJpc5rTnY2XHOVN+OC\\nfRjf3YduVtwXgCq9gZ2nCvkjNY8/juUTe7LA1Fp4MbV3RPt3cCeimxfXXOVNN2+nJoj44qr1Rnan\\nFbI7vZBdaYXsTisiJbcMvVG54Dr2GjUhPs708HOmd4ArI7t4MaBj80h8K3QG/jqez570Ig5kFLP/\\nTDFHskvP358Nv5n+6etsR+92rvQJcKV3gCvDOnnStRl8NwBZJVVsSs5hx8kCDmWVkphVQtY5s7HW\\nx8vRlr7t3RjQ0Z3+Hd0YHORBezeHJor4wvRGhb+O57PuSDabj+aSmFlyWb8dlQpCfJwZ2cWTUV29\\nGNnFiw7u1t2f8mo9W1Ly2JCUw4akbJJyyuq8rlKBRq3CqNRMbQ2w+WhundcHdnRnUpgfE3v40bud\\nq9V6SFTqDKw8kMGncafYlppH7U8lxMeJvu3d6Ha2F4S/i5ZynYHSKj3FVXoSMkr4I7Vm0qrNR3NZ\\nFJPE4CAP5g8OZEafgCa9FqhVWKHjtS0p/PuPVCr1RkJ8nLh7aCdm92t/wUe/aG1t6OLlRBcvJ27o\\nE8CZokq+3J3Gm78f45HVh/g07hTvTOnJ2Cae6vvHAxnM+noPRkXhPzf24baB5t0ArxXo4civ8wYx\\n4v2/mP3NXgJctQzp5GmhaC/sQEYxSTllXN+7Hc5WqBPNiUpRlAufWS0kPj6e8PDwxt5MgzVmXIqi\\n0O2VLWSUVJHz/LhG76710M8J/PuP44wL9mHNHQMtdselrErPjK/iWXs4m0GB7vx6x6AruvN2uWX+\\nv72nue/Hg+SV6wjxceKd63oSdQUPi0vNK+P5Dcl8HZ+OUYGBge68NL47kcHejX6iMxgVfk7I5PXf\\nUog7VQjUtNTcPrAj03u3o5e/62W1NpVX69mYnMvPCZmsTswk72zyEnGVN/83NIgpPf0v+L1bsq5X\\n6gysOZTF8t3prDuSbbqY7eCmZWywD30CXAnzd6Gnvwt+Lvam8q29Y300p4zknFL2nSlmU3JOnYuV\\nPgGu3Ng3gBl9AprsorewQsevh7L4KSGT9Uey69y1DfFxooe/C53O3k1v56oFQGcwojfWtF4l5ZSS\\nlF3K4exSckqrTeu2d9MywFvNbSPDGNPNu8kuQNILK1h3JJt1R7LZmJxTp3usq1ZDqK8z7Vy1+LnY\\n4+dsj0oFZdU1rSFFFXqSc0s5lFVK+Tnl4Ghnw7BOHozpVpP4NtUFoqIoHMwoqbmwTc7mj9R8qs55\\naLqTnQ1h/i74OtubWqQK87Kxd/WisKKmxepYXhkn8ivqfG5nT0fGBnsTGezDmG7eeDo2TWtChc7A\\nttSaC/WNybkcyCiu83onTwd6+LkQ4Ko922pog9bWhsLaFsWSKk4UVJCSW/cCv4efM1EhvkSF+DCy\\nq1eTdTPPKqli/ZFs1h7OZv3hDIqra44FGrWKEF9nQnycCPF1pounI1pbGzRqFbY2KkoqDZw82+qb\\nml/O7rTCOr+7Xu1cmNqzHVN7+dMnoGnqWqXOwPoj2azYd4ZfDmWZ4qm96TOskwfdfZ3p5uNMVy/H\\nOi3SlToDSTmlJGTUtMxvP1HAn8fzTclWZ09H5oR3YE7/DlxlwePaxY7rR3NK+WTHKT7fecp0rhjW\\nyYPrerZjSk+/y755lVdWzeajuXy+6xQxSTkoSs1x5K7BQTwwsnOTJPNVegMf/HWClzYdJb9cR3s3\\nLS+OD2Fu/45mt6bnlVXz7PojfBx7EqNS013t/Wk9L+uZnFd6Pl224yR3rzyAg60NP97an3EhlnsQ\\n7trDWUz+zy48HGyJf2gEgR6OFvvs+jy99jBLNqfw/dxwrm/kLn/NIZe4WAxmJ1RLlixh//79qFQq\\nnnrqKXr37m1WANbU2HHVVrTv5oZzQyNWtNUJmUz5fBehfs7sWDDc4oMCdQYj81bs56v4mq5zMXcO\\npqOHeQfRS5V5fnk19/xwkBX7zuBoZ8Pz40JYMKKzxe6OH8osYVFMEisP1EzxOaqrFy9Hd2dYZ8vf\\nyanUGVi+O503fz/G0dwyVCq4NtSPu4YEMT7EB80VJL3VeiM/Hczgo9iT/H6s5rkU/i72zBsUyJ2D\\nAgnyrHsQvdK6rigKsScKWB6fzop9ZyisqDlBX93elWm92jGxh5/ZFz4n88vZmJzDz4lZxCRlozPU\\nHJLCO7hxY98AbugTQCdPy54UMoor+Tkhk58OZvLbsVzTNrt5OxEd6suoLl4M7+yJr8vlP/BaURRS\\ncsvYkpLLbyk1d3Zzy2oSLK1GTUQ3byb1qLljbck78DqDkdgTBaw7e2F77kV6N28nokJ8GBzkQf+O\\n7nTzdrqs5N1oVDhVWEF8eiG/peTxW0ouh84Zo+DvYk9UiA9RIb6MDfa26IPBM4sr2Xw0lw3JOWxI\\nyqnz/LxzBg1OAAAgAElEQVTe7VwZF+LDkCAP+gS40tnT8bz9qa+uF1XoOJhRzN7Txfx2LJctR3NN\\nz3dRqWrq2thgHyK7+TCss4fFuqAZjQr7zxSzMTmHjck5bDv+d0Jor1EzsosnY4N9GNXVizA/l8tO\\nugsrdMSn1bQ6/pGaz+/HcqnQ1XyuVqNmZBcvxoX4EBXiQ5i/i8USEr3BSNypQlPCvie9yPSav6MN\\nU/p0ILq7LxFXeZ83O9mlPnffmWJTi8im5FyqDTX709nTket6+jO1lz9DO3latDtqld7AxuRcVuw7\\nzc8JWaYWtS5ejtzQO4DoUB+GBHmadf4pKK8mJimH1YlZrE7MNCVowzp5MLd/R2b0DbjiMTz/rOs6\\ng5HViZl8tP0km862mHk72TFvYCDzhwTSxevKkrmT+eV8tvMUy3acIrOkClsbFXPCO/Do6K6E+rlc\\n+gMayGhU+O/e0zyz/ggn8itw02pYGHEVD4zsYrGbBnvTi7j3x4PEnizA38WeT2f04dpLdF0z93yq\\nKApLNh/lmXVJeDvZsfaOQQwIdDc39At6/8/j3PdTAsM7e/Lb/w25omuNi6ltOMgsqSK7CRoOmkMu\\nYfGEaufOnXz22Wd8/PHHHDt2jKeeeooVK1aYFYA1NXZc+88U0fetP5jWy58fbh3QKNs4XVRB7ze3\\nUl5tYNeDIxqtj7PRqPD4mkO8tTWVDm5afr59AP06NPxAcLEyX3c4i3nf7SejuIohQR4sv+lqi97N\\nO9ee9EKeXZ/E2sPZAER39+Wl6BCz9umfCit0fLj9BO9sO05WSRV2NuqzJ50udG+Ek87hrBI+jj3J\\nl7vTKazQoVLB+BBfburXnilh/rhoNWbX9ZTcMr7dc5rlu9M4dnacV4Crltnh7ZkT3sHi9a2gvJpV\\nCZl8t/8Mm5JzTa1fgwLdmdjDj7HBPoR3cGvwCUJnMLLrVCGbU3JZdzibHacKqD3y9evgxtSe/kzt\\n1Y4efs4Wu/g0GBWWb9jBUaM7vyRmkZBZYnqtb4Ar47v7MrSTB0OCPBqUkBiMCvvPFJkSnW3H803d\\nR+01akZ39WJCqC/R3X0t2oWytntaTFIOMUnZZJ9tjVOpoH8Hd6JCfBgU5EG/9m60c7W/rHI0GhWO\\n55ez93QRvx/LY0tKbp3B5b7OdowL8WFcsA+RwT6mFsKLuZy6rjcY2Z1exKaziU7syQJTUu1gW5OQ\\nRHbzYXgXT8L8XC47OajUGUjILGFPes3+bDqaU6fVsk+AK+OCfRgbXPPZlroorNQZ+PN4PjFJNUno\\nuUl1ezct44Jrkqsx3RqW/OoNRg5mlBB7soCtx/LYmJxDwdmbKbY2KkZ09iK6uy/Rob5UpCfRv39/\\ni+xPSaWedUey+elgBr8ezjYlOr7Odlwb6kdksDdjuvng14AbHrXOTdh/Tsg0JdZBHg7M6BPAjX0D\\n6NfBzaKtYmVVen48mMHy3elsTslFUTBNMT0nvANjg33MGoMZHx9Pv3792JNexIp9Z/h6TzoZxTU3\\nIEZ28eTuIZ2Y1tvf4mPUqvQGvtqdzhu/HyP5bA+DKWF+PH7NVQy1wM1Jo1Hhp4QMXt50lL2ni7Gz\\nUXPvsE48HdmtUcamGYwKb/1+jGfXJ1FtMHLn4EDemhR2wd+9OefTar2RB39O4MPtJwnycCBm/mBC\\nfBuni7uiKMxYHs/KAxksGhfM4qiQRtlOfFoh/f+9jZl9A/jvnMa/xm8OuYTFE6p33nmHgIAAbrjh\\nBgDGjx/PypUrcXauv3I0h0KoT2PHpSgKfd7ayqGsUo49GXFeq8GVMhgVIj+K5fdjeXwwvRf/N7ST\\nRT+/Pm/8lsITvx7GzkbNB9N6cXsDJ76or8zzy6t5+OdEvtydjq2NiuejQnhsdNdGu6tyrr+O5/P0\\nuiNsPdvKM713O16ICqGHf8MTn8TMEj7feYqPd5yktMqAq1bD3UOCeGBEFwLcLn0heKXKq/V8ty+D\\nD2NPsPNs10KtRs2EUF96OlZyw4g+dPd1vmi5FlboiDv5d2vH0bNdixxs1Uzr1Y5b+nckopt3k0xa\\nkFdWzY8HM/hu3xm2pOSa+v67aTWM6upFDz8XU///2gttBTAqChnFlSTnlJGUXdN97c/j+aaLMrUK\\nhnf2ZFqvdlzX09/iv8tznVvfT+SXs+ZQFr8kZtVpFYOalqRQP2fau9VM8dvO1R5FgUq9kSq9gYIK\\nHUnZZRzOLiE5p6xOt7ervJ0YG+zNtaF+jO7q1SRdC41GhX1niohJymH9kWy2nyioM47J19mOnv6u\\n+Drb4e1U85/GRkVxZc0MlkUVeo7mlpGYVXJe18IRnT2JuMqbcSE1XQsbOvmKOcf10io9f6TWJAyb\\nknPrJL9Q0xUvzM8FfxctLlobXO1t0dqqKazQkVtWTW5ZNcfzyzmcVXc8V4CrlrHB3jWtX8HmJQDm\\nyCiuZENSTfK7MTnH1FJaG1N3X2dC/ZwJdHfAwdYGe40ae42a4ko96UUVnC6q5OTZiRjO/X4CPRxq\\nEqh6WqEa63xapTew5WguPyVksiohs06C2tPfhQEd3Qk+27Wwq5cTDrZqVCoVKmq6WNaOQz2aW8aO\\nkwUczPj7u+3gpmXG2S7GAwPdm6RrYXphBd/sOc2Xu9NMNw8c7WyIuMqb6O6+RAZ709XL6aLH2JzS\\nKuLTi/hueyLbshRTF1A3rYa5/Tty95Ags85hDWUwKqxOzOS1LX93Zx/WyYMHRnbh2lDfBrdYVOuN\\nfLMnnde2pJCUU9OrY1bf9rw8obvFeynU58CZYuZ8u5cDGcUEejjwyfW96x1q0NC6frqoghu+jCf2\\nZAG92rmw7s5Bjd5VsqC8mr7/+oP0wgp++7+hjOxq+eeK3vLfvSzfnc6aeQMv2apnCc0hl7B4QvXs\\ns88yatQoIiMjAbjpppt4+eWX6dy58wUDaKvWpJazeEchs0KceCTczaKf/VlCCR8eKGF0By1vjPBo\\nskGvf56u5LnYAoqrFa7r6shj/d2wtzFv21vSKnhtVxF5lUaCPTQsGuxBiEfTTmWqKApxmVV8sL+E\\nQ/k6VEAvb1tGttcyor2WLm6aestWb1Q4Uazn9/RKNp6s4FhRzQW7t4OaWSFOTL/KCWc76wzkP1Gs\\nZ+PJCjaeqiC16O+B4fY2KkI8NHhobbBTg61ahQKcLtWTVmKgoOrvC3VHjYqB/vaM6qAloqMWJ1vr\\nTUpQWGVkd1YVcZlV7Mys4nTpxWen+qdAFxsG+tszwM+ecD973O2tO8FCmc7IgZxqDubpOJhbzcHc\\nakp1lz4UO2hUdHLV0M3dlnA/O/r72ePnaP0Z0kp1RvZmV3MkX0dSQc1/GWUX/45s1dDZVUNXd1u6\\numno62tHmKcdtmYeSywpt8LAzswqDufrOFak41ihnrxK40XX0dqoCPbQEOJhS4inLb287C547GhK\\nRkUhqUBHbEYVe7OrOV6kJ7P80r8fFdDZTUNvbzt6edvRx9uWIFfr7o/BqJBcqGNnZhU7M6vZl1NN\\nleHyL2HsbeBqH3sG+tszqJ093dw1qK20P4qicKRAR8yJCv46U8Xx4r+P07Zq6OCsoaOLBjd7FdUG\\nqDYqVOkVjhfr6/y2tDYqRnbQMi5Qy5AArdnn4ivdl7051Xx5qJS/ztS0kDloVIxsr2VskJa+Phc+\\n5pbqjOzIqOLP05X8eaaKwqqa2RMndHbgllBnOrk17fVAtUHhs4QSvjhUikGBiZ0deKifG25mnjN2\\nZ1Xx1F8F5FcaiQpy4JlBbjg00QQ/+3KquGtTHt4Oar6N9jV7H+qTWWZgyuosAl00rLjWx2q/I2to\\n1IRq1qxZLFmy5KIJlbWzyvo0RVzVeiNdl2ymoELHqWcjLTb4efvxfEZ+sJ12Lvbsf3RUkw2qrnU8\\nr5zpX+5i7+lierVzYcmEUK4N9b3kyba2zHecLODFjcmsPZyNvUbNonHBPDq6q1WfeaEoCqsTs3jz\\n92NsP5FvahHxd7Gnnas9Xo41d9rLdQaOZJdyLK/cNOjYzqamJejGvgFM7WX5LhZXIjGzhK9/30uO\\nyo3d6YUkZJaY4q5lo1bR2dORYJ+aqX/Hd/dleGfzxg40hZoWqFKO5tTcec4urUYFqFUqVCrwcbYj\\nxMeZYB8ngn2crTaF8eUeYxSlZirz9MJKThdVkFlShY1ahb2NGq2tDc52NgT7ONPBXWv1C/TLVaU3\\nkFdW04KTU1qFQVFw1driYq/B1V5DO1f7RmmFbqzjekF5NXnlOoordZRU6SmvNuDh+HcLnJvW+snT\\n5Sqt0pOcU8qZ4iqq9AYqdUaq9Eac7TVnW0m1tHPVNuj3b43zfLXeyPH8cpJzSknKLiM1v4xqvYKC\\ngqKAnUZNF09Hup19VEM3H6dmdWw+14n8ctYdyeav4/kczS0jOafMNFb1XH4u9oR3cKN/B3fcq3K5\\nK3pQs3pG4aHMEr7eUzPW9tzHgXg52tLd15kgD0dKqvTklf/dslvbWu/vYs/MqwN4eGRXs8dpW8q+\\n00XM+24/e9KL8HW244mIq5g/OAhn+8vrQp9ZXMkrW1J478/jqFUq/jU5jPuGd2ryY8RLG5N5dn0S\\nU3v588Mt/S22/UdWJ/KvrakWmaHwcjWHXMLiLVRLly7Fx8eHmTNnAjBmzBh+/vln6fJ3Af/aWjM9\\n50vRITwdGXzFn1dYoaPvW1tJa8Sm3MtRoTPw4KoElsWdQjk7c94LUSGMC/Gp90drNCp8tC6Wlafg\\nt5SaLnYju3jy8fW9G2Vs0ZXILa1ifVIOvyRmEXeqgLzy6jqzpXmePTmE+DgzqqsX1/X0x60ZPCTw\\nQs6t69V6I2XVeqoNClV6A0alZpxFc36AY0vVXI99rZmUuXVIuVuWoijkllVTUqWv6ZJpo8ZeY4Oz\\nvY3p/Nqcy1xRFHanFfFTQgYHM0pIyi4lNf/vm5A2ahVejrYEeTgS3d2XSWF+9Gvv1qyer6g3GHnz\\n92O8tOkoZdUGvBxtWTCiC8OcixgztP5x8dklVbz+WwofbD9Bhc5IJ08Hvrmpn0XGlpnDYFQY81Es\\nW4/l8eH0XtxtgaEhhRU6Or64EVd7W1KfjmiymxTNob5fLAazbmsMGzaMpUuXMnPmTBITE/H19b1g\\nMiXgzkFBvLAhmXe3HefhUV2vaCCyoijM/34/JwsqWDQu2GrJFICDrQ0f39CH+4d35vkNyaw8kMH4\\nZXH4u9jTw8+FUD9nung5cjyvZtD5/oxiU1ISFVLznKgRXTyb5V1db2d7Zod3YHZ4B9Oy2jvudjYq\\ni85q1tTsNGrsNM3voZNCCCFqqFQqfJzt8Wmh5xqVSsWAQPc6s9hV641kllTiqrVtES26Ghs1C8d0\\nY/6QIJZuO847246zKCYJFXDV1i30be9K3wA3qg1GDmeVciirhKScUnQGhY7uWp6JDObWAR2t2tPD\\nRq3i65uups9bW3no50SGd/a84smkPtp+gtIqA8+NDW62Lb7WYFZC1a9fP8LCwpg5cyYqlYpFixZZ\\nOq5WxUWr4f+GduLVLSks353GXUM6mf1Zn8Wd4vv9GQzv7MkzkVf28F5L6dnOle9v6c/+M0W8ujmF\\nHacK2JKSy5aUvx90qFZBqJ8LXRz1PDe5P/07Wn6q0MZmr7EhwE0OHkIIIURD2WnUjf5cpMbg6WjH\\noqgQHh7VlWVxJ/lfXAopxdV8vz+D7/dnmN7nbG/D1e3dmBvegTsGBzabZKODuwP/ubEv132+i5lf\\n72HnA8PN7iZaqTPwzrbjuNhrmD84yMKRtmxmd7x99NFHLRlHq7dgRGf+tTWVN39P5Y5BQWbNknY4\\nq4QFqxJwd7Dlm5uvbpJZ8BqiT4CbaerMsio9STmlpOaVE+ThSM92LjjY2tQ0l7bAZEoIIYQQbZeL\\nVsPDo7oyyrmQfv36kVZYwf4zxdhr1PTwc6G9W/Md4zqlpz/3DevEe3+d4JHVh/jw+gs/O/Zivo5P\\nJ7OkisdGd23WwxysofmMZGzl2rlqmdu/A5/GneKHAxnM6NuwB/2WV+uZ+dUeKnRGvrrp6mZ/l8fJ\\nXkO/Du4Wea6TEEIIIURzoVKpCPRwbPbXYud6Y1IPtqbm8VHsScZ08+b6Pg27Dq3WG3nj92PY2qh4\\nYGT9k9C1Zc2riaOVe+yartjaqLjnhwOcyC+/9ApnVeuNTP9yNwcyirlrSBDTezfsRyCEEEIIIdou\\nra0NK+aE42hnw5xv97L1WO6lVzrHw6sTSc4p47YBHRv9OVotkSRUTSjYx5n3pvYir1zH1M93UV6t\\nv+Q6eoORm7/Zw/ojOUwI9eXd63o2QaRCCCGEEKI1CfVz4Ydb+mNQFCZ+tpNdZx/IfCmf7zzF+3+d\\noFc7F96aHNbIUbZMklA1sflDgpg/OJB9Z4q547sDXGzWeqNR4c7vD7DyQAajunqx8pb+zfa5QEII\\nIYQQonkb392Xb2/uR3m1gfHLdpCQUXzR9+88VcDdKw/i4WDLT7cOwNleRgvVR67OreDdqT0ZEuTB\\nf/ee5l9bU+t9T15ZNfO/P8AXu9IY0NGd1bcPuKLp1oUQQgghhLi+TwCfzuhDfrmOsR/v4LeU3Hpv\\n8GeVVDHti93ojUb+O7sfXb2drBBtyyBpphXYa2z44db+hL/9B4+vOcTmo7lM792OyWF+GIwKb21N\\n5cPtJyirNhDm78K6OwfhqpXZVIQQQgghxJW7bWAgJVV6HliVSMSHsQzt5MHTkd2ICvEl9kQ+Px7M\\n5Lv9ZzhdVMmr14YS1d3X2iE3a5JQWUk7Vy2/3D6QO7/fz7oj2aw7ko1aBRq1mmqDkQBXLS+OD2H+\\n4CCcpHlVCCGEEEJY0IIRXRgU6MHLm47yy6Esrv10J452NpRXGwBw02p4/JquPH5NVytH2vzJlboV\\nhXd0Z8/Do0jNK+PHA5n8cDCD0io99w3vxK0DOjabh8IJIYQQQojWZ1CQB6vnDeTAmWKWbD7KzlOF\\nRFzlzfTe/ozp5iNj9y+TJFTNQBcvJx69piuPyh0AIYQQQgjRxHoHuPK/OeHWDqPFkrRTCCGEEEII\\nIcwkCZUQQgghhBBCmEmlXOxBSBYSHx/f2JsQQgghhBBCiEYTHl5/t8gmSaiEEEIIIYQQojWSLn9C\\nCCGEEEIIYSZJqIQQQgghhBDCTJJQCSGEEEIIIYSZJKESQgghhBBCCDNJQiWEEEIIIYQQZpKESggh\\nhBBCCCHMpLF2ANawZMkS9u/fj0ql4qmnnqJ3797WDqnVev3114mPj0ev13PXXXexZcsWEhMTcXd3\\nB2DevHmMHj3aukG2MnFxcTzwwAN069YNgODgYO644w4ef/xxDAYDPj4+vPHGG9jZ2Vk50tbl+++/\\nZ/Xq1aa/ExIS6NmzJ+Xl5Tg6OgLwxBNP0LNnT2uF2KokJydzzz33cOuttzJ79mwyMjLqreOrV6/m\\nyy+/RK1WM2PGDG644QZrh95i1VfmTz75JHq9Ho1GwxtvvIGPjw9hYWH069fPtN4XX3yBjY2NFSNv\\n2f5Z7gsXLqz3PCp13XL+WeYLFiygoKAAgMLCQvr27ctdd93FpEmTTMd0Dw8P3n33XWuG3eL985qx\\nV69eLee4rrQxcXFxyvz58xVFUZSUlBRlxowZVo6o9YqNjVXuuOMORVEUJT8/Xxk1apTyxBNPKFu2\\nbLFyZK3bjh07lPvvv7/OsoULFypr165VFEVR3nrrLeWbb76xRmhtRlxcnLJ48WJl9uzZSlJSkrXD\\naXXKysqU2bNnK88884zy1VdfKYpSfx0vKytTxo0bpxQXFysVFRXKtddeqxQUFFgz9BarvjJ//PHH\\nlV9//VVRFEX5+uuvlddee01RFEUZOHCg1eJsbeor9/rOo1LXLae+Mj/XwoULlf379ytpaWnK1KlT\\nrRBh61TfNWNLOq63uS5/sbGxREZGAtC1a1eKioooLS21clSt04ABA3jnnXcAcHV1paKiAoPBYOWo\\n2qa4uDjGjBkDwDXXXENsbKyVI2rd3n//fe655x5rh9Fq2dnZsWzZMnx9fU3L6qvj+/fvp1evXri4\\nuKDVaunXrx979uyxVtgtWn1lvmjRIqKiooCau/OFhYXWCq/Vqq/c6yN13XIuVuapqamUlJRIz6ZG\\nUN81Y0s6rre5hCo3NxcPDw/T356enuTk5FgxotbLxsbG1NVp5cqVjBw5EhsbG77++mvmzp3LQw89\\nRH5+vpWjbJ1SUlK4++67mTVrFn/99RcVFRWmLn5eXl5S5xvRgQMHaNeuHT4+PgC8++673HzzzTz3\\n3HNUVlZaObrWQaPRoNVq6yyrr47n5ubi6elpeo8c781XX5k7OjpiY2ODwWDg22+/ZdKkSQBUV1fz\\nyCOPMHPmTD7//HNrhNtq1FfuwHnnUanrlnOhMgdYvnw5s2fPNv2dm5vLggULmDlzZp0u36Lh6rtm\\nbEnH9TY5hupciqJYO4RWb9OmTaxcuZL//Oc/JCQk4O7uTmhoKJ988gnvvfcezz33nLVDbFU6derE\\nfffdR3R0NGlpacydO7dOy6DU+ca1cuVKpk6dCsDcuXMJCQkhMDCQRYsW8c033zBv3jwrR9j6XaiO\\nS923PIPBwOOPP87gwYMZMmQIAI8//jiTJ09GpVIxe/Zs+vfvT69evawcaesxZcqU886jV199dZ33\\nSF23vOrqauLj41m8eDEA7u7uPPDAA0yePJmSkhJuuOEGBg8efMnWRHFx514zjhs3zrS8uR/X21wL\\nla+vL7m5uaa/s7OzTXeSheVt27aNjz76iGXLluHi4sKQIUMIDQ0FICIiguTkZCtH2Pr4+fkxYcIE\\nVCoVgYGBeHt7U1RUZGodycrKkgN+I4qLizNd3IwdO5bAwEBA6ntjc3R0PK+O13e8l7pvWU8++SRB\\nQUHcd999pmWzZs3CyckJR0dHBg8eLPXewuo7j0pdb3y7du2q09XP2dmZ6dOnY2tri6enJz179iQ1\\nNdWKEbZ8/7xmbEnH9TaXUA0bNoyYmBgAEhMT8fX1xdnZ2cpRtU4lJSW8/vrrfPzxx6bZiO6//37S\\n0tKAmgvP2pnohOWsXr2azz77DICcnBzy8vKYNm2aqd5v2LCBESNGWDPEVisrKwsnJyfs7OxQFIVb\\nb72V4uJiQOp7Yxs6dOh5dbxPnz4cPHiQ4uJiysrK2LNnD/3797dypK3H6tWrsbW1ZcGCBaZlqamp\\nPPLIIyiKgl6vZ8+ePVLvLay+86jU9cZ38OBBunfvbvp7x44dvPLKKwCUl5dz5MgROnfubK3wWrz6\\nrhlb0nG9zXX569evH2FhYcycOROVSsWiRYusHVKrtXbtWgoKCnjwwQdNy6ZNm8aDDz6Ig4MDjo6O\\npoORsJyIiAgeffRRNm/ejE6nY/HixYSGhvLEE0+wYsUKAgICuO6666wdZquUk5Nj6tutUqmYMWMG\\nt956Kw4ODvj5+XH//fdbOcLWISEhgddee43Tp0+j0WiIiYnhzTffZOHChXXquK2tLY888gjz5s1D\\npVJx77334uLiYu3wW6T6yjwvLw97e3vmzJkD1Ez0tHjxYvz9/bn++utRq9VERETIAP4rUF+5z549\\n+7zzqFarlbpuIfWV+dKlS8nJyTH1OADo378/q1at4sYbb8RgMDB//nz8/PysGHnLVt8146uvvsoz\\nzzzTIo7rKqW5dD4UQgghhBBCiBamzXX5E0IIIYQQQghLkYRKCCGEEEIIIcwkCZUQQgghhBBCmEkS\\nKiGEEEIIIYQwkyRUQgghhBBCCGEmSaiEEEIIIYQQwkySUAkhhBBCCCGEmSShEkIIIYQQQggzSUIl\\nhBBCCCGEEGaShEoIIYQQQgghzCQJlRBCCCGEEEKYSRIqIYQQQgghhDCTJFRCCCGEEEIIYSZJqIQQ\\nQgghhBDCTJJQCSEaXUhICGPHjmX8+PGMHz+esWPH8tRTT1FeXm72Z8bFxTF27NhLvm/8+PHk5uaa\\nvZ3L1VTbaQxjx44lLi7ust+fnp5Ojx49LLb9iIgIdu/efdH35ObmsnnzZottsyFSU1PZtWtXg9Yx\\nGAzMnTuXiIgIkpKS6rz2448/0rdvX8aPH09kZCSRkZEsWbKE4uJi03vmzJnD8OHDTb+Z2v9yc3NN\\n/x49ejQ9e/Y0/f3CCy9YZH8vJC0tjbFjxzJlyhQAHn30UUaNGsW2bdu45ZZbSExMvOC6Bw4cYN68\\neVe0/e+++86s9QoLCxk6dCjPPPNMva8/+OCD/PDDDyxdupSnn366zmtGo5GbbrqJ7du3m7VtIUTb\\noLF2AEKItuGrr77C398fgOrqah566CE+/vhjHnrooUbd7vr16xv185t6O21VXFwc27dvZ8yYMU2+\\n7U2bNqHX6xkwYMBlr5Odnc2uXbs4cOAAtra2573et29fvvjiCwBKSkp48803mTNnDt999x329vYA\\nPPbYY6bk5Vy1dS0uLo5nnnmmyepefHw8Pj4+fPvttwD8+uuvxMTEEBgYyIgRIy66bu/evfnss8/M\\n3rbBYOD1119nxowZDV53zZo1zJkzh5UrV1JVVWUqX4C1a9dSUlLC9OnTWbp06XnrqtVqlixZwm23\\n3ca6devQarVm74MQovWSFiohRJOzs7NjxIgRHD58GKhJsF566SWioqKIiIjgo48+Mr137969TJs2\\njfHjxzNhwoQG3ykOCQkhMzOTuLg4brzxRl5++WXGjBnDtGnT2L9/P3PmzGHYsGG8++67pnXef/99\\noqKiiIyM5K677jK1HBQWFjJ37lxGjx7NggULePrpp00XYf/czltvvUV0dDQRERHs3LkTgIqKCh58\\n8EHTfr722mumbc6ZM4e3336b6OhoPvzwQyZOnFhnP6ZNm8amTZvO278Lxbp06VJeeOEF7r33XsaM\\nGcP1119PdnY2AAkJCVx77bVERUWxZMmSC5Zdamoqs2bNIjo6mrFjx7JmzZo6r3/++eemfayNLSsr\\ni1tuuYUJEyYQGRnJ22+/DdTc6X/77bdNrSkLFy48r4Xyn62OtX8nJibywgsvEBMTY0rAN23axKRJ\\nk3mAwbQAACAASURBVBgzZgy33347+fn558UfFxfHpEmTePXVV01lvm/fvgZ9F++99x4ff/wxy5cv\\n59VXXz1vG0eOHGHmzJmMHz+eKVOmsG3bNgwGA3PmzMFoNDJp0iSOHDlywTIGcHFx4fnnn8fJyYlV\\nq1Zd9L0NlZaWxs0338zYsWOZPn26qRXpzJkzzJs3j6ioKCZOnFhnu/WV7d69e3nzzTc5dOgQkydP\\nNu3fvHnz2Lp1a51WxlWrVhEVFUVUVBSPPfYY1dXVdb7bi/3eIyIi+N///sf111/P8OHDTWV+2223\\nUVJSwvjx40lLS2PdunVMnDiR6OhoJk2adNEW1lWrVjFx4kSGDRt2Xivn+++/z5133nneOpmZmURE\\nRLBnzx46depEnz59+P777838FoQQrZ4ihBCNLDg4WMnIyDD9XVhYqNx8883KBx98oCiKorz33nvK\\nLbfcolRVVSllZWXKddddp2zZskVRFEWZOHGismbNGkVRFOWnn35SIiMjFUVRlB07dpj+fTnb3rFj\\nhxIWFqbs2LFDMRqNyvTp05Vp0/6/vTsPiKrc/wf+nmERWUU2BcUtVwQl3HBHKHEpUX/eAHP9lqXc\\n8l5tEcpruZvLTQ21S3brZoZJ5fI10zJN87qBXsAl3EJhBGKTfdjm+f3B17mMzCAMg2dG3q+/mOc5\\nzznvOfOA8/FsU0RpaalISUkRffr0EUqlUiQnJwt/f39RVFQkqqurxezZs0V0dLQQQoi1a9eKhQsX\\nCiGESE5OFv369RNbtmyps52+ffuKH3/8UQghRExMjJg9e7YQQoidO3eKl156SahUKnH//n0xaNAg\\nceHCBSGEEC+++KKYO3euqK6uFhUVFWLQoEHi2rVrQgghFAqF8PPzE+Xl5Rrvrb6sW7ZsEf7+/iI9\\nPV2oVCoxb9489f6eOnWqiI2NFUII8f3334tevXqJs2fP1tl3r7zyivj444+FEEKcP39e+Pj4iIqK\\nCpGWliZ69OghYmJihBBC/Prrr2LIkCGioqJCrF27VmzdulUIIURpaan461//KrKyssT//u//ipCQ\\nEFFSUiKqqqrE/Pnz1VkDAgLEhQsX6nymtV9v2bJFREVFCSGEuHv3rvD19RUpKSlCCCF27NghXnvt\\ntTr5z549K3r37i0OHTokhBDi66+/FpMmTWrUZyGEEG+//bY6a23V1dVi3Lhx4uDBg0IIIZKSksTA\\ngQNFUVGRSEtLE717964zRgghvvnmGzFr1qw67f/4xz/EX//6V3WGffv2aR2vbf/oMmvWLPHll18K\\nIYT48ccfxfjx44UQQsydO1fs2LFDCCFEenq68PPzE2lpafXu24dz1/69fvAZpqWliSFDhojMzEyh\\nUqlERESEiImJ0cha3+97QECAWLRokaiqqhKZmZnCy8tLZGRk1NmfgwcPFunp6UIIIS5cuCBWr16t\\n9f1fv35dhIaGqpd7+eWX1X03btwQAwcOVH/OD+ZYWVmZmDx5sjhw4IB62UOHDomwsLB69zURtVw8\\nQkVEj8WMGTMQHByMwMBABAYGYsiQIer/GT5+/DjCw8NhaWkJa2trTJo0CUePHgVQ87/L48aNAwD4\\n+fkhLS1N7wz29vYYPHgwZDIZunfvjkGDBqF169bo3r07qqurkZeXh759++LEiROwtbWFXC6Hr6+v\\nepvx8fHqI0d9+/aFj4+P1u3Y2NggKCgIAODl5YV79+4BAObOnYtt27ZBJpPBwcEB3bt3R3p6unrc\\nqFGjIJfLYWFhgbFjx+LQoUMAao4YBAYGwtLSUmM79WUFgAEDBsDDwwMymQy9e/dGRkYGysvLkZyc\\njPHjxwOoufardevWWt/Htm3b1Ne9+Pn5oby8HNnZ2er+yZMnAwCGDRuGqqoq3L17F05OTvj1118R\\nHx8PS0tLbNq0Ca6urjhx4gRCQkJgbW0NMzMzTJkyBadPn27Ix1bHyZMnMWjQIPTo0QMAEBoaip9/\\n/hnV1dV1lrW2tlbPn2effRbXrl1DWVlZgz+L+qSnpyMnJwcTJkwAAHh7e8Pd3R3Jycl6vS9bW1sU\\nFRWpX69fv17j+qnnn3++UesrLy/HuXPn1HM2MDAQX3/9NSorK/Hvf/8b4eHhAAAPDw8MHjwYZ8+e\\nbdS+1eb06dPw9fWFm5sbZDIZNm7ciNmzZ2ssU9/vOwA899xzMDMzg5ubG5ycnJCRkVFnO05OToiN\\njYVCocCAAQMQGRmpNc93332n3m9+fn5ITU1VX+uYlJQELy+vOp9zVFQUxowZg+eee07d5uPjg6Sk\\nJAghGrQfiKhl4TVURPRYPLiGKi8vT336nrl5zZ+goqIirFmzBps2bQJQc0rQg2Ll4MGD+Ne//oWS\\nkhKoVKomfaGxsbFR/yyXy2FtbQ0AkMlkkMvlqK6uRllZGdasWaM+haigoACjR48GABQWFsLBwUG9\\nDjc3N63bsbOz09iOSqUCAKSmpmLt2rW4ffs25HI5MjMzMWXKFPWytdc9YcIEREZGYvHixfjpp5+0\\nXtBfX9aHc5iZmaG6uhr3798HUPPl/cF7t7e31/o+Tp06he3btyM/Px8ymQxCCPV7AQBHR0eNbRUW\\nFmL27NlQqVR4//338ccff2D69Ol47bXXkJeXp/H+HBwckJubq3W7j1JUVIT4+HgEBwer22xtbXH/\\n/n04OTlpLGtvbw+ZTKb+Gaj5HLOyshr8WeiSl5cHOzs79fofbCMvLw8dO3Zs9PtSKBQa+XVdQ9VQ\\n9+/fh0qlUs8DmUwGGxsbZGdnQwihMT8e5FapVDr3bUPk5+drzKfa1ys9UN/v+4PtPfBg3j5s+/bt\\n2L59O6ZMmYL27dsjKioKgwYN0limuroaBw8eRGlpKTZu3Aigpsg8ePAg5syZg9zcXLRt21ZjzNGj\\nR1FRUQF/f3+NdicnJ1RWVqKgoABt2rRp0L4gopaDBRURPVZt27bFjBkzsH79emzfvh0A4Orqirlz\\n5yIgIEBj2aysLLz77rvYu3cvevfujdTUVIwdO7ZZ833++edITU3Ft99+CxsbG/z9739HVlYWgJqC\\nrPZ1P9nZ2fD09GzwupcvXw4vLy9ER0fDzMwMoaGhOpcdOHAgqqqqcPz4cdy4cQNDhw5tVFZdHhQK\\nxcXFsLOzg0qlQkFBQZ3lKisr8Ze//AUffvghRo0aVedLL1BTwD0oqgoKCuDg4ABzc3PMmzcP8+bN\\nw++//46XX34Zfn5+cHZ21vhSfv/+fTg7O2us7+Evz7Xvelebq6srhg4dqnHdmy61t/ngfbZp0waR\\nkZEN/ix0cXJyQkFBAYQQ6qJKW1HXENXV1fjpp58QERHR6LG6ODo6QiaTIT8/H23btoUQAnfv3oW7\\nuzvkcrn6M6udWyaTNXjf6trmpUuX1K+Li4uhVCo1ltH1+94Ynp6eWLNmDVQqFfbt24fFixfj1KlT\\nGsv8+uuv6NGjh8bNMK5evYrIyEjMmTNH63/O9OnTB0uWLMGcOXMwdOhQeHh46J2RiFoOnvJHRI/d\\nnDlzcOnSJfXNGgIDA7F3715UV1dDCIFt27bh5MmTyMvLg7W1Nbp27Yqqqirs2bMHAFBSUtJs2XJz\\nc9G1a1fY2NhAoVDgl19+URdRPj4+6juqXbt2DUlJSY1ed+/evWFmZobTp0/jzp07Om8dL5fLMX78\\neKxYsQJjxozReqe4+rLqYmVlhV69euHHH38EUHOntvLy8jrLlZWVobS0FH379gVQU7xZWFhorP/g\\nwYMAak7zat26NTw9PfG3v/1NfSqfp6cnnJ2dIZPJMHr0aBw4cABlZWWoqqpCXFwcRo0apbFNFxcX\\nZGdnIzc3V3104QFzc3P16XDDhw9HfHy8+vTGpKQkrFy5Uuv7VSqV6htmHDlyBH379kWrVq0a9VnU\\n3nZtHTp0QLt27fD9998DAC5evIicnBydp4LqUlpaiqVLl8LBwUF9eqIhWFpaYtiwYfjuu+8A1Bxx\\nnDdvHiwsLDB8+HD179Pdu3cRHx+PoUOHNmrfajNq1ChcvHgR6enpEEJg2bJliIuL01hG1+97fSws\\nLKBSqVBcXIy8vDzMmTMHxcXFkMvl6Nevn8ZRwge+++479am3D/Tp0wdFRUVISUmBk5MT8vPzNfo7\\ndOiA3r17Y9asWYiKilIXXXl5ebCwsNB5NJeIWjYWVET02Nna2mLevHlYt24dhBAIDw+Hu7s7JkyY\\ngODgYNy6dQt+fn7o1asXRo4cibFjx+KFF17AmDFj0L9/f8yYMUNjfYZ4xs0DoaGhuHDhAsaOHYt1\\n69ZhyZIlOHPmDD777DPMnz8fv//+O5555hl8+umnCAwM1PpFTpf58+dj3bp1mDhxIs6fP48///nP\\n2Lp1KxISErQuP2HCBCgUCvX1To3JWp/33nsPMTExGDt2LJKSktCtW7c6y9jb2+Oll15CSEgIQkJC\\n4OnpiaCgILz66qsoKyuDtbU1VCoVJk6ciGXLlmHVqlUwNzdHaGio+m5+48ePh6+vL/z9/REcHIyR\\nI0diypQpmDhxItq1a4eZM2dqbLNTp06YOnUqQkJCEB4ejiFDhqj7hg0bhrNnz2Lq1KlwdXXFihUr\\nEBERgXHjxmH58uU695GHhwcSEhIwduxYfPzxx1i2bFmjP4uAgADExsbi9ddf12iXyWTYtGkTdu3a\\nhXHjxmHlypXYvHmz+lTS+vznP/9BcHAwnn32WQQHB6NVq1bYuXOn+jRYQ1m1ahWOHz+OwMBAfPjh\\nh9iwYQMA4P3338e5c+cQHByMiIgIrFy5Eu3bt2/UvtWmXbt2WL58OWbNmqU+mjxnzhyNZXT9vtfH\\nxcUFfn5+CAgIQGpqKkaMGIGpU6di/PjxWLRoEVatWqWxfGFhofp9PywwMBD79u2Dj48Prly5ovVI\\n1bx581BSUoJdu3YBABITE+Ht7f3I6+qIqGWSCV5hSURPgEWLFqmvyWhOtU/vev311+Hn54dZs2Y1\\ny7ZycnIwefJknDhxAmZmZs2yjSfZg+c0PTgaR/SwB0Xjo54xtnjxYnh7e9e5wQYREcAjVET0BMjP\\nz0dISEizb2fXrl2YP38+VCoVcnNzcf78efj6+jbb9rZs2YKwsDAWU0TNZP78+YiJial3mbS0NMTH\\nx+v1UGEiahlYUBGRyXN0dMTIkSObfTuTJ0+GpaUlnn32WYSFhWHu3LmNvl6mIXJychAYGIicnBzM\\nnTvX4OsnohrPP/88rKys1NeZPUylUiEyMhIrV65s0KmcRNQy8ZQ/IiIiIiIiPfEIFRERERERkZ4e\\ny3OodN3BioiIiIiIyBTouiPpY3uw76Nuifo4HY7dBhzfCA9lKhRWnYGAxRgXukDqWPQESEhIMKq5\\nTqaPc4oMjXOKDI1zigzNGOdUfQeIHltBZSwOx25Dp8P/fRJ9J+Vt4HAEDgMsqoiIiIiIqFFa3jVU\\nxzfqaG/+59cQEREREdGTpcUVVB7KVK3t7jraiYiIiIiIdGlxBZXCqrPW9ns62omIiIiIiHRpcQUV\\nAhbraF/0eHMQEREREZHJa3EF1bjQBbgzLhqFMhsAQLplR9wZF80bUhARERERUaO1uIIKqCmqFE//\\nXwE1dimLKSIiIiIi0kuLLKgAoG3X/gCAgt8TJU5CRERERESmqsUWVE95D6r5IeuqtEGIiIiIiKhZ\\nDR48uNnW3eIe7PuAR8euOC+zheP961JHISIiIiJ6IsVeUmD1sRu4mlWMPm62iArsjlBfD6ljGVSL\\nLajkcjnute6CHqWXUVRYADt7B6kjERERERE9MWIvKRC266L6dXJGkfq1vkXVt99+i5MnT+KPP/7A\\niBEj8Msvv0AulyMoKAhz585FZmYm3nzzTQBAVVUV1q1bB09Pz6a/mXq02IIKAArtu0FemozrlxPg\\nN3SM1HGIiIiIiEzGmwevYm/iPZ399wqVWttnfnUJSw5d09o3rZ87Qt3r325GRgY2bNiAqKgofPXV\\nVwCAsLAwBAcHIycnBxERERgyZAji4uKwe/duLFmypGFvSE8tuqASzt2ATOBeSjwLKiIiIiIiA6qs\\nFo1qbyhvb28kJyfjzp07mDlzJgCgpKQECoUCHTp0wMqVK7F161YUFhbCy8urSdtqiBZdUNm27wFc\\nBkrTkqWOQkRERERkUtY/1wfrn+ujs99nwwkkZxTVbW9vj8Q3Rukcl5CQUO92LSwsYGFhgdGjR2P5\\n8uUafZGRkRg+fDjCwsLwww8/4MSJE/W/CQNosXf5A4B2nj0AAJbZ2g85EhERERGRfqICu2ttjwx8\\nqsnr9vLywrlz51BWVgYhBFauXAmlUon8/Hx4enpCCIFjx46hsrKyydt6lBZ9hMraxg6ZFu3gVnwT\\nKpUKcnmLri+JiIiIiAzmwY0n1hy7iatZRejjZofIwKcMcpc/d3d3zJw5E9OnT4eZmRmCgoJgZWWF\\nF154AStWrICHhwdmzJiBpUuX4tdff23y9urTogsqAMiz74E+uSeRee8u3Dt0ljoOEREREdETI9TX\\nw6C3SZ8yZYr65+nTp2P69Oka/QEBAQgICFC/PnXqFADg3LlzBsvwsBZ/SEblVnPe583LFyROQkRE\\nREREpqbFF1T2nfsBALJvXnzEkkRERERERJpafEHVqc9AAECV4orESYiIiIiIyNS0+ILqqZ7eKIcF\\nbPNTpI5CREREREQmpsUXVBaWlsho3RnuZb+jsqJC6jhERERERGRCWnxBBQBFjr3QCpW4mcIH/BIR\\nERERUcOxoAJg7uEFALj7W7zESYiIiIiISJeTJ09i9+7dUsfQ0KCC6vr16wgKCsKuXbsAABkZGZgx\\nYwbCw8OxcOFCVJj4qXLO3XwBAAW3/yNxEiIiIiKiJ0fs7Uvw2bcR5p+9BZ99GxF7+1KT1jdy5EiE\\nh4cbKJ1hPPLBvqWlpVixYgX8/f3VbVu2bEF4eDjGjRuHTZs2IS4uzujeWGN09x6E+7GAPOuq1FGI\\niIiIiJ4IsbcvIeyXL9Wvk/Mz1K9Du/rqtc5vv/0WJ06cQE5ODjw9PXHp0iWEhYUhJSUFiYmJ6of9\\nHjhwALt27YJcLkf37t2xYsUKFBUV4fXXX4dSqcSoUaPw9ddf4+eff27y+3xkQWVpaYmYmBjExMSo\\n286dO4f3338fQM3TiD/99FOTLqjauXsiVe4Ax8IbUkchIiIiIjIJb144iL2pSTr775UWam2feSoW\\nSxK+19o3rbMPQuXuj9z2tWvXEB0djYKCAkycOBHHjh1DeXk5XnvtNUyfPh1lZWX45JNPYG9vj+nT\\npyMlJQXnz59Ht27d8O677+LLL7985DYa6pEFlbm5OczNNRcrKyuDpaUlAMDJyQnZ2dmP3FBCQoKe\\nEZvXg1z3WndGn5JEnDp5AtY2dhKnIlNmrHOdTBfnFBka5xQZGudUy5SVmYWKct2X/lSqqnW26xqX\\nlZkFuLvrnFOpqanIz8+Hi4sLbt++DaVSCTs7O6Snp0OpVCI3NxcJCQnIzc3FjBkzAAAKhQLnz5/H\\n2bNn0adPHyQkJMDFxQXl5eUGmbuPLKgeRQjRoOX8/PyauimDS0hIUOe6ecgbuJUIK3mFUWYl01B7\\nThEZAucUGRrnFBka51TL9a9HfO4++zYiOT+jbrtjeySGLNY5rr45defOHaSmpqKkpAR+fn4oKSlB\\n69at1T9bWlrC29sbr732Gvbv3w8XFxe88sor6NGjB27evIlu3brBz88PWVlZaNWqVYPnbn2Fl153\\n+bO2toZSqQQAZGVlwdXVVZ/VGBXrjt4AgIzrFyVOQkRERERk+qJ8xmhtj9TRbiglJSUwMzODi4sL\\nMjIycPnyZVRWVsLT0xOXL18GUHO3QEPRq6AaOnQojhw5AgA4evQoRowYYbBAUmnf82kAQNld3eeB\\nEhERERFRw4R29cVXo6bDx7E9zGVy+Di2x1ejput9Q4qGcnR0xLBhwzB16lR89NFHeOmll7BmzRpM\\nnjwZ8fHxmDFjBnJyciCXG+YJUo885e/y5ctYt24dFAoFzM3NceTIEWzYsAFLlizBnj174O7ujpCQ\\nEIOEkVLPvgNxBzK0yvlN6ihERERERE+E0K6+Bi2gpkyZgilTpqhf29jYqO/UV/vntWvXaoybM2cO\\nFAoFIiIiMGLECFy6dAkXLlwwSKZHFlR9+/bFF198Uaf9n//8p0ECGAs7ewdkWrijXckNqFQqg1Ws\\nREREREQkPTs7O3z22WeIjo4GALzzzjsGWW+Tb0rxJMlv0wPu2cehSLuNjp2ekjoOEREREREZiL29\\nPXbu3Gnw9fIwTG1ufQAAty7HSxyEiIiIiIhMAQuqWhy69AMA5N7inf6IiIiIiOjRWFDV0rnPQABA\\n9b0rEichIiIiIiJTwIKqlq49+kKJVrDNT5E6ChERERERmQAWVLWYm5vjnnVneCjvoKK8XOo4RERE\\nRET0CGPGjEFJSYlk22dB9ZCStr1ggSrcuJYodRQiIiIiIpNWcDYWt97xwdU55rj1jg8KzsZKHcng\\neNv0h1h4eAPp+3H3t3h49R8kdRwiIiIiIpNUcDYWiu1h6tfl6cnq1w5DQvVaZ3FxMRYvXozS0lIo\\nlUosXboUAPDxxx8jPj4eZmZmiI6ORnFxMd58803I5XJUV1dj/fr18PDwaPqb0oIF1UNcuvUHzgFF\\nqf+ROgoRERERkdHKin0ThRf26uyvzL+ntV3xj5n4Y+8SrX32A6cB3XUXW9nZ2Zg2bRqCgoJw5swZ\\nxMTEAAB69uyJRYsWYd26ddi/fz+qqqowdOhQRERE4MqVK8jOzmZB9bjkpafACYDXtRgcfuUYELAY\\n40IXqPsPx24Djm+EhzIVCqvOGv319TVlbHOt11THEhEREZEJqK5sXHsDODs7Y9u2bdi5cycqKipg\\nbW0NABg8eDAAwNvbG/Hx8QgNDcWf//xnFBUVYezYsfD19dV7m48iE0KIZlv7/0lISICfn19zb6bR\\nHs51OHYbOh2OqLNcUudQ2PcYisLr/4ZPat3zPpM611TRuvqaMra51mtqY++MizaJospY5zqZLs4p\\nMjTOKTI0zinS5dY7PihPT67T3qqjD7qt1H2/gvrm1EcffQSlUok33ngDycnJ+OCDD6BQKLB37144\\nOTnh+++/R0JCApYuXYqysjKcPn0an3/+OaZOnYqQkBC930t9mXiEqrbjG7U2+6TGAlq+5Gv019fX\\nlLHNtV4TG4vjmwATKKiIiIiIqIbzc1Ea11Cp2ydG6r3O/Px89OzZEwDw008/obKy5mhXfHw8xo4d\\ni8TERHTt2hWHDh1Cx44dERQUhDZt2uCHH35oUkFVH97lrxYPZarW9mrIcXPUGqgg09lfX19TxjbX\\nek1trLuOz4aIiIiIjJPDkFB4zP8KrTr6AGbmaNXRBx7zv9L7hhQAMGnSJPzzn//E3Llz4ePjg+zs\\nbAghcOPGDcyePRspKSmYNGkSOnfujOXLl2PmzJmIjo5GWFjdws5QeISqFoVVZ3RS3q7Tnm7VBc/P\\nXYLD52J09gOiWcY213pNbew9q87oV6eViIiIiIyZw5DQJhVQD/Px8cHhw4fVrwMDA7Uu5+Xlhbi4\\nOINttz48QlVbwGId7Yse3d9cY40xk5RjiYiIiIiMCI9Q1TIudAEOA8DxTXBXpuKeVWcgYJH6ZgiP\\n6m+uscaYqbnHmh1bhw4Vd1Eks0Fe8AcmcUMKIiIiImp5eJc/I8xFQHVVNS6+ZIdcSzcE/+N3qeM0\\nGOcUGRrnFBka5xQZGucUGZoxzqn6MvGUPzJKZuZmyLDuhvblaShXKqWOQ0RERESkFQsqMlplTj1h\\ngWqkXLkodRQiIiIiIq1YUJHRsuzgDQBI/y1e4iRERERERNrpdVOKc+fOYeHChejevTsAoEePHli6\\ndKlBgxG5dX8a+DdQdEf3k7SJiIiIiKSk913+Bg0ahC1bthgyC5GGnt6DkAXA/I9rUkchIiIiItKK\\np/yR0XJycUO2mTOcC29IHYWIiIiISCu9C6qbN2/i1VdfRVhYGE6fPm3ITERqOfbd4VKdg9zsLKmj\\nEBERERHVoddzqLKyspCQkIBx48YhLS0NM2fOxNGjR2Fpaal1+YSEhCYHpZYp8dsNGJIei+Qx0Xiq\\n72Cp4xARERFRC6XrOVR6XUPl5uaG8ePHAwA8PT3h7OyMrKwsdOzYsdEBpGSMDw0jTVm/+QPpsbBU\\nZpvEZ8U5RYbGOUWGxjlFhsY5RYZmjHOqvgNEep3yd+DAAezcuRMAkJ2djdzcXLi5uemXjqgeHXoN\\nAABUpCdLnISIiIiIqC69jlCNGTMGb7zxBo4dO4bKykq89957Ok/3I2qKnl5P4zeYoXVuitRRiIiI\\niIjq0KugsrW1xY4dOwydhaiOVlZWyGjVEe1Lb6G6qhpm5mZSRyIiIiIiUuNt08noFTr2hI0oQ+pt\\nHqUiIiIiIuPCgoqMnry9FwDg96sXJE5CRERERKSJBRUZvbZd+wMA8m9fkjgJEREREZEmFlRk9Lr2\\nHQQAEBlXJU5CRERERKSJBRUZPc/O3VEks4FDwXWpoxARERERaWBBRUZPLpcjy6Yr2penobS0ROo4\\nRERERERqLKjIJJQ594Y5VLh+WfdTqomIiIiIHjcWVGQSWnXwBgAoUlhQEREREZHxYEFFJqFdDz8A\\nQPGdRImTEBERERH9FwsqMgk9vQcCACyyf5M4CRERERHRf7GgIpPg2NYZWeaucC66IXUUIiIiIiI1\\nFlRkMnLte8C5Og9/ZCqkjkJEREREBIAFFZkQlVtvAMD1yxckTkJEREREVIMFFZkMu079AADZNy5K\\nnISIiIiIqAYLKjIZnr1rbkxRkX5Z4iRERERERDVYUJHJ6N67HyphDus83umPiIiIiIwDCyoyGZat\\nWuGeVSe4l91GdVW11HGIiIiIiFhQkWkpcuyJ1qIct29ekToKERERERELKjItZu5eAIDUq/ESJyEi\\nIiIiYkFFJqZt1/4AgPu3L0mchIiIiIiIBRWZmKe8B9X8kHlV2iBERERERGhCQbV69Wq88MILAuPb\\nbAAACRJJREFUCA0NRVJSkiEzEenk0bErCuW2aFNwXeooREREREQw12fQ+fPncefOHezZswe3bt1C\\nVFQU9uzZY+hszSb29iWsTvoZV/Mz0SftBKJ8xiC0q69m3/0s9GnjptH3qP7mGmuMmaQaK5fLkW9h\\nB8/ydCTNkuN3axekDngZC/9nJQBg88530Tk+Bl1KsxvVZ8ix+yXarjHuiyd9LOeUae3HJ30s5xTn\\no5RjOadMK5MpjH14ThkzmRBCNHbQ5s2b4e7ujmnTpgEAgoODERcXB1tbW63LJyQkwM/Pr2lJDST2\\n9iWE/fJlnfavRk0HAJ19oV19JRlrjJmkHLt557t45uSqOv3Hn5oKAAi4+U2j+7ye/n+4cjGuxYw1\\nxkymONYYM5niWGPMZIpjjTGTKY41xkymONYYM3FfmO7YH0e+YxRFVX31jF4F1dKlSzFq1CgEBQUB\\nAMLDw7Fq1Sp06dKl0QEeN599G5Gcn1Gn3UJuBgCoVNV9vpGF3Azu1va4V1qos7+5xhpjJinH/v34\\nh+hZklOnn4iIiIiePNet3RCyPVPqGPXWM3qd8vewhtRkCQkJhthUk13N1/6BaPsSX7uvorxC5zLN\\nOdYYM0k5tltJrtb+asggg9B6UeCj+nZ0GY35vx9vMWONMZMpjjXGTKY41hgzmeJYY8xkimONMZMp\\njjXGTNwXpju2S1m20dQRuuh1hGrr1q1wcXFBaGgoACAwMBD79+83iVP+dB2h8nFsDwHo7EsMWSzJ\\nWGPMJOXY/fPd0L30jzr9163dIIPQqy9ke6be6zXFscaYyRTHGmMmUxxrjJlMcawxZjLFscaYyRTH\\nGmMm7gvTHyu1+uoZve7yN2zYMBw5cgQAcOXKFbi6uuospoxNlM8Yre2RPmPq7ZNqrDFmknJs6oCX\\ntfbfGfCS3n1NWa8pjjXGTKY41hgzmeJYY8xkimONMZMpjjXGTKY41hgzSTXWGDOZ6lhjZvbee++9\\n19hB7du3x82bN7FlyxacOnUKy5Ytg7Ozs87lMzIy4O7u3pScBtPXsT16ObjgRmEOcpQl8HZsjw8H\\nT0JoV1+NvtzyUvR1bKfue3jsw/3NNdYYM0k5dsjTY7A7rwrV2ddhX1WGW9auuOj/Fyz8n5V69zVl\\nvaY41hgzmeJYY8xkimONMZMpjjXGTKY41hgzmeJYY8zEfWH6Y6VWXz2j1yl/jWXs5z0SERERERHV\\nx6B3+SMiIiIiIiI9r6EiIiIiIiIiFlRERERERER6Y0FFRERERESkJxZUREREREREemJBRURERERE\\npCdzqQNIYfXq1UhMTIRMJkNUVBR8fHykjkQm6oMPPkBCQgKqqqrwyiuvwNvbG2+99Raqq6vh4uKC\\n9evXw9LSUuqYZEKUSiUmTpyIBQsWwN/fn/OJmuzAgQP45JNPYG5ujtdffx09e/bkvCK9lZSU4O23\\n30ZBQQEqKysREREBFxcXPHisac+ePfH+++9LG5JMwvXr17FgwQLMnj0bL774IjIyMrT+bTpw4AA+\\n//xzyOVy/OlPf8K0adOkjl5HiztCdf78edy5cwd79uzBqlWrsGrVKqkjkYk6e/Ysbty4gT179uCT\\nTz7B6tWrsWXLFoSHh2P37t3o1KkT4uLipI5JJmb79u1wcHAAAM4narL8/HxER0dj9+7d2LFjB44d\\nO8Z5RU3y3XffoUuXLvjiiy+wefNm9XepqKgoxMbGori4GL/88ovUMcnIlZaWYsWKFfD391e3afvb\\nVFpaiujoaHz22Wf44osv8Pnnn+P+/fsSJteuxRVUZ86cQVBQEACgW7duKCgoQHFxscSpyBQNHDgQ\\nmzdvBgDY29ujrKwM586dQ2BgIAAgICAAZ86ckTIimZhbt27h5s2bGD16NABwPlGTnTlzBv7+/rC1\\ntYWrqytWrFjBeUVN4ujoqP5CW1hYiDZt2kChUKjP9uGcooawtLRETEwMXF1d1W3a/jYlJibC29sb\\ndnZ2sLKywtNPP42LFy9KFVunFldQ5eTkwNHRUf26bdu2yM7OljARmSozMzNYW1sDAOLi4jBy5EiU\\nlZWpT51xcnLi3KJGWbduHZYsWaJ+zflETZWeng6lUolXX30V4eHhOHPmDOcVNcmECRNw7949PPPM\\nM3jxxRfx1ltvwd7eXt3POUUNYW5uDisrK402bX+bcnJy0LZtW/Uyxvq9vUVeQ1WbEELqCGTifvrp\\nJ8TFxeHTTz/Fs88+q27n3KLG2LdvH/r374+OHTtq7ed8In3dv38fH330Ee7du4eZM2dqzCXOK2qs\\n/fv3w93dHTt37sRvv/2GiIgI2NnZqfs5p8gQdM0jY51fLa6gcnV1RU5Ojvr1H3/8ARcXFwkTkSk7\\ndeoUduzYgU8++QR2dnawtraGUqmElZUVsrKyNA5lE9XnxIkTSEtLw4kTJ5CZmQlLS0vOJ2oyJycn\\n+Pr6wtzcHJ6enrCxsYGZmRnnFent4sWLGD58OACgV69eKC8vR1VVlbqfc4r0pe3fPG3f2/v37y9h\\nSu1a3Cl/w4YNw5EjRwAAV65cgaurK2xtbSVORaaoqKgIH3zwAT7++GO0adMGADB06FD1/Dp69ChG\\njBghZUQyIR9++CG++eYbfP3115g2bRoWLFjA+URNNnz4cJw9exYqlQr5+fkoLS3lvKIm6dSpExIT\\nEwEACoUCNjY26NatG+Lj4wFwTpH+tP1t6tevH5KTk1FYWIiSkhJcvHgRAwYMkDhpXTJhrMfOmtGG\\nDRsQHx8PmUyGZcuWoVevXlJHIhO0Z88ebN26FV26dFG3rV27Fu+++y7Ky8vh7u6ONWvWwMLCQsKU\\nZIq2bt0KDw8PDB8+HG+//TbnEzVJbGys+k5+8+fPh7e3N+cV6a2kpARRUVHIzc1FVVUVFi5cCBcX\\nF/ztb3+DSqVCv379EBkZKXVMMnKXL1/GunXroFAoYG5uDjc3N2zYsAFLliyp87fphx9+wM6dOyGT\\nyfDiiy/i+eeflzp+HS2yoCIiIiIiIjKEFnfKHxERERERkaGwoCIiIiIiItITCyoiIiIiIiI9saAi\\nIiIiIiLSEwsqIiIiIiIiPbGgIiIiIiIi0hMLKiIiIiIiIj2xoCIiIiIiItLT/wdS+pc/d5tV7wAA\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Unnormalized\\\")\\n\",\n \"plot_x_and_rfft(x, rfft(x))\\n\",\n \"print(\\\"Normalized with 1 / sqrt(N)\\\")\\n\",\n \"plot_x_and_rfft(x, rfft(x, norm=\\\"ortho\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For correct resampling via `irfft` to the time domain, the normalization flag in `rfft` and `irfft` must be equal (None or \\\"ortho\\\").\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gj8kcqwTLXTEEII4aauTkrk8M1/4IWJE1TLodPibDQaWbBgAeHh/2ve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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, '--')\\n\",\n \"plt.plot(np.fft.irfft(rfft(x, norm=\\\"ortho\\\"), norm=\\\"ortho\\\"), ':')\\n\",\n \"plt.legend(['raw', 'resampled'])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Resampling With Coefficients k>=10 Rejected\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What happens if one tries to resample the signal from the first 10 coefficients only and discard the rest? How does the plot look like?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 14.14213562 +0.00000000e+00j, 7.07106781 +1.93774590e-15j,\\n\",\n \" 7.07106781 -1.72812614e-15j, 7.07106781 -5.70583545e-15j,\\n\",\n \" 7.07106781 +4.21786636e-15j, 7.07106781 -3.98762969e-15j,\\n\",\n \" 7.07106781 -1.21015308e-14j, 7.07106781 -6.01337816e-15j,\\n\",\n \" 7.07106781 +6.47656704e-15j, 7.07106781 -5.10788198e-15j,\\n\",\n \" 0.00000000 +0.00000000e+00j, 0.00000000 +0.00000000e+00j,\\n\",\n \" 0.00000000 +0.00000000e+00j, 0.00000000 +0.00000000e+00j,\\n\",\n \" 0.00000000 +0.00000000e+00j])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fft_coeffs = rfft(x, norm=\\\"ortho\\\")\\n\",\n \"trunc_fft_coeffs = np.ones(len(fft_coeffs)) * 0+0j\\n\",\n \"trunc_fft_coeffs[0:10] = fft_coeffs[0:10]\\n\",\n \"trunc_fft_coeffs[0:15] # the remaining entries are zero\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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HkfnCfE9iQ20MCyk/uwOxyEVk8Ik+AshBCiIUazHa1ahU6jZmXWQars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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(np.fft.irfft(fft_coeffs, norm=\\\"ortho\\\")) # plot resampled with all coefficients\\n\",\n \"plt.plot(np.fft.irfft(trunc_fft_coeffs, norm=\\\"ortho\\\")) # plot resampled with truncated coefficients\\n\",\n \"plt.legend([\\\"not truncated\\\", \\\"truncated\\\"])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see an information loss without these coefficients.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# What about a non-integer amount of cycles?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Truncate the time series from above. The number of cycles is not integer any more.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y = x[0:-50] # length is now 150, some periods are not integer any more\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unnormalized\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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rAe7Afrwn6wLuwD6+HGXavBp1m6/K1duxbLly8HAOTl5aGgoAC+vr7N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\\nxxsG7NxPxXwAAAAASUVORK5CYII=\\n\",\n 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8J8NI6rNRA1qMvf+vXrsWzZMgBAfn4+CgsL4e/v37DobmOSJGF671AY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\\nKCIpqIQQQgghhBCiiP4fYBdgs1rC6mQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Unnormalized\\\")\\n\",\n \"plot_x_and_rfft(y, rfft(y))\\n\",\n \"print(\\\"Normalized with 1 / sqrt(N)\\\")\\n\",\n \"plot_x_and_rfft(y, rfft(y, norm=\\\"ortho\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"About the first 11 coefficients seem to be important. Plot the inverted coefficients / resampled signal.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FZXnBU2ZeATz6Gsy5aOIoQQQlTL6opzgIca/K6hdMu2dBQhhBCiWlZX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\\nmzMREZGD+V+eS4YeNp9u1gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(y, '--')\\n\",\n \"plt.plot(np.fft.irfft(rfft(y, norm=\\\"ortho\\\"), norm=\\\"ortho\\\"), ':')\\n\",\n \"plt.legend([\\\"raw\\\", \\\"resampled\\\"])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We do not have an analytical description of the signal any more, but it seems that it is perfectly described by the DFT coefficients, as it can be resampled without an error.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Signal Corrupted With Noise\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"noise = np.random.normal(loc=0.0, scale=1, size=200)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lJJdc4EQRBEEmLiXHhpLPwIpphylq1NljNBEAQhwxeKAiDLuSBo1Wxl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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_with_noise = x + noise\\n\",\n \"plt.plot(x_with_noise)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Whoosh, this signal seems corrupted. What are the results of the DFT?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unnormalized\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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Nx+rbAzM+LLg7EUlqn0HY4QQgghhBAN7uqSl0nernqOpPFIQtVArE2N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gCTO7rpOZLbRxKqevDOMC8AFuy+oOdIhBBCCCGE0A+VWsM/Z67gamVC\\nL087fYdz20hCVQ96etox3MuJvRfSOHIxQ9/hCCGEEEIIcdv5X8wgLb+ECR1cUSrv/vWnyklCVU/e\\nHV4+ShWp50jEnSizoIQ3tp0jNa9Y36EIIYQQQtTJX6FJwL1V7geSUNWbPs3sGdzSkV0RqRyPy9R3\\nOOIO80NAHJ/ui+KtHef1HYoQooFSqTUExEoVhBCiYdJotPx95gp2ZkYMaOGg73BuK0mo6tG7w1sB\\n8IGMUola2nFetzj0yqAE4jIK9ByNuB6NRktidhH+MemcT87VdzjiHjLfL5LeS46w+cwVfYcihBBV\\nBCVkcTm7iPEdXDEyuLdSjHvr095iA1o40q+5PdvPpRCdlq/vcKql0Wg5HJPOsoA4StWyGHFDkF1Y\\nytHYTEwNlZSqtXy6L0rfIVUrr1jFQ7+dYG/kvbnm2v6oNDp8cQCLN7bj8cFu+i09is/CQ5IAi9si\\np6iUb/0vArA65JKeoxFCv7RaLap7+BrmUHQ6sQ3w3FNe7jfJ+95pl15OEqp69mjXxsC/O1VDcfJS\\nNnM2hdPkwz30X3qUWRtCG+yF+71m74U01BotrwxsQUtHC34OjCchs1DfYVXx5YFo1oRc5sk/Q++5\\nZFyr1fL61nOcTc6lg5sV93dyY0YXDwpLNczbdk7f4Yl7wI8BcWQXqQDYejaZnKJSPUck7lTnk3OZ\\nvjqES1kN7zxTU0/8cRrPD/eSknvvzTtOzC5i8A8BPLj2hL5DqUSr1bIxLAlLEwOGeTnpO5zbThKq\\neja+gysGSgV/hTWchOpwTDpdFx1i4aEY8kvUPNa9MW7WJny4+wIRKXn6Dq9OXvj7DAOWHsEvIgWt\\nVqvvcG7Kzghdud/Ydi68NaQVpWotn+3/N9nNLVLx3MYwXttyFo1GP581ObeYLw/oFq++mFHAisCE\\nWr2+sFTN3M3hnLyUfSvCu+X8L2YQlJDF+PauBL3Unz8e7srqGV3o3sSWdacSOSrdPUUNpeQW89m+\\nKPKLVTV+TbFKzcJDMViaGPBS/2YUqTRskrI/UQfFKjUPrA5h3alEvjoYre9w6uRCah4rghJIzCni\\n5U3h+g7nttt6Nhm1RsvR2MwGdfM1NCmH6PQCxrR1wdTIQN/h3HaSUNUzBwtjBrVwIDA+q8GUAn3g\\nF4lGC6umdyZ5/nB+ntqZbyd6U6LW8PSG0DsuIdkSfoUl/hc5FJPBiGXHGfR9wB07UVur1bLzfAr2\\n5kZ0a2zLg74eNLM3Z/mxeC5nF3LiUhY+Cw+x9EgsXxyIZvbGML18Xwt2R5JfoubdYV6YGSlZsDuS\\nolJ1jV///dFYvjoYw9N/Naz9raBERWJ20Q2f91VZMvnKgOYVjymVChaNbw/AS5vC9Zbsin9ptVqK\\nVA179PTZjWHM23aO93ZF1Pg1a0Iuk5RTzKyenjzTuykAv59MvEUR1q/E7CLWnbxcq+OFuHXe3hFB\\nWJJu7ueq4EsUq6p+L6uCE9hwuuHuX18ciEarBUcLY347eZmdZXOQ7xVbziZX/PeG0IbzPf1dtpjv\\nJO97q7tfOUmoboEpnXQ708YGMEoVGJ/JngtpDGnlyENdG2NsqPvKJ3q7cl97Fw5Ep7MyqPJoQ1Zh\\nKSUN9KIkr1jFc3+fwchAwfqHfBnd1pmD0en0XnKET/beeQsrn0vOIyGriOFeThgoFRgZKHlzSEtK\\n1BomrQym5zf+RKXlM3dgCzq7W/NDQByvbz13W5OSqLR8fgyIo6WjBW8Pa8XzfZtxKbuIHwPiavT6\\nghIVn+/XJSSB8VkcjE6/leHWWKlaQ58lR2j16T4upF57pDYyNY/NZ5Pp3sSWPs3sK/2sV1N7pnV2\\nJyghi7UnZF6LPqk1Wib/GszQv5LZ2MBKrsv5x6RXlIN/43/xuvtdOY1Gyxf7ozAyUPDygOZ4OVni\\n08iG3ZGppDXgZRZKVBq+2B9F68/2MX3NCdp/cYAt4VdqdewqLFVzJinnFkZZ/5JyijjUQI5x/3Ug\\nKo2vDkbTytGCZ3p7klFQyqYzyZWeE5max//WneKRdafILar5KOrtkphdxK9Bl2jpaIHfUz0xUCp4\\n5q/QWo343skKSlTsiUzF084MpQL+PN1wjnUBsboO18O8HPUciX5IQnULTOjghlIBGxrASf3jPbok\\n462hrSo9rlAo+HaiN5YmBszdcpaU3GIC4zOZufYEzu/tovPXBxtkY435uyKIzyzktUEteaCzO9ue\\n6MHh2b1xtDDm64Mxd9wk1fJyv5FtnCsee7hrYzztzAiMz8LOzIhdT/Xgi3Ht2PVUT9o4W/LFgWg+\\n3HP7kse3d5xHpdHy8eg2GBkoeW1QS6xMDPl474UancR+DIgjObeYUWWfsaHM3fvyQDSnEnMoKFHz\\n5J+h1xxhWngwBq0W5g5sgUJRdZHCT8e0xdRQybxt5+vlpF6i0rDwYPRtmRuw41zyHVv2ezWtVsuL\\n/5zh77ArFKm1TFkVzKJDMfoOqxKNRsuczWcB3b5Uqtby6pazN3zdpvArRKTmM9OnER42ZgBM7+yB\\nSqO9ZaXlKbnF9Fniz+6I2jeg0Wi07I5IpdNXB3lt6zlMDQ14uGsj4jMLue+XIMb8FFijRDIhs5Ce\\ni/3x/vIgv90hNyvWnbxMu88PMOC7ow2ueU9WYSkP/34SpULB6hldeKFvMwB+Ol75xtiHuy+g0UJB\\niZo/G+Ao1cJDMZSoNbw2qAVdGtkwd0ALYjMKed/v3uiuvCcyjSKVhhk+Hgxo4UBAXMMo+9NqtZxM\\nzKapvRl25sb6DkcvJKG6BVysTOjf3IGjsZlcztbfjn4mKYdN4cn09LRjYDXrATS2M+PDkW3IKCil\\n3ef76bHYn7UnLuNqZcK55Dy6Lz7Mgag0PURevZOXsll0+CItHMwrJYh9mzswtbM7afkl7LtGvNmF\\npZy6nM3fYUksPBjNokMx7I5IJTG7SK8laOWlCsNb/zuB09hQycppnXmmtyenXxnA8Na6RMTZyoTd\\ns3rS1N6Md3dGMGr5MT7wi2T7ueR6u1NdqtZwMb2AvLLEIDghi/WnEunW2JYpZYv0OVgY83L/5qTk\\nlfDtkdjrvl9hqZrP90djaWLA6hldGNDCgV0RqZy6fP25VHEZBWQX3rpJ9xdS83jfLxIXKxNGtHbi\\nYHQ6PwfGV3leal4xK4MSaGpvxsQO1Xct8rQ3Z+7AFiTmFNVLsrj2xCXmbD7LC/+cuen3uhaNRssr\\nm8MZ/VMg3l8e4O0d5ylsgCVZWq2WZQFxfHck9rollYsOxbD0SCzeblb8OMQBVysTXt4Uzkv/nEFd\\nx1LMJ/84zfhfAust4Vx36jJBCVlM6+zO52Pb0q+5PZvCk6974a3VavmsbJ96dVCLisendnYHbl3Z\\n3x+nEzkam1njssT4zAI2RRcwY80J3N73Y/iyY0Sk5vFs76ZEzBvEr9O7cPqVAQxp5ciO8yn4LDyE\\nf8y1R3GC4rPovvgwoUk5GCgVPPtXGPGZDaOEvjpZhaU8uOYE09ecqCiha2jLpzy3MYyErCLeHtqK\\nHp52tHGxok9TO3ZHplV0i4tMzWPtiUs0dzBHoYBfqjkm6lNmQQk/BMTiZm3Cw10bAbrlaprZm/P1\\noZg7do5ubWwO140ojmvnwv2ddMeBhjBnPzGniNS8Enw8bPQdit5IQnWLlK8QXV5Tqg/lF3dvDmlZ\\n7Z11gOf6NqOnpx0ZhaXc196FPbN6Evf2UH56oBO5xSqG/XiM5cdqVtp1K6k1WmZtCEWt0fL95I6Y\\n/WfC47SyC4z1p6peYPwYEIvdOzvp8vUhJq0MZs7ms7y8KZzhy47h8cFuHN/dxarg2jVZqImg+Czm\\n74qotkYdIL9YxcHoDDq7W+NmbVrpZwNbOvLd5I64/ufxRrZm7JnVi/auVuw8n8p7uyIY81Mg7h/s\\nrpcyp6c3hNL8471YvbkDqze3M+SHAAA+G9u20j708oDm2JkZ8dm+qOuOpPwYEMeV3GKe79sMBwtj\\n5g1uqXu/ahKP9PwSlvpfpMfiwzT9aC/NPtp7S+6QarVant4QRrFKw5KJHfh5aiesTQ15dcvZKvOp\\nvj8aR5FKw0v9mmN4nTU1Xh/cEg8bUz7bH0XYTZYolZ8w159K5HRi/V8glKg0PPTbSb4+GENrJwvc\\nrE35aM8FvL84UKcRiVtFo9Hy/N9nmLUhlNkbw5iwIojMgpIqz/srNJFXtpzF3dqUbY/3wNfFhGMv\\n9KW9qxWLD19k/C+B1e6jYUk5fOAXWXHz4GopucX8dDyezeHJdPzyIO/uvLmEs7BUzbxt5zAxVPLJ\\nGN3f0qLx7VEodPPvqhtZV2u0vLL5LMfjsxjf3oW2LlYVP2tsZ0a/5vYcikm/JTftytfFC4jLJPxK\\n1bXWCkvV/B2WxOy/wmj96T48P9zLguNZ/H7yMgZKBQ/5NiL4pX4sneyNfdnd6nauVuye1ZNfp3em\\nqFTDyOXHqy2N23A6kQHfHSE5r5iF49vz45SOZBepePj3U3VOjm+lyNQ8On55gN9OXqZHE1tC5w5k\\ndFtnDsVkcDC6YdyQ9I9JZ+2Jy3RvYlvpZuQTPTwBKpoMfbRHNzr1+di2DGnpyJHYTCJrMJp4uyw9\\nEktesZo5/VtgYqi7BjA3NuSHKd6oNVoeWXey2r/nJYcvYj5vGwO/O8pn+6I4nZit97m8xSo1I348\\nxuPrT1Ubc3U0Gi1bzyXjZGlM9yZ2TPJ2Kyv70/9I4omyZLaLJFSivpVPytNX2V9Mej6/n7yMt5sV\\nY9q6XPN5BkoFe2b1JPHdYWx6rDtDvJxQKBQ83qMJe2b1wsbUkKf+DGWFHu5U5Rap8ItI4Z0d5+n3\\n7RGCErJ40MeDYa2rtuPs3dQeDxtTNoZdqTT/K79YxVvbz2NtYsjsPk35Ymw7Njziy7qZPrwzrBWT\\nvF0pVmmYsym8XidNX8oqZNTyY7zvF8mzf1XfSOJgTDolak2lcr+aaOFowZlXB5L03jC2PN6dd4d5\\nYWSg5PE/Tt/U0H9mQQlrT1zGxcqEUW2caelggbmRAY90bcSglpVrom3NjJg3uCWZhbrRzVXBCVU+\\nY2Gpms/2RWFhbMCc/rpmDiNaO9HJ3Zo/TicSk64rKU3JLeaJ9adxe9+P5/4+Q3BCFoNbOlKkUvPA\\nqhAe/f1kvbaI/jXoEvui0hjbzoUpHd3wsDHj87FtyS5S8dzfYYDuYvZYXCZLj1zExtSQx7o3ue57\\nWpoY8sOUjpSqtTy2/tQNS0+LStUUlFQ9iRaVqvGLTMXCWHex8O7OmjcuqIncIhXjfg7kt5OX6eVp\\nx9EX+hL+6kBeGdCcixkFDF92jFe3nK3XC1eVWsPJS9kk5VTf/CP8Si7TVoewLCCuYs5GsUrNjLUn\\nKkadhrZyZMvZZLouOsypy9m67RSRwkv/nGHm2pNYGBuw7YnuNLbTlcQ1sTPH/7k+DPNyZNu5FLy/\\nPFCxGG5BiYp5W8/h8/Uh3tsVwbJqbhgdKhs9GdvOBWdLYxbs1iWcIQlZddoGCw/GkJBVxEv9mtPU\\n3hwAn0a2/K9bY85cyeWn45WPrzlFpdz3SyALD8XQ1sWSxRM6VHnP6V080Gqrv4l0M4pK1eyPSsPI\\nQHcD5b831LRaLWN+Os6klcF8dzSWxJwixrZz4RUfa8JfHcjld4exakYXfBrZVnlvhULBw10b8+fD\\nvpSoNYz66TgHonTLRmw+c4Uh3wdw/6oQDJQKNj/WnZf6N+ex7o2Z6O3Kweh0vm5gXemKVWqmrgoh\\nIauId4d54f9cH1o6WvDOMC8AFuxuGPN6P96ru4G18L72lRZbvb+TG1YmhvwSGE9ESh5rQi7RwdWK\\niR3cKo55te3o+l/11aynoETF4sMXsTUzYlYvz0o/G97amWd6exKWlMuj605V+p1bzybz4ibdaP+h\\nmHTmbTtH568O0e7zA5yt5mbB7fLz8QT8IlP5JTCBnt/416gMNvhSFsm5xYxt64KBUlGpGqo2LfA3\\nhiZxrp4XpT95WXcj8V5OqAz1HcDdyt3GlD5N7TgUk05ybjEuVia39fd/ui8KjRbeGNwKpbL60aly\\nFiaGWJhU3RX6t3Dg2Iv96PDFAT7eG8UjXRvf8L3qy+nEbPovPUpO2QWWQgF9m9nz9X3tq32+Uqng\\ngU7uLDwUg19kKmPb6ZLIXwITSC8o5Z1hrfhgZJtqXztv6zk+2x/FH6cTebhsHbGboVJrmL7mBOkF\\npThbGvMzBdsrAAAgAElEQVRLYAI+HjbMLqtZL7fjXPn8qbqt1+BqbcrYdqaMbedCI1tTnvozlEfW\\nnWTPrF51+p5+P5moSy77N+e1spGk63llYAuMDBS8vTOCR34/xargS3w2pi02ZkZotVr+OJ3Ildxi\\n5g1uiaOlbv9XKBS8PqglM9ae4LN9UXi7WfPOzgiyCktp42zJkz2bMKOLB67WpkSk5DHztxP8GnyJ\\ngzHpbH6sO95u1rX+XFdLyS3mlS3hWJoYsHRSh4pRtyd7ePLbicv8HXaFET8eIyghi8yyksO3h7bC\\nyvTGh8qx7VyY6evBmpDLfH0wpso21Gq1HI/PYkVgPOtOJWJnZsT51wdVai+7LyqNghI1rwxozrG4\\nTDaHJxMYn0n3JnY1+nwFJSr8IlIZ1NIRGzOjSj+LyyhgwoogTiXmMLadC+sf8sHcWPe5vryvPQ/6\\nNGL6mhC+PBDNhdQ81jzog2U1x4UbUWu0HLmYgV9kKkdjMzgen0VBiRozIyW/PejDhKs6QJ26nM3Q\\nHwJILyhl/alEXtkSzoM+jYhKy2fvhTT6NrNny+PdsTIx5L1dEXy05wI9v/HHQKmgoER3A8TG1JB1\\nD/nS+T8nclszI3Y+2ZPFh2N4Y/t5xq8IYmpnd47HZxKbUUgTOzPiMwvxi0hlzoAWlV57oGzkZN7g\\nlnR0s2a+XwSLDsUwbc0JQucOqDJCfj1Xcor4ZN8FnCyNeWNI5X3io1Ft+ON0Ii9vCmdjWBJ9mznQ\\nyd2aN7ef42xyHiPbOLFupm+V7xJgSkc3nv/7DL+fvFwl/nLFKjXGBsprVihotdoqPzsYnU5hqYYX\\n+zXj95OXWR1ySTdPsOwzbwxLYn9UOgNbOLBgZGt6eNphZKAkJCSEdq5W1f2aKiZ4u/HXI12Z/Gsw\\no386jpu1KTHpurKzQS0dWDS+Ax3ddX/rCoWCZVM6EhCbyVs7zjPMy6nKd60vb20/z6nEHB7v3oT3\\nR7aueLynpx3DvBzZHZnG0YsZ9P5PM5urxWcWEJqUy/nkPM6n5GGghG8mdqgYgblZpy5ns+N8Cv2b\\n21eJw8LEkOld3Fl2LJ4JK4LQaOG94V4olQomeLtia2bEquBLLBjZ+roj9NdyLjmX3kuO0LWRDd9M\\n7FBplLWmknOLWR18iV+C4knLL7nm8XjR+A6cTc7jr9AkPtxzgXeHexGamMP0NSGYGio5NLsPnnZm\\n7I5MZXN4MutPJdJriT9/POTLiFre1LxZhaVqPtwTibmxAQ/6eLD8WDxdFx1m9fQu3HeN0nK4qtyv\\n/b83ye/v5M6BaF2zmxf7N7/WSyucuJTF5F+D6elpR8ALfW/+w5Q5WVbG79OoYfxt6kOdR6g+/vhj\\npk6dyrRp0wgNDa3PmO4aUzq5o9XC37exvjUlt5gH15xg+bF4WjpacH+nm2tf2dLRgpllFzhbzybf\\n+AW18OqWs0xbHVLt6M3CgzHkFKl4tndTtj3RnYwFIzn8XB+cr5OYTq0o+7sM6BKbrw5GY2ak5Pn/\\nJDNXm9XLE4VCV05QH97dFYH/xQzu7+RG0Ev9cLI05qVN4ZXKP1RqDTvOp2BlYkgvz2ufbGvqiR5N\\nGN/ehf1R6XVeW+SXwHhduU5ZbfqNGCgVvDygBWdf1ZW47L2QRtdFh2n1yT68Pt3P2zsisDA2qNRq\\nHHR3RZvZm7PsWDzP/30GrVbLkokdCJs7gDkDWlSUObZ2tuTo8315a2grYjMKmbn2ZLUjP2l5xfx5\\nOpHNZ66wJzKVoxczqu1OlVukYsKKIDIKSvlwZBua2JlX/EypVLD8gU6YGirxi0zFytSQp3o2YeOj\\nXXl/ROsq73Utiyd0wMXKhHd3RXC+7A5gfrGKb/0v0v6LA/T6xp9lx+IpVmmIyyzkj/+UamwpO2GO\\n7+DKR6N1NwDe2XHjUapilZolhy/S4uN9TFwZTNvP97PhdGLF39b+KN13cyoxh6d6NuHvR7tWJFPl\\nujSyIeCFvgxp5cim8GT6fXukxnc9tVotO84l8/j6U7jO92PAd0f5aM8FDkSn09zenEe6NkKhUDDp\\n12C+PhiNVqvVjUR+H0BGYSlf39eOD0a2xs7MiB8D4th7IY372rvgN6sntmZGGCgVfDiqDVse746z\\npTFNbM2YM6A5e2b1JPn94dcc5VWW7aMhL/eni4c1608lkpBVxOuDWnLutYF0cLXiYHR6ldHp/VFp\\nmBsb0K2xLVamhnx1X3te7NecqLT8ikY/NfXa1nPkFatZMLJ1lcTI1dqUFVM7l13opfHerggmrAji\\nbHIeL/VvxpbHulebTAE4WZowsrUTwQnZrDt5ucrP90elYfvWTvosOcKu8/+u16dSa1gTcon2n+/H\\n69P9VUZKy8v97mvvyqPdGpNRUFpRul6i0jBv23kMlQp+vL8jfZs7VBrxqI1x7V35+9FuqDW6zm1P\\n9GhC6NwB7Humd0UyVc7R0oSV0zpTqtYy5dfgGpWhpeYVczgm/ZaVdu2OSOWrgzG0crRg0YSqN/re\\nLR+l2nPtuVSrghNo+tFexv0cyKtbz/JzYDzLjsXzy02OCl2tvPT/jSGtqv15ednf+ZQ8OrhaVVTX\\nmBkZML2LO4k5RfjVocGGVqvlpX/CySosZc+FNDp+eZBXt5ytcefAnKJSHlgVjMcHu3l161mi0wqY\\n3sWj0lzCqxkbKvnzYV887cx4b1cEPwbEMu6XQPKK1aya3oWujW1xsjRhhk8j1j3ky9oHu1Cs0jD6\\np+N863+x1p+vOldyihj8/VH232Du+Q9HY0nKKeaFvs1Ydn8nVs/oQqlaw/gVQRi/thXT17dh8cZ2\\nmn20h+3n/r3u2hKejLGBstKiuZO8XVHUouxviX8sAMfi6neO/4nL2bhYmVSZvnAvqdMIVWBgIHFx\\ncaxfv57o6GjefPNN1q9fX9+x3fEmebvy8qZw/gpN4umytUNuFa1Wy6rgS8zZHE5GQSndGtuyclrn\\nOt1V+q8X+zfj58B4Fh2Oue7dk9pYd/JyxUKxj3RtxKiryhIzC0pYfyqRFg7mLJnYocajLd2b2NLU\\n3oxNZ5IravzjMguZ3acpTpbXTsSaOZgzpq0LW88mE5yQRdfGVUtVamrX+RQ+2RtFcwdzlt/fCRsz\\nIzY83JUhP+hKWX5+oBO7IlL543QiqXklTO7oVtHK/mYoFAp+eqATx788yFs7zjO0la60Lj6rkAup\\n+eTlqPC9zutPJ2YTcimbce1can1A9LQ3Z+vj3dkYlsT2slE3BQoUChjT1rlidKqcoYGS+SO8eHTd\\nKf7XrTGfjG57zUTZyEDJh6PakJRTxC+BCXzjf7HS3fiMghK6LT5MbEblE4ObtQlrZvgwuJWuVLGg\\nRMV9vwQSEJfJjC4e1SbYXk6WnJ47ALVGSxtny2ve1b8ee3NjvpvkzeRfg/nf+tO0sShl0997yCws\\nxdhAyQOd3Hmse2NaOlrQ6tN9LD0SWzEqqtVq2RyejL25Eb087TA0UDK0lSN+kakcik6nfzWNZQB+\\nO3GJedvOkZBVhIWxAdO7eLAxLIn7V4Uwtp0LvTzteHdXBArghynezOrV9Jrx25kbs+PJHjy3MYxl\\nx+Jp9ck+zIwMKNVoUKm1dGtiy4aHu1b6vrRaLbM3hvH9UV1pmKuVCbN6eTKunQt9mtljW5YQPN+3\\nGeN+CeSVzWcJjM9ix/kU8opV/DqtMw+VbYM3h7Ri5/kUYjMKmNXLs8rxa2w7F8a2G1br76W9qxXH\\nXujHmpBLdG9iS4eykc4RrZ04cyWXwzEZFaXEKbnFnE3OY5iXY6W/zQ9GtmZDaCKf7Y9iehePGo3G\\n+MekszrkEj6NbCouXP9rSid3pnRyJy2vmKOxmRyPz6SLhw1TyiacX8/C8e05EJ3O0xtC6elpV1FO\\neCmrkKmrQyhRawiIy2Tk8uP0aGLLRG83lh+LIzr93wYPvwZfqljbCnQJlaWJAX2b2dPY1pTP90ez\\n/Hgc0308+DEgjqi0fGb3aYqXk+UN47uRMe1cuPDGICxNDCvmWl3LiDbOvDvMiw92R9Jt0WFWTe/C\\n+GrOR5kFJXx5IJrFhy+SX6Jm0fj2Nbpzfy15xSqe+OM0cZmFTO/izrTOHigV8Mi6kxgqFfw2s/qR\\n3L7NHRjU0oGd51OrHWWOSc9n9sYwrEwMeX1QS9q6WOJqZcKQHwL4ZO8FHuve+KZHqaLS8vnzdCJd\\nPKwZUU2pPEDXxjZ4u1kRlpTLu2WjU+Ue696E74/GsSIwgdHXmTpQnW3nUvCLTGVoK0ee79uMFzed\\n4csD0fx+8jL7nul13f1Ho9Hy8G8n2RSeTCd3ax7v3oQZPh44WFx/H3GyNGHTY93oveQIT2/QlW9/\\nOKp1tX9LM3wa0czenAkrgnj+7zMk5hTx8ei2tfqM/7XsWDz7o9JJzA7lzKsDq73+yitW8cm+KN28\\n3bLkcKZvI7zdrHhz+3kyC0pRa7VotFrCknIZ81Mg7wxrxaPdGhOalMOoNs6V9jdXa1P6N3fgYLRu\\nPmV5J9DqpOQW89uJf2++/B12heeuc7O5ptLzS4jPLKxztc3dok4JVUBAAEOHDgWgRYsWZGdnk5eX\\nh6XlzR9g7yZN7Mzp3sSW/dHppOUVV7mwrE9P/hHKz4HxWBgbsGh8e57r2wyDeirP83azZmgrR/Zc\\nSOPU5eybLrdIyCzkmb/CMDVUUqTS8MWB6EoJ1ZqQyxSpNDzV07NWpWsKhYKpnTz4bH8U288l8/n+\\naJQKeOUa5TBXm92nKVvPJrP0SCwrpnWueDynqJTI1PwaJVlB8VnM/O0kxgZK/njo3zKd/i0cWDyh\\nA7M3hnHfL0EAOFkaM7tP04pa+/pQfhd35PLjDPz+KCUqTcUipwYKeD7nDO+PaI21adU73uV18o91\\nr1vJo0KhYHJHdyZ3vPFFIOhaw0/t7F7jC4bPxrTlnzNXeG9XBA90cqeRrVnFSTc2o5BHuzWmg6sV\\nBaVqknKKWH4snqE/BvDG4Ja8OaQVk1YGcyA6nckd3fh1eudr7lf1cZE4qaMb93dy48/TSRxDt/jk\\ne8O9eKZ300qlv/9N4k9cyiYxp4iHfBtVnIgXjGrDngv+vL3zPAef7V0lyTt5KZsH157E1FDJ3IEt\\neG1QC5wsTYhMzePpDaFsPZvM1rPJuFiZsOFhX/o2rz4pu5qRgZIfpnSkg6s1y47FoVCAoVJBiVrL\\n4ZgM+i09wu5ZPWliZ45Wq+WFv8/w/dE4Orlb890kb3p62lW7fX0b23L8hX6M/TmQ9acSMVAq+O1B\\nH6Z28ah4joFSwZh2tbtwqyljQyWP9ag8F254aye+OqgrEy5PqA6WzZ8a2KLyvEFLE0OWTvLmvl+C\\nmLUhlIPP9r7u8Uml1jB7o27uxtJJ3jc8HjtamnBfB9da3bRq5WTJkokdeGz9aWauPcGBZ3uj1upG\\ncVLzSlgysQN9m9mzYHckG8OucDw+C2MDJc/09uSRro3pv/QoCw/GMKvsWBuTnk9kaj7j27tgbKik\\nlZMlA1s4sD8qnZCELN73i9CVXw6vv+PW1SPFN/L+yNa0drbgiT9OM2FFEG8PbcXsPk1Jyinmck4R\\nIQlZLDp8kazCUlytTHTzNzeH08bZsk5lXYnZRYz9+XjF/JBjcZnM2XwWDxtTknKK+XRM2+ueG94Z\\n5sX+qADmbTvH9id6VJRNqsuOXXnFalbP6MJM33+rAp7p3ZSvD8awIjDhpm/Efr5fV/o/b3Cra94g\\nUigU/DC5I/uj05j8nwVZfRvZ0MHVik3hV2p1DVNSNi/ZQKlg4fj2dHCzZlhrJxbsjuSTvVHM/isM\\nv1k9rxnTR3svsCk8mSGtHNn5ZI9a3Rju5G7Dqumdmbr6BDN9PHjzGiNzoFtHMPDFfgz78Rif7I1i\\ncEtHhnrVLSnQarX8WtbcKiI1n1XBl6ocbwCW+F8kNa+E+cO9Kt1E6ORuw7YnelR6bnl53oLdF/j5\\nuO69ry73K3d/RzcORqczcUUwX4xry4AW1a8Dtfx4HCVqDa8Pasln+6PYGJZULwlVedfee7nDH9Sx\\n5C8tLQ07u3/vttjb25Oa2nC6QzUkUzq6odZo2RRev+VyV/srNJGfA+Pp4qGbFPxi/+b1lkyVe6ns\\nDt/Nru2i1mh5+PeTZBWWsmRiB4a2cqw4WYPuoPTjsTiMDBQ82q32F/flZX+vbjnH6cQcHujkTjOH\\nG5+wh3s50dLRgnUnL5Oer+skFpdRQLdFh+m26DC/Bl27BCMmPZ/pq0PovvgwafklLBzfHt//nGSf\\n6e3JByNb879ujdn5ZA8S3x3Gt5O8631u3Yg2zrwxpCVKhYJ2rlZM7+LBW0Nb4W5hwKJDF2nz2X5+\\nP3G5UhlMsUrNmpBLOFsa37KL2erU5u6ro6UJn49tR16xmpc3hQO6UpZt51IY5uXITw904pWBLXhn\\nmBffTe6I/3N9aGpnzsd7o2i0YA9+kamMbuvMbw/61Muo7Y0sneTNY90b83Z3G+LfGcr8Ea2rfNez\\n+zTVPbes1HRLWUntfVedMHt62jG2nQuHYzLYVjb6d7XycqJNj3Xji3HtKkZivZws2ft0L1ZO68wj\\nXXUd12qSTJVTKBQ8368ZYa8OJHTuQE7MGUDY3AG8NqgFkan59P32CJGpeby8KZxvyxpH7JnVk97N\\n7K+bZDS2M8P/uT68OrAFWx/vXimZ0od+zR0wNVSyK+LfbXsgSpdQDWpZdXuNa+/KJG9X/C9mVLTZ\\nzytWceRiBoei0ytNhv/+aByhSTk81r0xPT1rNgeuLh7t1pj7O7lxJDaTj/dGMWeTrjPggz4ezO7T\\nlM4eNvz1aDdOvzKA7yZ7c/GtIXw3uSM9PO2Y6evBhbT8in2vfF7nqLb/Jh9P9tRdFI7+6TjpBaW8\\nMaTldUf8b7UZPo049kI/mjuY8+GeC7i9vxufhYcY93Mg8/0iUSp0Xeqi3xzM5se6Y2SgZOrqkOu2\\nvy9Va/gxIJYt4VcqOkKeTsymx+LDnLycw5M9m3Dp3aEsGt9eN/KfWcjglo7MHXj9m3UDWzgwso0T\\n+6PS6bf0SEXToM/3R3EkNpP7O7nxoE/lv4FXB7bA1FDJx3svVGqwVFuXswtZGZRAK0eLiq7D19K7\\nmT1vDfWq8rerUCh4rHtjStVaVlznHPhfS/wvciEtn2d6eVaMBpsZGfDRqDaMbOPEngtp1+yAvP1c\\nMu/tiqCJnRnrZtbteD25ozsp7w9nxbTON6w08LQ35/eZPhgqFfxv3ak6L9fhfzGDmPQChns5YWqo\\nZL5fRJVS4uzCUr7YH429uVHFNdX1+DSyJeTl/oxu60xiWVOfsdWMFP6vu+4YEJSQxcDvAhi1/FiV\\npUlK1Rq+PxqHpYkBbw5tSU9POw5Gp5NaD0uunLgsHf4AFNo6FBi/8847DBgwoGKUavr06Xz88cc0\\na1Z9phsSEnJzUd7BLuWpmLA5hd5uJnwzqOYXNDWVUaTmgW2pFKo0rB3lTFPrW9NnRKPVcv+2VBLz\\nVGwZ74KjWd1KEVadzeObUzkMbGTKF/3sOH6lmOf2ZzCsiSmf9LXnVGoxT+xOr/j/2tJqtUzemkJ8\\nru5AtmakE23sq5+D8F9rzuWx6GQOL3axpo+7Cc/tSyelUIOREhTAz8MdaWv/7x2lYrWW707nsD4y\\nH5UG2tkb8UIXa7q66O9i41qK1VpWnc1jRXguJRro427Cez1tsTc1YE98IfP8M5nZxoKXfBruAVGj\\n1fLE7jRC00r5XztLfj2Xh5OZkrUjnbA1rbo/5pVo+CQom11xhXR3MWbhQAdMDG5PU5Wa0Gi1TN6S\\nQkqhmm0TXJm9L53o7FL2THbF0ujfi4jorFJm7EjFw9KA9aOdK7qvXcgsZfqOVLwdjPhluGOdShTr\\nYmV4Lt+ezsXEAIrV0NzGkB+GOGBfzXdwJ3huXzrHrhSzc6LuuHb/1hSS8tUcuN8Vw2qSw5QCNfdv\\n1SUeDmZKEnLVlJ9Em9sY8nBbS7q5mjB1WwoKYOM4Z+xu8bbJKdEwfXsqKQW6WFraGrJiuCNmNygn\\njs4qZer2VDo7GfPTMEdeOpCOf2IxW8c742qhO5cUq7WM/vsK2SVaXMyV/DXWBVND/f8d5ZRoWHoq\\nh8xiDU5mBjiZK3E1N6CvuymWxv9+7u0XC3g3IIsmVgasHOGEtXHVbbI1poD5x/7t4OhhYUBmsYYC\\nlZbnO1vxcNvKJcBJ+SrsTQ1qdDwpVmv5NCiLLTGF2Jkoecrbii9DsrEzVbJ+tDM2JlXj+Tokm98i\\n8nmjmw2TW1nUdtMA8FVINr9H5PN2dxsmtKzbewBkFamZuCWFYrWWX4Y70uaqc6BWq+Xn8Dz8Lxcx\\noJEpwzzNMDdUMHFLCgYK2DjOBdv/fL64HBVTt6fgZGbAn2N0yUe5hFwVD+1MpUStrXK+vdWWheWy\\nLCyXsc3MmN+r9jdAPjiWxeaYAr4f7MDRpCJWn8tnjo81M9roqh60Wi2LT+aw5nw+z3W24tF2NW/Q\\nodFq+aPsOmNm22tXUZxJK+Hb0zkEJ5egVMC8bjZMKvvud8cV8saRTKZ6WfBqV5uKa7G3e9gwoUXd\\n9w+At45ksiuukH/GOdPI6u7vdefrW/0Eijp9cmdnZ9LS/p10l5KSgpPT9YdJrxWAPoWEhNzyuHwB\\nnxOHCEzMoXlb73pdQVqr1TJpZTBZxRoWjW/P5JuoE6+J10tiefavMI7m2fB+36oT9dPzS1h0KIbu\\nTWwZ175q2crJS9l8t+4QrlYm/PlkfxwtTfDValkecYi9CTnYN23LoUjdBPx5o7vg26r6YesbeTj1\\nPB/uucBwLyceHNazxq9r3raEH8/s5s/oElZHFJJRqOGLse1o72rJmJ8Deet4PsEv+eBkaUJCZiGT\\nfw0mKCGfZvbmfDy6DQ90cr9tXRBrKyQkhB8eHcirafnM2hDK3gtpzPTLZOW0zhxMiwVg3rhutK9h\\nly59We2eg8/CQ6w4m4eRgYJ/nuh93bv//Xvq6tDbOFvWy1y12rrRMebl/GjmbD7L5hQzIjJLGe7l\\nxICe3So9xxc4nB3Gt0diOVJgxytld8U/WxWs+3diF7rWcn7DzfD1hbZHYpm9MYw2zpYceLb3be9i\\nej21Pa5PyY3m2JazJJu60dnLmYs5fgz3cqJHt67XfM3XhnHM2hCKkUrJgBa2dPGwITW/mHUnE5l/\\nLAsjAwWlal2zlaF9br6kpib+dEln4HdHsTYxZMcz/WnpeOOLJF9gZcxxdpxPId+2KSGpV2jnYsmY\\n/pVLjx6/Es7XB2P4fHxH+lyjE+rtOJ/+16BeN36Ory/km+o6uX4VrmHrE92qPGdxxEkgi+f7NiMq\\nLZ/j8ZkYGir4Y0anisVTb8amblq+OxLLS5vC+SxYdzd/7UPdGNy6+jLEr1oVsfGjvayNKmH+lL4Y\\nGyqJzShg5/kUfBrZVMzHqm6bq9Qa3tx+nt8jEmlkY8rbk/vc9FysdfbJjPkpkLcD8wl+ybdiLtP7\\nuyL4oWxpmDPppSw9nYuzpTH5pbp9f0jvqvu+L/BKvu778Mu0ruiOeDwuk1f9TpJXqmXltM7MrEOF\\nys34trOGkG/82XoxmycGdqh2fl65/273/GIV+/7yo4mdGU+N6sX9haX889FeVkUUMX9yHwyU8PSG\\nMNacz8fDxpRPH+hbbWfl67nO4aiCL/DwcC1+Eak89PtJPg7MxsTOlXeGteLFo0cA+GBSd7ycLLH1\\nzOebU/s4kWPCgpv8u43bsx8bU0PuG9D9lt7Y08cxproYrqVOVxh9+vRh165dAISHh+Ps7Czzp65j\\nSkc3VBptRVlFfVl74jL/nLnCgBYO1+1iV18e9m2EnZkR3wfE4h+TXtFxTaXW8N2RWLw+3ceHey4w\\n+ddgDsdUXrAxObeYCSuDUGlg5bTOldpozx3YAo0W3t11nj9OJdLS0aLacpuaerq3J2PaOvP5uNpN\\nMLUzN2Z6Fw8Sc4rILlKxYmpn5g5qwai2LnwwojXxmYVMW32CfRfS6LroEEEJWTzctRHhrw1kWheP\\nBptMXa2FowV+T/Xkq/vakVWoYvRPgew4n0KPJrYNPpkC6OhuzdyyOXFf39f+hqVUCoWCju7Wekmm\\nauLRbo0xM1LyzWFdl6nq6uNBN3fE3tyID3ZHkpxbzNkruWwITaJrY5tar2NWH57t05Rzrw0k8MV+\\nDSqZqovyyfq7zqdyMPra5X5Xe6qXJ+kLRpCxYAT7n+3N1+Pbs3qGD9FvDual/s0wNlDSvYktT/eq\\nvhHFrdCvuQMHnu1NwAt9a5RMlSvvwvnIupMUlmoYVc3+tGBka/Y+3YuHfGvWAbSh+Wh0G3o3tWPb\\nuZQqnc20Wi37otJwtDBm0fj2bH+yB2kfjCD9gxH1kkyB7jg0u28z9j3Tq2ydqlYMv0YyBbpGA0/3\\n9iQ+s5An/zxN3yX+NPtoL8/8FUaPxf7MWHOCuIyCKq9Lzi1m2I/H+OJANF5OFux8qme9tF8f3daF\\n94Z7EZtRyINrT6DWaPl07wXm+0XSzN6c0LkD+GVqJ4Z7OZFeUEpnd+vr7vtvD2uFu7VuIfSzV3KZ\\nuzmc3kv8iUrL5+2hrXjkNidToJs7ump6F0wMlTz15+lalcL9feYKecVqHunaCKVSgYOFMXMHtiAt\\nv4S5W8Lp+Y0/q8sa4gQ8X/tkqjYUCgUj2jjryt7tdR0PJ64I4khsJqPaOFfME27haEEnd2t2R6bW\\nucwRdCXPEal5dPawuW1VEg1Vnb5VHx8f2rdvz7Rp01AoFLz33nv1HdddZXJHN97cfp4Np5PqZZ0j\\n0NVHP//3GSxNDFgx9dqT7OuThYkhL/Rrxvt+kfRbehQbU0OGeTkRmZpPaFIOViaGvNS/Gd/6xzJp\\nZTBBL/Wjqb05RaVqJqwIIj6zkGc7WVWZHDy1sztvbj/HmhBd95mneja5qT9MDxsztv5ncmdNvTmk\\nFfGZhTzft1mlyeFvDmlFUEIWm8OT2ReVhqFSwZKJHZjdp+kddxBRKhXMGdCCwS0dmbH2BOeS83iy\\n5xwdJB0AACAASURBVO278LtZn4xpw6xenjWaG9fQ2Zkb86BPo4qFXcddYw6bvbkxC0a2YfbGMN7a\\nfp7CUjVaLbwz1Etv+1+bOqwp0xC1d7XC3dqU3RdSK9a3GdjyxqPj1XWla2JnzsLxHfh4dFuUCm7L\\nfL2r9avFPLlyg1s50tndmlOJusYL1SVU5saGFR0z70QGSgVTO7tzNDaTHedSeOKq411UWj6Xs4u4\\nv5NbxXlUoVBgeAvKg/s1d+DCG4Nr9NzXBrXkh6NxrAq+hEIBg1s6Mq69C2tPXOL3k5fZGJbElJZm\\ndMuPoaBUTX6JmpVBCVzOLmKitysrp3WutgFRXb07zIughCy2n0th8PdHORSTQRM7M/Y904um9uZ4\\nu1nzv+5NyCwowcRQed1939LEkC/HtWPG2hN0+uogKo2WFg7m/DK18zW7md4O7Vyt+GhUG+ZuOcuL\\n/4Tz20yfGr1uZdn8squv717u35xvj1xk2THdsf2Z3p4sHN++3tYXuxEvJ92yIyOXHa+Yw/9836aV\\nnjO5oxund+aw7VwyM3zqdrMkNDEHrRa6eNzcGpF3gzqnyXPnzq3POO5qXk6WeLtZsSsilZyi0no5\\nyL2y+SxZhaX8MMX7tl5YvjvMC99GNuw4n8KO8ylsKBvuf6x7Yz4e3RYXKxO8/s/enYdFVbYPHP/O\\nDCCyuYCgoLgviKKGmkumAiZuadoCKq5lmVaWLWqbqaWmtlhq5q+3zco3rUxT0kwtKyVFU9wXRBEV\\n2UF2Zp7fH8S8IjOACA7L/bkur8s523PPmWfOnJtnOQ0cePK7CIb/Zz9/TO/NExuOsO9CEmPu8mBi\\n26KDbK11Wmbc24KZm45jo9OWaTKK8tLSxZ5fnijaj0Sr1fBFcBd6f/gn8ek5rB/nW6abl8qks0cd\\nDszow4HoFPq0uP1nYd0pGo2mWiRTBab1bsb/hV3Ep5ETTeubf19Teniy6q8o/rP/Ihqgk7uT2RYt\\nUXoajYb72jbgs/3RfHXwEvY2Onxv8+GUt/LgX0vTaDTM7NeSkK8PYW+j454qdC24FYO93Hhm4zG2\\nniycUO3895lB/UuRRN9JjZxs2TC+K5EJGYzyaYR7nfzHWTx9T3O+PhTD7C0n+OpkOl+dPGbcp2BC\\njuf7tSz3P7RotRq+HN2Fru/u4ffIRNydbNn5RE/jVP0FSjusIaiLO2vCLrD7XALP9W3B/MC2RZ6N\\nZwkz7m3Bt4cv882hGCZ0a1xsSyLkP5h559n8h5Df2DLsaJufNL700wneHuplfDTEndTIyZbfp/Vi\\n3DeHyMo1MPCm9zKyYyNe+/kU30dcLXNCdUhm+DOyfO2tIR70cef1baf46XjZ/xJQ4M/zifz3n8vc\\n7VmXx8w826SiaLUahnk3ZJh3Q5RSnI5LR6fVFLqQTO3VjKNX0lj5VxQ+y3YTlZhJz6b1+L+HO3Hs\\nyD8mj/vY3U15f895BrdztegMUsWpU9ua8Gf7ALc2Q11lZmdjZdG/CIr8xPbz4M54uRbf4mOl0/Le\\ncG8CVu9DAa8OMD8Vsrg197XJT6iuZ+sJbNegzA+rraoe6ezOh3+cp7tnvWpzbbtZKxd7WrvY88vp\\nOHLyDMZuwLv+ndXRr5IlVIDJWVe1Wg1jfRszsmNDVm7dR8uWLbGz1mFvo8OzXu1bmob+VtW3s2Hz\\n5O4s3X2O2f6taHkLXUtvptFo+Glyd+LTcyo05lul02pY/aAPXd/bw5PfRRDxQr8ifyC5cS63L8Mv\\noRQm/xA8rmuTcuuVVFZ1alvz46TuJte1d3OgTQN7Qk9eIyMnr0wJrczw9z+SUN0hD/o04vVtp9hw\\n5EqRhConz8CZ+HSOx6ZxPTuPdq4OeLk5Gh+IeSODQTHjx/xnm7w3ovQPva0IGo2Gtq6mx869N8Kb\\nE9fS2HU2Ac96tflhYjfjMzhMcbS1Iupl/0p/g1hdbzaEZZX2R9e/TQOe7NWM6ORMHuhQ/FTIovQG\\ntHFBowGlij5/qiaw1mnZ90wfS4dR4QZ7ufL+nvP8cT4Rv9YuKKXYdTaeRk61aNPg9mY6u9PsbKzo\\n36Q2vh3v7HXAu6FjoWc13g47Gys8K0Gr1M06e9ThmT7Neee3SN7acYb5g9oB+a1Rk/57mJ1n4rH/\\nfiuOtaxIzsyltrWWhzpVvetx/vMjG7Hw17O0XrgLK50GpRRujrXYPqVHkdZGg0Hxys8ncXOoxdRe\\nzbCx0nIoJgVbKy3tzNwL1iSVryZXU+0bOuLl5kDoiWukZeVx9GoqG45cYdupOE5du06eoejs9e5O\\ntjzcuRGLhngZb+TXHrzEgegURnfxqNBnm9wua52WDeO7snT3Ocb5Ni7VwPXKnkwJURmsGNXR0iFU\\nOy4OtbjLow7hl1LoJy221VZBQrX1RCx+rV04Hnuda9dzGHOXh/z+iELeGNiW9Ycvs3jXWUbf5cGx\\nq2k8tv4IyZm5tK5rhYO9HalZeRgUPHq3Z7mOV7uTJnX3ZOPRq6Tn6NEAmXkGDkSn8NPx2CLdFPdH\\nJ7Pw17MArPwrireHtufo1TS6eNS54+NFKyNJqO6gUR0bsWDHGZrM/4WUrDwA7G10dG1SF283R7wb\\nOuBQy4qT165zPDaNQzGpvPf7ef48n8SG8b7Ut7Nh9paT1LbWsmjIrc1gZwn17Wx4a3Dlj1MIIeYF\\ntuXnk3F0vemh3KL6uLeFM3Y2OraevMbS+73ZeSZ//FRl7O4nLMuhlhUfPNCREZ/up+/Kv4i7noOd\\njY41D/nQ2SqOrl1LMY95FdDKxZ7jL/Y3vj56JZWOS38j9GRckYRq678P/u7fypnfziUw4tP9gHT3\\nKyAJ1R00+i4PFu86i0ajYUK3Jjzo04iANi5mu5Fl5OQx9bsIvjhwibve+Z1+rVy4nJrFawPa0KRe\\n7TscvRBCVF+DvdwYfAef5yXuPFtrHf6tXNh8PJbzCRnsOlc5J6QQlcPwDg0Z7u3Gj8di6ezuxDdj\\n76KdmyPh4fEl71xFeTd0pHEdW7aduobeoNDdMKxk68lYrLQaNk7sxoWkTGZsPMbOs/EEVOEZQMuT\\nJFR3kJebIzGvDaCOrXWpnotjZ2PFZ0Gd6dm0Hs9sPMZ3R67g7mTLi/1b3oFohRBCiOplkJcrm4/H\\nsuVELLvPJtCsfu1qNWuoKF9fjO7CtlNx3O/tViPGUGs0GgZ5ubJm30X+vphEz2b5s35eTc3iQHQK\\n/Vs542RrTcdG1ux4ogdX07JpWMWfRVhepNPjHdbAodYtPWRUo9HwRK9m7JneC79WLnzySKcKfSic\\nEEIIUV0VPGdr2W/nSMrMpX8NnIRElJ6TrTUPdXKvEclUgYLvSOjJa8ZlP5+MA2DIDa34Go2GRk62\\nMv7wX5JQVRHdPevx69SeBJp46KIQQgghStasvh3t3RyISswEqNIPLBaiIvi3dsFapymUUG09mf9w\\n4MFecg9qjiRUQgghhKgxbhwr17+VzOooxI2cbK25p3l9DkSnEJuWTa7ewPZTcTSrX1umRy+GJFRC\\nCCGEqDEK/srepoE9HnVkgichblbQ7W/bqWvsjUoiJSuPwe3cpHtfMSShEkIIIUSNcU/z+gzxcmXG\\nvS0sHYoQlZJxHNWJa2w5kd/db0h76e5XHJndQAghhBA1hrVOy0+P3m3pMISotLwbOtKkri3bTsXR\\n0KkWtlZaeeh5CaSFSgghhBBCCAH8O316O1eSMnM5EXud/q1csLORNpjiSEIlhBBCCCGEMBp0w6zS\\nMrtfySShEkIIIYQQQhj5t26AtS5/EgpJqEom7XdCCCGEEEIII0dbK6b2asbV1GxaONtbOpxKTxIq\\nIYQQQgghRCHvj+hg6RCqDOnyJ4QQQgghhBBlJAmVEEIIIYQQQpSRRimlKrqQ8PDwii5CCCGEEEII\\nISqMr6+vyeV3JKESQgghhBBCiOpIuvwJIYQQQgghRBlJQiWEEEIIIYQQZSQJlRBCCCGEEEKUkSRU\\nQgghhBBCCFFGklAJIYQQQgghRBlJQiWEEEIIIYQQZWRl6QAs4a233uLw4cNoNBrmzJmDj4+PpUOq\\ntt5++23Cw8PJy8vj8ccfZ+fOnRw7doy6desCMHnyZPr162fZIKuZsLAwnnnmGVq3bg1AmzZtePTR\\nR3nxxRfR6/U0aNCAJUuWYGNjY+FIq5f169ezadMm4+ujR4/SoUMHMjIysLOzA+Cll16iQ4cOlgqx\\nWjl9+jRPPvkkEyZMYOzYsVy5csVkHd+0aROff/45Wq2Whx9+mIceesjSoVdZps757NmzycvLw8rK\\niiVLltCgQQO8vb256667jPt99tln6HQ6C0Zetd183mfNmmXyd1Tqevm5+Zw//fTTJCUlAZCcnEzn\\nzp15/PHHGTZsmPGaXq9ePZYvX27JsKu8m+8ZO3bsWHWu66qGCQsLU1OmTFFKKXX27Fn18MMPWzii\\n6mvv3r3q0UcfVUoplZiYqPr27ateeukltXPnTgtHVr3t27dPPfXUU4WWzZo1S23dulUppdSyZcvU\\nV199ZYnQaoywsDA1d+5cNXbsWHXq1ClLh1PtpKenq7Fjx6pXXnlFffnll0op03U8PT1d3XfffSo1\\nNVVlZmaqIUOGqKSkJEuGXmWZOucvvvii2rJli1JKqbVr16rFixcrpZTq3r27xeKsbkydd1O/o1LX\\ny4+pc36jWbNmqcOHD6vo6Gj1wAMPWCDC6snUPWNVuq7XuC5/e/fuJSAgAICWLVuSkpLC9evXLRxV\\n9dStWzfef/99AJycnMjMzESv11s4qpopLCwMf39/APr378/evXstHFH1tmLFCp588klLh1Ft2djY\\nsGbNGlxdXY3LTNXxw4cP07FjRxwdHbG1teWuu+7i4MGDlgq7SjN1zl9//XUGDhwI5P91Pjk52VLh\\nVVumzrspUtfLT3HnPDIykrS0NOnZVAFM3TNWpet6jUuo4uPjqVevnvF1/fr1iYuLs2BE1ZdOpzN2\\nddqwYQP33nsvOp2OtWvXMm7cOJ599lkSExMtHGX1dPbsWZ544gmCg4P5888/yczMNHbxc3Z2ljpf\\ngY4cOUKjRo1o0KABAMuXL2fMmDG89tprZGVlWTi66sHKygpbW9tCy0zV8fj4eOrXr2/cRq73ZWfq\\nnNvZ2aHT6dDr9Xz99dcMGzYMgJycHGbOnElQUBCffvqpJcKtNkydd6DI76jU9fJj7pwDfPHFF4wd\\nO9b4Oj4+nqeffpqgoKBCXb7FrTN1z1iVrus1cgzVjZRSlg6h2tuxYwcbNmzgP//5D0ePHqVu3bp4\\neXnx8ccf8+GHH/Laa69ZOsRqpVmzZkyfPp1BgwYRHR3NuHHjCrUMSp2vWBs2bOCBBx4AYNy4cbRt\\n2xZPT09ef/11vvrqKyZPnmzhCKs/c3Vc6n750+v1vPjii/To0YOePXsC8OKLL3L//fej0WgYO3Ys\\nXbt2pWPHjhaOtPoYPnx4kd/RLl26FNpG6nr5y8nJITw8nLlz5wJQt25dnnnmGe6//37S0tJ46KGH\\n6NGjR4mtiaJ4N94z3nfffcbllf26XuNaqFxdXYmPjze+vnbtmvEvyaL87dmzh48++og1a9bg6OhI\\nz5498fLyAsDPz4/Tp09bOMLqx83NjcGDB6PRaPD09MTFxYWUlBRj60hsbKxc8CtQWFiY8eZmwIAB\\neHp6AlLfK5qdnV2ROm7qei91v3zNnj2bpk2bMn36dOOy4OBg7O3tsbOzo0ePHlLvy5mp31Gp6xVv\\n//79hbr6OTg4MGrUKKytralfvz4dOnQgMjLSghFWfTffM1al63qNS6h69+7Ntm3bADh27Biurq44\\nODhYOKrqKS0tjbfffpvVq1cbZyN66qmniI6OBvJvPAtmohPlZ9OmTXzyyScAxMXFkZCQwMiRI431\\nfvv27fTp08eSIVZbsbGx2NvbY2Njg1KKCRMmkJqaCkh9r2i9evUqUsc7depEREQEqamppKenc/Dg\\nQbp27WrhSKuPTZs2YW1tzdNPP21cFhkZycyZM1FKkZeXx8GDB6XelzNTv6NS1yteREQE7dq1M77e\\nt28fCxcuBCAjI4OTJ0/SvHlzS4VX5Zm6Z6xK1/Ua1+Xvrrvuwtvbm6CgIDQaDa+//rqlQ6q2tm7d\\nSlJSEjNmzDAuGzlyJDNmzKB27drY2dkZL0ai/Pj5+fH888/z66+/kpuby9y5c/Hy8uKll17iv//9\\nL+7u7owYMcLSYVZLcXFxxr7dGo2Ghx9+mAkTJlC7dm3c3Nx46qmnLBxh9XD06FEWL15MTEwMVlZW\\nbNu2jaVLlzJr1qxCddza2pqZM2cyefJkNBoN06ZNw9HR0dLhV0mmznlCQgK1atUiJCQEyJ/oae7c\\nuTRs2JAHH3wQrVaLn5+fDOC/DabO+9ixY4v8jtra2kpdLyemzvkHH3xAXFycsccBQNeuXdm4cSOP\\nPPIIer2eKVOm4ObmZsHIqzZT94yLFi3ilVdeqRLXdY2qLJ0PhRBCCCGEEKKKqXFd/oQQQgghhBCi\\nvEhCJYQQQgghhBBlJAmVEEIIIYQQQpSRJFRCCCGEEEIIUUaSUAkhhBBCCCFEGUlCJYQQQgghhBBl\\nJAmVEEIIIYQQQpSRJFRCCCGEEEIIUUaSUAkhhBBCCCFEGUlCJYQQQgghhBBlJAmVEEIIIYQQQpSR\\nJFRCCCGEEEIIUUaSUAkhhBBCCCFEGUlCJYQQQgghhBBlJAmVEKLCtW3blgEDBhAYGEhgYCADBgxg\\nzpw5ZGRklPmYYWFhDBgwoMTtAgMDiY+PL3M5pXWnyqkIAwYMICwsrNTbX7p0ifbt25db+X5+fhw4\\ncKDYbeLj4/n111/LrcxbERkZyf79+29pH71ez7hx4/Dz8+PUqVOF1n3//fd07tyZwMBAAgICCAgI\\n4K233iI1NdW4TUhICPfcc4/xO1PwLz4+3vj/fv360aFDB+PrefPmlcv7NSc6OpoBAwYwfPhwAJ5/\\n/nn69u3Lnj17GD9+PMeOHTO775EjR5g8efJtlf/tt9+Wab/k5GR69erFK6+8YnL9jBkz+O677/jg\\ngw94+eWXC60zGAyMHj2av/76q0xlCyFqBitLByCEqBm+/PJLGjZsCEBOTg7PPvssq1ev5tlnn63Q\\ncn/++ecKPf6dLqemCgsL46+//sLf3/+Ol71jxw7y8vLo1q1bqfe5du0a+/fv58iRI1hbWxdZ37lz\\nZz777DMA0tLSWLp0KSEhIXz77bfUqlULgBdeeMGYvNyooK6FhYXxyiuv3LG6Fx4eToMGDfj6668B\\n2LJlC9u2bcPT05M+ffoUu6+Pjw+ffPJJmcvW6/W8/fbbPPzww7e8708//URISAgbNmwgOzvbeH4B\\ntm7dSlpaGqNGjeKDDz4osq9Wq+Wtt95i4sSJhIaGYmtrW+b3IISovqSFSghxx9nY2NCnTx9OnDgB\\n5CdYCxYsYODAgfj5+fHRRx8Ztz106BAjR44kMDCQwYMH3/Jfitu2bcvVq1cJCwvjkUce4c0338Tf\\n35+RI0dy+PBhQkJC6N27N8uXLzfus2LFCgYOHEhAQACPP/64seUgOTmZcePG0a9fP55++mlefvll\\n403YzeUsW7aMQYMG4efnx99//w1AZmYmM2bMML7PxYsXG8sMCQnh3XffZdCgQaxatYqhQ4cWeh8j\\nR45kx44dRd6fuVg/+OAD5s2bx7Rp0/D39+fBBx/k2rVrABw9epQhQ4YwcOBA3nrrLbPnLjIykuDg\\nYAYNGsSAAQP46aefCq3/9NNPje+xILbY2FjGjx/P4MGDCQgI4N133wXy/9L/7rvvGltTZs2aVaSF\\n8uZWx4LXx44dY968eWzbts2YgO/YsYNhw4bh7+/PpEmTSExMLBJ/WFgYw4YNY9GiRcZz/s8//9zS\\nZ/Hhhx+yevVqvvjiCxYtWlSkjJMnTxIUFERgYCDDhw9nz5496PV6QkJCMBgMDBs2jJMnT5o9xwCO\\njo688cYb2Nvbs3HjxmK3vVXR0dGMGTOGAQMGMGrUKGMr0uXLl5k8eTIDBw5k6NChhco1dW4PHTrE\\n0qVLOX78OPfff7/x/U2ePJnffvutUCvjxo0bGThwIAMHDuSFF14gJyen0Gdb3Pfdz8+PdevW8eCD\\nD3LPPfcYz/nEiRNJS0sjMDCQ6OhoQkNDGTp0KIMGDWLYsGHFtrBu3LiRoUOH0rt37yKtnCtWrOCx\\nxx4rss/Vq1fx8/Pj4MGDNGvWjE6dOrF+/foyfgpCiGpPCSFEBWvTpo26cuWK8XVycrIaM2aMWrly\\npVJKqQ8//FCNHz9eZWdnq/T0dDVixAi1c+dOpZRSQ4cOVT/99JNSSqkffvhBBQQEKKWU2rdvn/H/\\npSl73759ytvbW+3bt08ZDAY1atQoNXLkSJWRkaFOnTql2rdvr7KyslRERITq2bOnSktLU3q9Xk2Y\\nMEGtWLFCKaXUokWL1DPPPKOUUioiIkJ16tRJLV++vEg5HTp0UL/88otSSqk1a9aoCRMmKKWU+uST\\nT9Sjjz6qDAaDSk5OVt27d1f79+9XSik1duxYNWnSJKXX61VOTo7q3r27OnHihFJKqZiYGOXr66uy\\ns7MLvbfiYl2+fLnq2bOnunTpkjIYDGrKlCnG8z1q1Ci1bt06pZRSW7duVe3atVP79u0rcu4ef/xx\\ntXr1aqWUUn///bfy8fFROTk5Kjo6WrVp00atWbNGKaXUH3/8oXr06KFycnLUokWL1AcffKCUUioj\\nI0M9++yzKjY2Vv30009qxIgRKj09XeXl5ampU6caY+3fv7/av39/kc/0xtfLly9Xc+bMUUopdfHi\\nRdWlSxd16tQppZRSH330kXrqqaeKxL9v3z7l5eWltmzZopRS6ttvv1XDhw+/pc9CKaVeeuklY6w3\\n0uv1atCgQWrz5s1KKaWOHDmiunXrptLS0lR0dLTy8vIqso9SSn333Xdq/PjxRZZ//PHH6tlnnzXG\\nsHHjRpP7mzo/5owfP1599dVXSimlfvnlFzV48GCllFKTJk1SH330kVJKqUuXLilfX18VHR1d7Lm9\\nOe4bv9cFn2F0dLTq0aOHunr1qjIYDGratGlqzZo1hWIt7vvev39/9dxzz6m8vDx19epV5e3tra5c\\nuVLkfN59993q0qVLSiml9u/fr9566y2T7//06dMqKCjIuN1jjz1mXHfmzBnVrVs34+dcUMcyMzPV\\nAw88oDZt2mTcdsuWLSo4OLjYcy2EqLmkhUoIcUeEhIQQGBiIv78//v7+9OjRw/iX4V27djF69Ghs\\nbGyws7Nj+PDhbN++Hcj/6/KgQYMA8PX1JTo6uswxODk5cffdd6PRaGjdujXdu3endu3atG7dGr1e\\nT2JiIh06dGD37t04ODig1Wrp0qWLscwDBw4YW446dOiAj4+PyXLs7e0JCAgAwNvbm8uXLwMwadIk\\nVq5ciUajoU6dOrRu3ZpLly4Z9+vbty9arRZra2sGDhzIli1bgPwWA39/f2xsbAqVU1ysAF27dsXD\\nwwONRoOXlxdXrlwhOzubiIgIBg8eDOSP/apdu7bJ97Fy5UrjuBdfX1+ys7OJi4szrn/ggQcA6N27\\nN3l5eVy8eBFnZ2f++OMPDhw4gI2NDe+88w6urq7s3r2bESNGYGdnh06nY+TIkfz555+l+diK+P33\\n3+nevTtt2rQBICgoiJ07d6LX64tsa2dnZ6w/9913HydOnCAzM7PUn0VxLl26RHx8PEOGDAGgY8eO\\nuLu7ExERUab35eDgQFpamvH1kiVLCo2fuv/++2/peNnZ2YSFhRnrrL+/P99++y25ubn89ddfjB49\\nGgAPDw/uvvtu9u3bd0vn1pQ///yTLl264ObmhkajYdmyZUyYMKHQNsV93wGGDRuGTqfDzc0NZ2dn\\nrly5UqQcZ2dn1q1bR0xMDF27dmX27Nkm4/nhhx+M583X15eoqCjjWMcjR47g7e1d5HOeM2cOfn5+\\nDBs2zLjMx8eHI0eOoJQq1XkQQtQsMoZKCHFHFIyhSkxMNHbfs7LKvwSlpaWxcOFC3nnnHSC/S1BB\\nsrJ582a++OIL0tPTMRgMt3VDY29vb/y/VqvFzs4OAI1Gg1arRa/Xk5mZycKFC41diFJSUujXrx8A\\nqamp1KlTx3gMNzc3k+U4OjoWKsdgMAAQFRXFokWLiIyMRKvVcvXqVUaOHGnc9sZjDxkyhNmzZzNz\\n5kx27NhhckB/cbHeHIdOp0Ov15OcnAzk37wXvHcnJyeT72PPnj2sWrWKpKQkNBoNSinjewGoV69e\\nobJSU1OZMGECBoOBN954g2vXrjFmzBieeuopEhMTC72/OnXqkJCQYLLckqSlpXHgwAECAwONyxwc\\nHEhOTsbZ2bnQtk5OTmg0GuP/If9zjI2NLfVnYU5iYiKOjo7G4xeUkZiYSJMmTW75fcXExBSK39wY\\nqtJKTk7GYDAY64FGo8He3p64uDiUUoXqR0HcBoPB7LktjaSkpEL16cbxSgWK+74XlFegoN7ebNWq\\nVaxatYqRI0fSqFEj5syZQ/fu3Qtto9fr2bx5MxkZGSxbtgzITzI3b97MxIkTSUhIoH79+oX22b59\\nOzk5OfTs2bPQcmdnZ3Jzc0lJSaFu3bqlOhdCiJpDEiohxB1Vv359QkJCWLJkCatWrQLA1dWVSZMm\\n0b9//0LbxsbG8sorr7B+/Xq8vLyIiopi4MCBFRrf559/TlRUFN9//z329va8++67xMbGAvkJ2Y3j\\nfuLi4vD09Cz1sefNm4e3tzcrVqxAp9MRFBRkdttu3bqRl5fHrl27OHPmDL169bqlWM0pSBSuX7+O\\no6MjBoOBlJSUItvl5uYyY8YM3nvvPfr27VvkphfyE7iCpColJYU6depgZWXFlClTmDJlCufPn+ex\\nxx7D19cXFxeXQjflycnJuLi4FDrezTfPN856dyNXV1d69epVaNybOTeWWfA+69aty+zZs0v9WZjj\\n7OxMSkoKSiljUmUqqSsNvV7Pjh07mDZt2i3va069evXQaDQkJSVRv359lFJcvHgRd3d3tFqtZEdw\\nFwAAIABJREFU8TO7MW6NRlPqc2uuzEOHDhlfX79+naysrELbmPu+3wpPT08WLlyIwWBg48aNzJw5\\nkz179hTa5o8//qBNmzaFJsM4fvw4s2fPZuLEiSb/ONO+fXtmzZrFxIkT6dWrFx4eHmWOUQhRc0iX\\nPyHEHTdx4kQOHTpknKzB39+f9evXo9frUUqxcuVKfv/9dxITE7Gzs6NFixbk5eXx3//+F4D09PQK\\niy0hIYEWLVpgb29PTEwMv/32mzGJ8vHxMc6oduLECY4cOXLLx/by8kKn0/Hnn39y4cIFs1PHa7Va\\nBg8ezPz58/Hz8zM5U1xxsZpja2tLu3bt+OWXX4D8mdqys7OLbJeZmUlGRgYdOnQA8pM3a2vrQsff\\nvHkzkN/Nq3bt2nh6evLaa68Zu/J5enri4uKCRqOhX79+bNq0iczMTPLy8tiwYQN9+/YtVGaDBg2I\\ni4sjISHB2LpQwMrKytgd7p577uHAgQPG7o1HjhxhwYIFJt9vVlaWccKMbdu20aFDB2rVqnVLn8WN\\nZd+ocePGNGzYkK1btwJw8OBB4uPjzXYFNScjI4NXX32VOnXqGLsnlgcbGxt69+7NDz/8AOS3OE6Z\\nMgVra2vuuece4/fp4sWLHDhwgF69et3SuTWlb9++HDx4kEuXLqGU4vXXX2fDhg2FtjH3fS+OtbU1\\nBoOB69evk5iYyMSJE7l+/TparZZOnToVaiUs8MMPPxi73hZo3749aWlpnDp1CmdnZ5KSkgqtb9y4\\nMV5eXowfP545c+YYk67ExESsra3NtuYKIWo2SaiEEHecg4MDU6ZMYfHixSilGD16NO7u7gwZMoTA\\nwEDOnTuHr68v7dq1495772XgwIE88sgj+Pn50blzZ0JCQgodrzyecVMgKCiI/fv3M3DgQBYvXsys\\nWbPYu3cvn332GVOnTuX8+fMMGDCA//znP/j7+5u8kTNn6tSpLF68mKFDh/L3338zffp0PvjgA8LD\\nw01uP2TIEGJiYozjnW4l1uLMnTuXNWvWMHDgQI4cOULLli2LbOPk5MSjjz7KiBEjGDFiBJ6engQE\\nBPDEE0+QmZmJnZ0dBoOBoUOH8vrrr/Pmm29iZWVFUFCQcTa/wYMH06VLF3r27ElgYCD33nsvI0eO\\nZOjQoTRs2JBx48YVKrNp06aMGjWKESNGMHr0aHr06GFc17t3b/bt28eoUaNwdXVl/vz5TJs2jUGD\\nBjFv3jyz58jDw4Pw8HAGDhzI6tWref3112/5s+jfvz/r1q3j6aefLrRco9HwzjvvsHbtWgYNGsSC\\nBQt4//33jV1Ji/PPP/8QGBjIfffdR2BgILVq1eKTTz4xdoMtL2+++Sa7du3C39+f9957j6VLlwLw\\nxhtvEBYWRmBgINOmTWPBggU0atTols6tKQ0bNmTevHmMHz/e2Jo8ceLEQtuY+74Xp0GDBvj6+tK/\\nf3+ioqLo06cPo0aNYvDgwTz33HO8+eabhbZPTU01vu+b+fv7s3HjRnx8fDh27JjJlqopU6aQnp7O\\n2rVrATh8+DAdO3YscVydEKJm0igZYSmEqAaee+4545iMinRj966nn34aX19fxo8fXyFlxcfH88AD\\nD7B79250Ol2FlFGdFTynqaA1ToibFSSNJT1jbObMmXTs2LHIBBtCCAHSQiWEqAaSkpIYMWJEhZez\\ndu1apk6disFgICEhgb///psuXbpUWHnLly8nODhYkikhKsjUqVNZs2ZNsdtER0dz4MCBMj1UWAhR\\nM0hCJYSo8urVq8e9995b4eU88MAD2NjYcN999xEcHMykSZNuebxMacTHx+Pv7098fDyTJk0q9+ML\\nIfLdf//92NraGseZ3cxgMDB79mwWLFhQqq6cQoiaSbr8CSGEEEIIIUQZlaqF6vTp0wQEBBgHZ86a\\nNYthw4YREhJCSEgIu3fvrsgYhRBCCCGEEKJSKnE6oYyMDObPn1/kIXfPPfdcqZ8hYW4GKyGEEEII\\nIYSoCszNSFpiQmVjY8OaNWtKHLRZ1gAsIXTdSti1DI+sKGJsm0H/mQwKetLSYYlqIDw8vFLVdVH1\\nSZ0S5U3qlChvUqdEeauMdaq4BqISu/xZWVlha2tbZPnatWsZN24czz77LImJibcX4R0Uum4lTUOn\\n0TQrEisMNM2KpGnotPwkSwghhBBCCCFuQaknpfjggw+oV68eY8eOZe/evdStWxcvLy8+/vhjrl69\\nymuvvWZ238rU5S/2owdplhNVZPl5m+Y0fGL9nQ9ICCGEEEIIUemVucufKTeOp/Lz82Pu3LllDuBO\\nO5Jz0eTyxjkX6VRJYhRVV2VsohZVm9QpUd6kTonyJnVKlLfKWKduq8ufKU899RTR0dFA/pPoW7du\\nXbbILCDGtpnJ5ZfNLBdCCCGEEEIIc0psoTp69CiLFy8mJiYGKysrtm3bxtixY5kxYwa1a9fGzs6O\\nhQsX3olYy0f/mRA6zcTy5+58LEIIIYQQQogqrcSEqkOHDnz55ZdFlg8cOLBCAqpog4KeJBRw/vkF\\nHFQG0bWaYfB7QWb5E0IIIYQQQtyyMnX5q+oGBT3J2dZBADiPWSHJlBBCCCGEEKJMamRCBVDb3QuA\\na+cjLByJEEIIIYQQoqqqsQlVg+YdAMiMOWHhSIQQQgghhBAV6e67766wY5dp2vTqoFnbTqQCuoQz\\nlg5FCCGEEEKIamndoRje+vUMx2Ov097NgTn+rQnq4mHpsMpVjU2oPBo2JFJbjzqp5y0dihBCCCGE\\nENXOukMxBK89aHwdcSXN+LqsSdX333/P77//zrVr1+jTpw+//fYbWq2WgIAAJk2axNWrV3nhhRcA\\nyMvLY/HixXh6et7+mylGjU2oNBoNV2s1oX1mBHlZGVjZ2lk6JCGEEEIIIaqMFzYfZ/3hy2bXX07N\\nMrl83DeHmLXF9LCbhzq5E+RefLlXrlxh6dKlzJkzh2+++QaA4OBgAgMDiY+PZ9q0afTo0YMNGzbw\\n9ddfM2vWrNK9oTKqsQkVQIpDU7SZR7h45ggtOvawdDhCCCGEEEJUG7l6dUvLS6tjx45ERERw4cIF\\nxo0bB0B6ejoxMTE0btyYBQsW8MEHH5Camoq3t/dtlVUaNTqhyq3bFOIg5qwkVEIIIYQQQtyKJcPa\\ns2RYe7PrfZbuJuJKWtHljZw4/Hxfs/uFh4cXW661tTXW1tb069ePefPmFVo3e/Zs7rnnHoKDg/n5\\n55/ZvXt38W+iHNTYWf4ArJybA5By4ZiFIxFCCCGEEKJ6mePf2uTy2f6tbvvY3t7ehIWFkZmZiVKK\\nBQsWkJWVRVJSEp6eniil+PXXX8nNzb3tskpSo1uo6jZqAYD+2mkLRyKEEEIIIUT1UjDxxMJfz3I8\\nNo32bo7M9m9VLrP8ubu7M27cOMaMGYNOpyMgIABbW1seeeQR5s+fj4eHByEhIbz66qv88ccft11e\\ncWp0QtXIzZ0MbLFLOmvpUIQQQgghhKh2grp4lOs06SNHjjT+f8yYMYwZM6bQ+v79+9O/f3/j6z17\\n9gAQFhZWbjHcrEZ3+bOz0XGpVhNcMi+iDHpLhyOEEEIIIYSoYmp0QgWQ7NicWiqH1CuRlg5FCCGE\\nEEIIUcXU+IRK75w/WO7CqX8sHIkQQgghhBCiqqnxCVVtj/ypHuPOH7VwJEIIIYQQQoiqpsYnVK7N\\n8x/2lXX5pIUjEUIIIYQQQlQ1pUqoTp8+TUBAAGvXrgXgypUrhISEMHr0aJ555hlycnIqNMiK1KxN\\nR/LQYhUvU6cLIYQQQgghbk2JCVVGRgbz58+nZ8+exmXLly9n9OjRfP311zRt2pQNGzZUaJAVqblr\\nPWJ0Dalz/bylQxFCCCGEEEIU4/fff+frr7+2dBiFlJhQ2djYsGbNGlxdXY3LwsLC8Pf3B/Lnet+7\\nd2/FRVjBdFoNcXZNccpLITc1ztLhCCGEEEIIUW2sizyEz8ZlWH32Ij4bl7Eu8tBtHe/ee+9l9OjR\\n5RRd+Sjxwb5WVlZYWRXeLDMzExsbGwCcnZ2Ji6vaiUhGvVaQtpfYyAgad/azdDhCCCGEEEJUeesi\\nDxH821fG1xFJV4yvg1p0KdMxv//+e3bv3k18fDyenp4cOnSI4OBgTp06xeHDh40P+920aRNr165F\\nq9XSunVr5s+fT1paGk8//TRZWVn07duXb7/9lp07d972+ywxoSqJUqpU24WHh99uURUiPDyc67YN\\nATj4x3Zi9XUsHJGo6iprXRdVl9QpUd6kTonyJnWqZnr/8iF2pESbXR+Xm2Fyechv3/DcXxtNrguo\\n04Rn3LuYrVNRUVEkJSVx7NgxpkyZwj333MOLL77I+++/T58+fXjvvfdo164dJ0+eZPr06djb2zNv\\n3jx++OEHTpw4gaOjI9OnT+eXX34hOzu7XOpumRIqOzs7srKysLW1JTY2tlB3QHN8fX3LUlSFCg8P\\nx9fXl8iYi3AaauckVMo4RdVRUKeEKC9Sp0R5kzolypvUqZrLzXAZm6xYs+vzctNNL8eATS0b08ds\\n6AaYzx0uXLhAVFQUzZo1o1+/fqSnp+Pi4sKAAQNIT09n6dKl+Pr6EhcXx8cffwxAbGws7u7uHD58\\nmMDAQHx9ffHw8GD79u2lrrvFJV5lSqh69erFtm3bGD58ONu3b6dPnz5lOUyl0bhVJwAMsacsHIkQ\\nQgghhBBVw5Juw1jSbZjZ9T4blxGRdKXo8nqNODxiptn9StNqpNPpjP+/eXhSTk4O8+bN48cff6RB\\ngwY8/vjjQH7POq02fwoJjUZTYhmlVeKkFEePHiUkJIQffviBL774gpCQEKZPn87GjRsZPXo0ycnJ\\njBgxotwCsoQ2TRsTr6mLXXKkpUMRQgghhBCiWpjjY3pugtlmlpeX9PR0dDodDRo04MqVKxw9epTc\\n3Fw8PT05evQokD9bYHkpsYWqQ4cOfPnll0WWf/rpp+UWhKU529tw0caTzlkRGHIy0drUtnRIQggh\\nhBBCVGkFE08sPLKT48mxtK/rxmwfvzJPSFFa9erVo3fv3owaNYp27drx6KOPsnDhQr788kuefPJJ\\nQkJC6NWrl7G16nbd9qQU1UWqY3O02UfIuHwKh2adLR2OEEIIIYQQVV5Qiy7lmkCNHDmSkSNHGl/b\\n29sbZ+q78f+LFi0qtN/EiROJiYlh2rRp9OnTh0OHDrF///5yiUkSqn/pG7SBeIg+fRgvSahKJXTd\\nSti1DI+sKGJsm0H/mQwKerLSHlcIIYQQQtRcjo6OfPbZZ6xYsQKAl19+uVyOKwnVv+w8vOAEJEQd\\ntUj5VS2JCF23kqah04yvm2ZFQug0QuG24q6o4wohhBBCiJrNycmJTz75pNyPWz4dB6sB1xYdAMi6\\nfPKOl12QRDTNisQKA02zImkaOi0/yfp3fejjLTkyXkfo4y2Ny0t77LLuW6xdy8wsf6dyHlcIIYQQ\\nQogKIC1U/2rRvA0Z1MIq4fQdLVfp87D9ZZ7JdfbbX2PTtXO0Cv9fMnErLTYV2drjkRVlcrm7meWW\\nPq4QQgghhBAVQRKqf7VydWCzVWNapUWhDAY0ZZj1o6Ruezeuj7VpTG6DttSJO4xb3jWTx3PRJ+AS\\nbqZlZtc7UFJSVFxrz20mVElWzjTIiyuyPNHa5baOm2DVALe8og+Iu2zbjE63dWQhhBBCCCHKnyRU\\n/6plpeNa7aa0TztHbsJFbBo0u6X9zbUG/azPJWDYaHatX0nT3+ca13vkXISYi2RhTbLGkboqrcgx\\nY3UNaKCPR4sqsq40LTbmWnvMLS+t2AunccxLMbnOLTeWnYtH06XXfST+/A7Zl49Ty709LsPmUKdH\\nULHH3f3T1zibSNIA6P/cbcUshBBCCCFERZAxVDfIrNcKgKSoiFvf2UxrkOf2GZx+yhWPG5KpG8Xa\\neJASuMjkuqz75hJt29zkuiu1mpQYUqqujsnluRorEq5Gl7i/KQa9ngPvBGNLDgc8RnHBtiW56Lhg\\n25J93s9xxro5DY9/w5X/m0j2pQgw6Mm+FEHMqmBS9q0ze9zfQ/9LnfUTyMOaiPZPcNnaHYBkrRMX\\nBq2QCSmEEEIIIYRJfn5+pKenW6x8SahuYNWwLQCXzx0p9T5KKWL/2YFnVqTJ9QZgu00vDGhMrnfP\\niWZQ0JNcGLSiUHJiTCL6zzS5Xy1DFskJprsKAmxZ/Rr19Ukm19VW2Rx7+S6O/72z+Ddnwrb/e53m\\nyQc5VO9eRs//lkGrz9Lp8zwGrT7LpBeX0WXhQbOJ3Nn1pseK/bHtOxzWjUOh4XrwVzzy0iqazfsH\\nPVrSnFpIMiWEEEIIUUWl7FvHuZd9OD7RinMv+xT7B/aqSrr83aBuU2/4G1IvHjO7zY3joBKsGqDR\\nWeOafclMugQXa7Xg8RW/s3t6m/xugDcpGBs0KOhJ47imG8cKDQp6klCAXe/gnhXFZdumpFvVof31\\nQ+x7uTfd5v2Gs6t74Ri/WErzv+YTr6vPtbumYh+x7t99m6H6PUvC5fN0PvIuOSsG8t2WB7C7Gl6q\\n6drPRITR6K/FJGjr0fu5L7DSFc3HPRvUJUVftPsigE38KbKSr2Fb19V4HhtnRVEXhQENiY98Tb/A\\nUQC0aOjCFqvGeKSeLPOYNiGEEEIIYTkp+9YRsyrY+Lqg1xJQ4lAQc65fv87MmTPJyMggKyuLV199\\nFYDVq1dz4MABdDodK1as4Pr167zwwgtotVr0ej1LlizBw8Pj9t+UCZJQ3aBJyw7o0aKumZ7p7+Zx\\nUm55sZAHJ61bcbVJAP0iPyqyj8ZvJrWtdfktTTfsa1SKsUE3J1u5uXmsmxuM76UNHHj1HrrM/Q3X\\nRvldAH/5dhVNfn2RJK0TTtO3cu9ddwMLjPsW2PqjL67fT8Qrar1xWXGzAOZkZRK5YgxNyCN5yLv0\\n8WxqNt5zOk/a6KOKLNdh4PQz7lyu3YKmmWcKrdOiyExN+N9rrYYrjl60TLpI5pVT+c8JE0IIIYQQ\\nlUbsuhdI3b/e7PrcpMsml8d8PI5r62eZXOfU7SFobT7ZiouL46GHHiIgIIC9e/eyZs0aANq2bctz\\nzz3H4sWL+fHHH8nLy6NXr15MmzaNY8eOERcXV2EJlfzZ/wZt3etzSeuGfbLp7nuanUtNLq+tM/Dk\\n66vMd9uD4rv13SJrayuC5/2XQ82CaZJ1npMvd2H7Y02JGK/FfcuTZGON9ZQf6XzX3WaPMXh4MIk2\\nrqZXmnjm00/vPU2TzHMc9BjJsAfHFxtfqHuIyeXb7fpx3qY5njclU+bKzXLzASD66N5iyxNCCCGE\\nEJWQPvfWlpeCi4sL27ZtIzg4mKVLl5KcnAzA3Xfn3/d27NiR8+fP07t3b3788UcWLVpETk4OnTt3\\nLnOZJZEWqhu4O9nyk3UTmmb/Td71BKwcnI3rlEFP4+wo0/tlXQDMd9srUNL6W2Gl0xL02lq+n3kB\\n76S/QP+/1p3a5HDhwnHo2a/YYzTMiTG5vElWJH/v+I6EuCuw+10aZ0XRFgMJ2roMfuH/Soyt/6jH\\nef6TTB7L2EALfTSRuiassXuQcZNnMKyTO8cmWqHDUGS/m2curN3sLjgJcaf+pu3ASSWWK4QQQggh\\n7hy3oCW4BS0xu/7cyz75k5TdpFYTH1ouOGx2v0vh4WbXff7557i5ubFkyRIiIiJ4++23AdBo/jcA\\nR6PR0KZNG3788Uf+/PNP3nnnHUaNGsWIESNK87ZumbRQ3UCj0ZDi1AKArJgTxuUqL5eT7z1icvpy\\nyB8HZQk6nRaHjKumV5poZbpZjJm4tSgcvnyQpj8/RdOsSHT/TqnhbEgmbNs3JR43qIsH4ybPYG77\\nz+jqupG57T9j3OQZBHXxQKvVcMlMuTefx8btu2NAg/7SoRLLFEIIIYQQlYvLsDmmlw+dXeZjJiUl\\n4enpCcCOHTvIzc1v7Tpw4AAAhw8fpkWLFmzZsoUzZ84QEBDAM888w9GjR8tcZkkkobqJwaU1ALGR\\n+dm0ISeL8++PRB3+jkitmX6XFnxGkofZVjPTywsxM4PgYe+nSNU4mN6nFIka5CdVh5/vS+6SoRx+\\nvi9BXW44d2bKvfk8tm/SkPM6Dxzjj6EMRVu0hBBCCCFE5VWnRxAeU7+hVhMf0FlRq4kPHlO/KfOE\\nFADDhw/n008/ZdKkSfj4+BAXF4dSijNnzjBhwgROnTrF8OHDadasGfPmzWPcuHGsWLGC4ODgkg9e\\nRtLl7yb2jdvDCYg/H0Hz7Awuvj+CrGO/8Kd1Z84PWYMu++8bZtxrBv2fs+i03jG2zYqdPbA4RWcQ\\nbAb9nyM46EmOjF9hcp9SJWplLPfm8+hZrzYbbFrRMnM3OdfOUath69suWwghhBBC3Dl1egTdVgJ1\\nMx8fH0JDQ42v/f39TW7n7e3Nhg0byq3c4khCdZM2hvwH3jqEreLUoS9QOenssunOtq5L+GG4Lzpt\\n13IbB1UubmP2QDA/rut2ErXbKfdGGo2GlPreELOb9PMHJKESQgghhBCVTpm6/IWFhdGjRw9CQkII\\nCQlh/vz55R2XRaTsW0eTX18EQAOonPwnLv/j4s+X43qi05p72pTllOfsgYWUslteRdM0zp+R5eqJ\\nsDtarhBCCCGEEKVR5haq7t27s3z58vKMxeLiN79lcvmzajN1apteVxmU5+yBNx6zNN3yKlr91l0h\\nDK6fNz/bixBCCCGEEJYiXf5ukBVzHFNtUCr25B2PpTKoiETtVnl5uhOldafR1QiUUoWmxBRCCCGE\\nEMLSNEop03OBFyMsLIw33ngDT09PUlJSmD59Or179za7fXgxc8lXJrEfPUiznKgiy8/bNKfhE+af\\nAi0qzrUMPce+eJ7BOXvIGv8jqk7FPOFaCCGEEEKI4vj6+ppcXqYWqmbNmjF9+nQGDRpEdHQ048aN\\nY/v27djY2NxyAJYUHh5eKK6htg/xdk7Rh5N9ZPsgmyth/DWBUorN61tDzh5aOebgVMk/h5vrlBC3\\nS+qUKG9Sp0R5kzolyltlrFPFNRCVaVIKNzc3Bg8ejEajwdPTExcXF2JjY8scYGVxselgnnd8nlO6\\nZuSi45SuGc87Ps/FpkMsHVqNpdFoyHbzAeB65H4LRyOEEEIIIURhZWqh2rRpE3FxcUyePJm4uDgS\\nEhJwc3Mr79juuDn+rQleey+hte4ttPwb/1YWikgA2De7C05D0pkDuFs6GCGEEEIIIW5QpoTKz8+P\\n559/nl9//ZXc3Fzmzp1bbHe/qiKoS/74nIW/nuV4bBrt3RyZ7d/KuFxYRitPDy5qG+IefUgmphBC\\nCCGEEJVKmRIqBwcHPvroo/KOpVII6uIhCVQl06GhE/9YtcQz60/yEqOxdva0dEhCCCGEEEIAZRxD\\nJcSd5N3QkRNWLQHIlOdRCSGEEEKISkQSKlHpuTrYEG3fFoCsCwctHI0QQgghhBD/IwmVqPQ0Gg26\\nJl0ASI88YOFohBBCCCGE+B9JqESV0KxxY2K0rmScD6cMz6IWQgghhBCiQkhCJaoE74aOHLdqgSY9\\njryky5YORwghKkzKvnWce9mH4xOtOPeyDyn71lk6JCGEEMWQhEpUCR0aOnLcKv95YFlRMjGFEKJ6\\nStm3jphVwWRfigCDnuxLEcSsCpakSgghKjFJqESVkN9C9e9MfzIxhRCimorf/Jbp5T8tvMORCCGE\\nKC1JqESV4GxvQ1zd9oC0UAkhqq/sy8dvabkQQgjLk4RKVBmNPZpwRetCZlTFtFBV1LgFGQ8hKhOp\\nj5WXITsdg8b0z3KmS/M7HI2oKULXrST08ZYcGa8j9PGWhK5baemQRDVXHX+HrCwdgBCllf+A3xY0\\nSv6bvOSrWNVtWG7HLhi3UKBg3AJAnR5BFXrc0E2L4JfleKRdJcaxIQx4mkH3zypzmZaWsm8d8Zvf\\nIvvycWq5t8dl2JzbOofi1pn7DCqqnldVla2uXv36ObT6XJPr1nh2x7cCy65s50LcGaHrVtI0dJrx\\nddOsSAidRigwKOhJywVmIfI9KL2ynqvq+jskLVSiyrhxYoryHkdV0riF0E2LCH3KnSMTtIQ+5Z6f\\nBJXC2e9eNbk8ct3z5CZEE/rjWzT9bjZNU69gpRRNU6/Q9LvZpT5+ZfsrT0UOqK9s77WyMvcZxP24\\ngMhvZprc5+z3r93hKAsr6bOtiM++sk3+kHrge5J3f8wp+wbMahvIKfsG5Gk0GIBUXS3+a+tcYWVX\\ntnMh7qBdy8wsf+fOxlEJ3O73oCb9Rt3Ouaqu40QloSpH6yIP4bNxGVafvYjPxmWsizxk6ZCqFW+3\\n/01MUZ7jqFReDlkxx0yuy4qOYN/8PmVKenITLlLr2lmT62olxXDmOU+afP+y6Z1/WV5i3LdzQauI\\nC78hJ4vYdS+YXHftu1dQ+rwyl2vJHzpLdYcpKNd6efci5ZqLSenzuLZhjsnjxX3/KrWSTT9ywPra\\nufJ/A6Vk7rNN3Lma3IRoEra9VyE3+5XpRz038RKX//MoGmtblvkGsbmRNyO7jaNT3+f4sFlvnPTZ\\nvBZTcQ81r0zn4mY16Sa1JBVxLjyyokwudzezvDo7u/4NM8vnlbhvZf2jxO38scrcOv31RM6vfd5k\\neSWdq9yEi2RdOmpyXVZM1R4nKl3+ysm6yEME//aV8XVE0hXj66AWXSwVlsWsizzEW0d2cjw5lvZ1\\n3Zjj43fb56F9Q0eO3UZCFbpuJexahkdWFDG2zdDcO51uDbTEbVmKRhlM7qNB4XT2D5Praoe+jd5v\\nCjqH+samb9uY45x1bYGujhuZ5/aZ/YtFvLUdh52b4XfV9AXEPS22xPdT3E1Qcc3mt9vcfnMzf73+\\nT5CXHEPS7o/Rp8Wb3Cf32jlOTq2Lyk4vU7lnv3uV2qaWf/8aviXsW6pulzfVDfrPZFDQk4R+s4Km\\nP0837nunusMU1w0HMLkuLOIr6qacNvsZoNESb22LS05GkVVZWiuyr56mVsM2txV3WbqAmKvHVz9/\\ngqsl7FfQjbEs3U6yYo6hMbX80lHyUuOwcmpwR7rjKoOemNUhGNKTsAlaysG4eLih29+AoFxTAAAg\\nAElEQVR/PLsy5NoJ7ovcS+a5v6ndsnu5lq+/nkjWpaNmz4VSCo3G1NqKV9J3t6TP3tz3uqJjLi6m\\nytRNKif2LHqNFVYqp8i6y7ZN6VSmo5aPivzumfoMarl7USv+lMntreNPl3jMsv4eFxdTwX4VVWeK\\nWw+YXHdt/WxyEy5go5TJMm3MnCtl0JO440OubXgZDab3vWrVEO8S31XlpVHKzFkpR+Hh4fj6VmTv\\n77Ipz7h8Ni4jIulK0eX1GnF4hOluNtXVzcllgW/6jrntpKrJvF/YEPkAjtocUIZSX3huvkG9UQa1\\n2F+7DX0zI4qsm9NqMPPPhqIzcwEA0NVzR2/iYcPJdvUIretJ8OXDRdYt7zaGj6avJfSpRjRNLXrb\\neMXeBf+VcWbLNGRd5+QTdcBUIqizov1/cs2ei3Mv++T/Fe0mtZr40HJB0VhvdPMFuFCx9vVBq0Of\\nVjRunb0zeRkpaFRekXWZLl74LjOdWCqlSDvwPdEfPmjyhs+ABq8V8egc6puN+exsb3JMzJCmc3DG\\nefALHD95ApcjnxdZf9mmCW45Megoeo4v2LZk0GrTrY83Ku6mwNTn49jlfjJO/c7R94Jw0qcUOV4O\\n1ug1WmqrbJPl6eq4QV4u+vTEIutqNfHh7YYdeWZ/0e9m/s7W1B/wNLXc25O4/b1b/mE3Vzc8pn5T\\n7I//8YlWYNAXXaHRUKfnGJL/+srkD7ACHLsM4/qhzcWWeXPMDp2HknnmTzJO/W42JrQ6Ml2aU9tE\\nC/OFUQsZdP+scrvhi9v8FnEbXsbhruGMbRXAnmtRPN62B3uvXeB4ciwGpeiefIlP/lmHrWdnms/d\\nj0Zn+m+hJSYYBTGnXuWykxsOzbricu5PDOlJZuOz9w6g4djlZF08XCFjS0x/D4aRGbmfS6uC0KcU\\n/eOSjVsbXEa8xuXVY4usK/jszV3zLwxaUWJSVdJnW9oxijfHVJr1Nx/XqfvDZJ0/wMX3hqFPvVZk\\n34Lr9q3edKeEfcvl/zyKykozuT7R04/e83aUOpkuz/up0E2LaPrd7CLLC757UHxdL+m6a+43zJxk\\njQN3r7yEzq6OyfWG3GxOPmZn+vdYo6XtigS2//rRLcfkMfUblFJc/mi0yXUlXZfN/dbrnFyp7z+N\\nxJ2r0KcUvf/Q1nZC6fWonPQi60CDbdt7uXT6MC4quei5QIPTwJmcyHXB8NfHeGRFEWfTiHq2OmxT\\nL3Jd58RPVr0Iyv65yL55aPEIXsLfSbVg9zt39A8hpVVcPZeEqpzisvrsRfQmvkxWGi25E96+rWPf\\nTmtPRbQUlaQik8tZb85n3Omi4z08pn4DYPKi5Db6PY6uf4sGuUV/jOI1dVng8yW77A8wIOkAj138\\nmxYZCUTaObPGszuhru34MfwLWl0vmiTEW9uR5dqKxpcjwMTX6JS9C7MDnqfF6T1Fjjvu4fkEtehi\\n9ofDgAbX4a9g49aGhK1vGy+UzoNfxJCZQtym+SZvNADQ6nDoNNjkjWadXiGk/LUWTCWIWh1eazLR\\nWFmbvUCfneVFzpWTRXa1qteYVotPkXZok9kfhourxphMThTgMvgF6vQeT/alCGO51vWbAJAbH4UC\\nkwkV5F/8nQc9j1W9xiRuezc/5kZe2HsHkBt/nrSDP5rZs3gFZ8hUuXq0dPhPttkbWyjmpuCB+fR0\\nbszl/5tYdCedFeiLJp2liSkPLR0/zSH17/VmP4PPnTz4edPim+pjN5rXcWPG2V3kxkeZLLe475f7\\nlC+xa92LC28HkBt3vsj64hL1lL1fE7M6xOSNSMF+oY+3zG+Fu4kercn6BJBd35MOr/xB+vFfTZ9n\\n4ISdG14ZRb9Dm1zb00mfQdOEKJP7JdVyIK9DIA3CNxRZd+MNX3GtI8bvV8xxUHq0dvXYPP7/ePHE\\nXzzYzIdv+4UYb2R/vHCUETs/4/2zOwm4dAi34HdwDny2SNnmbsrcn/iKuv/f3nlHV1FtDfx3S5Kb\\nctN7SCOEQIDQeyfSFBQLz6Ag6hMLKr5PEYQn+mygggWwAyrioyhY0dAFRJoUQ+gQSCEhDdJ77r3f\\nHyEhYc7chEReQM9vLZeLezIzZ2b22WfvffbZ0/sedV2jt+MnXS9Gl21TtB3XhdDGlAgarfAd1XZe\\nrK0EqbWrGrcajVCnXvFHiPSYzuiFc8+7Sf5lKUaT0lmoDoaoBt9UnlPK6FkM7j+RvD2ryFqjTNV2\\n6Xsf2QfXYVOsnGcqHLzwHzGFi5vfU3EQw3GLfpyM5f9S3qXBqOr0cOkJOLYdQvGxLYo2NaNb5+CC\\nqfAiZVp7Zjk8RlSAG/2TPse/NJFUmwDsKwvxsVzEZchk/CcsRKOtf3eIyJ5q7MqKWqAxz9aRdpM+\\noyInTfisAh5bwc7MRPH7G/U8A3vHkPLuaKGeKsLAKsNIHiz9VtgnvVsAfvd/xI7kwzWOUZrRB4fw\\nPvgl7qPiQrLq/Zg1OrQWZdDo3M0zGNBnHMfeGoldTqryOJ0tWnOlcOxpDUbchz2FuayIi+vfUbTb\\nBUZRlnJItU+NRqtnQtRmfBJ+ZF7BPEVzPo44I3LEYJ++LU+7zKTC4EHf/C1MKl5NS1MKZ3SBbLPt\\nxh2lm4ROGjQsEPK/QDpUKvxZ/So3VeK94j/kVZQq2prqRDRltedarhRZ41o6lzuebI17/inF7zpH\\nD9BoMBWqpDupUIGOjksr0X72LBaRk2HREJNYzKwk5b6ZVzveyQq3EOK2vY1eMIxMGh1xU84wYe1G\\n8EwGQzGUOkB2ECtGjyCmcwAA85c8T/C+xYSWZHHGwZMtHi25I/MYPqXKFYpqNHaOOLUfTsH+bwRt\\nTljKCq/iKVxG7+qHfas+FOxbo2izC2inmiZl0epp91lVmlLe7pVkr51zeRIdNQOXXjF892AorU2J\\nimOtGcYAhla9eUtn5PETGxRtv7bowqC8ZPU0N8Css0NrUq7onLdtwWyHfzI/92W0gndfiY5UQ7DQ\\nmAewa9Ge5NBelMb9pIg6mopy+PWZlniXiCcH1b7q7XCNfpJjm5YRYFIaXol2LdFgIbhMaRCctgll\\n4MITuNjbqL6DR35bzScndxPk6EpacT5tXb0pN5s4kZfFrMj+3PvfR4URS3S2aLQ6LBUlV3U/AGj1\\nRH5WUceoPm8XhJuHD05pe9DY2GGpUL6famNw1BPTeLNgrqJ9pvH/eLXgXeG7qw+ddyvatB3DyMxj\\nyiCKTxsADm192+rKtIgiGwPBI57mxJmzeBxZoWhPGvEefUI9hE7E9MhRHAzpwaExz+Bu51Cn7YUD\\n61j4+w9s2PcFThpoNecYNh6Bdf5GbSUWQGNjR2VFufB+zjp6M8p+MSPLttcxcBY53MW54Jv5LbqA\\nlPfuqpOGWI3O2Ydsv964nfhO0Xaq4+O0GXAnx3bF0nqf8v0Ve7XHMecklkplupnG1gH36Mnk7/2a\\nigtJinatoztmwSpsQ7AAhpCulAnSxo1dxpAWvx5jY+T8WqHV49pvIqd+X4OXQJ+YNFp0KinrevdA\\nAiZ9TknyH2QKCtK86fAgOd0f4Zv7u6PTVml2s9nCA0s2MmbXI0SYEnEZ8CD+D3yCRqsTXuNqneX6\\nVqxLEvZy5uWeqgE0a2gNThRVlGNvUspUfVSi4+uYw+z/aUmdcfCp/R0EmDN4ovQrtGZxBU6zRocx\\najhFcT8r2px7jSN771fYilbh68FaILEhaPS2wvFl4x2G3/0fs++d8XhXKPV9il0IrbyMwtWtU/pQ\\nxrjOZ2BLDxyOfavQGR7dxvDPbbfibVaOzwSbUKLePMyupBzGfaksLNbTvYyFJyfgiNKWbmhWyLXm\\nmjhUs2fPJi4uDo1Gw8yZM4mKimpUB5qTP6NfhRVl3LFlKRvTxHmjywaMY3xY46+httrT3s2X+DHi\\nTYH1HXut0xAj1rzOyXylgetrbyT5H89jo6KYG8KR+/VoBJEeq2g0XNS64W5SDvAkQ0vWP/g284/+\\nKjzUS+dGVlwUI7VrmVT4PS1Lsjhj78Uip9u4J+bfnLdNIPzDf9C6SHm/Sc5+TA9eTvx5ZXTR12jH\\n3NGRnMwq5JWNVziIHik4eR1j0+7FGCuVikXn5EnYnCPonb2FhrOxyxiOP+yokn6g49ygl2nxizLC\\nmh8yGNeM/ZhL8oXPAqBMZ4OdwLBKcvZj5EJxwYNqHnn+BZ5KeUXx+zu+03nrzq6kLfknZkEkNs21\\nBUM73c3IjOMK4zd80EO83TGa09PCMeULIr++rZlrc7fwulONU0kLHcUrx+4nrCJReU+GMBj8tDBt\\naI++Az0q44WT3UWjN64FWaqGvpmqSVK4yqTRMr3vb2ji1gijf0kj3wcQ9mmqcSp/eA3l3dvaYzKb\\nmbPlNEczCon0cWJmdDh9wx0JWzOHECc3jt0+Dd2lqHN2aRG91i4goeACh7e9e/XjCw0ufSdQGL9O\\nmJIEGop8u+CYrjRgy1yCaPfvzZSc3Sd0AHOKy/F7aSNDirYqJu6jvsN5N22ccOzl6A2EdL+TvF3L\\nEa1iVKKjY+8ZYCcwnEsd8Mnow6fZDxBSoDQ20uxd8S3Ja5QjZ4GqlSfhirYXrrN2MtivlaLNbDEz\\netOn2P3+Na+e2ECx1gFbcylpdsHYRd1CuCaTvL1fiYMdaHAI60Fxwh5he4VGy7Dgn8ksVBpevYLc\\n2PFkX44/aNMIuWgCtdKW1Yzy7B9nCw0+W59W+Dz2NTteGUWwSRn1r0SHnqu/FzPg3v8Bcnd8Ll49\\n0+pIxYMAs3IMpGh9sL97IX4bpwlXgXVGTyoLLohTW7V6Ij8tZ9QbtzL32FpF+3Ntb+H14+ugEe8n\\nyRDGgAXH+TE1vk4my9NtB7Hk+zSePPE07StPYx/eB3NJPmVpxxqUSp8ddR+uJ79DX6qcSyqMXrR9\\nYTe23i3rrGDZeoehsXeh7Ozvqv1Nd3AnavQMMldNQ5hlYQUz4DH4Uc5uX4WrSZnimmRoyciPE1h5\\nMJU5m09zNKOASB8jD/YI5J3tZ7DJOs6q/P/DYFZmEJwzejPsvQzVQNahiVr0gv6agVX+HRmWdQqP\\nCuXe1hOOXkS5+Yrl3D8S33veIfmtkeK5Xqsn4JFlquPHFHUHE6b9m7mCeWaqcSqd/V2494SySvFz\\nLs9y98Qp3Nu1heJZzYhuRUznAA5N1KEXBEmrA9hAk45tTqz5DY0qSrF3716SkpJYtWoVCQkJzJw5\\nk1WrVjWpk/9LatLgctKJTNna6DS4C6VF3LxxMXuzUxgV2Ja7gqN4+8h2juZmYLSxI6e8hGO5IgOj\\nYZRWVnA4R7wt+3BOOq/+sYnH2/ZhfeqJOspwaruBWLAInSmAo7n1FzxoLKfzs8ksES/3ppcU0P3H\\n+SzqcxcJBRdUUxGtpSmavSPQZSijsLa+rbGYzVQI9jxUeEXyRtnNvJGnjJKuiujJ0qO/4m/vTJrA\\nkVjQ72Zmplwg9uIoYh1GQXXg2Aypv5wlbupgRgX34s2jyonugxZdOZohXilKLyhjwnKVKpAXAjE6\\n22NfKd4nYyrJRe/sDUCsdwSzu913+Vl5RxBja6DEq6Vw/0eJd0umZvehhXGqwkBNNd7CwRndVPdm\\nWbQ6nm89jLnHfhLe60jx3dSg63wXU3NLFNfdrhnAo359sBcUSwDwyktjREAEd/QZy3+O7eBobgah\\nRndSinLZdGIXI1pEEFSQLTQWSzIS+MSjOymC+91oP5CKZwYSu/JZEO2vG/w0eT16MzXxFoUjt527\\n+PrM0wSXKSdmt4JMDri0IKT4onCSPOvkg8liEjoCCQ4erDuRwciOd3HWMQTttnfwL00kzRACg5+u\\nSXmIBfjl7Zo208D/o4/XULZsOsXdy+o6LvHnCxj35QGGD8qjwmxiRtSQGmcKwNPgyE9D/0mvtQs5\\n5eBO6yJleqtdYBS5ZcVimfJpReTDS1WNX52bn9CZAkgrs9DZpxW2Pq0UUeuyShNjPvudskozsXYD\\niLUbUPfgnBKWhw7iP4eVqXcfth/Nl49+SWnKIaExkmATgM6uXGhWjw/qw/KECj5wvJU3Cz5RtFfc\\nPJ2UjQsIzlfq1yRHT1o/uIzihTerr26pxDDDSy7SXuBMAWg1Wv474F4mx1Wt0jqYq+QqqOws/P4e\\n+UAFNtiiDHYk2ISwPuoDhqaPEL7bRAcvkmfdxLfx6TUGThtvJyrMZnYn5/DYmkPcbvQSpmBlOHjg\\nWXxReK8mNBxoOZGuZ5aqrABrcWzRTryf0z8SuFxsQWSkgjgF1fP2l3nkNzO5HrcwL1P5/r7r/gx3\\n7hMXIqpEQ4q9K6ElynF91smH9g99SsnZfcI+p9iFMF9/uzAQMt9xArEbDUwyTOBfKIM76yOeJTTu\\nQ2FgJ9k2iPd2fcPPPhFYsCh00ZnW/bErSBb2Se/WAtcBD5L9/SuIHJDg8iR+TI1XFNR6YOcK3hs5\\nlhmFb7Ig5QlCT+2saa8uTlAYvw7n3cq0cgDPQ18IfwewKcji9LNh6Fz9MOVeHkPl6VVB6SSDK1sD\\nopiYoNzj+GHYQN7s/wB2v30hdjIC2nMsL4MwQYp+ktGXFd5TOOjgJHxHH9jeyUggpnNATfZINXd0\\n8GPgJ+Xoc8VOq8+lzBiXXjEKPZZbVsJZR0/CBWPvlIMPs00vsz/4c+adVqYaLvLrj29xW6YiWCnq\\n+DitOgzDLkBlDAVEqo6fcyG3cNu7v5JgNwALKObGDYYBxF6Ag4J584z/CO7t2gJUnhXAWQdPwgWp\\nr2cdPGsKnagdm2oIEWaFpBlCmrVISkNolEO1a9cubrrpJgDCwsLIy8ujsLAQJyenP7Vz14KmVuO7\\nbOyno9doKTObuC+sK4v7/QMbrY6J4d0ByC8vpfMP7zDn0BaGB7RmgG9YA89bZRjfFdyB5WcPitPQ\\nAK1Gw6yD63g1biNltZaS43POM3GH9TKdZouFj47v5OGIXmg16rnRV7v/Krkwh+h1H5FbUcLEVl05\\neCGt5tgn2/Zjd1YSS07tpefaBXXuqvY7AKy+H+/RM7mwWLkR2ev2l1Rzp5/X9mWDYSAdw6Db8S9q\\nUus+De7JWjd/HgjvzsKeY/gx5Shzat3vjEv3Oz5X6SwBHM2oWk1JjhjEVItyojsXMQhjgY7cEmVU\\nJdTdnulDWvHYmnihfZWV5MVFtxZ456Qo2ko8QwF1Wc4uLWJfQFemCYzfRUE9OJJYQLzAQNWcL2Bj\\nYjGeKs7YSXt3fvZpU6WAr7jX4y17C59RNZkFZaz6I40ih4GcDbyZ09lFRPoY6RvqxvpdSUR/tItY\\nt2BcLigVabZrAGtv+ic6rZaHInrW/L49PYHhGxZxx5alrLYLILRUmcN+WhuIBoQGeZSPEaiq1je/\\nKK0m7fKsvRdJ3R7iqZjJdPh2Hod92tSkgVWj0xwl4Jg4nc+k0RL09F7mL3qZl899pGj/2mss9p45\\ntBYUh1gU1AMHrz94atg4cir6MNuhtCrw4+bLzKjLzzivR29mGx6rNTb78HzL1sR0DqDzW9soLL9i\\n4teXsyHrECFGN+GKeYSLN98Mmcjb5/7gDUFwwGbYv1hwYD3TVWSqK1XGxM7Ei/DLW/iXJpFmCMYy\\n6Gnsu/0Dz1d9hEZ3izLlCgJUpR1NXPEH289cZGxHP8a09+WNLQk10cxxnf2Zt/sQX7uHUNhW6fDG\\nugbR+o8NTBk1Q7ih+7NWnTFj5tGI3uzMTFSM+YnhWYz5TMdUbxQr0/cFTsBlKCDQNQuDerLu4HbW\\n2PoTUa68twSbUNp6O1KRqiwbbLjkRKjhamfPPad3CtuS9N4sNExgXqHym0IfOdxG7PnvOBvUg3mC\\nYMhXPrdzm16nMHDySioY/OFOFu1OJjWgG3PzlXIxN7gXk87uJ6JU6Wwl2QcyftYSfnz8Z1oLjKsE\\nBy/6jJhOmUCfe466/GxFRipUBZO+ELz7+NPnOJebgKatC3gIZMPBTJSDh9i5dPJiuc+tvJCwWNH2\\nYWB37FJP0HP0TKEjN19/OxfDb2XquXyFzAwY8TCcucii4905JzBSYxPbMtJwF/MqlIb+e749+fnE\\nLoId3YgV6CJjUT75gx7B7ssnFMf6xMzFpVcMKb99hX22cu9riXs4sw6sV/wOMOvQWiYO60flF2YQ\\nxPbydizFTXhklTN9zuBCcKlSR6bbOpHsHki39OPCKrgWG3tmzdjAki9eqaOTt7W7ldWOzhxY/wlv\\nRN5P5Dllls1cn66kOGcL5fx9/+7E/nYQraFqHlAEFINvEd5LhdnE16l7yfTfQUKSBxGibBRHLzpY\\nzAp7Kv7iee7YspRwlbG3yOk2egW6s6P4Vqa2LVfI6gbdIEy5IaQLZCYxuS0jAU8VeaweQ1eOn+8P\\npzN+wa8UlpkY096X7w4r58YvYjpx/8o/hPOm/oI48FmbT4J7CFdTFwd3Z0w9x57sMY7g7a8Jfo+p\\nN2jb3DTKocrOzqZdu8vFDd3d3cnKyrohHKrZh5QbNwHmHNpy1XuSTJeW2IcHRCjS2JxtDXw5YBz9\\nf/6A8dtXcOi2Z3C1ExV+FhvG1atLI/wjWJemLOW5uO9YLpYV89w+Zc4ugLfBiec7RjNlj3JDvp1O\\nz2O7vmFZwgHGBLVnWcJ+hdN0tY7n+eJ8otd9THJRLrO7jmRGVLTibx6K6Mn4sC4M37CIckE+8ZQ9\\n36FRyRiefWgzMS0780tApHAS7WHnzuKSU0SJjCsPd/p4pfJajhl61J28J7fpw/u976i5L9G9Rfo4\\nCdP2vJ1ssVgszIwawric84qJrsuFYKEzBTD75rbEdA7g/d8SheeuNFt4z7cXLwscqpc823Du+3c4\\nmS+uAvjknu/ApQWZgmexzsFPda+3BRixaA+3eXZitsBwXtGyHzM6DGEOKO6Vwou8dHADszrdJHTS\\np3x3mIvFFSwY054n+4fWaesX6s6E5Qd51b0zcwUO1detBhAt2BQ9wDeMb4ZM5LbNn/N+cEfmnVA6\\nVEscxvL5uE5MXPGHom1GdNVqwMozB/mXtvQK2Shl9or/kFkqXmE0WcyccfQUGmUJ9l7csXAfcDPF\\n3maFcbWucBivjXJnamGu4v3scO9HifYCIzbVjaxXj79Ki5m88lKe2P2tog2qZLikUpD+4ZmCRWPm\\nidYDWBOXzuzNp+qkA8Z0DmCwXytW9Z8oDA7EJhwBlxZkCWRqo5M/H1GVwjHu90Bweheqp4J9wIEd\\nfOMoNkTOOHjQzmRGr6v7fqf/dIxVf6TRL9SdL8Z1xmCj454uLWrazxXlMj/zCJRaiNUNIbZ918t7\\nFHN98LLN5sWDG/jNsxM64zNMKl5TZYzYBLCoZUfW+YTzxYAY1XTsm1p70cLFntgs5cr0qdjjHJ0+\\njQ+yCusYfCs8byeWSNCmsCisE/OOKR2qj9yG4qDR8jJKh+pUz3sQr09dJqhEnFbrb8rmVIuRTE3V\\n1DW8nG9lY3tH0BUTWxQN3v5MKvzhkjx6sii4O7FGXxZXVmCvt6lzThd7G9ZN6kWnxav52UW8OhLr\\nXaUHRMbix6EduWPZDG4K7i42JoO7M32HHxN6TKPD4aV1gxkNKFzwwoH1nBI4GFRmgFOVPhM5IFgs\\nfKJi4H4S1IOfM28l31tbd9y6DyPWy4d165fQsbInfgIDN2DAeCZ0NXPPdiOx1J1n7gst5Lnonuim\\n/ig0UrUaOOk/gqlpVxj6QT2JDfanh0cw64c/xLrU43WCfj28gvj01F76pJ1jcMBEJuWsq6Nr+hR0\\nIexAKj9obmcWym+KveoexWmV/ac55SW8e2ojD5SJM1pMaEi3MxJQpszsOO3oybKQfrx6RLnq8n5E\\nNOu827JLxaEKKMxg0srDrDzaExx6Xh57Z6Fbl1z2FcQxrsKRoS7/YlLhd1XPSt+CRSFdiXX2RKuJ\\nAG8/JhX+ePlZeEYT6+uHve4wTwWP4vUfle9gRa35oDqQHOzkSrnJxLniPNztHPiv3828fFq5+vZR\\nYDfmxX7ImKD2fH56H0dzM/B3cCa9uIAKi4kE3bArxl7V+9nIaCqe7MfKg6GM+9F0WY+V24FNKfb6\\n85TlexKLYIU+o5CbPtpFK8+2JHs/rAz82PUnhiq9XK3vPR1syCgsx8FWx6oJXflHJ3/V1Lu5WxOE\\ntknkpUCkiDJTJa/FbVJdTU1s3V/12GoWe7iyXjDPpHq48VS9RzcvjdpDNWvWLAYOHFizSjVu3Dhm\\nz55NaGio8O/37//zPsLaVHoeWolJVBkIDXuirCvxmJM/c1pQKCDc4MqK1mLfeVHGYT7OiGeoSxCz\\ng/oIS5CqnbeFrRPftRnN+twkPs88ypnSPFoaXLjfO5LhrsENuh/RsV0dvXkr7QAb88RVadrau5FQ\\nmke5ICUixM6Z1RFVkZz1uUl8lnmEM6X56DUayi1mHvCO5HFf6wuzan2uDxedLUWmSiqtFDC4Wqy9\\nu2rWJ5bw753issLRgQZm9XLlt6KUmuccYueMKTOIxBR3evraMSLYwPITRZzJq6Sli577I40MD7G3\\neu4ARx2pIVsYmancNxTr0wZ7rZ4SQS43VO3NcdXZkSMoxECFLe2z+3P4gvLYJzoa2X0hl30uO4TX\\nXe/Tlr0CmRrqEsS3FxM4X1FEX6M/g51bsPLCCc6W5hNqcKa3rjXL9jgQ5WnD2G5FLM0+WtP2gHc7\\nOjp48n5iArElR6xeV0RuqYnn4k+yz/YP4bEH9XeydnAU6xNL+PxogfAd3Hb8R1LLlY6TDg02Gi2l\\ngr0J4QZXuh45x6yUjxVt03weRhs8lj3pZWQWW5FVl0xFsZKZrcNpHVDKIwmbKbvKPRHVshzzcyan\\nc2u9X10FROwGkx7NqZ5YzEoz5rU+bgwPsVfVRY5aPXYaHRcFMqVFw2Svzqze60y6PgO8ksGuCMoc\\nodiIxvkCIy7ECw3YqeFjSOBhbgq0Z0NyCWfzKnEzaMkuMRPirGfJUE9c7Or2t8BUzkMJm0gozcMt\\nJ4yc1BaK8xoM5RB8iFKbIigygs4Edpcjq74X27F2sPq+X4CeK9IwqagpGy1UCPFg8kIAABsJSURB\\nVF5tR08b/tXTlidSNjDg/GHVghei/YAH/Xqwtr04Sl5N8pIRQsf0hKMXPw/8hMUnssErper5lxtA\\nXwE6E4/7RrFunycJdVKWLBBwAtwyGO4azKuBvRXz06bcZGYm78RsBrTKh9GqoB0JxiOMENxPrE8b\\n2jt4kFiaT//z8WJnrNANjEr9d7d9N54NDwcuzzPVOqOPkx9HSy6yr0glnd6iYXXL23nu/GahLAfo\\nnEktMjEyb7+yT85dIaGb8LR2zrmUtYgHjQUu+INT7mU5zwoiTONDUehe0gVpvqF2znwdcYtybF4i\\n3LVKJ/37yInL48eiBa0ZCtzwyOrAW/28aO9pqzh2c14K0xN3iDdlprSFPG9wyWSk7bdXNZcE2xqZ\\n5NOeyG8eVZW5RSqO6dS2o4gtHyO85h7tneRmubG6aJLqee+wXyK4GQAL+J4Bz3NVTodZVzWuLZoq\\n+bzgB+fDET0MjUsmuqATaIHu+lB+L8qg3KYQ2wonbndpw7Ph4azPTeLfycpV4O6O3swJ7sfwry8w\\nTHPFfmrnW4j1DwdnsWN6l1tr1u70o1SgzsNd9ay4uSp9/8o5qmt4PqtKfkdrssGU0BnK6wbkDTqq\\nzumSCYHHFOd2yYhkuFswX51UyuMTHY3c367KMbpyfD3g3Y7hrsGqtkn1XHHlsX62jpSbTWRWluCi\\nsyVPUBjERWfLx2HRtDK4Cp9Vankhtx0Xp5E2xEb/X/GnFqVYuHAhXl5exMRU3Vx0dDTff/+96grV\\n9VSUoimFGvSfP4tJ8LisVa+rNJsYGPshOzMT+aL/OCa0qvscCivKcPnyecwCB6MhVfGacj+hX79G\\nYqH690fU6O4ZSISzF1+eUVZpWT7gXsaFWV/pU+tzK6MnZouZM4XK4hFOelsCHV2r9qSJd17jqHWg\\nyKJUHr62rmRW5GG+yndXmyujOI/2DmbFH6n8euYi4Z6OPNQriC/3n+NIegG2ei2lFWbuivLjy3s7\\nY6e3XoRDFCG6u5M/doteosJGaegbKp0pmfQCHb6bJ9xjF+Xmx0DnzixMEq9eDvFrRaC2BauS9lOq\\nK8BgMvJAyz54uZt5I/4XylTKdtuUG8m8bybrjmcqVjiGtnXlnm3/ZYNKcRZdaltevzmSZ+OU1QNr\\nUClpVH2/tSNtkd5O9Ap2Y038eS4WV6BrsxuTXmnsBxk8SRon/jbQ4ZzzzNwfy48p4spoeo2WZQPG\\nqVbKHP9hKsP4UREZ3MhoKuaOqlqxEVQymjY4jA93JlFQpnzOUX7OxE0dqFops/rxiJR2tSwrrut9\\nFryT6abrSvwRZ8oEK1gBLgZWjO/CoG2vC3WRDi2PBg3n/eRYRRvmS8ZMuS3YCqprmTXcHRZF/u5V\\nYgfjfBhcCODKl79gTDue7N8SqBs1Nuj0FFWW83ibPnz4jU2VsS9Ap6/EFHIADMrCE9pzbTHN+qf4\\nwEtEzdsqjNB6ONhQWGaizKS8cEPen0WtdJdFg+VB5T7ParaezubNFY8w77SysufUtrcoV2IuMal1\\nTz7pO1YsjxozrXuc5mTReV7uPJxZnYbWNK1OjCNm639x0NvgmtGFlKJcRQAgyqEVllb7rM5BatVm\\n3WztySkXV9OzNTkQP/ZJtqUn8PBO5R45qJoTCgUVzFobfThx17NWq9w+9d1hMt2Upfztz7djapfe\\nykJBwIrxXfDxKWVI7EdC57JmHKgwNiSK1rpwXtt65LLTe8kZWzF6BLhkCvs7zmsIq7aa0Gu1PNgj\\nkN8SL9bo3ns6B3ChuIJ5acvBVlnACJOeDs4tOFyQgkWrtOhtTY4sHTLGajXg22bfxZwTSr09I+Iu\\nzkb2osXxXxTjOqn1ILLjOpCpO6eQGfK86R3shnvBu7x5Unne51rfyU85EzELHqVOA0vHdeKfe5dR\\nZlDaCYEFnXEtDxCO2yg/Z+aN82P0piV1tklUM8CnJXuzkykVzH/VsqymE3yNdthG7iW5SJniaFth\\npPxEF+XNUCVTon1E1Xx4fCeTd30DlTZV/9kV15GZnkFutPzqDTAI9q2XOFYFB1wy6wa5sqrGbdzU\\ngfVWglZbvQL1KtI3+YfzzZCJ/JRyrM5qans3X5afOYiLrYHvox9gYK1tMBaLhWUJ+3li97cUCCq+\\nwvXzTdc/vShF3759WbhwITExMRw5cgRvb+8bIt0PqErPEgjBo216WT2uqKIMW62eEkGFs0hXH9Xj\\n9FodXw4YR8fv3+bhnV/z2qFNnM6/QJjRg5ZO7uzMShIaMPWdtxq1+5kRNaTeY1OKxGW59RotbV29\\niRcY6056Ww5cSOX3bGUqGsDr8VvqdajU+vxKl+EAwrZFfccS07Izmg9fFCuPUkeKsoOEkRrbCy1p\\nF3ReOOk35BmDeAPlpF5BzPz5OHO3JjB97eXrll4KXd/ewbdeZ0rt3ACVGYHQQnk/ZecDyCgo499R\\n0cJn9Uy7QTy/8iJY2tadzC76YfQqYMv508DpmtFfqi/gw+T1kAz+Ds6kpRjBQ5muVJHRAo9Z6+pM\\ndNUFD1aM78LPQx/Cf9XLwjQ5TcBJ/h0vdrac9La83eNWHll9CIu/8m9Kzwdw1+e/syb+sjzGpxcQ\\nn16AnV7DO7e1Y+qZX4VeRmrZBbJLi9iUdrLGIA939sDLzokdmYlYsOCgt6G4Ujyuq1NARXvrZvvk\\nEXtemRIW5VcV+at+p6IJ6a1t4lLs1fvyIl19hPLawc0PCwjbbLQ6MksK6lz3SNZFLJ5pOOkd2Dbu\\nTpxnKEvPA6TmlTLg/Z3QykE4vkyl9rz/czG4tFUYSLpiNwJap5Nso1Je3uzIykETWBnUnv/Ueo53\\nhnTg4PFdpPslgDG7ajWllsGweI8zT/ZvqZi4iy4Z0X28Q9juky80cDr4GYl7ZiD2iw+Jtn9g6yve\\nu1WbmdHhQof4vTs6MF6loExD3t+h8/liHVbmoPztEhkFZYz78gCZupuhbZnCgO05fAo7j2wXfrpj\\nT1ZVJkIduUjPp52vMzOiWzGkzRB6rF3ACwfXs/jkHlKL8wlwcCalKA8nG1vWD5tE/7mHwexdtdpR\\n+34LClh2h/U5SG0M3RUShc3n04XOZbmumIhv3lB9Hl42Ltzk0p0V2ZsUbcPdu1q9bkzLzow/nwqF\\nSlmuKPDi5RFtiPQxqhqSthZ7yhHsI9GAl8GRrFLluzVo9XydeAg4BLWr3RuKIPAYM09nkKTyceUj\\nlceInTSB2z/bx0e7LpeQjz9fwIzqbwK2EzhTALpK4osSEebWASZdidXnBBDcazJTtaUKmWvV4zHG\\nhbgJ091XdIpm/K5UoczotRp2TunHlC3lTNWJzjuZdge0wnHdzteZe7sGMuOkhhTBLWu8UpjZZpBw\\n3M6IbsXQgAACHV05XXBB0b49Q6y/4HIhLzWdkF5QhqYwTyzL+kJmRLeig6+R12vtA60tU2o81qYP\\nm9NOsSYpvko/Qo3M7CrzYNsJU5WjJMJQBL4JVat5tX8LPEZ8dgEz9xex8OgO4aGvxG2qev8umVha\\n7cPimYHF1QdcnIEACivKmLFfHLDNLCnEaGMQbqG4pUVb7t+ximHrP+HRiN78kp7A0dwMnPS25FWU\\nYrSxY3JEHz44oVwlbIhN29w0yqHq0qUL7dq1IyYmBo1Gw4svvvhn9+uaUVt5HMlJx8fBSFpxPl+f\\nPcTDrXvXqX5VjclsZty2/wqdKaj/RYcaPZgQ1pUPju/kRF7VnouT+VmczM/C1caeu0I6sDpRWaWl\\nIQJUnzK0htqkX30ONccm2j8c3xUvCR3BhlQQbEif1doCK1qTYlAaMx7FrcjJd8WcgmKSTCtw5o1R\\nbRvteKqh12l5c3QkX8WlkZSjjLS+sSWhzr6Pq6W9fRjxgvux5HnTcvZm/m9ASx4OGMEXSb9RqivE\\nttKJdrpInv4iiwvFFYByMivOgxbdSjlXrHSmvQyOHL9jGn3n7yY+xVlxXR9zIPn6CkoEuU6vbjpF\\nTOcALpSJN6xWYkItU7PUVMmkiF4stCslPkUnjGjWdqZq09LDkX8NaMmnF8WybLJY8F7xYh1JPZ6X\\nxXGyCHR05cPed5BfXsY9260bhKLxpDa5Vu/NAnVnWW1fXnV+en2BElFbiamC3j+9x7qhD9Vc9+U/\\nNvDiwUqe7zQcB72t6nUDXQ3EdApg7kFx+ghZQVX/z1PKlEarIenByeg+e1aoEyr1VTIheo4Phvcg\\n8Mt54FRLHqsn/fOV/J4VqrpP9I34X5gZHSN8BzOjw9FoNFToxXvg1H6vjTWHePbmU41+f9OSjwp1\\nmHuhuHCRyWzh3v8eIL2gjLmjBtEiuEsdx7RaP74Sp3QuoK5OrpaLK6OskyP6MH3/TzUR9ur/T20/\\nkN7eIUT6iPd6RvoYG6TP1caQwWSkVC/4aG2lDRR4gGu60EjNKs9nxdYKoYO/7YIOhli/btU4sChk\\nObJWMETN2K3UiVfVdBoNC3qKV3s+7Xc3wU5u3LJpCbmCVbnEwouqSfBHczMYFuFNoKuBE1lK4znE\\nzR6TvQcppUonwd/OjSN3Ps2A2PeFAdJ2blUBRbXnBLBgyBCmAPd47KjJaJgU1o8FQy7PndX2VDs3\\n3zoBJ2tjxNp5V7qJV/erdWuaoLpq1e8XrY5bgLMqWTl6jZY2rt7CrI/qwKvo3JP7BLMm/jwby8TB\\nKDetC7NvbgvAuEbYA2p7pRccq3KGNBqVAvIa6jpTtbB4nmPOIXEbVMlc69Wvc6rW/rrqvbovHlzP\\nmYKLVKp8+8yaDXhPWBd87I2M2rSkpv9ATSDo1S4jmBLZn/6+oUKZut5plEMFMHWq9W8gXc9UK4/9\\n+/fTpUsXbt/yOd8nH2H2oc11Uh6gainyX3u/58eUo9zkH87EVt2YG7/1qp2XX1WiH4FOrnw9eCIr\\nzxxslFNU+36uFmuTfn2TZDs336at+Fjps7W2N/sPYtyPpYpJ9L3RQy4ZOQgnyaY4nvVxLk8cHayO\\nVjeWKoO9QHE//+wZyM/HMnltU3VaSlU6QTlwEDDamfFytCWrSJkO087HmSMl4n7llJVgtDGoXvfd\\n8e1UI/NH0gsYt2w/gfYeJBYrJ4AoKysr1TKjdt1Pxkbx6OpDwhSQU5cMDDVZHhfaie+Tj1AsCIa4\\n2dpzS2BVZTWN5uplo76J2xr1OWNXBn5Ek0rt/j7XYQjH8jJ4JW4TvX9ayFOR/Vl19g+O5Gag02jw\\nMjhZve6boyKJ6RzAuhOZQic+yqEVFgeLVQNJTSdUG20iWji6Yqe1oUzwIUeL3yl6rFWmXlVzNDeD\\nmDHW30Fj+lQbNcP6at6fQqb6ewt12MU8N+b+cppnB9ctTfHqxpNsPpXN6EgfnhnUEo1GI3YSrATI\\n6kOUvg2wJjGeFzoNa9D9NkaXTgrrL0xNfjJsKK/26Y/LZ6+qruZpAIvAwT9aUL/ebUgwRA1rMlXf\\nPKOWzqTTaGlbz/s7rVJd7VxeKct6jhDqv7d63YyrnT0zVbIZGhpQXDBkSB0Hqja17anaTnpDnrHa\\neevTrfXJujWHuDGB5NrPSXTuh3oF4zHvJHkGZWDcmN9S2I+GclTl0ztaNPx2yxOcLshmwnblR8Rn\\ndbyJV+M2CZ0tDRrWD5vEv/Z+L3SAHPW2dZyp2pzMz6anVxBnCi4IV2Pr0zfR/uEEOrpySvCt0iUn\\n9zIlsr+qTF3vNNqh+qug0Wj4tN/ddPr+bf7zxwYG+Lask9s5/+ivvHfsN9q7+rJ68H242No36kO9\\naoPi2CVhbuyE1BTqU/7W+tSUVMOmUKXIRlQp2rOXFO3oy4r2Wkz69VHfakNjsTapFJdXEjZ7C+kF\\nygk6xN3B6mQ2O6n+yUjtumqReTudlpV/pIGLJwQqHaqBzp3pE+JmPTXIynUX7jhr9Rlbk2X959MU\\nx8EVkftGyoa1ibu+48C6M2ZtUlHrb5CTGw//tpoXDl4uhWyyWHhgxyoMOj0xnTtbva6aUztjdNUY\\nsja+GqsT1FaLNFDjGJ4XBAEaYjxdKz11Ne9PfGxdHXbfgADe3X6WaWuPcbG4gtk3t0Gj0bDpZBYv\\nbTxJsJs9S8d1EhY1+jPuVS2qXP17U4IH1lgwZAhsgUUJ4tUPtYyEoIoIXPyMjda7TQqG1POcremS\\nphjz1uaZhszl1tqvBU2VmWs1rpsSSFZDp9VQmOUBpcoV07QC53r7ZA01mWnv5ksv72B6eQej12iF\\nff4u+YhK6rEvQwNaM6vjTcJnsbjvWMZvXyHcB6rXaNk9aorqHqqGvIMzBcr9b3Btv5H6v6BRRSmu\\nluvVy6zdr50ZiQyI/QAfeyfibnsGT4Mj3yTGc9cvX+Brb2T3qCcJclL76kL9NKV4xPVKU1bVrlmf\\nrGyivJbXFBma9W04bSr6Z9diEizb6LWamqIIomdR30ZUa6jd6/J7OxPq4cgti/dw0SZVuMJRvQm2\\nMTLTlGd8o4+9q9WfasVmGnq/Vjci1zO+GvN+63s/TZHXxvapOUi6WMzQj3dzKruI6HBPUvNKOZ5Z\\n5Wy+MiKC54e2rvccDb3XK2Xqeh0jKw+mMu7HdQp9smL0CEDs4F9rvQuNl6l6iwBYOW9zzTMN5X9t\\n5zVlXF8LnaBWsKK6UE1jadJ83YBj1Z5FQ3RCY59jQ/XN9eg7WOuTdKhq9WvOoc3M3B+L0caO4spy\\nzBYLtlodu0ZNobNH0xRWU40CyfVNtaFZe7P3tZ7kmqLAmzQZWTGq63PymkJjneUbfexdrf5UqzDX\\n0IqW/2uaMun/1cgsKKPH/F+FezL/TMP5Spm6nsdIUxz865FrpXubm+vVzvtfcS0d3uZwHq+lTmjo\\nua9HmfrTq/z9VQl2rFqBqp3nXGY2cSIvs8kOVXMsuUv+d6ht9r6WNGUfQFPSH63mp1+j9Mf6rmv1\\nuL/Z2GvKXprmoClFDf5qeBvtcLQVVwads/n0NTOer+cxYm3cN1YnNCfXSvdKmpdrlRYLTZSZxqaz\\nX0OdcD3rm6YgHapavB7/i/D3OYe2/GlCdKMLjOT64Voq8MbSFCfvWvJ3GnvNtb+xKfyd3k99iCq5\\nQdOL3NSHfAcSSdP4qzm811In/BX1jXSoalHfxlyJ5HrjelPg16OT93fjrxr9+7twLVd5JRKJRHJt\\nkA5VLW60VBmJ5HrkenPy/o78FaN/fxeu11VeiUQikaij8v3svyczVVJirudUGYlEIpH8dYjpHMCK\\n8V2I8nNGr9UQ5ed83VRyk0gkEokYuUJVC5kqI5FIJJLmRq7ySiQSyY3F/6xsukQikUgkEolEIpHc\\nqDTrd6gkEolEIpFIJBKJ5K+I3EMlkUgkEolEIpFIJI1EOlQSiUQikUgkEolE0kikQyWRSCQSiUQi\\nkUgkjUQ6VBKJRCKRSCQSiUTSSKRDJZFIJBKJRCKRSCSN5G/5HarZs2cTFxeHRqNh5syZREVFNXeX\\nJDcob775Jvv376eyspJHHnmEDh06MG3aNEwmE15eXsydOxdbW9vm7qbkBqK0tJRRo0YxefJkevfu\\nLeVJ0mR++OEHFi9ejF6vZ8qUKUREREi5kjSaoqIipk+fTl5eHhUVFTz++ON4eXnxn//8B4CIiAhe\\neuml5u2k5Ibg5MmTTJ48mfvvv5/x48dz/vx5oW764YcfWLp0KVqtln/84x+MHTu2ubuu4G+3QrV3\\n716SkpJYtWoVr732Gq+99lpzd0lyg7J7925OnTrFqlWrWLx4MbNnz2bBggXcc889LF++nODgYFav\\nXt3c3ZTcYHz44Ye4uLgASHmSNJmcnBzef/99li9fzkcffcTmzZulXEmaxLfffktoaCjLli1j/vz5\\nNbbUzJkzWblyJYWFhWzbtq25uym5zikuLuaVV16hd+/eNb+JdFNxcTHvv/8+n3/+OcuWLWPp0qXk\\n5uY2Y8/F/O0cql27dnHTTTcBEBYWRl5eHoWFhc3cK8mNSPfu3Zk/fz4Azs7OlJSUsGfPHqKjowEY\\nPHgwu3btas4uSm4wEhISOH36NIMGDQKQ8iRpMrt27aJ37944OTnh7e3NK6+8IuVK0iTc3NxqDNr8\\n/HxcXV1JTU2tyfaRMiVpCLa2tixatAhvb++a30S6KS4ujg4dOmA0GjEYDHTp0oUDBw40V7dV+ds5\\nVNnZ2bi5udX8293dnaysrGbskeRGRafT4eDgAMDq1asZMGAAJSUlNakzHh4eUrYkV8Ubb7zBc889\\nV/NvKU+SpnLu3DlKS0t59NFHueeee9i1a5eUK0mTuOWWW0hLS2Po0KGMHz+eadOm4ezsXNMuZUrS\\nEPR6PQaDoc5vIt2UnZ2Nu7t7zd9cr3b733IPVW0sFktzd0Fyg7Np0yZWr17Np59+yrBhw2p+l7Il\\nuRq+++47OnXqRGBgoLBdypOkseTm5vLee++RlpbGfffdV0eWpFxJrpbvv/8ef39/lixZwvHjx3n8\\n8ccxGo017VKmJH8GanJ0vcrX386h8vb2Jjs7u+bfmZmZeHl5NWOPJDcyv/76Kx999BGLFy/GaDTi\\n4OBAaWkpBoOBjIyMOkvZEok1tm7dSkpKClu3biU9PR1bW1spT5Im4+HhQefOndHr9QQFBeHo6IhO\\np5NyJWk0Bw4coF+/fgC0adOGsrIyKisra9qlTEkai2jOE9ntnTp1asZeivnbpfz17duX9evXA3Dk\\nyBG8vb1xcnJq5l5JbkQKCgp48803+fjjj3F1dQWgT58+NfK1YcMG+vfv35xdlNxAvPvuu6xZs4av\\nvvqKsWPHMnnyZClPkibTr18/du/ejdlsJicnh+LiYilXkiYRHBxMXFwcAKmpqTg6OhIWFsa+ffsA\\nKVOSxiPSTR07diQ+Pp78/HyKioo4cOAA3bp1a+aeKtFYrte1s2vIvHnz2LdvHxqNhhdffJE2bdo0\\nd5ckNyCrVq1i4cKFhIaG1vz2+uuv8/zzz1NWVoa/vz9z5szBxsamGXspuRFZuHAhAQEB9OvXj+nT\\np0t5kjSJlStX1lTye+yxx+jQoYOUK0mjKSoqYubMmVy4cIHKykqeeuopvLy8eOGFFzCbzXTs2JEZ\\nM2Y0dzcl1zmHDx/mjTfeIDU1Fb1ej4+PD/PmzeO5555T6KZ169axZMkSNBoN48eP59Zbb23u7iv4\\nWzpUEolEIpFIJBKJRPJn8LdL+ZNIJBKJRCKRSCSSPwvpUEkkEolEIpFIJBJJI5EOlUQikUgkEolE\\nIpE0EulQSSQSiUQikUgkEkkjkQ6VRCKRSCQSiUQikTQS6VBJJBKJRCKRSCQSSSORDpVEIpFIJBKJ\\nRCKRNBLpUEkkEolEIpFIJBJJI/l/BavkuuZkDAUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Unnormalized\\\")\\n\",\n \"plot_x_and_rfft(x_with_noise, rfft(x_with_noise))\\n\",\n \"print(\\\"Normalized with 1 / sqrt(N)\\\")\\n\",\n \"plot_x_and_rfft(x_with_noise, rfft(x_with_noise, norm=\\\"ortho\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this small amount of noise, one can still see the importance of the first 15 DFT coefficients. With more noise (i.e. a larger stddev), the result gets, unsurprisingly, worse.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Gaussian Noise\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"How is gaussian noise treated by the DFT?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"noise = np.random.normal(loc=0.0, scale=1, size=200)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unnormalized\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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M6hicDZQxUBlTwCAKCdhLK/ZjU/L03WuWKi2FnDr4GipGLU9ethtk2+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\\nkppf0Xn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0pQOHmfSiIa9z1UzTr2muiOkLT4urHjPLsnUkqEqNOaYI1QJSew6CwO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\\nlItLAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Unnormalized\\\")\\n\",\n \"plot_x_and_rfft(noise, rfft(noise))\\n\",\n \"print(\\\"Normalized\\\")\\n\",\n \"plot_x_and_rfft(noise, rfft(noise, norm=\\\"ortho\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For a random noise, all coefficients seem to be relevant. There seems to be no pattern.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Polynomial Signal\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Discrete Fourier Transformation of an arbitrary polynomial series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"z = np.linspace(-1, 1, n, endpoint=False)\\n\",\n \"polynomial = z + z**2 - z**3 + z**4 - z**5 # some arbitrary composition\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unnormalized\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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9Np3PPGDizf/TeMJk5cQURERER0K5hQ3YaUchkm92yO87PC8XLfe6E3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\\n4iQ1VCIiIiIiIk76PzWEpgmCfg11AAAAAElFTkSuQmCC\\n\",\n 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jmAJ74Ix/k4nbXDIyIiIiKqsZhQPUBaezngt2e6Yu/TXdApv+OKlqv2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\\nDFRCCCGEEEIIYaX/B+qWRqe0s0slAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Unnormalized\\\")\\n\",\n \"plot_x_and_rfft(polynomial, rfft(polynomial))\\n\",\n \"print(\\\"Normalized with 1 / sqrt(N)\\\")\\n\",\n \"plot_x_and_rfft(polynomial, rfft(polynomial, norm=\\\"ortho\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Discard all DFT coefficients with k >= 10 and k >= 5 and plot the resampling results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fft_coeffs = rfft(polynomial, norm=\\\"ortho\\\")\\n\",\n \"trunc_fft_coeffs_10 = np.ones(len(fft_coeffs)) * 0+0j\\n\",\n \"trunc_fft_coeffs_5 = np.ones(len(fft_coeffs)) * 0+0j\\n\",\n \"trunc_fft_coeffs_10[0:10] = fft_coeffs[0:10]\\n\",\n \"trunc_fft_coeffs_5[0:5] = fft_coeffs[0:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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e1vf6OoqIjk5GQWLVrUYroxT7puwCCePQYHamVwlhBCiDOrrq6muLgY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\\nCIJCRBEWBEEQBIX8H6Or8JMXlJ9yAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(polynomial)\\n\",\n \"plt.plot(np.fft.irfft(rfft(polynomial, norm=\\\"ortho\\\"), norm=\\\"ortho\\\"))\\n\",\n \"plt.plot(np.fft.irfft(trunc_fft_coeffs_10, norm=\\\"ortho\\\"))\\n\",\n \"plt.plot(np.fft.irfft(trunc_fft_coeffs_5, norm=\\\"ortho\\\"))\\n\",\n \"plt.legend([\\\"raw\\\", \\\"resampled_full\\\", \\\"resampled_k<10\\\", \\\"resampled_k<5\\\"])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Conclusion\\n\",\n \"\\n\",\n \"- For k=0, the exponential function reduces to 1 and the coefficient is simply the sum of all series values \\n\",\n \"- The DFT k=1 coefficient correlates the signal to an oscillation with 1 period in the signal, the k=2 coefficient correlates it to an oscillation with 2 periods in the signal, and so on\\n\",\n \"- If you want you can make the statement: Taking the first DFT coefficients corresponds to a *lowpass* behavior, taking the last DFT coefficients corresponds to a *highpass* behavior, and you can realize whatever bandpass you want by picking the coefficients of interest\\n\",\n \"- Discrete Fourier Transformation (DFT) is not restricted to obviously periodic signals\\n\",\n \"- If the input signal is real valued, one can exploit the symmetry of the DFT coefficients\\n\",\n \"\\n\",\n \"# Further Research\\n\",\n \"- If you are interested in the symmetry of the DFT and the important but unmentioned [Nyquist Frequency](https://en.wikipedia.org/wiki/Nyquist_frequency), you can find many valuable resources in the wild world wide web\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/advanced/perform-PCA-on-extracted-features.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*tsfresh* returns a great number of features. Depending on the dynamics of the inspected time series, some of them maybe highly correlated. \\n\",\n \"\\n\",\n \"A common technique to deal with such highly correlated features are transformations such as a principal component analysis (PCA). This notebooks shows you how to perform a PCA on the extracted features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"\\n\",\n \"class PCAForPandas(PCA):\\n\",\n \" \\\"\\\"\\\"This class is just a small wrapper around the PCA estimator of sklearn including normalization to make it \\n\",\n \" compatible with pandas DataFrames.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, **kwargs):\\n\",\n \" self._z_scaler = StandardScaler()\\n\",\n \" super(self.__class__, self).__init__(**kwargs)\\n\",\n \"\\n\",\n \" self._X_columns = None\\n\",\n \"\\n\",\n \" def fit(self, X, y=None):\\n\",\n \" \\\"\\\"\\\"Normalize X and call the fit method of the base class with numpy arrays instead of pandas data frames.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" X = self._prepare(X)\\n\",\n \"\\n\",\n \" self._z_scaler.fit(X.values, y)\\n\",\n \" z_data = self._z_scaler.transform(X.values, y)\\n\",\n \"\\n\",\n \" return super(self.__class__, self).fit(z_data, y)\\n\",\n \"\\n\",\n \" def fit_transform(self, X, y=None):\\n\",\n \" \\\"\\\"\\\"Call the fit and the transform method of this class.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" X = self._prepare(X)\\n\",\n \"\\n\",\n \" self.fit(X, y)\\n\",\n \" return self.transform(X, y)\\n\",\n \"\\n\",\n \" def transform(self, X, y=None):\\n\",\n \" \\\"\\\"\\\"Normalize X and call the transform method of the base class with numpy arrays instead of pandas data frames.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" X = self._prepare(X)\\n\",\n \"\\n\",\n \" z_data = self._z_scaler.transform(X.values, y)\\n\",\n \"\\n\",\n \" transformed_ndarray = super(self.__class__, self).transform(z_data)\\n\",\n \"\\n\",\n \" pandas_df = pd.DataFrame(transformed_ndarray)\\n\",\n \" pandas_df.columns = [\\\"pca_{}\\\".format(i) for i in range(len(pandas_df.columns))]\\n\",\n \"\\n\",\n \" return pandas_df\\n\",\n \"\\n\",\n \" def _prepare(self, X):\\n\",\n \" \\\"\\\"\\\"Check if the data is a pandas DataFrame and sorts the column names.\\n\",\n \"\\n\",\n \" :raise AttributeError: if pandas is not a DataFrame or the columns of the new X is not compatible with the \\n\",\n \" columns from the previous X data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if not isinstance(X, pd.DataFrame):\\n\",\n \" raise AttributeError(\\\"X is not a pandas DataFrame\\\")\\n\",\n \"\\n\",\n \" X.sort_index(axis=1, inplace=True)\\n\",\n \"\\n\",\n \" if self._X_columns is not None:\\n\",\n \" if self._X_columns != list(X.columns):\\n\",\n \" raise AttributeError(\\\"The columns of the new X is not compatible with the columns from the previous X data\\\")\\n\",\n \" else:\\n\",\n \" self._X_columns = list(X.columns)\\n\",\n \"\\n\",\n \" return X\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Load robot failure example\\n\",\n \"\\n\",\n \"Splits the data set in a train (1 <= id <= 87) and a test set (87 <= id <= 88). It is assumed that the selection process is done in the past (train) and features for future (test) data sets should be determined. The id 87 is overlapping so that the correctness of the procedure can be easily shown.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\\n\",\n \" from pandas.core import datetools\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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idtimeF_xF_yF_zT_xT_yT_z
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\"\n ],\n \"text/plain\": [\n \" id time F_x F_y F_z T_x T_y T_z\\n\",\n \"0 1 0 -1 -1 63 -3 -1 0\\n\",\n \"1 1 1 0 0 62 -3 -1 0\\n\",\n \"2 1 2 -1 -1 61 -3 0 0\\n\",\n \"3 1 3 -1 -1 63 -2 -1 0\\n\",\n \"4 1 4 -1 -1 63 -3 -1 0\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\\n\",\n \"from tsfresh.feature_extraction import extract_features\\n\",\n \"from tsfresh.feature_selection import select_features\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"from tsfresh.feature_extraction import ComprehensiveFCParameters, MinimalFCParameters, settings\\n\",\n \"\\n\",\n \"download_robot_execution_failures()\\n\",\n \"df, y = load_robot_execution_failures()\\n\",\n \"df_train = df.iloc[(df.id <= 87).values]\\n\",\n \"y_train = y[0:-1]\\n\",\n \"\\n\",\n \"df_test = df.iloc[(df.id >= 87).values]\\n\",\n \"y_test = y[-2:]\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Train\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Extract train features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Extraction: 100%|██████████| 522/522 [00:00<00:00, 4771.52it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"X_train = extract_features(df_train, column_id='id', column_sort='time', default_fc_parameters=MinimalFCParameters(),\\n\",\n \" impute_function=impute)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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5 rows × 24 columns

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\"\n ],\n \"text/plain\": [\n \"variable T_y__variance T_y__standard_deviation F_z__standard_deviation \\\\\\n\",\n \"id \\n\",\n \"83 51.706667 7.190735 51.266450 \\n\",\n \"84 1563.528889 39.541483 291.988082 \\n\",\n \"85 14.755556 3.841296 14.501494 \\n\",\n \"86 2788.595556 52.807154 121.420189 \\n\",\n \"87 6415.715556 80.098162 204.966621 \\n\",\n \"\\n\",\n \"variable F_z__variance F_x__standard_deviation F_x__variance \\\\\\n\",\n \"id \\n\",\n \"83 2628.248889 5.329165 28.400000 \\n\",\n \"84 85257.040000 36.585729 1338.515556 \\n\",\n \"85 210.293333 4.616877 21.315556 \\n\",\n \"86 14742.862222 38.235179 1461.928889 \\n\",\n \"87 42011.315556 57.753268 3335.440000 \\n\",\n \"\\n\",\n \"variable T_x__variance T_x__standard_deviation F_y__variance \\\\\\n\",\n \"id \\n\",\n \"83 1058.728889 32.538114 4.862222 \\n\",\n \"84 6875.848889 82.920739 1143.555556 \\n\",\n \"85 40.995556 6.402777 8.088889 \\n\",\n \"86 202.426667 14.227673 257.315556 \\n\",\n \"87 70.995556 8.425886 564.382222 \\n\",\n \"\\n\",\n \"variable F_y__standard_deviation ... F_z__sum_values F_z__median \\\\\\n\",\n \"id ... \\n\",\n \"83 2.205045 ... -1103.0 -53.0 \\n\",\n \"84 33.816498 ... -10671.0 -912.0 \\n\",\n \"85 2.844097 ... 423.0 32.0 \\n\",\n \"86 16.041058 ... -2216.0 -110.0 \\n\",\n \"87 23.756730 ... -14137.0 -1036.0 \\n\",\n \"\\n\",\n \"variable F_y__maximum F_x__minimum F_x__maximum T_x__minimum \\\\\\n\",\n \"id \\n\",\n \"83 -8.0 -28.0 -14.0 70.0 \\n\",\n \"84 83.0 -110.0 -25.0 180.0 \\n\",\n \"85 15.0 4.0 19.0 -46.0 \\n\",\n \"86 69.0 21.0 148.0 -95.0 \\n\",\n \"87 162.0 171.0 342.0 -142.0 \\n\",\n \"\\n\",\n \"variable T_z__minimum T_y__minimum T_z__maximum F_z__maximum \\n\",\n \"id \\n\",\n \"83 -16.0 -23.0 -10.0 -24.0 \\n\",\n \"84 -28.0 12.0 0.0 -208.0 \\n\",\n \"85 -7.0 -1.0 0.0 50.0 \\n\",\n \"86 -10.0 14.0 8.0 -14.0 \\n\",\n \"87 13.0 222.0 44.0 -486.0 \\n\",\n \"\\n\",\n \"[5 rows x 24 columns]\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_train_filtered = select_features(X_train, y_train)\\n\",\n \"X_train_filtered.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Principal Component Analysis on train features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
pca_0pca_1pca_2pca_3
83-1.8282460.5109620.070269-0.102048
843.7413403.6614481.263409-0.115073
85-2.361277-0.105445-0.0784770.292859
860.2615760.1297251.5867371.390926
874.3379373.2015851.2488124.419234
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" pca_0 pca_1 pca_2 pca_3\\n\",\n \"83 -1.828246 0.510962 0.070269 -0.102048\\n\",\n \"84 3.741340 3.661448 1.263409 -0.115073\\n\",\n \"85 -2.361277 -0.105445 -0.078477 0.292859\\n\",\n \"86 0.261576 0.129725 1.586737 1.390926\\n\",\n \"87 4.337937 3.201585 1.248812 4.419234\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pca_train = PCAForPandas(n_components=4)\\n\",\n \"X_train_pca = pca_train.fit_transform(X_train_filtered)\\n\",\n \"\\n\",\n \"# add index plus 1 to keep original index from robot example\\n\",\n \"X_train_pca.index += 1\\n\",\n \"\\n\",\n \"X_train_pca.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Extract test features\\n\",\n \"\\n\",\n \"Only the selected features from the train data are extracted.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Extraction: 100%|██████████| 12/12 [00:00<00:00, 2301.09it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"X_test_filtered = extract_features(df_test, column_id='id', column_sort='time',\\n\",\n \" kind_to_fc_parameters=settings.from_columns(X_train_filtered.columns),\\n\",\n \" impute_function=impute)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
variableF_x__maximumF_x__minimumF_x__standard_deviationF_x__varianceF_y__maximumF_y__standard_deviationF_y__varianceF_z__maximumF_z__meanF_z__median...T_x__minimumT_x__standard_deviationT_x__varianceT_y__minimumT_y__standard_deviationT_y__varianceT_z__maximumT_z__minimumT_z__standard_deviationT_z__variance
id
87342.0171.057.7532683335.440000162.023.756730564.382222-486.0-942.466667-1036.0...-142.08.42588670.995556222.080.0981626415.71555644.013.09.90398398.088889
88-6.0-13.02.0612834.2488895.01.2036981.44888953.040.00000042.0...-29.04.05736616.462222-27.02.6280546.9066676.03.00.8844330.782222
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2 rows × 24 columns

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\"\n ],\n \"text/plain\": [\n \"variable F_x__maximum F_x__minimum F_x__standard_deviation F_x__variance \\\\\\n\",\n \"id \\n\",\n \"87 342.0 171.0 57.753268 3335.440000 \\n\",\n \"88 -6.0 -13.0 2.061283 4.248889 \\n\",\n \"\\n\",\n \"variable F_y__maximum F_y__standard_deviation F_y__variance F_z__maximum \\\\\\n\",\n \"id \\n\",\n \"87 162.0 23.756730 564.382222 -486.0 \\n\",\n \"88 5.0 1.203698 1.448889 53.0 \\n\",\n \"\\n\",\n \"variable F_z__mean F_z__median ... T_x__minimum \\\\\\n\",\n \"id ... \\n\",\n \"87 -942.466667 -1036.0 ... -142.0 \\n\",\n \"88 40.000000 42.0 ... -29.0 \\n\",\n \"\\n\",\n \"variable T_x__standard_deviation T_x__variance T_y__minimum \\\\\\n\",\n \"id \\n\",\n \"87 8.425886 70.995556 222.0 \\n\",\n \"88 4.057366 16.462222 -27.0 \\n\",\n \"\\n\",\n \"variable T_y__standard_deviation T_y__variance T_z__maximum T_z__minimum \\\\\\n\",\n \"id \\n\",\n \"87 80.098162 6415.715556 44.0 13.0 \\n\",\n \"88 2.628054 6.906667 6.0 3.0 \\n\",\n \"\\n\",\n \"variable T_z__standard_deviation T_z__variance \\n\",\n \"id \\n\",\n \"87 9.903983 98.088889 \\n\",\n \"88 0.884433 0.782222 \\n\",\n \"\\n\",\n \"[2 rows x 24 columns]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_test_filtered\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Principal Component Analysis on test features\\n\",\n \"\\n\",\n \"The PCA components of the id 87 are the same as in the previous train PCA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
pca_0pca_1pca_2pca_3
874.3379373.2015851.2488124.419234
88-2.510650-0.139986-0.4691030.243084
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" pca_0 pca_1 pca_2 pca_3\\n\",\n \"87 4.337937 3.201585 1.248812 4.419234\\n\",\n \"88 -2.510650 -0.139986 -0.469103 0.243084\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_test_pca = pca_train.transform(X_test_filtered)\\n\",\n \"\\n\",\n \"# reset index to keep original index from robot example\\n\",\n \"X_test_pca.index = [87, 88]\\n\",\n \"\\n\",\n \"X_test_pca\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The Benjamini Yekutieli (BY) procedure is a multiple testing procedure that can be used to control the accumulation in type 1 errors when comparing multiple hypothesis at the same time.\\n\",\n \"\\n\",\n \"In the tsfresh filtering the BY procedure is used to decide which features to use and which to keep. \\n\",\n \"\\n\",\n \"The method is based on a line, the so called rejection line, that is compared to the sequence of ordered p-values. In this notebook, we will visualize that rejection line.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\\n\",\n \"from tsfresh import defaults, extract_features\\n\",\n \"from tsfresh.feature_selection.relevance import calculate_relevance_table\\n\",\n \"from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"from tsfresh.feature_extraction import ComprehensiveFCParameters\\n\",\n \"\\n\",\n \"matplotlib.rcParams[\\\"figure.figsize\\\"] = [16, 6]\\n\",\n \"matplotlib.rcParams[\\\"font.size\\\"] = 14\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Parameter setting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"FDR_LEVEL = defaults.FDR_LEVEL\\n\",\n \"HYPOTHESES_INDEPENDENT = defaults.HYPOTHESES_INDEPENDENT\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Load robot data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"download_robot_execution_failures()\\n\",\n \"df, y = load_robot_execution_failures()\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Extract Features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X = extract_features(df, \\n\",\n \" column_id='id', column_sort='time',\\n\",\n \" default_fc_parameters=ComprehensiveFCParameters(),\\n\",\n \" impute_function=impute)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# drop constant features\\n\",\n \"print(X.shape)\\n\",\n \"X = X.loc[:, X.apply(pd.Series.nunique) != 1] \\n\",\n \"print(X.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Calculate p-values and Benjamini-Yekutieli Procedure\\n\",\n \"\\n\",\n \"tsfresh has implemented two different feature significance tests, the Mann-Whitney-U test and the Kolmogorov-Smirnov test. In the following, both of them are being illustrated to show a scientific report of the feature selection process and to give a comparison of the differences of both methods.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Mann-Whitney-U\\n\",\n \"Run significance test with Mann-Whitney-U test. Returns the p-values of the features and whether they are rejected or not.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_mann = calculate_relevance_table(X, y, fdr_level=FDR_LEVEL, test_for_binary_target_real_feature='mann')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print(\\\"# total \\\\t\\\", len(df_pvalues_mann))\\n\",\n \"print(\\\"# relevant \\\\t\\\", (df_pvalues_mann[\\\"relevant\\\"] == True).sum())\\n\",\n \"print(\\\"# irrelevant \\\\t\\\", (df_pvalues_mann[\\\"relevant\\\"] == False).sum(),\\n\",\n \" \\\"( # constant\\\", (df_pvalues_mann[\\\"type\\\"] == \\\"const\\\").sum(), \\\")\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_mann.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Kolmogorov-Smirnov\\n\",\n \"Run significance test with Kolmogorov-Smirnov test. Returns the p-values of the features and whether they are rejected or not.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_smir = calculate_relevance_table(X, y, fdr_level=FDR_LEVEL, test_for_binary_target_real_feature='smir')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print(\\\"# total \\\\t\\\", len(df_pvalues_smir))\\n\",\n \"print(\\\"# relevant \\\\t\\\", (df_pvalues_smir[\\\"relevant\\\"] == True).sum())\\n\",\n \"print(\\\"# irrelevant \\\\t\\\", (df_pvalues_smir[\\\"relevant\\\"] == False).sum(),\\n\",\n \" \\\"( # constant\\\", (df_pvalues_smir[\\\"type\\\"] == \\\"const\\\").sum(), \\\")\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_smir.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Calculate rejection line\\n\",\n \"With the rejection line it is determined whether a feature is relevant or irrelevant.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_rejection_line(df_pvalues, hypothesis_independent, fdr_level):\\n\",\n \" \\n\",\n \" m = len(df_pvalues.loc[~(df_pvalues.type == \\\"const\\\")])\\n\",\n \" K = list(range(1, m + 1))\\n\",\n \" \\n\",\n \" if hypothesis_independent:\\n\",\n \" C = [1] * m\\n\",\n \" else:\\n\",\n \" C = [sum([1.0 / k for k in K])] * m\\n\",\n \"\\n\",\n \" return [fdr_level * k / m * 1.0 / c for k, c in zip(K, C)] \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Mann-Whitney-U\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"rejection_line_mann = calc_rejection_line(df_pvalues_mann, HYPOTHESES_INDEPENDENT, FDR_LEVEL)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Kolmogorov-Smirnov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"rejection_line_smir = calc_rejection_line(df_pvalues_smir, HYPOTHESES_INDEPENDENT, FDR_LEVEL)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Plot ordered p-values and rejection line\\n\",\n \"\\n\",\n \"In the plot, the p-values are ordered from low to high. Constant features (green points) are always irrelevant but are not considered for calculating the rejection line (red line).\\n\",\n \"\\n\",\n \"For nice plotting, the p-values are divided in the three groups relevant, irrelevant and constant (which are also irrelevant).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Mann-Whitney-U\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_mann.index = pd.Series(range(0, len(df_pvalues_mann.index)))\\n\",\n \"\\n\",\n \"df_pvalues_mann.p_value.where(df_pvalues_mann.relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"relevant features\\\")\\n\",\n \"\\n\",\n \"df_pvalues_mann.p_value.where(~df_pvalues_mann.relevant & (df_pvalues_mann.type != \\\"const\\\"))\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant features\\\")\\n\",\n \"\\n\",\n \"df_pvalues_mann.p_value.fillna(1).where(df_pvalues_mann.type == \\\"const\\\")\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant (constant) features\\\")\\n\",\n \"\\n\",\n \"plt.plot(rejection_line_mann, label=\\\"rejection line (FDR = \\\" + str(FDR_LEVEL) + \\\")\\\")\\n\",\n \"plt.xlabel(\\\"Feature #\\\")\\n\",\n \"plt.ylabel(\\\"p-value\\\")\\n\",\n \"plt.title(\\\"Mann-Whitney-U\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Kolmogorov-Smirnov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_pvalues_smir.index = pd.Series(range(0, len(df_pvalues_smir.index)))\\n\",\n \"\\n\",\n \"df_pvalues_smir.p_value.where(df_pvalues_smir.relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"relevant features\\\")\\n\",\n \"\\n\",\n \"df_pvalues_smir.p_value.where(~df_pvalues_smir.relevant & (df_pvalues_smir.type != \\\"const\\\"))\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant features\\\")\\n\",\n \"\\n\",\n \"df_pvalues_smir.p_value.fillna(1).where(df_pvalues_smir.type == \\\"const\\\")\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant (constant) features\\\")\\n\",\n \"\\n\",\n \"plt.plot(rejection_line_smir, label=\\\"rejection line (FDR = \\\" + str(FDR_LEVEL) + \\\")\\\")\\n\",\n \"plt.xlabel(\\\"Feature #\\\")\\n\",\n \"plt.ylabel(\\\"p-value\\\")\\n\",\n \"plt.title(\\\"Kolmogorov-Smirnov\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Plot zoomed ordered p-values and rejection line\\n\",\n \"Since the intersection of the ordered p-values and the rejection line is not clearly visible, a zoomed plot is provided.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Mann-Whitney-U\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"last_rejected_index = (df_pvalues_mann[\\\"relevant\\\"] == True).sum() - 1\\n\",\n \"margin = 20\\n\",\n \"a = max(last_rejected_index - margin, 0)\\n\",\n \"b = min(last_rejected_index + margin, len(df_pvalues_mann) - 1)\\n\",\n \"\\n\",\n \"df_pvalues_mann[a:b].p_value.where(df_pvalues_mann[a:b].relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"relevant features\\\")\\n\",\n \"df_pvalues_mann[a:b].p_value.where(~df_pvalues_mann[a:b].relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant features\\\")\\n\",\n \"plt.plot(np.arange(a, b), rejection_line_mann[a:b], label=\\\"rejection line (FDR = \\\" + str(FDR_LEVEL) + \\\")\\\")\\n\",\n \"plt.xlabel(\\\"Feature #\\\")\\n\",\n \"plt.ylabel(\\\"p-value\\\")\\n\",\n \"plt.title(\\\"Mann-Whitney-U\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Kolmogorov-Smirnov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"last_rejected_index = (df_pvalues_smir[\\\"relevant\\\"] == True).sum() - 1\\n\",\n \"margin = 20\\n\",\n \"a = max(last_rejected_index - margin, 0)\\n\",\n \"b = min(last_rejected_index + margin, len(df_pvalues_smir) - 1)\\n\",\n \"\\n\",\n \"df_pvalues_smir[a:b].p_value.where(df_pvalues_smir[a:b].relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"relevant features\\\")\\n\",\n \"df_pvalues_smir[a:b].p_value.where(~df_pvalues_smir[a:b].relevant)\\\\\\n\",\n \" .plot(style=\\\".\\\", label=\\\"irrelevant features\\\")\\n\",\n \"plt.plot(np.arange(a, b), rejection_line_smir[a:b], label=\\\"rejection line (FDR = \\\" + str(FDR_LEVEL) + \\\")\\\")\\n\",\n \"plt.xlabel(\\\"Feature #\\\")\\n\",\n \"plt.ylabel(\\\"p-value\\\")\\n\",\n \"plt.title(\\\"Kolmogorov-Smirnov\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.plot()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "setup.cfg", "content": "# This file is used to configure your project.\n# Read more about the various options under:\n# http://setuptools.readthedocs.io/en/latest/setuptools.html#configuring-setup-using-setup-cfg-files\n[metadata]\nname = tsfresh\nsummary = tsfresh extracts relevant characteristics from time series\nauthor = Maximilian Christ, Nils Braun, Julius Neuffer, Andreas W. Kempa-Liehr\nauthor_email = max.christ@me.com\nlicense = MIT\nhome_page = https://github.com/blue-yonder/tsfresh\n# Add here all kinds of additional classifiers as defined under\n# https://pypi.python.org/pypi?%3Aaction=list_classifiers\nclassifier =\n Development Status :: 4 - Beta\n Programming Language :: Python\n Programming Language :: Python :: 3\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 Programming Language :: Python :: 3 :: Only\n Operating System :: Unix\n Operating System :: Microsoft :: Windows\n Operating System :: MacOS\n Intended Audience :: Science/Research\n Topic :: Scientific/Engineering\n Topic :: Software Development\n\n\n[options]\npackages = find:\n# DON'T CHANGE THE FOLLOWING LINE! IT WILL BE UPDATED BY PYSCAFFOLD!\nsetup_requires =\n pyscaffold>=3.3a0,<4\n# Add here dependencies of your project (semicolon/line-separated), e.g.\ninstall_requires =\n requests>=2.9.1\n numpy >=1.15.1\n pandas>=0.25.0\n scipy>=1.14.0;python_version>='3.10'\n scipy>=1.2.0;python_version<'3.10'\n statsmodels>=0.13\n patsy>=0.4.1\n pywavelets\n scikit-learn>=0.22.0\n tqdm>=4.10.0\n stumpy>=1.7.2\n cloudpickle\n\n# Require a specific Python version, e.g. Python 2.7 or >= 3.4\npython_requires = >= 3.9\n\n[options.packages.find]\nwhere = .\nexclude =\n tests*\n\n[options.extras_require]\ndask =\n dask[dataframe]>=2.9.0\n distributed>=2.11.0\nmatrixprofile =\n matrixprofile>=1.1.10,<2.0.0\n\ndocs =\n Sphinx>=6.1.3\n sphinx_rtd_theme>=1.2.0\n b2luigi\n docutils>=0.18.1\n\n# Add here test requirements (semicolon/line-separated)\ntesting =\n %(dask)s\n pytest>=4.4.0\n pytest-cov>=2.6.1\n pytest-xdist>=1.26.1\n pytest-benchmark>=3.0.0\n mock>=2.0.0\n matplotlib>=2.0.0\n seaborn>=0.7.1\n ipython>=5.3.0\n notebook>=4.4.1\n pandas-datareader>=0.5.0\n pre-commit\n\n[options.entry_points]\nconsole_scripts =\n run_tsfresh = tsfresh.scripts.run_tsfresh:main\n\n\n[easy_install]\nfind_links = https://pypi.org/\nindex_url = https://pypi.org/simple\n\n[test]\n# py.test options when running `python setup.py test`\n# addopts = --verbose\nextras = True\n\n[tool:pytest]\n# Options for py.test:\n# Specify command line options as you would do when invoking py.test directly.\n# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml\n# in order to write a coverage file that can be read by Jenkins.\njunit_family = xunit2\naddopts =\n --cov tsfresh --cov-report term-missing\n --verbose\n #-n auto\ntestpaths = tests\nfilterwarnings =\n\n[aliases]\ndists = bdist_wheel\ndocs = build_sphinx\n\n[bdist_wheel]\nuniversal = 1\n\n[build_sphinx]\nsource_dir = docs\nbuild_dir = docs/_build\n\n[pbr]\n# Let pbr run sphinx-apidoc\nautodoc_tree_index_modules = True\n\n[devpi:upload]\n# Options for the devpi: PyPI server and packaging tool\n# VCS export must be deactivated since we are using setuptools-scm\nno-vcs = 1\nformats = bdist_wheel\n# do not upload the docs as we host them on read-the-docs\nwith-docs = 0\n\n[pycodestyle]\nmax-line-length = 120\n\n[pyscaffold]\n# PyScaffold's parameters when the project was created.\n# This will be used when updating. Do not change!\nversion = 3.2.3\npackage = tsfresh\n\n[tox:tox]\n# Tox global conifguration options\nminversion = 4.0.0\n# Which python versions to test for\nenvlist = py39, py310, py311, py312\n# Don't throw an error if a specific version of python is not installed\nskip_missing_interpreters = True\n# Ensure that tox uses a virtual environment to build a source distribution\nisolated_build = True\n\n# Tox configuration options for each version of python\n[testenv]\ncommands =\n # display the python version that tests are being run on\n python -V\n # install package requirements before running tests\n python -m pip install .[testing]\n # run the tests\n python -m pytest tests/\n" }, { "path": "setup.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"\n Setup file for tsfresh.\n Use setup.cfg to configure your project.\n\n This file was generated with PyScaffold 3.2.3.\n PyScaffold helps you to put up the scaffold of your new Python project.\n Learn more under: https://pyscaffold.org/\n\"\"\"\nimport sys\n\nfrom pkg_resources import VersionConflict, require\nfrom setuptools import setup\n\ntry:\n require(\"setuptools>=38.3\")\nexcept VersionConflict:\n print(\"Error: version of setuptools is too old (<38.3)!\")\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n setup(use_pyscaffold=True)\n" }, { "path": "tests/__init__.py", "content": "" }, { "path": "tests/benchmark.py", "content": "# Testcase for benchmarking tsfresh feature extraction and selection\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom tsfresh import extract_features, extract_relevant_features\nfrom tsfresh.feature_extraction.settings import (\n ComprehensiveFCParameters,\n MinimalFCParameters,\n)\n\n\ndef create_data(time_series_length, num_ids, random_seed=42):\n np.random.seed(random_seed)\n\n df = pd.concat(\n [\n pd.DataFrame(\n {\n \"id\": [i] * time_series_length,\n \"time\": range(time_series_length),\n \"value\": np.random.randn(time_series_length).cumsum(),\n }\n )\n for i in range(num_ids)\n ]\n )\n\n return df\n\n\ndef test_benchmark_small_data(benchmark):\n df = create_data(5, 200)\n\n benchmark(\n extract_features,\n df,\n column_id=\"id\",\n column_sort=\"time\",\n n_jobs=0,\n disable_progressbar=True,\n )\n\n\ndef test_benchmark_large_data(benchmark):\n df = create_data(500, 20)\n\n benchmark(\n extract_features,\n df,\n column_id=\"id\",\n column_sort=\"time\",\n n_jobs=0,\n disable_progressbar=True,\n )\n\n\ndef test_benchmark_with_selection(benchmark):\n df = create_data(500, 20)\n y = pd.Series(np.random.choice([0, 1], 20))\n\n benchmark(\n extract_relevant_features,\n df,\n y,\n column_id=\"id\",\n column_sort=\"time\",\n n_jobs=0,\n disable_progressbar=True,\n )\n" }, { "path": "tests/fixtures.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport warnings\nfrom contextlib import contextmanager\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\n\n\n@contextmanager\ndef warning_free():\n \"\"\"Small helper to surpress all warnings\"\"\"\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n yield\n\n\n# todo: add test cases for float data\n# todo: add test cases for nans, -infs, infs\n# todo: add test cases with time series of length one\n\n\nclass DataTestCase(TestCase):\n def create_test_data_sample(self):\n cid = np.repeat([10, 500], 40)\n ckind = np.repeat([\"a\", \"b\", \"a\", \"b\"], 20)\n csort = [\n 30,\n 53,\n 26,\n 35,\n 42,\n 25,\n 17,\n 67,\n 20,\n 68,\n 46,\n 12,\n 0,\n 74,\n 66,\n 31,\n 32,\n 2,\n 55,\n 59,\n 56,\n 60,\n 34,\n 69,\n 47,\n 15,\n 49,\n 8,\n 50,\n 73,\n 23,\n 62,\n 24,\n 33,\n 22,\n 70,\n 3,\n 38,\n 28,\n 75,\n 39,\n 36,\n 64,\n 13,\n 72,\n 52,\n 40,\n 16,\n 58,\n 29,\n 63,\n 79,\n 61,\n 78,\n 1,\n 10,\n 4,\n 6,\n 65,\n 44,\n 54,\n 48,\n 11,\n 14,\n 19,\n 43,\n 76,\n 7,\n 51,\n 9,\n 27,\n 21,\n 5,\n 71,\n 57,\n 77,\n 41,\n 18,\n 45,\n 37,\n ]\n cval = [\n 11,\n 9,\n 67,\n 45,\n 30,\n 58,\n 62,\n 19,\n 56,\n 29,\n 0,\n 27,\n 36,\n 43,\n 33,\n 2,\n 24,\n 71,\n 41,\n 28,\n 50,\n 40,\n 39,\n 7,\n 53,\n 23,\n 16,\n 37,\n 66,\n 38,\n 6,\n 47,\n 3,\n 61,\n 44,\n 42,\n 78,\n 31,\n 21,\n 55,\n 15,\n 35,\n 25,\n 32,\n 69,\n 65,\n 70,\n 64,\n 51,\n 46,\n 5,\n 77,\n 26,\n 73,\n 76,\n 75,\n 72,\n 74,\n 10,\n 57,\n 4,\n 14,\n 68,\n 22,\n 18,\n 52,\n 54,\n 60,\n 79,\n 12,\n 49,\n 63,\n 8,\n 59,\n 1,\n 13,\n 20,\n 17,\n 48,\n 34,\n ]\n df = pd.DataFrame({\"id\": cid, \"kind\": ckind, \"sort\": csort, \"val\": cval})\n df = df.set_index(\"id\", drop=False)\n df.index.name = None\n return df\n\n def create_test_data_sample_wide(self):\n rec = np.rec.array(\n [\n (0, 10, 0, 11, 50),\n (1, 10, 1, 9, 40),\n (2, 10, 2, 67, 39),\n (3, 10, 3, 45, 7),\n (4, 10, 4, 30, 53),\n (5, 10, 5, 58, 23),\n (6, 10, 6, 62, 16),\n (7, 10, 7, 19, 37),\n (8, 10, 8, 56, 66),\n (9, 10, 9, 29, 38),\n (10, 10, 10, 0, 6),\n (11, 10, 11, 27, 47),\n (12, 10, 12, 36, 3),\n (13, 10, 13, 43, 61),\n (14, 10, 14, 33, 44),\n (15, 10, 15, 2, 42),\n (16, 10, 16, 24, 78),\n (17, 10, 17, 71, 31),\n (18, 10, 18, 41, 21),\n (19, 10, 19, 28, 55),\n (20, 500, 0, 15, 4),\n (21, 500, 1, 35, 14),\n (22, 500, 2, 25, 68),\n (23, 500, 3, 32, 22),\n (24, 500, 4, 69, 18),\n (25, 500, 5, 65, 52),\n (26, 500, 6, 70, 54),\n (27, 500, 7, 64, 60),\n (28, 500, 8, 51, 79),\n (29, 500, 9, 46, 12),\n (30, 500, 10, 5, 49),\n (31, 500, 11, 77, 63),\n (32, 500, 12, 26, 8),\n (33, 500, 13, 73, 59),\n (34, 500, 14, 76, 1),\n (35, 500, 15, 75, 13),\n (36, 500, 16, 72, 20),\n (37, 500, 17, 74, 17),\n (38, 500, 18, 10, 48),\n (39, 500, 19, 57, 34),\n ],\n dtype=[\n (\"index\", \" 1 / 0.3)\n self.assertTrue(abs(0.4 - float(before) / (before + beyond)) < 0.006)\n\n\nclass LoadDriftBifTestCase(unittest.TestCase):\n def setUp(self):\n np.random.seed(42)\n\n def test_classification_labels(self):\n X, y = load_driftbif(10, 100)\n self.assertEqual(set(y), {0, 1})\n\n def test_regression_labels(self):\n Nsamples = 10\n X, y = load_driftbif(Nsamples, 100, classification=False)\n self.assertEqual(\n y.size,\n np.unique(y).size,\n \"For regression the target vector is expected to not contain any dublicated labels.\",\n )\n\n def test_default_dimensionality(self):\n Nsamples = 10\n Nt = 100\n X, y = load_driftbif(Nsamples, Nt)\n self.assertEqual(X.shape, (2 * Nt * Nsamples, 4))\n\n def test_configured_dimensionality(self):\n Nsamples = 10\n Nt = 100\n X, y = load_driftbif(Nsamples, Nt, m=3)\n self.assertEqual(X.shape, (3 * Nt * Nsamples, 4))\n" }, { "path": "tests/integrations/examples/test_har_dataset.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport shutil\nimport tempfile\nfrom unittest import TestCase\n\nfrom pandas import DataFrame, Series\n\nfrom tsfresh.examples.har_dataset import (\n download_har_dataset,\n load_har_classes,\n load_har_dataset,\n)\n\n\nclass HumanActivityTestCase(TestCase):\n def setUp(self):\n self.temporary_folder = tempfile.mkdtemp()\n\n download_har_dataset(folder_name=self.temporary_folder)\n self.data = load_har_dataset(folder_name=self.temporary_folder)\n self.classes = load_har_classes(folder_name=self.temporary_folder)\n\n def tearDown(self):\n shutil.rmtree(self.temporary_folder)\n\n def test_characteristics_downloaded_robot_execution_failures(self):\n self.assertEqual(len(self.data), 7352)\n self.assertIsInstance(self.data, DataFrame)\n\n self.assertEqual(len(self.classes), 7352)\n self.assertIsInstance(self.classes, Series)\n\n def test_index(self):\n self.assertCountEqual(self.data.index, self.classes.index)\n" }, { "path": "tests/integrations/examples/test_robot_execution_failures.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport os\nimport shutil\nimport tempfile\nfrom unittest import TestCase\n\nimport numpy as np\nfrom pandas import DataFrame, Series\n\nfrom tsfresh import extract_features\nfrom tsfresh.examples.robot_execution_failures import (\n download_robot_execution_failures,\n load_robot_execution_failures,\n)\n\n\nclass RobotExecutionFailuresTestCase(TestCase):\n def setUp(self):\n self.temporary_folder = tempfile.mkdtemp()\n self.temporary_file = os.path.join(self.temporary_folder, \"data\")\n\n download_robot_execution_failures(file_name=self.temporary_file)\n self.X, self.y = load_robot_execution_failures(file_name=self.temporary_file)\n\n def tearDown(self):\n shutil.rmtree(self.temporary_folder)\n\n def test_characteristics_downloaded_robot_execution_failures(self):\n self.assertEqual(len(self.X), 1320)\n self.assertIsInstance(self.X, DataFrame)\n self.assertIsInstance(self.y, Series)\n self.assertCountEqual(\n [\"id\", \"time\", \"F_x\", \"F_y\", \"F_z\", \"T_x\", \"T_y\", \"T_z\"],\n list(self.X.columns),\n )\n\n def test_extraction_runs_through(self):\n df = extract_features(self.X[self.X.id < 3], column_id=\"id\", column_sort=\"time\")\n\n self.assertCountEqual(df.index.values, [1, 2])\n self.assertGreater(len(df), 0)\n\n def test_binary_target_is_default(self):\n _, y = load_robot_execution_failures(file_name=self.temporary_file)\n\n assert len(y.unique()) == 2\n\n def test_multilabel_target_on_request(self):\n _, y = load_robot_execution_failures(\n multiclass=True, file_name=self.temporary_file\n )\n\n assert len(y.unique()) > 2\n assert y.dtype == object\n" }, { "path": "tests/integrations/test_bindings.py", "content": "from unittest import TestCase\n\nimport pandas as pd\nfrom dask import dataframe as dd\n\nfrom tsfresh.convenience.bindings import dask_feature_extraction_on_chunk\nfrom tsfresh.feature_extraction.settings import MinimalFCParameters\n\n\nclass DaskBindingsTestCase(TestCase):\n def test_feature_extraction(self):\n df = pd.DataFrame(\n {\n \"my_id\": [1, 1, 1, 2, 2, 2],\n \"my_kind\": [\"a\"] * 6,\n \"my_value\": [1, 2, 3, 4, 5, 6],\n }\n )\n\n df = dd.from_pandas(df, chunksize=3)\n\n df_grouped = df.groupby([\"my_id\", \"my_kind\"])\n\n features = dask_feature_extraction_on_chunk(\n df_grouped,\n column_id=\"my_id\",\n column_kind=\"my_kind\",\n column_value=\"my_value\",\n column_sort=None,\n default_fc_parameters=MinimalFCParameters(),\n )\n\n features = features.compute()\n\n self.assertEqual(list(sorted(features.columns)), [\"my_id\", \"value\", \"variable\"])\n self.assertEqual(len(features), 2 * len(MinimalFCParameters()))\n" }, { "path": "tests/integrations/test_feature_extraction.py", "content": "# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nfrom unittest import TestCase\n\nimport dask.dataframe as dd\nimport pandas as pd\n\nfrom tsfresh import extract_features, extract_relevant_features\nfrom tsfresh.examples.driftbif_simulation import load_driftbif\nfrom tsfresh.feature_extraction import MinimalFCParameters\n\n\nclass FeatureExtractionTestCase(TestCase):\n def setUp(self):\n df, y = load_driftbif(100, 10, classification=True, seed=42)\n\n df[\"my_id\"] = df[\"id\"].astype(\"str\")\n del df[\"id\"]\n\n self.df = df\n\n def test_pandas(self):\n df = self.df\n\n # Test shape and a single entry (to see if it works at all)\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n column_value=\"value\",\n default_fc_parameters=MinimalFCParameters(),\n )\n self.assertIn(\"1__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"1__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 20))\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n default_fc_parameters=MinimalFCParameters(),\n )\n self.assertIn(\"1__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"1__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 20))\n\n X = extract_features(\n df.drop(columns=[\"dimension\"]),\n column_id=\"my_id\",\n column_sort=\"time\",\n default_fc_parameters=MinimalFCParameters(),\n )\n self.assertIn(\"value__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"value__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 10))\n\n X = extract_features(\n df.drop(columns=[\"dimension\", \"time\"]),\n column_id=\"my_id\",\n default_fc_parameters=MinimalFCParameters(),\n )\n self.assertIn(\"value__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"value__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 10))\n\n def test_pandas_no_pivot(self):\n df = self.df\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n column_value=\"value\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n )\n X = pd.DataFrame(X, columns=[\"my_id\", \"variable\", \"value\"])\n self.assertIn(\"1__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"1__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 20, 3))\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n )\n X = pd.DataFrame(X, columns=[\"my_id\", \"variable\", \"value\"])\n self.assertIn(\"1__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"1__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 20, 3))\n\n X = extract_features(\n df.drop(columns=[\"dimension\"]),\n column_id=\"my_id\",\n column_sort=\"time\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n )\n X = pd.DataFrame(X, columns=[\"my_id\", \"variable\", \"value\"])\n self.assertIn(\"value__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"value__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 10, 3))\n\n X = extract_features(\n df.drop(columns=[\"dimension\", \"time\"]),\n column_id=\"my_id\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n )\n X = pd.DataFrame(X, columns=[\"my_id\", \"variable\", \"value\"])\n self.assertIn(\"value__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"value__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 10, 3))\n\n def test_dask(self):\n df = dd.from_pandas(self.df, npartitions=1)\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n column_value=\"value\",\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"1__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"1__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 20))\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"1__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"1__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 20))\n\n X = extract_features(\n df.drop(columns=[\"dimension\"]),\n column_id=\"my_id\",\n column_sort=\"time\",\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"value__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"value__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 10))\n\n X = extract_features(\n df.drop(columns=[\"dimension\", \"time\"]),\n column_id=\"my_id\",\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"value__mean\", X.columns)\n self.assertAlmostEqual(X.loc[\"5\", \"value__mean\"], 5.516e-05, 4)\n self.assertIn(\"11\", X.index)\n self.assertEqual(X.shape, (100, 10))\n\n def test_dask_no_pivot(self):\n df = dd.from_pandas(self.df, npartitions=1)\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n column_value=\"value\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"1__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"1__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 20, 3))\n\n X = extract_features(\n df,\n column_id=\"my_id\",\n column_sort=\"time\",\n column_kind=\"dimension\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"1__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"1__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 20, 3))\n\n X = extract_features(\n df.drop(columns=[\"dimension\"]),\n column_id=\"my_id\",\n column_sort=\"time\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"value__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"value__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 10, 3))\n\n X = extract_features(\n df.drop(columns=[\"dimension\", \"time\"]),\n column_id=\"my_id\",\n pivot=False,\n default_fc_parameters=MinimalFCParameters(),\n ).compute()\n self.assertIn(\"value__mean\", X[\"variable\"].values)\n self.assertAlmostEqual(\n X[(X[\"my_id\"] == \"5\") & (X[\"variable\"] == \"value__mean\")][\"value\"].iloc[0],\n 5.516e-05,\n 4,\n )\n self.assertEqual(X.shape, (100 * 10, 3))\n" }, { "path": "tests/integrations/test_full_pipeline.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport os\nimport shutil\nimport tempfile\nfrom unittest import TestCase\n\nimport pandas as pd\nfrom sklearn.pipeline import Pipeline\n\nfrom tests.fixtures import warning_free\nfrom tsfresh.examples.robot_execution_failures import (\n download_robot_execution_failures,\n load_robot_execution_failures,\n)\nfrom tsfresh.transformers import RelevantFeatureAugmenter\n\n\nclass FullPipelineTestCase_robot_failures(TestCase):\n def setUp(self):\n self.temporary_folder = tempfile.mkdtemp()\n temporary_file = os.path.join(self.temporary_folder, \"data\")\n\n download_robot_execution_failures(file_name=temporary_file)\n self.timeseries, self.y = load_robot_execution_failures(\n file_name=temporary_file\n )\n self.df = pd.DataFrame(index=self.timeseries.id.unique())\n\n # shrink the time series for this test\n self.timeseries = self.timeseries[[\"id\", \"time\", \"F_x\"]]\n\n def tearDown(self):\n shutil.rmtree(self.temporary_folder)\n\n def test_relevant_extraction(self):\n self.assertGreater(len(self.y), 0)\n self.assertGreater(len(self.df), 0)\n self.assertGreater(len(self.timeseries), 0)\n\n relevant_augmenter = RelevantFeatureAugmenter(\n column_id=\"id\", column_sort=\"time\"\n )\n relevant_augmenter.set_timeseries_container(self.timeseries)\n\n pipe = Pipeline([(\"relevant_augmenter\", relevant_augmenter)])\n\n with warning_free():\n pipe.fit(self.df, self.y)\n extracted_features = pipe.transform(self.df)\n\n some_expected_features = {\n \"F_x__abs_energy\",\n \"F_x__absolute_sum_of_changes\",\n \"F_x__autocorrelation__lag_1\",\n \"F_x__binned_entropy__max_bins_10\",\n \"F_x__count_above_mean\",\n \"F_x__longest_strike_above_mean\",\n \"F_x__maximum\",\n \"F_x__mean_abs_change\",\n \"F_x__minimum\",\n \"F_x__quantile__q_0.1\",\n \"F_x__range_count__max_1__min_-1\",\n \"F_x__spkt_welch_density__coeff_2\",\n \"F_x__standard_deviation\",\n \"F_x__value_count__value_0\",\n \"F_x__variance\",\n \"F_x__variance_larger_than_standard_deviation\",\n }\n\n self.assertGreaterEqual(set(extracted_features.columns), some_expected_features)\n self.assertGreater(len(extracted_features), 0)\n" }, { "path": "tests/integrations/test_notebooks.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport os\nimport subprocess\nimport tempfile\nfrom unittest import TestCase\n\nimport nbformat\nimport pytest\n\ndefault_timeout = 900\n\n\ndef _notebook_run(path, timeout=default_timeout):\n \"\"\"\n Execute a singular ipython notebook at path and returns the cells and outputs\n\n :returns (parsed nb object, execution errors)\n \"\"\"\n\n dirname, _ = os.path.split(path)\n execproc_timeout = \"--ExecutePreprocessor.timeout=%d\" % timeout\n\n # Do not run notebook tests on Travis. notebooks tests should only be\n # run in the local developer testing context and notebook tests often\n # cause time out failures on Travis builds see (github #409, #410)\n try:\n if os.environ[\"TRAVIS\"]:\n return [], []\n except BaseException:\n pass\n\n # Ensure temporary files are not auto-deleted as processes have limited\n # permissions to re-use file handles under WinNT-based operating systems.\n fname = \"\"\n with tempfile.NamedTemporaryFile(mode=\"w+t\", suffix=\".ipynb\", delete=False) as fout:\n fname = fout.name\n\n args = [\n \"jupyter\",\n \"nbconvert\",\n \"--to\",\n \"notebook\",\n \"--execute\",\n execproc_timeout,\n ]\n args += [\"--ExecutePreprocessor.kernel_name=python3\"]\n args += [\"--output\", fout.name, path]\n subprocess.check_call(args)\n\n fout.seek(0)\n nb = nbformat.read(fout, nbformat.current_nbformat)\n os.remove(fname)\n\n errors = [\n output\n for cell in nb.cells\n if \"outputs\" in cell\n for output in cell[\"outputs\"]\n if output.output_type == \"error\"\n ]\n return nb, errors\n\n\n@pytest.mark.skipif(\n os.environ.get(\"TEST_NOTEBOOKS\") != \"y\", reason=\"Disabled notebook testing\"\n)\nclass NotebooksTestCase(TestCase):\n def test_basic_example(self):\n nb, errors = _notebook_run(\n \"notebooks/01 Feature Extraction and Selection.ipynb\",\n default_timeout,\n )\n self.assertEqual(errors, [])\n\n def test_pipeline_example(self):\n nb, errors = _notebook_run(\n \"notebooks/02 sklearn Pipeline.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_extraction_settings(self):\n nb, errors = _notebook_run(\n \"notebooks/03 Feature Extraction Settings.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_multiclass_selection_example(self):\n nb, errors = _notebook_run(\n \"notebooks/04 Multiclass Selection Example.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_timeseries_forecasting(self):\n nb, errors = _notebook_run(\n \"notebooks/05 Timeseries Forecasting.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_timeseries_forecasting_exprt(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb\",\n default_timeout,\n )\n self.assertEqual(errors, [])\n\n def test_inspect_dft_features(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/inspect_dft_features.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_feature_extraction_with_datetime_index(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/feature_extraction_with_datetime_index.ipynb\",\n default_timeout,\n )\n self.assertEqual(errors, [])\n\n def test_friedrich_coefficients(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/friedrich_coefficients.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_inspect_dft_features(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/inspect_dft_features.ipynb\", default_timeout\n )\n self.assertEqual(errors, [])\n\n def test_perform_PCA_on_extracted_features(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/perform-PCA-on-extracted-features.ipynb\",\n default_timeout,\n )\n self.assertEqual(errors, [])\n\n def test_visualize_benjamini_yekutieli_procedure(self):\n nb, errors = _notebook_run(\n \"notebooks/advanced/visualize-benjamini-yekutieli-procedure.ipynb\",\n default_timeout,\n )\n self.assertEqual(errors, [])\n" }, { "path": "tests/integrations/test_relevant_feature_extraction.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\nimport pandas.testing as pdt\n\nfrom tests.fixtures import DataTestCase\nfrom tsfresh import extract_features, extract_relevant_features, select_features\nfrom tsfresh.feature_extraction.settings import ComprehensiveFCParameters\nfrom tsfresh.utilities.dataframe_functions import impute\n\n\nclass RelevantFeatureExtractionDataTestCase(DataTestCase):\n \"\"\"\n Test case for the relevant_feature_extraction function\n \"\"\"\n\n def test_functional_equality(self):\n \"\"\"\n `extract_relevant_features` should be equivalent to running first `extract_features` with impute and\n `select_features` afterwards.\n Meaning it should produce the same relevant features and the values of these features should be identical.\n :return:\n \"\"\"\n df, y = self.create_test_data_sample_with_target()\n\n relevant_features = extract_relevant_features(\n df,\n y,\n column_id=\"id\",\n column_value=\"val\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n )\n\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_value=\"val\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n impute_function=impute,\n )\n selected_features = select_features(extracted_features, y)\n\n self.assertEqual(\n set(relevant_features.columns),\n set(selected_features.columns),\n \"Should select the same columns:\\n\\t{}\\n\\nvs.\\n\\n\\t{}\".format(\n relevant_features.columns, selected_features.columns\n ),\n )\n\n relevant_columns = relevant_features.columns\n relevant_index = relevant_features.index\n self.assertTrue(\n relevant_features.equals(\n selected_features.loc[relevant_index][relevant_columns]\n ),\n \"Should calculate the same feature values\",\n )\n\n\nclass RelevantFeatureExtractionTestCase(TestCase):\n def setUp(self):\n np.random.seed(42)\n y = pd.Series(np.random.binomial(1, 0.5, 20), index=range(20))\n df = pd.DataFrame(index=range(100))\n\n df[\"a\"] = np.random.normal(0, 1, 100)\n df[\"b\"] = np.random.normal(0, 1, 100)\n df[\"id\"] = np.repeat(range(20), 5)\n\n X = pd.DataFrame(index=range(20))\n X[\"f1\"] = np.random.normal(0, 1, 20)\n X[\"f2\"] = np.random.normal(0, 1, 20)\n\n self.df = df\n self.X = X\n self.y = y\n\n def test_extracted_features_contain_X_features(self):\n X = extract_relevant_features(self.df, self.y, self.X, column_id=\"id\")\n self.assertIn(\"f1\", X.columns)\n self.assertIn(\"f2\", X.columns)\n pdt.assert_series_equal(self.X[\"f1\"], X[\"f1\"])\n pdt.assert_series_equal(self.X[\"f2\"], X[\"f2\"])\n pdt.assert_index_equal(self.X[\"f1\"].index, X[\"f1\"].index)\n pdt.assert_index_equal(self.X[\"f2\"].index, X[\"f2\"].index)\n\n def test_extraction_null_as_column_name(self):\n\n df1 = pd.DataFrame(\n data={\n 0: range(10),\n 1: np.repeat([0, 1], 5),\n 2: np.repeat([0, 1, 2, 3, 4], 2),\n }\n )\n X1 = extract_features(df1, column_id=1, column_sort=2)\n self.assertEqual(len(X1), 2)\n\n df2 = pd.DataFrame(\n data={\n 1: range(10),\n 0: np.repeat([0, 1], 5),\n 2: np.repeat([0, 1, 2, 3, 4], 2),\n }\n )\n X2 = extract_features(df2, column_id=0, column_sort=2)\n self.assertEqual(len(X2), 2)\n\n df3 = pd.DataFrame(\n data={\n 0: range(10),\n 2: np.repeat([0, 1], 5),\n 1: np.repeat([0, 1, 2, 3, 4], 2),\n }\n )\n X3 = extract_features(df3, column_id=2, column_sort=1)\n self.assertEqual(len(X3), 2)\n\n def test_raises_mismatch_index_df_and_y_df_more(self):\n y = pd.Series(range(3), index=[1, 2, 3])\n df_dict = {\n \"a\": pd.DataFrame({\"val\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}),\n \"b\": pd.DataFrame({\"val\": [5, 6, 7, 8, 12, 13], \"id\": [4, 4, 3, 3, 2, 2]}),\n }\n self.assertRaises(\n ValueError,\n extract_relevant_features,\n df_dict,\n y,\n None,\n None,\n None,\n \"id\",\n None,\n \"val\",\n )\n\n def test_raises_mismatch_index_df_and_y_y_more(self):\n y = pd.Series(range(4), index=[1, 2, 3, 4])\n df = pd.DataFrame({\"val\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]})\n self.assertRaises(\n ValueError,\n extract_relevant_features,\n df,\n y,\n None,\n None,\n None,\n \"id\",\n None,\n \"val\",\n )\n\n def test_raises_y_not_series(self):\n y = np.arange(10)\n df_dict = {\n \"a\": pd.DataFrame({\"val\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}),\n \"b\": pd.DataFrame({\"val\": [5, 6, 7, 8, 12, 13], \"id\": [4, 4, 3, 3, 2, 2]}),\n }\n self.assertRaises(\n AssertionError,\n extract_relevant_features,\n df_dict,\n y,\n None,\n None,\n None,\n \"id\",\n None,\n \"val\",\n )\n\n def test_raises_y_not_more_than_one_label(self):\n y = pd.Series(1, index=[1, 2, 3])\n df_dict = {\n \"a\": pd.DataFrame({\"val\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}),\n \"b\": pd.DataFrame({\"val\": [5, 6, 7, 8, 12, 13], \"id\": [4, 4, 3, 3, 2, 2]}),\n }\n self.assertRaises(\n AssertionError,\n extract_relevant_features,\n df_dict,\n y,\n None,\n None,\n None,\n \"id\",\n None,\n \"val\",\n )\n" }, { "path": "tests/units/__init__.py", "content": "" }, { "path": "tests/units/feature_extraction/__init__.py", "content": "" }, { "path": "tests/units/feature_extraction/test_data.py", "content": "import math\nfrom unittest import TestCase\nfrom unittest.mock import Mock\n\nimport numpy as np\nimport pandas as pd\nfrom dask import dataframe as dd\n\nfrom tests.fixtures import DataTestCase\nfrom tsfresh.feature_extraction.data import (\n LongTsFrameAdapter,\n PartitionedTsData,\n TsDictAdapter,\n WideTsFrameAdapter,\n to_tsdata,\n)\nfrom tsfresh.utilities.distribution import MultiprocessingDistributor\n\nTEST_DATA_EXPECTED_TUPLES = [\n (\n 10,\n \"a\",\n pd.Series(\n [\n 36,\n 71,\n 27,\n 62,\n 56,\n 58,\n 67,\n 11,\n 2,\n 24,\n 45,\n 30,\n 0,\n 9,\n 41,\n 28,\n 33,\n 19,\n 29,\n 43,\n ],\n index=[10] * 20,\n ),\n ),\n (\n 10,\n \"b\",\n pd.Series(\n [\n 78,\n 37,\n 23,\n 44,\n 6,\n 3,\n 21,\n 61,\n 39,\n 31,\n 53,\n 16,\n 66,\n 50,\n 40,\n 47,\n 7,\n 42,\n 38,\n 55,\n ],\n index=[10] * 20,\n ),\n ),\n (\n 500,\n \"a\",\n pd.Series(\n [\n 76,\n 72,\n 74,\n 75,\n 32,\n 64,\n 46,\n 35,\n 15,\n 70,\n 57,\n 65,\n 51,\n 26,\n 5,\n 25,\n 10,\n 69,\n 73,\n 77,\n ],\n index=[500] * 20,\n ),\n ),\n (\n 500,\n \"b\",\n pd.Series(\n [\n 8,\n 60,\n 12,\n 68,\n 22,\n 17,\n 18,\n 63,\n 49,\n 34,\n 20,\n 52,\n 48,\n 14,\n 79,\n 4,\n 1,\n 59,\n 54,\n 13,\n ],\n index=[500] * 20,\n ),\n ),\n]\n\nWIDE_TEST_DATA_EXPECTED_TUPLES = [\n (\n 10,\n \"a\",\n pd.Series(\n [\n 11,\n 9,\n 67,\n 45,\n 30,\n 58,\n 62,\n 19,\n 56,\n 29,\n 0,\n 27,\n 36,\n 43,\n 33,\n 2,\n 24,\n 71,\n 41,\n 28,\n ],\n index=list(range(20)),\n ),\n ),\n (\n 10,\n \"b\",\n pd.Series(\n [\n 50,\n 40,\n 39,\n 7,\n 53,\n 23,\n 16,\n 37,\n 66,\n 38,\n 6,\n 47,\n 3,\n 61,\n 44,\n 42,\n 78,\n 31,\n 21,\n 55,\n ],\n index=list(range(20)),\n ),\n ),\n (\n 500,\n \"a\",\n pd.Series(\n [\n 15,\n 35,\n 25,\n 32,\n 69,\n 65,\n 70,\n 64,\n 51,\n 46,\n 5,\n 77,\n 26,\n 73,\n 76,\n 75,\n 72,\n 74,\n 10,\n 57,\n ],\n index=list(range(20, 40)),\n ),\n ),\n (\n 500,\n \"b\",\n pd.Series(\n [\n 4,\n 14,\n 68,\n 22,\n 18,\n 52,\n 54,\n 60,\n 79,\n 12,\n 49,\n 63,\n 8,\n 59,\n 1,\n 13,\n 20,\n 17,\n 48,\n 34,\n ],\n index=list(range(20, 40)),\n ),\n ),\n]\n\n\nclass DataAdapterTestCase(DataTestCase):\n def test_long_tsframe(self):\n df = self.create_test_data_sample()\n data = LongTsFrameAdapter(df, \"id\", \"kind\", \"val\", \"sort\")\n\n self.assert_tsdata(data, TEST_DATA_EXPECTED_TUPLES)\n\n def test_long_tsframe_no_value_column(self):\n df = self.create_test_data_sample()\n data = LongTsFrameAdapter(df, \"id\", \"kind\", None, \"sort\")\n\n self.assert_tsdata(data, TEST_DATA_EXPECTED_TUPLES)\n\n def test_wide_tsframe(self):\n df = self.create_test_data_sample_wide()\n data = WideTsFrameAdapter(df, \"id\", \"sort\")\n\n self.assert_tsdata(data, WIDE_TEST_DATA_EXPECTED_TUPLES)\n\n def test_wide_tsframe_without_sort(self):\n df = self.create_test_data_sample_wide()\n del df[\"sort\"]\n data = WideTsFrameAdapter(df, \"id\")\n\n self.assert_tsdata(data, WIDE_TEST_DATA_EXPECTED_TUPLES)\n\n def test_dict_tsframe(self):\n df = {key: df for key, df in self.create_test_data_sample().groupby(\"kind\")}\n data = TsDictAdapter(df, \"id\", \"val\", \"sort\")\n\n self.assert_tsdata(data, TEST_DATA_EXPECTED_TUPLES)\n\n def assert_tsdata(self, data, expected):\n self.assertEqual(len(data), len(expected))\n self.assertEqual(sum(1 for _ in data), len(data))\n self.assert_data_chunk_object_equal(data, expected)\n\n def assert_data_chunk_object_equal(self, result, expected):\n dic_result = {str(x[0]) + \"_\" + str(x[1]): x[2] for x in result}\n dic_expected = {str(x[0]) + \"_\" + str(x[1]): x[2] for x in expected}\n for k in dic_result.keys():\n pd.testing.assert_series_equal(\n dic_result[k], dic_expected[k], check_names=False\n )\n\n def test_simple_data_sample_two_timeseries(self):\n df = pd.DataFrame(\n {\"id\": [10] * 4, \"kind\": [\"a\"] * 2 + [\"b\"] * 2, \"val\": [36, 71, 78, 37]}\n )\n df.set_index(\"id\", drop=False, inplace=True)\n df.index.name = None\n\n result = to_tsdata(df, \"id\", \"kind\", \"val\")\n expected = [\n (10, \"a\", pd.Series([36, 71], index=[10] * 2, name=\"val\")),\n (10, \"b\", pd.Series([78, 37], index=[10] * 2, name=\"val\")),\n ]\n self.assert_data_chunk_object_equal(result, expected)\n\n def test_simple_data_sample_four_timeseries(self):\n df = self.create_test_data_sample()\n df.index.name = None\n df.sort_values(by=[\"id\", \"kind\", \"sort\"], inplace=True)\n\n result = to_tsdata(df, \"id\", \"kind\", \"val\", \"sort\")\n expected = TEST_DATA_EXPECTED_TUPLES\n\n self.assert_data_chunk_object_equal(result, expected)\n\n def test_with_dictionaries_two_rows(self):\n test_df = pd.DataFrame(\n [\n {\"value\": 2, \"sort\": 2, \"id\": \"id_1\"},\n {\"value\": 1, \"sort\": 1, \"id\": \"id_1\"},\n ]\n )\n test_dict = {\"a\": test_df, \"b\": test_df}\n\n result = to_tsdata(\n test_dict, column_id=\"id\", column_value=\"value\", column_sort=\"sort\"\n )\n expected = [\n (\"id_1\", \"a\", pd.Series([1, 2], index=[1, 0], name=\"value\")),\n (\"id_1\", \"b\", pd.Series([1, 2], index=[1, 0], name=\"value\")),\n ]\n self.assert_data_chunk_object_equal(result, expected)\n\n def test_with_dictionaries_two_rows(self):\n test_df = pd.DataFrame([{\"value\": 1, \"id\": \"id_1\"}, {\"value\": 2, \"id\": \"id_1\"}])\n test_dict = {\"a\": test_df, \"b\": test_df}\n\n result = to_tsdata(test_dict, column_id=\"id\", column_value=\"value\")\n expected = [\n (\"id_1\", \"a\", pd.Series([1, 2], index=[0, 1], name=\"value\")),\n (\"id_1\", \"b\", pd.Series([1, 2], index=[0, 1], name=\"value\")),\n ]\n self.assert_data_chunk_object_equal(result, expected)\n\n def test_wide_dataframe_order_preserved_with_sort_column(self):\n \"\"\"verifies that the order of the sort column from a wide time series container is preserved\"\"\"\n\n test_df = pd.DataFrame(\n {\n \"id\": [\"a\", \"a\", \"b\"],\n \"v1\": [3, 2, 1],\n \"v2\": [13, 12, 11],\n \"sort\": [103, 102, 101],\n }\n )\n\n result = to_tsdata(test_df, column_id=\"id\", column_sort=\"sort\")\n expected = [\n (\"a\", \"v1\", pd.Series([2, 3], index=[1, 0], name=\"v1\")),\n (\"a\", \"v2\", pd.Series([12, 13], index=[1, 0], name=\"v2\")),\n (\"b\", \"v1\", pd.Series([1], index=[2], name=\"v1\")),\n (\"b\", \"v2\", pd.Series([11], index=[2], name=\"v2\")),\n ]\n self.assert_data_chunk_object_equal(result, expected)\n\n def test_dask_dataframe_with_kind(self):\n test_df = dd.from_pandas(\n pd.DataFrame({\"id\": [1, 2], \"kind\": [\"a\", \"a\"], \"value\": [1, 2]}),\n npartitions=1,\n )\n\n result = to_tsdata(test_df, column_id=\"id\", column_kind=\"kind\")\n self.assertEqual(result.column_id, \"id\")\n self.assertEqual(result.column_kind, \"kind\")\n self.assertEqual(result.column_value, \"value\")\n\n def test_f(chunk):\n return pd.DataFrame(\n {\"id\": chunk[0], \"variable\": chunk[1], \"value\": chunk[2]}\n )\n\n return_f = result.apply(\n test_f, meta=((\"id\", \"int\"), (\"variable\", \"int\"), (\"value\", \"int\"))\n ).compute()\n pd.testing.assert_frame_equal(\n return_f.reset_index(drop=True),\n pd.DataFrame({\"id\": [1, 2], \"variable\": [\"a\", \"a\"], \"value\": [1.0, 2.0]}),\n )\n\n def test_dask_dataframe_without_kind(self):\n test_df = dd.from_pandas(\n pd.DataFrame({\"id\": [1, 2], \"value_a\": [1, 2], \"value_b\": [3, 4]}),\n npartitions=1,\n )\n\n result = to_tsdata(test_df, column_id=\"id\")\n self.assertEqual(result.column_id, \"id\")\n\n def test_f(chunk):\n return pd.DataFrame(\n {\"id\": chunk[0], \"variable\": chunk[1], \"value\": chunk[2]}\n )\n\n return_f = result.apply(\n test_f, meta=((\"id\", \"int\"), (\"variable\", \"int\"), (\"value\", \"int\"))\n ).compute()\n pd.testing.assert_frame_equal(\n return_f.sort_values(\"value\").reset_index(drop=True),\n pd.DataFrame(\n {\n \"id\": [1, 2, 1, 2],\n \"variable\": [\"value_a\", \"value_a\", \"value_b\", \"value_b\"],\n \"value\": [1.0, 2.0, 3.0, 4.0],\n }\n ),\n )\n\n test_df = dd.from_pandas(\n pd.DataFrame(\n {\"id\": [1, 1], \"sort\": [2, 1], \"value_a\": [1, 2], \"value_b\": [3, 4]}\n ),\n npartitions=1,\n )\n\n result = to_tsdata(test_df, column_id=\"id\", column_sort=\"sort\")\n self.assertEqual(result.column_id, \"id\")\n\n def test_f(chunk):\n return pd.DataFrame(\n {\"id\": chunk[0], \"variable\": chunk[1], \"value\": chunk[2]}\n )\n\n return_f = result.apply(\n test_f, meta=((\"id\", \"int\"), (\"variable\", \"int\"), (\"value\", \"int\"))\n ).compute()\n\n pd.testing.assert_frame_equal(\n return_f.reset_index(drop=True),\n pd.DataFrame(\n {\n \"id\": [1, 1, 1, 1],\n \"variable\": [\"value_a\", \"value_a\", \"value_b\", \"value_b\"],\n \"value\": [2.0, 1.0, 4.0, 3.0],\n }\n ),\n )\n\n def test_with_wrong_input(self):\n test_df = pd.DataFrame([{\"id\": 0, \"kind\": \"a\", \"value\": 3, \"sort\": np.nan}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", \"kind\", \"value\", \"sort\")\n\n test_df = pd.DataFrame([{\"id\": 0, \"kind\": \"a\", \"value\": 3, \"sort\": 1}])\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"strange_id\", \"kind\", \"value\", \"sort\"\n )\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"strange_id\", \"kind\", \"value\", \"sort\"\n )\n\n test_df = pd.DataFrame(\n [{\"id\": 0, \"kind\": \"a\", \"value\": 3, \"value_2\": 1, \"sort\": 1}]\n )\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"strange_id\", \"kind\", None, \"sort\"\n )\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"strange_id\", \"kind\", None, \"sort\"\n )\n\n test_df = pd.DataFrame([{\"id\": 0, \"kind\": \"a\", \"value\": 3, \"sort\": 1}])\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"id\", \"strange_kind\", \"value\", \"sort\"\n )\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(\n ValueError, to_tsdata, test_df, \"id\", \"strange_kind\", \"value\", \"sort\"\n )\n\n test_df = pd.DataFrame([{\"id\": np.nan, \"kind\": \"a\", \"value\": 3, \"sort\": 1}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", \"kind\", \"value\", \"sort\")\n\n test_df = pd.DataFrame([{\"id\": 0, \"kind\": np.nan, \"value\": 3, \"sort\": 1}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", \"kind\", \"value\", \"sort\")\n\n test_df = pd.DataFrame([{\"id\": 2}, {\"id\": 1}])\n test_dd = dd.from_pandas(test_df, npartitions=1)\n test_dict = {\"a\": test_df, \"b\": test_df}\n\n # column_id needs to be given\n self.assertRaises(ValueError, to_tsdata, test_df, None, \"a\", \"b\", None)\n self.assertRaises(ValueError, to_tsdata, test_dd, None, \"a\", \"b\", None)\n self.assertRaises(ValueError, to_tsdata, test_df, None, \"a\", \"b\", \"a\")\n self.assertRaises(ValueError, to_tsdata, test_dd, None, \"a\", \"b\", \"a\")\n self.assertRaises(ValueError, to_tsdata, test_dict, None, \"a\", \"b\", None)\n self.assertRaises(ValueError, to_tsdata, test_dict, None, \"a\", \"b\", \"a\")\n\n # If there are more than one column, the algorithm can not choose the correct column\n self.assertRaises(ValueError, to_tsdata, test_dict, \"id\", None, None, None)\n\n test_dict = {\n \"a\": pd.DataFrame([{\"id\": 2, \"value_a\": 3}, {\"id\": 1, \"value_a\": 4}]),\n \"b\": pd.DataFrame([{\"id\": 2}, {\"id\": 1}]),\n }\n\n # If there are more than one column, the algorithm can not choose the correct column\n self.assertRaises(ValueError, to_tsdata, test_dict, \"id\", None, None, None)\n\n test_df = pd.DataFrame([{\"id\": 0, \"value\": np.nan}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, \"value\", None)\n\n test_df = pd.DataFrame([{\"id\": 0, \"value\": np.nan}])\n self.assertRaises(ValueError, to_tsdata, test_df, None, None, \"value\", None)\n\n test_df = pd.DataFrame([{\"id\": 0, \"a_\": 3, \"b\": 5, \"sort\": 1}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n\n test_df = pd.DataFrame([{\"id\": 0, \"a__c\": 3, \"b\": 5, \"sort\": 1}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n\n test_df = pd.DataFrame([{\"id\": 0}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, None)\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, None)\n\n test_df = pd.DataFrame([{\"id\": 0, \"sort\": 0}])\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n test_df = dd.from_pandas(test_df, npartitions=1)\n self.assertRaises(ValueError, to_tsdata, test_df, \"id\", None, None, \"sort\")\n\n test_df = [1, 2, 3]\n self.assertRaises(ValueError, to_tsdata, test_df, \"a\", \"b\", \"c\", \"d\")\n\n\nclass PivotListTestCase(TestCase):\n def test_empty_list(self):\n mock_ts_data = Mock()\n mock_ts_data.df_id_type = str\n\n return_df = PartitionedTsData.pivot(mock_ts_data, [])\n\n self.assertEqual(len(return_df), 0)\n self.assertEqual(len(return_df.index), 0)\n self.assertEqual(len(return_df.columns), 0)\n\n def test_different_input(self):\n mock_ts_data = Mock()\n mock_ts_data.df_id_type = str\n\n input_list = [\n (\"a\", \"b\", 1),\n (\"a\", \"c\", 2),\n (\"A\", \"b\", 3),\n (\"A\", \"c\", 4),\n (\"X\", \"Y\", 5),\n ]\n return_df = PartitionedTsData.pivot(mock_ts_data, input_list)\n\n self.assertEqual(len(return_df), 3)\n self.assertEqual(set(return_df.index), {\"a\", \"A\", \"X\"})\n self.assertEqual(set(return_df.columns), {\"b\", \"c\", \"Y\"})\n\n self.assertEqual(return_df.loc[\"a\", \"b\"], 1)\n self.assertEqual(return_df.loc[\"a\", \"c\"], 2)\n self.assertEqual(return_df.loc[\"A\", \"b\"], 3)\n self.assertEqual(return_df.loc[\"A\", \"c\"], 4)\n self.assertEqual(return_df.loc[\"X\", \"Y\"], 5)\n\n def test_long_input(self):\n mock_ts_data = Mock()\n mock_ts_data.df_id_type = str\n\n input_list = []\n for i in range(100):\n for j in range(100):\n input_list.append((i, j, i * j))\n\n return_df = PartitionedTsData.pivot(mock_ts_data, input_list)\n\n self.assertEqual(len(return_df), 100)\n self.assertEqual(len(return_df.columns), 100)\n # every cell should be filled\n self.assertEqual(np.sum(np.sum(np.isnan(return_df))), 0)\n # test if all entries are there\n self.assertEqual(return_df.sum().sum(), 24502500)\n" }, { "path": "tests/units/feature_extraction/test_extraction.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport os\nimport tempfile\n\nimport numpy as np\nimport pandas as pd\nfrom mock import Mock\n\nfrom tests.fixtures import DataTestCase\nfrom tsfresh.feature_extraction.extraction import extract_features\nfrom tsfresh.feature_extraction.settings import (\n ComprehensiveFCParameters,\n PickableSettings,\n)\nfrom tsfresh.utilities.distribution import IterableDistributorBaseClass, MapDistributor\n\n\nclass ExtractionTestCase(DataTestCase):\n \"\"\"The unit tests in this module make sure if the time series features are created properly\"\"\"\n\n def setUp(self):\n self.n_jobs = 1\n self.directory = tempfile.gettempdir()\n\n def test_extract_features(self):\n # todo: implement more methods and test more aspects\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n )\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n\n df_sts = self.create_one_valued_time_series()\n extracted_features_sts = extract_features(\n df_sts,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n )\n\n self.assertIsInstance(extracted_features_sts, pd.DataFrame)\n self.assertTrue(\n np.all(extracted_features_sts.a__maximum == np.array([1.0, 6.0]))\n )\n self.assertTrue(\n np.all(extracted_features_sts.a__sum_values == np.array([1.0, 11.0]))\n )\n self.assertTrue(\n np.all(extracted_features_sts.a__count_above_mean == np.array([0, 1]))\n )\n\n def test_extract_features_uses_only_kind_to_fc_settings(self):\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n kind_to_fc_parameters={\"a\": {\"maximum\": None, \"minimum\": None}},\n )\n assert len(extracted_features) == 2\n\n def test_extract_features_for_one_time_series(self):\n # todo: implement more methods and test more aspects\n df = self.create_test_data_sample()\n settings = ComprehensiveFCParameters()\n\n extracted_features = extract_features(\n df,\n default_fc_parameters=settings,\n column_value=\"val\",\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n\n df_sts = self.create_one_valued_time_series()\n extracted_features_sts = extract_features(\n df_sts,\n default_fc_parameters=settings,\n column_value=\"val\",\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n )\n\n self.assertIsInstance(extracted_features_sts, pd.DataFrame)\n self.assertTrue(\n np.all(extracted_features_sts.a__maximum == np.array([1.0, 6.0]))\n )\n self.assertTrue(\n np.all(extracted_features_sts.a__sum_values == np.array([1.0, 11.0]))\n )\n self.assertTrue(\n np.all(extracted_features_sts.a__count_above_mean == np.array([0, 1]))\n )\n\n def test_extract_features_for_index_based_functions(self):\n df = self.create_test_data_sample_with_time_index()\n\n settings = {\n \"linear_trend_timewise\": [{\"attr\": \"slope\"}],\n \"linear_trend\": [{\"attr\": \"slope\"}],\n }\n\n extracted_features = extract_features(\n df,\n default_fc_parameters=settings,\n column_value=\"val\",\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n\n slope_a = extracted_features['a__linear_trend_timewise__attr_\"slope\"'].values\n slope_b = extracted_features['b__linear_trend_timewise__attr_\"slope\"'].values\n\n self.assertAlmostEqual(slope_a[0], -0.001347117)\n self.assertAlmostEqual(slope_a[1], 0.052036340)\n self.assertAlmostEqual(slope_b[0], 0.021898496)\n self.assertAlmostEqual(slope_b[1], -0.012312)\n\n # Test that the index of the returned df is the ID and not the timestamp\n self.assertTrue(extracted_features.index.dtype != df.index.dtype)\n self.assertTrue(extracted_features.index.dtype == df[\"id\"].dtype)\n self.assertEqual(\n sorted(extracted_features.index.unique().tolist()),\n sorted(df[\"id\"].unique().tolist()),\n )\n\n def test_extract_features_custom_function(self):\n df = self.create_test_data_sample()\n\n def custom_function(x, p):\n return len(x) + p\n\n settings = PickableSettings(\n {\n \"mean\": None,\n custom_function: [{\"p\": 1}, {\"p\": -1}],\n }\n )\n\n extracted_features = extract_features(\n df,\n default_fc_parameters=settings,\n column_value=\"val\",\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n\n mean_a = extracted_features[\"a__mean\"].values\n custom_function_a_1 = extracted_features[\"a__custom_function__p_1\"].values\n custom_function_a_m1 = extracted_features[\"a__custom_function__p_-1\"].values\n\n self.assertAlmostEqual(mean_a[0], 34.55)\n self.assertAlmostEqual(mean_a[1], 50.85)\n self.assertAlmostEqual(custom_function_a_1[0], 21)\n self.assertAlmostEqual(custom_function_a_1[1], 21)\n self.assertAlmostEqual(custom_function_a_m1[0], 19)\n self.assertAlmostEqual(custom_function_a_m1[1], 19)\n\n def test_extract_features_after_randomisation(self):\n df = self.create_test_data_sample()\n df_random = df.copy().sample(frac=1)\n\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n ).sort_index()\n extracted_features_from_random = extract_features(\n df_random,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n ).sort_index()\n\n self.assertCountEqual(\n extracted_features.columns, extracted_features_from_random.columns\n )\n\n for col in extracted_features:\n self.assertIsNone(\n np.testing.assert_array_almost_equal(\n extracted_features[col], extracted_features_from_random[col]\n )\n )\n\n def test_profiling_file_written_out(self):\n\n df = pd.DataFrame(\n data={\"id\": np.repeat([1, 2], 10), \"val\": np.random.normal(0, 1, 20)}\n )\n profiling_filename = os.path.join(self.directory, \"test_profiling.txt\")\n X = extract_features(\n df,\n column_id=\"id\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n profile=True,\n profiling_filename=profiling_filename,\n )\n\n self.assertTrue(os.path.isfile(profiling_filename))\n os.remove(profiling_filename)\n\n def test_profiling_cumulative_file_written_out(self):\n\n PROFILING_FILENAME = os.path.join(\n self.directory, \"test_profiling_cumulative.txt\"\n )\n PROFILING_SORTING = \"cumulative\"\n\n df = pd.DataFrame(\n data={\"id\": np.repeat([1, 2], 10), \"val\": np.random.normal(0, 1, 20)}\n )\n extract_features(\n df,\n column_id=\"id\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n profile=True,\n profiling_filename=PROFILING_FILENAME,\n profiling_sorting=PROFILING_SORTING,\n )\n\n self.assertTrue(os.path.isfile(PROFILING_FILENAME))\n os.remove(PROFILING_FILENAME)\n\n def test_extract_features_without_settings(self):\n df = pd.DataFrame(\n data={\n \"id\": np.repeat([1, 2], 10),\n \"value1\": np.random.normal(0, 1, 20),\n \"value2\": np.random.normal(0, 1, 20),\n }\n )\n X = extract_features(df, column_id=\"id\", n_jobs=self.n_jobs)\n self.assertIn(\"value1__maximum\", list(X.columns))\n self.assertIn(\"value2__maximum\", list(X.columns))\n\n def test_extract_features_with_and_without_parallelization(self):\n df = self.create_test_data_sample()\n\n features_parallel = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=2,\n )\n\n features_serial = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=0,\n )\n\n self.assertCountEqual(features_parallel.columns, features_serial.columns)\n\n for col in features_parallel.columns:\n np.testing.assert_array_almost_equal(\n features_parallel[col], features_serial[col]\n )\n\n def test_extract_index_preservation(self):\n df = self.create_test_data_nearly_numerical_indices()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertEqual(set(df[\"id\"]), set(extracted_features.index))\n\n def test_extract_features_alphabetically_sorted(self):\n df = self.create_test_data_sample()\n\n features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n )\n\n for col_name in features.columns:\n # split out the configuration of the features calculator\n col_name_chunks = col_name.split(\"__\")\n # the name is always at the beginning, so remove it. Also remove the kind of the column\n col_name_chunks = col_name_chunks[2:]\n\n self.assertEqual(col_name_chunks, list(sorted(col_name_chunks)))\n\n\nclass ParallelExtractionTestCase(DataTestCase):\n def setUp(self):\n self.n_jobs = 2\n\n # only calculate some features to reduce load on travis ci\n self.name_to_param = {\n \"maximum\": None,\n \"sum_values\": None,\n \"abs_energy\": None,\n \"minimum\": None,\n \"mean\": None,\n \"median\": None,\n }\n\n def test_extract_features(self):\n # todo: implement more methods and test more aspects\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n n_jobs=self.n_jobs,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n\n\nclass DistributorUsageTestCase(DataTestCase):\n def setUp(self):\n # only calculate some features to reduce load on travis ci\n self.name_to_param = {\"maximum\": None}\n\n def test_distributor_map_reduce_is_called(self):\n df = self.create_test_data_sample()\n\n mock = Mock(spec=IterableDistributorBaseClass)\n mock.close.return_value = None\n mock.map_reduce.return_value = []\n\n X = extract_features(\n timeseries_container=df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n default_fc_parameters=self.name_to_param,\n distributor=mock,\n )\n\n self.assertTrue(mock.map_reduce.called)\n\n def test_distributor_close_is_called(self):\n df = self.create_test_data_sample()\n\n mock = MapDistributor()\n mock.close = Mock()\n mock.close.return_value = None\n\n X = extract_features(\n timeseries_container=df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n default_fc_parameters=self.name_to_param,\n distributor=mock,\n )\n\n self.assertTrue(mock.close.called)\n" }, { "path": "tests/units/feature_extraction/test_feature_calculations.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport math\nimport unittest\nfrom random import shuffle\nfrom unittest import TestCase\n\nfrom tsfresh.examples.driftbif_simulation import velocity\nfrom tsfresh.feature_extraction.feature_calculators import *\nfrom tsfresh.feature_extraction.feature_calculators import (\n _aggregate_on_chunks,\n _estimate_friedrich_coefficients,\n _get_length_sequences_where,\n _into_subchunks,\n _roll,\n)\n\n\nclass FeatureCalculationTestCase(TestCase):\n def setUp(self):\n # There will be a lot of warnings in the feature calculators.\n # Just ignore all of them in these tests\n warnings.simplefilter(\"ignore\")\n\n def tearDown(self):\n warnings.resetwarnings()\n\n def assertIsNaN(self, result):\n self.assertTrue(np.isnan(result), msg=\"{} is not np.nan\")\n\n def assertEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):\n expected_result = f(input_to_f, *args, **kwargs)\n self.assertEqual(\n expected_result,\n result,\n msg=\"Not equal for lists: {} != {}\".format(expected_result, result),\n )\n expected_result = f(np.array(input_to_f), *args, **kwargs)\n self.assertEqual(\n expected_result,\n result,\n msg=\"Not equal for numpy.arrays: {} != {}\".format(expected_result, result),\n )\n expected_result = f(pd.Series(input_to_f, dtype=\"float64\"), *args, **kwargs)\n self.assertEqual(\n expected_result,\n result,\n msg=\"Not equal for pandas.Series: {} != {}\".format(expected_result, result),\n )\n\n def assertTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):\n self.assertTrue(f(input_to_f, *args, **kwargs), msg=\"Not true for lists\")\n self.assertTrue(\n f(np.array(input_to_f), *args, **kwargs), msg=\"Not true for numpy.arrays\"\n )\n self.assertTrue(\n f(pd.Series(input_to_f), *args, **kwargs), msg=\"Not true for pandas.Series\"\n )\n\n def assertAllTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):\n self.assertTrue(\n all(dict(f(input_to_f, *args, **kwargs)).values()), msg=\"Not true for lists\"\n )\n self.assertTrue(\n all(dict(f(np.array(input_to_f), *args, **kwargs)).values()),\n msg=\"Not true for numpy.arrays\",\n )\n self.assertTrue(\n all(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()),\n msg=\"Not true for pandas.Series\",\n )\n\n def assertFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):\n self.assertFalse(f(input_to_f, *args, **kwargs), msg=\"Not false for lists\")\n self.assertFalse(\n f(np.array(input_to_f), *args, **kwargs), msg=\"Not false for numpy.arrays\"\n )\n self.assertFalse(\n f(pd.Series(input_to_f), *args, **kwargs), msg=\"Not false for pandas.Series\"\n )\n\n def assertAllFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):\n self.assertFalse(\n any(dict(f(input_to_f, *args, **kwargs)).values()),\n msg=\"Not false for lists\",\n )\n self.assertFalse(\n any(dict(f(np.array(input_to_f), *args, **kwargs)).values()),\n msg=\"Not false for numpy.arrays\",\n )\n self.assertFalse(\n any(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()),\n msg=\"Not false for pandas.Series\",\n )\n\n def assertAlmostEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):\n expected_result = f(input_to_f, *args, **kwargs)\n self.assertAlmostEqual(\n expected_result,\n result,\n msg=\"Not almost equal for lists: {} != {}\".format(expected_result, result),\n )\n expected_result = f(np.array(input_to_f), *args, **kwargs)\n self.assertAlmostEqual(\n expected_result,\n result,\n msg=\"Not almost equal for numpy.arrays: {} != {}\".format(\n expected_result, result\n ),\n )\n expected_result = f(pd.Series(input_to_f, dtype=\"float64\"), *args, **kwargs)\n self.assertAlmostEqual(\n expected_result,\n result,\n msg=\"Not almost equal for pandas.Series: {} != {}\".format(\n expected_result, result\n ),\n )\n\n def assertIsNanOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):\n self.assertTrue(\n np.isnan(f(input_to_f, *args, **kwargs)), msg=\"Not NaN for lists\"\n )\n self.assertTrue(\n np.isnan(f(np.array(input_to_f), *args, **kwargs)),\n msg=\"Not NaN for numpy.arrays\",\n )\n self.assertTrue(\n np.isnan(f(pd.Series(input_to_f, dtype=\"float64\"), *args, **kwargs)),\n msg=\"Not NaN for pandas.Series\",\n )\n\n def assertEqualPandasSeriesWrapper(self, f, input_to_f, result, *args, **kwargs):\n self.assertEqual(\n f(pd.Series(input_to_f), *args, **kwargs),\n result,\n msg=\"Not equal for pandas.Series: {} != {}\".format(\n f(pd.Series(input_to_f), *args, **kwargs), result\n ),\n )\n\n def test__roll(self):\n x = np.random.normal(size=30)\n for shift in [0, 1, 10, 11, 30, 31, 50, 51, 150, 151]:\n np.testing.assert_array_equal(_roll(x, shift), np.roll(x, shift))\n np.testing.assert_array_equal(_roll(x, -shift), np.roll(x, -shift))\n\n def test___get_length_sequences_where(self):\n self.assertEqualOnAllArrayTypes(\n _get_length_sequences_where,\n [0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1],\n [1, 3, 1, 2],\n )\n self.assertEqualOnAllArrayTypes(\n _get_length_sequences_where,\n [0, True, 0, 0, True, True, True, 0, 0, True, 0, True, True],\n [1, 3, 1, 2],\n )\n self.assertEqualOnAllArrayTypes(\n _get_length_sequences_where,\n [0, True, 0, 0, 1, True, 1, 0, 0, True, 0, 1, True],\n [1, 3, 1, 2],\n )\n self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [0] * 10, [0])\n self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [], [0])\n\n def test__into_subchunks(self):\n np.testing.assert_array_equal(\n _into_subchunks(range(7), 3, 2), np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6]])\n )\n np.testing.assert_array_equal(\n _into_subchunks(range(5), 3), np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4]])\n )\n\n def test_variance_larger_than_standard_deviation(self):\n self.assertFalseOnAllArrayTypes(\n variance_larger_than_standard_deviation, [-1, -1, 1, 1, 1]\n )\n self.assertTrueOnAllArrayTypes(\n variance_larger_than_standard_deviation, [-1, -1, 1, 1, 2]\n )\n\n def test_large_standard_deviation(self):\n self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)\n self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)\n self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0)\n self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.25)\n self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.3)\n self.assertFalseOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.5)\n\n def test_symmetry_looking(self):\n self.assertAllTrueOnAllArrayTypes(\n symmetry_looking, [-1, -1, 1, 1], [dict(r=0.05), dict(r=0.75)]\n )\n self.assertAllFalseOnAllArrayTypes(\n symmetry_looking, [-1, -1, 1, 1], [dict(r=0)]\n )\n self.assertAllFalseOnAllArrayTypes(\n symmetry_looking, [-1, -1, -1, -1, 1], [dict(r=0.05)]\n )\n self.assertAllTrueOnAllArrayTypes(\n symmetry_looking, [-2, -2, -2, -1, -1, -1], [dict(r=0.05)]\n )\n self.assertAllTrueOnAllArrayTypes(\n symmetry_looking, [-0.9, -0.900001], [dict(r=0.05)]\n )\n\n def test_has_duplicate_max(self):\n self.assertTrueOnAllArrayTypes(has_duplicate_max, [2.1, 0, 0, 2.1, 1.1])\n self.assertFalseOnAllArrayTypes(\n has_duplicate_max, np.array([2.1, 0, 0, 2, 1.1])\n )\n self.assertTrueOnAllArrayTypes(has_duplicate_max, [1, 1, 1, 1])\n self.assertFalseOnAllArrayTypes(has_duplicate_max, np.array([0]))\n self.assertTrueOnAllArrayTypes(has_duplicate_max, np.array([1, 1]))\n\n def test_has_duplicate_min(self):\n self.assertTrueOnAllArrayTypes(has_duplicate_min, [-2.1, 0, 0, -2.1, 1.1])\n self.assertFalseOnAllArrayTypes(has_duplicate_min, [2.1, 0, -1, 2, 1.1])\n self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1, 1, 1]))\n self.assertFalseOnAllArrayTypes(has_duplicate_min, np.array([0]))\n self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1]))\n\n def test_has_duplicate(self):\n self.assertTrueOnAllArrayTypes(has_duplicate, np.array([-2.1, 0, 0, -2.1]))\n self.assertTrueOnAllArrayTypes(has_duplicate, [-2.1, 2.1, 2.1, 2.1])\n self.assertFalseOnAllArrayTypes(has_duplicate, [1.1, 1.2, 1.3, 1.4])\n self.assertFalseOnAllArrayTypes(has_duplicate, [1])\n self.assertFalseOnAllArrayTypes(has_duplicate, [])\n\n def test_sum(self):\n self.assertEqualOnAllArrayTypes(sum_values, [1, 2, 3, 4.1], 10.1)\n self.assertEqualOnAllArrayTypes(sum_values, [-1.2, -2, -3, -4], -10.2)\n self.assertEqualOnAllArrayTypes(sum_values, [], 0)\n\n def test_agg_autocorrelation_returns_correct_values(self):\n\n param = [{\"f_agg\": \"mean\", \"maxlag\": 10}]\n x = [1, 1, 1, 1, 1, 1, 1]\n expected_res = 0\n res = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_10']\n self.assertAlmostEqual(res, expected_res, places=4)\n\n x = [1, 2, -3]\n expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)\n res = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_10']\n self.assertAlmostEqual(res, expected_res, places=4)\n\n np.random.seed(42)\n x = np.random.normal(size=3000)\n expected_res = 0\n res = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_10']\n self.assertAlmostEqual(res, expected_res, places=2)\n\n param = [{\"f_agg\": \"median\", \"maxlag\": 10}]\n x = [1, 1, 1, 1, 1, 1, 1]\n expected_res = 0\n res = dict(agg_autocorrelation(x, param=param))['f_agg_\"median\"__maxlag_10']\n self.assertAlmostEqual(res, expected_res, places=4)\n\n x = [1, 2, -3]\n expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)\n res = dict(agg_autocorrelation(x, param=param))['f_agg_\"median\"__maxlag_10']\n self.assertAlmostEqual(res, expected_res, places=4)\n\n def test_agg_autocorrelation_returns_max_lag_does_not_affect_other_results(self):\n\n param = [{\"f_agg\": \"mean\", \"maxlag\": 1}, {\"f_agg\": \"mean\", \"maxlag\": 10}]\n x = range(10)\n res1 = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_1']\n res10 = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_10']\n self.assertAlmostEqual(res1, 0.77777777, places=4)\n self.assertAlmostEqual(res10, -0.64983164983165, places=4)\n\n param = [{\"f_agg\": \"mean\", \"maxlag\": 1}]\n x = range(10)\n res1 = dict(agg_autocorrelation(x, param=param))['f_agg_\"mean\"__maxlag_1']\n self.assertAlmostEqual(res1, 0.77777777, places=4)\n\n def test_partial_autocorrelation(self):\n\n # Test for altering time series\n # len(x) < max_lag\n param = [{\"lag\": lag} for lag in range(10)]\n x = [1, 2, 1, 2, 1, 2]\n expected_res = [(\"lag_0\", 1.0), (\"lag_1\", -1.0), (\"lag_2\", np.nan)]\n res = partial_autocorrelation(x, param=param)\n self.assertAlmostEqual(res[0][1], expected_res[0][1], places=4)\n self.assertAlmostEqual(res[1][1], expected_res[1][1], places=4)\n self.assertIsNaN(res[2][1])\n\n # Linear signal\n param = [{\"lag\": lag} for lag in range(10)]\n x = np.linspace(0, 1, 3000)\n expected_res = [(\"lag_0\", 1.0), (\"lag_1\", 1.0), (\"lag_2\", 0)]\n res = partial_autocorrelation(x, param=param)\n self.assertAlmostEqual(res[0][1], expected_res[0][1], places=2)\n self.assertAlmostEqual(res[1][1], expected_res[1][1], places=2)\n self.assertAlmostEqual(res[2][1], expected_res[2][1], places=2)\n\n # Random noise\n np.random.seed(42)\n x = np.random.normal(size=3000)\n param = [{\"lag\": lag} for lag in range(10)]\n expected_res = [(\"lag_0\", 1.0), (\"lag_1\", 0), (\"lag_2\", 0)]\n res = partial_autocorrelation(x, param=param)\n self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)\n self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)\n self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)\n\n # On a simulated AR process\n np.random.seed(42)\n param = [{\"lag\": lag} for lag in range(10)]\n # Simulate AR process\n T = 3000\n epsilon = np.random.randn(T)\n x = np.repeat(1.0, T)\n for t in range(T - 1):\n x[t + 1] = 0.5 * x[t] + 2 + epsilon[t]\n expected_res = [(\"lag_0\", 1.0), (\"lag_1\", 0.5), (\"lag_2\", 0)]\n res = partial_autocorrelation(x, param=param)\n self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)\n self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)\n self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)\n\n # Some pathological cases\n param = [{\"lag\": lag} for lag in range(10)]\n # List of length 1\n res = partial_autocorrelation([1], param=param)\n for lag_no, lag_val in res:\n self.assertIsNaN(lag_val)\n # Empty list\n res = partial_autocorrelation([], param=param)\n for lag_no, lag_val in res:\n self.assertIsNaN(lag_val)\n # List contains only zeros\n res = partial_autocorrelation(np.zeros(100), param=param)\n for lag_no, lag_val in res:\n if lag_no == \"lag_0\":\n self.assertEqual(lag_val, 1.0)\n else:\n self.assertIsNaN(lag_val)\n\n def test_augmented_dickey_fuller(self):\n # todo: add unit test for the values of the test statistic\n\n # the adf hypothesis test checks for unit roots,\n # so H_0 = {random drift} vs H_1 = {AR(1) model}\n\n # H0 is true\n np.random.seed(seed=42)\n x = np.cumsum(np.random.uniform(size=100))\n param = [\n {\"autolag\": \"BIC\", \"attr\": \"teststat\"},\n {\"autolag\": \"BIC\", \"attr\": \"pvalue\"},\n {\"autolag\": \"BIC\", \"attr\": \"usedlag\"},\n ]\n expected_index = [\n 'attr_\"teststat\"__autolag_\"BIC\"',\n 'attr_\"pvalue\"__autolag_\"BIC\"',\n 'attr_\"usedlag\"__autolag_\"BIC\"',\n ]\n\n res = augmented_dickey_fuller(x=x, param=param)\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertGreater(res['attr_\"pvalue\"__autolag_\"BIC\"'], 0.10)\n self.assertEqual(res['attr_\"usedlag\"__autolag_\"BIC\"'], 0)\n\n # H0 should be rejected for AR(1) model with x_{t} = 1/2 x_{t-1} + e_{t}\n np.random.seed(seed=42)\n e = np.random.normal(0.1, 0.1, size=100)\n m = 50\n x = [0] * m\n x[0] = 100\n for i in range(1, m):\n x[i] = x[i - 1] * 0.5 + e[i]\n param = [\n {\"autolag\": \"AIC\", \"attr\": \"teststat\"},\n {\"autolag\": \"AIC\", \"attr\": \"pvalue\"},\n {\"autolag\": \"AIC\", \"attr\": \"usedlag\"},\n ]\n expected_index = [\n 'attr_\"teststat\"__autolag_\"AIC\"',\n 'attr_\"pvalue\"__autolag_\"AIC\"',\n 'attr_\"usedlag\"__autolag_\"AIC\"',\n ]\n\n res = augmented_dickey_fuller(x=x, param=param)\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertLessEqual(res['attr_\"pvalue\"__autolag_\"AIC\"'], 0.05)\n self.assertEqual(res['attr_\"usedlag\"__autolag_\"AIC\"'], 0)\n\n # Check if LinAlgError and ValueError are catched\n res_linalg_error = augmented_dickey_fuller(\n x=np.repeat(np.nan, 100), param=param\n )\n res_value_error = augmented_dickey_fuller(x=[], param=param)\n for index, val in res_linalg_error:\n self.assertIsNaN(val)\n for index, val in res_value_error:\n self.assertIsNaN(val)\n\n # Should return NaN if \"attr\" is unknown\n res_attr_error = augmented_dickey_fuller(\n x=x, param=[{\"autolag\": \"AIC\", \"attr\": \"\"}]\n )\n for index, val in res_attr_error:\n self.assertIsNaN(val)\n\n def test_abs_energy(self):\n self.assertEqualOnAllArrayTypes(abs_energy, [1, 1, 1], 3)\n self.assertEqualOnAllArrayTypes(abs_energy, [1, 2, 3], 14)\n self.assertEqualOnAllArrayTypes(abs_energy, [-1, 2, -3], 14)\n self.assertAlmostEqualOnAllArrayTypes(abs_energy, [-1, 1.3], 2.69)\n self.assertEqualOnAllArrayTypes(abs_energy, [1], 1)\n\n def test_cid_ce(self):\n self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=True)\n self.assertEqualOnAllArrayTypes(cid_ce, [0, 4], 2, normalize=True)\n self.assertEqualOnAllArrayTypes(cid_ce, [100, 104], 2, normalize=True)\n\n self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=False)\n self.assertEqualOnAllArrayTypes(cid_ce, [0.5, 3.5, 7.5], 5, normalize=False)\n self.assertEqualOnAllArrayTypes(\n cid_ce, [-4.33, -1.33, 2.67], 5, normalize=False\n )\n\n def test_lempel_ziv_complexity(self):\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [1, 1, 1], 2.0 / 3, bins=2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [1, 1, 1], 2.0 / 3, bins=5\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [1, 1, 1, 1, 1, 1, 1], 0.4285714285, bins=2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [1, 1, 1, 2, 1, 1, 1], 0.5714285714, bins=2\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6], 0.8, bins=10\n )\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity,\n [-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6],\n 0.4,\n bins=10,\n )\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, np.linspace(0, 1, 10), 0.6, bins=3\n )\n self.assertAlmostEqualOnAllArrayTypes(\n lempel_ziv_complexity, [1, 1, 2, 3, 4, 5, 6, 0, 7, 8], 0.6, bins=3\n )\n\n def test_fourier_entropy(self):\n self.assertAlmostEqualOnAllArrayTypes(\n fourier_entropy, [1, 2, 1], 0.693147180, bins=2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n fourier_entropy, [1, 2, 1], 0.693147180, bins=5\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n fourier_entropy, [1, 1, 2, 1, 1, 1, 1], 0.5623351446188083, bins=5\n )\n self.assertAlmostEqualOnAllArrayTypes(\n fourier_entropy, [1, 1, 1, 1, 2, 1, 1], 1.0397207708399179, bins=5\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n fourier_entropy,\n [-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],\n 1.5607104090414063,\n bins=10,\n )\n self.assertIsNanOnAllArrayTypes(\n fourier_entropy, [-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6], bins=10\n )\n\n def test_permutation_entropy(self):\n self.assertAlmostEqualOnAllArrayTypes(\n permutation_entropy,\n [4, 7, 9, 10, 6, 11, 3],\n 1.054920167,\n dimension=3,\n tau=1,\n )\n\n # should grow\n self.assertAlmostEqualOnAllArrayTypes(\n permutation_entropy,\n [1, -1, 1, -1, 1, -1, 1, -1],\n 0.6931471805599453,\n dimension=3,\n tau=1,\n )\n self.assertAlmostEqualOnAllArrayTypes(\n permutation_entropy,\n [1, -1, 1, -1, 1, 1, 1, -1],\n 1.3296613488547582,\n dimension=3,\n tau=1,\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n permutation_entropy,\n [-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],\n 1.0397207708399179,\n dimension=3,\n tau=2,\n )\n # nan is treated like any other number\n self.assertAlmostEqualOnAllArrayTypes(\n permutation_entropy,\n [-1, 4.3, 5, 1, -4.5, 1, 5, np.nan, -3.4, 6],\n 1.0397207708399179,\n dimension=3,\n tau=2,\n )\n\n # if too short, return nan\n self.assertIsNanOnAllArrayTypes(\n permutation_entropy, [1, -1], dimension=3, tau=1\n )\n\n def test_ratio_beyond_r_sigma(self):\n\n x = [0, 1] * 10 + [10, 20, -30] # std of x is 7.21, mean 3.04\n self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 3.0 / len(x), r=1)\n self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 2.0 / len(x), r=2)\n self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 1.0 / len(x), r=3)\n self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 0, r=20)\n\n def test_mean_abs_change(self):\n self.assertEqualOnAllArrayTypes(mean_abs_change, [-2, 2, 5], 3.5)\n self.assertEqualOnAllArrayTypes(mean_abs_change, [1, 2, -1], 2)\n\n def test_mean_change(self):\n self.assertEqualOnAllArrayTypes(mean_change, [-2, 2, 5], 3.5)\n self.assertEqualOnAllArrayTypes(mean_change, [1, 2, -1], -1)\n self.assertEqualOnAllArrayTypes(mean_change, [10, 20], 10)\n self.assertIsNanOnAllArrayTypes(mean_change, [1])\n self.assertIsNanOnAllArrayTypes(mean_change, [])\n\n def test_mean_second_derivate_central(self):\n self.assertEqualOnAllArrayTypes(\n mean_second_derivative_central, list(range(10)), 0\n )\n self.assertEqualOnAllArrayTypes(mean_second_derivative_central, [1, 3, 5], 0)\n self.assertEqualOnAllArrayTypes(\n mean_second_derivative_central, [1, 3, 7, -3], -3\n )\n\n def test_median(self):\n self.assertEqualOnAllArrayTypes(median, [1, 1, 2, 2], 1.5)\n self.assertEqualOnAllArrayTypes(median, [0.5, 0.5, 2, 3.5, 10], 2)\n self.assertEqualOnAllArrayTypes(median, [0.5], 0.5)\n self.assertIsNanOnAllArrayTypes(median, [])\n\n def test_mean(self):\n self.assertEqualOnAllArrayTypes(mean, [1, 1, 2, 2], 1.5)\n self.assertEqualOnAllArrayTypes(mean, [0.5, 0.5, 2, 3.5, 10], 3.3)\n self.assertEqualOnAllArrayTypes(mean, [0.5], 0.5)\n self.assertIsNanOnAllArrayTypes(mean, [])\n\n def test_length(self):\n self.assertEqualOnAllArrayTypes(length, [1, 2, 3, 4], 4)\n self.assertEqualOnAllArrayTypes(length, [1, 2, 3], 3)\n self.assertEqualOnAllArrayTypes(length, [1, 2], 2)\n self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.nan], 4)\n self.assertEqualOnAllArrayTypes(length, [], 0)\n\n def test_standard_deviation(self):\n self.assertAlmostEqualOnAllArrayTypes(standard_deviation, [1, 1, -1, -1], 1)\n self.assertAlmostEqualOnAllArrayTypes(\n standard_deviation, [1, 2, -2, -1], 1.58113883008\n )\n self.assertIsNanOnAllArrayTypes(standard_deviation, [])\n\n def test_variation_coefficient(self):\n self.assertIsNanOnAllArrayTypes(\n variation_coefficient,\n [1, 1, -1, -1],\n )\n self.assertAlmostEqualOnAllArrayTypes(\n variation_coefficient, [1, 2, -3, -1], -7.681145747868608\n )\n self.assertAlmostEqualOnAllArrayTypes(\n variation_coefficient, [1, 2, 4, -1], 1.2018504251546631\n )\n self.assertIsNanOnAllArrayTypes(variation_coefficient, [])\n\n def test_variance(self):\n self.assertAlmostEqualOnAllArrayTypes(variance, [1, 1, -1, -1], 1)\n self.assertAlmostEqualOnAllArrayTypes(variance, [1, 2, -2, -1], 2.5)\n self.assertIsNanOnAllArrayTypes(variance, [])\n\n def test_skewness(self):\n self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2, 2], 0)\n self.assertAlmostEqualOnAllArrayTypes(\n skewness, [1, 1, 1, 2, 2], 0.6085806194501855\n )\n self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1], 0)\n self.assertIsNanOnAllArrayTypes(skewness, [1, 1])\n\n def test_kurtosis(self):\n self.assertAlmostEqualOnAllArrayTypes(\n kurtosis, [1, 1, 1, 2, 2], -3.333333333333333\n )\n self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 1], 0)\n self.assertIsNanOnAllArrayTypes(kurtosis, [1, 1, 1])\n\n def test_root_mean_square(self):\n self.assertAlmostEqualOnAllArrayTypes(\n root_mean_square, [1, 1, 1, 2, 2], 1.4832396974191\n )\n self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [0], 0)\n self.assertIsNanOnAllArrayTypes(root_mean_square, [])\n self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [1], 1)\n self.assertAlmostEqualOnAllArrayTypes(root_mean_square, [-1], 1)\n\n def test_mean_n_absolute_max(self):\n self.assertIsNanOnAllArrayTypes(mean_n_absolute_max, [], number_of_maxima=1)\n self.assertIsNanOnAllArrayTypes(\n mean_n_absolute_max, [12, 3], number_of_maxima=10\n )\n self.assertRaises(\n AssertionError, mean_n_absolute_max, [12, 3], number_of_maxima=0\n )\n self.assertRaises(\n AssertionError, mean_n_absolute_max, [12, 3], number_of_maxima=-1\n )\n self.assertAlmostEqualOnAllArrayTypes(\n mean_n_absolute_max, [-1, -5, 4, 10], 6.33333333333, number_of_maxima=3\n )\n self.assertAlmostEqualOnAllArrayTypes(\n mean_n_absolute_max, [0, -5, -9], 7.000000, number_of_maxima=2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n mean_n_absolute_max, [0, 0, 0], 0, number_of_maxima=1\n )\n\n def test_absolute_sum_of_changes(self):\n self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, 1, 1, 1, 2, 1], 2)\n self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, -1, 1, -1], 6)\n self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1], 0)\n self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [], 0)\n\n def test_longest_strike_below_mean(self):\n self.assertEqualOnAllArrayTypes(\n longest_strike_below_mean, [1, 2, 1, 1, 1, 2, 2, 2], 3\n )\n self.assertEqualOnAllArrayTypes(\n longest_strike_below_mean, [1, 2, 3, 4, 5, 6], 3\n )\n self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 3, 4, 5], 2)\n self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 1], 1)\n self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [], 0)\n\n def test_longest_strike_above_mean(self):\n self.assertEqualOnAllArrayTypes(\n longest_strike_above_mean, [1, 2, 1, 2, 1, 2, 2, 1], 2\n )\n self.assertEqualOnAllArrayTypes(\n longest_strike_above_mean, [1, 2, 3, 4, 5, 6], 3\n )\n self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 3, 4, 5], 2)\n self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 1], 1)\n self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [], 0)\n\n def test_count_above_mean(self):\n self.assertEqualOnAllArrayTypes(count_above_mean, [1, 2, 1, 2, 1, 2], 3)\n self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1, 2], 1)\n self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1], 0)\n self.assertEqualOnAllArrayTypes(count_above_mean, [], 0)\n\n def test_count_below_mean(self):\n self.assertEqualOnAllArrayTypes(count_below_mean, [1, 2, 1, 2, 1, 2], 3)\n self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1, 2], 5)\n self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1], 0)\n self.assertEqualOnAllArrayTypes(count_below_mean, [], 0)\n\n def test_last_location_maximum(self):\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_maximum, [1, 2, 1, 2, 1], 0.8\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_maximum, [1, 2, 1, 1, 2], 1.0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_maximum, [2, 1, 1, 1, 1], 0.2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_maximum, [1, 1, 1, 1, 1], 1.0\n )\n self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1], 1.0)\n self.assertIsNanOnAllArrayTypes(last_location_of_maximum, [])\n\n def test_first_location_of_maximum(self):\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_maximum, [1, 2, 1, 2, 1], 0.2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_maximum, [1, 2, 1, 1, 2], 0.2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_maximum, [2, 1, 1, 1, 1], 0.0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_maximum, [1, 1, 1, 1, 1], 0.0\n )\n self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1], 0.0)\n self.assertIsNanOnAllArrayTypes(first_location_of_maximum, [])\n\n def test_last_location_of_minimum(self):\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_minimum, [1, 2, 1, 2, 1], 1.0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_minimum, [1, 2, 1, 2, 2], 0.6\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_minimum, [2, 1, 1, 1, 2], 0.8\n )\n self.assertAlmostEqualOnAllArrayTypes(\n last_location_of_minimum, [1, 1, 1, 1, 1], 1.0\n )\n self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1], 1.0)\n self.assertIsNanOnAllArrayTypes(last_location_of_minimum, [])\n\n def test_first_location_of_minimum(self):\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_minimum, [1, 2, 1, 2, 1], 0.0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_minimum, [2, 2, 1, 2, 2], 0.4\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_minimum, [2, 1, 1, 1, 2], 0.2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n first_location_of_minimum, [1, 1, 1, 1, 1], 0.0\n )\n self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1], 0.0)\n self.assertIsNanOnAllArrayTypes(first_location_of_minimum, [])\n\n def test_percentage_of_doubled_datapoints(self):\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1, 2, 3, 4], 0.4\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1.5, 2, 3], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_datapoints_to_all_datapoints, [1], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_datapoints_to_all_datapoints,\n [1.111, -2.45, 1.111, 2.45],\n 0.5,\n )\n self.assertIsNanOnAllArrayTypes(\n percentage_of_reoccurring_datapoints_to_all_datapoints, []\n )\n\n def test_ratio_of_doubled_values(self):\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.25\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_values_to_all_values, [1, 1.5, 2, 3], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_values_to_all_values, [1], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n percentage_of_reoccurring_values_to_all_values,\n [1.111, -2.45, 1.111, 2.45],\n 1.0 / 3.0,\n )\n self.assertIsNanOnAllArrayTypes(\n percentage_of_reoccurring_values_to_all_values, []\n )\n\n def test_sum_of_reoccurring_values(self):\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_values, [1, 1, 2, 3, 4, 4], 5\n )\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_values, [1, 1.5, 2, 3], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1], 0)\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_values, [1.111, -2.45, 1.111, 2.45], 1.111\n )\n self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [], 0)\n\n def test_sum_of_reoccurring_data_points(self):\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_data_points, [1, 1, 2, 3, 4, 4], 10\n )\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_data_points, [1, 1.5, 2, 3], 0\n )\n self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1], 0)\n self.assertAlmostEqualOnAllArrayTypes(\n sum_of_reoccurring_data_points, [1.111, -2.45, 1.111, 2.45], 2.222\n )\n self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [], 0)\n\n def test_uniqueness_factor(self):\n self.assertAlmostEqualOnAllArrayTypes(\n ratio_value_number_to_time_series_length, [1, 1, 2, 3, 4], 0.8\n )\n self.assertAlmostEqualOnAllArrayTypes(\n ratio_value_number_to_time_series_length, [1, 1.5, 2, 3], 1\n )\n self.assertAlmostEqualOnAllArrayTypes(\n ratio_value_number_to_time_series_length, [1], 1\n )\n self.assertAlmostEqualOnAllArrayTypes(\n ratio_value_number_to_time_series_length, [1.111, -2.45, 1.111, 2.45], 0.75\n )\n self.assertIsNanOnAllArrayTypes(ratio_value_number_to_time_series_length, [])\n\n def test_fft_coefficient(self):\n x = range(10)\n param = [\n {\"coeff\": 0, \"attr\": \"real\"},\n {\"coeff\": 1, \"attr\": \"real\"},\n {\"coeff\": 2, \"attr\": \"real\"},\n {\"coeff\": 0, \"attr\": \"imag\"},\n {\"coeff\": 1, \"attr\": \"imag\"},\n {\"coeff\": 2, \"attr\": \"imag\"},\n {\"coeff\": 0, \"attr\": \"angle\"},\n {\"coeff\": 1, \"attr\": \"angle\"},\n {\"coeff\": 2, \"attr\": \"angle\"},\n {\"coeff\": 0, \"attr\": \"abs\"},\n {\"coeff\": 1, \"attr\": \"abs\"},\n {\"coeff\": 2, \"attr\": \"abs\"},\n ]\n expected_index = [\n 'attr_\"real\"__coeff_0',\n 'attr_\"real\"__coeff_1',\n 'attr_\"real\"__coeff_2',\n 'attr_\"imag\"__coeff_0',\n 'attr_\"imag\"__coeff_1',\n 'attr_\"imag\"__coeff_2',\n 'attr_\"angle\"__coeff_0',\n 'attr_\"angle\"__coeff_1',\n 'attr_\"angle\"__coeff_2',\n 'attr_\"abs\"__coeff_0',\n 'attr_\"abs\"__coeff_1',\n 'attr_\"abs\"__coeff_2',\n ]\n\n res = pd.Series(dict(fft_coefficient(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['attr_\"imag\"__coeff_0'], 0, places=6)\n self.assertAlmostEqual(res['attr_\"real\"__coeff_0'], sum(x), places=6)\n self.assertAlmostEqual(res['attr_\"angle\"__coeff_0'], 0, places=6)\n self.assertAlmostEqual(res['attr_\"abs\"__coeff_0'], sum(x), places=6)\n\n x = [0, 1, 0, 0]\n res = pd.Series(dict(fft_coefficient(x, param)))\n # see documentation of fft in numpy\n # should return array([1. + 0.j, 0. - 1.j, -1. + 0.j])\n self.assertAlmostEqual(res['attr_\"imag\"__coeff_0'], 0, places=6)\n self.assertAlmostEqual(res['attr_\"real\"__coeff_0'], 1, places=6)\n self.assertAlmostEqual(res['attr_\"imag\"__coeff_1'], -1, places=6)\n self.assertAlmostEqual(res['attr_\"angle\"__coeff_1'], -90, places=6)\n self.assertAlmostEqual(res['attr_\"real\"__coeff_1'], 0, places=6)\n self.assertAlmostEqual(res['attr_\"imag\"__coeff_2'], 0, places=6)\n self.assertAlmostEqual(res['attr_\"real\"__coeff_2'], -1, places=6)\n\n # test what happens if coeff is biger than time series lenght\n x = range(5)\n param = [{\"coeff\": 10, \"attr\": \"real\"}]\n expected_index = ['attr_\"real\"__coeff_10']\n\n res = pd.Series(dict(fft_coefficient(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertIsNaN(res['attr_\"real\"__coeff_10'])\n\n def test_fft_aggregated(self):\n param = [\n {\"aggtype\": \"centroid\"},\n {\"aggtype\": \"variance\"},\n {\"aggtype\": \"skew\"},\n {\"aggtype\": \"kurtosis\"},\n ]\n expected_index = [\n 'aggtype_\"centroid\"',\n 'aggtype_\"variance\"',\n 'aggtype_\"skew\"',\n 'aggtype_\"kurtosis\"',\n ]\n\n x = np.arange(10)\n res = pd.Series(dict(fft_aggregated(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['aggtype_\"centroid\"'], 1.135, places=3)\n self.assertAlmostEqual(res['aggtype_\"variance\"'], 2.368, places=3)\n self.assertAlmostEqual(res['aggtype_\"skew\"'], 1.249, places=3)\n self.assertAlmostEqual(res['aggtype_\"kurtosis\"'], 3.643, places=3)\n\n # Scalar multiplying the distribution should not change the results:\n x = 10 * x\n res = pd.Series(dict(fft_aggregated(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['aggtype_\"centroid\"'], 1.135, places=3)\n self.assertAlmostEqual(res['aggtype_\"variance\"'], 2.368, places=3)\n self.assertAlmostEqual(res['aggtype_\"skew\"'], 1.249, places=3)\n self.assertAlmostEqual(res['aggtype_\"kurtosis\"'], 3.643, places=3)\n\n # The fft of a sign wave is a dirac delta, variance and skew should be near zero, kurtosis should be near 3:\n # However, in the discrete limit, skew and kurtosis blow up in a manner that is noise dependent and are\n # therefore bad features, therefore an nan should be returned for these values\n x = np.sin(2 * np.pi / 10 * np.arange(30))\n res = pd.Series(dict(fft_aggregated(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['aggtype_\"centroid\"'], 3.0, places=5)\n self.assertAlmostEqual(res['aggtype_\"variance\"'], 0.0, places=5)\n self.assertIsNaN(res['aggtype_\"skew\"'])\n self.assertIsNaN(res['aggtype_\"kurtosis\"'])\n\n # Gaussian test:\n def normal(y, mean_, sigma_):\n return (\n 1\n / (2 * np.pi * sigma_**2)\n * np.exp(-((y - mean_) ** 2) / (2 * sigma_**2))\n )\n\n mean_ = 500.0\n sigma_ = 1.0\n range_ = int(2 * mean_)\n x = list(map(lambda x: normal(x, mean_, sigma_), range(range_)))\n\n # The fourier transform of a Normal dist in the positive halfspace is a half normal,\n # Hand calculated values of centroid and variance based for the half-normal dist:\n # (Ref: https://en.wikipedia.org/wiki/Half-normal_distribution)\n expected_fft_centroid = (range_ / (2 * np.pi * sigma_)) * np.sqrt(2 / np.pi)\n expected_fft_var = (range_ / (2 * np.pi * sigma_)) ** 2 * (1 - 2 / np.pi)\n\n # Calculate values for unit test:\n res = pd.Series(dict(fft_aggregated(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n\n # Compare against hand calculated values:\n rel_diff_allowed = 0.02\n self.assertAlmostEqual(\n res['aggtype_\"centroid\"'],\n expected_fft_centroid,\n delta=rel_diff_allowed * expected_fft_centroid,\n )\n self.assertAlmostEqual(\n res['aggtype_\"variance\"'],\n expected_fft_var,\n delta=rel_diff_allowed * expected_fft_var,\n )\n\n def test_number_peaks(self):\n x = np.array([0, 1, 2, 1, 0, 1, 2, 3, 4, 5, 4, 3, 2, 1])\n self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 1)\n self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 2)\n self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 3)\n self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 4)\n self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 5)\n self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 6)\n\n def test_mass_quantile(self):\n\n x = [1] * 101\n param = [{\"q\": 0.5}]\n expected_index = [\"q_0.5\"]\n res = index_mass_quantile(x, param)\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"q_0.5\"], 0.5, places=1)\n\n # Test for parts of pandas series\n x = pd.Series([0] * 55 + [1] * 101)\n param = [{\"q\": 0.5}]\n expected_index = [\"q_0.5\"]\n res = index_mass_quantile(x[x > 0], param)\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"q_0.5\"], 0.5, places=1)\n\n x = [0] * 1000 + [1]\n param = [{\"q\": 0.5}, {\"q\": 0.99}]\n expected_index = [\"q_0.5\", \"q_0.99\"]\n res = index_mass_quantile(x, param)\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"q_0.5\"], 1, places=1)\n self.assertAlmostEqual(res[\"q_0.99\"], 1, places=1)\n\n x = [0, 1, 1, 0, 0, 1, 0, 0]\n param = [{\"q\": 0.30}, {\"q\": 0.60}, {\"q\": 0.90}]\n expected_index = [\"q_0.3\", \"q_0.6\", \"q_0.9\"]\n res = index_mass_quantile(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"q_0.3\"], 0.25, places=1)\n self.assertAlmostEqual(res[\"q_0.6\"], 0.375, places=1)\n self.assertAlmostEqual(res[\"q_0.9\"], 0.75, places=1)\n\n x = [0, 0, 0]\n param = [{\"q\": 0.5}]\n expected_index = [\"q_0.5\"]\n res = index_mass_quantile(x, param)\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertTrue(np.isnan(res[\"q_0.5\"]))\n\n x = []\n param = [{\"q\": 0.5}]\n expected_index = [\"q_0.5\"]\n res = index_mass_quantile(x, param)\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertTrue(np.isnan(res[\"q_0.5\"]))\n\n def test_number_cwt_peaks(self):\n x = [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1]\n self.assertEqualOnAllArrayTypes(number_cwt_peaks, x, 2, 2)\n\n def test_spkt_welch_density(self):\n\n # todo: improve tests\n x = range(10)\n param = [{\"coeff\": 1}, {\"coeff\": 10}]\n expected_index = [\"coeff_1\", \"coeff_10\"]\n res = pd.Series(dict(spkt_welch_density(x, param)))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertIsNaN(res[\"coeff_10\"])\n\n def test_cwt_coefficients(self):\n x = [0.1, 0.2, 0.3]\n param = [\n {\"widths\": (1, 2, 3), \"coeff\": 2, \"w\": 1},\n {\"widths\": (1, 3), \"coeff\": 2, \"w\": 3},\n {\"widths\": (1, 3), \"coeff\": 5, \"w\": 3},\n ]\n shuffle(param)\n\n expected_index = [\n \"coeff_2__w_1__widths_(1, 2, 3)\",\n \"coeff_2__w_3__widths_(1, 3)\",\n \"coeff_5__w_3__widths_(1, 3)\",\n ]\n\n res = cwt_coefficients(x, param)\n res = pd.Series(dict(res))\n\n # todo: add unit test for the values\n self.assertCountEqual(list(res.index), expected_index)\n self.assertTrue(math.isnan(res[\"coeff_5__w_3__widths_(1, 3)\"]))\n\n def test_ar_coefficient(self):\n\n # Test for X_i = 2.5 * X_{i-1} + 1\n param = [{\"k\": 1, \"coeff\": 0}, {\"k\": 1, \"coeff\": 1}]\n shuffle(param)\n\n x = [1] + 9 * [0]\n for i in range(1, len(x)):\n x[i] = 2.5 * x[i - 1] + 1\n\n res = ar_coefficient(x, param)\n expected_index = [\"coeff_0__k_1\", \"coeff_1__k_1\"]\n\n res = pd.Series(dict(res))\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"coeff_0__k_1\"], 1, places=2)\n self.assertAlmostEqual(res[\"coeff_1__k_1\"], 2.5, places=2)\n\n # Test for X_i = 1.4 * X_{i-1} - 1 X_{i-2} + 1\n param = [\n {\"k\": 1, \"coeff\": 0},\n {\"k\": 1, \"coeff\": 1},\n {\"k\": 2, \"coeff\": 0},\n {\"k\": 2, \"coeff\": 1},\n {\"k\": 2, \"coeff\": 2},\n {\"k\": 2, \"coeff\": 3},\n ]\n shuffle(param)\n\n x = [1, 1] + 5 * [0]\n for i in range(2, len(x)):\n x[i] = (-2) * x[i - 2] + 3.5 * x[i - 1] + 1\n\n res = ar_coefficient(x, param)\n expected_index = [\n \"coeff_0__k_1\",\n \"coeff_1__k_1\",\n \"coeff_0__k_2\",\n \"coeff_1__k_2\",\n \"coeff_2__k_2\",\n \"coeff_3__k_2\",\n ]\n\n res = pd.Series(dict(res))\n\n self.assertIsInstance(res, pd.Series)\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res[\"coeff_0__k_2\"], 1, places=2)\n self.assertAlmostEqual(res[\"coeff_1__k_2\"], 3.5, places=2)\n self.assertAlmostEqual(res[\"coeff_2__k_2\"], -2, places=2)\n self.assertTrue(np.isnan(res[\"coeff_3__k_2\"]))\n\n def test_time_reversal_asymmetry_statistic(self):\n x = [1] * 10\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 0\n )\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 1\n )\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 3\n )\n\n x = [1, 2, -3, 4]\n # 1/2 * ( (4^2 * -3 + 3 * 2^2) + (3^2*2)-(2*1^1)) = 1/2 * (-48+12+18-2) = 20/2\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, -10, 1\n )\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n time_reversal_asymmetry_statistic, x, 0, 3\n )\n\n def test_number_crossing_m(self):\n x = [10, -10, 10, -10]\n self.assertEqualOnAllArrayTypes(number_crossing_m, x, 3, 0)\n self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 10)\n\n x = [10, 20, 20, 30]\n self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 0)\n self.assertEqualOnAllArrayTypes(number_crossing_m, x, 1, 15)\n\n def test_c3(self):\n x = [1] * 10\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 0)\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 1)\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 2)\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 3)\n\n x = [1, 2, -3, 4]\n # 1/2 *(1*2*(-3)+2*(-3)*4) = 1/2 *(-6-24) = -30/2\n self.assertAlmostEqualOnAllArrayTypes(c3, x, -15, 1)\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 2)\n self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 3)\n\n def test_binned_entropy(self):\n self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 100, 0, 10)\n self.assertAlmostEqualOnAllArrayTypes(\n binned_entropy,\n [10] * 10 + [1],\n -(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),\n 10,\n )\n self.assertAlmostEqualOnAllArrayTypes(\n binned_entropy,\n [10] * 10 + [1],\n -(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),\n 10,\n )\n self.assertAlmostEqualOnAllArrayTypes(\n binned_entropy,\n [10] * 10 + [1],\n -(10 / 11 * math.log(10 / 11) + 1 / 11 * math.log(1 / 11)),\n 100,\n )\n self.assertAlmostEqualOnAllArrayTypes(\n binned_entropy, list(range(10)), -math.log(1 / 10), 100\n )\n self.assertAlmostEqualOnAllArrayTypes(\n binned_entropy, list(range(100)), -math.log(1 / 2), 2\n )\n\n def test_sample_entropy(self):\n # \"random\" list -> large entropy\n ts = [\n 1,\n 4,\n 5,\n 1,\n 7,\n 3,\n 1,\n 2,\n 5,\n 8,\n 9,\n 7,\n 3,\n 7,\n 9,\n 5,\n 4,\n 3,\n 9,\n 1,\n 2,\n 3,\n 4,\n 2,\n 9,\n 6,\n 7,\n 4,\n 9,\n 2,\n 9,\n 9,\n 6,\n 5,\n 1,\n 3,\n 8,\n 1,\n 5,\n 3,\n 8,\n 4,\n 1,\n 2,\n 2,\n 1,\n 6,\n 5,\n 3,\n 6,\n 5,\n 4,\n 8,\n 9,\n 6,\n 7,\n 5,\n 3,\n 2,\n 5,\n 4,\n 2,\n 5,\n 1,\n 6,\n 5,\n 3,\n 5,\n 6,\n 7,\n 8,\n 5,\n 2,\n 8,\n 6,\n 3,\n 8,\n 2,\n 7,\n 1,\n 7,\n 3,\n 5,\n 6,\n 2,\n 1,\n 3,\n 7,\n 3,\n 5,\n 3,\n 7,\n 6,\n 7,\n 7,\n 2,\n 3,\n 1,\n 7,\n 8,\n ]\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 2.38262780)\n # This is not very complex, so it gives a small value\n ts = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.25131442)\n # however adding a 2 increases complexity\n ts = [1, 1, 2, 1, 1, 1, 1, 1, 1, 1]\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)\n # and it does not matter where\n ts = [1, 1, 1, 2, 1, 1, 1, 1, 1, 1]\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)\n # negative numbers also work\n ts = [1, -1, 1, -1, 1, -1]\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.69314718)\n # nan gives nan\n ts = [1, -1, 1, np.nan, 1, -1]\n self.assertIsNanOnAllArrayTypes(sample_entropy, ts)\n # this is not a very \"random\" list, so it should give a small entropy\n ts = list(range(1000))\n self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.0010314596066622707)\n\n def test_autocorrelation(self):\n self.assertAlmostEqualOnAllArrayTypes(\n autocorrelation, [1, 2, 1, 2, 1, 2], -1, 1\n )\n self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 2)\n self.assertAlmostEqualOnAllArrayTypes(\n autocorrelation, [1, 2, 1, 2, 1, 2], -1, 3\n )\n self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 4)\n self.assertAlmostEqualOnAllArrayTypes(\n autocorrelation, pd.Series([0, 1, 2, 0, 1, 2]), -0.75, 2\n )\n # Autocorrelation lag is larger than length of the time series\n self.assertIsNanOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 200)\n self.assertIsNanOnAllArrayTypes(autocorrelation, [np.nan], 0)\n self.assertIsNanOnAllArrayTypes(autocorrelation, [], 0)\n # time series with length 1 has no variance, therefore no result for autocorrelation at lag 0\n self.assertIsNanOnAllArrayTypes(autocorrelation, [1], 0)\n\n def test_quantile(self):\n self.assertAlmostEqualOnAllArrayTypes(\n quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 1.0, 0.2\n )\n self.assertAlmostEqualOnAllArrayTypes(\n quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 0.9\n )\n self.assertAlmostEqualOnAllArrayTypes(\n quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 1.0\n )\n self.assertAlmostEqualOnAllArrayTypes(quantile, [1], 1, 0.5)\n self.assertIsNanOnAllArrayTypes(quantile, [], 0.5)\n\n def test_mean_abs_change_quantiles(self):\n\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 1,\n ql=0.1,\n qh=0.9,\n isabs=True,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 0,\n ql=0.15,\n qh=0.18,\n isabs=True,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, 0, 0, 0], 0.5, ql=0, qh=1, isabs=True, f_agg=\"mean\"\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, 0, 0, 0],\n 0.5,\n ql=0.1,\n qh=1,\n isabs=True,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, 0, 0, 0],\n 0,\n ql=0.1,\n qh=0.6,\n isabs=True,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, -9, 0, 0], 5, ql=0, qh=1, isabs=True, f_agg=\"mean\"\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, -9, 0, 0],\n 0.5,\n ql=0.1,\n qh=1,\n isabs=True,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, -9, 0, 0, 1, 0],\n 0.75,\n ql=0.1,\n qh=1,\n isabs=True,\n f_agg=\"mean\",\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 1,\n ql=0.1,\n qh=0.9,\n isabs=False,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 0,\n ql=0.15,\n qh=0.18,\n isabs=False,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, 0, 0, 0], 0, ql=0, qh=1, isabs=False, f_agg=\"mean\"\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, 0, 0, 0],\n 0,\n ql=0.1,\n qh=1,\n isabs=False,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, 0, 0, 0],\n 0,\n ql=0.1,\n qh=0.6,\n isabs=False,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, -9, 0, 0], 0, ql=0, qh=1, isabs=False, f_agg=\"mean\"\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, -9, 0, 0],\n 0.5,\n ql=0.1,\n qh=1,\n isabs=False,\n f_agg=\"mean\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n [0, 1, -9, 0, 0, 1, 0],\n 0.25,\n ql=0.1,\n qh=1,\n isabs=False,\n f_agg=\"mean\",\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 0,\n ql=0.1,\n qh=0.9,\n isabs=True,\n f_agg=\"std\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, 0, 0, 0], 0.5, ql=0, qh=1, isabs=True, f_agg=\"std\"\n )\n\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles,\n list(range(10)),\n 0,\n ql=0.1,\n qh=0.9,\n isabs=False,\n f_agg=\"std\",\n )\n self.assertAlmostEqualOnAllArrayTypes(\n change_quantiles, [0, 1, 0, 1, 0], 1, ql=0, qh=1, isabs=False, f_agg=\"std\"\n )\n\n def test_value_count(self):\n self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 10, value=1)\n self.assertEqualPandasSeriesWrapper(value_count, list(range(10)), 1, value=0)\n self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 0, value=0)\n self.assertEqualPandasSeriesWrapper(value_count, [np.nan, 0, 1] * 3, 3, value=0)\n self.assertEqualPandasSeriesWrapper(\n value_count, [-np.inf, 0, 1] * 3, 3, value=0\n )\n self.assertEqualPandasSeriesWrapper(value_count, [np.inf, 0, 1] * 3, 3, value=0)\n self.assertEqualPandasSeriesWrapper(\n value_count, [0.1, 0.2, 0.3] * 3, 3, value=0.2\n )\n self.assertEqualPandasSeriesWrapper(\n value_count, [np.nan, 0, 1] * 3, 3, value=np.nan\n )\n self.assertEqualPandasSeriesWrapper(\n value_count, [-np.inf, 0, 1] * 3, 3, value=-np.inf\n )\n self.assertEqualPandasSeriesWrapper(\n value_count, [np.inf, 0, 1] * 3, 3, value=np.inf\n )\n\n def test_range_count(self):\n self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=1, max=1)\n self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=0.9, max=1)\n self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 10, min=1, max=1.1)\n self.assertEqualPandasSeriesWrapper(\n range_count, list(range(10)), 9, min=0, max=9\n )\n self.assertEqualPandasSeriesWrapper(\n range_count, list(range(10)), 10, min=0, max=10\n )\n self.assertEqualPandasSeriesWrapper(\n range_count, list(range(0, -10, -1)), 9, min=-10, max=0\n )\n self.assertEqualPandasSeriesWrapper(\n range_count, [np.nan, np.inf, -np.inf] + list(range(10)), 10, min=0, max=10\n )\n\n def test_approximate_entropy(self):\n self.assertEqualOnAllArrayTypes(approximate_entropy, [1], 0, m=2, r=0.5)\n self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2], 0, m=2, r=0.5)\n self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)\n self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)\n self.assertAlmostEqualOnAllArrayTypes(\n approximate_entropy, [12, 13, 15, 16, 17] * 10, 0.282456191, m=2, r=0.9\n )\n self.assertRaises(\n ValueError, approximate_entropy, x=[12, 13, 15, 16, 17] * 10, m=2, r=-0.5\n )\n\n def test_absolute_maximum(self):\n self.assertEqualOnAllArrayTypes(absolute_maximum, [-5, 0, 1], 5)\n self.assertEqualOnAllArrayTypes(absolute_maximum, [0], 0)\n self.assertIsNanOnAllArrayTypes(absolute_maximum, [])\n\n def test_max_langevin_fixed_point(self):\n \"\"\"\n Estimating the intrinsic velocity of a dissipative soliton\n \"\"\"\n default_params = {\"m\": 3, \"r\": 30}\n\n # active Brownian motion\n ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)\n v = ds.simulate(100000, v0=np.zeros(1))\n v0 = max_langevin_fixed_point(v[:, 0], **default_params)\n self.assertLess(abs(ds.deterministic - v0), 0.001)\n\n # Brownian motion\n ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)\n v = ds.simulate(10000, v0=np.zeros(1))\n v0 = max_langevin_fixed_point(v[:, 0], **default_params)\n self.assertLess(v0, 0.001)\n\n def test_linear_trend(self):\n # check linear up trend\n x = range(10)\n param = [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ]\n res = linear_trend(x, param)\n\n res = pd.Series(dict(res))\n\n expected_index = [\n 'attr_\"pvalue\"',\n 'attr_\"intercept\"',\n 'attr_\"rvalue\"',\n 'attr_\"slope\"',\n 'attr_\"stderr\"',\n ]\n\n self.assertEqual(len(res), 5)\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['attr_\"pvalue\"'], 0)\n self.assertAlmostEqual(res['attr_\"stderr\"'], 0)\n self.assertAlmostEqual(res['attr_\"intercept\"'], 0)\n self.assertAlmostEqual(res['attr_\"slope\"'], 1.0)\n\n # check p value for random trend\n np.random.seed(42)\n x = np.random.uniform(size=100)\n param = [{\"attr\": \"rvalue\"}]\n res = linear_trend(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertLess(abs(res['attr_\"rvalue\"']), 0.1)\n\n # check slope and intercept decreasing trend with intercept\n x = [42 - 2 * x for x in range(10)]\n param = [{\"attr\": \"intercept\"}, {\"attr\": \"slope\"}]\n res = linear_trend(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertAlmostEqual(res['attr_\"intercept\"'], 42)\n self.assertAlmostEqual(res['attr_\"slope\"'], -2)\n\n def test__aggregate_on_chunks(self):\n self.assertListEqual(\n _aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg=\"max\", chunk_len=2),\n [1, 3],\n )\n self.assertListEqual(\n _aggregate_on_chunks(x=pd.Series([1, 1, 3, 3]), f_agg=\"max\", chunk_len=2),\n [1, 3],\n )\n\n self.assertListEqual(\n _aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg=\"min\", chunk_len=2),\n [0, 2],\n )\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([0, 1, 2, 3, 5]), f_agg=\"min\", chunk_len=2\n ),\n [0, 2, 5],\n )\n\n self.assertListEqual(\n _aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg=\"mean\", chunk_len=2),\n [0.5, 2.5],\n )\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([0, 1, 0, 4, 5]), f_agg=\"mean\", chunk_len=2\n ),\n [0.5, 2, 5],\n )\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([0, 1, 0, 4, 5]), f_agg=\"mean\", chunk_len=3\n ),\n [1 / 3, 4.5],\n )\n\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([0, 1, 2, 3, 5, -2]), f_agg=\"median\", chunk_len=2\n ),\n [0.5, 2.5, 1.5],\n )\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([-10, 5, 3, -3, 4, -6]), f_agg=\"median\", chunk_len=3\n ),\n [3, -3],\n )\n self.assertListEqual(\n _aggregate_on_chunks(\n x=pd.Series([0, 1, 2, np.nan, 5]), f_agg=\"median\", chunk_len=2\n ),\n [0.5, 2, 5],\n )\n\n def test_agg_linear_trend(self):\n x = pd.Series(range(9), index=range(9))\n param = [\n {\"attr\": \"intercept\", \"chunk_len\": 3, \"f_agg\": \"max\"},\n {\"attr\": \"slope\", \"chunk_len\": 3, \"f_agg\": \"max\"},\n {\"attr\": \"intercept\", \"chunk_len\": 3, \"f_agg\": \"min\"},\n {\"attr\": \"slope\", \"chunk_len\": 3, \"f_agg\": \"min\"},\n {\"attr\": \"intercept\", \"chunk_len\": 3, \"f_agg\": \"mean\"},\n {\"attr\": \"slope\", \"chunk_len\": 3, \"f_agg\": \"mean\"},\n {\"attr\": \"intercept\", \"chunk_len\": 3, \"f_agg\": \"median\"},\n {\"attr\": \"slope\", \"chunk_len\": 3, \"f_agg\": \"median\"},\n ]\n expected_index = [\n 'attr_\"intercept\"__chunk_len_3__f_agg_\"max\"',\n 'attr_\"slope\"__chunk_len_3__f_agg_\"max\"',\n 'attr_\"intercept\"__chunk_len_3__f_agg_\"min\"',\n 'attr_\"slope\"__chunk_len_3__f_agg_\"min\"',\n 'attr_\"intercept\"__chunk_len_3__f_agg_\"mean\"',\n 'attr_\"slope\"__chunk_len_3__f_agg_\"mean\"',\n 'attr_\"intercept\"__chunk_len_3__f_agg_\"median\"',\n 'attr_\"slope\"__chunk_len_3__f_agg_\"median\"',\n ]\n\n res = agg_linear_trend(x=x, param=param)\n\n res = pd.Series(dict(res))\n self.assertEqual(len(res), 8)\n self.maxDiff = 2000\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"max\"'], 2)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"max\"'], 3)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"min\"'], 0)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"min\"'], 3)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"mean\"'], 1)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"mean\"'], 3)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"median\"'], 1)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"median\"'], 3)\n\n x = pd.Series([np.nan, np.nan, np.nan, -3, -3, -3])\n res = agg_linear_trend(x=x, param=param)\n\n res = pd.Series(dict(res))\n\n self.assertIsNaN(res['attr_\"intercept\"__chunk_len_3__f_agg_\"max\"'])\n self.assertIsNaN(res['attr_\"slope\"__chunk_len_3__f_agg_\"max\"'])\n self.assertIsNaN(res['attr_\"intercept\"__chunk_len_3__f_agg_\"min\"'])\n self.assertIsNaN(res['attr_\"slope\"__chunk_len_3__f_agg_\"min\"'])\n self.assertIsNaN(res['attr_\"intercept\"__chunk_len_3__f_agg_\"mean\"'])\n self.assertIsNaN(res['attr_\"slope\"__chunk_len_3__f_agg_\"mean\"'])\n self.assertIsNaN(res['attr_\"intercept\"__chunk_len_3__f_agg_\"median\"'])\n self.assertIsNaN(res['attr_\"slope\"__chunk_len_3__f_agg_\"median\"'])\n\n x = pd.Series([np.nan, np.nan, -3, -3, -3, -3])\n res = agg_linear_trend(x=x, param=param)\n\n res = pd.Series(dict(res))\n\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"max\"'], -3)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"max\"'], 0)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"min\"'], -3)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"min\"'], 0)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"mean\"'], -3)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"mean\"'], 0)\n self.assertAlmostEqual(res['attr_\"intercept\"__chunk_len_3__f_agg_\"median\"'], -3)\n self.assertAlmostEqual(res['attr_\"slope\"__chunk_len_3__f_agg_\"median\"'], 0)\n\n def test_energy_ratio_by_chunks(self):\n x = pd.Series(range(90), index=range(90))\n param = [{\"num_segments\": 6, \"segment_focus\": i} for i in range(6)]\n output = energy_ratio_by_chunks(x=x, param=param)\n\n self.assertAlmostEqual(output[0][1], 0.0043, places=3)\n self.assertAlmostEqual(output[1][1], 0.0316, places=3)\n self.assertAlmostEqual(output[2][1], 0.0871, places=3)\n self.assertAlmostEqual(output[3][1], 0.1709, places=3)\n self.assertAlmostEqual(output[4][1], 0.2829, places=3)\n self.assertAlmostEqual(output[5][1], 0.4232, places=3)\n\n # Sum of the ratios should be 1.0\n sum = 0.0\n for name, dat in output:\n sum = sum + dat\n self.assertAlmostEqual(sum, 1.0)\n\n x = pd.Series(1, index=range(10))\n param = [{\"num_segments\": 3, \"segment_focus\": i} for i in range(3)]\n output = energy_ratio_by_chunks(x=x, param=param)\n\n self.assertAlmostEqual(output[0][1], 0.4, places=3)\n self.assertAlmostEqual(output[1][1], 0.3, places=3)\n self.assertAlmostEqual(output[2][1], 0.3, places=3)\n\n # Sum of the ratios should be 1.0\n sum = 0.0\n for name, dat in output:\n sum = sum + dat\n self.assertAlmostEqual(sum, 1.0)\n\n x = pd.Series(0, index=range(10))\n param = [{\"num_segments\": 3, \"segment_focus\": i} for i in range(3)]\n output = energy_ratio_by_chunks(x=x, param=param)\n self.assertIsNaN(output[0][1])\n self.assertIsNaN(output[1][1])\n self.assertIsNaN(output[2][1])\n\n def test_linear_trend_timewise_hours(self):\n \"\"\"Test linear_trend_timewise function with hour intervals.\"\"\"\n x = pd.Series(\n [0, 1, 3, 6],\n index=pd.DatetimeIndex(\n [\n \"2018-01-01 04:00:00\",\n \"2018-01-01 05:00:00\",\n \"2018-01-01 07:00:00\",\n \"2018-01-01 10:00:00\",\n ]\n ),\n )\n\n param = [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ]\n res = linear_trend_timewise(x, param)\n\n res = pd.Series(dict(res))\n\n expected_index = [\n 'attr_\"pvalue\"',\n 'attr_\"intercept\"',\n 'attr_\"rvalue\"',\n 'attr_\"slope\"',\n 'attr_\"stderr\"',\n ]\n\n self.assertEqual(len(res), 5)\n self.assertCountEqual(list(res.index), expected_index)\n self.assertAlmostEqual(res['attr_\"pvalue\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"stderr\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"intercept\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"slope\"'], 1.0, places=3)\n\n def test_linear_trend_timewise_days(self):\n \"\"\"Test linear_trend_timewise function with day intervals.\"\"\"\n # Try with different days\n x = pd.Series(\n [0, 24, 48, 72],\n index=pd.DatetimeIndex(\n [\n \"2018-01-01 04:00:00\",\n \"2018-01-02 04:00:00\",\n \"2018-01-03 04:00:00\",\n \"2018-01-04 04:00:00\",\n ]\n ),\n )\n\n param = [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ]\n res = linear_trend_timewise(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertAlmostEqual(res['attr_\"pvalue\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"stderr\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"intercept\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"slope\"'], 1.0, places=3)\n\n def test_linear_trend_timewise_seconds(self):\n \"\"\"Test linear_trend_timewise function with second intervals.\"\"\"\n # Try with different days\n x = pd.Series(\n [0, 1 / float(3600), 2 / float(3600), 3 / float(3600)],\n index=pd.DatetimeIndex(\n [\n \"2018-01-01 04:00:01\",\n \"2018-01-01 04:00:02\",\n \"2018-01-01 04:00:03\",\n \"2018-01-01 04:00:04\",\n ]\n ),\n )\n\n param = [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ]\n res = linear_trend_timewise(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertAlmostEqual(res['attr_\"pvalue\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"stderr\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"intercept\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"slope\"'], 1.0, places=3)\n\n def test_linear_trend_timewise_years(self):\n \"\"\"Test linear_trend_timewise function with year intervals.\"\"\"\n # Try with different days\n x = pd.Series(\n [\n 0,\n 365 * 24,\n 365 * 48,\n 365 * 72 + 24,\n ], # Add 24 to the last one since it's a leap year\n index=pd.DatetimeIndex(\n [\n \"2018-01-01 04:00:00\",\n \"2019-01-01 04:00:00\",\n \"2020-01-01 04:00:00\",\n \"2021-01-01 04:00:00\",\n ]\n ),\n )\n\n param = [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ]\n res = linear_trend_timewise(x, param)\n\n res = pd.Series(dict(res))\n\n self.assertAlmostEqual(res['attr_\"pvalue\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"stderr\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"intercept\"'], 0, places=3)\n self.assertAlmostEqual(res['attr_\"slope\"'], 1.0, places=3)\n\n def test_change_quantiles(self):\n \"\"\"Test change_quantiles function when changing from `sum` to `np.sum`.\"\"\"\n np.random.seed(0)\n res = change_quantiles(np.random.rand(10000) * 1000, 0.1, 0.2, False, \"mean\")\n self.assertAlmostEqual(res, -0.9443846621365727)\n\n def test_count_above(self):\n self.assertEqualPandasSeriesWrapper(count_above, [1] * 10, 1, t=1)\n self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 1, t=0)\n self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 0.5, t=5)\n self.assertEqualPandasSeriesWrapper(\n count_above, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2\n )\n self.assertEqualPandasSeriesWrapper(count_above, [np.nan, 0, 1] * 3, 2 / 3, t=0)\n self.assertEqualPandasSeriesWrapper(\n count_above, [-np.inf, 0, 1] * 3, 2 / 3, t=0\n )\n self.assertEqualPandasSeriesWrapper(count_above, [np.inf, 0, 1] * 3, 1, t=0)\n self.assertEqualPandasSeriesWrapper(\n count_above, [np.nan, 0, 1] * 3, 0, t=np.nan\n )\n self.assertEqualPandasSeriesWrapper(\n count_above, [-np.inf, 0, np.inf] * 3, 1, t=-np.inf\n )\n self.assertEqualPandasSeriesWrapper(\n count_above, [np.inf, 0, 1] * 3, 1 / 3, t=np.inf\n )\n\n def test_count_below(self):\n self.assertEqualPandasSeriesWrapper(count_below, [1] * 10, 1, t=1)\n self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 1 / 10, t=0)\n self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 6 / 10, t=5)\n self.assertEqualPandasSeriesWrapper(\n count_below, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2\n )\n self.assertEqualPandasSeriesWrapper(count_below, [np.nan, 0, 1] * 3, 1 / 3, t=0)\n self.assertEqualPandasSeriesWrapper(\n count_below, [-np.inf, 0, 1] * 3, 2 / 3, t=0\n )\n self.assertEqualPandasSeriesWrapper(count_below, [np.inf, 0, 1] * 3, 1 / 3, t=0)\n self.assertEqualPandasSeriesWrapper(\n count_below, [np.nan, 0, 1] * 3, 0, t=np.nan\n )\n self.assertEqualPandasSeriesWrapper(\n count_below, [-np.inf, 0, np.inf] * 3, 1 / 3, t=-np.inf\n )\n self.assertEqualPandasSeriesWrapper(\n count_below, [np.inf, 0, 1] * 3, 1, t=np.inf\n )\n\n def test_benford_correlation(self):\n # A test with list of random values\n np.random.seed(42)\n random_list = np.random.uniform(size=100)\n\n # Fibonacci series is known to match the Newcomb-Benford's Distribution\n fibonacci_list = [0, 1]\n for i in range(2, 200):\n fibonacci_list.append(fibonacci_list[i - 1] + fibonacci_list[i - 2])\n\n # A list of equally distributed digits (returns NaN)\n equal_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n # A list containing NaN\n list_with_nan = [\n 1.354,\n 0.058,\n 0.055,\n 0.99,\n 3.15,\n np.nan,\n 0.3,\n 2.3,\n 0,\n 0.59,\n 0.74,\n ]\n\n self.assertAlmostEqual(benford_correlation(random_list), 0.39458056)\n self.assertAlmostEqual(benford_correlation(fibonacci_list), 0.998003988)\n self.assertAlmostEqual(benford_correlation(list_with_nan), 0.10357511)\n self.assertIsNaN(benford_correlation(equal_list))\n\n def test_query_similarity_count(self):\n np.random.seed(42)\n query = np.random.uniform(size=10)\n threshold = 3.0\n x = np.random.uniform(size=100)\n\n # z-normalized Euclidean distances\n\n param = [{\"query\": query}]\n self.assertAlmostEqual(query_similarity_count(x, param=param)[0][1], 0.0)\n\n param = [{\"query\": query, \"threshold\": threshold}]\n self.assertAlmostEqual(query_similarity_count(x, param=param)[0][1], 6.0)\n\n # non-normalized Euclidean distances\n\n param = [{\"query\": query, \"normalize\": False}]\n self.assertAlmostEqual(query_similarity_count(x, param=param)[0][1], 0.0)\n\n param = [{\"query\": query, \"threshold\": threshold, \"normalize\": False}]\n self.assertAlmostEqual(query_similarity_count(x, param=param)[0][1], 91.0)\n\n @unittest.skipIf(\n not getattr(matrix_profile, \"dependency_available\", True),\n \"Missing matrixprofile dependency\",\n )\n def test_matrix_profile_window(self):\n # Test matrix profile output with specified window\n np.random.seed(9999)\n ts = np.random.uniform(size=2**10)\n w = 2**5\n subq = ts[0:w]\n ts[0:w] = subq\n ts[w + 100 : w + 100 + w] = subq\n param = [\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"min\"},\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"max\"},\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"mean\"},\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"median\"},\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"25\"},\n {\"threshold\": 0.98, \"windows\": 36, \"feature\": \"75\"},\n ]\n\n self.assertAlmostEqual(matrix_profile(ts, param=param)[0][1], 2.825786727580335)\n\n @unittest.skipIf(\n not getattr(matrix_profile, \"dependency_available\", True),\n \"Missing matrixprofile dependency\",\n )\n def test_matrix_profile_no_window(self):\n # Test matrix profile output with no window specified\n np.random.seed(9999)\n ts = np.random.uniform(size=2**10)\n w = 2**5\n subq = ts[0:w]\n ts[0:w] = subq\n ts[w + 100 : w + 100 + w] = subq\n\n param = [\n {\"threshold\": 0.98, \"feature\": \"min\"},\n {\"threshold\": 0.98, \"feature\": \"max\"},\n {\"threshold\": 0.98, \"feature\": \"mean\"},\n {\"threshold\": 0.98, \"feature\": \"median\"},\n {\"threshold\": 0.98, \"feature\": \"25\"},\n {\"threshold\": 0.98, \"feature\": \"75\"},\n ]\n\n # Test matrix profile output with no window specified\n self.assertAlmostEqual(matrix_profile(ts, param=param)[0][1], 2.825786727580335)\n\n @unittest.skipIf(\n not getattr(matrix_profile, \"dependency_available\", True),\n \"Missing matrixprofile dependency\",\n )\n def test_matrix_profile_nan(self):\n # Test matrix profile of NaNs (NaN output)\n ts = np.random.uniform(size=2**6)\n ts[:] = np.nan\n\n param = [\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"min\"},\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"max\"},\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"mean\"},\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"median\"},\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"25\"},\n {\"threshold\": 0.98, \"windows\": None, \"feature\": \"75\"},\n ]\n\n self.assertTrue(np.isnan(matrix_profile(ts, param=param)[0][1]))\n\n\nclass FriedrichTestCase(TestCase):\n def test_estimate_friedrich_coefficients(self):\n \"\"\"\n Estimate friedrich coefficients\n \"\"\"\n default_params = {\"m\": 3, \"r\": 30}\n\n # active Brownian motion\n ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)\n v = ds.simulate(10000, v0=np.zeros(1))\n coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)\n self.assertLess(abs(coeff[-1]), 0.0001)\n\n # Brownian motion\n ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)\n v = ds.simulate(10000, v0=np.zeros(1))\n coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)\n self.assertLess(abs(coeff[-1]), 0.0001)\n\n def test_friedrich_coefficients(self):\n # Test binning error returns vector of NaNs\n param = [{\"coeff\": coeff, \"m\": 2, \"r\": 30} for coeff in range(4)]\n x = np.zeros(100)\n res = pd.Series(dict(friedrich_coefficients(x, param)))\n\n expected_index = [\n \"coeff_0__m_2__r_30\",\n \"coeff_1__m_2__r_30\",\n \"coeff_2__m_2__r_30\",\n \"coeff_3__m_2__r_30\",\n ]\n self.assertCountEqual(list(res.index), expected_index)\n self.assertTrue(np.sum(np.isnan(res)), 3)\n\n def test_friedrich_number_of_returned_features_is_equal_to_number_of_parameters(\n self,\n ):\n \"\"\"unit test for issue 501\"\"\"\n param = [\n {\"m\": 3, \"r\": 5, \"coeff\": 2},\n {\"m\": 3, \"r\": 5, \"coeff\": 3},\n {\"m\": 3, \"r\": 2, \"coeff\": 3},\n ]\n x = np.zeros(100)\n res = pd.Series(dict(friedrich_coefficients(x, param)))\n\n expected_index = [\"coeff_2__m_3__r_5\", \"coeff_3__m_3__r_5\", \"coeff_3__m_3__r_2\"]\n self.assertCountEqual(list(res.index), expected_index)\n self.assertTrue(np.sum(np.isnan(res)), 3)\n\n def test_friedrich_equal_to_snapshot(self):\n param = [{\"coeff\": coeff, \"m\": 2, \"r\": 30} for coeff in range(4)]\n x = np.array(\n [\n -0.53,\n -0.61,\n -1.26,\n -0.88,\n -0.34,\n 0.58,\n 2.86,\n -0.47,\n 0.78,\n -0.45,\n -0.27,\n 0.43,\n 1.72,\n 0.26,\n 1.02,\n -0.09,\n 0.65,\n 1.49,\n -0.95,\n -1.02,\n -0.64,\n -1.63,\n -0.71,\n -0.43,\n -1.69,\n 0.05,\n 1.58,\n 1.1,\n 0.55,\n -1.02,\n ]\n )\n\n res = pd.Series(dict(friedrich_coefficients(x, param)))\n\n self.assertAlmostEqual(res[\"coeff_0__m_2__r_30\"], -0.24536975738843042)\n self.assertAlmostEqual(res[\"coeff_1__m_2__r_30\"], -0.533309548662685)\n self.assertAlmostEqual(res[\"coeff_2__m_2__r_30\"], 0.2759399238199404)\n" }, { "path": "tests/units/feature_extraction/test_settings.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport pickle\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.testing import assert_frame_equal\n\nfrom tsfresh.feature_extraction import feature_calculators\nfrom tsfresh.feature_extraction.extraction import extract_features\nfrom tsfresh.feature_extraction.settings import (\n ComprehensiveFCParameters,\n EfficientFCParameters,\n IndexBasedFCParameters,\n MinimalFCParameters,\n PickableSettings,\n TimeBasedFCParameters,\n from_columns,\n include_function,\n)\n\n\nclass TestSettingsObject(TestCase):\n \"\"\"\n This tests the base class ComprehensiveFCParameters\n \"\"\"\n\n def test_range_count_correctly_configured(self):\n fset = ComprehensiveFCParameters()\n params_range_count = fset[\"range_count\"]\n for param in params_range_count:\n assert param[\"min\"] < param[\"max\"]\n\n def test_from_column_raises_on_wrong_column_format(self):\n\n self.assertRaises(TypeError, from_columns, 42)\n self.assertRaises(TypeError, from_columns, [42])\n self.assertRaises(ValueError, from_columns, [\"This is not a column name\"])\n self.assertRaises(ValueError, from_columns, [\"This__neither\"])\n self.assertRaises(ValueError, from_columns, [\"This__also__not\"])\n\n def test_from_column_correct_for_selected_columns(self):\n tsn = \"TEST_TIME_SERIES\"\n\n # Aggregate functions\n feature_names = [\n tsn + \"__sum_values\",\n tsn + \"__median\",\n tsn + \"__length\",\n tsn + \"__sample_entropy\",\n ]\n\n # Aggregate functions with params\n feature_names += [\n tsn + \"__quantile__q_10\",\n tsn + \"__quantile__q_70\",\n tsn + \"__number_peaks__n_30\",\n tsn + \"__value_count__value_inf\",\n tsn + \"__value_count__value_-inf\",\n tsn + \"__value_count__value_nan\",\n ]\n\n # Apply functions\n feature_names += [\n tsn + \"__ar_coefficient__k_20__coeff_4\",\n tsn + \"__ar_coefficient__coeff_10__k_-1\",\n ]\n\n kind_to_fc_parameters = from_columns(feature_names)\n self.assertCountEqual(\n list(kind_to_fc_parameters[tsn].keys()),\n [\n \"sum_values\",\n \"median\",\n \"length\",\n \"sample_entropy\",\n \"quantile\",\n \"number_peaks\",\n \"ar_coefficient\",\n \"value_count\",\n ],\n )\n\n self.assertIsNone(kind_to_fc_parameters[tsn][\"sum_values\"])\n self.assertEqual(\n kind_to_fc_parameters[tsn][\"ar_coefficient\"],\n [{\"k\": 20, \"coeff\": 4}, {\"k\": -1, \"coeff\": 10}],\n )\n\n self.assertEqual(\n kind_to_fc_parameters[tsn][\"value_count\"],\n [{\"value\": np.inf}, {\"value\": -np.inf}, {\"value\": np.nan}],\n )\n\n def test_from_column_correct_for_comprehensive_fc_parameters(self):\n fset = ComprehensiveFCParameters()\n X_org = extract_features(\n pd.DataFrame({\"value\": [1, 2, 3], \"id\": [1, 1, 1]}),\n default_fc_parameters=fset,\n column_id=\"id\",\n column_value=\"value\",\n n_jobs=0,\n )\n inferred_fset = from_columns(X_org)\n X_new = extract_features(\n pd.DataFrame({\"value\": [1, 2, 3], \"id\": [1, 1, 1]}),\n kind_to_fc_parameters=inferred_fset,\n column_id=\"id\",\n column_value=\"value\",\n n_jobs=0,\n )\n assert_frame_equal(X_org.sort_index(), X_new.sort_index())\n\n def test_from_columns_ignores_columns(self):\n\n tsn = \"TEST_TIME_SERIES\"\n feature_names = [\n tsn + \"__sum_values\",\n tsn + \"__median\",\n tsn + \"__length\",\n tsn + \"__sample_entropy\",\n ]\n feature_names += [\"THIS_COL_SHOULD_BE_IGNORED\"]\n\n kind_to_fc_parameters = from_columns(\n feature_names,\n columns_to_ignore=[\"THIS_COL_SHOULD_BE_IGNORED\", \"THIS_AS_WELL\"],\n )\n\n self.assertCountEqual(\n list(kind_to_fc_parameters[tsn].keys()),\n [\"sum_values\", \"median\", \"length\", \"sample_entropy\"],\n )\n\n def test_default_calculates_all_features(self):\n \"\"\"\n Test that by default a ComprehensiveFCParameters object should be set up to calculate all features defined\n in tsfresh.feature_extraction.feature_calculators\n \"\"\"\n settings = ComprehensiveFCParameters()\n all_feature_calculators = [\n name\n for name, func in feature_calculators.__dict__.items()\n if include_function(func, exclusion_attr=\"input_type\")\n ]\n\n for calculator in all_feature_calculators:\n self.assertIn(\n calculator,\n settings,\n msg=\"Default ComprehensiveFCParameters object does not setup calculation of {}\".format(\n calculator\n ),\n )\n\n def test_from_columns_correct_for_different_kind_datatypes(self):\n \"\"\"The `settings.from_columns()` function is supposed to save the feature extraction / selection results so it\n can be reused later. It works by parsing the column names of the extracted dataframes. An unfortunate side\n effect of this is that when used with the 'long' format time series input, the typing information about the\n 'kind' column is lost. For example, even if the 'kind' values are in int32, in the resulting settings dict, the\n type of the top level keys (representing different kind values) will be str\n \"\"\"\n df = pd.DataFrame(\n {\n \"id\": [1, 1, 1, 1],\n \"time\": [1, 1, 2, 2],\n \"kind\": [1, 2, 1, 2],\n \"value\": [1, 2, 3, 4],\n }\n )\n\n features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=\"kind\",\n column_value=\"value\",\n default_fc_parameters=MinimalFCParameters(),\n )\n sample_settings = from_columns(features)\n X = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=\"kind\",\n column_value=\"value\",\n kind_to_fc_parameters=sample_settings,\n )\n assert X.shape == (1, 2 * len(MinimalFCParameters()))\n\n\nclass TestEfficientFCParameters(TestCase):\n \"\"\"\n This tests the EfficientFCParameters( class\n \"\"\"\n\n def test_extraction_runs_through(self):\n rfs = EfficientFCParameters()\n data = pd.DataFrame(\n [[0, 0, 0, 0], [1, 0, 0, 0]], columns=[\"id\", \"time\", \"kind\", \"value\"]\n )\n\n extracted_features = extract_features(\n data,\n default_fc_parameters=rfs,\n column_kind=\"kind\",\n column_value=\"value\",\n column_sort=\"time\",\n column_id=\"id\",\n )\n\n self.assertCountEqual(extracted_features.index, [0, 1])\n\n def test_contains_all_non_high_comp_cost_features(self):\n \"\"\"\n Test that by default a EfficientFCParameters object should be set up to calculate all features defined\n in tsfresh.feature_extraction.feature_calculators that do not have the attribute \"high_comp_cost\"\n \"\"\"\n rfs = EfficientFCParameters()\n all_feature_calculators = [\n name\n for name, func in feature_calculators.__dict__.items()\n if include_function(func, exclusion_attr=\"high_comp_cost\")\n ]\n\n for calculator in all_feature_calculators:\n self.assertIn(\n calculator,\n rfs,\n msg=\"Default EfficientFCParameters object does not setup calculation of {}\".format(\n calculator\n ),\n )\n\n def test_contains_all_time_based_features(self):\n \"\"\"\n Test that by default a TimeBasedFCParameters object should be set up to calculate all\n features defined in tsfresh.feature_extraction.feature_calculators that have the\n attribute \"index_type\" == pd.DatetimeIndex\n \"\"\"\n rfs = TimeBasedFCParameters()\n all_feature_calculators = [\n name\n for name, func in feature_calculators.__dict__.items()\n if not getattr(func, \"index_type\", False) != pd.DatetimeIndex\n ]\n\n for calculator in all_feature_calculators:\n self.assertIn(\n calculator,\n rfs,\n msg=\"Default TimeBasedFCParameters object does not setup calculation of {}\".format(\n calculator\n ),\n )\n\n def test_contains_all_index_based_features(self):\n \"\"\"\n Test that by default a IndexBasedFCParameters object should be set up to calculate all\n features defined in tsfresh.feature_extraction.feature_calculators that have the\n attribute \"input\" == \"pd.Series\"\n \"\"\"\n rfs = IndexBasedFCParameters()\n all_feature_calculators = [\n name\n for name, func in feature_calculators.__dict__.items()\n if getattr(func, \"input\", None) == \"pd.Series\"\n ]\n\n for calculator in all_feature_calculators:\n self.assertIn(\n calculator,\n rfs,\n msg=f\"Default IndexBasedFCParameters object does not setup calculation of {calculator}.\",\n )\n\n\nclass TestMinimalSettingsObject(TestCase):\n def test_all_minimal_features_in(self):\n mfs = MinimalFCParameters()\n\n self.assertIn(\"mean\", mfs)\n self.assertIn(\"median\", mfs)\n self.assertIn(\"minimum\", mfs)\n self.assertIn(\"maximum\", mfs)\n self.assertIn(\"length\", mfs)\n self.assertIn(\"sum_values\", mfs)\n self.assertIn(\"standard_deviation\", mfs)\n self.assertIn(\"variance\", mfs)\n self.assertNotIn(\"fft_coefficient\", mfs)\n\n def test_extraction_runs_through(self):\n mfs = MinimalFCParameters()\n\n data = pd.DataFrame(\n [[0, 0, 0, 0], [1, 0, 0, 0]], columns=[\"id\", \"time\", \"kind\", \"value\"]\n )\n\n extracted_features = extract_features(\n data,\n default_fc_parameters=mfs,\n column_kind=\"kind\",\n column_value=\"value\",\n column_sort=\"time\",\n column_id=\"id\",\n )\n\n self.assertCountEqual(\n extracted_features.columns,\n [\n \"0__median\",\n \"0__standard_deviation\",\n \"0__sum_values\",\n \"0__maximum\",\n \"0__variance\",\n \"0__minimum\",\n \"0__mean\",\n \"0__length\",\n \"0__root_mean_square\",\n \"0__absolute_maximum\",\n ],\n )\n self.assertCountEqual(extracted_features.index, [0, 1])\n\n\nclass TestSettingPickability(TestCase):\n def test_settings_pickable(self):\n settings = PickableSettings()\n settings[\"test\"] = 3\n settings[lambda x: x + 1] = None\n\n def f(x):\n return x - 2\n\n settings[f] = {\"this\": \"is a test\"}\n\n dumped_settings = pickle.dumps(settings)\n settings = pickle.loads(dumped_settings)\n\n self.assertIn(\"test\", settings)\n self.assertEqual(len(settings), 3)\n\n for key in settings:\n self.assertTrue(\n not callable(key)\n or (key(3) == 4 and settings[key] is None)\n or (key(3) == 1 and settings[key] == {\"this\": \"is a test\"})\n )\n\n\nclass TestIncludeFunction(TestCase):\n def test_no_fctype(self):\n class NoFCType:\n pass\n\n assert not include_function(NoFCType())\n\n def test_fctype(self):\n class FCType:\n fctype = \"testclass\"\n\n class FCTypeWithInstalledDependency:\n fctype = \"testclass\"\n dependency_available = True\n\n class FCTypeWithoutInstalledDependency:\n fctype = \"testclass\"\n dependency_available = False\n\n assert include_function(FCType())\n assert include_function(FCType(), exclusion_attr=\"exclude_this\")\n assert include_function(FCTypeWithInstalledDependency())\n assert include_function(\n FCTypeWithInstalledDependency(), exclusion_attr=\"exclude_this\"\n )\n assert not include_function(FCTypeWithoutInstalledDependency())\n assert not include_function(\n FCTypeWithoutInstalledDependency(), exclusion_attr=\"exclude_this\"\n )\n\n def test_fctype_default_exclusion(self):\n class FCType:\n fctype = \"testclass\"\n input_type = True\n\n class FCTypeWithInstalledDependency:\n fctype = \"testclass\"\n input_type = True\n dependency_available = True\n\n class FCTypeWithoutInstalledDependency:\n fctype = \"testclass\"\n input_type = True\n dependency_available = False\n\n assert not include_function(FCType())\n assert not include_function(FCTypeWithInstalledDependency())\n assert not include_function(FCTypeWithoutInstalledDependency())\n\n def test_fctype_exclude(self):\n class FCType:\n fctype = \"testclass\"\n exclude_this = \"Arbitrary value\"\n\n class FCTypeWithInstalledDependency:\n fctype = \"testclass\"\n exclude_this = \"Arbitrary value\"\n dependency_available = True\n\n class FCTypeWithoutInstalledDependency:\n fctype = \"testclass\"\n exclude_this = \"Arbitrary value\"\n dependency_available = False\n\n assert not include_function(FCType(), exclusion_attr=\"exclude_this\")\n assert not include_function(\n FCTypeWithInstalledDependency(), exclusion_attr=\"exclude_this\"\n )\n assert not include_function(\n FCTypeWithoutInstalledDependency(), exclusion_attr=\"exclude_this\"\n )\n" }, { "path": "tests/units/feature_selection/__init__.py", "content": "" }, { "path": "tests/units/feature_selection/test_checks.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom tests.fixtures import warning_free\nfrom tsfresh.defaults import TEST_FOR_BINARY_TARGET_REAL_FEATURE\nfrom tsfresh.feature_selection.significance_tests import (\n target_binary_feature_binary_test,\n target_binary_feature_real_test,\n target_real_feature_binary_test,\n target_real_feature_real_test,\n)\n\n\n@pytest.fixture()\ndef binary_series_with_nan():\n return pd.Series([np.nan, 1, 1])\n\n\n@pytest.fixture()\ndef real_series_with_nan():\n return pd.Series([np.nan, 1, 2])\n\n\n@pytest.fixture()\ndef binary_series():\n return pd.Series([0, 1, 1])\n\n\n@pytest.fixture()\ndef real_series():\n return pd.Series([0, 1, 2])\n\n\nclass TestChecksBinaryReal:\n \"\"\"\n Test the checks for the `target_binary_feature_real_test`.\n \"\"\"\n\n def test_check_target_is_binary(self, real_series):\n with pytest.raises(ValueError):\n target_binary_feature_real_test(\n x=real_series, y=real_series, test=TEST_FOR_BINARY_TARGET_REAL_FEATURE\n )\n\n def test_checks_test_function(self, binary_series, real_series):\n with pytest.raises(ValueError):\n target_binary_feature_real_test(\n x=real_series, y=binary_series, test=\"other_unknown_function\"\n )\n\n def test_checks_feature_nan(self, real_series_with_nan, binary_series):\n with pytest.raises(ValueError):\n target_binary_feature_real_test(\n x=real_series_with_nan,\n y=binary_series,\n test=TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n )\n\n def test_checks_target_nan(self, binary_series_with_nan, real_series):\n with pytest.raises(ValueError):\n target_binary_feature_real_test(\n x=real_series,\n y=binary_series_with_nan,\n test=TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n )\n\n def test_check_feature_is_series(self, binary_series, real_series):\n with pytest.raises(TypeError):\n target_binary_feature_real_test(x=real_series.values, y=binary_series)\n\n def test_check_feature_is_series(self, binary_series, real_series):\n with pytest.raises(TypeError):\n target_binary_feature_real_test(x=real_series, y=binary_series.values)\n\n\nclass TestChecksBinaryBinary:\n \"\"\"\n Test the checks for the `target_binary_feature_binary_test`.\n \"\"\"\n\n def test_checks_feature_is_binary(self, binary_series, real_series):\n with pytest.raises(ValueError):\n target_binary_feature_binary_test(x=real_series, y=binary_series)\n\n def test_checks_target_is_binary(self, binary_series, real_series):\n with pytest.raises(ValueError):\n target_binary_feature_binary_test(x=binary_series, y=real_series)\n\n def test_checks_feature_is_series(self, binary_series):\n with pytest.raises(TypeError):\n target_binary_feature_binary_test(x=binary_series.values, y=binary_series)\n\n def test_checks_target_is_series(self, binary_series):\n with pytest.raises(TypeError):\n target_binary_feature_binary_test(x=binary_series, y=binary_series.values)\n\n def test_checks_feature_nan(self, binary_series_with_nan, binary_series):\n with pytest.raises(ValueError):\n target_binary_feature_binary_test(x=binary_series_with_nan, y=binary_series)\n\n def test_checks_target_nan(self, binary_series_with_nan, binary_series):\n with pytest.raises(ValueError):\n target_binary_feature_binary_test(x=binary_series, y=binary_series_with_nan)\n\n\nclass TestChecksRealReal:\n \"\"\"\n Test the checks for the `target_real_feature_real_test`.\n \"\"\"\n\n def test_checks_feature_is_series(self, real_series):\n with pytest.raises(TypeError):\n target_real_feature_real_test(x=real_series.values, y=real_series)\n\n def test_checks_target_is_series(self, real_series):\n with pytest.raises(TypeError):\n target_real_feature_real_test(x=real_series, y=real_series.values)\n\n def test_checks_feature_nan(self, real_series_with_nan, real_series):\n with pytest.raises(ValueError):\n target_real_feature_real_test(x=real_series_with_nan, y=real_series)\n\n def test_checks_target_nan(self, real_series_with_nan, real_series):\n with pytest.raises(ValueError):\n target_real_feature_real_test(x=real_series, y=real_series_with_nan)\n\n\nclass TestChecksRealBinary:\n \"\"\"\n Test the checks for the `target_real_feature_binary_test`.\n \"\"\"\n\n def test_feature_is_binary(self, real_series):\n with pytest.raises(ValueError):\n target_real_feature_binary_test(x=real_series, y=real_series)\n\n def test_feature_is_series(self, real_series, binary_series):\n with pytest.raises(TypeError):\n target_real_feature_binary_test(x=binary_series.values, y=real_series)\n\n def test_feature_is_series(self, real_series, binary_series):\n with pytest.raises(TypeError):\n target_real_feature_binary_test(x=binary_series, y=real_series.values)\n\n def test_checks_feature_nan(self, binary_series_with_nan, real_series):\n with pytest.raises(ValueError):\n target_real_feature_binary_test(x=binary_series_with_nan, y=real_series)\n\n def test_checks_target_nan(self, real_series_with_nan, binary_series):\n with pytest.raises(ValueError):\n target_real_feature_binary_test(x=binary_series, y=real_series_with_nan)\n" }, { "path": "tests/units/feature_selection/test_fdr_control.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom statsmodels.stats.multitest import multipletests\n\n\n@pytest.mark.parametrize(\n \"p_value, ind, fdr, expected\",\n [\n ([0, 0, 0], True, 0.10, [True, True, True]),\n ([0, 0, 0], False, 0.10, [True, True, True]),\n ([0.1, 0.15, 0.2, 0], True, 0.20, [True, True, True, True]),\n ([0.1, 0.15, 0.2, 0], False, 0.20, [False, False, False, True]),\n ([0.1, 0.1, 0.05], True, 0.20, [True, True, True]),\n ([0.1, 0.11, 0.05], False, 0.20, [False, False, False]),\n ([0.1, 0.1, 0.05], False, 0.20, [True, True, True]),\n (\n [0.00356, 0.01042, 0.01208, 0.02155, 0.03329, 0.11542],\n True,\n 0.05,\n [True, True, True, True, True, False],\n ),\n (\n [0.00356, 0.01042, 0.01208, 0.02155, 0.03329, 0.11542],\n False,\n 0.05,\n [False, False, False, False, False, False],\n ),\n ([0.11, 0.001, 0.05], False, 0.20, [False, True, True]),\n ],\n)\ndef test_fdr_control(p_value, ind, fdr, expected):\n df = pd.DataFrame({\"p_value\": p_value})\n method = \"fdr_bh\" if ind else \"fdr_by\"\n df[\"relevant\"] = multipletests(pvals=df.p_value, alpha=fdr, method=method)[0]\n result = df[\"relevant\"].values\n expected = np.array(expected)\n assert (result == expected).all()\n" }, { "path": "tests/units/feature_selection/test_feature_significance.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\n\nfrom tsfresh.feature_selection.relevance import calculate_relevance_table\n\n\nclass FeatureSignificanceTestCase(TestCase):\n \"\"\"Test cases for the whole feature selection algorithm.\"\"\"\n\n def setUp(self):\n \"\"\"Fix the random seed.\"\"\"\n np.random.seed(seed=42)\n\n def test_binary_target_mixed_case(self):\n # Mixed case with binomial target\n np.random.seed(42)\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=range(1000))\n\n z = y - np.random.binomial(1, 0.1, 1000) + np.random.binomial(1, 0.1, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n\n X[\"rel1\"] = z\n X[\"rel2\"] = y * np.abs(np.random.normal(0, 1, 1000)) + np.random.normal(\n 0, 1, 1000\n )\n X[\"rel3\"] = y + np.random.normal(0, 0.3, 1000)\n X[\"rel4\"] = y**2 + np.random.normal(0, 1, 1000)\n X[\"rel5\"] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)\n\n X[\"irr_constant\"] = 1.113344\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"irr2\"] = np.random.poisson(1, 1000)\n X[\"irr3\"] = np.random.binomial(1, 0.3, 1000)\n X[\"irr4\"] = np.random.normal(0, 1, 1000)\n X[\"irr5\"] = np.random.poisson(1, 1000)\n X[\"irr6\"] = np.random.binomial(1, 0.3, 1000)\n X[\"irr7\"] = np.random.normal(0, 1, 1000)\n X[\"irr8\"] = np.random.poisson(1, 1000)\n X[\"irr9\"] = np.random.binomial(1, 0.3, 1000)\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\", \"rel3\", \"rel4\", \"rel5\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n # Test type outputs\n for i in range(1, 6):\n row = df_bh.loc[\"rel{}\".format(i)]\n self.assertEqual(row.feature, \"rel{}\".format(i))\n if i == 1:\n self.assertEqual(row.type, \"binary\")\n else:\n self.assertEqual(row.type, \"real\")\n\n for i in range(1, 10):\n row = df_bh.loc[\"irr{}\".format(i)]\n self.assertEqual(row.feature, \"irr{}\".format(i))\n if i not in [3, 6, 9]:\n self.assertEqual(row.type, \"real\")\n else:\n self.assertEqual(row.type, \"binary\")\n\n self.assertFalse(row.relevant)\n\n # Assert that all of the relevant features are kept.\n # THIS FAILS!\n # self.assertEqual(len(kept_feature), 5)\n\n def test_binary_target_binary_features(self):\n # Binomial random variables and binomial target\n y = pd.Series(np.random.binomial(1, 0.5, 5000))\n X = pd.DataFrame(index=range(5000))\n\n for i in range(10):\n X[\"irr{}\".format(i)] = np.random.binomial(1, 0.1, 5000)\n\n for i in range(10, 20):\n X[\"irr{}\".format(i)] = np.random.binomial(1, 0.8, 5000)\n\n z = y - np.random.binomial(1, 0.01, 5000) + np.random.binomial(1, 0.01, 5000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel1\"] = z\n\n z = y - np.random.binomial(1, 0.05, 5000) + np.random.binomial(1, 0.05, 5000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel2\"] = z\n\n z = y - np.random.binomial(1, 0.10, 5000) + np.random.binomial(1, 0.10, 5000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel3\"] = z\n\n z = y - np.random.binomial(1, 0.15, 5000) + np.random.binomial(1, 0.15, 5000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel4\"] = z\n\n z = y - np.random.binomial(1, 0.20, 5000) + np.random.binomial(1, 0.20, 5000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel5\"] = z\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\", \"rel3\", \"rel4\", \"rel5\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n # Test type outputs\n for i in range(1, 6):\n row = df_bh.loc[\"rel{}\".format(i)]\n self.assertEqual(row.feature, \"rel{}\".format(i))\n self.assertEqual(row.type, \"binary\")\n\n for i in range(1, 20):\n row = df_bh.loc[\"irr{}\".format(i)]\n self.assertEqual(row.feature, \"irr{}\".format(i))\n self.assertEqual(row.type, \"binary\")\n\n self.assertFalse(row.relevant)\n\n def test_binomial_target_realvalued_features(self):\n # Real valued random variables and binomial target\n y = pd.Series(np.random.binomial(1, 0.5, 5000))\n X = pd.DataFrame(index=range(5000))\n\n for i in range(10):\n X[\"irr{}\".format(i)] = np.random.normal(1, 0.3, 5000)\n\n for i in range(10, 20):\n X[\"irr{}\".format(i)] = np.random.normal(1, 0.5, 5000)\n\n for i in range(20, 30):\n X[\"irr{}\".format(i)] = np.random.normal(1, 0.8, 5000)\n\n X[\"rel1\"] = y * np.random.normal(0, 1, 5000) + np.random.normal(0, 1, 5000)\n X[\"rel2\"] = y + np.random.normal(0, 1, 5000)\n X[\"rel3\"] = y**2 + np.random.normal(0, 1, 5000)\n X[\"rel4\"] = np.sqrt(y) + np.random.binomial(2, 0.1, 5000)\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\", \"rel3\", \"rel4\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n # Test type outputs\n for i in range(1, 5):\n row = df_bh.loc[\"rel{}\".format(i)]\n self.assertEqual(row.feature, \"rel{}\".format(i))\n self.assertEqual(row.type, \"real\")\n\n for i in range(1, 30):\n row = df_bh.loc[\"irr{}\".format(i)]\n self.assertEqual(row.feature, \"irr{}\".format(i))\n self.assertEqual(row.type, \"real\")\n\n self.assertFalse(row.relevant)\n\n def test_real_target_mixed_case(self):\n # Mixed case with real target\n y = pd.Series(np.random.normal(0, 1, 5000))\n X = pd.DataFrame(index=range(5000))\n\n z = y.copy()\n z[z <= 0] = 0\n z[z > 0] = 1\n\n X[\"rel1\"] = z\n X[\"rel2\"] = y\n X[\"rel3\"] = y**2\n X[\"rel4\"] = np.sqrt(abs(y))\n\n X[\"irr1\"] = np.random.normal(0, 1, 5000)\n X[\"irr2\"] = np.random.poisson(1, 5000)\n X[\"irr3\"] = np.random.binomial(1, 0.1, 5000)\n X[\"irr4\"] = np.random.normal(0, 1, 5000)\n X[\"irr5\"] = np.random.poisson(1, 5000)\n X[\"irr6\"] = np.random.binomial(1, 0.05, 5000)\n X[\"irr7\"] = np.random.normal(0, 1, 5000)\n X[\"irr8\"] = np.random.poisson(1, 5000)\n X[\"irr9\"] = np.random.binomial(1, 0.2, 5000)\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\", \"rel3\", \"rel4\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n # Test type outputs\n for i in range(1, 5):\n row = df_bh.loc[\"rel{}\".format(i)]\n self.assertEqual(row.feature, \"rel{}\".format(i))\n if i == 1:\n self.assertEqual(row.type, \"binary\")\n else:\n self.assertEqual(row.type, \"real\")\n\n for i in range(1, 10):\n row = df_bh.loc[\"irr{}\".format(i)]\n self.assertEqual(row.feature, \"irr{}\".format(i))\n if i in [3, 6, 9]:\n self.assertEqual(row.type, \"binary\")\n else:\n self.assertEqual(row.type, \"real\")\n\n self.assertFalse(row.relevant)\n\n def test_real_target_binary_features(self):\n # Mixed case with real target\n y = pd.Series(np.random.normal(0, 1, 1000))\n X = pd.DataFrame(index=range(1000))\n\n z = y - np.random.binomial(1, 0.20, 1000) + np.random.binomial(1, 0.20, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel1\"] = z\n\n z = y - np.random.binomial(1, 0.10, 1000) + np.random.binomial(1, 0.10, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel2\"] = z\n\n X[\"irr1\"] = np.random.binomial(0, 0.1, 1000)\n X[\"irr2\"] = np.random.binomial(0, 0.15, 1000)\n X[\"irr3\"] = np.random.binomial(0, 0.05, 1000)\n X[\"irr4\"] = np.random.binomial(0, 0.2, 1000)\n X[\"irr5\"] = np.random.binomial(0, 0.25, 1000)\n X[\"irr6\"] = np.random.binomial(0, 0.01, 1000)\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n def test_all_features_good(self):\n # Mixed case with real target\n y = pd.Series(np.random.normal(0, 1, 1000))\n X = pd.DataFrame(index=range(1000))\n\n z = y - np.random.binomial(1, 0.20, 1000) + np.random.binomial(1, 0.20, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel1\"] = z\n\n z = y - np.random.binomial(1, 0.10, 1000) + np.random.binomial(1, 0.10, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n X[\"rel2\"] = z\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n # Make sure all selected variables are relevant\n for kept_feature in feat_rej:\n self.assertIn(kept_feature, [\"rel1\", \"rel2\"])\n\n self.assertGreater(len(feat_rej), 0)\n\n def test_all_features_bad(self):\n # Mixed case with real target\n y = pd.Series(np.random.normal(0, 1, 1000))\n X = pd.DataFrame(index=range(1000))\n\n X[\"irr1\"] = np.random.binomial(0, 0.1, 1000)\n X[\"irr2\"] = np.random.binomial(0, 0.15, 1000)\n X[\"irr3\"] = np.random.binomial(0, 0.05, 1000)\n X[\"irr4\"] = np.random.binomial(0, 0.2, 1000)\n X[\"irr5\"] = np.random.binomial(0, 0.25, 1000)\n X[\"irr6\"] = np.random.binomial(0, 0.01, 1000)\n\n df_bh = calculate_relevance_table(X, y)\n feat_rej = df_bh.loc[df_bh.relevant].feature\n\n self.assertEqual(len(feat_rej), 0)\n" }, { "path": "tests/units/feature_selection/test_relevance.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport warnings\n\nimport mock\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom tsfresh.feature_selection.relevance import (\n calculate_relevance_table,\n combine_relevance_tables,\n get_feature_type,\n infer_ml_task,\n)\n\n\nclass TestInferMLTask:\n def test_infers_classification_for_integer_target(self):\n y = pd.Series([1, 2, 3])\n assert \"classification\" == infer_ml_task(y)\n\n def test_infers_classification_for_boolean_target(self):\n y = pd.Series([True, False, False])\n assert \"classification\" == infer_ml_task(y)\n\n def test_infers_classification_for_object_target(self):\n y = pd.Series([\"high\", \"low\"])\n assert \"classification\" == infer_ml_task(y)\n\n def test_infers_regression_for_float_target(self):\n y = pd.Series([1.0, 1.5, 1.7])\n assert \"regression\" == infer_ml_task(y)\n\n\nclass TestCalculateRelevanceTable:\n @pytest.fixture()\n def y_binary(self):\n return pd.Series([0, 1, 1])\n\n @pytest.fixture()\n def y_real(self):\n return pd.Series([0.1, 0.2, 0.1])\n\n @pytest.fixture()\n def y_multi(self):\n y0 = np.zeros(100)\n y1 = np.repeat(1, 100)\n y2 = np.repeat(2, 100)\n return pd.Series(np.uint8(np.concatenate([y0, y1, y2])))\n\n @pytest.fixture()\n def X(self):\n df = pd.DataFrame()\n df[\"feature_binary\"] = [1, 1, 0]\n df[\"feature_real\"] = [0.1, 0.2, 0.3]\n return df\n\n def test_restrict_ml_task_options(self, X, y_binary):\n with pytest.raises(ValueError):\n calculate_relevance_table(X, y_binary, ml_task=\"some_other_task\")\n\n def test_constant_feature_irrelevant(self, y_binary):\n X = pd.DataFrame([1, 1, 1], columns=[\"feature_binary\"])\n\n relevance_table = calculate_relevance_table(X, y_binary)\n assert \"feature_binary\" == relevance_table.index[0]\n assert \"constant\" == relevance_table.type[0]\n assert np.isnan(relevance_table.p_value[0])\n assert not relevance_table.relevant[0]\n\n @mock.patch(\"tsfresh.feature_selection.relevance.target_binary_feature_real_test\")\n @mock.patch(\"tsfresh.feature_selection.relevance.target_binary_feature_binary_test\")\n def test_target_binary_calls_correct_tests(\n self,\n significance_test_feature_binary_mock,\n significance_test_feature_real_mock,\n X,\n y_binary,\n ):\n significance_test_feature_binary_mock.return_value = 0.5\n significance_test_feature_real_mock.return_value = 0.7\n relevance_table = calculate_relevance_table(X, y_binary, n_jobs=0)\n\n assert 0.5 == relevance_table.loc[\"feature_binary\"].p_value\n assert 0.7 == relevance_table.loc[\"feature_real\"].p_value\n assert 2 == significance_test_feature_binary_mock.call_count\n assert 2 == significance_test_feature_real_mock.call_count\n\n @mock.patch(\"tsfresh.feature_selection.relevance.target_real_feature_real_test\")\n @mock.patch(\"tsfresh.feature_selection.relevance.target_real_feature_binary_test\")\n def test_target_real_calls_correct_tests(\n self,\n significance_test_feature_binary_mock,\n significance_test_feature_real_mock,\n X,\n y_real,\n ):\n significance_test_feature_binary_mock.return_value = 0.5\n significance_test_feature_real_mock.return_value = 0.7\n\n relevance_table = calculate_relevance_table(X, y_real, n_jobs=0)\n\n assert 0.5 == relevance_table.loc[\"feature_binary\"].p_value\n assert 0.7 == relevance_table.loc[\"feature_real\"].p_value\n\n assert significance_test_feature_binary_mock.call_count == 1\n pd.testing.assert_series_equal(\n significance_test_feature_binary_mock.call_args[0][0], X[\"feature_binary\"]\n )\n pd.testing.assert_series_equal(\n significance_test_feature_binary_mock.call_args[1][\"y\"], y_real\n )\n\n assert significance_test_feature_real_mock.call_count == 1\n pd.testing.assert_series_equal(\n significance_test_feature_real_mock.call_args[0][0], X[\"feature_real\"]\n )\n pd.testing.assert_series_equal(\n significance_test_feature_real_mock.call_args[1][\"y\"], y_real\n )\n\n @mock.patch(\"tsfresh.feature_selection.relevance.target_real_feature_real_test\")\n @mock.patch(\"tsfresh.feature_selection.relevance.target_real_feature_binary_test\")\n def test_warning_for_no_relevant_feature(\n self,\n significance_test_feature_binary_mock,\n significance_test_feature_real_mock,\n X,\n y_real,\n ):\n significance_test_feature_binary_mock.return_value = 0.95\n significance_test_feature_real_mock.return_value = 0.95\n\n with pytest.warns(RuntimeWarning) as record:\n _ = calculate_relevance_table(\n X, y_real, n_jobs=0, ml_task=\"regression\", show_warnings=True\n )\n assert len(record) >= 1\n assert (\n \"No feature was found relevant for regression for fdr level = 0.05 (which corresponds \"\n \"to the maximal percentage of irrelevant features, consider using an higher fdr level \"\n \"or add other features.\"\n ) in [str(warning.message) for warning in record]\n\n def test_multiclass_requires_classification(self, X, y_real):\n with pytest.raises(AssertionError):\n calculate_relevance_table(X, y_real, multiclass=True, ml_task=\"regression\")\n\n def test_multiclass_n_significant_error(self, X, y_binary):\n with pytest.raises(AssertionError):\n calculate_relevance_table(\n X, y_binary, multiclass=True, n_significant=3, ml_task=\"classification\"\n )\n\n def test_multiclass_relevance_table_columns(self, X, y_binary):\n y = y_binary.copy()\n y[2] = 2\n relevance_table = calculate_relevance_table(X, y, multiclass=True)\n\n assert len(relevance_table.columns) == 10\n\n def test_multiclass_correct_features_relevant(self, y_multi):\n X_multi = pd.DataFrame()\n X_multi[\"relevant_0\"] = np.concatenate([np.zeros(298), np.array([0.01, -0.01])])\n X_multi[\"relevant_3\"] = X_multi[\"relevant_0\"].copy()\n X_multi[\"relevant_3\"][y_multi == 0] = np.random.uniform(2, 3, 100)\n X_multi[\"relevant_2\"] = X_multi[\"relevant_3\"].copy()\n X_multi[\"relevant_2\"][y_multi == 1] = np.random.uniform(-2, -1, 100)\n\n relevance_table = calculate_relevance_table(\n X_multi, y_multi, multiclass=True, ml_task=\"classification\", n_significant=3\n )\n assert relevance_table.loc[\"relevant_3\", \"relevant\"]\n assert not relevance_table.loc[\"relevant_2\", \"relevant\"]\n assert not relevance_table.loc[\"relevant_0\", \"relevant\"]\n\n # the distributions of all 3 classes under a one vs. rest scheme will be separated enough for\n # this feature to be relevant for predicting 3 classes\n assert relevance_table.loc[\"relevant_3\", \"n_significant\"] == 3\n\n # due to the distribution of this feature where y_multi == 0 being contained inside the range of\n # y_multi != 0 it will not pass the Mann-Whitney U test under a one vs. rest scheme for that class\n assert relevance_table.loc[\"relevant_2\", \"n_significant\"] == 2\n assert relevance_table.loc[\"relevant_0\", \"n_significant\"] == 0\n\n\nclass TestCombineRelevanceTables:\n @pytest.fixture()\n def relevance_table(self):\n relevance_table = pd.DataFrame(\n index=pd.Series([\"f1\", \"f2\", \"f3\", \"f4\"], name=\"feature\")\n )\n relevance_table[\"relevant\"] = [True, False, True, False]\n relevance_table[\"type\"] = [\"real\"] * 4\n relevance_table[\"p_value\"] = [0.1, 0.2, 0.3, 0.4]\n return relevance_table\n\n def test_disjuncts_relevance(self, relevance_table):\n relevance_table_2 = relevance_table.copy()\n relevance_table_2.relevant = [False, True, True, False]\n result = combine_relevance_tables([relevance_table, relevance_table_2])\n\n assert ([True, True, True, False] == result.relevant).all()\n\n def test_respects_index(self, relevance_table):\n relevance_table_2 = relevance_table.copy()\n relevance_table_2.reindex(reversed(relevance_table.index))\n\n result = combine_relevance_tables([relevance_table, relevance_table_2])\n\n assert ([True, False, True, False] == result.relevant).all()\n\n def test_aggregates_p_value(self, relevance_table):\n relevance_table_2 = relevance_table.copy()\n relevance_table_2.p_value = [0.2, 0.1, 0.4, 0.3]\n result = combine_relevance_tables([relevance_table, relevance_table_2])\n\n assert (np.array([0.1, 0.1, 0.3, 0.3]) == result.p_value).all()\n\n\nclass TestGetFeatureType:\n def test_binary(self):\n feature = pd.Series([0.0, 1.0, 1.0])\n assert \"binary\" == get_feature_type(feature)\n\n def test_constant(self):\n feature = pd.Series([0.0, 0.0, 0.0])\n assert \"constant\" == get_feature_type(feature)\n\n def test_real(self):\n feature = pd.Series([0.0, 1.0, 2.0])\n assert \"real\" == get_feature_type(feature)\n" }, { "path": "tests/units/feature_selection/test_selection.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom tsfresh.feature_selection.selection import select_features\n\n\nclass TestSelectFeatures:\n def test_assert_list(self):\n with pytest.raises(AssertionError):\n select_features(pd.DataFrame(index=range(2)), [1, 2, 3])\n\n def test_assert_one_row_X(self):\n X = pd.DataFrame([1], index=[1])\n y = pd.Series([1], index=[1])\n with pytest.raises(AssertionError):\n select_features(X, y)\n\n def test_assert_one_label_y(self):\n X = pd.DataFrame([10, 10], index=[1, 2])\n y = pd.Series([1, 1], index=[1, 2])\n with pytest.raises(AssertionError):\n select_features(X, y)\n\n def test_assert_different_index(self):\n X = pd.DataFrame(list(range(3)), index=[1, 2, 3])\n y = pd.Series(range(3), index=[1, 3, 4])\n with pytest.raises(ValueError):\n select_features(X, y)\n\n def test_assert_shorter_y(self):\n X = pd.DataFrame([1, 2], index=[1, 2])\n y = np.array([1])\n with pytest.raises(AssertionError):\n select_features(X, y)\n\n def test_assert_X_is_DataFrame(self):\n X = np.array([[1, 2], [1, 2]])\n y = np.array([1])\n with pytest.raises(AssertionError):\n select_features(X, y)\n\n def test_selects_for_each_class(self):\n df = pd.DataFrame()\n df[\"f1\"] = [10] * 10 + list(range(10)) + list(range(10))\n df[\"f2\"] = list(range(10)) + [10] * 10 + list(range(10))\n df[\"f3\"] = list(range(10)) + list(range(10)) + [10] * 10\n df[\"y\"] = [0] * 10 + [1] * 10 + [2] * 10\n\n y = df.y\n X = df.drop([\"y\"], axis=1)\n X_relevant = select_features(X, y, ml_task=\"classification\")\n assert {\"f1\", \"f2\", \"f3\"} == set(X_relevant.columns)\n\n def test_multiclass_selects_correct_n_significant(self):\n df = pd.DataFrame()\n N = 10\n constants = [N] * N\n increase = list(range(N))\n\n df[\"f1\"] = constants + increase + increase\n df[\"f2\"] = increase + constants + increase\n df[\"f3\"] = increase + increase + constants\n df[\"y\"] = [0] * N + [1] * N + [2] * N\n\n y = df.y\n X = df.drop([\"y\"], axis=1)\n X_relevant = select_features(\n X,\n y,\n ml_task=\"classification\",\n multiclass=True,\n n_significant=1,\n fdr_level=0.01,\n )\n assert {\"f1\", \"f2\", \"f3\"} == set(X_relevant.columns)\n X_relevant = select_features(\n X,\n y,\n ml_task=\"classification\",\n multiclass=True,\n n_significant=2,\n fdr_level=0.01,\n )\n assert len(X_relevant.columns) == 0\n X_relevant = select_features(\n X,\n y,\n ml_task=\"classification\",\n multiclass=True,\n n_significant=3,\n fdr_level=0.01,\n )\n assert len(X_relevant.columns) == 0\n" }, { "path": "tests/units/feature_selection/test_significance_tests.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom tsfresh.defaults import TEST_FOR_BINARY_TARGET_REAL_FEATURE\nfrom tsfresh.feature_selection.significance_tests import (\n target_binary_feature_binary_test,\n target_binary_feature_real_test,\n target_real_feature_binary_test,\n target_real_feature_real_test,\n)\n\n\n@pytest.fixture()\ndef set_random_seed():\n np.random.seed(seed=42)\n\n\n@pytest.fixture()\ndef binary_feature(set_random_seed):\n return pd.Series(np.random.binomial(1, 0.5, 250), name=\"TEST\")\n\n\n@pytest.fixture()\ndef binary_target_not_related(set_random_seed):\n return pd.Series(np.random.binomial(1, 0.5, 250))\n\n\n@pytest.fixture()\ndef real_feature(set_random_seed):\n return pd.Series(np.random.normal(0, 1, 250), name=\"TEST\")\n\n\n@pytest.fixture()\ndef real_target_not_related(set_random_seed):\n return pd.Series(np.random.normal(0, 1, 250))\n\n\nclass TestUnsignificant:\n @pytest.fixture()\n def minimal_p_value_for_unsignificant_features(self):\n return 0.05\n\n def test_feature_selection_target_binary_features_binary(\n self,\n minimal_p_value_for_unsignificant_features,\n binary_feature,\n binary_target_not_related,\n ):\n \"\"\"\n Test if the p_value returned by target_binary_feature_binary_test is\n large enough for highly unsignificant features.\n \"\"\"\n p_value = target_binary_feature_binary_test(\n binary_feature, binary_target_not_related\n )\n assert minimal_p_value_for_unsignificant_features < p_value\n\n def test_feature_selection_target_binary_features_realvalued(\n self,\n minimal_p_value_for_unsignificant_features,\n real_feature,\n binary_target_not_related,\n ):\n \"\"\"\n Test if the p_value returned by target_binary_feature_binary_test is\n large enough for highly unsignificant features.\n \"\"\"\n p_value = target_binary_feature_real_test(\n real_feature, binary_target_not_related, TEST_FOR_BINARY_TARGET_REAL_FEATURE\n )\n assert minimal_p_value_for_unsignificant_features < p_value\n\n def test_feature_selection_target_realvalued_features_binary(\n self,\n minimal_p_value_for_unsignificant_features,\n binary_feature,\n real_target_not_related,\n ):\n \"\"\"\n Test if the p_value returned by target_real_feature_binary_test is\n large enough for highly unsignificant features.\"\"\"\n p_value = target_real_feature_binary_test(\n binary_feature, real_target_not_related\n )\n assert minimal_p_value_for_unsignificant_features < p_value\n\n def test_feature_selection_target_realvalued_features_realvalued(\n self,\n minimal_p_value_for_unsignificant_features,\n real_feature,\n real_target_not_related,\n ):\n \"\"\"\n Test if the p_value returned by target_real_feature_real_test is\n large enough for highly unsignificant features.\n \"\"\"\n p_value = target_real_feature_real_test(real_feature, real_target_not_related)\n assert minimal_p_value_for_unsignificant_features < p_value\n\n\nclass TestSignificant:\n @pytest.fixture()\n def maximal_p_value_for_significant_features(self):\n return 0.15\n\n def test_feature_selection_target_binary_features_binary(\n self, maximal_p_value_for_significant_features, binary_feature\n ):\n \"\"\"\n Test if the p_value returned by target_binary_feature_binary_test is\n low enough for highly significant features.\n \"\"\"\n y = binary_feature - pd.Series(\n np.random.binomial(1, 0.1, 250) + np.random.binomial(1, 0.1, 250)\n )\n y[y == -1] = 0\n y[y == -2] = 0\n y[y == 2] = 1\n\n p_value = target_binary_feature_binary_test(binary_feature, y)\n assert maximal_p_value_for_significant_features > p_value\n\n def test_feature_selection_target_binary_features_realvalued_mann(\n self, maximal_p_value_for_significant_features, real_feature\n ):\n \"\"\"\n Test if the p_value returned by target_binary_feature_real_test is\n low enough for highly significant features.\n \"\"\"\n y = pd.Series(np.ndarray(250))\n y[real_feature >= 0.3] = 1\n y[real_feature < 0.3] = 0\n y -= pd.Series(np.random.binomial(1, 0.1, 250))\n y[y == -1] = 0\n y[y == 2] = 1\n\n p_value = target_binary_feature_real_test(\n real_feature, y, TEST_FOR_BINARY_TARGET_REAL_FEATURE\n )\n assert maximal_p_value_for_significant_features > p_value\n\n def test_feature_selection_target_binary_features_realvalued_smir(\n self, maximal_p_value_for_significant_features, real_feature\n ):\n \"\"\"\n Test if the p_value returned by target_binary_feature_real_test is\n low enough for highly significant features.\n \"\"\"\n y = pd.Series(np.ndarray(250))\n y[real_feature >= 0.3] = 1\n y[real_feature < 0.3] = 0\n y -= pd.Series(np.random.binomial(1, 0.2, 250))\n y[y == -1] = 0\n y[y == 2] = 1\n\n p_value = target_binary_feature_real_test(real_feature, y, test=\"smir\")\n assert maximal_p_value_for_significant_features > p_value\n\n def test_feature_selection_target_realvalued_features_binary(\n self, maximal_p_value_for_significant_features, binary_feature\n ):\n \"\"\"\n Test if the p_value returned by target_real_feature_binary_test is\n low enough for highly significant features.\n \"\"\"\n y = binary_feature * pd.Series(np.random.normal(0, 1, 250)) + pd.Series(\n np.random.normal(0, 0.25, 250)\n )\n\n p_value = target_real_feature_binary_test(binary_feature, y)\n assert maximal_p_value_for_significant_features > p_value\n\n def test_feature_selection_target_realvalued_features_realvalued(\n self, maximal_p_value_for_significant_features, real_feature\n ):\n \"\"\"\n Test if the p_value returned by target_real_feature_real_test is\n low enough for highly significant features.\n \"\"\"\n y = real_feature + pd.Series(np.random.normal(0, 1, 250))\n\n p_value = target_real_feature_real_test(real_feature, y)\n\n assert maximal_p_value_for_significant_features > p_value\n" }, { "path": "tests/units/scripts/__init__.py", "content": "" }, { "path": "tests/units/scripts/test_run_tsfresh.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\nimport os\nimport shutil\nimport tempfile\nfrom unittest import TestCase\n\nfrom mock import patch\n\nfrom tsfresh.scripts import run_tsfresh\n\n\nclass RunTSFreshTestCase(TestCase):\n \"\"\"\n Test the command line interface to tsfresh. This does not test the tsfresh functionality (this is tested elsewhere),\n but mocks the extract_features functionality by just outputting the same df as going in into the function.\n \"\"\"\n\n def setUp(self):\n # Create a temporary directory\n self.test_dir = tempfile.mkdtemp()\n # Change into the tmp dir\n self.curr_dir = os.getcwd()\n os.chdir(self.test_dir)\n\n def extract_features_mock(df, **kwargs):\n # just mirror the input\n return df\n\n # Patcher object to be disabled in tearDown\n self.patcher = patch(\n \"tsfresh.scripts.run_tsfresh.extract_features\",\n side_effect=extract_features_mock,\n )\n # Mocked extract function\n self.mocked_extract_function = self.patcher.start()\n\n def tearDown(self):\n # Remove the directory after the test\n os.chdir(self.curr_dir)\n shutil.rmtree(self.test_dir)\n # Disable the mocking\n self.patcher.stop()\n\n def call_main_function(self, input_csv_string=None, arguments=\"\"):\n # Helper function to call \"main\" from the script after creating the input file out of the input string.\n input_file_name = \"temporary_csv_file.csv\"\n\n if input_csv_string:\n with open(os.path.join(self.test_dir, input_file_name), \"w\") as f:\n f.write(input_csv_string)\n\n output_file_name = \"temporary_output_csv_file.csv\"\n arguments_with_filenames = \"{input_file_name} {arguments} --output-file-name {output_file_name}\".format(\n input_file_name=input_file_name,\n arguments=arguments,\n output_file_name=output_file_name,\n )\n\n run_tsfresh.main(arguments_with_filenames.split())\n\n if os.path.exists(os.path.join(self.test_dir, output_file_name)):\n with open(os.path.join(self.test_dir, output_file_name), \"r\") as f:\n return f.read()\n else:\n return None\n\n def test_invalid_arguments(self):\n self.assertRaises(\n SystemExit, self.call_main_function, arguments=\"--invalid-argument\"\n )\n\n def test_csv_without_headers_wrong_arguments(self):\n self.assertRaises(\n AttributeError, self.call_main_function, arguments=\"--column-id invalid\"\n )\n\n def test_csv_without_headers(self):\n input_csv = \"1 1 1 1\\n1 1 1 1\"\n output_csv = (\n \",id,time,value\\n\"\n \"0,0,0,1\\n\"\n \"1,0,1,1\\n\"\n \"2,0,2,1\\n\"\n \"3,0,3,1\\n\"\n \"4,1,0,1\\n\"\n \"5,1,1,1\\n\"\n \"6,1,2,1\\n\"\n \"7,1,3,1\\n\"\n )\n\n result_csv = self.call_main_function(input_csv_string=input_csv)\n\n self.assertEqual(result_csv, output_csv)\n\n called_kwargs = self.mocked_extract_function.call_args[1]\n\n self.assertEqual(called_kwargs[\"column_id\"], \"id\")\n self.assertEqual(called_kwargs[\"column_value\"], \"value\")\n self.assertIsNone(called_kwargs[\"column_kind\"])\n self.assertEqual(called_kwargs[\"column_sort\"], \"time\")\n\n def test_csv_with_header(self):\n input_csv = \"ID SORT KIND VALUE\\n0 0 a 0\\n1 0 a 1\\n0 0 b 1\\n1 0 b 3\"\n output_csv = \",ID,SORT,KIND,VALUE\\n0,0,0,a,0\\n1,1,0,a,1\\n2,0,0,b,1\\n3,1,0,b,3\\n\"\n\n result_csv = self.call_main_function(\n input_csv_string=input_csv,\n arguments=\"--column-id ID --column-sort SORT --column-value VALUE --column-kind KIND --csv-with-headers\",\n )\n\n self.assertEqual(result_csv, output_csv)\n\n called_kwargs = self.mocked_extract_function.call_args[1]\n\n self.assertEqual(called_kwargs[\"column_id\"], \"ID\")\n self.assertEqual(called_kwargs[\"column_value\"], \"VALUE\")\n self.assertEqual(called_kwargs[\"column_kind\"], \"KIND\")\n self.assertEqual(called_kwargs[\"column_sort\"], \"SORT\")\n" }, { "path": "tests/units/transformers/__init__.py", "content": "" }, { "path": "tests/units/transformers/test_feature_augmenter.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport numpy as np\nimport pandas as pd\n\nfrom tests.fixtures import DataTestCase\nfrom tsfresh.feature_extraction.settings import ComprehensiveFCParameters\nfrom tsfresh.transformers import FeatureAugmenter\n\n\nclass FeatureAugmenterTestCase(DataTestCase):\n def setUp(self):\n self.test_df = self.create_test_data_sample()\n\n fc_parameters = {\"length\": None}\n self.kind_to_fc_parameters = {\n \"a\": fc_parameters.copy(),\n \"b\": fc_parameters.copy(),\n }\n\n def test_fit_and_transform(self):\n augmenter = FeatureAugmenter(\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n n_jobs=0,\n disable_progressbar=True,\n )\n\n # Fit should do nothing\n returned_df = augmenter.fit()\n self.assertCountEqual(returned_df.__dict__, augmenter.__dict__)\n self.assertRaises(RuntimeError, augmenter.transform, None)\n\n augmenter.set_timeseries_container(self.test_df)\n\n # Add features to all time series\n X_with_index = pd.DataFrame([{\"feature_1\": 1}] * 2, index=[10, 500])\n X_transformed = augmenter.transform(X_with_index)\n\n # Require same shape\n for i in X_transformed.index:\n self.assertIn(i, X_with_index.index)\n\n for i in X_with_index.index:\n self.assertIn(i, X_transformed.index)\n\n self.assertEqual(X_transformed.shape, (2, 3))\n\n # Preserve old features\n self.assertCountEqual(\n list(X_transformed.columns), [\"feature_1\", \"a__length\", \"b__length\"]\n )\n\n # Features are not allowed to be NaN\n for index, row in X_transformed.iterrows():\n print((index, row))\n self.assertFalse(np.isnan(row[\"a__length\"]))\n self.assertFalse(np.isnan(row[\"b__length\"]))\n\n def test_add_features_to_only_a_part(self):\n augmenter = FeatureAugmenter(\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n n_jobs=0,\n disable_progressbar=True,\n )\n\n augmenter.set_timeseries_container(self.test_df)\n\n X_with_not_all_ids = pd.DataFrame([{\"feature_1\": 1}], index=[10])\n X_transformed = augmenter.transform(X_with_not_all_ids)\n\n for i in X_transformed.index:\n self.assertIn(i, X_with_not_all_ids.index)\n\n for i in X_with_not_all_ids.index:\n self.assertIn(i, X_transformed.index)\n\n self.assertEqual(X_transformed.shape, (1, 3))\n self.assertEqual(X_transformed.index, [10])\n\n # Features are not allowed to be NaN\n for index, row in X_transformed.iterrows():\n print((index, row))\n self.assertFalse(np.isnan(row[\"a__length\"]))\n self.assertFalse(np.isnan(row[\"b__length\"]))\n\n def test_no_ids_present(self):\n augmenter = FeatureAugmenter(\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n n_jobs=0,\n disable_progressbar=True,\n )\n\n augmenter.set_timeseries_container(self.test_df)\n\n X_with_not_all_ids = pd.DataFrame([{\"feature_1\": 1}], index=[-999])\n self.assertRaisesRegex(\n AttributeError,\n r\"The ids of the time series container\",\n augmenter.transform,\n X_with_not_all_ids,\n )\n" }, { "path": "tests/units/transformers/test_feature_selector.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nfrom builtins import range\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\n\nfrom tsfresh.transformers.feature_selector import FeatureSelector\n\n\nclass FeatureSelectorTestCase(TestCase):\n def setUp(self):\n np.random.seed(0)\n\n def test_not_fitted(self):\n selector = FeatureSelector()\n\n X = pd.DataFrame()\n\n self.assertRaises(RuntimeError, selector.transform, X)\n\n def test_extract_relevant_features(self):\n selector = FeatureSelector()\n np.random.seed(42)\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=list(range(1000)))\n\n z = y - np.random.binomial(1, 0.1, 1000) + np.random.binomial(1, 0.1, 1000)\n z[z == -1] = 0\n z[z == 2] = 1\n\n X[\"rel1\"] = z\n X[\"rel2\"] = y * np.abs(np.random.normal(0, 1, 1000)) + np.random.normal(\n 0, 0.1, 1000\n )\n X[\"rel3\"] = y + np.random.normal(0, 1, 1000)\n X[\"rel4\"] = y**2 + np.random.normal(0, 1, 1000)\n X[\"rel5\"] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)\n\n X[\"irr_constant\"] = 1.113344\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"irr2\"] = np.random.poisson(1, 1000)\n X[\"irr3\"] = np.random.binomial(1, 0.3, 1000)\n X[\"irr4\"] = np.random.normal(0, 1, 1000)\n X[\"irr5\"] = np.random.poisson(1, 1000)\n X[\"irr6\"] = np.random.binomial(1, 0.3, 1000)\n X[\"irr7\"] = np.random.normal(0, 1, 1000)\n X[\"irr8\"] = np.random.poisson(1, 1000)\n X[\"irr9\"] = np.random.binomial(1, 0.3, 1000)\n\n returned_selector = selector.fit(X, y)\n self.assertIs(returned_selector, selector)\n self.assertEqual(\n sorted(selector.relevant_features), [\"rel1\", \"rel2\", \"rel3\", \"rel4\", \"rel5\"]\n )\n\n new_X = X.copy()\n\n selected_X = selector.transform(new_X)\n\n self.assertEqual(\n sorted(selector.relevant_features), sorted(list(selected_X.columns))\n )\n self.assertEqual(len(selector.features), len(X.columns))\n\n def test_nothing_relevant(self):\n selector = FeatureSelector()\n\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=list(range(1000)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"irr2\"] = np.random.normal(2, 1, 1000)\n\n selector.fit(X, y)\n\n transformed_X = selector.transform(X.copy())\n\n self.assertEqual(list(transformed_X.columns), [])\n self.assertEqual(list(transformed_X.index), list(X.index))\n\n def test_with_numpy_array(self):\n selector = FeatureSelector()\n\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=list(range(1000)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"rel1\"] = y\n\n y_numpy = y.values\n X_numpy = X.values\n\n selector.fit(X, y)\n selected_X = selector.transform(X)\n\n selector.fit(X_numpy, y_numpy)\n selected_X_numpy = selector.transform(X_numpy)\n\n self.assertTrue((selected_X_numpy == selected_X.values).all())\n\n self.assertTrue(selected_X_numpy.shape, (1, 1000))\n\n def test_feature_importance(self):\n selector = FeatureSelector()\n self.assertIsNone(selector.feature_importances_)\n\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=list(range(1000)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"rel1\"] = y\n\n selector.fit(X, y)\n\n self.assertEqual(selector.feature_importances_.shape, (2,))\n\n def test_feature_importance(self):\n selector = FeatureSelector()\n self.assertIsNone(selector.p_values)\n\n y = pd.Series(np.random.binomial(1, 0.5, 1000))\n X = pd.DataFrame(index=list(range(1000)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 1000)\n X[\"rel1\"] = y\n\n selector.fit(X, y)\n\n self.assertEqual(selector.p_values.shape, (2,))\n np.testing.assert_almost_equal(\n selector.p_values, 1.0 - selector.feature_importances_\n )\n\n def test_multiclass_relevant_features_selected(self):\n y0 = np.zeros(100)\n y1 = np.repeat(1, 100)\n y2 = np.repeat(2, 100)\n y_multi = pd.Series(np.uint8(np.concatenate([y0, y1, y2])))\n X_multi = pd.DataFrame()\n X_multi[\"irrelevant\"] = np.concatenate([np.zeros(298), np.array([0.01, -0.01])])\n X_multi[\"relevant\"] = X_multi[\"irrelevant\"].copy()\n X_multi[\"relevant\"][y_multi == 0] = np.random.uniform(2, 3, 100)\n\n selector = FeatureSelector(\n multiclass=True, n_significant=3, ml_task=\"classification\"\n )\n\n selector.fit(X_multi, y_multi)\n\n self.assertEqual(selector.relevant_features, [\"relevant\"])\n\n def test_multiclass_importance_p_values(self):\n y0 = np.zeros(100)\n y1 = np.repeat(1, 100)\n y2 = np.repeat(2, 100)\n y = pd.Series(np.uint8(np.concatenate([y0, y1, y2])))\n X = pd.DataFrame(index=list(range(300)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 300)\n X[\"rel1\"] = y\n\n selector = FeatureSelector(\n multiclass=True,\n ml_task=\"classification\",\n n_significant=2,\n multiclass_p_values=\"all\",\n )\n selector.fit(X, y)\n\n self.assertEqual(selector.p_values.shape, (2, 3))\n self.assertEqual(selector.feature_importances_.shape, (2, 3))\n\n selector = FeatureSelector(\n multiclass=True,\n ml_task=\"classification\",\n n_significant=2,\n multiclass_p_values=\"min\",\n )\n selector.fit(X, y)\n\n self.assertEqual(selector.p_values.shape, (2,))\n self.assertEqual(selector.feature_importances_.shape, (2,))\n\n def test_multiclass_max_avg_p_values(self):\n y0 = np.zeros(100)\n y1 = np.repeat(1, 100)\n y2 = np.repeat(2, 100)\n y = pd.Series(np.uint8(np.concatenate([y0, y1, y2])))\n X = pd.DataFrame(index=list(range(300)))\n\n X[\"irr1\"] = np.random.normal(0, 1, 300)\n X[\"rel1\"] = y\n\n selector = FeatureSelector(\n multiclass=True,\n ml_task=\"classification\",\n n_significant=2,\n multiclass_p_values=\"max\",\n )\n selector.fit(X, y)\n\n assert (selector.p_values > 0.1).all()\n selector = FeatureSelector(\n multiclass=True,\n ml_task=\"classification\",\n n_significant=2,\n multiclass_p_values=\"avg\",\n )\n selector.fit(X, y)\n\n assert (selector.p_values > 0.1).all()\n" }, { "path": "tests/units/transformers/test_per_column_imputer.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport warnings\nfrom builtins import range\nfrom unittest import TestCase\n\nimport numpy as np\nimport numpy.testing as npt\nimport pandas as pd\nimport pandas.testing as pdt\nfrom sklearn.exceptions import NotFittedError\n\nfrom tsfresh.transformers.per_column_imputer import PerColumnImputer\n\n\nclass PerColumnImputerTestCase(TestCase):\n def setUp(self):\n np.random.seed(0)\n\n def test_not_fitted(self):\n imputer = PerColumnImputer()\n\n X = pd.DataFrame()\n\n self.assertRaises(NotFittedError, imputer.transform, X)\n\n def test_only_nans_and_infs(self):\n imputer = PerColumnImputer()\n\n X = pd.DataFrame(index=list(range(100)))\n\n X[\"NaNs\"] = np.nan * np.ones(100)\n X[\"PINF\"] = np.inf * np.ones(100)\n X[\"NINF\"] = -np.inf * np.ones(100)\n\n with warnings.catch_warnings(record=True) as w:\n imputer.fit(X)\n self.assertEqual(len(w), 1)\n self.assertEqual(\n \"The columns ['NaNs' 'PINF' 'NINF'] did not have any finite values. Filling with zeros.\",\n str(w[0].message),\n )\n\n selected_X = imputer.transform(X)\n\n self.assertTrue((selected_X.values == 0).all())\n\n def test_with_numpy_array(self):\n imputer = PerColumnImputer()\n\n X = pd.DataFrame(index=list(range(100)))\n\n X[\"NaNs\"] = np.nan * np.ones(100)\n X[\"PINF\"] = np.inf * np.ones(100)\n X[\"NINF\"] = -np.inf * np.ones(100)\n\n X_numpy = X.values.copy()\n\n with warnings.catch_warnings(record=True) as w:\n imputer.fit(X)\n self.assertEqual(len(w), 1)\n self.assertEqual(\n \"The columns ['NaNs' 'PINF' 'NINF'] did not have any finite values. Filling with zeros.\",\n str(w[0].message),\n )\n\n selected_X = imputer.transform(X)\n\n # re-initialize for new dicts\n imputer = PerColumnImputer()\n with warnings.catch_warnings(record=True) as w:\n imputer.fit(X_numpy)\n self.assertEqual(len(w), 1)\n self.assertEqual(\n \"The columns [0 1 2] did not have any finite values. Filling with zeros.\",\n str(w[0].message),\n )\n\n selected_X_numpy = imputer.transform(X_numpy)\n\n npt.assert_array_equal(selected_X.values, selected_X_numpy.values)\n\n self.assertTrue(selected_X_numpy.shape, (1, 100))\n\n def test_standard_replacement_behavior(self):\n imputer = PerColumnImputer()\n\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]\n X = pd.DataFrame({\"a\": data})\n true_X = pd.DataFrame({\"a\": truth})\n\n imputer.fit(X)\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_partial_preset_col_to_NINF_given(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]\n X = pd.DataFrame({\"a\": data})\n true_X = pd.DataFrame({\"a\": truth})\n\n col_to_min = {\"a\": -100}\n imputer = PerColumnImputer(col_to_NINF_repl_preset=col_to_min)\n\n imputer.fit(X)\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_partial_preset_col_to_PINF_given(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]\n X = pd.DataFrame({\"a\": data})\n true_X = pd.DataFrame({\"a\": truth})\n\n col_to_max = {\"a\": 100}\n imputer = PerColumnImputer(col_to_PINF_repl_preset=col_to_max)\n\n imputer.fit(X)\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_partial_preset_col_to_NAN_given(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]\n X = pd.DataFrame({\"a\": data})\n true_X = pd.DataFrame({\"a\": truth})\n\n col_to_median = {\"a\": 1}\n imputer = PerColumnImputer(col_to_NAN_repl_preset=col_to_median)\n\n imputer.fit(X)\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_different_shapes_fitted_and_transformed(self):\n imputer = PerColumnImputer()\n\n X = pd.DataFrame(index=list(range(10)))\n X[\"a\"] = np.ones(10)\n\n imputer.fit(X)\n X[\"b\"] = np.ones(10)\n\n self.assertRaises(ValueError, imputer.transform, X)\n\n def test_preset_has_higher_priority_than_fit(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth = [-100.0, 100.0, 0.0, 100.0, -100.0, 1.0, 1.0]\n\n X = pd.DataFrame({\"a\": data})\n true_X = pd.DataFrame({\"a\": truth})\n\n col_to_median = {\"a\": 0}\n imputer = PerColumnImputer(col_to_NAN_repl_preset=col_to_median)\n imputer.fit(X)\n\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_only_parameters_of_last_fit_count(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n data_2 = [-np.inf, np.inf, np.nan, 10.0, -10.0, 3.0, 3.0]\n truth_a = [-10.0, 10.0, 3.0, 10.0, -10.0, 3.0, 3.0]\n truth_b = [-10.0, 10.0, 3.0, 10.0, -10.0, 3.0, 3.0]\n\n X = pd.DataFrame({\"a\": data, \"b\": data})\n X_2 = pd.DataFrame({\"a\": data_2, \"b\": data_2})\n true_X = pd.DataFrame({\"a\": truth_a, \"b\": truth_b})\n\n imputer = PerColumnImputer()\n\n imputer.fit(X)\n imputer.fit(X_2)\n\n selected_X = imputer.transform(X_2)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_only_subset_of_columns_given(self):\n data = [-np.inf, np.inf, np.nan, 100.0, -100.0, 1.0, 1.0]\n truth_a = [-100.0, 100.0, 0.0, 100.0, -100.0, 1.0, 1.0]\n truth_b = [-100.0, 100.0, 1.0, 100.0, -100.0, 1.0, 1.0]\n X = pd.DataFrame({\"a\": data, \"b\": data})\n true_X = pd.DataFrame({\"a\": truth_a, \"b\": truth_b})\n\n col_to_median = {\"a\": 0}\n imputer = PerColumnImputer(col_to_NAN_repl_preset=col_to_median)\n\n imputer.fit(X)\n selected_X = imputer.transform(X)\n\n pdt.assert_frame_equal(selected_X, true_X)\n\n def test_NINF_preset_contains_more_columns_than_dataframe_to_fit(self):\n X = pd.DataFrame(index=list(range(10)))\n X[\"a\"] = np.ones(10)\n\n col_to_min = {\"a\": 0, \"b\": 0}\n\n imputer = PerColumnImputer(col_to_NINF_repl_preset=col_to_min)\n\n self.assertRaises(ValueError, imputer.fit, X)\n\n def test_PINF_preset_contains_more_columns_than_dataframe_to_fit(self):\n X = pd.DataFrame(index=list(range(10)))\n X[\"a\"] = np.ones(10)\n\n col_to_max = {\"a\": 0, \"b\": 0}\n\n imputer = PerColumnImputer(col_to_PINF_repl_preset=col_to_max)\n\n self.assertRaises(ValueError, imputer.fit, X)\n\n def test_NAN_preset_contains_more_columns_than_dataframe_to_fit(self):\n X = pd.DataFrame(index=list(range(10)))\n X[\"a\"] = np.ones(10)\n\n col_to_median = {\"a\": 0, \"b\": 0}\n\n imputer = PerColumnImputer(col_to_NAN_repl_preset=col_to_median)\n\n self.assertRaises(ValueError, imputer.fit, X)\n" }, { "path": "tests/units/transformers/test_relevant_feature_augmenter.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport mock\nimport numpy as np\nimport pandas as pd\nfrom sklearn import model_selection\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import Pipeline\n\nfrom tests.fixtures import DataTestCase, warning_free\nfrom tsfresh.feature_extraction import MinimalFCParameters\nfrom tsfresh.transformers.relevant_feature_augmenter import RelevantFeatureAugmenter\n\n\nclass RelevantFeatureAugmenterTestCase(DataTestCase):\n def setUp(self):\n self.test_df = self.create_test_data_sample()\n fc_parameters = {\"length\": None}\n self.kind_to_fc_parameters = {\n \"a\": fc_parameters.copy(),\n \"b\": fc_parameters.copy(),\n }\n\n def test_not_fitted(self):\n augmenter = RelevantFeatureAugmenter()\n\n X = pd.DataFrame()\n\n self.assertRaises(RuntimeError, augmenter.transform, X)\n\n def test_no_timeseries(self):\n augmenter = RelevantFeatureAugmenter()\n\n X = pd.DataFrame()\n y = pd.Series(dtype=\"float64\")\n\n self.assertRaises(RuntimeError, augmenter.fit, X, y)\n self.assertRaises(RuntimeError, augmenter.fit_transform, X, y)\n\n def test_nothing_relevant(self):\n augmenter = RelevantFeatureAugmenter(\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n )\n\n y = pd.Series({10: 1, 500: 0})\n X = pd.DataFrame(index=[10, 500])\n\n augmenter.set_timeseries_container(self.test_df)\n augmenter.fit(X, y)\n transformed_X = augmenter.transform(X.copy())\n\n fit_transformed_X = augmenter.fit_transform(X, y)\n\n self.assertEqual(list(transformed_X.columns), [])\n self.assertEqual(list(transformed_X.index), list(X.index))\n self.assertEqual(list(fit_transformed_X.columns), [])\n self.assertEqual(list(fit_transformed_X.index), list(X.index))\n\n def test_filter_only_tsfresh_features_true(self):\n \"\"\"\n The boolean flag `filter_only_tsfresh_features` makes sure that only the time series based features are\n filtered. This unit tests checks that\n \"\"\"\n\n augmenter = RelevantFeatureAugmenter(\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n filter_only_tsfresh_features=True,\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n )\n\n y = pd.Series({10: 1, 500: 0})\n X = pd.DataFrame(index=[10, 500])\n X[\"pre_feature\"] = 0\n\n augmenter.set_timeseries_container(self.test_df)\n augmenter.fit(X, y)\n transformed_X = augmenter.transform(X.copy())\n\n fit_transformed_X = augmenter.fit_transform(X, y)\n\n self.assertEqual(\n sum([\"pre_feature\" == column for column in transformed_X.columns]), 1\n )\n self.assertEqual(\n sum([\"pre_feature\" == column for column in fit_transformed_X.columns]), 1\n )\n\n def test_filter_only_tsfresh_features_false(self):\n \"\"\"\n The boolean flag `filter_only_tsfresh_features` makes sure that only the time series based features are\n filtered. This unit tests checks that\n \"\"\"\n\n augmenter = RelevantFeatureAugmenter(\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n filter_only_tsfresh_features=False,\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n )\n\n df, y = self.create_test_data_sample_with_target()\n X = pd.DataFrame(index=np.unique(df.id))\n X[\"pre_drop\"] = 0\n X[\"pre_keep\"] = y\n\n augmenter.set_timeseries_container(df)\n augmenter.fit(X, y)\n transformed_X = augmenter.transform(X.copy())\n\n fit_transformed_X = augmenter.fit_transform(X, y)\n\n self.assertEqual(\n sum([\"pre_keep\" == column for column in transformed_X.columns]), 1\n )\n self.assertEqual(\n sum([\"pre_drop\" == column for column in transformed_X.columns]), 0\n )\n self.assertEqual(\n sum([\"pre_keep\" == column for column in fit_transformed_X.columns]), 1\n )\n self.assertEqual(\n sum([\"pre_drop\" == column for column in fit_transformed_X.columns]), 0\n )\n\n @mock.patch(\"tsfresh.transformers.feature_selector.calculate_relevance_table\")\n def test_does_impute(self, calculate_relevance_table_mock):\n df = pd.DataFrame([[1, 1, 1], [2, 1, 1]], columns=[\"id\", \"time\", \"value\"])\n X = pd.DataFrame(index=[1])\n y = pd.Series([0, 1])\n fc_parameters = {\"autocorrelation\": [{\"lag\": 2}]}\n\n calculate_relevance_table_mock.return_value = pd.DataFrame(\n columns=[\"feature\", \"p_value\", \"relevant\"]\n )\n augmenter = RelevantFeatureAugmenter(\n column_id=\"id\", column_sort=\"time\", default_fc_parameters=fc_parameters\n )\n augmenter.set_timeseries_container(df)\n with warning_free():\n augmenter.fit(X, y)\n\n assert calculate_relevance_table_mock.call_count == 1\n assert not calculate_relevance_table_mock.call_args[0][0].isnull().any().any()\n\n def test_no_ids_present(self):\n augmenter = RelevantFeatureAugmenter(\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n filter_only_tsfresh_features=False,\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n )\n\n df, y = self.create_test_data_sample_with_target()\n X_with_wrong_ids = pd.DataFrame(index=[-999])\n\n augmenter.set_timeseries_container(df)\n\n self.assertRaisesRegex(\n AttributeError,\n r\"The ids of the time series container\",\n augmenter.fit,\n X_with_wrong_ids,\n y,\n )\n self.assertRaisesRegex(\n AttributeError,\n r\"The ids of the time series container\",\n augmenter.fit_transform,\n X_with_wrong_ids,\n y,\n )\n\n def test_multiclass_selection(self):\n augmenter = RelevantFeatureAugmenter(\n column_value=\"val\",\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n multiclass=True,\n n_significant=3,\n )\n\n df, y = self.create_test_data_sample_with_multiclass_target()\n X = pd.DataFrame(index=np.unique(df.id))\n\n augmenter.set_timeseries_container(df)\n fit_transformed_X = augmenter.fit_transform(X, y)\n\n self.assertEqual(len(fit_transformed_X.columns), 4)\n\n\ndef test_relevant_augmentor_cross_validated():\n \"\"\"\n Validates that the RelevantFeatureAugmenter can be cloned in pipelines, see issue 537\n \"\"\"\n n = 16 # number of samples, needs to be divisable by 4\n index = range(n)\n df_ts = pd.DataFrame(\n {\n \"time\": [10, 11] * n,\n \"id\": np.repeat(index, 2),\n \"value\": [0, 1] * (n // 4)\n + [1, 2] * (n // 4)\n + [10, 11] * (n // 4) # class 0\n + [12, 14] * (n // 4),\n }\n )\n y = pd.Series(data=[0] * (n // 2) + [1] * (n // 2), index=index)\n X = pd.DataFrame(index=index)\n augmenter = RelevantFeatureAugmenter(\n column_id=\"id\",\n column_sort=\"time\",\n timeseries_container=df_ts,\n default_fc_parameters=MinimalFCParameters(),\n disable_progressbar=True,\n show_warnings=False,\n fdr_level=0.90,\n )\n pipeline = Pipeline(\n [\n (\"augmenter\", augmenter),\n (\"classifier\", RandomForestClassifier(random_state=1)),\n ]\n )\n\n scores = model_selection.cross_val_score(pipeline, X, y, cv=2)\n assert (scores == np.array([1, 1])).all()\n" }, { "path": "tests/units/utilities/__init__.py", "content": "" }, { "path": "tests/units/utilities/test_dataframe_functions.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\nimport warnings\nfrom unittest import TestCase\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.testing import assert_frame_equal, assert_series_equal\n\nfrom tests.fixtures import warning_free\nfrom tsfresh import extract_relevant_features\nfrom tsfresh.feature_extraction.settings import MinimalFCParameters\nfrom tsfresh.utilities import dataframe_functions\n\n\nclass RollingTestCase(TestCase):\n def test_with_wrong_input(self):\n test_df = pd.DataFrame(\n {\n \"id\": [0, 0],\n \"kind\": [\"a\", \"b\"],\n \"value\": [3, 3],\n \"sort\": [np.nan, np.nan],\n }\n )\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n test_df = pd.DataFrame(\n {\"id\": [0, 0], \"kind\": [\"a\", \"b\"], \"value\": [3, 3], \"sort\": [1, 1]}\n )\n self.assertRaises(\n AttributeError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"strange_id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=None,\n column_sort=\"sort\",\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n test_df = {\"a\": pd.DataFrame([{\"id\": 0}])}\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_sort=None,\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=None,\n column_sort=None,\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_sort=None,\n column_kind=None,\n rolling_direction=0,\n n_jobs=0,\n )\n\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=None,\n column_sort=None,\n column_kind=None,\n rolling_direction=0,\n n_jobs=0,\n )\n\n test_df = pd.DataFrame(\n {\"id\": [0, 0], \"kind\": [\"a\", \"b\"], \"value\": [3, 3], \"sort\": [1, 1]}\n )\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n max_timeshift=0,\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_kind=\"kind\",\n column_sort=\"sort\",\n min_timeshift=-1,\n rolling_direction=1,\n n_jobs=0,\n )\n\n def test_assert_single_row(self):\n test_df = pd.DataFrame([{\"id\": np.nan, \"kind\": \"a\", \"value\": 3, \"sort\": 1}])\n self.assertRaises(\n ValueError,\n dataframe_functions.roll_time_series,\n df_or_dict=test_df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n rolling_direction=1,\n n_jobs=0,\n )\n\n def test_positive_rolling(self):\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": range(4)}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": range(20, 22)}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n \"\"\" df_full is\n a b time id\n 0 1 5 0 1\n 1 2 6 1 1\n 2 3 7 2 1\n 3 4 8 3 1\n 4 10 12 20 2\n 5 11 13 21 2\n \"\"\"\n correct_indices = [\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 2),\n (1, 3),\n (1, 3),\n (1, 3),\n (1, 3),\n (2, 20),\n (2, 21),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 1.0,\n 2.0,\n 1.0,\n 2.0,\n 3.0,\n 1.0,\n 2.0,\n 3.0,\n 4.0,\n 10.0,\n 10.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 5.0,\n 6.0,\n 5.0,\n 6.0,\n 7.0,\n 5.0,\n 6.0,\n 7.0,\n 8.0,\n 12.0,\n 12.0,\n 13.0,\n ]\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n max_timeshift=4,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n max_timeshift=2,\n n_jobs=0,\n )\n correct_indices = [\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 2),\n (1, 3),\n (1, 3),\n (1, 3),\n (2, 20),\n (2, 21),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 1.0,\n 2.0,\n 1.0,\n 2.0,\n 3.0,\n 2.0,\n 3.0,\n 4.0,\n 10.0,\n 10.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 5.0,\n 6.0,\n 5.0,\n 6.0,\n 7.0,\n 6.0,\n 7.0,\n 8.0,\n 12.0,\n 12.0,\n 13.0,\n ]\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n max_timeshift=2,\n min_timeshift=2,\n n_jobs=0,\n )\n\n correct_indices = [(1, 2), (1, 2), (1, 2), (1, 3), (1, 3), (1, 3)]\n correct_values_a = [1.0, 2.0, 3.0, 2.0, 3.0, 4.0]\n correct_values_b = [5.0, 6.0, 7.0, 6.0, 7.0, 8.0]\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n def test_negative_rolling(self):\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": range(4)}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": range(20, 22)}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n \"\"\" df_full is\n a b time id\n 0 1 5 0 1\n 1 2 6 1 1\n 2 3 7 2 1\n 3 4 8 3 1\n 4 10 12 20 2\n 5 11 13 21 2\n \"\"\"\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 20),\n (2, 20),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 2.0,\n 3.0,\n 4.0,\n 2.0,\n 3.0,\n 4.0,\n 3.0,\n 4.0,\n 4.0,\n 10.0,\n 11.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 6.0,\n 7.0,\n 8.0,\n 6.0,\n 7.0,\n 8.0,\n 7.0,\n 8.0,\n 8.0,\n 12.0,\n 13.0,\n 13.0,\n ]\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n max_timeshift=None,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n max_timeshift=1,\n n_jobs=0,\n )\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 20),\n (2, 20),\n (2, 21),\n ]\n correct_values_a = [1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 10.0, 11.0, 11.0]\n correct_values_b = [5.0, 6.0, 6.0, 7.0, 7.0, 8.0, 8.0, 12.0, 13.0, 13.0]\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n max_timeshift=2,\n n_jobs=0,\n )\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 20),\n (2, 20),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 2.0,\n 3.0,\n 2.0,\n 3.0,\n 4.0,\n 3.0,\n 4.0,\n 4.0,\n 10.0,\n 11.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 6.0,\n 7.0,\n 6.0,\n 7.0,\n 8.0,\n 7.0,\n 8.0,\n 8.0,\n 12.0,\n 13.0,\n 13.0,\n ]\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n max_timeshift=4,\n n_jobs=0,\n )\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 20),\n (2, 20),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 2.0,\n 3.0,\n 4.0,\n 2.0,\n 3.0,\n 4.0,\n 3.0,\n 4.0,\n 4.0,\n 10.0,\n 11.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 6.0,\n 7.0,\n 8.0,\n 6.0,\n 7.0,\n 8.0,\n 7.0,\n 8.0,\n 8.0,\n 12.0,\n 13.0,\n 13.0,\n ]\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-1,\n min_timeshift=2,\n max_timeshift=3,\n n_jobs=0,\n )\n\n correct_indices = [(1, 0), (1, 0), (1, 0), (1, 0), (1, 1), (1, 1), (1, 1)]\n correct_values_a = [1.0, 2.0, 3.0, 4.0, 2.0, 3.0, 4.0]\n correct_values_b = [5.0, 6.0, 7.0, 8.0, 6.0, 7.0, 8.0]\n\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n def test_rolling_with_larger_shift(self):\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": range(4)}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": range(20, 22)}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n \"\"\" df_full is\n a b time id\n 0 1 5 0 1\n 1 2 6 1 1\n 2 3 7 2 1\n 3 4 8 3 1\n 4 10 12 20 2\n 5 11 13 21 2\n \"\"\"\n correct_indices = [\n (1, 1),\n (1, 1),\n (1, 3),\n (1, 3),\n (1, 3),\n (1, 3),\n (2, 21),\n (2, 21),\n ]\n correct_values_a = [1.0, 2.0, 1.0, 2.0, 3.0, 4.0, 10.0, 11.0]\n correct_values_b = [5.0, 6.0, 5.0, 6.0, 7.0, 8.0, 12.0, 13.0]\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=2,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 2),\n (1, 2),\n (2, 20),\n (2, 20),\n ]\n correct_values_a = [1.0, 2.0, 3.0, 4.0, 3.0, 4.0, 10.0, 11.0]\n correct_values_b = [5.0, 6.0, 7.0, 8.0, 7.0, 8.0, 12.0, 13.0]\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=-2,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n def test_stacked_rolling(self):\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": range(4)}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": range(20, 22)}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n df_stacked = pd.concat(\n [\n df_full[[\"time\", \"id\", \"a\"]].rename(columns={\"a\": \"_value\"}),\n df_full[[\"time\", \"id\", \"b\"]].rename(columns={\"b\": \"_value\"}),\n ],\n ignore_index=True,\n )\n df_stacked[\"kind\"] = [\"a\"] * 6 + [\"b\"] * 6\n\n \"\"\" df_stacked is\n time id _value kind\n 0 0 1 1 a\n 1 1 1 2 a\n 2 2 1 3 a\n 3 3 1 4 a\n 4 20 2 10 a\n 5 21 2 11 a\n 6 0 1 5 b\n 7 1 1 6 b\n 8 2 1 7 b\n 9 3 1 8 b\n 10 20 2 12 b\n 11 21 2 13 b\n \"\"\"\n\n df = dataframe_functions.roll_time_series(\n df_stacked,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=\"kind\",\n rolling_direction=-1,\n n_jobs=0,\n )\n\n correct_indices = (\n [(1, 0)] * 2 * 4\n + [(1, 1)] * 2 * 3\n + [(1, 2)] * 2 * 2\n + [(1, 3)] * 2 * 1\n + [(2, 20)] * 2 * 2\n + [(2, 21)] * 2 * 1\n )\n self.assertListEqual(list(df[\"id\"].values), correct_indices)\n\n self.assertListEqual(list(df[\"kind\"].values), [\"a\", \"b\"] * 13)\n self.assertListEqual(\n list(df[\"_value\"].values),\n [\n 1.0,\n 5.0,\n 2.0,\n 6.0,\n 3.0,\n 7.0,\n 4.0,\n 8.0,\n 2.0,\n 6.0,\n 3.0,\n 7.0,\n 4.0,\n 8.0,\n 3.0,\n 7.0,\n 4.0,\n 8.0,\n 4.0,\n 8.0,\n 10.0,\n 12.0,\n 11.0,\n 13.0,\n 11.0,\n 13.0,\n ],\n )\n\n def test_dict_rolling(self):\n df_dict = {\n \"a\": pd.DataFrame(\n {\"_value\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}\n ),\n \"b\": pd.DataFrame(\n {\"_value\": [5, 6, 7, 8, 12, 13], \"id\": [1, 1, 1, 1, 2, 2]}\n ),\n }\n df = dataframe_functions.roll_time_series(\n df_dict,\n column_id=\"id\",\n column_sort=None,\n column_kind=None,\n rolling_direction=-1,\n n_jobs=0,\n )\n \"\"\" df is\n {a: _value id\n 1.0 1\n 2.0 1\n 3.0 1\n 4.0 1\n 10.0 2\n 11.0 2,\n\n b: _value id\n 5.0 1\n 6.0 1\n 7.0 1\n 8.0 1\n 12.0 2\n 13.0 2\n }\n \"\"\"\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 0),\n (2, 0),\n (2, 1),\n ]\n self.assertListEqual(list(df[\"a\"][\"id\"].values), correct_indices)\n\n self.assertListEqual(list(df[\"b\"][\"id\"].values), correct_indices)\n\n self.assertListEqual(\n list(df[\"a\"][\"_value\"].values),\n [1.0, 2.0, 3.0, 4.0, 2.0, 3.0, 4.0, 3.0, 4.0, 4.0, 10.0, 11.0, 11.0],\n )\n self.assertListEqual(\n list(df[\"b\"][\"_value\"].values),\n [5.0, 6.0, 7.0, 8.0, 6.0, 7.0, 8.0, 7.0, 8.0, 8.0, 12.0, 13.0, 13.0],\n )\n\n def test_dict_rolling_maxshift_1(self):\n df_dict = {\n \"a\": pd.DataFrame(\n {\"_value\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}\n ),\n \"b\": pd.DataFrame(\n {\"_value\": [5, 6, 7, 8, 12, 13], \"id\": [1, 1, 1, 1, 2, 2]}\n ),\n }\n df = dataframe_functions.roll_time_series(\n df_dict,\n column_id=\"id\",\n column_sort=None,\n column_kind=None,\n rolling_direction=-1,\n max_timeshift=1,\n n_jobs=0,\n )\n \"\"\" df is\n {a: _value id\n 1.0 1\n 2.0 1\n 3.0 1\n 4.0 1\n 10.0 2\n 11.0 2,\n\n b: _value id\n 5.0 1\n 6.0 1\n 7.0 1\n 8.0 1\n 12.0 2\n 13.0 2\n }\n \"\"\"\n\n correct_indices = [\n (1, 0),\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 3),\n (2, 0),\n (2, 0),\n (2, 1),\n ]\n\n self.assertListEqual(list(df[\"a\"][\"id\"].values), correct_indices)\n self.assertListEqual(list(df[\"b\"][\"id\"].values), correct_indices)\n\n self.assertListEqual(\n list(df[\"a\"][\"_value\"].values),\n [1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 10.0, 11.0, 11.0],\n )\n self.assertListEqual(\n list(df[\"b\"][\"_value\"].values),\n [5.0, 6.0, 6.0, 7.0, 7.0, 8.0, 8.0, 12.0, 13.0, 13.0],\n )\n\n def test_order_rolling(self):\n\n first_class = pd.DataFrame({\"x\": [1, 2, 3, 4], \"time\": [1, 15, 132, 145]})\n second_class = pd.DataFrame({\"x\": [5, 6, 7], \"time\": [16, 133, 146]})\n\n first_class[\"initial_id\"] = 1\n second_class[\"initial_id\"] = 2\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n # Do not show the warning on non-equidistant time\n with warning_free():\n window_size = 2\n\n df_rolled = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"initial_id\",\n column_sort=\"time\",\n min_timeshift=window_size - 1,\n max_timeshift=window_size - 1,\n )\n\n \"\"\" df is\n {x: _value id\n 1.0 1\n 2.0 1\n 3.0 1\n 4.0 1\n 5.0 2\n 6.0 2\n 7.0 2,\n }\n \"\"\"\n\n correct_indices = [\n (1, 15),\n (1, 15),\n (1, 132),\n (1, 132),\n (1, 145),\n (1, 145),\n (2, 133),\n (2, 133),\n (2, 146),\n (2, 146),\n ]\n\n self.assertListEqual(list(df_rolled[\"id\"]), correct_indices)\n\n def test_warning_on_non_uniform_time_steps(self):\n with warnings.catch_warnings(record=True) as w:\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": [1, 2, 4, 5]}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": list(range(20, 22))}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertGreaterEqual(len(w), 1)\n self.assertIn(\n \"Your time stamps are not uniformly sampled, which makes rolling \"\n \"nonsensical in some domains.\",\n [str(warning.message) for warning in w],\n )\n\n def test_multicore_rolling(self):\n first_class = pd.DataFrame(\n {\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8], \"time\": range(4)}\n )\n second_class = pd.DataFrame(\n {\"a\": [10, 11], \"b\": [12, 13], \"time\": range(20, 22)}\n )\n\n first_class[\"id\"] = 1\n second_class[\"id\"] = 2\n\n df_full = pd.concat([first_class, second_class], ignore_index=True)\n\n \"\"\" df_full is\n a b time id\n 0 1 5 0 1\n 1 2 6 1 1\n 2 3 7 2 1\n 3 4 8 3 1\n 4 10 12 20 2\n 5 11 13 21 2\n \"\"\"\n correct_indices = [\n (1, 0),\n (1, 1),\n (1, 1),\n (1, 2),\n (1, 2),\n (1, 2),\n (1, 3),\n (1, 3),\n (1, 3),\n (1, 3),\n (2, 20),\n (2, 21),\n (2, 21),\n ]\n correct_values_a = [\n 1.0,\n 1.0,\n 2.0,\n 1.0,\n 2.0,\n 3.0,\n 1.0,\n 2.0,\n 3.0,\n 4.0,\n 10.0,\n 10.0,\n 11.0,\n ]\n correct_values_b = [\n 5.0,\n 5.0,\n 6.0,\n 5.0,\n 6.0,\n 7.0,\n 5.0,\n 6.0,\n 7.0,\n 8.0,\n 12.0,\n 12.0,\n 13.0,\n ]\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n df = dataframe_functions.roll_time_series(\n df_full,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=None,\n rolling_direction=1,\n n_jobs=0,\n )\n\n self.assertListEqual(list(df[\"id\"]), correct_indices)\n self.assertListEqual(list(df[\"a\"].values), correct_values_a)\n self.assertListEqual(list(df[\"b\"].values), correct_values_b)\n\n\nclass CheckForNanTestCase(TestCase):\n def test_all_columns(self):\n test_df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], index=[0, 1])\n\n # should not raise an exception\n dataframe_functions.check_for_nans_in_columns(test_df)\n\n test_df = pd.DataFrame([[1, 2, 3], [4, np.nan, 6]], index=[0, 1])\n\n self.assertRaises(\n ValueError, dataframe_functions.check_for_nans_in_columns, test_df\n )\n\n def test_not_all_columns(self):\n test_df = pd.DataFrame(\n [[1, 2, 3], [4, np.nan, 6]], index=[0, 1], columns=[\"a\", \"b\", \"c\"]\n )\n\n self.assertRaises(\n ValueError, dataframe_functions.check_for_nans_in_columns, test_df\n )\n self.assertRaises(\n ValueError,\n dataframe_functions.check_for_nans_in_columns,\n test_df,\n [\"a\", \"b\"],\n )\n self.assertRaises(\n ValueError, dataframe_functions.check_for_nans_in_columns, test_df, [\"b\"]\n )\n self.assertRaises(\n ValueError, dataframe_functions.check_for_nans_in_columns, test_df, \"b\"\n )\n self.assertRaises(\n ValueError,\n dataframe_functions.check_for_nans_in_columns,\n test_df,\n [\"c\", \"b\"],\n )\n\n dataframe_functions.check_for_nans_in_columns(test_df, columns=[\"a\", \"c\"])\n dataframe_functions.check_for_nans_in_columns(test_df, columns=\"a\")\n\n\nclass ImputeTestCase(TestCase):\n def test_impute_zero(self):\n df = pd.DataFrame([{\"value\": np.nan}])\n dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0])\n\n df = pd.DataFrame([{\"value\": np.inf}])\n dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0])\n\n df = pd.DataFrame([{\"value\": -np.inf}])\n dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0])\n\n df = pd.DataFrame(\n [{\"value\": -np.inf}, {\"value\": np.nan}, {\"value\": np.inf}, {\"value\": 1}]\n )\n dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0, 0, 0, 1])\n\n df = pd.DataFrame(\n [{\"value\": -np.inf}, {\"value\": np.nan}, {\"value\": np.inf}, {\"value\": 1}]\n )\n df = df.astype(np.float64)\n df = dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0, 0, 0, 1])\n\n df = pd.DataFrame(\n [{\"value\": -np.inf}, {\"value\": np.nan}, {\"value\": np.inf}, {\"value\": 1}]\n )\n df = df.astype(np.float32)\n df = dataframe_functions.impute_dataframe_zero(df)\n self.assertEqual(list(df.value), [0, 0, 0, 1])\n\n df = pd.DataFrame([])\n dataframe_functions.impute_dataframe_zero(df)\n\n self.assertEqual(len(df), 0)\n\n def test_toplevel_impute(self):\n df = pd.DataFrame(\n np.transpose([[0, 1, 2, np.nan], [1, np.inf, 2, 3], [1, -3, -np.inf, 3]]),\n columns=[\"value_a\", \"value_b\", \"value_c\"],\n )\n\n dataframe_functions.impute(df)\n\n self.assertEqual(list(df.value_a), [0, 1, 2, 1])\n self.assertEqual(list(df.value_b), [1, 3, 2, 3])\n self.assertEqual(list(df.value_c), [1, -3, -3, 3])\n\n df = pd.DataFrame(\n np.transpose(\n [[0, 1, 2, np.nan], [1, np.inf, 2, np.nan], [np.nan, -3, -np.inf, 3]]\n ),\n columns=[\"value_a\", \"value_b\", \"value_c\"],\n )\n df = df.astype(np.float64)\n dataframe_functions.impute(df)\n\n self.assertEqual(list(df.value_a), [0, 1, 2, 1])\n self.assertEqual(list(df.value_b), [1, 2, 2, 1.5])\n self.assertEqual(list(df.value_c), [0, -3, -3, 3])\n\n df = pd.DataFrame(\n np.transpose(\n [[0, 1, 2, np.nan], [1, np.inf, 2, 3], [np.inf, -3, -np.inf, 3]]\n ),\n columns=[\"value_a\", \"value_b\", \"value_c\"],\n )\n df = df.astype(np.float32)\n dataframe_functions.impute(df)\n\n self.assertEqual(list(df.value_a), [0, 1, 2, 1])\n self.assertEqual(list(df.value_b), [1, 3, 2, 3])\n self.assertEqual(list(df.value_c), [3, -3, -3, 3])\n\n df = pd.DataFrame([])\n dataframe_functions.impute(df)\n\n self.assertEqual(len(df), 0)\n\n def test_impute_range(self):\n def get_df():\n return pd.DataFrame(\n np.transpose(\n [[0, 1, 2, np.nan], [1, np.inf, 2, 3], [1, -3, -np.inf, 3]]\n ),\n columns=[\"value_a\", \"value_b\", \"value_c\"],\n )\n\n # check if values are replaced correctly\n df = get_df()\n col_to_max = {\"value_a\": 200, \"value_b\": 200, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -134, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_b\": 55, \"value_c\": 55}\n dataframe_functions.impute_dataframe_range(\n df, col_to_max, col_to_min, col_to_median\n )\n self.assertEqual(list(df.value_a), [0, 1, 2, 55])\n self.assertEqual(list(df.value_b), [1, 200, 2, 3])\n self.assertEqual(list(df.value_c), [1, -3, -134, 3])\n\n # check for error if column key is missing\n df = get_df()\n col_to_max = {\"value_a\": 200, \"value_b\": 200, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -134, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_c\": 55}\n self.assertRaises(\n ValueError,\n dataframe_functions.impute_dataframe_range,\n df,\n col_to_max,\n col_to_min,\n col_to_median,\n )\n\n # check for no error if column key is too much\n col_to_max = {\"value_a\": 200, \"value_b\": 200, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -134, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_b\": 55, \"value_c\": 55, \"value_d\": 55}\n dataframe_functions.impute_dataframe_range(\n df, col_to_max, col_to_min, col_to_median\n )\n\n # check for error if replacement value is not finite\n df = get_df()\n col_to_max = {\"value_a\": 200, \"value_b\": np.nan, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -134, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_b\": 55, \"value_c\": 55}\n self.assertRaises(\n ValueError,\n dataframe_functions.impute_dataframe_range,\n df,\n col_to_max,\n col_to_min,\n col_to_median,\n )\n df = get_df()\n col_to_max = {\"value_a\": 200, \"value_b\": 200, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -np.inf, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_b\": 55, \"value_c\": 55}\n self.assertRaises(\n ValueError,\n dataframe_functions.impute_dataframe_range,\n df,\n col_to_max,\n col_to_min,\n col_to_median,\n )\n\n df = get_df()\n col_to_max = {\"value_a\": 200, \"value_b\": 200, \"value_c\": 200}\n col_to_min = {\"value_a\": -134, \"value_b\": -134, \"value_c\": -134}\n col_to_median = {\"value_a\": 55, \"value_b\": 55, \"value_c\": np.inf}\n self.assertRaises(\n ValueError,\n dataframe_functions.impute_dataframe_range,\n df,\n col_to_max,\n col_to_min,\n col_to_median,\n )\n\n df = pd.DataFrame([0, 1, 2, 3, 4], columns=[\"test\"])\n col_dict = {\"test\": 0}\n dataframe_functions.impute_dataframe_range(df, col_dict, col_dict, col_dict)\n\n self.assertEqual(df.columns, [\"test\"])\n self.assertListEqual(list(df[\"test\"].values), [0, 1, 2, 3, 4])\n\n df = pd.DataFrame([])\n dataframe_functions.impute_dataframe_range(df, {}, {}, {})\n\n self.assertEqual(len(df), 0)\n\n\nclass RestrictTestCase(TestCase):\n def test_restrict_dataframe(self):\n df = pd.DataFrame({\"id\": [1, 2, 3] * 2})\n\n df_restricted = dataframe_functions.restrict_input_to_index(df, \"id\", [2])\n self.assertEqual(list(df_restricted.id), [2, 2])\n\n df_restricted2 = dataframe_functions.restrict_input_to_index(\n df, \"id\", [1, 2, 3]\n )\n self.assertTrue(df_restricted2.equals(df))\n\n def test_restrict_dict(self):\n kind_to_df = {\n \"a\": pd.DataFrame({\"id\": [1, 2, 3]}),\n \"b\": pd.DataFrame({\"id\": [3, 4, 5]}),\n }\n\n kind_to_df_restricted = dataframe_functions.restrict_input_to_index(\n kind_to_df, \"id\", [3]\n )\n self.assertEqual(list(kind_to_df_restricted[\"a\"].id), [3])\n self.assertEqual(list(kind_to_df_restricted[\"b\"].id), [3])\n\n kind_to_df_restricted2 = dataframe_functions.restrict_input_to_index(\n kind_to_df, \"id\", [1, 2, 3, 4, 5]\n )\n self.assertTrue(kind_to_df_restricted2[\"a\"].equals(kind_to_df[\"a\"]))\n self.assertTrue(kind_to_df_restricted2[\"b\"].equals(kind_to_df[\"b\"]))\n\n def test_restrict_wrong(self):\n other_type = np.array([1, 2, 3])\n\n self.assertRaises(\n TypeError,\n dataframe_functions.restrict_input_to_index,\n other_type,\n \"id\",\n [1, 2, 3],\n )\n\n\nclass GetRangeValuesPerColumnTestCase(TestCase):\n def test_ignores_non_finite_values(self):\n df = pd.DataFrame([0, 1, 2, 3, np.nan, np.inf, -np.inf], columns=[\"value\"])\n\n (\n col_to_max,\n col_to_min,\n col_to_median,\n ) = dataframe_functions.get_range_values_per_column(df)\n\n self.assertEqual(col_to_max, {\"value\": 3})\n self.assertEqual(col_to_min, {\"value\": 0})\n self.assertEqual(col_to_median, {\"value\": 1.5})\n\n def test_range_values_correct_with_even_length(self):\n df = pd.DataFrame([0, 1, 2, 3], columns=[\"value\"])\n\n (\n col_to_max,\n col_to_min,\n col_to_median,\n ) = dataframe_functions.get_range_values_per_column(df)\n\n self.assertEqual(col_to_max, {\"value\": 3})\n self.assertEqual(col_to_min, {\"value\": 0})\n self.assertEqual(col_to_median, {\"value\": 1.5})\n\n def test_range_values_correct_with_uneven_length(self):\n df = pd.DataFrame([0, 1, 2], columns=[\"value\"])\n\n (\n col_to_max,\n col_to_min,\n col_to_median,\n ) = dataframe_functions.get_range_values_per_column(df)\n\n self.assertEqual(col_to_max, {\"value\": 2})\n self.assertEqual(col_to_min, {\"value\": 0})\n self.assertEqual(col_to_median, {\"value\": 1})\n\n def test_no_finite_values_yields_0(self):\n df = pd.DataFrame([np.nan, np.inf, -np.inf], columns=[\"value\"])\n\n with warnings.catch_warnings(record=True) as w:\n (\n col_to_max,\n col_to_min,\n col_to_median,\n ) = dataframe_functions.get_range_values_per_column(df)\n\n self.assertEqual(len(w), 1)\n self.assertEqual(\n str(w[0].message),\n \"The columns ['value'] did not have any finite values. Filling with zeros.\",\n )\n\n self.assertEqual(col_to_max, {\"value\": 0})\n self.assertEqual(col_to_min, {\"value\": 0})\n self.assertEqual(col_to_median, {\"value\": 0})\n\n\nclass MakeForecastingFrameTestCase(TestCase):\n def test_make_forecasting_frame_list(self):\n df, y = dataframe_functions.make_forecasting_frame(\n x=range(4), kind=\"test\", max_timeshift=1, rolling_direction=1\n )\n expected_df = pd.DataFrame(\n {\n \"id\": [(\"id\", 1), (\"id\", 2), (\"id\", 3)],\n \"kind\": [\"test\"] * 3,\n \"value\": [0, 1, 2],\n \"time\": [0, 1, 2],\n }\n )\n\n expected_y = pd.Series(\n data=[1, 2, 3], index=[(\"id\", 1), (\"id\", 2), (\"id\", 3)], name=\"value\"\n )\n assert_frame_equal(\n df.sort_index(axis=1).reset_index(drop=True), expected_df.sort_index(axis=1)\n )\n assert_series_equal(y, expected_y)\n\n def test_make_forecasting_frame_range(self):\n df, y = dataframe_functions.make_forecasting_frame(\n x=np.arange(4), kind=\"test\", max_timeshift=1, rolling_direction=1\n )\n expected_df = pd.DataFrame(\n {\n \"id\": list(zip([\"id\"] * 3, np.arange(1, 4))),\n \"kind\": [\"test\"] * 3,\n \"value\": np.arange(3),\n \"time\": [0, 1, 2],\n }\n )\n expected_y = pd.Series(\n data=[1, 2, 3], index=[(\"id\", 1), (\"id\", 2), (\"id\", 3)], name=\"value\"\n )\n assert_frame_equal(\n df.sort_index(axis=1).reset_index(drop=True), expected_df.sort_index(axis=1)\n )\n assert_series_equal(y, expected_y)\n\n def test_make_forecasting_frame_pdSeries(self):\n\n t_index = pd.date_range(\"1/1/2011\", periods=4, freq=\"H\")\n df, y = dataframe_functions.make_forecasting_frame(\n x=pd.Series(data=range(4), index=t_index),\n kind=\"test\",\n max_timeshift=1,\n rolling_direction=1,\n )\n\n time_shifts = pd.DatetimeIndex(\n [\"2011-01-01 01:00:00\", \"2011-01-01 02:00:00\", \"2011-01-01 03:00:00\"],\n freq=\"H\",\n )\n expected_y = pd.Series(\n data=[1, 2, 3], index=zip([\"id\"] * 3, time_shifts), name=\"value\"\n )\n expected_df = pd.DataFrame(\n {\n \"id\": list(\n zip(\n [\"id\"] * 3,\n pd.DatetimeIndex(\n [\n \"2011-01-01 01:00:00\",\n \"2011-01-01 02:00:00\",\n \"2011-01-01 03:00:00\",\n ]\n ),\n )\n ),\n \"kind\": [\"test\"] * 3,\n \"value\": [0, 1, 2],\n \"time\": pd.DatetimeIndex(\n [\n \"2011-01-01 00:00:00\",\n \"2011-01-01 01:00:00\",\n \"2011-01-01 02:00:00\",\n ]\n ),\n }\n )\n assert_frame_equal(\n df.sort_index(axis=1).reset_index(drop=True), expected_df.sort_index(axis=1)\n )\n assert_series_equal(y, expected_y)\n\n def test_make_forecasting_frame_feature_extraction(self):\n t_index = pd.date_range(\"1/1/2011\", periods=4, freq=\"H\")\n df, y = dataframe_functions.make_forecasting_frame(\n x=pd.Series(data=range(4), index=t_index),\n kind=\"test\",\n max_timeshift=1,\n rolling_direction=1,\n )\n\n extract_relevant_features(\n df,\n y,\n column_id=\"id\",\n column_sort=\"time\",\n column_value=\"value\",\n default_fc_parameters=MinimalFCParameters(),\n )\n\n\nclass GetIDsTestCase(TestCase):\n def test_get_id__correct_DataFrame(self):\n df = pd.DataFrame({\"_value\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]})\n self.assertEqual(dataframe_functions.get_ids(df, \"id\"), {1, 2})\n\n def test_get_id__correct_dict(self):\n df_dict = {\n \"a\": pd.DataFrame(\n {\"_value\": [1, 2, 3, 4, 10, 11], \"id\": [1, 1, 1, 1, 2, 2]}\n ),\n \"b\": pd.DataFrame(\n {\"_value\": [5, 6, 7, 8, 12, 13], \"id\": [4, 4, 3, 3, 2, 2]}\n ),\n }\n self.assertEqual(dataframe_functions.get_ids(df_dict, \"id\"), {1, 2, 3, 4})\n\n def test_get_id_wrong(self):\n other_type = np.array([1, 2, 3])\n self.assertRaises(TypeError, dataframe_functions.get_ids, other_type, \"id\")\n\n\nclass AddSubIdTestCase(TestCase):\n def test_no_parameters(self):\n dataframe = pd.DataFrame({\"value\": [1, 2, 3, 4, 5, 6, 7, 8, 9]})\n extended_dataframe = dataframe_functions.add_sub_time_series_index(dataframe, 2)\n\n self.assertEqual(list(extended_dataframe[\"id\"]), [0, 0, 1, 1, 2, 2, 3, 3, 4])\n assert_series_equal(dataframe[\"value\"], extended_dataframe[\"value\"])\n\n def test_id_parameters(self):\n dataframe = pd.DataFrame(\n {\"value\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"id\": [1, 1, 1, 1, 2, 2, 2, 2, 2]}\n )\n\n extended_dataframe = dataframe_functions.add_sub_time_series_index(\n dataframe, 2, column_id=\"id\"\n )\n\n self.assertEqual(\n list(extended_dataframe[\"id\"]),\n [(0, 1), (0, 1), (1, 1), (1, 1), (0, 2), (0, 2), (1, 2), (1, 2), (2, 2)],\n )\n assert_series_equal(dataframe[\"value\"], extended_dataframe[\"value\"])\n\n def test_kind_parameters(self):\n dataframe = pd.DataFrame(\n {\n \"value\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n \"id\": [1, 1, 1, 1, 2, 2, 2, 2, 2],\n \"kind\": [0, 1, 0, 1, 0, 1, 0, 1, 0],\n }\n )\n\n extended_dataframe = dataframe_functions.add_sub_time_series_index(\n dataframe, 2, column_id=\"id\", column_kind=\"kind\"\n ).sort_index()\n\n self.assertEqual(\n list(extended_dataframe[\"id\"]),\n [(0, 1), (0, 1), (0, 1), (0, 1), (0, 2), (0, 2), (0, 2), (0, 2), (1, 2)],\n )\n assert_series_equal(dataframe[\"value\"], extended_dataframe[\"value\"])\n assert_series_equal(dataframe[\"kind\"], extended_dataframe[\"kind\"])\n\n def test_sort_parameters(self):\n dataframe = pd.DataFrame(\n {\n \"value\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n \"id\": [1, 1, 1, 1, 2, 2, 2, 2, 2],\n \"kind\": [0, 1, 0, 1, 0, 1, 0, 1, 0],\n \"sort\": [9, 8, 7, 6, 5, 4, 3, 2, 1],\n }\n )\n\n extended_dataframe = dataframe_functions.add_sub_time_series_index(\n dataframe, 2, column_id=\"id\", column_kind=\"kind\", column_sort=\"sort\"\n )\n\n self.assertEqual(\n list(extended_dataframe[\"id\"]),\n [(0, 2), (0, 2), (0, 2), (0, 2), (1, 2), (0, 1), (0, 1), (0, 1), (0, 1)],\n )\n self.assertEqual(list(extended_dataframe[\"value\"]), [9, 8, 7, 6, 5, 4, 3, 2, 1])\n self.assertEqual(list(extended_dataframe[\"kind\"]), [0, 1, 0, 1, 0, 1, 0, 1, 0])\n self.assertEqual(list(extended_dataframe[\"sort\"]), [1, 2, 3, 4, 5, 6, 7, 8, 9])\n\n def test_dict_input(self):\n dataframe = pd.DataFrame(\n {\"value\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"id\": [1, 1, 1, 1, 2, 2, 2, 2, 2]}\n )\n\n extended_dataframe = dataframe_functions.add_sub_time_series_index(\n {\"1\": dataframe}, 2, column_id=\"id\"\n )\n\n self.assertIn(\"1\", extended_dataframe)\n\n extended_dataframe = extended_dataframe[\"1\"]\n\n self.assertEqual(\n list(extended_dataframe[\"id\"]),\n [(0, 1), (0, 1), (1, 1), (1, 1), (0, 2), (0, 2), (1, 2), (1, 2), (2, 2)],\n )\n assert_series_equal(dataframe[\"value\"], extended_dataframe[\"value\"])\n" }, { "path": "tests/units/utilities/test_distribution.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport sys\nfrom unittest import TestCase, skipIf\n\nimport numpy as np\nimport pandas as pd\nfrom distributed import Client, LocalCluster\n\nfrom tests.fixtures import DataTestCase\nfrom tsfresh import extract_features\nfrom tsfresh.utilities.distribution import (\n ClusterDaskDistributor,\n LocalDaskDistributor,\n MultiprocessingDistributor,\n)\nfrom tsfresh.utilities.profiling import get_n_jobs, set_n_jobs\n\n\nclass MultiprocessingDistributorTestCase(TestCase):\n def test_n_jobs(self):\n curr = get_n_jobs()\n self.assertEqual(curr, get_n_jobs())\n\n set_n_jobs(2)\n self.assertEqual(2, get_n_jobs())\n\n set_n_jobs(4)\n self.assertEqual(4, get_n_jobs())\n\n set_n_jobs(curr)\n self.assertEqual(curr, get_n_jobs())\n\n def test_partition(self):\n\n distributor = MultiprocessingDistributor(n_workers=1)\n\n data = [1, 3, 10, -10, 343.0]\n distro = distributor.partition(data, 3)\n self.assertEqual(next(distro), [1, 3, 10])\n self.assertEqual(next(distro), [-10, 343.0])\n\n data = np.arange(10)\n distro = distributor.partition(data, 2)\n self.assertEqual(next(distro), [0, 1])\n self.assertEqual(next(distro), [2, 3])\n\n def test__calculate_best_chunk_size(self):\n\n distributor = MultiprocessingDistributor(n_workers=2)\n self.assertEqual(distributor.calculate_best_chunk_size(10), 1)\n self.assertEqual(distributor.calculate_best_chunk_size(11), 2)\n self.assertEqual(distributor.calculate_best_chunk_size(100), 10)\n self.assertEqual(distributor.calculate_best_chunk_size(101), 11)\n\n distributor = MultiprocessingDistributor(n_workers=3)\n self.assertEqual(distributor.calculate_best_chunk_size(10), 1)\n self.assertEqual(distributor.calculate_best_chunk_size(30), 2)\n self.assertEqual(distributor.calculate_best_chunk_size(31), 3)\n\n\nclass LocalDaskDistributorTestCase(DataTestCase):\n def test_local_dask_cluster_extraction_one_worker(self):\n\n Distributor = LocalDaskDistributor(n_workers=1)\n\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n distributor=Distributor,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n\n def test_local_dask_cluster_extraction_two_worker(self):\n\n Distributor = LocalDaskDistributor(n_workers=2)\n\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n distributor=Distributor,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n\n\nclass ClusterDaskDistributorTestCase(DataTestCase):\n @skipIf(sys.version_info < (3, 6, 0), \"dask requires python >= 3.6\")\n def test_dask_cluster_extraction_one_worker(self):\n cluster = LocalCluster(\n n_workers=1, threads_per_worker=1, dashboard_address=None\n )\n client = Client(cluster)\n address = client.scheduler_info()[\"address\"]\n Distributor = ClusterDaskDistributor(address=address)\n\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n distributor=Distributor,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n cluster.close()\n\n @skipIf(sys.version_info < (3, 6, 0), \"dask requires python >= 3.6\")\n def test_dask_cluster_extraction_two_workers(self):\n cluster = LocalCluster(\n n_workers=2, threads_per_worker=1, dashboard_address=None\n )\n client = Client(cluster)\n address = client.scheduler_info()[\"address\"]\n Distributor = ClusterDaskDistributor(address=address)\n\n df = self.create_test_data_sample()\n extracted_features = extract_features(\n df,\n column_id=\"id\",\n column_sort=\"sort\",\n column_kind=\"kind\",\n column_value=\"val\",\n distributor=Distributor,\n )\n\n self.assertIsInstance(extracted_features, pd.DataFrame)\n self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))\n self.assertTrue(\n np.all(extracted_features.a__sum_values == np.array([691, 1017]))\n )\n self.assertTrue(\n np.all(extracted_features.a__abs_energy == np.array([32211, 63167]))\n )\n self.assertTrue(\n np.all(extracted_features.b__sum_values == np.array([757, 695]))\n )\n self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))\n self.assertTrue(\n np.all(extracted_features.b__abs_energy == np.array([36619, 35483]))\n )\n self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))\n self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))\n cluster.close()\n" }, { "path": "tests/units/utilities/test_string_manipilations.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nfrom unittest import TestCase\n\nfrom tsfresh.utilities.string_manipulation import convert_to_output_format\n\n\nclass StringUtilities(TestCase):\n def test_convert_to_output_format(self):\n out = convert_to_output_format({\"p1\": 1, \"p2\": \"a\"})\n expected_out = 'p1_1__p2_\"a\"'\n self.assertEqual(out, expected_out)\n\n out = convert_to_output_format({\"list\": [1, 2, 4]})\n expected_out = \"list_[1, 2, 4]\"\n self.assertEqual(out, expected_out)\n\n out = convert_to_output_format({\"list\": [\"a\", \"b\", \"c\"]})\n expected_out = \"list_['a', 'b', 'c']\"\n self.assertEqual(out, expected_out)\n\n def test_convert_to_output_format_wrong_order(self):\n out = convert_to_output_format({\"width\": 1, \"coeff\": \"a\"})\n expected_out = 'coeff_\"a\"__width_1'\n self.assertEqual(out, expected_out)\n\n out = convert_to_output_format({\"c\": 1, \"b\": 2, \"a\": 3})\n expected_out = \"a_3__b_2__c_1\"\n self.assertEqual(out, expected_out)\n" }, { "path": "tsfresh/__init__.py", "content": "# todo: here should go a top level description (see for example the numpy top level __init__.py)\n\n\"\"\"\nAt the top level we export the three most important submodules of tsfresh, which are:\n\n * :mod:`~tsfresh.extract_features`\n * :mod:`~tsfresh.select_features`\n * :mod:`~tsfresh.extract_relevant_features`\n\"\"\"\n\n\nfrom importlib.metadata import version\n\ntry:\n __version__ = version(\"tsfresh\")\nexcept Exception:\n __version__ = \"unknown\"\n\n\n# Set default logging handler to avoid \"No handler found\" warnings.\nimport logging\nfrom logging import NullHandler\n\nlogging.getLogger(__name__).addHandler(NullHandler())\n\n\nfrom tsfresh.convenience.relevant_extraction import ( # noqa: E402\n extract_relevant_features,\n)\nfrom tsfresh.feature_extraction import extract_features # noqa: E402\nfrom tsfresh.feature_selection import select_features # noqa: E402\n" }, { "path": "tsfresh/convenience/__init__.py", "content": "\"\"\"\nThe :mod:`~tsfresh.convenience` submodule contains methods that allow the user to extract and filter features\nconveniently.\n\"\"\"\n" }, { "path": "tsfresh/convenience/bindings.py", "content": "from functools import partial\n\nimport pandas as pd\n\nfrom tsfresh.feature_extraction.extraction import _do_extraction_on_chunk\nfrom tsfresh.feature_extraction.settings import ComprehensiveFCParameters\n\n\ndef _feature_extraction_on_chunk_helper(\n df,\n column_id,\n column_kind,\n column_sort,\n column_value,\n default_fc_parameters,\n kind_to_fc_parameters,\n):\n \"\"\"\n Helper function wrapped around _do_extraction_on_chunk to use the correct format\n of the \"chunk\" and output a pandas dataframe.\n Is used e.g. in the convenience functions for dask and spark.\n\n For the definitions of the parameters, please see these convenience functions.\n \"\"\"\n if default_fc_parameters is None and kind_to_fc_parameters is None:\n default_fc_parameters = ComprehensiveFCParameters()\n elif default_fc_parameters is None and kind_to_fc_parameters is not None:\n default_fc_parameters = {}\n\n if column_sort is not None:\n df = df.sort_values(column_sort)\n\n chunk = df[column_id].iloc[0], df[column_kind].iloc[0], df[column_value]\n features = _do_extraction_on_chunk(\n chunk,\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n )\n features = pd.DataFrame(features, columns=[column_id, \"variable\", \"value\"])\n features[\"value\"] = features[\"value\"].astype(\"double\")\n\n return features[[column_id, \"variable\", \"value\"]]\n\n\ndef dask_feature_extraction_on_chunk(\n df,\n column_id,\n column_kind,\n column_value,\n column_sort=None,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n):\n \"\"\"\n Extract features on a grouped dask dataframe given the column names and the extraction settings.\n This wrapper function should only be used if you have a dask dataframe as input.\n All format handling (input and output) needs to be done before or after that.\n\n Examples\n ========\n\n For example if you want to extract features on the robot example dataframe (stored as csv):\n\n Import statements:\n\n >>> from dask import dataframe as dd\n >>> from tsfresh.convenience.bindings import dask_feature_extraction_on_chunk\n >>> from tsfresh.feature_extraction.settings import MinimalFCParameters\n\n Read in the data\n\n >>> df = dd.read_csv(\"robot.csv\")\n\n Prepare the data into correct format.\n The format needs to be a grouped dataframe (grouped by time series id and feature kind),\n where each group chunk consists of a dataframe with exactly 4 columns: ``column_id``,\n ``column_kind``, ``column_sort`` and ``column_value``.\n You can find the description of the columns in :ref:`data-formats-label`.\n Please note: for this function to work you need to have all columns present!\n If necessary create the columns and fill them with dummy values.\n\n >>> df = df.melt(id_vars=[\"id\", \"time\"],\n ... value_vars=[\"F_x\", \"F_y\", \"F_z\", \"T_x\", \"T_y\", \"T_z\"],\n ... var_name=\"kind\", value_name=\"value\")\n >>> df_grouped = df.groupby([\"id\", \"kind\"])\n\n Call the feature extraction\n\n >>> features = dask_feature_extraction_on_chunk(df_grouped, column_id=\"id\", column_kind=\"kind\",\n ... column_sort=\"time\", column_value=\"value\",\n ... default_fc_parameters=MinimalFCParameters())\n\n Write out the data in a tabular format\n\n >>> features = features.categorize(columns=[\"variable\"])\n >>> features = features.reset_index(drop=True) \\\\\n ... .pivot_table(index=\"id\", columns=\"variable\", values=\"value\", aggfunc=\"mean\")\n >>> features.to_csv(\"output\")\n\n\n :param df: A dask dataframe grouped by id and kind.\n :type df: dask.dataframe.groupby.DataFrameGroupBy\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters. This means\n that kinds, for which kind_of_fc_parameters doe not have any entries, will be ignored by\n the feature selection.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The name of the id column to group by.\n :type column_id: str\n\n :param column_sort: The name of the sort column.\n :type column_sort: str or None\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n :type column_kind: str\n\n :param column_value: The name for the column keeping the value itself.\n :type column_value: str\n\n :return: A dask dataframe with the columns ``column_id``, \"variable\" and \"value\". The index is taken\n from the grouped dataframe.\n :rtype: dask.dataframe.DataFrame (id int64, variable object, value float64)\n\n \"\"\"\n feature_extraction = partial(\n _feature_extraction_on_chunk_helper,\n column_id=column_id,\n column_kind=column_kind,\n column_sort=column_sort,\n column_value=column_value,\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n )\n return df.apply(\n feature_extraction,\n meta=[(column_id, \"int64\"), (\"variable\", \"object\"), (\"value\", \"float64\")],\n )\n\n\ndef spark_feature_extraction_on_chunk(\n df,\n column_id,\n column_kind,\n column_value,\n column_sort=None,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n):\n \"\"\"\n Extract features on a grouped spark dataframe given the column names and the extraction settings.\n This wrapper function should only be used if you have a spark dataframe as input.\n All format handling (input and output) needs to be done before or after that.\n\n Examples\n ========\n\n For example if you want to extract features on the robot example dataframe (stored as csv):\n\n Import statements:\n\n >>> from tsfresh.convenience.bindings import spark_feature_extraction_on_chunk\n >>> from tsfresh.feature_extraction.settings import MinimalFCParameters\n\n Read in the data\n\n >>> df = spark.read(...)\n\n Prepare the data into correct format.\n The format needs to be a grouped dataframe (grouped by time series id and feature kind),\n where each group chunk consists of a dataframe with exactly 4 columns: ``column_id``,\n ``column_kind``, ``column_sort`` and ``column_value``.\n You can find the description of the columns in :ref:`data-formats-label`.\n Please note: for this function to work you need to have all columns present!\n If necessary create the columns and fill them with dummy values.\n\n >>> df = ...\n >>> df_grouped = df.groupby([\"id\", \"kind\"])\n\n Call the feature extraction\n\n >>> features = spark_feature_extraction_on_chunk(df_grouped, column_id=\"id\", column_kind=\"kind\",\n ... column_sort=\"time\", column_value=\"value\",\n ... default_fc_parameters=MinimalFCParameters())\n\n Write out the data in a tabular format\n\n >>> features = features.groupby(\"id\").pivot(\"variable\").sum(\"value\")\n >>> features.write.csv(\"output\")\n\n\n :param df: A spark dataframe grouped by id and kind.\n :type df: pyspark.sql.group.GroupedData\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters.\n This means that kinds, for which kind_of_fc_parameters doe not have any entries,\n will be ignored by the feature selection.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The name of the id column to group by.\n :type column_id: str\n\n :param column_sort: The name of the sort column.\n :type column_sort: str or None\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n :type column_kind: str\n\n :param column_value: The name for the column keeping the value itself.\n :type column_value: str\n\n :return: A dask dataframe with the columns ``column_id``, \"variable\" and \"value\".\n :rtype: pyspark.sql.DataFrame[id: bigint, variable: string, value: double]\n\n \"\"\"\n from pyspark.sql.functions import PandasUDFType, pandas_udf\n\n feature_extraction = partial(\n _feature_extraction_on_chunk_helper,\n column_id=column_id,\n column_kind=column_kind,\n column_sort=column_sort,\n column_value=column_value,\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n )\n\n type_string = \"{column_id} long, variable string, value double\".format(\n column_id=column_id\n )\n feature_extraction_udf = pandas_udf(type_string, PandasUDFType.GROUPED_MAP)(\n feature_extraction\n )\n\n return df.apply(feature_extraction_udf)\n" }, { "path": "tsfresh/convenience/relevant_extraction.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport pandas as pd\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_extraction import extract_features\nfrom tsfresh.feature_selection import select_features\nfrom tsfresh.utilities.dataframe_functions import (\n get_ids,\n impute,\n restrict_input_to_index,\n)\n\n\ndef extract_relevant_features(\n timeseries_container,\n y,\n X=None,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n column_id=None,\n column_sort=None,\n column_kind=None,\n column_value=None,\n show_warnings=defaults.SHOW_WARNINGS,\n disable_progressbar=defaults.DISABLE_PROGRESSBAR,\n profile=defaults.PROFILING,\n profiling_filename=defaults.PROFILING_FILENAME,\n profiling_sorting=defaults.PROFILING_SORTING,\n test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,\n test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,\n test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,\n fdr_level=defaults.FDR_LEVEL,\n hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,\n n_jobs=defaults.N_PROCESSES,\n distributor=None,\n chunksize=defaults.CHUNKSIZE,\n ml_task=\"auto\",\n):\n \"\"\"\n High level convenience function to extract time series features from `timeseries_container`. Then return feature\n matrix `X` possibly augmented with relevant features with respect to target vector `y`.\n\n For more details see the documentation of :func:`~tsfresh.feature_extraction.extraction.extract_features` and\n :func:`~tsfresh.feature_selection.selection.select_features`.\n\n Examples\n ========\n\n >>> from tsfresh.examples import load_robot_execution_failures\n >>> from tsfresh import extract_relevant_features\n >>> df, y = load_robot_execution_failures()\n >>> X = extract_relevant_features(df, y, column_id='id', column_sort='time')\n\n :param timeseries_container: The pandas.DataFrame with the time series to compute the features for, or a\n dictionary of pandas.DataFrames.\n See :func:`~tsfresh.feature_extraction.extraction.extract_features`.\n\n :param X: A DataFrame containing additional features\n :type X: pandas.DataFrame\n\n :param y: The target vector\n :type y: pandas.Series\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The name of the id column to group by. Please see :ref:`data-formats-label`.\n :type column_id: str\n\n :param column_sort: The name of the sort column. Please see :ref:`data-formats-label`.\n :type column_sort: str\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n Please see :ref:`data-formats-label`.\n :type column_kind: str\n\n :param column_value: The name for the column keeping the value itself. Please see :ref:`data-formats-label`.\n :type column_value: str\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as a\n singular time series for one id and one kind. If you set the chunksize\n to 10, then it means that one task is to calculate all features for 10\n time series. If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param distributor: Advanced parameter: set this to a class name that you want to use as a\n distributor. See the utilities/distribution.py for more information. Leave to None, if you want\n TSFresh to choose the best distributor.\n :type distributor: class\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param profile: Turn on profiling during feature extraction\n :type profile: bool\n\n :param profiling_sorting: How to sort the profiling results (see the documentation of the profiling package for\n more information)\n :type profiling_sorting: basestring\n\n :param profiling_filename: Where to save the profiling results.\n :type profiling_filename: basestring\n\n :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature\n (currently unused)\n :type test_for_binary_target_binary_feature: str\n\n :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature\n :type test_for_binary_target_real_feature: str\n\n :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature (currently unused)\n :type test_for_real_target_binary_feature: str\n\n :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)\n :type test_for_real_target_real_feature: str\n\n :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage of irrelevant\n features among all created features.\n :type fdr_level: float\n\n :param hypotheses_independent: Can the significance of the features be assumed to be independent?\n Normally, this should be set to False as the features are never\n independent (e.g. mean and median)\n :type hypotheses_independent: bool\n\n :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.\n Defaults to `'auto'`, meaning the intended task is inferred from `y`.\n If `y` has a boolean, integer or object dtype, the task is assumed to be classification,\n else regression.\n :type ml_task: str\n\n :return: Feature matrix X, possibly extended with relevant time series features.\n \"\"\"\n\n assert isinstance(\n y, pd.Series\n ), \"y needs to be a pandas.Series, received type: {}.\".format(type(y))\n assert (\n len(set(y)) > 1\n ), \"Feature selection is only possible if more than 1 label/class is provided\"\n\n if X is not None:\n timeseries_container = restrict_input_to_index(\n timeseries_container, column_id, X.index\n )\n\n ids_container = get_ids(df_or_dict=timeseries_container, column_id=column_id)\n ids_y = set(y.index)\n if ids_container != ids_y:\n if len(ids_container - ids_y) > 0:\n raise ValueError(\n \"The following ids are in the time series container but are missing in y: \"\n \"{}\".format(ids_container - ids_y)\n )\n if len(ids_y - ids_container) > 0:\n raise ValueError(\n \"The following ids are in y but are missing inside the time series container: \"\n \"{}\".format(ids_y - ids_container)\n )\n\n X_ext = extract_features(\n timeseries_container,\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n show_warnings=show_warnings,\n disable_progressbar=disable_progressbar,\n profile=profile,\n profiling_filename=profiling_filename,\n profiling_sorting=profiling_sorting,\n n_jobs=n_jobs,\n chunksize=chunksize,\n column_id=column_id,\n column_sort=column_sort,\n column_kind=column_kind,\n column_value=column_value,\n distributor=distributor,\n impute_function=impute,\n )\n\n X_sel = select_features(\n X_ext,\n y,\n test_for_binary_target_binary_feature=test_for_binary_target_binary_feature,\n test_for_binary_target_real_feature=test_for_binary_target_real_feature,\n test_for_real_target_binary_feature=test_for_real_target_binary_feature,\n test_for_real_target_real_feature=test_for_real_target_real_feature,\n fdr_level=fdr_level,\n hypotheses_independent=hypotheses_independent,\n n_jobs=n_jobs,\n show_warnings=show_warnings,\n chunksize=chunksize,\n ml_task=ml_task,\n )\n\n if X is None:\n X = X_sel\n else:\n X = pd.merge(X, X_sel, left_index=True, right_index=True, how=\"left\")\n\n return X\n" }, { "path": "tsfresh/defaults.py", "content": "import os\nfrom multiprocessing import cpu_count\n\nn_cores = int(os.getenv(\"NUMBER_OF_CPUS\") or cpu_count())\n\nCHUNKSIZE = None\nN_PROCESSES = max(1, n_cores // 2)\nPROFILING = False\nPROFILING_SORTING = \"cumulative\"\nPROFILING_FILENAME = \"profile.txt\"\nIMPUTE_FUNCTION = None\nDISABLE_PROGRESSBAR = False\nSHOW_WARNINGS = False\nPARALLELISATION = True\nTEST_FOR_BINARY_TARGET_BINARY_FEATURE = \"fisher\"\nTEST_FOR_BINARY_TARGET_REAL_FEATURE = \"mann\"\nTEST_FOR_REAL_TARGET_BINARY_FEATURE = \"mann\"\nTEST_FOR_REAL_TARGET_REAL_FEATURE = \"kendall\"\nFDR_LEVEL = 0.05\nHYPOTHESES_INDEPENDENT = False\nWRITE_SELECTION_REPORT = False\nRESULT_DIR = \"logging\"\n" }, { "path": "tsfresh/examples/__init__.py", "content": "\"\"\"\nModule with exemplary data sets to play around with.\n\nSee for eample the :ref:`quick-start-label` section on how to use them.\n\"\"\"\nfrom .driftbif_simulation import load_driftbif\nfrom .har_dataset import download_har_dataset, load_har_classes, load_har_dataset\nfrom .robot_execution_failures import (\n download_robot_execution_failures,\n load_robot_execution_failures,\n)\n" }, { "path": "tsfresh/examples/driftbif_simulation.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\n# Thanks to Andreas W. Kempa-Liehr for providing this snippet\n\nimport logging\n\nimport numpy as np\nimport pandas as pd\n\n_logger = logging.getLogger(__name__)\n\n\nclass velocity:\n \"\"\"\n Simulates the velocity of a dissipative soliton (kind of self organized particle) [6]_.\n The equilibrium velocity without noise R=0 for\n $\\tau>1.0/\\\\kappa_3$ is $\\\\kappa_3 \\\\sqrt{(tau - 1.0/\\\\kappa_3)/Q}.\n Before the drift-bifurcation $\\tau \\\\le 1.0/\\\\kappa_3$ the velocity is zero.\n\n References\n ----------\n\n .. [6] Andreas Kempa-Liehr (2013, p. 159-170)\n Dynamics of Dissipative Soliton\n Dissipative Solitons in Reaction Diffusion Systems.\n Springer: Berlin\n\n\n >>> ds = velocity(tau=3.5) # Dissipative soliton with equilibrium velocity 1.5e-3\n >>> print(ds.label) # Discriminating before or beyond Drift-Bifurcation\n 1\n\n # Equilibrium velocity\n >>> print(ds.deterministic)\n 0.0015191090506254991\n\n # Simulated velocity as a time series with 20000 time steps being disturbed by Gaussian white noise\n >>> v = ds.simulate(20000)\n \"\"\"\n\n def __init__(self, tau=3.8, kappa_3=0.3, Q=1950.0, R=3e-4, delta_t=0.05, seed=None):\n \"\"\"\n :param tau: Bifurcation parameter determining the intrinsic velocity of the dissipative soliton,\n which is zero for tau<=1.0/kappa_3 and np.sqrt(kappa_3**3/Q * (tau - 1.0/kappa_3)) otherwise\n :type tau: float\n :param kappa_3: Inverse bifurcation point.\n :type kappa_3:\n :param Q: Shape parameter of dissipative soliton\n :type Q: float\n :param R: Noise amplitude\n :type R: float\n :param delta_t: temporal discretization\n :type delta_t: float\n \"\"\"\n # done: add start seed\n\n self.delta_t = delta_t\n self.kappa_3 = kappa_3\n self.Q = Q\n self.tau = tau\n self.a = self.delta_t * kappa_3**2 * (tau - 1.0 / kappa_3)\n self.b = self.delta_t * Q / kappa_3\n self.label = int(tau > 1.0 / kappa_3)\n self.c = np.sqrt(self.delta_t) * R\n self.delta_t = self.delta_t\n\n if seed is not None:\n np.random.seed(seed)\n\n if tau <= 1.0 / kappa_3:\n self.deterministic = 0.0\n else:\n self.deterministic = kappa_3**1.5 * np.sqrt((tau - 1.0 / kappa_3) / Q)\n\n def __call__(self, v):\n \"\"\"\n returns deterministic dynamic = acceleration (without noise)\n\n :param v: initial velocity vector\n :rtype v: ndarray\n :return: velocity vector of next time step\n :return type: ndarray\n \"\"\"\n\n return v * (1.0 + self.a - self.b * np.dot(v, v))\n\n def simulate(self, N, v0=np.zeros(2)):\n \"\"\"\n\n :param N: number of time steps\n :type N: int\n :param v0: initial velocity vector\n :type v0: ndarray\n :return: time series of velocity vectors with shape (N, v0.shape[0])\n :rtype: ndarray\n \"\"\"\n\n v = [v0] # first value is initial condition\n n = N - 1 # Because we are returning the initial condition,\n # only (N-1) time steps are computed\n gamma = np.random.randn(n, v0.size)\n for i in range(n):\n next_v = self.__call__(v[i]) + self.c * gamma[i]\n v.append(next_v)\n v_vec = np.array(v)\n return v_vec\n\n\ndef sample_tau(n=10, kappa_3=0.3, ratio=0.5, rel_increase=0.15):\n \"\"\"\n Return list of control parameters\n\n :param n: number of samples\n :type n: int\n :param kappa_3: inverse bifurcation point\n :type kappa_3: float\n :param ratio: ratio (default 0.5) of samples before and beyond drift-bifurcation\n :type ratio: float\n :param rel_increase: relative increase from bifurcation point\n :type rel_increase: float\n :return: tau. List of sampled bifurcation parameter\n :rtype tau: list\n \"\"\"\n assert ratio > 0 and ratio <= 1\n assert kappa_3 > 0\n assert rel_increase > 0 and rel_increase <= 1\n tau_c = 1.0 / kappa_3\n\n tau_max = tau_c * (1.0 + rel_increase)\n tau = tau_c + (tau_max - tau_c) * (np.random.rand(n) - ratio)\n return tau.tolist()\n\n\ndef load_driftbif(n, length, m=2, classification=True, kappa_3=0.3, seed=False):\n \"\"\"\n Simulates n time-series with length time steps each for the m-dimensional velocity of a dissipative soliton\n\n classification=True:\n target 0 means tau<=1/0.3, Dissipative Soliton with Brownian motion (purely noise driven)\n target 1 means tau> 1/0.3, Dissipative Soliton with Active Brownian motion (intrinsiv velocity with overlaid noise)\n\n classification=False:\n target is bifurcation parameter tau\n\n :param n: number of samples\n :type n: int\n :param length: length of the time series\n :type length: int\n :param m: number of spatial dimensions (default m=2) the dissipative soliton is propagating in\n :type m: int\n :param classification: distinguish between classification (default True) and regression target\n :type classification: bool\n :param kappa_3: inverse bifurcation parameter (default 0.3)\n :type kappa_3: float\n :param seed: random seed (default False)\n :type seed: float\n :return: X, y. Time series container and target vector\n :rtype X: pandas.DataFrame\n :rtype y: pandas.DataFrame\n \"\"\"\n\n # todo: add ratio of classes\n\n if m > 2:\n logging.warning(\n \"You set the dimension parameter for the dissipative soliton to m={}, however it is only\"\n \"properly defined for m=1 or m=2.\".format(m)\n )\n\n id = np.repeat(range(n), length * m)\n dimensions = list(np.repeat(range(m), length)) * n\n\n labels = list()\n values = list()\n\n ls_tau = sample_tau(n, kappa_3=kappa_3)\n\n for i, tau in enumerate(ls_tau):\n ds = velocity(tau=tau, kappa_3=kappa_3, seed=seed)\n if classification:\n labels.append(ds.label)\n else:\n labels.append(ds.tau)\n values.append(ds.simulate(length, v0=np.zeros(m)).transpose().flatten())\n time = np.stack([ds.delta_t * np.arange(length)] * n * m).flatten()\n\n df = pd.DataFrame(\n {\n \"id\": id,\n \"time\": time,\n \"value\": np.stack(values).flatten(),\n \"dimension\": dimensions,\n }\n )\n y = pd.Series(labels)\n y.index = range(n)\n\n return df, y\n" }, { "path": "tsfresh/examples/har_dataset.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\n\"\"\"\nThis module implements functions to download and load the Human Activity Recognition dataset [4]_.\nA description of the data set can be found in [5]_.\n\n\nReferences\n----------\n\n.. [4] https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\n.. [5] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. (2013)\n A Public Domain Dataset for Human Activity Recognition Using Smartphones.\n 21th European Symposium on Artificial Neural Networks,\n Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.\n\n\"\"\"\n\nimport logging\nimport os\nimport shutil\nfrom io import BytesIO\nfrom zipfile import ZipFile\n\nimport pandas as pd\nimport requests\n\n_logger = logging.getLogger(__name__)\n\nmodule_path = os.path.dirname(__file__)\ndata_file_name = os.path.join(module_path, \"data\", \"UCI HAR Dataset\")\n\n\ndef download_har_dataset(folder_name=data_file_name):\n \"\"\"\n Download human activity recognition dataset from UCI ML Repository and store it at /tsfresh/notebooks/data.\n\n Examples\n ========\n\n >>> from tsfresh.examples import har_dataset\n >>> har_dataset.download_har_dataset()\n \"\"\"\n\n zipurl = \"https://github.com/MaxBenChrist/human-activity-dataset/blob/master/UCI%20HAR%20Dataset.zip?raw=True\"\n\n if not os.access(module_path, os.W_OK):\n raise RuntimeError(\n \"You don't have the necessary permissions to download the Human Activity Dataset \"\n \"Set into the module path. Consider installing the module in a virtualenv you \"\n \"own or run this function with appropriate permissions.\"\n )\n\n if os.path.exists(os.path.join(folder_name, \"UCI HAR Dataset\")):\n _logger.warning(\"You have already downloaded the Human Activity Data Set.\")\n return\n\n os.makedirs(folder_name, exist_ok=True)\n\n r = requests.get(zipurl, stream=True)\n if r.status_code != 200:\n raise RuntimeError(\n \"Could not download the Human Activity Data Set from GitHub.\"\n \"HTTP status code: {}\".format(r.status_code)\n )\n\n with ZipFile(BytesIO(r.content)) as zfile:\n zfile.extractall(path=folder_name)\n\n\ndef load_har_dataset(folder_name=data_file_name):\n data_file_name_dataset = os.path.join(\n folder_name,\n \"UCI HAR Dataset\",\n \"train\",\n \"Inertial Signals\",\n \"body_acc_x_train.txt\",\n )\n try:\n return pd.read_csv(data_file_name_dataset, delim_whitespace=True, header=None)\n except OSError:\n raise OSError(\n \"File {} was not found. Have you downloaded the dataset with download_har_dataset() \"\n \"before?\".format(data_file_name_dataset)\n )\n\n\ndef load_har_classes(folder_name=data_file_name):\n data_file_name_classes = os.path.join(\n folder_name, \"UCI HAR Dataset\", \"train\", \"y_train.txt\"\n )\n try:\n return pd.read_csv(\n data_file_name_classes, delim_whitespace=True, header=None\n ).squeeze()\n except OSError:\n raise OSError(\n \"File {} was not found. Have you downloaded the dataset with download_har_dataset() \"\n \"before?\".format(data_file_name_classes)\n )\n" }, { "path": "tsfresh/examples/robot_execution_failures.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\n\"\"\"\nThis module implements functions to download the Robot Execution Failures LP1 Data Set [1]_, [2]_, [3]_ and load it as\nas DataFrame.\n\n*Important:* You need to download the data set yourself, either manually or via the function\n:func:`~tsfresh.examples.robot_execution_failures.download_robot_execution_failures`\n\nReferences\n----------\n.. [1] https://archive.ics.uci.edu/ml/datasets/Robot+Execution+Failures\n.. [2] Lichman, M. (2013).\n UCI Machine Learning Repository [https://archive.ics.uci.edu/ml].\n Irvine, CA: University of California, School of Information and Computer Science.\n.. [3] Camarinha-Matos, L.M., L. Seabra Lopes, and J. Barata (1996).\n Integration and Learning in Supervision of Flexible Assembly Systems.\n \"IEEE Transactions on Robotics and Automation\", 12 (2), 202-219\n\n\"\"\"\n\nimport logging\nimport os\nfrom builtins import map\n\nimport pandas as pd\nimport requests\n\n_logger = logging.getLogger(__name__)\n\nUCI_MLD_REF_MSG = (\n \"The example data could not be found. You need to download the Robot Execution Failures \"\n \"LP1 Data Set from the UCI Machine Learning Repository. To do so, you can call the function \"\n \"tsfresh.examples.robot_execution_failures.download_robot_execution_failures\"\n)\nUCI_MLD_REF_URL = \"https://raw.githubusercontent.com/MaxBenChrist/robot-failure-dataset/master/lp1.data.txt\"\n\nmodule_path = os.path.dirname(__file__)\ndata_file_name = os.path.join(module_path, \"data\", \"robotfailure-mld\", \"lp1.data\")\n\n\ndef download_robot_execution_failures(file_name=data_file_name):\n \"\"\"\n Download the Robot Execution Failures LP1 Data Set[#1] from the UCI Machine Learning Repository [#2] and store it\n locally.\n\n :return:\n\n Examples\n ========\n\n >>> from tsfresh.examples import download_robot_execution_failures\n >>> download_robot_execution_failures()\n \"\"\"\n if os.path.exists(file_name):\n _logger.warning(\n \"You have already downloaded the Robot Execution Failures LP1 Data Set.\"\n )\n return\n\n os.makedirs(os.path.dirname(file_name), exist_ok=True)\n\n if not os.access(os.path.dirname(file_name), os.W_OK):\n raise RuntimeError(\n \"You don't have the necessary permissions to download the Robot Execution Failures LP1 Data \"\n \"Set into the module path. Consider installing the module in a virtualenv you \"\n \"own or run this function with appropriate permissions.\"\n )\n\n r = requests.get(UCI_MLD_REF_URL)\n\n if r.status_code != 200:\n raise RuntimeError(\n \"Could not download the Robot Execution Failures LP1 Data Set from the UCI Machine Learning \"\n \"Repository. HTTP status code: {}\".format(r.status_code)\n )\n\n with open(file_name, \"w\") as f:\n f.write(r.text)\n\n\ndef load_robot_execution_failures(multiclass=False, file_name=data_file_name):\n \"\"\"\n Load the Robot Execution Failures LP1 Data Set[1].\n The Time series are passed as a flat DataFrame.\n\n Examples\n ========\n\n >>> from tsfresh.examples import load_robot_execution_failures\n >>> df, y = load_robot_execution_failures()\n >>> print(df.shape)\n (1320, 8)\n\n :param multiclass: If True, return all target labels. The default returns only \"normal\" vs all other labels.\n :type multiclass: bool\n :return: time series data as :class:`pandas.DataFrame` and target vector as :class:`pandas.Series`\n :rtype: tuple\n \"\"\"\n if not os.path.exists(file_name):\n raise RuntimeError(UCI_MLD_REF_MSG)\n\n id_to_target = {}\n df_rows = []\n\n with open(file_name) as f:\n cur_id = 0\n time = 0\n\n for line in f.readlines():\n # New sample --> increase id, reset time and determine target\n if line[0] not in [\"\\t\", \"\\n\"]:\n cur_id += 1\n time = 0\n if multiclass:\n id_to_target[cur_id] = line.strip()\n else:\n id_to_target[cur_id] = line.strip() == \"normal\"\n # Data row --> split and convert values, create complete df row\n elif line[0] == \"\\t\":\n values = list(map(int, line.split(\"\\t\")[1:]))\n df_rows.append([cur_id, time] + values)\n time += 1\n\n df = pd.DataFrame(\n df_rows, columns=[\"id\", \"time\", \"F_x\", \"F_y\", \"F_z\", \"T_x\", \"T_y\", \"T_z\"]\n )\n y = pd.Series(id_to_target)\n\n return df, y\n" }, { "path": "tsfresh/feature_extraction/__init__.py", "content": "\"\"\"\nThe :mod:`tsfresh.feature_extraction` module contains methods to extract the features from the time series\n\"\"\"\n\nfrom tsfresh.feature_extraction.extraction import extract_features\nfrom tsfresh.feature_extraction.settings import (\n ComprehensiveFCParameters,\n EfficientFCParameters,\n MinimalFCParameters,\n)\n" }, { "path": "tsfresh/feature_extraction/data.py", "content": "import itertools\nfrom collections import defaultdict, namedtuple\nfrom typing import Iterable, Sized\n\nimport pandas as pd\n\ntry:\n from dask import dataframe as dd\nexcept ImportError: # pragma: no cover\n dd = None\n\n\ndef _binding_helper(f, kwargs, column_sort, column_id, column_kind, column_value):\n def wrapped_feature_extraction(x):\n if column_sort is not None:\n x = x.sort_values(column_sort)\n\n chunk = Timeseries(\n x[column_id].iloc[0], x[column_kind].iloc[0], x[column_value]\n )\n result = f(chunk, **kwargs)\n\n result = pd.DataFrame(result, columns=[column_id, \"variable\", \"value\"])\n result[\"value\"] = result[\"value\"].astype(\"double\")\n\n return result[[column_id, \"variable\", \"value\"]]\n\n return wrapped_feature_extraction\n\n\nclass Timeseries(namedtuple(\"Timeseries\", [\"id\", \"kind\", \"data\"])):\n \"\"\"\n Timeseries tuple used for feature extraction.\n\n Make sure `kind` is of type `str` to allow inference\n of feature settings in `feature_extraction.settings.from_columns`.\n \"\"\"\n\n\nclass TsData:\n \"\"\"\n TsData provides access to time series data for internal usage.\n\n Distributors will use this data class to apply functions on the data.\n All derived classes must either implement the `apply` method,\n which is used to apply the given function directly on the data\n or the __iter__ method, which can be used to get an iterator of\n Timeseries instances (which distributors can use to apply the function on).\n Other methods can be overwritten if a more efficient solution exists for the underlying data store.\n \"\"\"\n\n pass\n\n\nclass PartitionedTsData(Iterable[Timeseries], Sized, TsData):\n \"\"\"\n Special class of TsData, which can be partitioned.\n Derived classes should implement __iter__ and __len__.\n \"\"\"\n\n def __init__(self, df, column_id):\n self.df_id_type = df[column_id].dtype\n\n def pivot(self, results):\n \"\"\"\n Helper function to turn an iterable of tuples with three entries into a dataframe.\n\n The input ``list_of_tuples`` needs to be an iterable with tuples containing three\n entries: (a, b, c).\n Out of this, a pandas dataframe will be created with all a's as index,\n all b's as columns and all c's as values.\n\n It basically does a pd.pivot(first entry, second entry, third entry),\n but optimized for non-pandas input (= python list of tuples).\n\n This function is called in the end of the extract_features call.\n \"\"\"\n return_df_dict = defaultdict(dict)\n for chunk_id, variable, value in results:\n # we turn it into a nested mapping `column -> index -> value`\n return_df_dict[variable][chunk_id] = value\n\n # the mapping column -> {index -> value}\n # is now a dict of dicts. The pandas dataframe\n # constructor will peel this off:\n # first, the keys of the outer dict (the column)\n # will turn into a column header and the rest into a column\n # the rest is {index -> value} which will be turned into a\n # column with index.\n # All index will be aligned.\n return_df = pd.DataFrame(return_df_dict, dtype=float)\n\n # copy the type of the index\n return_df.index = return_df.index.astype(self.df_id_type)\n\n # Sort by index to be backward compatible\n return_df = return_df.sort_index()\n\n return return_df\n\n\ndef _check_colname(*columns):\n \"\"\"\n Check if given column names conflict with `settings.from_columns` (ends with '_' or contains '__').\n\n :param columns: the column names to check\n :type columns: str\n\n :return: None\n :rtype: None\n :raise: ``ValueError`` if column names are invalid.\n \"\"\"\n\n for col in columns:\n if str(col).endswith(\"_\"):\n raise ValueError(\n \"Dict keys are not allowed to end with '_': {}\".format(col)\n )\n\n if \"__\" in str(col):\n raise ValueError(\n \"Dict keys are not allowed to contain '__': {}\".format(col)\n )\n\n\ndef _check_nan(df, *columns):\n \"\"\"\n Raise a ``ValueError`` if one of the columns does not exist or contains NaNs.\n\n :param df: the pandas DataFrame to test for NaNs\n :type df: pandas.DataFrame\n :param columns: a list of columns to test for NaNs. If left empty, all columns of the DataFrame will be tested.\n :type columns: str\n\n :return: None\n :rtype: None\n :raise: ``ValueError`` if ``NaNs`` are found in the DataFrame.\n \"\"\"\n\n for col in columns:\n if col not in df.columns:\n raise ValueError(\"Column not found: {}\".format(col))\n\n if df[col].isnull().any():\n raise ValueError(\"Column must not contain NaN values: {}\".format(col))\n\n\ndef _get_value_columns(df, *other_columns):\n value_columns = [col for col in df.columns if col not in other_columns]\n\n if len(value_columns) == 0:\n raise ValueError(\n \"Could not guess the value column! Please hand it to the function as an argument.\"\n )\n\n return value_columns\n\n\nclass WideTsFrameAdapter(PartitionedTsData):\n def __init__(self, df, column_id, column_sort=None, value_columns=None):\n \"\"\"\n Adapter for Pandas DataFrames in wide format, where multiple columns contain different time series for\n the same id.\n\n :param df: the data frame\n :type df: pd.DataFrame\n\n :param column_id: the name of the column containing time series group ids\n :type column_id: str\n\n :param column_sort: the name of the column to sort on\n :type column_sort: str|None\n\n :param value_columns: list of column names to treat as time series values.\n If `None` or empty, all columns except `column_id` and `column_sort` will be used.\n :type value_columns: list[str]|None\n \"\"\"\n if column_id is None:\n raise ValueError(\"A value for column_id needs to be supplied\")\n\n _check_nan(df, column_id)\n\n if not value_columns:\n value_columns = _get_value_columns(df, column_id, column_sort)\n\n _check_nan(df, *value_columns)\n _check_colname(*value_columns)\n\n self.value_columns = value_columns\n\n if column_sort is not None:\n _check_nan(df, column_sort)\n\n self.column_sort = column_sort\n self.df_grouped = df.groupby(column_id)\n\n super().__init__(df, column_id)\n\n def __len__(self):\n return self.df_grouped.ngroups * len(self.value_columns)\n\n def __iter__(self):\n for group_name, group in self.df_grouped:\n if self.column_sort is not None:\n group = group.sort_values(self.column_sort)\n\n for kind in self.value_columns:\n yield Timeseries(group_name, kind, group[kind])\n\n\nclass LongTsFrameAdapter(PartitionedTsData):\n def __init__(self, df, column_id, column_kind, column_value=None, column_sort=None):\n \"\"\"\n Adapter for Pandas DataFrames in long format, where different time series for the same id are\n labeled by column `column_kind`.\n\n :param df: the data frame\n :type df: pd.DataFrame\n\n :param column_id: the name of the column containing time series group ids\n :type column_id: str\n\n :param column_kind: the name of the column containing time series kinds for each id\n :type column_kind: str\n\n :param column_value: None or the name of the column containing time series values\n If `None`, try to guess it from the remaining, unused columns.\n :type column_value: str|None\n\n :param column_sort: the name of the column to sort on\n :type column_sort: str|None\n \"\"\"\n if column_id is None:\n raise ValueError(\"A value for column_id needs to be supplied\")\n if column_kind is None:\n raise ValueError(\"A value for column_kind needs to be supplied\")\n\n if column_value is None:\n possible_value_columns = _get_value_columns(\n df, column_id, column_sort, column_kind\n )\n if len(possible_value_columns) != 1:\n raise ValueError(\n \"Could not guess the value column, as the number of unused columns os not equal to 1.\"\n f\"These columns where currently unused: {','.join(possible_value_columns)}\"\n \"Please hand it to the function as an argument.\"\n )\n self.column_value = possible_value_columns[0]\n else:\n self.column_value = column_value\n\n _check_nan(df, column_id, column_kind, self.column_value)\n\n if column_sort is not None:\n _check_nan(df, column_sort)\n\n self.column_sort = column_sort\n self.df_grouped = df.groupby([column_id, column_kind])\n\n super().__init__(df, column_id)\n\n def __len__(self):\n return len(self.df_grouped)\n\n def __iter__(self):\n for group_key, group in self.df_grouped:\n if self.column_sort is not None:\n group = group.sort_values(self.column_sort)\n yield Timeseries(group_key[0], str(group_key[1]), group[self.column_value])\n\n\nclass TsDictAdapter(PartitionedTsData):\n def __init__(self, ts_dict, column_id, column_value, column_sort=None):\n \"\"\"\n Adapter for a dict, which maps different time series kinds to Pandas DataFrames.\n\n :param ts_dict: a dict of data frames\n :type ts_dict: dict[str, pd.DataFrame]\n\n :param column_id: the name of the column containing time series group ids\n :type column_id: str\n\n :param column_value: the name of the column containing time series values\n :type column_value: str\n\n :param column_sort: the name of the column to sort on\n :type column_sort: str|None\n \"\"\"\n _check_colname(*list(ts_dict.keys()))\n for df in ts_dict.values():\n _check_nan(df, column_id, column_value)\n\n self.column_value = column_value\n\n if column_sort is not None:\n for key, df in ts_dict.items():\n _check_nan(df, column_sort)\n\n self.grouped_dict = {\n key: df.sort_values([column_sort]).groupby(column_id)\n for key, df in ts_dict.items()\n }\n else:\n self.grouped_dict = {\n key: df.groupby(column_id) for key, df in ts_dict.items()\n }\n\n super().__init__(df, column_id)\n\n def __iter__(self):\n for kind, grouped_df in self.grouped_dict.items():\n for ts_id, group in grouped_df:\n yield Timeseries(ts_id, str(kind), group[self.column_value])\n\n def __len__(self):\n return sum(grouped_df.ngroups for grouped_df in self.grouped_dict.values())\n\n\nclass DaskTsAdapter(TsData):\n def __init__(\n self, df, column_id, column_kind=None, column_value=None, column_sort=None\n ):\n if column_id is None:\n raise ValueError(\"column_id must be set\")\n\n if column_id not in df.columns:\n raise ValueError(f\"Column not found: {column_id}\")\n\n # Get all columns, which are not id, kind or sort\n possible_value_columns = _get_value_columns(\n df, column_id, column_sort, column_kind\n )\n\n # The user has already a kind column. That means we just need to group by id (and additionally by id)\n if column_kind is not None:\n if column_kind not in df.columns:\n raise ValueError(f\"Column not found: {column_kind}\")\n\n self.df = df.groupby([column_id, column_kind])\n\n # We assume the last remaining column is the value - but there needs to be one!\n if column_value is None:\n if len(possible_value_columns) != 1:\n raise ValueError(\n \"Could not guess the value column! Please hand it to the function as an argument.\"\n )\n column_value = possible_value_columns[0]\n else:\n # Ok, the user has no kind, so it is in Wide format.\n # That means we have do melt before we can group.\n # TODO: here is some room for optimization!\n # we could choose the same way as for the Wide and LongTsAdapter\n\n # We first choose a name for our future kind column\n column_kind = \"kind\"\n\n # if the user has specified a value column, use it\n # if not, just go with every remaining columns\n if column_value is not None:\n value_vars = [column_value]\n else:\n value_vars = possible_value_columns\n column_value = \"value\"\n\n # Make sure we are not reusing a column that already exists\n while column_value in df.columns:\n column_value += \"_\"\n\n _check_colname(*value_vars)\n\n id_vars = [column_id, column_sort] if column_sort else [column_id]\n\n # Now melt and group\n df_melted = df.melt(\n id_vars=id_vars,\n value_vars=value_vars,\n var_name=column_kind,\n value_name=column_value,\n )\n\n self.df = df_melted.groupby([column_id, column_kind])\n\n self.column_id = column_id\n self.column_kind = column_kind\n self.column_value = column_value\n self.column_sort = column_sort\n\n def apply(self, f, meta, **kwargs):\n \"\"\"\n Apply the wrapped feature extraction function \"f\"\n onto the data.\n Before that, turn the data into the correct form of Timeseries instances\n usable the the feature extraction.\n After the call, turn it back into pandas dataframes\n for further processing.\n \"\"\"\n bound_function = _binding_helper(\n f,\n kwargs,\n self.column_sort,\n self.column_id,\n self.column_kind,\n self.column_value,\n )\n return self.df.apply(bound_function, meta=meta)\n\n def pivot(self, results):\n \"\"\"\n The extract features function for dask returns a\n dataframe of [id, variable, value].\n Turn this into a pivoted dataframe, where only the variables are the columns\n and the ids are the rows.\n\n Attention: this is highly non-optimized!\n \"\"\"\n results = results.reset_index(drop=True).persist()\n results = results.categorize(columns=[\"variable\"])\n feature_table = results.pivot_table(\n index=self.column_id, columns=\"variable\", values=\"value\", aggfunc=\"sum\"\n )\n\n return feature_table\n\n\ndef to_tsdata(\n df, column_id=None, column_kind=None, column_value=None, column_sort=None\n):\n \"\"\"\n Wrap supported data formats as a TsData object, i.e. an iterable of individual time series.\n\n E.g. the DataFrame\n\n ==== ====== =========\n id kind val\n ==== ====== =========\n 1 a -0.21761\n 1 a -0.613667\n 1 a -2.07339\n 2 b -0.576254\n 2 b -1.21924\n ==== ====== =========\n\n into\n\n Iterable((1, 'a', pd.Series([-0.217610, -0.613667, -2.073386]),\n (2, 'b', pd.Series([-0.576254, -1.219238]))\n\n :param df: one of the supported input formats\n :type df: pd.DataFrame|dict|TsData\n\n :param column_id: The name of the id column to group by.\n :type column_id: str|None\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n :type column_kind: str|None\n\n :param column_value: The name for the column keeping the value itself.\n :type column_value: str|None\n\n :param column_sort: The name for the column to sort on.\n :type column_sort: str|None\n\n :return: a data adapter\n :rtype: TsData\n \"\"\"\n if isinstance(df, TsData):\n return df\n\n elif isinstance(df, pd.DataFrame):\n if column_kind is not None:\n return LongTsFrameAdapter(\n df, column_id, column_kind, column_value, column_sort\n )\n else:\n if column_value is not None:\n return WideTsFrameAdapter(df, column_id, column_sort, [column_value])\n else:\n return WideTsFrameAdapter(df, column_id, column_sort)\n\n elif isinstance(df, dict):\n return TsDictAdapter(df, column_id, column_value, column_sort)\n\n elif dd and isinstance(df, dd.DataFrame):\n return DaskTsAdapter(df, column_id, column_kind, column_value, column_sort)\n\n else:\n raise ValueError(\n \"df must be a DataFrame or a dict of DataFrames. \"\n \"See https://tsfresh.readthedocs.io/en/latest/text/data_formats.html\"\n )\n" }, { "path": "tsfresh/feature_extraction/extraction.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nThis module contains the main function to interact with tsfresh: extract features\n\"\"\"\n\nimport logging\nimport warnings\nfrom collections.abc import Iterable\n\nimport pandas as pd\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_extraction import feature_calculators\nfrom tsfresh.feature_extraction.data import to_tsdata\nfrom tsfresh.feature_extraction.settings import ComprehensiveFCParameters\nfrom tsfresh.utilities import profiling\nfrom tsfresh.utilities.distribution import (\n ApplyDistributor,\n DistributorBaseClass,\n MapDistributor,\n MultiprocessingDistributor,\n)\nfrom tsfresh.utilities.string_manipulation import convert_to_output_format\n\n_logger = logging.getLogger(__name__)\n\n\ndef extract_features(\n timeseries_container,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n column_id=None,\n column_sort=None,\n column_kind=None,\n column_value=None,\n chunksize=defaults.CHUNKSIZE,\n n_jobs=defaults.N_PROCESSES,\n show_warnings=defaults.SHOW_WARNINGS,\n disable_progressbar=defaults.DISABLE_PROGRESSBAR,\n impute_function=defaults.IMPUTE_FUNCTION,\n profile=defaults.PROFILING,\n profiling_filename=defaults.PROFILING_FILENAME,\n profiling_sorting=defaults.PROFILING_SORTING,\n distributor=None,\n pivot=True,\n):\n \"\"\"\n Extract features from\n\n * a :class:`pandas.DataFrame` containing the different time series\n\n or\n\n * a dictionary of :class:`pandas.DataFrame` each containing one type of time series\n\n In both cases a :class:`pandas.DataFrame` with the calculated features will be returned.\n\n For a list of all the calculated time series features, please see the\n :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters` class,\n which is used to control which features with which parameters are calculated.\n\n For a detailed explanation of the different parameters (e.g. the columns) and data formats\n please see :ref:`data-formats-label`.\n\n Examples\n ========\n\n >>> from tsfresh.examples import load_robot_execution_failures\n >>> from tsfresh import extract_features\n >>> df, _ = load_robot_execution_failures()\n >>> X = extract_features(df, column_id='id', column_sort='time')\n\n :param timeseries_container: The pandas.DataFrame with the time series to compute the features for, or a\n dictionary of pandas.DataFrames.\n :type timeseries_container: pandas.DataFrame or dict\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters. This means that kinds, for\n which kind_of_fc_parameters doe not have any entries, will be ignored by the feature selection.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The name of the id column to group by. Please see :ref:`data-formats-label`.\n :type column_id: str\n\n :param column_sort: The name of the sort column. Please see :ref:`data-formats-label`.\n :type column_sort: str\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n Please see :ref:`data-formats-label`.\n :type column_kind: str\n\n :param column_value: The name for the column keeping the value itself. Please see :ref:`data-formats-label`.\n :type column_value: str\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as a\n singular time series for one id and one kind. If you set the chunksize\n to 10, then it means that one task is to calculate all features for 10\n time series. If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param impute_function: None, if no imputing should happen or the function to call for\n imputing the result dataframe. Imputing will never happen on the input data.\n :type impute_function: None or callable\n\n :param profile: Turn on profiling during feature extraction\n :type profile: bool\n\n :param profiling_sorting: How to sort the profiling results (see the documentation of the profiling package for\n more information)\n :type profiling_sorting: basestring\n\n :param profiling_filename: Where to save the profiling results.\n :type profiling_filename: basestring\n\n :param distributor: Advanced parameter: set this to a class name that you want to use as a\n distributor. See the utilities/distribution.py for more information. Leave to None, if you want\n TSFresh to choose the best distributor.\n :type distributor: class\n\n :return: The (maybe imputed) DataFrame containing extracted features.\n :rtype: pandas.DataFrame\n \"\"\"\n\n # Always use the standardized way of storing the data.\n # See the function normalize_input_to_internal_representation for more information.\n\n # Use the standard setting if the user did not supply ones himself.\n if default_fc_parameters is None and kind_to_fc_parameters is None:\n default_fc_parameters = ComprehensiveFCParameters()\n elif default_fc_parameters is None and kind_to_fc_parameters is not None:\n default_fc_parameters = {}\n\n # If requested, do profiling (advanced feature)\n if profile:\n profiler = profiling.start_profiling()\n\n with warnings.catch_warnings():\n if not show_warnings:\n warnings.simplefilter(\"ignore\")\n else:\n warnings.simplefilter(\"default\")\n\n result = _do_extraction(\n df=timeseries_container,\n column_id=column_id,\n column_value=column_value,\n column_kind=column_kind,\n column_sort=column_sort,\n n_jobs=n_jobs,\n chunk_size=chunksize,\n disable_progressbar=disable_progressbar,\n show_warnings=show_warnings,\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n distributor=distributor,\n pivot=pivot,\n )\n\n # Impute the result if requested\n if impute_function is not None:\n impute_function(result)\n\n # Turn off profiling if it was turned on\n if profile:\n profiling.end_profiling(\n profiler, filename=profiling_filename, sorting=profiling_sorting\n )\n\n return result\n\n\ndef _do_extraction(\n df,\n column_id,\n column_value,\n column_kind,\n column_sort,\n default_fc_parameters,\n kind_to_fc_parameters,\n n_jobs,\n chunk_size,\n disable_progressbar,\n show_warnings,\n distributor,\n pivot,\n):\n \"\"\"\n Wrapper around the _do_extraction_on_chunk, which calls it on all chunks in the data frame.\n A chunk is a subset of the data, with a given kind and id - so a single time series.\n\n The data is separated out into those single time series and the _do_extraction_on_chunk is\n called on each of them. The results are then combined into a single pandas DataFrame.\n\n The call is either happening in parallel or not and is showing a progress bar or not depending\n on the given flags.\n\n :param df: The dataframe in the normalized format which is used for extraction.\n :type df: pd.DataFrame\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The name of the id column to group by.\n :type column_id: str\n\n :param column_kind: The name of the column keeping record on the kind of the value.\n :type column_kind: str\n\n :param column_value: The name for the column keeping the value itself.\n :type column_value: str\n\n :param chunk_size: The size of one chunk for the parallelization\n :type chunk_size: None or int\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param distributor: Advanced parameter: See the utilities/distribution.py for more information.\n Leave to None, if you want TSFresh to choose the best distributor.\n :type distributor: DistributorBaseClass\n\n :return: the extracted features\n :rtype: pd.DataFrame\n \"\"\"\n\n data = to_tsdata(df, column_id, column_kind, column_value, column_sort)\n\n if distributor is None:\n if isinstance(data, Iterable):\n if n_jobs == 0 or n_jobs == 1:\n distributor = MapDistributor(\n disable_progressbar=disable_progressbar,\n progressbar_title=\"Feature Extraction\",\n )\n else:\n distributor = MultiprocessingDistributor(\n n_workers=n_jobs,\n disable_progressbar=disable_progressbar,\n progressbar_title=\"Feature Extraction\",\n show_warnings=show_warnings,\n )\n else:\n distributor = ApplyDistributor(\n meta=[\n (data.column_id, \"int64\"),\n (\"variable\", \"object\"),\n (\"value\", \"float64\"),\n ]\n )\n\n if not isinstance(distributor, DistributorBaseClass):\n raise ValueError(\"the passed distributor is not an DistributorBaseClass object\")\n\n kwargs = dict(\n default_fc_parameters=default_fc_parameters,\n kind_to_fc_parameters=kind_to_fc_parameters,\n show_warnings=show_warnings,\n )\n\n result = distributor.map_reduce(\n _do_extraction_on_chunk,\n data=data,\n chunk_size=chunk_size,\n function_kwargs=kwargs,\n )\n\n if not pivot:\n return result\n\n return_df = data.pivot(result)\n return return_df\n\n\ndef _do_extraction_on_chunk(\n chunk, default_fc_parameters, kind_to_fc_parameters, show_warnings=True\n):\n \"\"\"\n Main function of this module: use the feature calculators defined in the\n default_fc_parameters or kind_to_fc_parameters parameters and extract all\n features on the chunk.\n\n The chunk consists of the chunk id, the chunk kind and the data (as a Series),\n which is then converted to a numpy array - so a single time series.\n\n Returned is a list of the extracted features. Each one is a tuple consisting of\n { the id of the chunk,\n the feature name in the format ____,\n the numeric value of the feature or np.nan , }\n\n The are in the form described in :mod:`~tsfresh.utilities.string_manipulation`.\n\n :param chunk: A tuple of sample_id, kind, data\n :param default_fc_parameters: A dictionary of feature calculators.\n :param kind_to_fc_parameters: A dictionary of fc_parameters for special kinds or None.\n :param show_warnings: Surpress warnings (some feature calculators are quite verbose)\n :return: A list of calculated features.\n \"\"\"\n sample_id, kind, data = chunk\n if kind_to_fc_parameters and kind in kind_to_fc_parameters:\n fc_parameters = kind_to_fc_parameters[kind]\n else:\n fc_parameters = default_fc_parameters\n\n def _f():\n for f_or_function_name, parameter_list in fc_parameters.items():\n if callable(f_or_function_name):\n func = f_or_function_name\n else:\n func = getattr(feature_calculators, f_or_function_name)\n\n # If the function uses the index, pass is at as a pandas Series.\n # Otherwise, convert to numpy array\n if getattr(func, \"input\", None) == \"pd.Series\":\n # If it has a required index type, check that the data has the right index type.\n index_type = getattr(func, \"index_type\", None)\n if index_type is not None:\n try:\n assert isinstance(data.index, index_type)\n except AssertionError:\n warnings.warn(\n \"{} requires the data to have a index of type {}. Results will \"\n \"not be calculated\".format(f_or_function_name, index_type)\n )\n continue\n x = data\n else:\n x = data.values\n\n if getattr(func, \"fctype\", None) == \"combiner\":\n result = func(x, param=parameter_list)\n else:\n if parameter_list:\n result = (\n (convert_to_output_format(param), func(x, **param))\n for param in parameter_list\n )\n else:\n result = [(\"\", func(x))]\n\n for key, item in result:\n feature_name = str(kind) + \"__\" + func.__name__\n if key:\n feature_name += \"__\" + str(key)\n yield (sample_id, feature_name, item)\n\n with warnings.catch_warnings():\n if not show_warnings:\n warnings.simplefilter(\"ignore\")\n else:\n warnings.simplefilter(\"default\")\n\n return list(_f())\n" }, { "path": "tsfresh/feature_extraction/feature_calculators.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nThis module contains the feature calculators that take time series as input and calculate the values of the feature.\nThere are two types of features:\n\n1. feature calculators which calculate a single number (simple)\n2. feature calculators which calculate a bunch of features for a list of parameters at once,\n to use e.g. cached results (combiner). They return a list of (key, value) pairs for each input parameter.\n\nThey are specified using the \"fctype\" parameter of each feature calculator, which is added using the\nset_property function. Only functions in this python module, which have a parameter called \"fctype\" are\nseen by tsfresh as a feature calculator. Others will not be calculated.\n\nFeature calculators of type combiner should return the concatenated parameters sorted\nalphabetically ascending.\n\"\"\"\n\nimport functools\nimport itertools\nimport warnings\nfrom builtins import range\nfrom collections import defaultdict\nfrom typing import Literal\n\nimport numpy as np\nimport pandas as pd\nimport pywt\nimport stumpy\nfrom numpy.linalg import LinAlgError\nfrom scipy.signal import find_peaks_cwt, welch\nfrom scipy.stats import linregress\nfrom statsmodels.tools.sm_exceptions import MissingDataError\nfrom statsmodels.tsa.ar_model import AutoReg\n\ntry:\n import matrixprofile as mp\n from matrixprofile.exceptions import NoSolutionPossible\nexcept ImportError:\n mp = None\n\n\nfrom tsfresh.utilities.string_manipulation import convert_to_output_format\n\nwith warnings.catch_warnings():\n # Ignore warnings of the patsy package\n warnings.simplefilter(\"ignore\", DeprecationWarning)\n\n\nfrom statsmodels.tsa.stattools import acf, adfuller, pacf\n\n# todo: make sure '_' works in parameter names in all cases, add a warning if not\n\n\ndef _roll(a, shift):\n \"\"\"\n Roll 1D array elements. Improves the performance of numpy.roll() by reducing the overhead introduced from the\n flexibility of the numpy.roll() method such as the support for rolling over multiple dimensions.\n\n Elements that roll beyond the last position are re-introduced at the beginning. Similarly, elements that roll\n back beyond the first position are re-introduced at the end (with negative shift).\n\n Examples\n --------\n >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> _roll(x, shift=2)\n >>> array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])\n\n >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> _roll(x, shift=-2)\n >>> array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1])\n\n >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> _roll(x, shift=12)\n >>> array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])\n\n Benchmark\n ---------\n >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> %timeit _roll(x, shift=2)\n >>> 1.89 µs ± 341 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n\n >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> %timeit np.roll(x, shift=2)\n >>> 11.4 µs ± 776 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n\n :param a: the input array\n :type a: array_like\n :param shift: the number of places by which elements are shifted\n :type shift: int\n\n :return: shifted array with the same shape as a\n :return type: ndarray\n \"\"\"\n if not isinstance(a, np.ndarray):\n a = np.asarray(a)\n idx = shift % len(a)\n return np.concatenate([a[-idx:], a[:-idx]])\n\n\ndef _get_length_sequences_where(x):\n \"\"\"\n This method calculates the length of all sub-sequences where the array x is either True or 1.\n\n Examples\n --------\n >>> x = [0,1,0,0,1,1,1,0,0,1,0,1,1]\n >>> _get_length_sequences_where(x)\n >>> [1, 3, 1, 2]\n\n >>> x = [0,True,0,0,True,True,True,0,0,True,0,True,True]\n >>> _get_length_sequences_where(x)\n >>> [1, 3, 1, 2]\n\n >>> x = [0,True,0,0,1,True,1,0,0,True,0,1,True]\n >>> _get_length_sequences_where(x)\n >>> [1, 3, 1, 2]\n\n :param x: An iterable containing only 1, True, 0 and False values\n :return: A list with the length of all sub-sequences where the array is either True or False. If no ones or Trues\n contained, the list [0] is returned.\n \"\"\"\n if len(x) == 0:\n return [0]\n\n res = [len(list(group)) for value, group in itertools.groupby(x) if value == 1]\n return res if len(res) > 0 else [0]\n\n\ndef _estimate_friedrich_coefficients(x, m, r):\n \"\"\"\n Coefficients of polynomial :math:`h(x)`, which has been fitted to\n the deterministic dynamics of Langevin model\n .. math::\n \\\\dot{x}(t) = h(x(t)) + \\\\mathcal{N}(0,R)\n\n As described by\n\n Friedrich et al. (2000): Physics Letters A 271, p. 217-222\n *Extracting model equations from experimental data*\n\n For short time-series this method is highly dependent on the parameters.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param m: order of polynomial to fit for estimating fixed points of dynamics\n :type m: int\n :param r: number of quantiles to use for averaging\n :type r: float\n\n :return: coefficients of polynomial of deterministic dynamics\n :return type: ndarray\n \"\"\"\n assert m > 0, f\"Order of polynomial need to be positive integer, found {m}\"\n\n df = pd.DataFrame({\"signal\": x[:-1], \"delta\": np.diff(x)})\n try:\n df[\"quantiles\"] = pd.qcut(df.signal, r)\n except (ValueError, IndexError):\n return [np.nan] * (m + 1)\n\n quantiles = df.groupby(\"quantiles\")\n\n result = pd.DataFrame(\n {\"x_mean\": quantiles.signal.mean(), \"y_mean\": quantiles.delta.mean()}\n )\n result.dropna(inplace=True)\n\n try:\n return np.polyfit(result.x_mean, result.y_mean, deg=m)\n except (np.linalg.LinAlgError, ValueError):\n return [np.nan] * (m + 1)\n\n\ndef _aggregate_on_chunks(x, f_agg, chunk_len):\n \"\"\"\n Takes the time series x and constructs a lower sampled version of it by applying the aggregation function f_agg on\n consecutive chunks of length chunk_len\n\n :param x: the time series to calculate the aggregation of\n :type x: numpy.ndarray\n :param f_agg: The name of the aggregation function that should be an attribute of the pandas.Series\n :type f_agg: str\n :param chunk_len: The size of the chunks where to aggregate the time series\n :type chunk_len: int\n :return: A list of the aggregation function over the chunks\n :return type: list\n \"\"\"\n return [\n getattr(x[i * chunk_len : (i + 1) * chunk_len], f_agg)()\n for i in range(int(np.ceil(len(x) / chunk_len)))\n ]\n\n\ndef _into_subchunks(x, subchunk_length, every_n=1):\n \"\"\"\n Split the time series x into subwindows of length \"subchunk_length\", starting every \"every_n\".\n\n For example, the input data if [0, 1, 2, 3, 4, 5, 6] will be turned into a matrix\n\n 0 2 4\n 1 3 5\n 2 4 6\n\n with the settings subchunk_length = 3 and every_n = 2\n \"\"\"\n len_x = len(x)\n\n assert subchunk_length > 1\n assert every_n > 0\n\n # how often can we shift a window of size subchunk_length over the input?\n num_shifts = (len_x - subchunk_length) // every_n + 1\n shift_starts = every_n * np.arange(num_shifts)\n indices = np.arange(subchunk_length)\n\n indexer = np.expand_dims(indices, axis=0) + np.expand_dims(shift_starts, axis=1)\n return np.asarray(x)[indexer]\n\n\ndef set_property(key, value):\n \"\"\"\n This method returns a decorator that sets the property key of the function to value\n \"\"\"\n\n def decorate_func(func):\n setattr(func, key, value)\n if func.__doc__ and key == \"fctype\":\n func.__doc__ = (\n func.__doc__ + \"\\n\\n *This function is of type: \" + value + \"*\\n\"\n )\n return func\n\n return decorate_func\n\n\n@set_property(\"fctype\", \"simple\")\ndef variance_larger_than_standard_deviation(x):\n \"\"\"\n Is variance higher than the standard deviation?\n\n Boolean variable denoting if the variance of x is greater than its standard deviation. Is equal to variance of x\n being larger than 1\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: bool\n \"\"\"\n y = np.var(x)\n return y > np.sqrt(y)\n\n\n@set_property(\"fctype\", \"simple\")\ndef ratio_beyond_r_sigma(x, r):\n \"\"\"\n Ratio of values that are more than r * std(x) (so r times sigma) away from the mean of x.\n\n :param x: the time series to calculate the feature of\n :type x: iterable\n :param r: the ratio to compare with\n :type r: float\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.sum(np.abs(x - np.mean(x)) > r * np.std(x)) / x.size\n\n\n@set_property(\"fctype\", \"simple\")\ndef large_standard_deviation(x, r):\n \"\"\"\n Does time series have *large* standard deviation?\n\n Boolean variable denoting if the standard dev of x is higher than 'r' times the range = difference between max and\n min of x. Hence it checks if\n\n .. math::\n\n std(x) > r * (max(X)-min(X))\n\n According to a rule of the thumb, the standard deviation should be a forth of the range of the values.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param r: the percentage of the range to compare with\n :type r: float\n :return: the value of this feature\n :return type: bool\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.std(x) > (r * (np.max(x) - np.min(x)))\n\n\n@set_property(\"fctype\", \"combiner\")\ndef symmetry_looking(x, param):\n \"\"\"\n Boolean variable denoting if the distribution of x *looks symmetric*. This is the case if\n\n .. math::\n\n | mean(X)-median(X)| < r * (max(X)-min(X))\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"r\": x} with x (float) is the percentage of the range to compare with\n :type param: list\n :return: the value of this feature\n :return type: List[Tuple[str, bool]]\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n mean_median_difference = np.abs(np.mean(x) - np.median(x))\n max_min_difference = np.max(x) - np.min(x)\n return [\n (f\"r_{r['r']}\", mean_median_difference < (r[\"r\"] * max_min_difference))\n for r in param\n ]\n\n\n@set_property(\"fctype\", \"simple\")\ndef has_duplicate_max(x):\n \"\"\"\n Checks if the maximum value of x is observed more than once\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: bool\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.sum(x == np.max(x)) >= 2\n\n\n@set_property(\"fctype\", \"simple\")\ndef has_duplicate_min(x):\n \"\"\"\n Checks if the minimal value of x is observed more than once\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: bool\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.sum(x == np.min(x)) >= 2\n\n\n@set_property(\"fctype\", \"simple\")\ndef has_duplicate(x):\n \"\"\"\n Checks if any value in x occurs more than once\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: bool\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return x.size != np.unique(x).size\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef sum_values(x):\n \"\"\"\n Calculates the sum over the time series values\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if len(x) == 0:\n return 0\n\n return np.sum(x)\n\n\n@set_property(\"fctype\", \"combiner\")\ndef agg_autocorrelation(x, param):\n \"\"\"\n Descriptive statistics on the autocorrelation of the time series.\n\n Calculates the value of an aggregation function :math:`f_{agg}` (e.g. the variance or the mean) over the\n autocorrelation :math:`R(l)` for different lags. The autocorrelation :math:`R(l)` for lag :math:`l` is defined as\n\n .. math::\n\n R(l) = \\\\frac{1}{(n-l)\\\\sigma^2} \\\\sum_{t=1}^{n-l}(X_{t}-\\\\mu )(X_{t+l}-\\\\mu)\n\n where :math:`X_i` are the values of the time series, :math:`n` its length. Finally, :math:`\\\\sigma^2` and\n :math:`\\\\mu` are estimators for its variance and mean\n (See `Estimation of the Autocorrelation function `_).\n\n The :math:`R(l)` for different lags :math:`l` form a vector. This feature calculator applies the aggregation\n function :math:`f_{agg}` to this vector and returns\n\n .. math::\n\n f_{agg} \\\\left( R(1), \\\\ldots, R(m)\\\\right) \\\\quad \\\\text{for} \\\\quad m = max(n, maxlag).\n\n Here :math:`maxlag` is the second parameter passed to this function.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"f_agg\": x, \"maxlag\", n} with x str, the name of a numpy function\n (e.g. \"mean\", \"var\", \"std\", \"median\"), its the name of the aggregator function that is applied to the\n autocorrelations. Further, n is an int and the maximal number of lags to consider.\n :type param: list\n :return: the value of this feature\n :return type: List[Tuple[str, float]]\n \"\"\"\n # if the time series is longer than the following threshold, we use fft to calculate the acf\n THRESHOLD_TO_USE_FFT = 1250\n var = np.var(x)\n n = len(x)\n max_maxlag = max([config[\"maxlag\"] for config in param])\n\n if np.abs(var) < 10**-10 or n == 1:\n a = [0] * len(x)\n else:\n a = acf(x, adjusted=True, fft=n > THRESHOLD_TO_USE_FFT, nlags=max_maxlag)[1:]\n return [\n (\n f'f_agg_\"{config[\"f_agg\"]}\"__maxlag_{config[\"maxlag\"]}',\n getattr(np, config[\"f_agg\"])(a[: int(config[\"maxlag\"])]),\n )\n for config in param\n ]\n\n\n@set_property(\"fctype\", \"combiner\")\ndef partial_autocorrelation(x, param):\n \"\"\"\n Calculates the value of the partial autocorrelation function at the given lag.\n\n The lag `k` partial autocorrelation of a time series :math:`\\\\lbrace x_t, t = 1 \\\\ldots T \\\\rbrace` equals the\n partial correlation of :math:`x_t` and :math:`x_{t-k}`, adjusted for the intermediate variables\n :math:`\\\\lbrace x_{t-1}, \\\\ldots, x_{t-k+1} \\\\rbrace` ([1]).\n\n Following [2], it can be defined as\n\n .. math::\n\n \\\\alpha_k = \\\\frac{ Cov(x_t, x_{t-k} | x_{t-1}, \\\\ldots, x_{t-k+1})}\n {\\\\sqrt{ Var(x_t | x_{t-1}, \\\\ldots, x_{t-k+1}) Var(x_{t-k} | x_{t-1}, \\\\ldots, x_{t-k+1} )}}\n\n with (a) :math:`x_t = f(x_{t-1}, \\\\ldots, x_{t-k+1})` and (b) :math:`x_{t-k} = f(x_{t-1}, \\\\ldots, x_{t-k+1})`\n being AR(k-1) models that can be fitted by OLS. Be aware that in (a), the regression is done on past values to\n predict :math:`x_t` whereas in (b), future values are used to calculate the past value :math:`x_{t-k}`.\n It is said in [1] that \"for an AR(p), the partial autocorrelations [ :math:`\\\\alpha_k` ] will be nonzero for `k<=p`\n and zero for `k>p`.\"\n With this property, it is used to determine the lag of an AR-Process.\n\n .. rubric:: References\n\n | [1] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015).\n | Time series analysis: forecasting and control. John Wiley & Sons.\n | [2] https://onlinecourses.science.psu.edu/stat510/node/62\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"lag\": val} with int val indicating the lag to be returned\n :type param: list\n :return: the value of this feature\n :return type: List[Tuple[str, float]]\n \"\"\"\n # Check the difference between demanded lags by param and possible lags to calculate (depends on len(x))\n max_demanded_lag = max([lag[\"lag\"] for lag in param])\n n = len(x)\n\n # Check if list is too short to make calculations\n if n <= 1:\n pacf_coeffs = [np.nan] * (max_demanded_lag + 1)\n else:\n # https://github.com/statsmodels/statsmodels/pull/6846\n # PACF limits lag length to 50% of sample size.\n if max_demanded_lag >= n // 2:\n max_lag = n // 2 - 1\n else:\n max_lag = max_demanded_lag\n if max_lag > 0:\n pacf_coeffs = list(pacf(x, method=\"ld\", nlags=max_lag))\n pacf_coeffs = pacf_coeffs + [np.nan] * max(0, (max_demanded_lag - max_lag))\n else:\n pacf_coeffs = [np.nan] * (max_demanded_lag + 1)\n\n return [(f\"lag_{lag['lag']}\", pacf_coeffs[lag[\"lag\"]]) for lag in param]\n\n\n@set_property(\"fctype\", \"combiner\")\ndef augmented_dickey_fuller(x, param):\n \"\"\"\n Does the time series have a unit root?\n\n The Augmented Dickey-Fuller test is a hypothesis test which checks whether a unit root is present in a time\n series sample. This feature calculator returns the value of the respective test statistic.\n\n See the statsmodels implementation for references and more details.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"attr\": x, \"autolag\": y} with x str, either \"teststat\", \"pvalue\" or \"usedlag\"\n and with y str, either of \"AIC\", \"BIC\", \"t-stats\" or None (See the documentation of adfuller() in\n statsmodels).\n :type param: list\n :return: the value of this feature\n :return type: List[Tuple[str, float]]\n \"\"\"\n\n @functools.lru_cache()\n def compute_adf(autolag):\n try:\n return adfuller(x, autolag=autolag)\n except LinAlgError:\n return np.nan, np.nan, np.nan\n except ValueError: # occurs if sample size is too small\n return np.nan, np.nan, np.nan\n except MissingDataError: # is thrown for e.g. inf or nan in the data\n return np.nan, np.nan, np.nan\n\n res = []\n for config in param:\n autolag = config.get(\"autolag\", \"AIC\")\n\n adf = compute_adf(autolag)\n index = f'attr_\"{config[\"attr\"]}\"__autolag_\"{autolag}\"'\n\n if config[\"attr\"] == \"teststat\":\n res.append((index, adf[0]))\n elif config[\"attr\"] == \"pvalue\":\n res.append((index, adf[1]))\n elif config[\"attr\"] == \"usedlag\":\n res.append((index, adf[2]))\n else:\n res.append((index, np.nan))\n return res\n\n\n@set_property(\"fctype\", \"simple\")\ndef abs_energy(x):\n \"\"\"\n Returns the absolute energy of the time series which is the sum over the squared values\n\n .. math::\n\n E = \\\\sum_{i=1,\\\\ldots, n} x_i^2\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.dot(x, x)\n\n\n@set_property(\"fctype\", \"simple\")\ndef cid_ce(x, normalize):\n \"\"\"\n This function calculator is an estimate for a time series complexity [1] (A more complex time series has more peaks,\n valleys etc.). It calculates the value of\n\n .. math::\n\n \\\\sqrt{ \\\\sum_{i=1}^{n-1} ( x_{i} - x_{i-1})^2 }\n\n .. rubric:: References\n\n | [1] Batista, Gustavo EAPA, et al (2014).\n | CID: an efficient complexity-invariant distance for time series.\n | Data Mining and Knowledge Discovery 28.3 (2014): 634-669.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param normalize: should the time series be z-transformed?\n :type normalize: bool\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n if normalize:\n s = np.std(x)\n if s != 0:\n x = (x - np.mean(x)) / s\n else:\n return 0.0\n\n x = np.diff(x)\n return np.sqrt(np.dot(x, x))\n\n\n@set_property(\"fctype\", \"simple\")\ndef mean_abs_change(x):\n \"\"\"\n Average over first differences.\n\n Returns the mean over the absolute differences between subsequent time series values which is\n\n .. math::\n\n \\\\frac{1}{n-1} \\\\sum_{i=1,\\\\ldots, n-1} | x_{i+1} - x_{i}|\n\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.mean(np.abs(np.diff(x)))\n\n\n@set_property(\"fctype\", \"simple\")\ndef mean_change(x):\n \"\"\"\n Average over time series differences.\n\n Returns the mean over the differences between subsequent time series values which is\n\n .. math::\n\n \\\\frac{1}{n-1} \\\\sum_{i=1,\\\\ldots, n-1} x_{i+1} - x_{i} = \\\\frac{1}{n-1} (x_{n} - x_{1})\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.asarray(x)\n return (x[-1] - x[0]) / (len(x) - 1) if len(x) > 1 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef mean_second_derivative_central(x):\n \"\"\"\n Returns the mean value of a central approximation of the second derivative\n\n .. math::\n\n \\\\frac{1}{2(n-2)} \\\\sum_{i=1,\\\\ldots, n-1} \\\\frac{1}{2} (x_{i+2} - 2 \\\\cdot x_{i+1} + x_i)\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.asarray(x)\n return (x[-1] - x[-2] - x[1] + x[0]) / (2 * (len(x) - 2)) if len(x) > 2 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef median(x):\n \"\"\"\n Returns the median of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.median(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef mean(x):\n \"\"\"\n Returns the mean of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.mean(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef length(x):\n \"\"\"\n Returns the length of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: int\n \"\"\"\n return len(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef standard_deviation(x):\n \"\"\"\n Returns the standard deviation of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.std(x)\n\n\n@set_property(\"fctype\", \"simple\")\ndef variation_coefficient(x):\n \"\"\"\n Returns the variation coefficient (standard error / mean, give relative value of variation around mean) of x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n avg = np.mean(x)\n if avg == 0:\n return np.nan\n return np.std(x) / avg\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef variance(x):\n \"\"\"\n Returns the variance of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.var(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"input\", \"pd.Series\")\ndef skewness(x):\n \"\"\"\n Returns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized\n moment coefficient G1).\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, pd.Series):\n x = pd.Series(x)\n return pd.Series.skew(x, skipna=False)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"input\", \"pd.Series\")\ndef kurtosis(x):\n \"\"\"\n Returns the kurtosis of x (calculated with the adjusted Fisher-Pearson standardized\n moment coefficient G2).\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, pd.Series):\n x = pd.Series(x)\n return pd.Series.kurtosis(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef root_mean_square(x):\n \"\"\"\n Returns the root mean square (rms) of the time series.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.sqrt(np.mean(np.square(x))) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef absolute_sum_of_changes(x):\n \"\"\"\n Returns the sum over the absolute value of consecutive changes in the series x\n\n .. math::\n\n \\\\sum_{i=1, \\\\ldots, n-1} \\\\mid x_{i+1}- x_i \\\\mid\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.sum(np.abs(np.diff(x)))\n\n\n@set_property(\"fctype\", \"simple\")\ndef longest_strike_below_mean(x):\n \"\"\"\n Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.max(_get_length_sequences_where(x < np.mean(x))) if x.size > 0 else 0\n\n\n@set_property(\"fctype\", \"simple\")\ndef longest_strike_above_mean(x):\n \"\"\"\n Returns the length of the longest consecutive subsequence in x that is bigger than the mean of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.max(_get_length_sequences_where(x > np.mean(x))) if x.size > 0 else 0\n\n\n@set_property(\"fctype\", \"simple\")\ndef count_above_mean(x):\n \"\"\"\n Returns the number of values in x that are higher than the mean of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n m = np.mean(x)\n return np.where(x > m)[0].size\n\n\n@set_property(\"fctype\", \"simple\")\ndef count_below_mean(x):\n \"\"\"\n Returns the number of values in x that are lower than the mean of x\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n m = np.mean(x)\n return np.where(x < m)[0].size\n\n\n@set_property(\"fctype\", \"simple\")\ndef last_location_of_maximum(x):\n \"\"\"\n Returns the relative last location of the maximum value of x.\n The position is calculated relatively to the length of x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.asarray(x)\n return 1.0 - np.argmax(x[::-1]) / len(x) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef first_location_of_maximum(x):\n \"\"\"\n Returns the first location of the maximum value of x.\n The position is calculated relatively to the length of x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.argmax(x) / len(x) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef last_location_of_minimum(x):\n \"\"\"\n Returns the last location of the minimal value of x.\n The position is calculated relatively to the length of x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.asarray(x)\n return 1.0 - np.argmin(x[::-1]) / len(x) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef first_location_of_minimum(x):\n \"\"\"\n Returns the first location of the minimal value of x.\n The position is calculated relatively to the length of x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n return np.argmin(x) / len(x) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef percentage_of_reoccurring_values_to_all_values(x):\n \"\"\"\n Returns the percentage of values that are present in the time series\n more than once.\n\n len(different values occurring more than once) / len(different values)\n\n This means the percentage is normalized to the number of unique values,\n in contrast to the percentage_of_reoccurring_datapoints_to_all_datapoints.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if len(x) == 0:\n return np.nan\n\n _, counts = np.unique(x, return_counts=True)\n\n if counts.shape[0] == 0:\n return 0.0\n\n return np.sum(counts > 1) / float(counts.shape[0])\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"input\", \"pd.Series\")\ndef percentage_of_reoccurring_datapoints_to_all_datapoints(x):\n \"\"\"\n Returns the percentage of non-unique data points. Non-unique means that they are\n contained another time in the time series again.\n\n # of data points occurring more than once / # of all data points\n\n This means the ratio is normalized to the number of data points in the time series,\n in contrast to the percentage_of_reoccurring_values_to_all_values.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if len(x) == 0:\n return np.nan\n\n if not isinstance(x, pd.Series):\n x = pd.Series(x)\n\n value_counts = x.value_counts()\n reoccuring_values = value_counts[value_counts > 1].sum()\n\n if np.isnan(reoccuring_values):\n return 0.0\n\n return reoccuring_values / x.size\n\n\n@set_property(\"fctype\", \"simple\")\ndef sum_of_reoccurring_values(x):\n \"\"\"\n Returns the sum of all values, that are present in the time series\n more than once.\n\n For example\n\n sum_of_reoccurring_values([2, 2, 2, 2, 1]) = 2\n\n as 2 is a reoccurring value, so it is summed up with all\n other reoccuring values (there is none), so the result is 2.\n\n This is in contrast to ``sum_of_reoccurring_data_points``,\n where each reoccuring value is only counted as often as\n it is present in the data.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n unique, counts = np.unique(x, return_counts=True)\n counts[counts < 2] = 0\n counts[counts > 1] = 1\n return np.sum(counts * unique)\n\n\n@set_property(\"fctype\", \"simple\")\ndef sum_of_reoccurring_data_points(x):\n \"\"\"\n Returns the sum of all data points, that are present in the time series\n more than once.\n\n For example\n\n sum_of_reoccurring_data_points([2, 2, 2, 2, 1]) = 8\n\n as 2 is a reoccurring value, so all 2's are summed up.\n\n This is in contrast to ``sum_of_reoccurring_values``,\n where each reoccuring value is only counted once.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n unique, counts = np.unique(x, return_counts=True)\n counts[counts < 2] = 0\n return np.sum(counts * unique)\n\n\n@set_property(\"fctype\", \"simple\")\ndef ratio_value_number_to_time_series_length(x):\n \"\"\"\n Returns a factor which is 1 if all values in the time series occur only once,\n and below one if this is not the case.\n In principle, it just returns\n\n # unique values / # values\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n if x.size == 0:\n return np.nan\n\n return np.unique(x).size / x.size\n\n\n@set_property(\"fctype\", \"combiner\")\ndef fft_coefficient(x, param):\n \"\"\"\n Calculates the fourier coefficients of the one-dimensional discrete Fourier Transform for real input by fast\n fourier transformation algorithm\n\n .. math::\n A_k = \\\\sum_{m=0}^{n-1} a_m \\\\exp \\\\left \\\\{ -2 \\\\pi i \\\\frac{m k}{n} \\\\right \\\\}, \\\\qquad k = 0,\n \\\\ldots , n-1.\n\n The resulting coefficients will be complex, this feature calculator can return the real part (attr==\"real\"),\n the imaginary part (attr==\"imag), the absolute value (attr=\"\"abs) and the angle in degrees (attr==\"angle).\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"coeff\": x, \"attr\": s} with x int and x >= 0, s str and in [\"real\", \"imag\",\n \"abs\", \"angle\"]\n :type param: list\n :return: the different feature values\n :return type: Iterator[Tuple[str, float]]\n \"\"\"\n\n assert (\n min((config[\"coeff\"] for config in param)) >= 0\n ), \"Coefficients must be positive or zero.\"\n assert {config[\"attr\"] for config in param} <= {\n \"imag\",\n \"real\",\n \"abs\",\n \"angle\",\n }, 'Attribute must be \"real\", \"imag\", \"angle\" or \"abs\"'\n\n fft = np.fft.rfft(x)\n\n def complex_agg(x, agg: Literal[\"real\", \"imag\", \"angle\", \"abs\"]):\n if agg == \"real\":\n return x.real\n elif agg == \"imag\":\n return x.imag\n elif agg == \"abs\":\n return np.abs(x)\n elif agg == \"angle\":\n return np.angle(x, deg=True)\n else:\n raise ValueError('`agg` must be \"real\", \"imag\", \"angle\" or \"abs\"')\n\n res = [\n complex_agg(fft[config[\"coeff\"]], config[\"attr\"])\n if config[\"coeff\"] < len(fft)\n else np.nan\n for config in param\n ]\n index = [f'attr_\"{config[\"attr\"]}\"__coeff_{config[\"coeff\"]}' for config in param]\n return zip(index, res)\n\n\n@set_property(\"fctype\", \"combiner\")\ndef fft_aggregated(x, param):\n \"\"\"\n Returns the spectral centroid (mean), variance, skew, and kurtosis of the absolute fourier transform spectrum.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"aggtype\": s} where s str and in [\"centroid\", \"variance\",\n \"skew\", \"kurtosis\"]\n :type param: list\n :return: the different feature values\n :return type: Iterator[Tuple[str, float]]\n \"\"\"\n\n assert {config[\"aggtype\"] for config in param} <= {\n \"centroid\",\n \"variance\",\n \"skew\",\n \"kurtosis\",\n }, 'Attribute must be \"centroid\", \"variance\", \"skew\", \"kurtosis\"'\n\n def get_moment(y, moment):\n \"\"\"\n Returns the (non centered) moment of the distribution y:\n E[y**moment] = \\\\sum_i[index(y_i)^moment * y_i] / \\\\sum_i[y_i]\n\n :param y: the discrete distribution from which one wants to calculate the moment\n :type y: pandas.Series or np.array\n :param moment: the moment one wants to calcalate (choose 1,2,3, ... )\n :type moment: int\n :return: the moment requested\n :return type: float\n \"\"\"\n return y.dot(np.arange(len(y), dtype=float) ** moment) / y.sum()\n\n def get_centroid(y):\n \"\"\"\n :param y: the discrete distribution from which one wants to calculate the centroid\n :type y: pandas.Series or np.array\n :return: the centroid of distribution y (aka distribution mean, first moment)\n :return type: float\n \"\"\"\n return get_moment(y, 1)\n\n def get_variance(y):\n \"\"\"\n :param y: the discrete distribution from which one wants to calculate the variance\n :type y: pandas.Series or np.array\n :return: the variance of distribution y\n :return type: float\n \"\"\"\n return get_moment(y, 2) - get_centroid(y) ** 2\n\n def get_skew(y):\n \"\"\"\n Calculates the skew as the third standardized moment.\n Ref: https://en.wikipedia.org/wiki/Skewness#Definition\n\n :param y: the discrete distribution from which one wants to calculate the skew\n :type y: pandas.Series or np.array\n :return: the skew of distribution y\n :return type: float\n \"\"\"\n\n var = get_variance(y)\n # In the limit of a dirac delta, skew should be 0 and variance 0. However, in the discrete limit,\n # the skew blows up as variance --> 0, hence return nan when variance is smaller than a resolution of 0.5:\n if var < 0.5:\n return np.nan\n centroid = get_centroid(y)\n return (get_moment(y, 3) - 3 * centroid * var - centroid**3) / get_variance(\n y\n ) ** (1.5)\n\n def get_kurtosis(y):\n \"\"\"\n Calculates the kurtosis as the fourth standardized moment.\n Ref: https://en.wikipedia.org/wiki/Kurtosis#Pearson_moments\n\n :param y: the discrete distribution from which one wants to calculate the kurtosis\n :type y: pandas.Series or np.array\n :return: the kurtosis of distribution y\n :return type: float\n \"\"\"\n\n var = get_variance(y)\n # In the limit of a dirac delta, kurtosis should be 3 and variance 0. However, in the discrete limit,\n # the kurtosis blows up as variance --> 0, hence return nan when variance is smaller than a resolution of 0.5:\n if var < 0.5:\n return np.nan\n centroid = get_centroid(y)\n return (\n get_moment(y, 4)\n - 4 * centroid * get_moment(y, 3)\n + 6 * get_moment(y, 2) * centroid**2\n - 3 * centroid\n ) / get_variance(y) ** 2\n\n calculation = {\n \"centroid\": get_centroid,\n \"variance\": get_variance,\n \"skew\": get_skew,\n \"kurtosis\": get_kurtosis,\n }\n\n fft_abs = np.abs(np.fft.rfft(x))\n\n res = [calculation[config[\"aggtype\"]](fft_abs) for config in param]\n index = [f'aggtype_\"{config[\"aggtype\"]}\"' for config in param]\n return zip(index, res)\n\n\n@set_property(\"fctype\", \"simple\")\ndef number_peaks(x, n):\n \"\"\"\n Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined as a\n subsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right.\n\n Hence in the sequence\n\n >>> x = [3, 0, 0, 4, 0, 0, 13]\n\n 4 is a peak of support 1 and 2 because in the subsequences\n\n >>> [0, 4, 0]\n >>> [0, 0, 4, 0, 0]\n\n 4 is still the highest value. Here, 4 is not a peak of support 3 because 13 is the 3th neighbour to the right of 4\n and its bigger than 4.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param n: the support of the peak\n :type n: int\n :return: the value of this feature\n :return type: float\n \"\"\"\n x_reduced = x[n:-n]\n\n res = None\n for i in range(1, n + 1):\n result_first = x_reduced > _roll(x, i)[n:-n]\n\n if res is None:\n res = result_first\n else:\n res &= result_first\n\n res &= x_reduced > _roll(x, -i)[n:-n]\n return np.sum(res)\n\n\n@set_property(\"fctype\", \"combiner\")\ndef index_mass_quantile(x, param):\n \"\"\"\n Calculates the relative index i of time series x where q% of the mass of x lies left of i.\n For example for q = 50% this feature calculator will return the mass center of the time series.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"q\": x} with x float\n :type param: list\n :return: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n\n x = np.asarray(x)\n abs_x = np.abs(x)\n s = np.sum(abs_x)\n\n if s == 0:\n # all values in x are zero or it has length 0\n return [(f\"q_{config['q']}\", np.nan) for config in param]\n\n # at least one value is not zero\n mass_centralized = np.cumsum(abs_x) / s\n return [\n (\n f\"q_{config['q']}\",\n (np.argmax(mass_centralized >= config[\"q\"]) + 1) / len(x),\n )\n for config in param\n ]\n\n\ndef _ricker(points, a):\n \"\"\"Custom implementation of the ricker wavelet, copied from scipy as scipy dropped it.\"\"\"\n A = 2 / (np.sqrt(3 * a) * (np.pi**0.25))\n wsq = a**2\n vec = np.arange(0, points) - (points - 1.0) / 2\n xsq = vec**2\n mod = 1 - xsq / wsq\n gauss = np.exp(-xsq / (2 * wsq))\n total = A * mod * gauss\n return total\n\n\n@set_property(\"fctype\", \"simple\")\ndef number_cwt_peaks(x, n):\n \"\"\"\n Number of different peaks in x.\n\n To estimamte the numbers of peaks, x is smoothed by a ricker wavelet for widths ranging from 1 to n. This feature\n calculator returns the number of peaks that occur at enough width scales and with sufficiently high\n Signal-to-Noise-Ratio (SNR)\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param n: maximum width to consider\n :type n: int\n :return: the value of this feature\n :return type: int\n \"\"\"\n return len(\n find_peaks_cwt(\n vector=x, widths=np.array(list(range(1, n + 1))), wavelet=_ricker\n )\n )\n\n\n@set_property(\"fctype\", \"combiner\")\ndef linear_trend(x, param):\n \"\"\"\n Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to\n length of the time series minus one.\n This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model.\n The parameters control which of the characteristics are returned.\n\n Possible extracted attributes are \"pvalue\", \"rvalue\", \"intercept\", \"slope\", \"stderr\", see the documentation of\n linregress for more information.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"attr\": x} with x an string, the attribute name of the regression model\n :type param: list\n :return: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n # todo: we could use the index of the DataFrame here\n lin_reg = linregress(range(len(x)), x)\n\n return [\n (f'attr_\"{config[\"attr\"]}\"', getattr(lin_reg, config[\"attr\"]))\n for config in param\n ]\n\n\n@set_property(\"fctype\", \"combiner\")\ndef cwt_coefficients(x, param):\n \"\"\"\n Calculates a Continuous wavelet transform for the Ricker wavelet, also known as the \"Mexican hat wavelet\" which is\n defined by\n\n .. math::\n \\\\frac{2}{\\\\sqrt{3a} \\\\pi^{\\\\frac{1}{4}}} (1 - \\\\frac{x^2}{a^2}) exp(-\\\\frac{x^2}{2a^2})\n\n where :math:`a` is the width parameter of the wavelet function.\n\n This feature calculator takes three different parameter: widths, coeff and w. The feature calculator takes all the\n different widths arrays and then calculates the cwt one time for each different width array. Then the values for the\n different coefficient for coeff and width w are returned. (For each dic in param one feature is returned)\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"widths\":x, \"coeff\": y, \"w\": z} with x array of int and y,z int\n :type param: list\n :return: the different feature values\n :return type: Iterator[Tuple[str, float]]\n \"\"\"\n\n calculated_cwt = {}\n res = []\n indices = []\n\n for parameter_combination in param:\n widths = tuple(parameter_combination[\"widths\"])\n w = parameter_combination[\"w\"]\n coeff = parameter_combination[\"coeff\"]\n\n if widths not in calculated_cwt:\n calculated_cwt[widths], _ = pywt.cwt(x, scales=widths, wavelet=\"mexh\")\n\n calculated_cwt_for_widths = calculated_cwt[widths]\n\n indices += [f\"coeff_{coeff}__w_{w}__widths_{widths}\"]\n\n i = widths.index(w)\n if calculated_cwt_for_widths.shape[1] <= coeff:\n res += [np.nan]\n else:\n res += [calculated_cwt_for_widths[i, coeff]]\n\n return zip(indices, res)\n\n\n@set_property(\"fctype\", \"combiner\")\ndef spkt_welch_density(x, param):\n \"\"\"\n This feature calculator estimates the cross power spectral density of the time series x at different frequencies.\n To do so, the time series is first shifted from the time domain to the frequency domain.\n\n The feature calculators returns the power spectrum of the different frequencies.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"coeff\": x} with x int\n :type param: list\n :return: the different feature values\n :return type: Iterator[Tuple[str, float]]\n \"\"\"\n\n max_length_per_segment = 256\n x = np.asarray(x)\n _, pxx = welch(x, nperseg=min(len(x), max_length_per_segment))\n coeff = [config[\"coeff\"] for config in param]\n indices = [f\"coeff_{i}\" for i in coeff]\n\n if len(pxx) <= np.max(\n coeff\n ): # There are fewer data points in the time series than requested coefficients\n\n # filter coefficients that are not contained in pxx\n reduced_coeff = [coefficient for coefficient in coeff if len(pxx) > coefficient]\n not_calculated_coefficients = [\n coefficient for coefficient in coeff if coefficient not in reduced_coeff\n ]\n\n # Fill up the rest of the requested coefficients with np.nans\n return zip(\n indices,\n list(pxx[reduced_coeff]) + [np.nan] * len(not_calculated_coefficients),\n )\n\n return zip(indices, pxx[coeff])\n\n\n@set_property(\"fctype\", \"combiner\")\ndef ar_coefficient(x, param):\n \"\"\"\n This feature calculator fits the unconditional maximum likelihood\n of an autoregressive AR(k) process.\n The k parameter is the maximum lag of the process\n\n .. math::\n\n X_{t}=\\\\varphi_0 +\\\\sum _{{i=1}}^{k}\\\\varphi_{i}X_{{t-i}}+\\\\varepsilon_{t}\n\n For the configurations from param which should contain the maxlag \"k\" and such an AR process is calculated. Then\n the coefficients :math:`\\\\varphi_{i}` whose index :math:`i` contained from \"coeff\" are returned.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"coeff\": x, \"k\": y} with x,y int\n :type param: list\n :return x: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n calculated_ar_params = {}\n\n x_as_list = list(x)\n\n res = {}\n\n for parameter_combination in param:\n k = parameter_combination[\"k\"]\n p = parameter_combination[\"coeff\"]\n\n column_name = f\"coeff_{p}__k_{k}\"\n\n if k not in calculated_ar_params:\n try:\n calculated_ar = AutoReg(x_as_list, lags=k, trend=\"c\")\n calculated_ar_params[k] = calculated_ar.fit().params\n except (ZeroDivisionError, LinAlgError, ValueError):\n calculated_ar_params[k] = [np.nan] * k\n\n mod = calculated_ar_params[k]\n if p <= k:\n try:\n res[column_name] = mod[p]\n except IndexError:\n res[column_name] = 0\n else:\n res[column_name] = np.nan\n\n return list(res.items())\n\n\n@set_property(\"fctype\", \"simple\")\ndef change_quantiles(x, ql, qh, isabs, f_agg):\n \"\"\"\n First fixes a corridor given by the quantiles ql and qh of the distribution of x.\n Then calculates the average, absolute value of consecutive changes of the series x inside this corridor.\n\n Think about selecting a corridor on the\n y-Axis and only calculating the mean of the absolute change of the time series inside this corridor.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param ql: the lower quantile of the corridor\n :type ql: float\n :param qh: the higher quantile of the corridor\n :type qh: float\n :param isabs: should the absolute differences be taken?\n :type isabs: bool\n :param f_agg: the aggregator function that is applied to the differences in the bin\n :type f_agg: str, name of a numpy function (e.g. mean, var, std, median)\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n if ql >= qh:\n return 0.0\n\n div = np.diff(x)\n if isabs:\n div = np.abs(div)\n # All values that originate from the corridor between the quantiles ql and qh will have the category 0,\n # other will be np.nan\n try:\n bin_cat = pd.qcut(x, [ql, qh], labels=False)\n bin_cat_0 = bin_cat == 0\n except ValueError: # Occurs when ql are qh effectively equal, e.g. x is not long enough or is too categorical\n return 0.0\n # We only count changes that start and end inside the corridor\n ind = (bin_cat_0 & _roll(bin_cat_0, 1))[1:]\n if np.sum(ind) == 0:\n return 0.0\n\n ind_inside_corridor = np.where(ind == 1)\n aggregator = getattr(np, f_agg)\n return aggregator(div[ind_inside_corridor])\n\n\n@set_property(\"fctype\", \"simple\")\ndef time_reversal_asymmetry_statistic(x, lag):\n \"\"\"\n Returns the time reversal asymmetry statistic.\n\n This function calculates the value of\n\n .. math::\n\n \\\\frac{1}{n-2lag} \\\\sum_{i=1}^{n-2lag} x_{i + 2 \\\\cdot lag}^2 \\\\cdot x_{i + lag} - x_{i + lag} \\\\cdot x_{i}^2\n\n which is\n\n .. math::\n\n \\\\mathbb{E}[L^2(X)^2 \\\\cdot L(X) - L(X) \\\\cdot X^2]\n\n where :math:`\\\\mathbb{E}` is the mean and :math:`L` is the lag operator. It was proposed in [1] as a\n promising feature to extract from time series.\n\n .. rubric:: References\n\n | [1] Fulcher, B.D., Jones, N.S. (2014).\n | Highly comparative feature-based time-series classification.\n | Knowledge and Data Engineering, IEEE Transactions on 26, 3026–3037.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param lag: the lag that should be used in the calculation of the feature\n :type lag: int\n :return: the value of this feature\n :return type: float\n \"\"\"\n n = len(x)\n x = np.asarray(x)\n if 2 * lag >= n:\n return 0.0\n\n one_lag = _roll(x, -lag)\n two_lag = _roll(x, 2 * -lag)\n return np.mean((two_lag * two_lag * one_lag - one_lag * x * x)[0 : (n - 2 * lag)])\n\n\n@set_property(\"fctype\", \"simple\")\ndef c3(x, lag):\n \"\"\"\n Uses c3 statistics to measure non linearity in the time series\n\n This function calculates the value of\n\n .. math::\n\n \\\\frac{1}{n-2lag} \\\\sum_{i=1}^{n-2lag} x_{i + 2 \\\\cdot lag} \\\\cdot x_{i + lag} \\\\cdot x_{i}\n\n which is\n\n .. math::\n\n \\\\mathbb{E}[L^2(X) \\\\cdot L(X) \\\\cdot X]\n\n where :math:`\\\\mathbb{E}` is the mean and :math:`L` is the lag operator. It was proposed in [1] as a measure of\n non linearity in the time series.\n\n .. rubric:: References\n\n | [1] Schreiber, T. and Schmitz, A. (1997).\n | Discrimination power of measures for nonlinearity in a time series\n | PHYSICAL REVIEW E, VOLUME 55, NUMBER 5\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param lag: the lag that should be used in the calculation of the feature\n :type lag: int\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n n = x.size\n\n if 2 * lag >= n:\n return 0.0\n\n return np.mean((_roll(x, 2 * -lag) * _roll(x, -lag) * x)[0 : (n - 2 * lag)])\n\n\n@set_property(\"fctype\", \"simple\")\ndef mean_n_absolute_max(x, number_of_maxima):\n \"\"\"\n Calculates the arithmetic mean of the n absolute maximum values of the time series.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param number_of_maxima: the number of maxima, which should be considered\n :type number_of_maxima: int\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n\n assert (\n number_of_maxima > 0\n ), f\" number_of_maxima={number_of_maxima} which is not greater than 1\"\n\n n_absolute_maximum_values = np.sort(np.absolute(x))[-number_of_maxima:]\n\n return np.mean(n_absolute_maximum_values) if len(x) > number_of_maxima else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\ndef binned_entropy(x, max_bins):\n \"\"\"\n First bins the values of x into max_bins equidistant bins.\n Then calculates the value of\n\n .. math::\n\n - \\\\sum_{k=0}^{min(max\\\\_bins, len(x))} p_k log(p_k) \\\\cdot \\\\mathbf{1}_{(p_k > 0)}\n\n where :math:`p_k` is the percentage of samples in bin :math:`k`.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param max_bins: the maximal number of bins\n :type max_bins: int\n :return: the value of this feature\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n\n # nan makes no sense here\n if np.isnan(x).any():\n return np.nan\n\n hist, bin_edges = np.histogram(x, bins=max_bins)\n probs = hist / x.size\n probs[probs == 0] = 1.0\n return -np.sum(probs * np.log(probs))\n\n\n# todo - include latex formula\n# todo - check if vectorizable\n@set_property(\"high_comp_cost\", True)\n@set_property(\"fctype\", \"simple\")\ndef sample_entropy(x):\n \"\"\"\n Calculate and return sample entropy of x.\n\n .. rubric:: References\n\n | [1] http://en.wikipedia.org/wiki/Sample_Entropy\n | [2] https://www.ncbi.nlm.nih.gov/pubmed/10843903?dopt=Abstract\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.array(x)\n\n # if one of the values is NaN, we can not compute anything meaningful\n if np.isnan(x).any():\n return np.nan\n\n m = 2 # common value for m, according to wikipedia...\n tolerance = 0.2 * np.std(\n x\n ) # 0.2 is a common value for r, according to wikipedia...\n\n # Split time series and save all templates of length m\n # Basically we turn [1, 2, 3, 4] into [1, 2], [2, 3], [3, 4]\n xm = _into_subchunks(x, m)\n\n # Now calculate the maximum distance between each of those pairs\n # np.abs(xmi - xm).max(axis=1)\n # and check how many are below the tolerance.\n # For speed reasons, we are not doing this in a nested for loop,\n # but with numpy magic.\n # Example:\n # if x = [1, 2, 3]\n # then xm = [[1, 2], [2, 3]]\n # so we will substract xm from [1, 2] => [[0, 0], [-1, -1]]\n # and from [2, 3] => [[1, 1], [0, 0]]\n # taking the abs and max gives us:\n # [0, 1] and [1, 0]\n # as the diagonal elements are always 0, we substract 1.\n B = np.sum([np.sum(np.abs(xmi - xm).max(axis=1) <= tolerance) - 1 for xmi in xm])\n\n # Similar for computing A\n xmp1 = _into_subchunks(x, m + 1)\n\n A = np.sum(\n [np.sum(np.abs(xmi - xmp1).max(axis=1) <= tolerance) - 1 for xmi in xmp1]\n )\n\n # Return SampEn\n return -np.log(A / B)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"high_comp_cost\", True)\ndef approximate_entropy(x, m, r):\n \"\"\"\n Implements a vectorized Approximate entropy algorithm.\n\n https://en.wikipedia.org/wiki/Approximate_entropy\n\n For short time-series this method is highly dependent on the parameters,\n but should be stable for N > 2000, see:\n\n Yentes et al. (2012) -\n *The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets*\n\n\n Other shortcomings and alternatives discussed in:\n\n Richman & Moorman (2000) -\n *Physiological time-series analysis using approximate entropy and sample entropy*\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param m: Length of compared run of data\n :type m: int\n :param r: Filtering level, must be positive\n :type r: float\n\n :return: Approximate entropy\n :return type: float\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n\n N = x.size\n r *= np.std(x)\n if r < 0:\n raise ValueError(\"Parameter r must be positive.\")\n if N <= m + 1:\n return 0\n\n def _phi(m):\n x_re = np.array([x[i : i + m] for i in range(N - m + 1)])\n C = np.sum(\n np.max(np.abs(x_re[:, np.newaxis] - x_re[np.newaxis, :]), axis=2) <= r,\n axis=0,\n ) / (N - m + 1)\n return np.sum(np.log(C)) / (N - m + 1.0)\n\n return np.abs(_phi(m) - _phi(m + 1))\n\n\n@set_property(\"fctype\", \"simple\")\ndef fourier_entropy(x, bins):\n \"\"\"\n Calculate the binned entropy of the power spectral density of the time series\n (using the welch method).\n\n Ref: https://hackaday.io/project/707-complexity-of-a-time-series/details\n Ref: https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html\n\n \"\"\"\n max_length_per_segment = 256\n x = np.asarray(x)\n _, pxx = welch(x, nperseg=min(len(x), max_length_per_segment))\n return binned_entropy(pxx / np.max(pxx), bins)\n\n\n@set_property(\"fctype\", \"simple\")\ndef lempel_ziv_complexity(x, bins):\n \"\"\"\n Calculate a complexity estimate based on the Lempel-Ziv compression\n algorithm.\n\n The complexity is defined as the number of dictionary entries (or sub-words) needed\n to encode the time series when viewed from left to right.\n For this, the time series is first binned into the given number of bins.\n Then it is converted into sub-words with different prefixes.\n The number of sub-words needed for this divided by the length of the time\n series is the complexity estimate.\n\n For example, if the time series (after binning in only 2 bins) would look like \"100111\",\n the different sub-words would be 1, 0, 01 and 11 and therefore the result is 4/6 = 0.66.\n\n Ref: https://github.com/Naereen/Lempel-Ziv_Complexity/blob/master/src/lempel_ziv_complexity.py\n\n \"\"\"\n x = np.asarray(x)\n\n bins = np.linspace(np.min(x), np.max(x), bins + 1)[1:]\n sequence = np.searchsorted(bins, x, side=\"left\")\n\n sub_strings = set()\n n = len(sequence)\n\n ind = 0\n inc = 1\n while ind + inc <= n:\n # convert to tuple in order to make it hashable\n sub_str = tuple(sequence[ind : ind + inc])\n if sub_str in sub_strings:\n inc += 1\n else:\n sub_strings.add(sub_str)\n ind += inc\n inc = 1\n return len(sub_strings) / n\n\n\n@set_property(\"fctype\", \"simple\")\ndef permutation_entropy(x, tau, dimension):\n \"\"\"\n Calculate the permutation entropy.\n\n Three steps are needed for this:\n\n 1. chunk the data into sub-windows of length D starting every tau.\n Following the example from the reference, a vector\n\n x = [4, 7, 9, 10, 6, 11, 3\n\n with D = 3 and tau = 1 is turned into\n\n [[ 4, 7, 9],\n [ 7, 9, 10],\n [ 9, 10, 6],\n [10, 6, 11],\n [ 6, 11, 3]]\n\n 2. replace each D-window by the permutation, that\n captures the ordinal ranking of the data.\n That gives\n\n [[0, 1, 2],\n [0, 1, 2],\n [1, 2, 0],\n [1, 0, 2],\n [1, 2, 0]]\n\n 3. Now we just need to count the frequencies of every permutation\n and return their entropy (we use log_e and not log_2).\n\n Ref: https://www.aptech.com/blog/permutation-entropy/\n Bandt, Christoph and Bernd Pompe.\n “Permutation entropy: a natural complexity measure for time series.”\n Physical review letters 88 17 (2002): 174102 .\n \"\"\"\n\n X = _into_subchunks(x, dimension, tau)\n if len(X) == 0:\n return np.nan\n # Now that is clearly black, magic, but see here:\n # https://stackoverflow.com/questions/54459554/numpy-find-index-in-sorted-array-in-an-efficient-way\n permutations = np.argsort(np.argsort(X))\n # Count the number of occurences\n _, counts = np.unique(permutations, axis=0, return_counts=True)\n # turn them into frequencies\n probs = counts / len(permutations)\n # and return their entropy\n return -np.sum(probs * np.log(probs))\n\n\n@set_property(\"fctype\", \"simple\")\ndef autocorrelation(x, lag):\n \"\"\"\n Calculates the autocorrelation of the specified lag, according to the formula [1]\n\n .. math::\n\n \\\\frac{1}{(n-l)\\\\sigma^{2}} \\\\sum_{t=1}^{n-l}(X_{t}-\\\\mu )(X_{t+l}-\\\\mu)\n\n where :math:`n` is the length of the time series :math:`X_i`, :math:`\\\\sigma^2` its variance and :math:`\\\\mu` its\n mean. `l` denotes the lag.\n\n .. rubric:: References\n\n [1] https://en.wikipedia.org/wiki/Autocorrelation#Estimation\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param lag: the lag\n :type lag: int\n :return: the value of this feature\n :return type: float\n \"\"\"\n # This is important: If a series is passed, the product below is calculated\n # based on the index, which corresponds to squaring the series.\n if isinstance(x, pd.Series):\n x = x.values\n if len(x) < lag:\n return np.nan\n # Slice the relevant subseries based on the lag\n y1 = x[: (len(x) - lag)]\n y2 = x[lag:]\n # Subtract the mean of the whole series x\n x_mean = np.mean(x)\n # The result is sometimes referred to as \"covariation\"\n sum_product = np.sum((y1 - x_mean) * (y2 - x_mean))\n # Return the normalized unbiased covariance\n v = np.var(x)\n if np.isclose(v, 0):\n return np.nan\n\n return sum_product / ((len(x) - lag) * v)\n\n\n@set_property(\"fctype\", \"simple\")\ndef quantile(x, q):\n \"\"\"\n Calculates the q quantile of x. This is the value of x greater than q% of the ordered values from x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param q: the quantile to calculate\n :type q: float\n :return: the value of this feature\n :return type: float\n \"\"\"\n if len(x) == 0:\n return np.nan\n return np.quantile(x, q)\n\n\n@set_property(\"fctype\", \"simple\")\ndef number_crossing_m(x, m):\n \"\"\"\n Calculates the number of crossings of x on m. A crossing is defined as two sequential values where the first value\n is lower than m and the next is greater, or vice-versa. If you set m to zero, you will get the number of zero\n crossings.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param m: the threshold for the crossing\n :type m: float\n :return: the value of this feature\n :return type: int\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n # From https://stackoverflow.com/questions/3843017/efficiently-detect-sign-changes-in-python\n # However, we are not going with the fastest version as it breaks with pandas\n positive = x > m\n return np.where(np.diff(positive))[0].size\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef maximum(x):\n \"\"\"\n Calculates the highest value of the time series x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.max(x)\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef absolute_maximum(x):\n \"\"\"\n Calculates the highest absolute value of the time series x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.max(np.absolute(x)) if len(x) > 0 else np.nan\n\n\n@set_property(\"fctype\", \"simple\")\n@set_property(\"minimal\", True)\ndef minimum(x):\n \"\"\"\n Calculates the lowest value of the time series x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.min(x)\n\n\n@set_property(\"fctype\", \"simple\")\ndef value_count(x, value):\n \"\"\"\n Count occurrences of `value` in time series x.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param value: the value to be counted\n :type value: int or float\n :return: the count\n :rtype: int\n \"\"\"\n if not isinstance(x, (np.ndarray, pd.Series)):\n x = np.asarray(x)\n\n if np.isnan(value):\n return np.isnan(x).sum()\n\n return x[x == value].size\n\n\n@set_property(\"fctype\", \"simple\")\ndef range_count(x, min, max):\n \"\"\"\n Count observed values within the interval [min, max).\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param min: the inclusive lower bound of the range\n :type min: int or float\n :param max: the exclusive upper bound of the range\n :type max: int or float\n :return: the count of values within the range\n :rtype: int\n \"\"\"\n return np.sum((x >= min) & (x < max))\n\n\n@set_property(\"fctype\", \"combiner\")\ndef friedrich_coefficients(x, param):\n \"\"\"\n Coefficients of polynomial :math:`h(x)`, which has been fitted to\n the deterministic dynamics of Langevin model\n\n .. math::\n \\\\dot{x}(t) = h(x(t)) + \\\\mathcal{N}(0,R)\n\n as described by [1].\n\n For short time-series this method is highly dependent on the parameters.\n\n .. rubric:: References\n\n | [1] Friedrich et al. (2000): Physics Letters A 271, p. 217-222\n | *Extracting model equations from experimental data*\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"m\": x, \"r\": y, \"coeff\": z} with x being positive integer,\n the order of polynomial to fit for estimating fixed points of\n dynamics, y positive float, the number of quantiles to use for averaging and finally z,\n a positive integer corresponding to the returned coefficient\n :type param: list\n :return: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n # calculated is dictionary storing the calculated coefficients {m: {r: friedrich_coefficients}}\n calculated = defaultdict(dict)\n # res is a dictionary containing the results {\"m_10__r_2__coeff_3\": 15.43}\n res = {}\n\n for parameter_combination in param:\n m = parameter_combination[\"m\"]\n r = parameter_combination[\"r\"]\n coeff = parameter_combination[\"coeff\"]\n\n assert coeff >= 0, f\"Coefficients must be positive or zero. Found {coeff}\"\n\n # calculate the current friedrich coefficients if they do not exist yet\n if m not in calculated or r not in calculated[m]:\n calculated[m][r] = _estimate_friedrich_coefficients(x, m, r)\n\n try:\n res[f\"coeff_{coeff}__m_{m}__r_{r}\"] = calculated[m][r][coeff]\n except IndexError:\n res[f\"coeff_{coeff}__m_{m}__r_{r}\"] = np.nan\n\n return list(res.items())\n\n\n@set_property(\"fctype\", \"simple\")\ndef max_langevin_fixed_point(x, r, m):\n \"\"\"\n Largest fixed point of dynamics :math:argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`,\n which has been fitted to the deterministic dynamics of Langevin model\n\n .. math::\n \\\\dot(x)(t) = h(x(t)) + R \\\\mathcal(N)(0,1)\n\n as described by\n\n Friedrich et al. (2000): Physics Letters A 271, p. 217-222\n *Extracting model equations from experimental data*\n\n For short time-series this method is highly dependent on the parameters.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param m: order of polynomial to fit for estimating fixed points of dynamics\n :type m: int\n :param r: number of quantiles to use for averaging\n :type r: float\n\n :return: Largest fixed point of deterministic dynamics\n :return type: float\n \"\"\"\n\n coeff = _estimate_friedrich_coefficients(x, m, r)\n\n try:\n max_fixed_point = np.max(np.real(np.roots(coeff)))\n except (np.linalg.LinAlgError, ValueError):\n return np.nan\n\n return max_fixed_point\n\n\n@set_property(\"fctype\", \"combiner\")\ndef agg_linear_trend(x, param):\n \"\"\"\n Calculates a linear least-squares regression for values of the time series that were aggregated over chunks versus\n the sequence from 0 up to the number of chunks minus one.\n\n This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model.\n\n The parameters attr controls which of the characteristics are returned. Possible extracted attributes are \"pvalue\",\n \"rvalue\", \"intercept\", \"slope\", \"stderr\", see the documentation of linregress for more information.\n\n The chunksize is regulated by \"chunk_len\". It specifies how many time series values are in each chunk.\n\n Further, the aggregation function is controlled by \"f_agg\", which can use \"max\", \"min\" or , \"mean\", \"median\"\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"attr\": x, \"chunk_len\": l, \"f_agg\": f} with x, f an string and l an int\n :type param: list\n :return: the different feature values\n :return type: Iterator[Tuple[str, float]]\n \"\"\"\n # todo: we could use the index of the DataFrame here\n\n calculated_agg = defaultdict(dict)\n res_data = []\n res_index = []\n\n for parameter_combination in param:\n\n chunk_len = parameter_combination[\"chunk_len\"]\n f_agg = parameter_combination[\"f_agg\"]\n\n if f_agg not in calculated_agg or chunk_len not in calculated_agg[f_agg]:\n if chunk_len >= len(x):\n calculated_agg[f_agg][chunk_len] = np.nan\n else:\n aggregate_result = _aggregate_on_chunks(x, f_agg, chunk_len)\n lin_reg_result = linregress(\n range(len(aggregate_result)), aggregate_result\n )\n calculated_agg[f_agg][chunk_len] = lin_reg_result\n\n attr = parameter_combination[\"attr\"]\n\n if chunk_len >= len(x):\n res_data.append(np.nan)\n else:\n res_data.append(getattr(calculated_agg[f_agg][chunk_len], attr))\n\n res_index.append(f'attr_\"{attr}\"__chunk_len_{chunk_len}__f_agg_\"{f_agg}\"')\n\n return zip(res_index, res_data)\n\n\n@set_property(\"fctype\", \"combiner\")\ndef energy_ratio_by_chunks(x, param):\n \"\"\"\n Calculates the sum of squares of chunk i out of N chunks expressed as a ratio with the sum of squares over the whole\n series.\n\n Takes as input parameters the number num_segments of segments to divide the series into and segment_focus\n which is the segment number (starting at zero) to return a feature on.\n\n If the length of the time series is not a multiple of the number of segments, the remaining data points are\n distributed on the bins starting from the first. For example, if your time series consists of 8 entries, the\n first two bins will contain 3 and the last two values, e.g. `[ 0., 1., 2.], [ 3., 4., 5.]` and `[ 6., 7.]`.\n\n Note that the answer for `num_segments = 1` is a trivial \"1\" but we handle this scenario\n in case somebody calls it. Sum of the ratios should be 1.0.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries {\"num_segments\": N, \"segment_focus\": i} with N, i both ints\n :return: the feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n res_data = []\n res_index = []\n full_series_energy = np.sum(x**2)\n\n for parameter_combination in param:\n num_segments = parameter_combination[\"num_segments\"]\n segment_focus = parameter_combination[\"segment_focus\"]\n assert segment_focus < num_segments\n assert num_segments > 0\n\n if full_series_energy == 0:\n res_data.append(np.nan)\n else:\n res_data.append(\n np.sum(np.array_split(x, num_segments)[segment_focus] ** 2.0)\n / full_series_energy\n )\n\n res_index.append(f\"num_segments_{num_segments}__segment_focus_{segment_focus}\")\n\n # Materialize as list for Python 3 compatibility with name handling\n return list(zip(res_index, res_data))\n\n\n@set_property(\"fctype\", \"combiner\")\n@set_property(\"input\", \"pd.Series\")\n@set_property(\"index_type\", pd.DatetimeIndex)\ndef linear_trend_timewise(x, param):\n \"\"\"\n Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to\n length of the time series minus one.\n This feature uses the index of the time series to fit the model, which must be of a datetime\n dtype.\n The parameters control which of the characteristics are returned.\n\n Possible extracted attributes are \"pvalue\", \"rvalue\", \"intercept\", \"slope\", \"stderr\", see the documentation of\n linregress for more information.\n\n :param x: the time series to calculate the feature of. The index must be datetime.\n :type x: pandas.Series\n :param param: contains dictionaries {\"attr\": x} with x an string, the attribute name of the regression model\n :type param: list\n :return: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n ix = x.index\n\n # Get differences between each timestamp and the first timestamp in seconds.\n # Then convert to hours and reshape for linear regression\n seconds_per_hr = 3600\n times_seconds = (ix - ix[0]).total_seconds()\n times_hours = np.asarray(times_seconds / float(seconds_per_hr))\n\n lin_reg = linregress(times_hours, x.values)\n\n return [\n (f'attr_\"{config[\"attr\"]}\"', getattr(lin_reg, config[\"attr\"]))\n for config in param\n ]\n\n\n@set_property(\"fctype\", \"simple\")\ndef count_above(x, t):\n \"\"\"\n Returns the percentage of values in x that are higher than t\n\n :param x: the time series to calculate the feature of\n :type x: pandas.Series\n :param t: value used as threshold\n :type t: float\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.sum(x >= t) / len(x)\n\n\n@set_property(\"fctype\", \"simple\")\ndef count_below(x, t):\n \"\"\"\n Returns the percentage of values in x that are lower than t\n\n :param x: the time series to calculate the feature of\n :type x: pandas.Series\n :param t: value used as threshold\n :type t: float\n\n :return: the value of this feature\n :return type: float\n \"\"\"\n return np.sum(x <= t) / len(x)\n\n\n@set_property(\"fctype\", \"simple\")\ndef benford_correlation(x):\n \"\"\"\n Useful for anomaly detection applications [1][2]. Returns the correlation from first digit distribution when\n compared to the Newcomb-Benford's Law distribution [3][4].\n\n .. math::\n\n P(d)=\\\\log_{10}\\\\left(1+\\\\frac{1}{d}\\\\right)\n\n where :math:`P(d)` is the Newcomb-Benford distribution for :math:`d` that is the leading digit of the number\n {1, 2, 3, 4, 5, 6, 7, 8, 9}.\n\n .. rubric:: References\n\n | [1] A Statistical Derivation of the Significant-Digit Law, Theodore P. Hill, Statistical Science, 1995\n | [2] The significant-digit phenomenon, Theodore P. Hill, The American Mathematical Monthly, 1995\n | [3] The law of anomalous numbers, Frank Benford, Proceedings of the American philosophical society, 1938\n | [4] Note on the frequency of use of the different digits in natural numbers, Simon Newcomb, American Journal of\n | mathematics, 1881\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :return: the value of this feature\n :return type: float\n \"\"\"\n x = np.asarray(x)\n\n # retrieve first digit from data\n x = np.array(\n [int(str(np.format_float_scientific(i))[:1]) for i in np.abs(np.nan_to_num(x))]\n )\n\n # benford distribution\n benford_distribution = np.array([np.log10(1 + 1 / n) for n in range(1, 10)])\n\n data_distribution = np.array([(x == n).mean() for n in range(1, 10)])\n\n # np.corrcoef outputs the normalized covariance (correlation) between benford_distribution and data_distribution.\n # In this case returns a 2x2 matrix, the [0, 1] and [1, 1] are the values between the two arrays\n return np.corrcoef(benford_distribution, data_distribution)[0, 1]\n\n\n@set_property(\"fctype\", \"combiner\")\n@set_property(\"dependency_available\", mp is not None)\ndef matrix_profile(x, param):\n \"\"\"\n Calculates the 1-D Matrix Profile[1] and returns Tukey's Five Number Set plus the mean of that Matrix Profile.\n\n This feature is not supported anymore, since matrixprofile does not up to date with latest Python releases. To use\n it, you can install the extra with pip install tsfresh[matrixprofile].\n\n .. rubric:: References\n\n | [1] Yeh et.al (2016), IEEE ICDM\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries\n {\"sample_pct\": x, \"threshold\": y, \"feature\": z}\n with sample_pct and threshold being parameters of the matrixprofile\n package https://matrixprofile.docs.matrixprofile.org/api.html#matrixprofile-compute\n and feature being one of \"min\", \"max\", \"mean\", \"median\", \"25\", \"75\"\n and decides which feature of the matrix profile to extract\n :type param: list\n :return: the different feature values\n :return type: List[Tuple[str, float]]\n \"\"\"\n if mp is None:\n raise ImportError(\n \"Could not import matrixprofile but it was required. Please install the extra to use it.\"\n )\n\n x = np.asarray(x)\n\n def _calculate_mp(**kwargs):\n \"\"\"Calculate the matrix profile using the specified window, or the max subsequence if no window is specified\"\"\"\n try:\n if \"windows\" in kwargs:\n m_p = mp.compute(x, **kwargs)[\"mp\"]\n\n else:\n m_p = mp.algorithms.maximum_subsequence(x, include_pmp=True, **kwargs)[\n \"pmp\"\n ][-1]\n\n return m_p\n\n except NoSolutionPossible:\n return [np.nan]\n\n # The already calculated matrix profiles\n matrix_profiles = {}\n\n # The results\n res = {}\n\n for kwargs in param:\n kwargs = kwargs.copy()\n key = convert_to_output_format(kwargs)\n feature = kwargs.pop(\"feature\")\n\n featureless_key = convert_to_output_format(kwargs)\n if featureless_key not in matrix_profiles:\n matrix_profiles[featureless_key] = _calculate_mp(**kwargs)\n\n m_p = matrix_profiles[featureless_key]\n\n # Set all features to nan if Matrix Profile is nan (cannot be computed)\n if len(m_p) == 1:\n res[key] = np.nan\n\n # Handle all other Matrix Profile instances\n else:\n\n finite_indices = np.isfinite(m_p)\n\n feature_map = {\n \"min\": np.min,\n \"max\": np.max,\n \"mean\": np.mean,\n \"median\": np.median,\n \"25\": lambda data: np.percentile(data, 25),\n \"75\": lambda data: np.percentile(data, 75),\n }\n\n if feature in feature_map:\n res[key] = feature_map[feature](m_p[finite_indices])\n else:\n raise ValueError(f\"Unknown feature {feature} for the matrix profile\")\n\n return list(res.items())\n\n\n@set_property(\"fctype\", \"combiner\")\ndef query_similarity_count(x, param):\n \"\"\"\n This feature calculator accepts an input query subsequence parameter,\n compares the query (under z-normalized Euclidean distance) to all\n subsequences within the time series, and returns a count of the number\n of times the query was found in the time series (within some predefined\n maximum distance threshold). Note that this feature will always return\n `np.nan` when no query subsequence is provided and so users will need\n to enable this feature themselves.\n\n :param x: the time series to calculate the feature of\n :type x: numpy.ndarray\n :param param: contains dictionaries\n {\"query\": Q, \"threshold\": thr, \"normalize\": norm}\n with `Q` (numpy.ndarray), the query subsequence to compare the\n time series against. If `Q` is omitted then a value of zero\n is returned. Additionally, `thr` (float), the maximum\n z-normalized Euclidean distance threshold for which to\n increment the query similarity count. If `thr` is omitted\n then a default threshold of `thr=0.0` is used, which\n corresponds to finding exact matches to `Q`. Finally, for\n non-normalized (i.e., without z-normalization) Euclidean set\n `norm` (bool) to `False.\n :type param: list[dict]\n :return x: the different feature values\n :return type: List[Tuple[str, int | np.nan]]\n \"\"\"\n res = {}\n T = np.asarray(x).astype(float)\n\n for kwargs in param:\n key = convert_to_output_format(kwargs)\n normalize = kwargs.get(\"normalize\", True)\n threshold = kwargs.get(\"threshold\", 0.0)\n Q = kwargs.get(\"query\", None)\n Q = np.asarray(Q).astype(float)\n count = np.nan\n if Q is not None and Q.size >= 3:\n if normalize:\n distance_profile = stumpy.core.mass(Q, T)\n else:\n distance_profile = stumpy.core.mass_absolute(Q, T)\n count = np.sum(distance_profile <= threshold)\n\n res[key] = count\n\n return list(res.items())\n" }, { "path": "tsfresh/feature_extraction/settings.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nThis file contains methods/objects for controlling which features will be extracted when calling extract_features.\nFor the naming of the features, see :ref:`feature-naming-label`.\n\"\"\"\nimport logging\nfrom builtins import range\nfrom collections import UserDict\nfrom inspect import getfullargspec\nfrom itertools import product\n\nimport cloudpickle\nimport pandas as pd\n\nfrom tsfresh.feature_extraction import feature_calculators\nfrom tsfresh.utilities.string_manipulation import get_config_from_string\n\n_logger = logging.getLogger(__name__)\n\n\ndef from_columns(columns, columns_to_ignore=None):\n \"\"\"\n Creates a mapping from kind names to fc_parameters objects\n (which are itself mappings from feature calculators to settings)\n to extract only the features contained in the columns.\n To do so, for every feature name in columns this method\n\n 1. split the column name into col, feature, params part\n 2. decide which feature we are dealing with (aggregate with/without params or apply)\n 3. add it to the new name_to_function dict\n 4. set up the params\n\n :param columns: containing the feature names\n :type columns: list of str\n :param columns_to_ignore: columns which do not contain tsfresh feature names\n :type columns_to_ignore: list of str\n\n :return: The kind_to_fc_parameters object ready to be used in the extract_features function.\n :rtype: dict\n \"\"\"\n\n kind_to_fc_parameters = {}\n\n if columns_to_ignore is None:\n columns_to_ignore = []\n\n for col in columns:\n if col in columns_to_ignore:\n continue\n\n if not isinstance(col, str):\n raise TypeError(\"Column name {} should be a string or unicode\".format(col))\n\n # Split according to our separator into , , \n parts = col.split(\"__\")\n n_parts = len(parts)\n\n if n_parts == 1:\n raise ValueError(\n \"Splitting of columnname {} resulted in only one part.\".format(col)\n )\n\n kind = parts[0]\n feature_name = parts[1]\n\n if kind not in kind_to_fc_parameters:\n kind_to_fc_parameters[kind] = {}\n\n if not hasattr(feature_calculators, feature_name):\n raise ValueError(\"Unknown feature name {}\".format(feature_name))\n\n config = get_config_from_string(parts)\n if config:\n if feature_name in kind_to_fc_parameters[kind]:\n kind_to_fc_parameters[kind][feature_name].append(config)\n else:\n kind_to_fc_parameters[kind][feature_name] = [config]\n else:\n kind_to_fc_parameters[kind][feature_name] = None\n\n return kind_to_fc_parameters\n\n\ndef include_function(func, exclusion_attr=\"input_type\"):\n \"\"\"Helper function for selecting specific subset of functions subject to an exclusion attribute\n and the availability of optional dependencies.\n\n :param func: function to be tested for inclusion\n :type func: object\n :param exclusion_attr: function attribute qualifying as exclusion criterion\n :type exclusion_attr: str\n\n :return: Boolean indicating if the specific function matches the inclusion criteria.\n :rtype: bool\n \"\"\"\n decision = (\n hasattr(func, \"fctype\")\n and not hasattr(func, exclusion_attr)\n and not (\n hasattr(func, \"dependency_available\")\n and getattr(func, \"dependency_available\") is False\n )\n )\n return decision\n\n\nclass PickableSettings(UserDict):\n \"\"\"Base object for all settings, which is a pickable dict.\n For user-specified functions, the settings dictionary might include functions as a key.\n These functions unfortunately can not easily be transported to workers in multiprocessing\n or multi-cloud setups, as they are not pickable by default.\n Therefore, we change the pickle-behavior of this class and use cloudpickle for\n pickling and unpickling the keys of the dictionary, before pickling the full object.\n cloudpickle is able to pickle much more functions than pickle can and pickle will\n only see the already encoded keys (not the raw functions).\n \"\"\"\n\n def __getstate__(self):\n \"\"\"Called on pickling. Encode the keys by cloudpickling them\"\"\"\n state = {cloudpickle.dumps(key): value for key, value in self.items()}\n return state\n\n def __setstate__(self, state):\n \"\"\"Called on un-pickling. cloudunpickle the keys again\"\"\"\n state = {cloudpickle.loads(key): value for key, value in state.items()}\n # please note that the internal dictionary is stored as \"data\" in the UserDict\n self.__dict__.update(data=state)\n\n\n# todo: this classes' docstrings are not completely up-to-date\nclass ComprehensiveFCParameters(PickableSettings):\n \"\"\"\n Create a new ComprehensiveFCParameters instance. You have to pass this instance to the\n extract_feature instance.\n\n It is basically a dictionary (and also based on one), which is a mapping from\n string (the same names that are in the feature_calculators.py file) to a list of dictionary of parameters,\n which should be used when the function with this name is called.\n\n Only those strings (function names), that are keys in this dictionary, will be later used to extract\n features - so whenever you delete a key from this dict, you disable the calculation of this feature.\n\n You can use the settings object with\n\n >>> from tsfresh.feature_extraction import extract_features, ComprehensiveFCParameters\n >>> extract_features(df, default_fc_parameters=ComprehensiveFCParameters())\n\n to extract all features (which is the default nevertheless) or you change the ComprehensiveFCParameters\n object to other types (see below).\n \"\"\"\n\n def __init__(self):\n name_to_param = {}\n\n for name, func in feature_calculators.__dict__.items():\n if (\n callable(func)\n and hasattr(func, \"fctype\")\n and len(getfullargspec(func).args) == 1\n ):\n name_to_param[name] = None\n\n name_to_param.update(\n {\n \"time_reversal_asymmetry_statistic\": [\n {\"lag\": lag} for lag in range(1, 4)\n ],\n \"c3\": [{\"lag\": lag} for lag in range(1, 4)],\n \"cid_ce\": [{\"normalize\": True}, {\"normalize\": False}],\n \"symmetry_looking\": [{\"r\": r * 0.05} for r in range(20)],\n \"large_standard_deviation\": [{\"r\": r * 0.05} for r in range(1, 20)],\n \"quantile\": [\n {\"q\": q} for q in [0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9]\n ],\n \"autocorrelation\": [{\"lag\": lag} for lag in range(10)],\n \"agg_autocorrelation\": [\n {\"f_agg\": s, \"maxlag\": 40} for s in [\"mean\", \"median\", \"var\"]\n ],\n \"partial_autocorrelation\": [{\"lag\": lag} for lag in range(10)],\n \"number_cwt_peaks\": [{\"n\": n} for n in [1, 5]],\n \"number_peaks\": [{\"n\": n} for n in [1, 3, 5, 10, 50]],\n \"binned_entropy\": [{\"max_bins\": max_bins} for max_bins in [10]],\n \"index_mass_quantile\": [\n {\"q\": q} for q in [0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9]\n ],\n \"cwt_coefficients\": [\n {\"widths\": width, \"coeff\": coeff, \"w\": w}\n for width in [(2, 5, 10, 20)]\n for coeff in range(15)\n for w in (2, 5, 10, 20)\n ],\n \"spkt_welch_density\": [{\"coeff\": coeff} for coeff in [2, 5, 8]],\n \"ar_coefficient\": [\n {\"coeff\": coeff, \"k\": k} for coeff in range(10 + 1) for k in [10]\n ],\n \"change_quantiles\": [\n {\"ql\": ql, \"qh\": qh, \"isabs\": b, \"f_agg\": f}\n for ql in [0.0, 0.2, 0.4, 0.6, 0.8]\n for qh in [0.2, 0.4, 0.6, 0.8, 1.0]\n for b in [False, True]\n for f in [\"mean\", \"var\"]\n if ql < qh\n ],\n \"fft_coefficient\": [\n {\"coeff\": k, \"attr\": a}\n for a, k in product([\"real\", \"imag\", \"abs\", \"angle\"], range(100))\n ],\n \"fft_aggregated\": [\n {\"aggtype\": s} for s in [\"centroid\", \"variance\", \"skew\", \"kurtosis\"]\n ],\n \"value_count\": [{\"value\": value} for value in [0, 1, -1]],\n \"range_count\": [\n {\"min\": -1, \"max\": 1},\n {\"min\": -1e12, \"max\": 0},\n {\"min\": 0, \"max\": 1e12},\n ],\n \"approximate_entropy\": [\n {\"m\": 2, \"r\": r} for r in [0.1, 0.3, 0.5, 0.7, 0.9]\n ],\n \"friedrich_coefficients\": (\n lambda m: [\n {\"coeff\": coeff, \"m\": m, \"r\": 30} for coeff in range(m + 1)\n ]\n )(3),\n \"max_langevin_fixed_point\": [{\"m\": 3, \"r\": 30}],\n \"linear_trend\": [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ],\n \"agg_linear_trend\": [\n {\"attr\": attr, \"chunk_len\": i, \"f_agg\": f}\n for attr in [\"rvalue\", \"intercept\", \"slope\", \"stderr\"]\n for i in [5, 10, 50]\n for f in [\"max\", \"min\", \"mean\", \"var\"]\n ],\n \"augmented_dickey_fuller\": [\n {\"attr\": \"teststat\"},\n {\"attr\": \"pvalue\"},\n {\"attr\": \"usedlag\"},\n ],\n \"number_crossing_m\": [{\"m\": 0}, {\"m\": -1}, {\"m\": 1}],\n \"energy_ratio_by_chunks\": [\n {\"num_segments\": 10, \"segment_focus\": i} for i in range(10)\n ],\n \"ratio_beyond_r_sigma\": [\n {\"r\": x} for x in [0.5, 1, 1.5, 2, 2.5, 3, 5, 6, 7, 10]\n ],\n \"linear_trend_timewise\": [\n {\"attr\": \"pvalue\"},\n {\"attr\": \"rvalue\"},\n {\"attr\": \"intercept\"},\n {\"attr\": \"slope\"},\n {\"attr\": \"stderr\"},\n ],\n \"count_above\": [{\"t\": 0}],\n \"count_below\": [{\"t\": 0}],\n \"lempel_ziv_complexity\": [{\"bins\": x} for x in [2, 3, 5, 10, 100]],\n \"fourier_entropy\": [{\"bins\": x} for x in [2, 3, 5, 10, 100]],\n \"permutation_entropy\": [\n {\"tau\": 1, \"dimension\": x} for x in [3, 4, 5, 6, 7]\n ],\n \"query_similarity_count\": [{\"query\": None, \"threshold\": 0.0}],\n \"matrix_profile\": [\n {\"threshold\": 0.98, \"feature\": f}\n for f in [\"min\", \"max\", \"mean\", \"median\", \"25\", \"75\"]\n ],\n \"mean_n_absolute_max\": [\n {\n \"number_of_maxima\": 3,\n \"number_of_maxima\": 5,\n \"number_of_maxima\": 7,\n }\n ],\n }\n )\n\n # remove missing dependencies\n for name, func in feature_calculators.__dict__.items():\n if (\n callable(func)\n and hasattr(func, \"dependency_available\")\n and getattr(func, \"dependency_available\") is False\n ):\n name_to_param.pop(name)\n _logger.warning(\n f\"Dependency not available for {name}, this feature will be disabled!\"\n )\n\n super().__init__(name_to_param)\n\n\nclass MinimalFCParameters(ComprehensiveFCParameters):\n \"\"\"\n This class is a child class of the ComprehensiveFCParameters class\n and has the same functionality as its base class. The only difference is,\n that most of the feature calculators are disabled and only a small\n subset of calculators will be calculated at all. Those are denoted by an attribute called \"minimal\".\n\n Use this class for quick tests of your setup before calculating all\n features which could take some time depending of your data set size.\n\n You should use this object when calling the extract function, like so:\n\n >>> from tsfresh.feature_extraction import extract_features, MinimalFCParameters\n >>> extract_features(df, default_fc_parameters=MinimalFCParameters())\n \"\"\"\n\n def __init__(self):\n ComprehensiveFCParameters.__init__(self)\n\n for fname, f in feature_calculators.__dict__.items():\n if fname in self and (\n not hasattr(f, \"minimal\") or not getattr(f, \"minimal\")\n ):\n del self[fname]\n\n\nclass EfficientFCParameters(ComprehensiveFCParameters):\n \"\"\"\n This class is a child class of the ComprehensiveFCParameters class\n and has the same functionality as its base class.\n\n The only difference is, that the features with high computational costs are not calculated. Those are denoted by\n the attribute \"high_comp_cost\".\n\n You should use this object when calling the extract function, like so:\n\n >>> from tsfresh.feature_extraction import extract_features, EfficientFCParameters\n >>> extract_features(df, default_fc_parameters=EfficientFCParameters())\n \"\"\"\n\n def __init__(self):\n ComprehensiveFCParameters.__init__(self)\n\n # drop all features with high computational costs\n for fname, f in feature_calculators.__dict__.items():\n if fname in self and hasattr(f, \"high_comp_cost\"):\n del self[fname]\n\n\nclass IndexBasedFCParameters(ComprehensiveFCParameters):\n \"\"\"\n This class is a child class of the ComprehensiveFCParameters class\n and has the same functionality as its base class.\n\n The only difference is that only the features that require a pd.Series as an input are\n included. Those have an attribute \"input\" with value \"pd.Series\".\n \"\"\"\n\n def __init__(self):\n ComprehensiveFCParameters.__init__(self)\n # drop all features with high computational costs\n for fname, f in feature_calculators.__dict__.items():\n if fname in self and getattr(f, \"input\", None) != \"pd.Series\":\n del self[fname]\n\n\nclass TimeBasedFCParameters(ComprehensiveFCParameters):\n \"\"\"\n This class is a child class of the ComprehensiveFCParameters class\n and has the same functionality as its base class.\n\n The only difference is, that only the features that require a DatetimeIndex are included. Those\n have an attribute \"index_type\" with value pd.DatetimeIndex.\n \"\"\"\n\n def __init__(self):\n ComprehensiveFCParameters.__init__(self)\n # drop all features with high computational costs\n for fname, f in feature_calculators.__dict__.items():\n if fname in self and getattr(f, \"index_type\", False) != pd.DatetimeIndex:\n del self[fname]\n" }, { "path": "tsfresh/feature_selection/__init__.py", "content": "\"\"\"\nThe :mod:`~tsfresh.feature_selection` module contains feature selection algorithms.\nThose methods were suited to pick the best explaining features out of a massive amount of features.\nOften the features have to be picked in situations where one has more features than samples.\nTraditional feature selection methods can be not suitable for such situations which is why we propose a p-value based\napproach that inspects the significance of the features individually to avoid overfitting and spurious correlations.\n\"\"\"\n\n\nfrom tsfresh.feature_selection.selection import select_features\n" }, { "path": "tsfresh/feature_selection/relevance.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nContains a feature selection method that evaluates the importance of the different extracted features. To do so,\nfor every feature the influence on the target is evaluated by an univariate tests and the p-Value is calculated.\nThe methods that calculate the p-values are called feature selectors.\n\nAfterwards the Benjamini Hochberg procedure which is a multiple testing procedure decides which features to keep and\nwhich to cut off (solely based on the p-values).\n\"\"\"\n\nimport warnings\nfrom functools import partial, reduce\nfrom multiprocessing import Pool\n\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.stats.multitest import multipletests\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_selection.significance_tests import (\n target_binary_feature_binary_test,\n target_binary_feature_real_test,\n target_real_feature_binary_test,\n target_real_feature_real_test,\n)\nfrom tsfresh.utilities.distribution import initialize_warnings_in_workers\n\n\ndef calculate_relevance_table(\n X,\n y,\n ml_task=\"auto\",\n multiclass=False,\n n_significant=1,\n n_jobs=defaults.N_PROCESSES,\n show_warnings=defaults.SHOW_WARNINGS,\n chunksize=defaults.CHUNKSIZE,\n test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,\n test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,\n test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,\n fdr_level=defaults.FDR_LEVEL,\n hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,\n):\n \"\"\"\n Calculate the relevance table for the features contained in feature matrix `X` with respect to target vector `y`.\n The relevance table is calculated for the intended machine learning task `ml_task`.\n\n To accomplish this for each feature from the input pandas.DataFrame an univariate feature significance test\n is conducted. Those tests generate p values that are then evaluated by the Benjamini Hochberg procedure to\n decide which features to keep and which to delete.\n\n We are testing\n\n :math:`H_0` = the Feature is not relevant and should not be added\n\n against\n\n :math:`H_1` = the Feature is relevant and should be kept\n\n or in other words\n\n :math:`H_0` = Target and Feature are independent / the Feature has no influence on the target\n\n :math:`H_1` = Target and Feature are associated / dependent\n\n When the target is binary this becomes\n\n :math:`H_0 = \\\\left( F_{\\\\text{target}=1} = F_{\\\\text{target}=0} \\\\right)`\n\n :math:`H_1 = \\\\left( F_{\\\\text{target}=1} \\\\neq F_{\\\\text{target}=0} \\\\right)`\n\n Where :math:`F` is the distribution of the target.\n\n In the same way we can state the hypothesis when the feature is binary\n\n :math:`H_0 = \\\\left( T_{\\\\text{feature}=1} = T_{\\\\text{feature}=0} \\\\right)`\n\n :math:`H_1 = \\\\left( T_{\\\\text{feature}=1} \\\\neq T_{\\\\text{feature}=0} \\\\right)`\n\n Here :math:`T` is the distribution of the target.\n\n TODO: And for real valued?\n\n :param X: Feature matrix in the format mentioned before which will be reduced to only the relevant features.\n It can contain both binary or real-valued features at the same time.\n :type X: pandas.DataFrame\n\n :param y: Target vector which is needed to test which features are relevant. Can be binary or real-valued.\n :type y: pandas.Series or numpy.ndarray\n\n :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.\n Defaults to `'auto'`, meaning the intended task is inferred from `y`.\n If `y` has a boolean, integer or object dtype, the task is assumed to be classification,\n else regression.\n :type ml_task: str\n\n :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features\n are selected. Multiclass requires the features to be statistically significant for\n predicting n_significant classes.\n :type multiclass: bool\n\n :param n_significant: The number of classes for which features should be statistically significant predictors\n to be regarded as 'relevant'\n :type n_significant: int\n\n :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature\n (currently unused)\n :type test_for_binary_target_binary_feature: str\n\n :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature\n :type test_for_binary_target_real_feature: str\n\n :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature (currently unused)\n :type test_for_real_target_binary_feature: str\n\n :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)\n :type test_for_real_target_real_feature: str\n\n :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage of irrelevant\n features among all created features.\n :type fdr_level: float\n\n :param hypotheses_independent: Can the significance of the features be assumed to be independent?\n Normally, this should be set to False as the features are never\n independent (e.g. mean and median)\n :type hypotheses_independent: bool\n\n :param n_jobs: Number of processes to use during the p-value calculation\n :type n_jobs: int\n\n :param show_warnings: Show warnings during the p-value calculation (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as\n the data for one feature. If you set the chunksize\n to 10, then it means that one task is to filter 10 features.\n If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :return: A pandas.DataFrame with each column of the input DataFrame X as index with information on the significance\n of this particular feature. The DataFrame has the columns\n \"feature\",\n \"type\" (binary, real or const),\n \"p_value\" (the significance of this feature as a p-value, lower means more significant)\n \"relevant\" (True if the Benjamini Hochberg procedure rejected the null hypothesis [the feature is\n not relevant] for this feature).\n If the problem is `multiclass` with n classes, the DataFrame will contain n\n columns named \"p_value_CLASSID\" instead of the \"p_value\" column.\n `CLASSID` refers here to the different values set in `y`.\n There will also be n columns named `relevant_CLASSID`, indicating whether\n the feature is relevant for that class.\n :rtype: pandas.DataFrame\n \"\"\"\n\n # Make sure X and y both have the exact same indices\n y = y.sort_index()\n X = X.sort_index()\n\n assert list(y.index) == list(X.index), \"The index of X and y need to be the same\"\n\n if ml_task not in [\"auto\", \"classification\", \"regression\"]:\n raise ValueError(\n \"ml_task must be one of: 'auto', 'classification', 'regression'\"\n )\n elif ml_task == \"auto\":\n ml_task = infer_ml_task(y)\n\n if multiclass:\n assert (\n ml_task == \"classification\"\n ), \"ml_task must be classification for multiclass problem\"\n assert (\n len(y.unique()) >= n_significant\n ), \"n_significant must not exceed the total number of classes\"\n\n if len(y.unique()) <= 2:\n warnings.warn(\n \"Two or fewer classes, binary feature selection will be used (multiclass = False)\"\n )\n multiclass = False\n\n with warnings.catch_warnings():\n if not show_warnings:\n warnings.simplefilter(\"ignore\")\n else:\n warnings.simplefilter(\"default\")\n\n if n_jobs == 0 or n_jobs == 1:\n map_function = map\n else:\n pool = Pool(\n processes=n_jobs,\n initializer=initialize_warnings_in_workers,\n initargs=(show_warnings,),\n )\n map_function = partial(pool.map, chunksize=chunksize)\n\n relevance_table = pd.DataFrame(index=pd.Series(X.columns, name=\"feature\"))\n relevance_table[\"feature\"] = relevance_table.index\n relevance_table[\"type\"] = pd.Series(\n map_function(\n get_feature_type, [X[feature] for feature in relevance_table.index]\n ),\n index=relevance_table.index,\n )\n table_real = relevance_table[relevance_table.type == \"real\"].copy()\n table_binary = relevance_table[relevance_table.type == \"binary\"].copy()\n\n table_const = relevance_table[relevance_table.type == \"constant\"].copy()\n table_const[\"p_value\"] = np.nan\n table_const[\"relevant\"] = False\n\n if not table_const.empty:\n\n warnings.warn(\n \"[test_feature_significance] Constant features: {}\".format(\n \", \".join(map(str, table_const.feature))\n ),\n RuntimeWarning,\n )\n\n if len(table_const) == len(relevance_table):\n if n_jobs < 0 or n_jobs > 1:\n pool.close()\n pool.terminate()\n pool.join()\n return table_const\n\n if ml_task == \"classification\":\n tables = []\n for label in y.unique():\n _test_real_feature = partial(\n target_binary_feature_real_test,\n y=(y == label),\n test=test_for_binary_target_real_feature,\n )\n _test_binary_feature = partial(\n target_binary_feature_binary_test, y=(y == label)\n )\n tmp = _calculate_relevance_table_for_implicit_target(\n table_real,\n table_binary,\n X,\n _test_real_feature,\n _test_binary_feature,\n hypotheses_independent,\n fdr_level,\n map_function,\n )\n if multiclass:\n tmp = tmp.reset_index(drop=True)\n tmp.columns = tmp.columns.map(\n lambda x: x + \"_\" + str(label)\n if x != \"feature\" and x != \"type\"\n else x\n )\n tables.append(tmp)\n\n if multiclass:\n relevance_table = reduce(\n lambda left, right: pd.merge(\n left, right, on=[\"feature\", \"type\"], how=\"outer\"\n ),\n tables,\n )\n relevance_table[\"n_significant\"] = relevance_table.filter(\n regex=\"^relevant_\", axis=1\n ).sum(axis=1)\n relevance_table[\"relevant\"] = (\n relevance_table[\"n_significant\"] >= n_significant\n )\n relevance_table.index = relevance_table[\"feature\"]\n else:\n relevance_table = combine_relevance_tables(tables)\n\n elif ml_task == \"regression\":\n _test_real_feature = partial(target_real_feature_real_test, y=y)\n _test_binary_feature = partial(target_real_feature_binary_test, y=y)\n relevance_table = _calculate_relevance_table_for_implicit_target(\n table_real,\n table_binary,\n X,\n _test_real_feature,\n _test_binary_feature,\n hypotheses_independent,\n fdr_level,\n map_function,\n )\n\n if n_jobs < 0 or n_jobs > 1:\n pool.close()\n pool.terminate()\n pool.join()\n\n # set constant features to be irrelevant for all classes in multiclass case\n if multiclass:\n for column in relevance_table.filter(regex=\"^relevant_\", axis=1).columns:\n table_const[column] = False\n table_const[\"n_significant\"] = 0\n table_const.drop(columns=[\"p_value\"], inplace=True)\n\n relevance_table = pd.concat([relevance_table, table_const], axis=0)\n\n if sum(relevance_table[\"relevant\"]) == 0:\n warnings.warn(\n \"No feature was found relevant for {} for fdr level = {} (which corresponds to the maximal percentage \"\n \"of irrelevant features, consider using an higher fdr level or add other features.\".format(\n ml_task, fdr_level\n ),\n RuntimeWarning,\n )\n\n return relevance_table\n\n\ndef _calculate_relevance_table_for_implicit_target(\n table_real,\n table_binary,\n X,\n test_real_feature,\n test_binary_feature,\n hypotheses_independent,\n fdr_level,\n map_function,\n):\n table_real[\"p_value\"] = pd.Series(\n map_function(test_real_feature, [X[feature] for feature in table_real.index]),\n index=table_real.index,\n )\n table_binary[\"p_value\"] = pd.Series(\n map_function(\n test_binary_feature, [X[feature] for feature in table_binary.index]\n ),\n index=table_binary.index,\n )\n relevance_table = pd.concat([table_real, table_binary])\n method = \"fdr_bh\" if hypotheses_independent else \"fdr_by\"\n relevance_table[\"relevant\"] = multipletests(\n relevance_table.p_value, fdr_level, method\n )[0]\n return relevance_table.sort_values(\"p_value\")\n\n\ndef infer_ml_task(y):\n \"\"\"\n Infer the machine learning task to select for.\n The result will be either `'regression'` or `'classification'`.\n If the target vector only consists of integer typed values or objects, we assume the task is `'classification'`.\n Else `'regression'`.\n\n :param y: The target vector y.\n :type y: pandas.Series\n :return: 'classification' or 'regression'\n :rtype: str\n \"\"\"\n if y.dtype.kind in np.typecodes[\"AllInteger\"] or y.dtype == object:\n ml_task = \"classification\"\n else:\n ml_task = \"regression\"\n\n return ml_task\n\n\ndef combine_relevance_tables(relevance_tables):\n \"\"\"\n Create a combined relevance table out of a list of relevance tables,\n aggregating the p-values and the relevances.\n\n :param relevance_tables: A list of relevance tables\n :type relevance_tables: List[pd.DataFrame]\n :return: The combined relevance table\n :rtype: pandas.DataFrame\n \"\"\"\n\n def _combine(a, b):\n a.relevant |= b.relevant\n a.p_value = a.p_value.combine(b.p_value, min, 1)\n return a\n\n return reduce(_combine, relevance_tables)\n\n\ndef get_feature_type(feature_column):\n \"\"\"\n For a given feature, determine if it is real, binary or constant.\n Here binary means that only two unique values occur in the feature.\n\n :param feature_column: The feature column\n :type feature_column: pandas.Series\n :return: 'constant', 'binary' or 'real'\n \"\"\"\n n_unique_values = len(set(feature_column.values))\n if n_unique_values == 1:\n return \"constant\"\n elif n_unique_values == 2:\n return \"binary\"\n else:\n return \"real\"\n" }, { "path": "tsfresh/feature_selection/selection.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nThis module contains the filtering process for the extracted features. The filtering procedure can also be used on\nother features that are not based on time series.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_selection.relevance import calculate_relevance_table\nfrom tsfresh.utilities.dataframe_functions import check_for_nans_in_columns\n\n\ndef select_features(\n X,\n y,\n test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,\n test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,\n test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,\n fdr_level=defaults.FDR_LEVEL,\n hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,\n n_jobs=defaults.N_PROCESSES,\n show_warnings=defaults.SHOW_WARNINGS,\n chunksize=defaults.CHUNKSIZE,\n ml_task=\"auto\",\n multiclass=False,\n n_significant=1,\n):\n \"\"\"\n Check the significance of all features (columns) of feature matrix X and return a possibly reduced feature matrix\n only containing relevant features.\n\n The feature matrix must be a pandas.DataFrame in the format:\n\n +-------+-----------+-----------+-----+-----------+\n | index | feature_1 | feature_2 | ... | feature_N |\n +=======+===========+===========+=====+===========+\n | A | ... | ... | ... | ... |\n +-------+-----------+-----------+-----+-----------+\n | B | ... | ... | ... | ... |\n +-------+-----------+-----------+-----+-----------+\n | ... | ... | ... | ... | ... |\n +-------+-----------+-----------+-----+-----------+\n | ... | ... | ... | ... | ... |\n +-------+-----------+-----------+-----+-----------+\n | ... | ... | ... | ... | ... |\n +-------+-----------+-----------+-----+-----------+\n\n\n Each column will be handled as a feature and tested for its significance to the target.\n\n The target vector must be a pandas.Series or numpy.array in the form\n\n +-------+--------+\n | index | target |\n +=======+========+\n | A | ... |\n +-------+--------+\n | B | ... |\n +-------+--------+\n | . | ... |\n +-------+--------+\n | . | ... |\n +-------+--------+\n\n and must contain all id's that are in the feature matrix. If y is a numpy.array without index, it is assumed\n that y has the same order and length than X and the rows correspond to each other.\n\n Examples\n ========\n\n >>> from tsfresh.examples import load_robot_execution_failures\n >>> from tsfresh import extract_features, select_features\n >>> df, y = load_robot_execution_failures()\n >>> X_extracted = extract_features(df, column_id='id', column_sort='time')\n >>> X_selected = select_features(X_extracted, y)\n\n :param X: Feature matrix in the format mentioned before which will be reduced to only the relevant features.\n It can contain both binary or real-valued features at the same time.\n :type X: pandas.DataFrame\n\n :param y: Target vector which is needed to test which features are relevant. Can be binary or real-valued.\n :type y: pandas.Series or numpy.ndarray\n\n :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature\n (currently unused)\n :type test_for_binary_target_binary_feature: str\n\n :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature\n :type test_for_binary_target_real_feature: str\n\n :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature (currently unused)\n :type test_for_real_target_binary_feature: str\n\n :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)\n :type test_for_real_target_real_feature: str\n\n :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage of irrelevant\n features among all created features.\n :type fdr_level: float\n\n :param hypotheses_independent: Can the significance of the features be assumed to be independent?\n Normally, this should be set to False as the features are never\n independent (e.g. mean and median)\n :type hypotheses_independent: bool\n\n :param n_jobs: Number of processes to use during the p-value calculation\n :type n_jobs: int\n\n :param show_warnings: Show warnings during the p-value calculation (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as\n the data for one feature. If you set the chunksize\n to 10, then it means that one task is to filter 10 features.\n If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.\n Defaults to `'auto'`, meaning the intended task is inferred from `y`.\n If `y` has a boolean, integer or object dtype, the task is assumed to be classification,\n else regression.\n :type ml_task: str\n\n :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features\n are selected. Multiclass requires the features to be statistically significant for\n predicting n_significant features.\n :type multiclass: bool\n\n :param n_significant: The number of classes for which features should be statistically significant predictors\n to be regarded as 'relevant'. Only specify when multiclass=True\n :type n_significant: int\n\n :return: The same DataFrame as X, but possibly with reduced number of columns ( = features).\n :rtype: pandas.DataFrame\n\n :raises: ``ValueError`` when the target vector does not fit to the feature matrix\n or `ml_task` is not one of `'auto'`, `'classification'` or `'regression'`.\n \"\"\"\n assert isinstance(X, pd.DataFrame), \"Please pass features in X as pandas.DataFrame.\"\n check_for_nans_in_columns(X)\n assert isinstance(y, (pd.Series, np.ndarray)), (\n \"The type of target vector y must be one of: \" \"pandas.Series, numpy.ndarray\"\n )\n assert len(y) > 1, \"y must contain at least two samples.\"\n assert len(X) == len(y), \"X and y must contain the same number of samples.\"\n assert (\n len(set(y)) > 1\n ), \"Feature selection is only possible if more than 1 label/class is provided\"\n\n if isinstance(y, pd.Series) and set(X.index) != set(y.index):\n raise ValueError(\"Index of X and y must be identical if provided\")\n\n if isinstance(y, np.ndarray):\n y = pd.Series(y, index=X.index)\n\n relevance_table = calculate_relevance_table(\n X,\n y,\n ml_task=ml_task,\n multiclass=multiclass,\n n_significant=n_significant,\n n_jobs=n_jobs,\n show_warnings=show_warnings,\n chunksize=chunksize,\n test_for_binary_target_real_feature=test_for_binary_target_real_feature,\n fdr_level=fdr_level,\n hypotheses_independent=hypotheses_independent,\n )\n\n relevant_features = relevance_table[relevance_table.relevant].feature\n\n return X.loc[:, relevant_features]\n" }, { "path": "tsfresh/feature_selection/significance_tests.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\n# todo: complement this docstring and reference to christ paper\n\"\"\"\nContains the methods from the following paper about the FRESH algorithm [2]_\n\nFresh is based on hypothesis tests that individually check the significance of every generated feature on the target.\nIt makes sure that only features are kept, that are relevant for the regression or classification task at hand. FRESH\ndecide between four settings depending if the features and target are binary or not.\n\nThe four functions are named\n\n1. :func:`~tsfresh.feature_selection.significance_tests.target_binary_feature_binary_test`:\n Target and feature are both binary\n2. :func:`~tsfresh.feature_selection.significance_tests.target_binary_feature_real_test`:\n Target is binary and feature real\n3. :func:`~tsfresh.feature_selection.significance_tests.target_real_feature_binary_test`:\n Target is real and the feature is binary\n4. :func:`~tsfresh.feature_selection.significance_tests.target_real_feature_real_test`:\n Target and feature are both real\n\nReferences\n----------\n\n.. [2] Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2016).\n Distributed and parallel time series feature extraction for industrial big data applications.\n ArXiv e-prints: 1610.07717\n https://arxiv.org/abs/1610.07717\n\n\n\"\"\"\n\nimport warnings\nfrom builtins import str\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\n\ndef target_binary_feature_binary_test(x, y):\n \"\"\"\n Calculate the feature significance of a binary feature to a binary target as a p-value.\n Use the two-sided univariate fisher test from :func:`~scipy.stats.fisher_exact` for this.\n\n :param x: the binary feature vector\n :type x: pandas.Series\n\n :param y: the binary target vector\n :type y: pandas.Series\n\n :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance\n :rtype: float\n\n :raise: ``ValueError`` if the target or the feature is not binary.\n \"\"\"\n __check_if_pandas_series(x, y)\n _check_for_nans(x, y)\n\n # Check for correct value range\n __check_for_binary_feature(x)\n __check_for_binary_target(y)\n\n # Extract the unique values\n x0, x1 = np.unique(x.values)\n y0, y1 = np.unique(y.values)\n\n # Calculate contingency table\n n_y1_x0 = np.sum(y[x == x0] == y1)\n n_y0_x0 = len(y[x == x0]) - n_y1_x0\n n_y1_x1 = np.sum(y[x == x1] == y1)\n n_y0_x1 = len(y[x == x1]) - n_y1_x1\n\n table = np.array([[n_y1_x1, n_y1_x0], [n_y0_x1, n_y0_x0]])\n\n # Perform the Fisher test\n oddsratio, p_value = stats.fisher_exact(table, alternative=\"two-sided\")\n\n return p_value\n\n\ndef target_binary_feature_real_test(x, y, test):\n \"\"\"\n Calculate the feature significance of a real-valued feature to a binary target as a p-value.\n Use either the `Mann-Whitney U` or `Kolmogorov Smirnov` from :func:`~scipy.stats.mannwhitneyu` or\n :func:`~scipy.stats.ks_2samp` for this.\n\n :param x: the real-valued feature vector\n :type x: pandas.Series\n\n :param y: the binary target vector\n :type y: pandas.Series\n\n :param test: The significance test to be used. Either ``'mann'`` for the Mann-Whitney-U test\n or ``'smir'`` for the Kolmogorov-Smirnov test\n :type test: str\n\n :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance\n :rtype: float\n\n :raise: ``ValueError`` if the target is not binary.\n \"\"\"\n __check_if_pandas_series(x, y)\n _check_for_nans(x, y)\n\n # Check for correct value range\n __check_for_binary_target(y)\n\n # Extract the unique values\n y0, y1 = np.unique(y.values)\n\n # Divide feature according to target\n x_y1 = x[y == y1]\n x_y0 = x[y == y0]\n\n if test == \"mann\":\n # Perform Mann-Whitney-U test\n U, p_mannwhitu = stats.mannwhitneyu(\n x_y1, x_y0, use_continuity=True, alternative=\"two-sided\"\n )\n return p_mannwhitu\n elif test == \"smir\":\n # Perform Kolmogorov-Smirnov test\n KS, p_ks = stats.ks_2samp(x_y1, x_y0)\n return p_ks\n else:\n raise ValueError(\n \"Please use a valid entry for test_for_binary_target_real_feature. \"\n + \"Valid entries are 'mann' and 'smir'.\"\n )\n\n\ndef target_real_feature_binary_test(x, y):\n \"\"\"\n Calculate the feature significance of a binary feature to a real-valued target as a p-value.\n Use the `Kolmogorov-Smirnov` test from from :func:`~scipy.stats.ks_2samp` for this.\n\n :param x: the binary feature vector\n :type x: pandas.Series\n\n :param y: the real-valued target vector\n :type y: pandas.Series\n\n :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance.\n :rtype: float\n\n :raise: ``ValueError`` if the feature is not binary.\n \"\"\"\n __check_if_pandas_series(x, y)\n _check_for_nans(x, y)\n\n # Check for correct value range\n __check_for_binary_feature(x)\n\n # Extract the unique values\n x0, x1 = np.unique(x.values)\n\n # Divide target according to feature\n y_x1 = y[x == x1]\n y_x0 = y[x == x0]\n\n # Perform Kolmogorov-Smirnov test\n KS, p_value = stats.ks_2samp(y_x1, y_x0)\n\n return p_value\n\n\ndef target_real_feature_real_test(x, y):\n \"\"\"\n Calculate the feature significance of a real-valued feature to a real-valued target as a p-value.\n Use `Kendall's tau` from :func:`~scipy.stats.kendalltau` for this.\n\n :param x: the real-valued feature vector\n :type x: pandas.Series\n\n :param y: the real-valued target vector\n :type y: pandas.Series\n\n :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance.\n :rtype: float\n \"\"\"\n __check_if_pandas_series(x, y)\n _check_for_nans(x, y)\n\n tau, p_value = stats.kendalltau(x, y, method=\"asymptotic\")\n return p_value\n\n\ndef __check_if_pandas_series(x, y):\n \"\"\"\n Helper function to check if both x and y are pandas.Series. If not, raises a ``TypeError``.\n\n :param x: the first object to check.\n :type x: Any\n\n :param y: the second object to check.\n :type y: Any\n\n :return: None\n :rtype: None\n\n :raise: ``TypeError`` if one of the objects is not a pandas.Series.\n \"\"\"\n if not isinstance(x, pd.Series):\n raise TypeError(\"x should be a pandas Series\")\n if not isinstance(y, pd.Series):\n raise TypeError(\"y should be a pandas Series\")\n if not y.index.equals(x.index):\n raise ValueError(\"X and y need to have the same index!\")\n\n\ndef __check_for_binary_target(y):\n \"\"\"\n Helper function to check if a target column is binary.\n Checks if only the values true and false (or 0 and 1) are present in the values.\n\n :param y: the values to check for.\n :type y: pandas.Series or numpy.array\n\n :return: None\n :rtype: None\n\n :raises: ``ValueError`` if the values are not binary.\n \"\"\"\n if not set(y) == {0, 1}:\n if len(set(y)) > 2:\n raise ValueError(\"Target is not binary!\")\n\n warnings.warn(\n \"The binary target should have \"\n \"values 1 and 0 (or True and False). \"\n \"Instead found\" + str(set(y)),\n RuntimeWarning,\n )\n\n\ndef __check_for_binary_feature(x):\n \"\"\"\n Helper function to check if a feature column is binary.\n Checks if only the values true and false (or 0 and 1) are present in the values.\n\n :param y: the values to check for.\n :type y: pandas.Series or numpy.array\n\n :return: None\n :rtype: None\n\n :raises: ``ValueError`` if the values are not binary.\n \"\"\"\n if not set(x) == {0, 1}:\n if len(set(x)) > 2:\n raise ValueError(\n \"[target_binary_feature_binary_test] Feature is not binary!\"\n )\n\n warnings.warn(\n \"A binary feature should have only \"\n \"values 1 and 0 (incl. True and False). \"\n \"Instead found \" + str(set(x)) + \" in feature ''\" + str(x.name) + \"''.\",\n RuntimeWarning,\n )\n\n\ndef _check_for_nans(x, y):\n \"\"\"\n Helper function to check if target or feature contains NaNs.\n :param x: A feature\n :type x: pandas.Series\n :param y: The target\n :type y: pandas.Series\n :raises: `ValueError` if target or feature contains NaNs.\n \"\"\"\n if np.isnan(x.values).any():\n raise ValueError(\"Feature {} contains NaN values\".format(x.name))\n elif np.isnan(y.values).any():\n raise ValueError(\"Target contains NaN values\")\n" }, { "path": "tsfresh/scripts/__init__.py", "content": "" }, { "path": "tsfresh/scripts/data.txt", "content": " 1.1653150e-002 1.3109090e-002 1.1268850e-002 2.7830730e-002 2.3183500e-003 -1.8965500e-002 -6.1920230e-002 -9.4248130e-002 -7.9534200e-002 -7.0083760e-002 -1.9152480e-002 -2.2668950e-003 -3.0111470e-002 -1.1910260e-002 -1.5542110e-002 -1.6666170e-002 -6.6302790e-003 -2.3480930e-002 -1.7627590e-002 -2.1150170e-002 -3.2659070e-002 -2.2478410e-002 -1.0196890e-002 -3.1196910e-003 -1.6495820e-002 -2.0441880e-002 -2.7567620e-002 -5.5032500e-002 -5.7479710e-002 -4.7095990e-002 -2.8361690e-002 -1.7184890e-002 -7.3758860e-003 2.4778770e-002 2.3929340e-002 2.1248370e-002 9.6999630e-003 -3.5118900e-002 -1.0733260e-002 2.3672330e-002 1.4929500e-002 2.1906200e-002 5.0844080e-003 -1.3070070e-002 -7.9809120e-003 -1.2763990e-002 -9.8686730e-003 -3.6970220e-003 4.7806470e-003 7.9291720e-003 -5.4944340e-004 -9.1546860e-004 -4.9868180e-003 9.0750290e-003 3.3040610e-002 1.9763320e-002 1.0038920e-005 -1.7139270e-002 -2.9957720e-002 -9.4479730e-003 5.1335230e-003 -2.5121430e-003 3.2194320e-003 1.2330130e-002 9.2796290e-003 4.9297110e-003 3.9535960e-003 9.2144290e-003 1.6156130e-002 1.4079400e-002 1.3105210e-002 2.1246960e-002 2.6823440e-002 3.1054370e-002 3.7886060e-002 2.6462870e-002 -2.1441150e-002 -6.0567950e-002 -2.4725380e-002 2.0272130e-002 -1.9340940e-003 -8.9067130e-003 9.5376650e-003 -4.3316630e-003 -1.8638360e-003 4.9550750e-003 -5.3361360e-003 2.0007890e-003 -1.7075180e-003 -6.4634980e-003 2.0412210e-004 -3.6400110e-003 8.2683690e-005 -1.4416150e-003 -5.9593910e-003 3.4398910e-004 5.5799520e-004 2.3814270e-003 1.8277850e-003 -1.4560660e-003 -2.0005970e-004 -5.7041380e-004 2.6049420e-003 2.6436190e-003 1.5970290e-003 3.8177820e-003 1.6479750e-003 2.9432210e-003 5.5816380e-003 6.2567030e-003 5.1689760e-003 -2.5212320e-003 -6.3626070e-003 -2.4966130e-003 2.4552680e-003 4.4235590e-003 2.1102270e-003 -5.5878910e-004 -2.9442470e-003 -1.2468990e-003 2.8482550e-003 2.3545580e-003 2.7209850e-003 3.2552900e-003 1.3010800e-003 1.2915110e-003 1.4699970e-003 2.5738410e-003\r\n 9.2796290e-003 4.9297110e-003 3.9535960e-003 9.2144290e-003 1.6156130e-002 1.4079400e-002 1.3105210e-002 2.1246960e-002 2.6823440e-002 3.1054370e-002 3.7886060e-002 2.6462870e-002 -2.1441150e-002 -6.0567950e-002 -2.4725380e-002 2.0272130e-002 -1.9340940e-003 -8.9067130e-003 9.5376650e-003 -4.3316630e-003 -1.8638360e-003 4.9550750e-003 -5.3361360e-003 2.0007890e-003 -1.7075180e-003 -6.4634980e-003 2.0412210e-004 -3.6400110e-003 8.2683690e-005 -1.4416150e-003 -5.9593910e-003 3.4398910e-004 5.5799520e-004 2.3814270e-003 1.8277850e-003 -1.4560660e-003 -2.0005970e-004 -5.7041380e-004 2.6049420e-003 2.6436190e-003 1.5970290e-003 3.8177820e-003 1.6479750e-003 2.9432210e-003 5.5816380e-003 6.2567030e-003 5.1689760e-003 -2.5212320e-003 -6.3626070e-003 -2.4966130e-003 2.4552680e-003 4.4235590e-003 2.1102270e-003 -5.5878910e-004 -2.9442470e-003 -1.2468990e-003 2.8482550e-003 2.3545580e-003 2.7209850e-003 3.2552900e-003 1.3010800e-003 1.2915110e-003 1.4699970e-003 2.5738410e-003 5.7319450e-003 7.0656500e-003 5.1097580e-003 2.4339620e-003 2.0239410e-003 1.3819760e-003 1.9298470e-003 4.8752880e-003 3.7760940e-003 2.3999900e-003 2.5349960e-003 -1.1269110e-004 4.7346710e-004 2.4052440e-003 1.6299070e-003 2.2211010e-003 2.1347320e-003 2.4627530e-003 3.5252070e-003 1.2434190e-003 1.2865960e-005 2.3366350e-003 4.7126850e-003 4.8014380e-003 3.7976760e-003 3.2531130e-003 9.1773420e-004 -7.7316990e-004 2.5905310e-004 1.8800500e-004 -2.6874730e-004 5.0410870e-005 7.7157500e-004 4.2688870e-003 7.6185120e-003 4.5647830e-003 2.7213470e-004 7.1718250e-004 -9.5696540e-004 -3.1730870e-003 -1.4384740e-003 -3.4901240e-003 -5.3391930e-003 -1.5563700e-003 6.5998610e-004 1.7810490e-003 5.0006020e-003 6.5324410e-003 1.9051930e-003 -2.8812690e-003 -6.5461400e-004 -3.9610920e-004 -4.3843150e-003 -4.9458000e-003 -2.5334790e-003 1.6250470e-004 -1.0948130e-003 -4.5474160e-003 -5.3101700e-003 -4.0935450e-003 2.2000900e-003 7.7876000e-003 3.4334890e-003 -1.2386780e-003\r\n 5.7319450e-003 7.0656500e-003 5.1097580e-003 2.4339620e-003 2.0239410e-003 1.3819760e-003 1.9298470e-003 4.8752880e-003 3.7760940e-003 2.3999900e-003 2.5349960e-003 -1.1269110e-004 4.7346710e-004 2.4052440e-003 1.6299070e-003 2.2211010e-003 2.1347320e-003 2.4627530e-003 3.5252070e-003 1.2434190e-003 1.2865960e-005 2.3366350e-003 4.7126850e-003 4.8014380e-003 3.7976760e-003 3.2531130e-003 9.1773420e-004 -7.7316990e-004 2.5905310e-004 1.8800500e-004 -2.6874730e-004 5.0410870e-005 7.7157500e-004 4.2688870e-003 7.6185120e-003 4.5647830e-003 2.7213470e-004 7.1718250e-004 -9.5696540e-004 -3.1730870e-003 -1.4384740e-003 -3.4901240e-003 -5.3391930e-003 -1.5563700e-003 6.5998610e-004 1.7810490e-003 5.0006020e-003 6.5324410e-003 1.9051930e-003 -2.8812690e-003 -6.5461400e-004 -3.9610920e-004 -4.3843150e-003 -4.9458000e-003 -2.5334790e-003 1.6250470e-004 -1.0948130e-003 -4.5474160e-003 -5.3101700e-003 -4.0935450e-003 2.2000900e-003 7.7876000e-003 3.4334890e-003 -1.2386780e-003 4.5194670e-004 6.0393820e-004 -2.4845620e-003 -4.5616010e-003 -6.0023590e-003 -6.3816380e-003 -5.2758290e-003 -1.4890040e-003 5.1525920e-003 4.9513680e-003 7.3156820e-004 5.6312270e-004 -3.5969230e-003 -7.3706340e-003 -6.1132980e-003 -7.0069840e-003 -6.5153670e-003 -5.3029930e-003 -5.4969300e-003 -4.0168370e-003 -1.9010940e-003 6.4195350e-004 -2.3597730e-003 -7.3168380e-003 -3.6246110e-003 -6.4389180e-004 -1.3405370e-003 -8.4823500e-004 -3.7627120e-003 -5.9915130e-003 -4.0941780e-003 -7.8601150e-004 3.4583910e-003 3.7026630e-003 -7.4465180e-004 -3.8010510e-003 -2.6949470e-003 -2.3476190e-004 -2.4698710e-004 -7.0564350e-004 -2.1630180e-004 -4.1176740e-004 -5.2457120e-004 -1.6148500e-003 -4.1254420e-003 -3.7520650e-003 4.4799940e-005 9.8637740e-004 -8.9263980e-004 2.3791160e-005 5.1217810e-004 -7.1014710e-004 -8.4200860e-004 -3.2600560e-003 -4.9724740e-003 -3.5859920e-003 -3.8600370e-003 -3.5600490e-003 -9.7441320e-004 1.0520290e-003 7.7765290e-004 -7.4284610e-004 -1.9233560e-003 -4.3046170e-003\r\n 4.5194670e-004 6.0393820e-004 -2.4845620e-003 -4.5616010e-003 -6.0023590e-003 -6.3816380e-003 -5.2758290e-003 -1.4890040e-003 5.1525920e-003 4.9513680e-003 7.3156820e-004 5.6312270e-004 -3.5969230e-003 -7.3706340e-003 -6.1132980e-003 -7.0069840e-003 -6.5153670e-003 -5.3029930e-003 -5.4969300e-003 -4.0168370e-003 -1.9010940e-003 6.4195350e-004 -2.3597730e-003 -7.3168380e-003 -3.6246110e-003 -6.4389180e-004 -1.3405370e-003 -8.4823500e-004 -3.7627120e-003 -5.9915130e-003 -4.0941780e-003 -7.8601150e-004 3.4583910e-003 3.7026630e-003 -7.4465180e-004 -3.8010510e-003 -2.6949470e-003 -2.3476190e-004 -2.4698710e-004 -7.0564350e-004 -2.1630180e-004 -4.1176740e-004 -5.2457120e-004 -1.6148500e-003 -4.1254420e-003 -3.7520650e-003 4.4799940e-005 9.8637740e-004 -8.9263980e-004 2.3791160e-005 5.1217810e-004 -7.1014710e-004 -8.4200860e-004 -3.2600560e-003 -4.9724740e-003 -3.5859920e-003 -3.8600370e-003 -3.5600490e-003 -9.7441320e-004 1.0520290e-003 7.7765290e-004 -7.4284610e-004 -1.9233560e-003 -4.3046170e-003 -4.3623790e-003 -2.7653920e-003 -4.9047840e-003 -4.6821630e-003 -2.6686130e-004 4.1353650e-003 5.6256690e-003 9.3337260e-004 -1.5422450e-003 3.0197580e-003 7.4526450e-003 8.2210370e-003 4.6441160e-003 7.2693400e-004 -1.0385440e-003 -4.0508650e-003 -6.5189280e-003 -4.4996280e-003 -1.6821020e-003 -2.9650530e-003 -2.6032040e-003 1.7582890e-004 -1.4332690e-003 -3.9780640e-003 -5.5375860e-003 -5.9164140e-003 -3.2500770e-003 -1.1546230e-003 6.4784330e-004 1.7185740e-003 -1.2089120e-004 -3.2094970e-003 -5.9321400e-003 -5.5016650e-003 -3.1201670e-003 -1.4517700e-003 -7.7464810e-004 -1.8392710e-003 -3.1129970e-003 -3.7406470e-003 -1.7210340e-003 1.2133160e-003 -1.3801250e-003 -3.8615940e-003 -2.0102650e-003 -1.6564070e-003 -1.9431630e-004 2.9336980e-003 2.2245800e-003 1.9977070e-003 2.9546920e-003 -2.0019770e-003 -6.7912680e-003 -3.0589690e-003 1.2487700e-003 9.7765980e-004 2.2477230e-003 3.5297610e-003 -1.1697740e-004 -3.2751760e-003 -1.3250760e-003 5.9704400e-004 -2.4582460e-004 -2.6896940e-004\r\n -4.3623790e-003 -2.7653920e-003 -4.9047840e-003 -4.6821630e-003 -2.6686130e-004 4.1353650e-003 5.6256690e-003 9.3337260e-004 -1.5422450e-003 3.0197580e-003 7.4526450e-003 8.2210370e-003 4.6441160e-003 7.2693400e-004 -1.0385440e-003 -4.0508650e-003 -6.5189280e-003 -4.4996280e-003 -1.6821020e-003 -2.9650530e-003 -2.6032040e-003 1.7582890e-004 -1.4332690e-003 -3.9780640e-003 -5.5375860e-003 -5.9164140e-003 -3.2500770e-003 -1.1546230e-003 6.4784330e-004 1.7185740e-003 -1.2089120e-004 -3.2094970e-003 -5.9321400e-003 -5.5016650e-003 -3.1201670e-003 -1.4517700e-003 -7.7464810e-004 -1.8392710e-003 -3.1129970e-003 -3.7406470e-003 -1.7210340e-003 1.2133160e-003 -1.3801250e-003 -3.8615940e-003 -2.0102650e-003 -1.6564070e-003 -1.9431630e-004 2.9336980e-003 2.2245800e-003 1.9977070e-003 2.9546920e-003 -2.0019770e-003 -6.7912680e-003 -3.0589690e-003 1.2487700e-003 9.7765980e-004 2.2477230e-003 3.5297610e-003 -1.1697740e-004 -3.2751760e-003 -1.3250760e-003 5.9704400e-004 -2.4582460e-004 -2.6896940e-004 -1.0956640e-003 -3.2297430e-003 -3.4296850e-003 -4.6544660e-003 -4.5386040e-003 -1.3851410e-003 5.2406410e-004 4.7644000e-003 7.0118930e-003 4.9838150e-003 6.8107850e-003 6.0549310e-003 2.0591640e-003 1.2031080e-003 -1.0250920e-003 -2.3297930e-003 -1.4020340e-003 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4.0288270e-001 5.0185210e-001 4.1920950e-001 3.2183080e-001 3.0261570e-001 1.8409840e-001 2.4617700e-002 -5.4376680e-002 -9.5439940e-002 -1.0203310e-001 -1.0280860e-001 -1.1891640e-001 -1.4725100e-001 -2.0477180e-001 -2.5079760e-001 -2.6237060e-001 -2.5329600e-001 -2.1596830e-001 -1.4119590e-001 -6.4224780e-002 -5.5631420e-002 -1.0494090e-001 -1.4803570e-001 -1.5554960e-001 -1.2702700e-001 -8.9936100e-002 -4.9785180e-002 -8.8896890e-003 1.9348030e-002 6.7426810e-002 1.2421670e-001 1.3541280e-001 1.7411310e-001 3.6383980e-001 5.6162430e-001 5.2354690e-001 3.6654280e-001 1.0933630e-001 -2.8030480e-001 -2.5073540e-001 4.9390760e-002 1.2861010e-002 3.5632610e-002 7.1226830e-002 -8.8665300e-002 -7.0674730e-002 -1.1688740e-001 -2.1411710e-001 -2.1992790e-001 -2.7502420e-001 -2.2906230e-001 -2.4530900e-001 -2.4225990e-001 -1.7192860e-001 -2.2416360e-001 -1.5686030e-001 -1.1445160e-001 -1.2398320e-001 -4.7481350e-002 -3.4828960e-002 1.9014830e-002 5.3397070e-002 6.4815390e-002 1.0278350e-001 7.6053810e-002 1.1158510e-001 1.7558720e-001 2.7248390e-001 4.2896290e-001 4.9307280e-001 4.7214750e-001 4.1655320e-001 3.4686590e-001 1.8403780e-001 -3.3835110e-002 -1.1367570e-001 -9.6132820e-002 -5.8506690e-002 -4.8071090e-002 -7.7781600e-002 -1.1629420e-001 -1.8163210e-001 -2.2299490e-001 -2.6093200e-001 -3.0279360e-001 -2.9780140e-001 -2.7941940e-001 -2.2637150e-001 -1.4507560e-001 -8.3344240e-002 -8.0088450e-002 -1.1622240e-001 -1.0610020e-001 -8.9397650e-002 -5.5760440e-002 1.7739840e-002 5.4811330e-002 9.8336060e-002 1.4786620e-001 1.6844160e-001 2.1139970e-001 3.2885210e-001 5.3764340e-001 6.3859240e-001 4.9789300e-001 2.1412750e-001 -1.1450890e-001\r\n 7.1226830e-002 -8.8665300e-002 -7.0674730e-002 -1.1688740e-001 -2.1411710e-001 -2.1992790e-001 -2.7502420e-001 -2.2906230e-001 -2.4530900e-001 -2.4225990e-001 -1.7192860e-001 -2.2416360e-001 -1.5686030e-001 -1.1445160e-001 -1.2398320e-001 -4.7481350e-002 -3.4828960e-002 1.9014830e-002 5.3397070e-002 6.4815390e-002 1.0278350e-001 7.6053810e-002 1.1158510e-001 1.7558720e-001 2.7248390e-001 4.2896290e-001 4.9307280e-001 4.7214750e-001 4.1655320e-001 3.4686590e-001 1.8403780e-001 -3.3835110e-002 -1.1367570e-001 -9.6132820e-002 -5.8506690e-002 -4.8071090e-002 -7.7781600e-002 -1.1629420e-001 -1.8163210e-001 -2.2299490e-001 -2.6093200e-001 -3.0279360e-001 -2.9780140e-001 -2.7941940e-001 -2.2637150e-001 -1.4507560e-001 -8.3344240e-002 -8.0088450e-002 -1.1622240e-001 -1.0610020e-001 -8.9397650e-002 -5.5760440e-002 1.7739840e-002 5.4811330e-002 9.8336060e-002 1.4786620e-001 1.6844160e-001 2.1139970e-001 3.2885210e-001 5.3764340e-001 6.3859240e-001 4.9789300e-001 2.1412750e-001 -1.1450890e-001 -1.9364250e-001 -6.4987380e-002 -9.7122100e-002 -5.5734790e-002 4.1101470e-002 -6.9489150e-002 -1.0405840e-001 -1.4993890e-001 -2.6893410e-001 -2.7348300e-001 -3.2168610e-001 -3.0465180e-001 -2.4403760e-001 -2.6718840e-001 -2.0297790e-001 -1.5180510e-001 -1.1798920e-001 -5.6056010e-002 -3.3551020e-002 2.3934090e-002 2.5334680e-002 2.1370760e-003 6.2426190e-002 9.2001640e-002 1.0350340e-001 1.3482930e-001 1.4456340e-001 1.8128840e-001 2.7628710e-001 3.6625720e-001 4.4477150e-001 5.1891990e-001 5.1125800e-001 4.3076580e-001 2.2537710e-001 -9.7748440e-002 -1.9708090e-001 -9.3031620e-002 -9.8751400e-002 -9.2377730e-002 -3.7277040e-002 -7.6692140e-002 -1.1525600e-001 -1.7692850e-001 -2.7514570e-001 -3.0186110e-001 -3.3079970e-001 -3.3262600e-001 -2.5027560e-001 -1.5736190e-001 -5.8068760e-002 -1.8318130e-002 -2.2401950e-002 -3.7750860e-003 -4.1441830e-002 -8.3458000e-002 -8.5438830e-002 -1.0046990e-001 -6.7287170e-002 1.8514180e-002 9.7981260e-002 1.8598780e-001 2.7371140e-001 3.5367380e-001\r\n -1.9364250e-001 -6.4987380e-002 -9.7122100e-002 -5.5734790e-002 4.1101470e-002 -6.9489150e-002 -1.0405840e-001 -1.4993890e-001 -2.6893410e-001 -2.7348300e-001 -3.2168610e-001 -3.0465180e-001 -2.4403760e-001 -2.6718840e-001 -2.0297790e-001 -1.5180510e-001 -1.1798920e-001 -5.6056010e-002 -3.3551020e-002 2.3934090e-002 2.5334680e-002 2.1370760e-003 6.2426190e-002 9.2001640e-002 1.0350340e-001 1.3482930e-001 1.4456340e-001 1.8128840e-001 2.7628710e-001 3.6625720e-001 4.4477150e-001 5.1891990e-001 5.1125800e-001 4.3076580e-001 2.2537710e-001 -9.7748440e-002 -1.9708090e-001 -9.3031620e-002 -9.8751400e-002 -9.2377730e-002 -3.7277040e-002 -7.6692140e-002 -1.1525600e-001 -1.7692850e-001 -2.7514570e-001 -3.0186110e-001 -3.3079970e-001 -3.3262600e-001 -2.5027560e-001 -1.5736190e-001 -5.8068760e-002 -1.8318130e-002 -2.2401950e-002 -3.7750860e-003 -4.1441830e-002 -8.3458000e-002 -8.5438830e-002 -1.0046990e-001 -6.7287170e-002 1.8514180e-002 9.7981260e-002 1.8598780e-001 2.7371140e-001 3.5367380e-001 4.7434460e-001 5.2297550e-001 3.6254440e-001 1.5240240e-001 1.4356160e-002 -8.5905450e-002 -1.2760210e-001 -1.2650330e-001 -9.6833170e-002 -4.8860750e-002 -2.1647540e-002 -2.8557520e-002 -7.1333270e-002 -1.2004330e-001 -1.8941370e-001 -2.8147790e-001 -3.3663880e-001 -3.3678480e-001 -2.6605830e-001 -1.6571260e-001 -1.1916730e-001 -1.2813670e-001 -1.2917860e-001 -8.9647010e-002 -5.6524470e-002 -3.4915840e-002 -2.5197170e-003 4.7411640e-002 1.0513870e-001 1.1023750e-001 1.0209320e-001 1.2236640e-001 1.4784600e-001 2.4459340e-001 4.1681360e-001 5.5070570e-001 5.5930410e-001 4.6802090e-001 3.1795260e-001 8.2447800e-002 -1.2867170e-001 -1.6393880e-001 -8.3052470e-002 -2.9882640e-002 -3.5347020e-002 -4.4049450e-002 -6.5067100e-002 -1.1461830e-001 -1.8631700e-001 -2.7782600e-001 -3.2300940e-001 -3.5901200e-001 -4.0926240e-001 -3.6805250e-001 -2.9925500e-001 -2.1883690e-001 -1.0517590e-001 -9.9268240e-002 -1.0626530e-001 -6.1666710e-002 -7.0890360e-002 -5.0755210e-002 -1.9806750e-002 -1.1040150e-002\r\n" }, { "path": "tsfresh/scripts/measure_execution_time.py", "content": "# This script extracts the execution time for\n# various different settings of tsfresh\n# using different input data\n# Attention: it will run for ~half a day\n# Do these calculations in a controlled environment\n# (e.g. a cloud provider VM)\n# You will need to have b2luigi installed.\nimport json\nfrom time import time\n\nimport b2luigi as luigi\nimport numpy as np\nimport pandas as pd\n\nfrom tsfresh.feature_extraction import (\n ComprehensiveFCParameters,\n MinimalFCParameters,\n extract_features,\n)\n\n\nclass DataCreationTask(luigi.Task):\n \"\"\"Create random data for testing\"\"\"\n\n num_ids = luigi.IntParameter(default=100)\n time_series_length = luigi.IntParameter()\n random_seed = luigi.IntParameter()\n\n def output(self):\n yield self.add_to_output(\"data.csv\")\n\n def run(self):\n np.random.seed(self.random_seed)\n\n df = pd.concat(\n [\n pd.DataFrame(\n {\n \"id\": [i] * self.time_series_length,\n \"time\": range(self.time_series_length),\n \"value\": np.random.randn(self.time_series_length),\n }\n )\n for i in range(self.num_ids)\n ]\n )\n\n with self._get_output_target(\"data.csv\").open(\"w\") as f:\n df.to_csv(f)\n\n\n@luigi.requires(DataCreationTask)\nclass TimingTask(luigi.Task):\n \"\"\"Run tsfresh with the given parameters\"\"\"\n\n feature_parameter = luigi.DictParameter(hashed=True)\n n_jobs = luigi.IntParameter()\n try_number = luigi.IntParameter()\n\n def output(self):\n yield self.add_to_output(\"result.json\")\n\n def run(self):\n input_file = self._get_input_targets(\"data.csv\")[0]\n\n with input_file.open(\"r\") as f:\n df = pd.read_csv(f)\n\n start_time = time()\n extract_features(\n df,\n column_id=\"id\",\n column_sort=\"time\",\n n_jobs=self.n_jobs,\n default_fc_parameters=self.feature_parameter,\n disable_progressbar=True,\n )\n end_time = time()\n\n single_parameter_name = list(self.feature_parameter.keys())[0]\n single_parameter_params = self.feature_parameter[single_parameter_name]\n\n result_json = {\n \"time\": end_time - start_time,\n \"n_ids\": self.num_ids,\n \"n_jobs\": self.n_jobs,\n \"feature\": single_parameter_name,\n \"number_parameters\": len(single_parameter_params)\n if single_parameter_params\n else 0,\n \"time_series_length\": int((df[\"id\"] == 0).sum()),\n \"try_number\": self.try_number,\n }\n\n with self._get_output_target(\"result.json\").open(\"w\") as f:\n json.dump(result_json, f)\n\n\n@luigi.requires(DataCreationTask)\nclass FullTimingTask(luigi.Task):\n \"\"\"Run tsfresh with all calculators for comparison\"\"\"\n\n n_jobs = luigi.IntParameter()\n\n def output(self):\n yield self.add_to_output(\"result.json\")\n\n def run(self):\n input_file = self._get_input_targets(\"data.csv\")[0]\n\n with input_file.open(\"r\") as f:\n df = pd.read_csv(f)\n\n start_time = time()\n extract_features(\n df,\n column_id=\"id\",\n column_sort=\"time\",\n n_jobs=self.n_jobs,\n disable_progressbar=True,\n )\n end_time = time()\n\n result_json = {\n \"time\": end_time - start_time,\n \"n_ids\": self.num_ids,\n \"n_jobs\": self.n_jobs,\n \"time_series_length\": int((df[\"id\"] == 0).sum()),\n }\n\n with self._get_output_target(\"result.json\").open(\"w\") as f:\n json.dump(result_json, f)\n\n\nclass CombinerTask(luigi.Task):\n \"\"\"Collect all tasks into a single result.csv file\"\"\"\n\n def complete(self):\n return False\n\n def requires(self):\n settings = ComprehensiveFCParameters()\n for job in [0, 1, 4]:\n for time_series_length in [100, 500, 1000, 5000]:\n yield FullTimingTask(\n time_series_length=time_series_length,\n n_jobs=job,\n num_ids=10,\n random_seed=42,\n )\n yield FullTimingTask(\n time_series_length=time_series_length,\n n_jobs=job,\n num_ids=100,\n random_seed=42,\n )\n\n for feature_name in settings:\n yield TimingTask(\n feature_parameter={feature_name: settings[feature_name]},\n time_series_length=time_series_length,\n n_jobs=job,\n num_ids=100,\n try_number=0,\n random_seed=42,\n )\n\n for try_number in range(3):\n yield TimingTask(\n feature_parameter={feature_name: settings[feature_name]},\n n_jobs=job,\n try_number=try_number,\n num_ids=10,\n time_series_length=time_series_length,\n random_seed=42,\n )\n\n def output(self):\n yield self.add_to_output(\"results.csv\")\n\n def run(self):\n results = []\n\n for input_file in self._get_input_targets(\"result.json\"):\n with input_file.open(\"r\") as f:\n results.append(json.load(f))\n\n df = pd.DataFrame(results)\n\n with self._get_output_target(\"results.csv\").open(\"w\") as f:\n df.to_csv(f)\n\n\nif __name__ == \"__main__\":\n luigi.set_setting(\"result_path\", \"results\")\n luigi.process(CombinerTask())\n" }, { "path": "tsfresh/scripts/run_tsfresh.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2017\n\"\"\"\nThis script can be run with:\n\n\n.. code-block:: bash\n\n python run_tsfresh.py path_to_your_csv.csv\n\nA corresponding csv containing time series features will be\nsaved as features_path_to_your_csv.csv\n\nThere are a few limitations though\n\n- Currently this only samples to first 50 values.\n- Your csv must be space delimited.\n- Output is saved as path_to_your_csv.features.csv\n\n\"\"\"\n\nimport argparse\nimport os\nimport sys\n\nimport pandas as pd\n\nfrom tsfresh import extract_features\n\n\ndef _preprocess(df):\n \"\"\"\n given a DataFrame where records are stored row-wise, rearrange it\n such that records are stored column-wise.\n \"\"\"\n\n df = df.stack()\n\n df.index.rename([\"id\", \"time\"], inplace=True) # .reset_index()\n df.name = \"value\"\n df = df.reset_index()\n\n return df\n\n\ndef main(console_args=None):\n parser = argparse.ArgumentParser(\n description=\"Extract features from time series stored in a CSV file and \"\n \"write them back into another CSV file. The time series in the CSV \"\n \"file should either have one of the dataframe-formats described in \"\n \"http://tsfresh.readthedocs.io/en/latest/text/data_formats.html, \"\n \"which means you have to supply the --csv-with-headers flag \"\n \"or should be in the form \"\n \"[time series 1 values ..., time series 2 values ...] \"\n \"where you should not add the --csv-with-headers flag. \"\n \"The CSV is expected to be space-separated.\"\n )\n parser.add_argument(\n \"input_file_name\", help=\"File name of the input CSV file to read in.\"\n )\n parser.add_argument(\n \"--output-file-name\",\n help=\"File name of the output CSV file to write to. \"\n \"Defaults to input_file_name.features.csv\",\n default=None,\n )\n\n parser.add_argument(\n \"--column-sort\",\n help=\"Column name to be used to sort the rows. \"\n \"Only available when --csv-with-headers is enabled.\",\n default=None,\n )\n parser.add_argument(\n \"--column-kind\",\n help=\"Column name where the kind column can be found.\"\n \"Only available when --csv-with-headers is enabled.\",\n default=None,\n )\n parser.add_argument(\n \"--column-value\",\n help=\"Column name where the values can be found.\"\n \"Only available when --csv-with-headers is enabled.\",\n default=None,\n )\n parser.add_argument(\n \"--column-id\",\n help=\"Column name where the ids can be found.\"\n \"Only available when --csv-with-headers is enabled.\",\n default=None,\n )\n\n parser.add_argument(\"--csv-with-headers\", action=\"store_true\", help=\"\")\n print(console_args)\n args = parser.parse_args(console_args)\n\n if (\n args.column_id or args.column_kind or args.column_sort or args.column_value\n ) and (not args.csv_with_headers):\n raise AttributeError(\n \"You can only pass in column-value, column-kind, column-id or column-sort if \"\n \"--csv-with-headers is enabled.\"\n )\n\n if args.csv_with_headers:\n column_kind = args.column_kind\n column_sort = args.column_sort\n column_value = args.column_value\n column_id = args.column_id\n header = 0\n else:\n column_kind = None\n column_sort = \"time\"\n column_value = \"value\"\n column_id = \"id\"\n header = None\n\n # Read in CSV file\n input_file_name = args.input_file_name\n df = pd.read_csv(input_file_name, delim_whitespace=True, header=header)\n\n if not args.csv_with_headers:\n df = _preprocess(df)\n\n df_features = extract_features(\n df,\n column_kind=column_kind,\n column_sort=column_sort,\n column_value=column_value,\n column_id=column_id,\n )\n\n # re-cast index from float to int\n df_features.index = df_features.index.astype(\"int\")\n\n # write to disk\n default_out_file_name = os.path.splitext(input_file_name)[0] + \".features.csv\"\n output_file_name = args.output_file_name or default_out_file_name\n df_features.to_csv(output_file_name)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "tsfresh/scripts/test_timing.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2017\nimport time\nfrom glob import glob\nfrom subprocess import check_output\n\nimport pandas as pd\n\n\ndef simulate_with_length(length, df):\n from tsfresh import extract_features\n\n start = time.time()\n df = extract_features(df[:length], column_id=\"id\", column_sort=\"time\")\n end = time.time()\n\n duration = end - start\n\n print(\"Some checks with length\", length)\n print(100 * duration)\n print(len(df.columns), len(df))\n print(\n df[\n [\n \"a__abs_energy\",\n \"b__absolute_sum_of_changes\",\n \"f__time_reversal_asymmetry_statistic__lag_1\",\n ]\n ].head()\n )\n\n return {\"length\": length, \"duration\": duration}\n\n\ndef plot_results():\n from matplotlib import pyplot as plt\n\n plt.figure(figsize=(7, 7))\n\n baseline = (\n pd.read_csv(\"a57a09fe62a62fe0d2564a056f7fd99f58822312.dat\")\n .groupby(\"length\")\n .duration.mean()\n )\n\n for file_name in glob(\"*.dat\"):\n df = pd.read_csv(file_name).groupby(\"length\").duration.mean()\n\n plt.subplot(211)\n df.plot(label=file_name.replace(\".dat\", \"\"))\n\n plt.subplot(212)\n (baseline / df).plot(label=file_name.replace(\".dat\", \"\"))\n\n plt.subplot(211)\n plt.xlabel(\"DataFrame Length\")\n plt.ylabel(\"Extract Features Mean Duration\")\n plt.legend()\n\n plt.subplot(212)\n plt.xlabel(\"DataFrame Length\")\n plt.ylabel(\"Speedup\")\n plt.gca().axhline(1, color=\"black\", ls=\"--\")\n plt.legend()\n\n plt.savefig(\"timing.png\")\n\n\ndef measure_temporal_complexity():\n from tsfresh.examples.robot_execution_failures import (\n download_robot_execution_failures,\n load_robot_execution_failures,\n )\n\n download_robot_execution_failures()\n df, y = load_robot_execution_failures()\n\n commit_hash = (\n check_output([\"git\", \"log\", '--format=\"%H\"', \"-1\"])\n .decode(\"ascii\")\n .strip()\n .replace('\"', \"\")\n )\n\n lengths_to_test = [1, 5, 10, 60, 100, 400, 600, 1000, 2000]\n results = []\n\n for length in lengths_to_test:\n results.append(simulate_with_length(length, df))\n results.append(simulate_with_length(length, df))\n results.append(simulate_with_length(length, df))\n\n results = pd.DataFrame(results)\n results.to_csv(\"{hash}.dat\".format(hash=commit_hash))\n\n\nif __name__ == \"__main__\":\n measure_temporal_complexity()\n plot_results()\n" }, { "path": "tsfresh/transformers/__init__.py", "content": "\"\"\"\nThe module :mod:`~tsfresh.transformers` contains several transformers which can be used inside a sklearn pipeline.\n\n\"\"\"\n\nfrom tsfresh.transformers.feature_augmenter import FeatureAugmenter\nfrom tsfresh.transformers.feature_selector import FeatureSelector\nfrom tsfresh.transformers.per_column_imputer import PerColumnImputer\nfrom tsfresh.transformers.relevant_feature_augmenter import RelevantFeatureAugmenter\n" }, { "path": "tsfresh/transformers/feature_augmenter.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nimport tsfresh.defaults\nfrom tsfresh.feature_extraction import extract_features\nfrom tsfresh.utilities.dataframe_functions import restrict_input_to_index\n\n\nclass FeatureAugmenter(BaseEstimator, TransformerMixin):\n \"\"\"\n Sklearn-compatible estimator, for calculating and adding many features calculated from a given time series\n to the data. It is basically a wrapper around :func:`~tsfresh.feature_extraction.extract_features`.\n\n The features include basic ones like min, max or median, and advanced features like fourier\n transformations or statistical tests. For a list of all possible features, see the module\n :mod:`~tsfresh.feature_extraction.feature_calculators`. The column name of each added feature contains the name\n of the function of that module, which was used for the calculation.\n\n For this estimator, two datasets play a crucial role:\n\n 1. the time series container with the timeseries data. This container (for the format see :ref:`data-formats-label`)\n contains the data which is used for calculating the\n features. It must be groupable by ids which are used to identify which feature should be attached to which row\n in the second dataframe.\n\n 2. the input data X, where the features will be added to. Its rows are identifies by the index and each index in\n X must be present as an id in the time series container.\n\n Imagine the following situation: You want to classify 10 different financial shares and you have their development\n in the last year as a time series. You would then start by creating features from the metainformation of the\n shares, e.g. how long they were on the market etc. and filling up a table - the features of one stock in one row.\n This is the input array X, which each row identified by e.g. the stock name as an index.\n\n >>> df = pandas.DataFrame(index=[\"AAA\", \"BBB\", ...])\n >>> # Fill in the information of the stocks\n >>> df[\"started_since_days\"] = ... # add a feature\n\n You can then extract all the features from the time development of the shares, by using this estimator.\n The time series container must include a column of ids, which are the same as the index of X.\n\n >>> time_series = read_in_timeseries() # get the development of the shares\n >>> from tsfresh.transformers import FeatureAugmenter\n >>> augmenter = FeatureAugmenter(column_id=\"id\")\n >>> augmenter.set_timeseries_container(time_series)\n >>> df_with_time_series_features = augmenter.transform(df)\n\n The settings for the feature calculation can be controlled with the settings object.\n If you pass ``None``, the default settings are used.\n Please refer to :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters` for\n more information.\n\n This estimator does not select the relevant features, but calculates and adds all of them to the DataFrame. See the\n :class:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter` for calculating and selecting\n features.\n\n For a description what the parameters column_id, column_sort, column_kind and column_value mean, please see\n :mod:`~tsfresh.feature_extraction.extraction`.\n \"\"\"\n\n def __init__(\n self,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n column_id=None,\n column_sort=None,\n column_kind=None,\n column_value=None,\n timeseries_container=None,\n chunksize=tsfresh.defaults.CHUNKSIZE,\n n_jobs=tsfresh.defaults.N_PROCESSES,\n show_warnings=tsfresh.defaults.SHOW_WARNINGS,\n disable_progressbar=tsfresh.defaults.DISABLE_PROGRESSBAR,\n impute_function=tsfresh.defaults.IMPUTE_FUNCTION,\n profile=tsfresh.defaults.PROFILING,\n profiling_filename=tsfresh.defaults.PROFILING_FILENAME,\n profiling_sorting=tsfresh.defaults.PROFILING_SORTING,\n ):\n \"\"\"\n Create a new FeatureAugmenter instance.\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters. This means that kinds,\n for which kind_of_fc_parameters doe not have any entries, will be ignored by the feature selection.\n :type kind_to_fc_parameters: dict\n\n :param column_id: The column with the id. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_id: basestring\n :param column_sort: The column with the sort data. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_sort: basestring\n :param column_kind: The column with the kind data. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_kind: basestring\n :param column_value: The column with the values. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_value: basestring\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as a\n singular time series for one id and one kind. If you set the chunksize\n to 10, then it means that one task is to calculate all features for 10\n time series. If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param impute_function: None, if no imputing should happen or the function to call for imputing\n the result dataframe. Imputing will never happen on the input data.\n :type impute_function: None or function\n\n :param profile: Turn on profiling during feature extraction\n :type profile: bool\n\n :param profiling_sorting: How to sort the profiling results (see the documentation of the profiling package for\n more information)\n :type profiling_sorting: basestring\n\n :param profiling_filename: Where to save the profiling results.\n :type profiling_filename: basestring\n \"\"\"\n self.default_fc_parameters = default_fc_parameters\n self.kind_to_fc_parameters = kind_to_fc_parameters\n\n self.column_id = column_id\n self.column_sort = column_sort\n self.column_kind = column_kind\n self.column_value = column_value\n\n self.n_jobs = n_jobs\n self.chunksize = chunksize\n self.show_warnings = show_warnings\n self.disable_progressbar = disable_progressbar\n self.impute_function = impute_function\n self.profile = profile\n self.profiling_filename = profiling_filename\n self.profiling_sorting = profiling_sorting\n\n self.timeseries_container = timeseries_container\n\n def set_timeseries_container(self, timeseries_container):\n \"\"\"\n Set the timeseries, with which the features will be calculated. For a format of the time series container,\n please refer to :mod:`~tsfresh.feature_extraction.extraction`. The timeseries must contain the same indices\n as the later DataFrame, to which the features will be added (the one you will pass to :func:`~transform`). You\n can call this function as often as you like, to change the timeseries later (e.g. if you want to extract for\n different ids).\n\n :param timeseries_container: The timeseries as a pandas.DataFrame or a dict. See\n :mod:`~tsfresh.feature_extraction.extraction` for the format.\n :type timeseries_container: pandas.DataFrame or dict\n :return: None\n :rtype: None\n \"\"\"\n self.timeseries_container = timeseries_container\n\n def fit(self, X=None, y=None):\n \"\"\"\n The fit function is not needed for this estimator. It just does nothing and is here for compatibility reasons.\n\n :param X: Unneeded.\n :type X: Any\n\n :param y: Unneeded.\n :type y: Any\n\n :return: The estimator instance itself\n :rtype: FeatureAugmenter\n \"\"\"\n return self\n\n def transform(self, X):\n \"\"\"\n Add the features calculated using the timeseries_container and add them to the corresponding rows in the input\n pandas.DataFrame X.\n\n To save some computing time, you should only include those time serieses in the container, that you\n need. You can set the timeseries container with the method :func:`set_timeseries_container`.\n\n :param X: the DataFrame to which the calculated timeseries features will be added. This is *not* the\n dataframe with the timeseries itself.\n :type X: pandas.DataFrame\n\n :return: The input DataFrame, but with added features.\n :rtype: pandas.DataFrame\n \"\"\"\n if self.timeseries_container is None:\n raise RuntimeError(\n \"You have to provide a time series using the set_timeseries_container function before.\"\n )\n\n # Extract only features for the IDs in X.index\n timeseries_container_X = restrict_input_to_index(\n self.timeseries_container, self.column_id, X.index\n )\n\n extracted_features = extract_features(\n timeseries_container_X,\n default_fc_parameters=self.default_fc_parameters,\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n column_id=self.column_id,\n column_sort=self.column_sort,\n column_kind=self.column_kind,\n column_value=self.column_value,\n chunksize=self.chunksize,\n n_jobs=self.n_jobs,\n show_warnings=self.show_warnings,\n disable_progressbar=self.disable_progressbar,\n impute_function=self.impute_function,\n profile=self.profile,\n profiling_filename=self.profiling_filename,\n profiling_sorting=self.profiling_sorting,\n )\n\n X = pd.merge(\n X, extracted_features, left_index=True, right_index=True, how=\"left\"\n )\n\n return X\n" }, { "path": "tsfresh/transformers/feature_selector.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_selection.relevance import calculate_relevance_table\n\n\nclass FeatureSelector(BaseEstimator, TransformerMixin):\n \"\"\"\n Sklearn-compatible estimator, for reducing the number of features in a dataset to only those,\n that are relevant and significant to a given target. It is basically a wrapper around\n :func:`~tsfresh.feature_selection.feature_selector.check_fs_sig_bh`.\n\n The check is done by testing the hypothesis\n\n :math:`H_0` = the Feature is not relevant and can not be added`\n\n against\n\n :math:`H_1` = the Feature is relevant and should be kept\n\n using several statistical tests (depending on whether the feature or/and the target is binary\n or not). Using the Benjamini Hochberg procedure, only features in :math:`H_0` are rejected.\n\n This estimator - as most of the sklearn estimators - works in a two step procedure. First, it is fitted\n on training data, where the target is known:\n\n >>> import pandas as pd\n >>> X_train, y_train = pd.DataFrame(), pd.Series() # fill in with your features and target\n >>> from tsfresh.transformers import FeatureSelector\n >>> selector = FeatureSelector()\n >>> selector.fit(X_train, y_train)\n\n In this example the list of relevant features is empty:\n\n >>> selector.relevant_features\n >>> []\n\n The same holds for the feature importance:\n\n >>> selector.feature_importances_\n >>> array([], dtype=float64)\n\n The estimator keeps track on those features, that were relevant in the training step. If you\n apply the estimator after the training, it will delete all other features in the testing\n data sample:\n\n >>> X_test = pd.DataFrame()\n >>> X_selected = selector.transform(X_test)\n\n After that, X_selected will only contain the features that were relevant during the training.\n\n If you are interested in more information on the features, you can look into the member\n ``relevant_features`` after the fit.\n \"\"\"\n\n def __init__(\n self,\n test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,\n test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,\n test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,\n fdr_level=defaults.FDR_LEVEL,\n hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,\n n_jobs=defaults.N_PROCESSES,\n chunksize=defaults.CHUNKSIZE,\n ml_task=\"auto\",\n multiclass=False,\n n_significant=1,\n multiclass_p_values=\"min\",\n ):\n \"\"\"\n Create a new FeatureSelector instance.\n\n :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature\n (currently unused)\n :type test_for_binary_target_binary_feature: str\n\n :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature\n :type test_for_binary_target_real_feature: str\n\n :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature\n (currently unused)\n :type test_for_real_target_binary_feature: str\n\n :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)\n :type test_for_real_target_real_feature: str\n\n :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage\n of irrelevant features among all created features.\n :type fdr_level: float\n\n :param hypotheses_independent: Can the significance of the features be assumed to be independent?\n Normally, this should be set to False as the features are never\n independent (e.g. mean and median)\n :type hypotheses_independent: bool\n\n :param n_jobs: Number of processes to use during the p-value calculation\n :type n_jobs: int\n\n :param chunksize: Size of the chunks submitted to the worker processes\n :type chunksize: int\n\n :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.\n Defaults to `'auto'`, meaning the intended task is inferred from `y`.\n If `y` has a boolean, integer or object dtype, the task is assumed to be classification,\n else regression.\n :type ml_task: str\n\n :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features\n are selected. Multiclass requires the features to be statistically significant for\n predicting n_significant classes.\n :type multiclass: bool\n\n :param n_significant: The number of classes for which features should be statistically significant predictors\n to be regarded as 'relevant'\n :type n_significant: int\n\n :param multiclass_p_values: The desired method for choosing how to display multiclass p-values for each feature.\n Either `'avg'`, `'max'`, `'min'`, `'all'`. Defaults to `'min'`, meaning the p-value\n with the highest significance is chosen. When set to `'all'`, the attributes\n `self.feature_importances_` and `self.p_values` are of type pandas.DataFrame, where\n each column corresponds to a target class.\n :type multiclass_p_values: str\n \"\"\"\n self.relevant_features = None\n self.feature_importances_ = None\n self.p_values = None\n self.features = None\n\n self.test_for_binary_target_binary_feature = (\n test_for_binary_target_binary_feature\n )\n self.test_for_binary_target_real_feature = test_for_binary_target_real_feature\n self.test_for_real_target_binary_feature = test_for_real_target_binary_feature\n self.test_for_real_target_real_feature = test_for_real_target_real_feature\n\n self.fdr_level = fdr_level\n self.hypotheses_independent = hypotheses_independent\n\n self.n_jobs = n_jobs\n self.chunksize = chunksize\n self.ml_task = ml_task\n self.multiclass = multiclass\n self.n_significant = n_significant\n self.multiclass_p_values = multiclass_p_values\n\n def fit(self, X, y):\n \"\"\"\n Extract the information, which of the features are relevant using the given target.\n\n For more information, please see the :func:`~tsfresh.festure_selection.festure_selector.check_fs_sig_bh`\n function. All columns in the input data sample are treated as feature. The index of all\n rows in X must be present in y.\n\n :param X: data sample with the features, which will be classified as relevant or not\n :type X: pandas.DataFrame or numpy.array\n\n :param y: target vector to be used, to classify the features\n :type y: pandas.Series or numpy.array\n\n :return: the fitted estimator with the information, which features are relevant\n :rtype: FeatureSelector\n \"\"\"\n if not isinstance(X, pd.DataFrame):\n X = pd.DataFrame(X.copy())\n\n if not isinstance(y, pd.Series):\n y = pd.Series(y.copy())\n\n relevance_table = calculate_relevance_table(\n X,\n y,\n ml_task=self.ml_task,\n multiclass=self.multiclass,\n n_significant=self.n_significant,\n n_jobs=self.n_jobs,\n chunksize=self.chunksize,\n fdr_level=self.fdr_level,\n hypotheses_independent=self.hypotheses_independent,\n test_for_binary_target_real_feature=self.test_for_binary_target_real_feature,\n )\n self.relevant_features = relevance_table.loc[\n relevance_table.relevant\n ].feature.tolist()\n\n if self.multiclass:\n p_values_table = relevance_table.filter(regex=\"^p_value_*\", axis=1)\n if self.multiclass_p_values == \"all\":\n self.p_values = p_values_table\n self.feature_importances_ = 1.0 - p_values_table\n self.feature_importances_.columns = (\n self.feature_importances_.columns.str.lstrip(\"p_value\")\n )\n self.feature_importances_ = self.feature_importances_.add_prefix(\n \"importance_\"\n )\n elif self.multiclass_p_values == \"min\":\n self.p_values = p_values_table.min(axis=1).values\n elif self.multiclass_p_values == \"max\":\n self.p_values = p_values_table.max(axis=1).values\n elif self.multiclass_p_values == \"avg\":\n self.p_values = p_values_table.mean(axis=1).values\n\n if self.multiclass_p_values != \"all\":\n # raise p_values to the power of n_significant to increase importance\n # of features which are significant for more classes\n self.feature_importances_ = (\n 1.0 - self.p_values**relevance_table.n_significant.values\n )\n else:\n self.feature_importances_ = 1.0 - relevance_table.p_value.values\n self.p_values = relevance_table.p_value.values\n\n self.features = relevance_table.index.tolist()\n\n return self\n\n def transform(self, X):\n \"\"\"\n Delete all features, which were not relevant in the fit phase.\n\n :param X: data sample with all features, which will be reduced to only those that are relevant\n :type X: pandas.DataSeries or numpy.array\n\n :return: same data sample as X, but with only the relevant features\n :rtype: pandas.DataFrame or numpy.array\n \"\"\"\n if self.relevant_features is None:\n raise RuntimeError(\"You have to call fit before.\")\n\n if isinstance(X, pd.DataFrame):\n return X.copy().loc[:, self.relevant_features]\n else:\n return X[:, self.relevant_features]\n" }, { "path": "tsfresh/transformers/per_column_imputer.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.exceptions import NotFittedError\n\nfrom tsfresh.utilities.dataframe_functions import (\n get_range_values_per_column,\n impute_dataframe_range,\n)\n\n\nclass PerColumnImputer(BaseEstimator, TransformerMixin):\n \"\"\"\n Sklearn-compatible estimator, for column-wise imputing DataFrames by replacing all ``NaNs`` and ``infs``\n with with average/extreme values from the same columns. It is basically a wrapper around\n :func:`~tsfresh.utilities.dataframe_functions.impute`.\n\n Each occurring ``inf`` or ``NaN`` in the DataFrame is replaced by\n\n * ``-inf`` -> ``min``\n * ``+inf`` -> ``max``\n * ``NaN`` -> ``median``\n\n This estimator - as most of the sklearn estimators - works in a two step procedure. First, the ``.fit``\n function is called where for each column the min, max and median are computed.\n Secondly, the ``.transform`` function is called which replaces the occurances of ``NaNs`` and ``infs`` using\n the column-wise computed min, max and median values.\n \"\"\"\n\n def __init__(\n self,\n col_to_NINF_repl_preset=None,\n col_to_PINF_repl_preset=None,\n col_to_NAN_repl_preset=None,\n ):\n \"\"\"\n Create a new PerColumnImputer instance, optionally with dictionaries containing replacements for\n ``NaNs`` and ``infs``.\n\n :param col_to_NINF_repl: Dictionary mapping column names to ``-inf`` replacement values\n :type col_to_NINF_repl: dict\n :param col_to_PINF_repl: Dictionary mapping column names to ``+inf`` replacement values\n :type col_to_PINF_repl: dict\n :param col_to_NAN_repl: Dictionary mapping column names to ``NaN`` replacement values\n :type col_to_NAN_repl: dict\n \"\"\"\n self._col_to_NINF_repl = None\n self._col_to_PINF_repl = None\n self._col_to_NAN_repl = None\n self.col_to_NINF_repl_preset = col_to_NINF_repl_preset\n self.col_to_PINF_repl_preset = col_to_PINF_repl_preset\n self.col_to_NAN_repl_preset = col_to_NAN_repl_preset\n\n def fit(self, X, y=None):\n \"\"\"\n Compute the min, max and median for all columns in the DataFrame. For more information,\n please see the :func:`~tsfresh.utilities.dataframe_functions.get_range_values_per_column` function.\n\n :param X: DataFrame to calculate min, max and median values on\n :type X: pandas.DataFrame\n :param y: Unneeded.\n :type y: Any\n\n :return: the estimator with the computed min, max and median values\n :rtype: Imputer\n \"\"\"\n if not isinstance(X, pd.DataFrame):\n X = pd.DataFrame(X)\n\n col_to_max, col_to_min, col_to_median = get_range_values_per_column(X)\n\n if self.col_to_NINF_repl_preset is not None:\n if not set(X.columns) >= set(self.col_to_NINF_repl_preset.keys()):\n raise ValueError(\n \"Preset dictionary 'col_to_NINF_repl_preset' contain more keys \"\n \"than the column names in X\"\n )\n col_to_min.update(self.col_to_NINF_repl_preset)\n self._col_to_NINF_repl = col_to_min\n\n if self.col_to_PINF_repl_preset is not None:\n if not set(X.columns) >= set(self.col_to_PINF_repl_preset.keys()):\n raise ValueError(\n \"Preset dictionary 'col_to_PINF_repl_preset' contain more keys \"\n \"than the column names in X\"\n )\n col_to_max.update(self.col_to_PINF_repl_preset)\n self._col_to_PINF_repl = col_to_max\n\n if self.col_to_NAN_repl_preset is not None:\n if not set(X.columns) >= set(self.col_to_NAN_repl_preset.keys()):\n raise ValueError(\n \"Preset dictionary 'col_to_NAN_repl_preset' contain more keys \"\n \"than the column names in X\"\n )\n col_to_median.update(self.col_to_NAN_repl_preset)\n self._col_to_NAN_repl = col_to_median\n\n return self\n\n def transform(self, X):\n \"\"\"\n Column-wise replace all ``NaNs``, ``-inf`` and ``+inf`` in the DataFrame `X` with average/extreme\n values from the provided dictionaries.\n\n :param X: DataFrame to impute\n :type X: pandas.DataFrame\n\n :return: imputed DataFrame\n :rtype: pandas.DataFrame\n :raise RuntimeError: if the replacement dictionaries are still of None type.\n This can happen if the transformer was not fitted.\n \"\"\"\n\n if not isinstance(X, pd.DataFrame):\n X = pd.DataFrame(X)\n\n if (\n self._col_to_NINF_repl is None\n or self._col_to_PINF_repl is None\n or self._col_to_NAN_repl is None\n ):\n raise NotFittedError(\"PerColumnImputer is not fitted\")\n\n X = impute_dataframe_range(\n X, self._col_to_PINF_repl, self._col_to_NINF_repl, self._col_to_NAN_repl\n )\n\n return X\n" }, { "path": "tsfresh/transformers/relevant_feature_augmenter.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\nfrom functools import partial\n\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom tsfresh import defaults\nfrom tsfresh.feature_extraction.settings import from_columns\nfrom tsfresh.transformers.feature_augmenter import FeatureAugmenter\nfrom tsfresh.transformers.feature_selector import FeatureSelector\nfrom tsfresh.utilities.dataframe_functions import (\n get_range_values_per_column,\n impute_dataframe_range,\n)\n\n\n# Pro: It offers more control\n# Contra: The Transformer is more than an Augmenter\nclass RelevantFeatureAugmenter(BaseEstimator, TransformerMixin):\n \"\"\"\n Sklearn-compatible estimator to calculate relevant features out of a time series and add them to a data sample.\n\n As many other sklearn estimators, this estimator works in two steps:\n\n In the fit phase, all possible time series features are calculated using the time series, that is set by the\n set_timeseries_container function (if the features are not manually changed by handing in a\n feature_extraction_settings object). Then, their significance and relevance to the target is computed using\n statistical methods and only the relevant ones are selected using the Benjamini Hochberg procedure. These features\n are stored internally.\n\n In the transform step, the information on which features are relevant from the fit step is used and those features\n are extracted from the time series. These extracted features are then added to the input data sample.\n\n This estimator is a wrapper around most of the functionality in the tsfresh package. For more information on the\n subtasks, please refer to the single modules and functions, which are:\n\n * Settings for the feature extraction: :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters`\n * Feature extraction method: :func:`~tsfresh.feature_extraction.extraction.extract_features`\n * Extracted features: :mod:`~tsfresh.feature_extraction.feature_calculators`\n * Feature selection: :func:`~tsfresh.feature_selection.feature_selector.check_fs_sig_bh`\n\n This estimator works analogue to the :class:`~tsfresh.transformers.feature_augmenter.FeatureAugmenter` with\n the difference that this estimator does only output and calculate the relevant features,\n whereas the other outputs all features.\n\n Also for this estimator, two datasets play a crucial role:\n\n 1. the time series container with the timeseries data. This container (for the format see\n :mod:`~tsfresh.feature_extraction.extraction`) contains the data which is used for calculating the\n features. It must be groupable by ids which are used to identify which feature should be attached to which row\n in the second dataframe:\n\n 2. the input data, where the features will be added to.\n\n Imagine the following situation: You want to classify 10 different financial shares and you have their development\n in the last year as a time series. You would then start by creating features from the metainformation of the\n shares, e.g. how long they were on the market etc. and filling up a table - the features of one stock in one row.\n\n >>> # Fill in the information of the stocks and the target\n >>> X_train, X_test, y_train = pd.DataFrame(), pd.DataFrame(), pd.Series()\n\n You can then extract all the relevant features from the time development of the shares, by using this estimator:\n\n >>> train_time_series, test_time_series = read_in_timeseries() # get the development of the shares\n >>> from tsfresh.transformers import RelevantFeatureAugmenter\n >>> augmenter = RelevantFeatureAugmenter()\n >>> augmenter.set_timeseries_container(train_time_series)\n >>> augmenter.fit(X_train, y_train)\n >>> augmenter.set_timeseries_container(test_time_series)\n >>> X_test_with_features = augmenter.transform(X_test)\n\n X_test_with_features will then contain the same information as X_test (with all the meta information you have\n probably added) plus some relevant time series features calculated on the time series you handed in.\n\n Please keep in mind that the time series you hand in before fit or transform must contain data for the rows that are\n present in X.\n\n If your set filter_only_tsfresh_features to True, your manually-created features that were present in X_train (or\n X_test) before using this estimator are not touched. Otherwise, also those features are evaluated and may be\n rejected from the data sample, because they are irrelevant.\n\n For a description what the parameters column_id, column_sort, column_kind and column_value mean, please see\n :mod:`~tsfresh.feature_extraction.extraction`.\n\n You can control the feature extraction in the fit step (the feature extraction in the transform step is done\n automatically) as well as the feature selection in the fit step by handing in settings.\n However, the default settings which are used if you pass no flags are often quite sensible.\n \"\"\"\n\n def __init__(\n self,\n filter_only_tsfresh_features=True,\n default_fc_parameters=None,\n kind_to_fc_parameters=None,\n column_id=None,\n column_sort=None,\n column_kind=None,\n column_value=None,\n timeseries_container=None,\n chunksize=defaults.CHUNKSIZE,\n n_jobs=defaults.N_PROCESSES,\n show_warnings=defaults.SHOW_WARNINGS,\n disable_progressbar=defaults.DISABLE_PROGRESSBAR,\n profile=defaults.PROFILING,\n profiling_filename=defaults.PROFILING_FILENAME,\n profiling_sorting=defaults.PROFILING_SORTING,\n test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE,\n test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE,\n test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE,\n test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE,\n fdr_level=defaults.FDR_LEVEL,\n hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT,\n ml_task=\"auto\",\n multiclass=False,\n n_significant=1,\n multiclass_p_values=\"min\",\n ):\n \"\"\"\n Create a new RelevantFeatureAugmenter instance.\n\n\n :param filter_only_tsfresh_features: Whether to touch the manually-created features during feature selection or\n not.\n :type filter_only_tsfresh_features: bool\n\n :param default_fc_parameters: mapping from feature calculator names to parameters. Only those names\n which are keys in this dict will be calculated. See the class:`ComprehensiveFCParameters` for\n more information.\n :type default_fc_parameters: dict\n\n :param kind_to_fc_parameters: mapping from kind names to objects of the same type as the ones for\n default_fc_parameters. If you put a kind as a key here, the fc_parameters\n object (which is the value), will be used instead of the default_fc_parameters. This means that kinds,\n for which kind_of_fc_parameters doe not have any entries, will be ignored by the feature selection.\n :type kind_to_fc_parameters: dict\n :param column_id: The column with the id. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_id: basestring\n :param column_sort: The column with the sort data. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_sort: basestring\n :param column_kind: The column with the kind data. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_kind: basestring\n :param column_value: The column with the values. See :mod:`~tsfresh.feature_extraction.extraction`.\n :type column_value: basestring\n\n :param chunksize: The size of one chunk that is submitted to the worker\n process for the parallelisation. Where one chunk is defined as a\n singular time series for one id and one kind. If you set the chunksize\n to 10, then it means that one task is to calculate all features for 10\n time series. If it is set it to None, depending on distributor,\n heuristics are used to find the optimal chunksize. If you get out of\n memory exceptions, you can try it with the dask distributor and a\n smaller chunksize.\n :type chunksize: None or int\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param profile: Turn on profiling during feature extraction\n :type profile: bool\n\n :param profiling_sorting: How to sort the profiling results (see the documentation of the profiling package for\n more information)\n :type profiling_sorting: basestring\n\n :param profiling_filename: Where to save the profiling results.\n :type profiling_filename: basestring\n\n :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature\n (currently unused)\n :type test_for_binary_target_binary_feature: str\n\n :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature\n :type test_for_binary_target_real_feature: str\n\n :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature\n (currently unused)\n :type test_for_real_target_binary_feature: str\n\n :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused)\n :type test_for_real_target_real_feature: str\n\n :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage\n of irrelevant features among all created features.\n :type fdr_level: float\n\n :param hypotheses_independent: Can the significance of the features be assumed to be independent?\n Normally, this should be set to False as the features are never\n independent (e.g. mean and median)\n :type hypotheses_independent: bool\n\n :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`.\n Defaults to `'auto'`, meaning the intended task is inferred from `y`.\n If `y` has a boolean, integer or object dtype, the task is assumed to be classification,\n else regression.\n :type ml_task: str\n\n :param multiclass: Whether the problem is multiclass classification. This modifies the way in which features\n are selected. Multiclass requires the features to be statistically significant for\n predicting n_significant classes.\n :type multiclass: bool\n\n :param n_significant: The number of classes for which features should be statistically significant predictors\n to be regarded as 'relevant'\n :type n_significant: int\n\n :param multiclass_p_values: The desired method for choosing how to display multiclass p-values for each feature.\n Either `'avg'`, `'max'`, `'min'`, `'all'`. Defaults to `'min'`, meaning the p-value\n with the highest significance is chosen. When set to `'all'`, the attributes\n `self.feature_importances_` and `self.p_values` are of type pandas.DataFrame, where\n each column corresponds to a target class.\n :type multiclass_p_values: str\n \"\"\"\n self.filter_only_tsfresh_features = filter_only_tsfresh_features\n self.default_fc_parameters = default_fc_parameters\n self.kind_to_fc_parameters = kind_to_fc_parameters\n self.column_id = column_id\n self.column_sort = column_sort\n self.column_kind = column_kind\n self.column_value = column_value\n self.timeseries_container = timeseries_container\n self.chunksize = chunksize\n self.n_jobs = n_jobs\n self.show_warnings = show_warnings\n self.disable_progressbar = disable_progressbar\n self.profile = profile\n self.profiling_filename = profiling_filename\n self.profiling_sorting = profiling_sorting\n self.test_for_binary_target_binary_feature = (\n test_for_binary_target_binary_feature\n )\n self.test_for_binary_target_real_feature = test_for_binary_target_real_feature\n self.test_for_real_target_binary_feature = test_for_real_target_binary_feature\n self.test_for_real_target_real_feature = test_for_real_target_real_feature\n self.fdr_level = fdr_level\n self.hypotheses_independent = hypotheses_independent\n self.ml_task = ml_task\n self.multiclass = multiclass\n self.n_significant = n_significant\n self.multiclass_p_values = multiclass_p_values\n\n # attributes\n self.feature_extractor = None\n self.feature_selector = None\n\n def set_timeseries_container(self, timeseries_container):\n \"\"\"\n Set the timeseries, with which the features will be calculated. For a format of the time series container,\n please refer to :mod:`~tsfresh.feature_extraction.extraction`. The timeseries must contain the same indices\n as the later DataFrame, to which the features will be added (the one you will pass to :func:`~transform` or\n :func:`~fit`). You can call this function as often as you like, to change the timeseries later\n (e.g. if you want to extract for different ids).\n\n :param timeseries_container: The timeseries as a pandas.DataFrame or a dict. See\n :mod:`~tsfresh.feature_extraction.extraction` for the format.\n :type timeseries_container: pandas.DataFrame or dict\n :return: None\n :rtype: None\n \"\"\"\n self.timeseries_container = timeseries_container\n\n def fit(self, X, y):\n \"\"\"\n Use the given timeseries from :func:`~set_timeseries_container` and calculate features from it and add them\n to the data sample X (which can contain other manually-designed features).\n\n Then determine which of the features of X are relevant for the given target y.\n Store those relevant features internally to only extract them in the transform step.\n\n If filter_only_tsfresh_features is True, only reject newly, automatically added features. If it is False,\n also look at the features that are already present in the DataFrame.\n\n :param X: The data frame without the time series features. The index rows should be present in the timeseries\n and in the target vector.\n :type X: pandas.DataFrame or numpy.array\n\n :param y: The target vector to define, which features are relevant.\n :type y: pandas.Series or numpy.array\n\n :return: the fitted estimator with the information, which features are relevant.\n :rtype: RelevantFeatureAugmenter\n \"\"\"\n self._fit_and_augment(X, y)\n return self\n\n def transform(self, X):\n \"\"\"\n After the fit step, it is known which features are relevant, Only extract those from the time series handed in\n with the function :func:`~set_timeseries_container`.\n\n If filter_only_tsfresh_features is False, also delete the irrelevant,\n already present features in the data frame.\n\n :param X: the data sample to add the relevant (and delete the irrelevant) features to.\n :type X: pandas.DataFrame or numpy.array\n\n :return: a data sample with the same information as X, but with added relevant time series features and\n deleted irrelevant information (only if filter_only_tsfresh_features is False).\n :rtype: pandas.DataFrame\n \"\"\"\n\n if self.timeseries_container is None:\n raise RuntimeError(\n \"You have to provide a time series using the set_timeseries_container function before.\"\n )\n\n if self.feature_selector is None:\n raise RuntimeError(\"You have to call fit before calling transform.\")\n\n if self.feature_selector.relevant_features is None:\n raise RuntimeError(\"You have to call fit before calling transform.\")\n\n self.feature_extractor.set_timeseries_container(self.timeseries_container)\n\n relevant_time_series_features = set(\n self.feature_selector.relevant_features\n ) - set(pd.DataFrame(X).columns)\n relevant_extraction_settings = from_columns(relevant_time_series_features)\n\n # Set imputing strategy\n impute_function = partial(\n impute_dataframe_range,\n col_to_max=self.col_to_max,\n col_to_min=self.col_to_min,\n col_to_median=self.col_to_median,\n )\n\n relevant_feature_extractor = FeatureAugmenter(\n kind_to_fc_parameters=relevant_extraction_settings,\n default_fc_parameters={},\n column_id=self.feature_extractor.column_id,\n column_sort=self.feature_extractor.column_sort,\n column_kind=self.feature_extractor.column_kind,\n column_value=self.feature_extractor.column_value,\n chunksize=self.feature_extractor.chunksize,\n n_jobs=self.feature_extractor.n_jobs,\n show_warnings=self.feature_extractor.show_warnings,\n disable_progressbar=self.feature_extractor.disable_progressbar,\n impute_function=impute_function,\n profile=self.feature_extractor.profile,\n profiling_filename=self.feature_extractor.profiling_filename,\n profiling_sorting=self.feature_extractor.profiling_sorting,\n )\n\n relevant_feature_extractor.set_timeseries_container(\n self.feature_extractor.timeseries_container\n )\n\n X_augmented = relevant_feature_extractor.transform(X)\n\n if self.filter_only_tsfresh_features:\n return X_augmented.copy().loc[\n :, self.feature_selector.relevant_features + X.columns.tolist()\n ]\n else:\n return X_augmented.copy().loc[:, self.feature_selector.relevant_features]\n\n def fit_transform(self, X, y):\n \"\"\"\n Equivalent to :func:`~fit` followed by :func:`~transform`; however, this is faster than performing those steps\n separately, because it avoids re-extracting relevant features for training data.\n\n :param X: The data frame without the time series features. The index rows should be present in the timeseries\n and in the target vector.\n :type X: pandas.DataFrame or numpy.array\n\n :param y: The target vector to define, which features are relevant.\n :type y: pandas.Series or numpy.array\n\n :return: a data sample with the same information as X, but with added relevant time series features and\n deleted irrelevant information (only if filter_only_tsfresh_features is False).\n :rtype: pandas.DataFrame\n \"\"\"\n X_augmented = self._fit_and_augment(X, y)\n\n selected_features = X_augmented.copy().loc[\n :, self.feature_selector.relevant_features\n ]\n\n if self.filter_only_tsfresh_features:\n selected_features = pd.merge(\n selected_features, X, left_index=True, right_index=True, how=\"left\"\n )\n\n return selected_features\n\n def _fit_and_augment(self, X, y):\n \"\"\"\n Helper for the :func:`~fit` and :func:`~fit_transform` functions, which does most of the work described in\n :func:`~fit`.\n\n :param X: The data frame without the time series features. The index rows should be present in the timeseries\n and in the target vector.\n :type X: pandas.DataFrame or numpy.array\n\n :param y: The target vector to define, which features are relevant.\n :type y: pandas.Series or numpy.array\n\n :return: a data sample with the extraced time series features. If filter_only_tsfresh_features is False\n the data sample will also include the information in X.\n :rtype: pandas.DataFrame\n \"\"\"\n if self.timeseries_container is None:\n raise RuntimeError(\n \"You have to provide a time series using the set_timeseries_container function before.\"\n )\n\n self.feature_extractor = FeatureAugmenter(\n default_fc_parameters=self.default_fc_parameters,\n kind_to_fc_parameters=self.kind_to_fc_parameters,\n column_id=self.column_id,\n column_sort=self.column_sort,\n column_kind=self.column_kind,\n column_value=self.column_value,\n timeseries_container=self.timeseries_container,\n chunksize=self.chunksize,\n n_jobs=self.n_jobs,\n show_warnings=self.show_warnings,\n disable_progressbar=self.disable_progressbar,\n profile=self.profile,\n profiling_filename=self.profiling_filename,\n profiling_sorting=self.profiling_sorting,\n )\n\n self.feature_selector = FeatureSelector(\n test_for_binary_target_binary_feature=self.test_for_binary_target_binary_feature,\n test_for_binary_target_real_feature=self.test_for_binary_target_real_feature,\n test_for_real_target_binary_feature=self.test_for_real_target_binary_feature,\n test_for_real_target_real_feature=self.test_for_real_target_real_feature,\n fdr_level=self.fdr_level,\n hypotheses_independent=self.hypotheses_independent,\n n_jobs=self.n_jobs,\n chunksize=self.chunksize,\n ml_task=self.ml_task,\n multiclass=self.multiclass,\n n_significant=self.n_significant,\n multiclass_p_values=self.multiclass_p_values,\n )\n\n if self.filter_only_tsfresh_features:\n # Do not merge the time series features to the old features\n X_tmp = pd.DataFrame(index=X.index)\n else:\n X_tmp = X\n\n X_augmented = self.feature_extractor.transform(X_tmp)\n\n (\n self.col_to_max,\n self.col_to_min,\n self.col_to_median,\n ) = get_range_values_per_column(X_augmented)\n X_augmented = impute_dataframe_range(\n X_augmented,\n col_to_max=self.col_to_max,\n col_to_median=self.col_to_median,\n col_to_min=self.col_to_min,\n )\n\n self.feature_selector.fit(X_augmented, y)\n\n return X_augmented\n" }, { "path": "tsfresh/utilities/__init__.py", "content": "\"\"\"\nThis :mod:`~tsfresh.utilities` submodule contains several utility functions.\nThose should only be used internally inside tsfresh.\n\"\"\"\n" }, { "path": "tsfresh/utilities/dataframe_functions.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nUtility functions for handling the DataFrame conversions to the internal normalized format\n(see ``normalize_input_to_internal_representation``) or on how to handle ``NaN`` and ``inf`` in the DataFrames.\n\"\"\"\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nfrom tsfresh import defaults\nfrom tsfresh.utilities.distribution import (\n DistributorBaseClass,\n MapDistributor,\n MultiprocessingDistributor,\n)\n\n\ndef check_for_nans_in_columns(df, columns=None):\n \"\"\"\n Helper function to check for ``NaN`` in the data frame and raise a ``ValueError`` if there is one.\n\n :param df: the pandas DataFrame to test for NaNs\n :type df: pandas.DataFrame\n :param columns: a list of columns to test for NaNs. If left empty, all columns of the DataFrame will be tested.\n :type columns: list\n\n :return: None\n :rtype: None\n :raise: ``ValueError`` of ``NaNs`` are found in the DataFrame.\n \"\"\"\n if columns is None:\n columns = df.columns\n\n if pd.isnull(df.loc[:, columns]).any().any():\n if not isinstance(columns, list):\n columns = list(columns)\n raise ValueError(\n \"Columns {} of DataFrame must not contain NaN values\".format(\n df.loc[:, columns]\n .columns[pd.isnull(df.loc[:, columns]).sum() > 0]\n .tolist()\n )\n )\n\n\ndef impute(df_impute):\n \"\"\"\n Columnwise replaces all ``NaNs`` and ``infs`` from the DataFrame `df_impute` with average/extreme values from\n the same columns. This is done as follows: Each occurring ``inf`` or ``NaN`` in `df_impute` is replaced by\n\n * ``-inf`` -> ``min``\n * ``+inf`` -> ``max``\n * ``NaN`` -> ``median``\n\n If the column does not contain finite values at all, it is filled with zeros.\n\n This function modifies `df_impute` in place. After that, df_impute is guaranteed to not contain any non-finite\n values. Also, all columns will be guaranteed to be of type ``np.float64``.\n\n :param df_impute: DataFrame to impute\n :type df_impute: pandas.DataFrame\n\n :return df_impute: imputed DataFrame\n :rtype df_impute: pandas.DataFrame\n \"\"\"\n if len(df_impute) == 0:\n return df_impute\n\n col_to_max, col_to_min, col_to_median = get_range_values_per_column(df_impute)\n df_impute = impute_dataframe_range(df_impute, col_to_max, col_to_min, col_to_median)\n\n # Ensure a type of \"np.float64\"\n df_impute.astype(np.float64, copy=False)\n return df_impute\n\n\ndef impute_dataframe_zero(df_impute):\n \"\"\"\n Replaces all ``NaNs``, ``-infs`` and ``+infs`` from the DataFrame `df_impute` with 0s.\n The `df_impute` will be modified in place. All its columns will be into converted into dtype ``np.float64``.\n\n :param df_impute: DataFrame to impute\n :type df_impute: pandas.DataFrame\n\n :return df_impute: imputed DataFrame\n :rtype df_impute: pandas.DataFrame\n \"\"\"\n if len(df_impute) == 0:\n return df_impute\n\n df_impute.replace([np.inf, -np.inf], 0, inplace=True)\n df_impute.fillna(0, inplace=True)\n\n # Ensure a type of \"np.float64\"\n df_impute.astype(np.float64, copy=False)\n return df_impute\n\n\ndef impute_dataframe_range(df_impute, col_to_max, col_to_min, col_to_median):\n \"\"\"\n Columnwise replaces all ``NaNs``, ``-inf`` and ``+inf`` from the DataFrame `df_impute` with average/extreme values\n from the provided dictionaries.\n\n This is done as follows: Each occurring ``inf`` or ``NaN`` in `df_impute` is replaced by\n\n * ``-inf`` -> by value in col_to_min\n * ``+inf`` -> by value in col_to_max\n * ``NaN`` -> by value in col_to_median\n\n If a column of df_impute is not found in the one of the dictionaries, this method will raise a ValueError.\n Also, if one of the values to replace is not finite a ValueError is returned\n\n This function modifies `df_impute` in place. Afterwards df_impute is\n guaranteed to not contain any non-finite values.\n Also, all columns will be guaranteed to be of type ``np.float64``.\n\n :param df_impute: DataFrame to impute\n :type df_impute: pandas.DataFrame\n :param col_to_max: Dictionary mapping column names to max values\n :type col_to_max: dict\n :param col_to_min: Dictionary mapping column names to min values\n :type col_to_max: dict\n :param col_to_median: Dictionary mapping column names to median values\n :type col_to_max: dict\n\n :return df_impute: imputed DataFrame\n :rtype df_impute: pandas.DataFrame\n :raise ValueError: if a column of df_impute is missing in col_to_max, col_to_min or col_to_median or a value\n to replace is non finite\n \"\"\"\n if len(df_impute) == 0:\n return df_impute\n\n columns = df_impute.columns\n\n # Making sure col_to_median, col_to_max and col_to_min have entries for every column\n if (\n not set(columns) <= set(col_to_median.keys())\n or not set(columns) <= set(col_to_max.keys())\n or not set(columns) <= set(col_to_min.keys())\n ):\n raise ValueError(\n \"Some of the dictionaries col_to_median, col_to_max, col_to_min contains more or less keys \"\n \"than the column names in df\"\n )\n\n # check if there are non finite values for the replacement\n if (\n np.any(~np.isfinite(list(col_to_median.values())))\n or np.any(~np.isfinite(list(col_to_min.values())))\n or np.any(~np.isfinite(list(col_to_max.values())))\n ):\n raise ValueError(\n \"Some of the dictionaries col_to_median, col_to_max, col_to_min contains non finite values \"\n \"to replace\"\n )\n\n # Make the replacement dataframes as large as the real one\n col_to_max = pd.DataFrame([col_to_max] * len(df_impute), index=df_impute.index)\n col_to_min = pd.DataFrame([col_to_min] * len(df_impute), index=df_impute.index)\n col_to_median = pd.DataFrame(\n [col_to_median] * len(df_impute), index=df_impute.index\n )\n\n df_impute.where(df_impute.values != np.inf, other=col_to_max, inplace=True)\n df_impute.where(df_impute.values != -np.inf, other=col_to_min, inplace=True)\n df_impute.where(~np.isnan(df_impute.values), other=col_to_median, inplace=True)\n\n df_impute.astype(np.float64, copy=False)\n return df_impute\n\n\ndef get_range_values_per_column(df):\n \"\"\"\n Retrieves the finite max, min and mean values per column in the DataFrame `df` and stores them in three\n dictionaries. Those dictionaries `col_to_max`, `col_to_min`, `col_to_median` map the columnname to the maximal,\n minimal or median value of that column.\n\n If a column does not contain any finite values at all, a 0 is stored instead.\n\n :param df: the Dataframe to get columnswise max, min and median from\n :type df: pandas.DataFrame\n\n :return: Dictionaries mapping column names to max, min, mean values\n :rtype: (dict, dict, dict)\n \"\"\"\n data = df.values\n masked = np.ma.masked_invalid(data)\n columns = df.columns\n\n is_col_non_finite = masked.mask.sum(axis=0) == masked.data.shape[0]\n\n if np.any(is_col_non_finite):\n # We have columns that does not contain any finite value at all, so we will store 0 instead.\n warnings.warn(\n \"The columns {} did not have any finite values. Filling with zeros.\".format(\n df.iloc[:, np.where(is_col_non_finite)[0]].columns.values\n ),\n RuntimeWarning,\n )\n\n masked.data[:, is_col_non_finite] = 0 # Set the values of the columns to 0\n masked.mask[:, is_col_non_finite] = False # Remove the mask for this column\n\n # fetch max, min and median for all columns\n col_to_max = dict(zip(columns, np.max(masked, axis=0)))\n col_to_min = dict(zip(columns, np.min(masked, axis=0)))\n col_to_median = dict(zip(columns, np.ma.median(masked, axis=0)))\n\n return col_to_max, col_to_min, col_to_median\n\n\ndef restrict_input_to_index(df_or_dict, column_id, index):\n \"\"\"\n Restrict df_or_dict to those ids contained in index.\n\n :param df_or_dict: a pandas DataFrame or a dictionary.\n :type df_or_dict: pandas.DataFrame or dict\n :param column_id: it must be present in the pandas DataFrame or in all DataFrames in the dictionary.\n It is not allowed to have NaN values in this column.\n :type column_id: basestring\n :param index: Index containing the ids\n :type index: Iterable or pandas.Series\n\n :return df_or_dict_restricted: the restricted df_or_dict\n :rtype df_or_dict_restricted: dict or pandas.DataFrame\n :raise: ``TypeError`` if df_or_dict is not of type dict or pandas.DataFrame\n \"\"\"\n if isinstance(df_or_dict, pd.DataFrame):\n ids_in_df = set(df_or_dict[column_id])\n ids_in_index = set(index)\n present_ids = ids_in_index & ids_in_df\n if not present_ids:\n msg = \"The ids of the time series container and the index of the input data X do not share any identifier!\"\n raise AttributeError(msg)\n\n df_or_dict_restricted = df_or_dict[df_or_dict[column_id].isin(index)]\n elif isinstance(df_or_dict, dict):\n df_or_dict_restricted = {\n kind: restrict_input_to_index(df, column_id, index)\n for kind, df in df_or_dict.items()\n }\n else:\n raise TypeError(\"df_or_dict should be of type dict or pandas.DataFrame\")\n\n return df_or_dict_restricted\n\n\ndef get_ids(df_or_dict, column_id):\n \"\"\"\n Aggregates all ids in column_id from the time series container `\n\n :param df_or_dict: a pandas DataFrame or a dictionary.\n :type df_or_dict: pandas.DataFrame or dict\n :param column_id: it must be present in the pandas DataFrame or in all DataFrames in the dictionary.\n It is not allowed to have NaN values in this column.\n :type column_id: basestring\n\n :return: as set with all existing ids in energy_ratio_by_chunks\n :rtype: Set\n :raise: ``TypeError`` if df_or_dict is not of type dict or pandas.DataFrame\n \"\"\"\n if isinstance(df_or_dict, pd.DataFrame):\n return set(df_or_dict[column_id])\n elif isinstance(df_or_dict, dict):\n return set.union(*[set(df[column_id]) for _, df in df_or_dict.items()])\n else:\n raise TypeError(\"df_or_dict should be of type dict or pandas.DataFrame\")\n\n\ndef _roll_out_time_series(\n timeshift,\n grouped_data,\n rolling_direction,\n max_timeshift,\n min_timeshift,\n column_sort,\n column_id,\n):\n \"\"\"\n Internal helper function for roll_time_series.\n This function has the task to extract the rolled forecast data frame of the number `timeshift`.\n This means it has shifted a virtual window if size `max_timeshift` (or infinite)\n `timeshift` times in the positive direction (for positive `rolling_direction`) or in negative direction\n (for negative `rolling_direction`).\n It starts counting from the first data point for each id (and kind) (or the last one for negative\n `rolling_direction`).\n The rolling happens for each `id` and `kind` separately.\n Extracted data smaller than `min_timeshift` + 1 are removed.\n\n Implementation note:\n Even though negative rolling direction means, we let the window shift in negative direction over the data,\n the counting of `timeshift` still happens from the first row onwards. Example:\n\n 1 2 3 4\n\n If we do positive rolling, we extract the sub time series\n\n [ 1 ] input parameter: timeshift=1, new id: ([id=]X,[timeshift=]1)\n [ 1 2 ] input parameter: timeshift=2, new id: ([id=]X,[timeshift=]2)\n [ 1 2 3 ] input parameter: timeshift=3, new id: ([id=]X,[timeshift=]3)\n [ 1 2 3 4 ] input parameter: timeshift=4, new id: ([id=]X,[timeshift=]4)\n\n If we do negative rolling:\n\n [ 1 2 3 4 ] input parameter: timeshift=1, new id: ([id=]X,[timeshift=]1)\n [ 2 3 4 ] input parameter: timeshift=2, new id: ([id=]X,[timeshift=]2)\n [ 3 4 ] input parameter: timeshift=3, new id: ([id=]X,[timeshift=]3)\n [ 4 ] input parameter: timeshift=4, new id: ([id=]X,[timeshift=]4)\n\n If you now reverse the order of the negative examples, it looks like shifting the\n window from the back (but it is implemented to start counting from the beginning).\n\n \"\"\"\n\n def _f(x):\n if rolling_direction > 0:\n # For positive rolling, the right side of the window moves with `timeshift`\n shift_until = timeshift\n shift_from = max(shift_until - max_timeshift - 1, 0)\n\n df_temp = x.iloc[shift_from:shift_until] if shift_until <= len(x) else None\n else:\n # For negative rolling, the left side of the window moves with `timeshift`\n shift_from = max(timeshift - 1, 0)\n shift_until = shift_from + max_timeshift + 1\n\n df_temp = x.iloc[shift_from:shift_until]\n\n if df_temp is None or len(df_temp) < min_timeshift + 1:\n return\n\n df_temp = df_temp.copy()\n\n # and set the shift correctly\n if column_sort and rolling_direction > 0:\n timeshift_value = df_temp[column_sort].iloc[-1]\n elif column_sort and rolling_direction < 0:\n timeshift_value = df_temp[column_sort].iloc[0]\n else:\n timeshift_value = timeshift - 1\n # and now create new ones ids out of the old ones\n df_temp[\"id\"] = df_temp[column_id].apply(lambda row: (row, timeshift_value))\n\n return df_temp\n\n return [grouped_data.apply(_f)]\n\n\ndef roll_time_series(\n df_or_dict,\n column_id,\n column_sort=None,\n column_kind=None,\n rolling_direction=1,\n max_timeshift=None,\n min_timeshift=0,\n chunksize=defaults.CHUNKSIZE,\n n_jobs=defaults.N_PROCESSES,\n show_warnings=defaults.SHOW_WARNINGS,\n disable_progressbar=defaults.DISABLE_PROGRESSBAR,\n distributor=None,\n):\n \"\"\"\n This method creates sub windows of the time series. It rolls the (sorted) data frames for each kind and each id\n separately in the \"time\" domain (which is represented by the sort order of the sort column given by `column_sort`).\n\n For each rolling step, a new id is created by the scheme ({id}, {shift}), here id is the former id of\n the column and shift is the amount of \"time\" shifts.\n You can think of it as having a window of fixed length (the max_timeshift) moving one step at a time over\n your time series.\n Each cut-out seen by the window is a new time series with a new identifier.\n\n A few remarks:\n\n * This method will create new IDs!\n * The sign of rolling defines the direction of time rolling, a positive value means we are shifting\n the cut-out window foreward in time. The name of each new sub time series is given by the last time point.\n This means, the time series named `([id=]4,[timeshift=]5)` with a `max_timeshift` of 3 includes the data\n of the times 3, 4 and 5.\n A negative rolling direction means, you go in negative time direction over your data.\n The time series named `([id=]4,[timeshift=]5)` with `max_timeshift` of 3 would then include the data\n of the times 5, 6 and 7.\n The absolute value defines how much time to shift at each step.\n * It is possible to shift time series of different lengths, but:\n * We assume that the time series are uniformly sampled\n * For more information, please see :ref:`forecasting-label`.\n\n :param df_or_dict: a pandas DataFrame or a dictionary. The required shape/form of the object depends on the rest of\n the passed arguments.\n :type df_or_dict: pandas.DataFrame or dict\n\n :param column_id: it must be present in the pandas DataFrame or in all DataFrames in the dictionary.\n It is not allowed to have NaN values in this column.\n :type column_id: basestring\n\n :param column_sort: if not None, sort the rows by this column. It is not allowed to\n have NaN values in this column. If not given, will be filled by an increasing number,\n meaning that the order of the passed dataframes are used as \"time\" for the time series.\n :type column_sort: basestring or None\n\n :param column_kind: It can only be used when passing a pandas DataFrame (the dictionary is already assumed to be\n grouped by the kind). Is must be present in the DataFrame and no NaN values are allowed.\n If the kind column is not passed, it is assumed that each column in the pandas DataFrame (except the id or\n sort column) is a possible kind.\n :type column_kind: basestring or None\n\n :param rolling_direction: The sign decides, if to shift our cut-out window backwards or forwards in \"time\".\n The absolute value decides, how much to shift at each step.\n :type rolling_direction: int\n\n :param max_timeshift: If not None, the cut-out window is at maximum `max_timeshift` large. If none, it grows\n infinitely.\n :type max_timeshift: int\n\n :param min_timeshift: Throw away all extracted forecast windows smaller or equal than this. Must be larger\n than or equal 0.\n :type min_timeshift: int\n\n :param n_jobs: The number of processes to use for parallelization. If zero, no parallelization is used.\n :type n_jobs: int\n\n :param chunksize: How many shifts per job should be calculated.\n :type chunksize: None or int\n\n :param show_warnings: Show warnings during the feature extraction (needed for debugging of calculators).\n :type show_warnings: bool\n\n :param disable_progressbar: Do not show a progressbar while doing the calculation.\n :type disable_progressbar: bool\n\n :param distributor: Advanced parameter: set this to a class name that you want to use as a\n distributor. See the utilities/distribution.py for more information. Leave to None, if you want\n TSFresh to choose the best distributor.\n :type distributor: class\n\n :return: The rolled data frame or dictionary of data frames\n :rtype: the one from df_or_dict\n \"\"\"\n\n if rolling_direction == 0:\n raise ValueError(\"Rolling direction of 0 is not possible\")\n\n if max_timeshift is not None and max_timeshift <= 0:\n raise ValueError(\"max_timeshift needs to be positive!\")\n\n if min_timeshift < 0:\n raise ValueError(\"min_timeshift needs to be positive or zero!\")\n\n if isinstance(df_or_dict, dict):\n if column_kind is not None:\n raise ValueError(\n \"You passed in a dictionary and gave a column name for the kind. Both are not possible.\"\n )\n\n return {\n key: roll_time_series(\n df_or_dict=df_or_dict[key],\n column_id=column_id,\n column_sort=column_sort,\n column_kind=column_kind,\n rolling_direction=rolling_direction,\n max_timeshift=max_timeshift,\n min_timeshift=min_timeshift,\n chunksize=chunksize,\n n_jobs=n_jobs,\n show_warnings=show_warnings,\n disable_progressbar=disable_progressbar,\n distributor=distributor,\n )\n for key in df_or_dict\n }\n\n # Now we know that this is a pandas data frame\n df = df_or_dict\n\n if len(df) <= 1:\n raise ValueError(\n \"Your time series container has zero or one rows!. Can not perform rolling.\"\n )\n\n if column_id is not None:\n if column_id not in df:\n raise AttributeError(\n \"The given column for the id is not present in the data.\"\n )\n else:\n raise ValueError(\n \"You have to set the column_id which contains the ids of the different time series\"\n )\n\n if column_kind is not None:\n grouper = [column_kind, column_id]\n else:\n grouper = [\n column_id,\n ]\n\n if column_sort is not None:\n # Require no Nans in column\n if df[column_sort].isnull().any():\n raise ValueError(\"You have NaN values in your sort column.\")\n\n df = df.sort_values(column_sort)\n\n if df[column_sort].dtype != object:\n # if rolling is enabled, the data should be uniformly sampled in this column\n # Build the differences between consecutive time sort values\n\n differences = df.groupby(grouper)[column_sort].apply(\n lambda x: x.values[:-1] - x.values[1:]\n )\n # Write all of them into one big list\n differences = sum(map(list, differences), [])\n # Test if all differences are the same\n if differences and min(differences) != max(differences):\n warnings.warn(\n \"Your time stamps are not uniformly sampled, which makes rolling \"\n \"nonsensical in some domains.\"\n )\n\n # Roll the data frames if requested\n rolling_amount = np.abs(rolling_direction)\n rolling_direction = np.sign(rolling_direction)\n\n grouped_data = df.groupby(grouper)\n prediction_steps = grouped_data.count().max().max()\n\n max_timeshift = max_timeshift or prediction_steps\n\n # Todo: not default for columns_sort to be None\n if column_sort is None:\n df[\"sort\"] = range(df.shape[0])\n\n if rolling_direction > 0:\n range_of_shifts = list(reversed(range(prediction_steps, 0, -rolling_amount)))\n else:\n range_of_shifts = range(1, prediction_steps + 1, rolling_amount)\n\n if distributor is None:\n if n_jobs == 0 or n_jobs == 1:\n distributor = MapDistributor(\n disable_progressbar=disable_progressbar, progressbar_title=\"Rolling\"\n )\n else:\n distributor = MultiprocessingDistributor(\n n_workers=n_jobs,\n disable_progressbar=disable_progressbar,\n progressbar_title=\"Rolling\",\n show_warnings=show_warnings,\n )\n\n if not isinstance(distributor, DistributorBaseClass):\n raise ValueError(\"the passed distributor is not an DistributorBaseClass object\")\n\n kwargs = {\n \"grouped_data\": grouped_data,\n \"rolling_direction\": rolling_direction,\n \"max_timeshift\": max_timeshift,\n \"min_timeshift\": min_timeshift,\n \"column_sort\": column_sort,\n \"column_id\": column_id,\n }\n\n shifted_chunks = distributor.map_reduce(\n _roll_out_time_series,\n data=range_of_shifts,\n chunk_size=chunksize,\n function_kwargs=kwargs,\n )\n\n distributor.close()\n\n df_shift = pd.concat(shifted_chunks, ignore_index=True)\n\n return df_shift.sort_values(by=[\"id\", column_sort or \"sort\"])\n\n\ndef make_forecasting_frame(x, kind, max_timeshift, rolling_direction):\n \"\"\"\n Takes a singular time series x and constructs a DataFrame df and target vector y that can be used for a time series\n forecasting task.\n\n The returned df will contain, for every time stamp in x, the last max_timeshift data points as a new\n time series, such can be used to fit a time series forecasting model.\n\n See :ref:`forecasting-label` for a detailed description of the rolling process and how the feature matrix and target\n vector are derived.\n\n The returned time series container df, will contain the rolled time series as a flat data frame, the first format\n from :ref:`data-formats-label`.\n\n When x is a pandas.Series, the index will be used as id.\n\n :param x: the singular time series\n :type x: np.array or pd.Series\n :param kind: the kind of the time series\n :type kind: str\n :param rolling_direction: The sign decides, if to roll backwards (if sign is positive) or forwards in \"time\"\n :type rolling_direction: int\n :param max_timeshift: If not None, shift only up to max_timeshift. If None, shift as often as possible.\n :type max_timeshift: int\n\n :return: time series container df, target vector y\n :rtype: (pd.DataFrame, pd.Series)\n \"\"\"\n n = len(x)\n\n if isinstance(x, pd.Series):\n t = x.index\n else:\n t = range(n)\n\n df = pd.DataFrame({\"id\": [\"id\"] * n, \"time\": t, \"value\": x, \"kind\": kind})\n\n df_shift = roll_time_series(\n df,\n column_id=\"id\",\n column_sort=\"time\",\n column_kind=\"kind\",\n rolling_direction=rolling_direction,\n max_timeshift=max_timeshift,\n )\n\n # drop the rows which should actually be predicted\n def mask_first(x):\n \"\"\"\n this mask returns an array of 1s where the last entry is a 0\n \"\"\"\n result = np.ones(len(x))\n result[-1] = 0\n return result\n\n mask = df_shift.groupby([\"id\"])[\"id\"].transform(mask_first).astype(bool)\n df_shift = df_shift[mask]\n\n # Now create the target vector out of the values\n # of the input series - not including the first one\n # (as there is nothing to forecast from)\n y = df[\"value\"][1:]\n\n # make sure that the format is the same as the\n # df_shift index\n y.index = map(lambda x: (\"id\", x), y.index)\n\n return df_shift, y\n\n\ndef add_sub_time_series_index(\n df_or_dict, sub_length, column_id=None, column_sort=None, column_kind=None\n):\n \"\"\"\n Add a column \"id\" which contains:\n\n - if column_id is None: for each kind (or if column_kind is None for the full dataframe) a new index built by\n \"sub-packaging\" the data in packages of length \"sub_length\". For example if you have data with the\n length of 11 and sub_length is 2, you will get 6 new packages: 0, 0; 1, 1; 2, 2; 3, 3; 4, 4; 5.\n - if column_id is not None: the same as before, just for each id separately. The old column_id values are added\n to the new \"id\" column after a comma\n\n You can use this functions to turn a long measurement into sub-packages, where you want to extract features on.\n\n :param df_or_dict: a pandas DataFrame or a dictionary. The required shape/form of the object depends on the rest of\n the passed arguments.\n :type df_or_dict: pandas.DataFrame or dict\n :param column_id: it must be present in the pandas DataFrame or in all DataFrames in the dictionary.\n It is not allowed to have NaN values in this column.\n :type column_id: basestring or None\n :param column_sort: if not None, sort the rows by this column. It is not allowed to\n have NaN values in this column.\n :type column_sort: basestring or None\n :param column_kind: It can only be used when passing a pandas DataFrame (the dictionary is already assumed to be\n grouped by the kind). Is must be present in the DataFrame and no NaN values are allowed.\n If the kind column is not passed, it is assumed that each column in the pandas DataFrame (except the id or\n sort column) is a possible kind.\n :type column_kind: basestring or None\n\n :return: The data frame or dictionary of data frames with a column \"id\" added\n :rtype: the one from df_or_dict\n \"\"\"\n\n if isinstance(df_or_dict, dict):\n if column_kind is not None:\n raise ValueError(\n \"You passed in a dictionary and gave a column name for the kind. Both are not possible.\"\n )\n\n return {\n key: add_sub_time_series_index(\n df_or_dict=df_or_dict[key],\n sub_length=sub_length,\n column_id=column_id,\n column_sort=column_sort,\n column_kind=column_kind,\n )\n for key in df_or_dict\n }\n\n df = df_or_dict\n\n grouper = []\n\n if column_id is not None:\n grouper.append(column_id)\n if column_kind is not None:\n grouper.append(column_kind)\n\n def _add_id_column(df_chunk):\n chunk_length = len(df_chunk)\n last_chunk_number = chunk_length // sub_length\n reminder = chunk_length % sub_length\n\n indices = np.concatenate(\n [\n np.repeat(np.arange(last_chunk_number), sub_length),\n np.repeat(last_chunk_number, reminder),\n ]\n )\n assert len(indices) == chunk_length\n\n if column_id:\n indices = list(zip(indices, df_chunk[column_id]))\n\n if column_sort:\n df_chunk = df_chunk.sort_values(column_sort)\n\n df_chunk[\"id\"] = indices\n\n return df_chunk\n\n if grouper:\n df = df.groupby(grouper).apply(_add_id_column)\n else:\n df = _add_id_column(df)\n\n if column_sort:\n df = df.sort_values(column_sort)\n\n df = df.set_index(df.index.get_level_values(-1))\n\n return df\n" }, { "path": "tsfresh/utilities/distribution.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2017\n\"\"\"\nThis module contains the Distributor class, such objects are used to distribute the calculation of features.\nEssentially, a Distributor organizes the application of feature calculators to data chunks.\n\nDesign of this module by Nils Braun\n\"\"\"\n\nimport itertools\nimport math\nimport warnings\nfrom collections.abc import Generator, Iterable\nfrom functools import partial\nfrom itertools import islice, repeat, takewhile\nfrom multiprocessing import Pool\n\nfrom tqdm import tqdm\n\nfrom tsfresh.feature_extraction.data import TsData\n\n\ndef _function_with_partly_reduce(chunk_list, map_function, kwargs):\n \"\"\"\n Small helper function to call a function (map_function)\n on a list of data chunks (chunk_list) and convert the results into\n a flattened list.\n\n This function is used to send chunks of data with a size larger than 1 to\n the workers in parallel and process these on the worker.\n\n :param chunk_list: A list of data chunks to process.\n :type chunk_list: list\n :param map_function: A function, which is called on each chunk in the list separately.\n :type map_function: callable\n\n :return: A list of the results of the function evaluated on each chunk and flattened.\n :rtype: list\n \"\"\"\n kwargs = kwargs or {}\n results = (map_function(chunk, **kwargs) for chunk in chunk_list)\n results = list(itertools.chain.from_iterable(results))\n return results\n\n\ndef initialize_warnings_in_workers(show_warnings):\n \"\"\"\n Small helper function to initialize warnings module in multiprocessing workers.\n\n On Windows, Python spawns fresh processes which do not inherit from warnings\n state, so warnings must be enabled/disabled before running computations.\n\n :param show_warnings: whether to show warnings or not.\n :type show_warnings: bool\n \"\"\"\n warnings.catch_warnings()\n if not show_warnings:\n warnings.simplefilter(\"ignore\")\n else:\n warnings.simplefilter(\"default\")\n\n\nclass DistributorBaseClass:\n \"\"\"\n The distributor abstract base class.\n\n The main purpose of the instances of the DistributorBaseClass subclasses is to evaluate a function\n (called map_function) on a list of data items (called data).\n\n Dependent on the implementation of the distribute function, this is done in parallel or using a cluster of nodes.\n \"\"\"\n\n def map_reduce(\n self,\n map_function,\n data,\n function_kwargs=None,\n chunk_size=None,\n data_length=None,\n ):\n \"\"\"\n This method contains the core functionality of the DistributorBaseClass class.\n\n It maps the map_function to each element of the data and reduces the results to return a flattened list.\n\n It needs to be implemented for each of the subclasses.\n\n :param map_function: a function to apply to each data item.\n :type map_function: callable\n :param data: the data to use in the calculation\n :type data: iterable\n :param function_kwargs: parameters for the map function\n :type function_kwargs: dict of string to parameter\n :param chunk_size: If given, chunk the data according to this size. If not given, use an empirical value.\n :type chunk_size: int\n :param data_length: If the data is a generator, you have to set the length here. If it is none, the\n length is deduced from the len of the data.\n :type data_length: int\n\n :return: the calculated results\n :rtype: list\n \"\"\"\n raise NotImplementedError\n\n\nclass IterableDistributorBaseClass(DistributorBaseClass):\n \"\"\"\n Distributor Base Class that can handle all iterable items and calculate\n a map_function on each item separately.\n\n This is done on chunks of the data, meaning, that the DistributorBaseClass classes will chunk the data into chunks,\n distribute the data and apply the map_function functions on the items separately.\n\n Dependent on the implementation of the distribute function, this is done in parallel or using a cluster of nodes.\n \"\"\"\n\n @staticmethod\n def partition(data, chunk_size):\n \"\"\"\n This generator partitions an iterable into slices of length `chunk_size`.\n If the chunk size is not a divider of the data length, the last slice will be shorter.\n\n Taken from\n https://stackoverflow.com/questions/1915170/split-a-generator-iterable-every-n-items-in-python-splitevery\n\n The important part here is, that the iterable is only\n traversed once and the chunks are produced one at a time.\n This is good for both memory as well as speed.\n\n :param data: The data to partition.\n :type data: Iterable\n :param chunk_size: The chunk size. The last chunk might be smaller.\n :type chunk_size: int\n\n :return: A generator producing the chunks of data.\n :rtype: Generator[Iterable]\n \"\"\"\n # Make sure we have an iterable\n iterator = iter(data)\n\n # takewhile(true, ...) generates an iterator until the items are empty\n # (= we have reached the end)\n # The islice(iterator, n) gets the next n elements from the iterator.\n # The list(...) makes sure we do not pass\n return takewhile(\n bool, (list(islice(iterator, chunk_size)) for _ in repeat(None))\n )\n\n def __init__(self):\n \"\"\"\n Constructs the DistributorBaseClass class\n \"\"\"\n raise NotImplementedError\n\n def calculate_best_chunk_size(self, data_length):\n \"\"\"\n Calculates the best chunk size for a list of length data_length. The current implemented formula is more or\n less an empirical result for multiprocessing case on one machine.\n\n :param data_length: A length which defines how many calculations there need to be.\n :type data_length: int\n :return: the calculated chunk size\n :rtype: int\n\n TODO: Investigate which is the best chunk size for different settings.\n \"\"\"\n chunk_size, extra = divmod(data_length, self.n_workers * 5)\n if extra:\n chunk_size += 1\n return chunk_size\n\n def map_reduce(\n self,\n map_function,\n data,\n function_kwargs=None,\n chunk_size=None,\n data_length=None,\n ):\n \"\"\"\n This method contains the core functionality of the DistributorBaseClass class.\n\n It maps the map_function to each element of the data and reduces the results to return a flattened list.\n\n How the jobs are calculated, is determined by the classes\n :func:`tsfresh.utilities.distribution.DistributorBaseClass.distribute` method,\n which can distribute the jobs in multiple threads, across multiple processing units etc.\n\n To not transport each element of the data individually, the data is split into chunks, according to the chunk\n size (or an empirical guess if none is given). By this, worker processes not tiny but adequate sized parts of\n the data.\n\n :param map_function: a function to apply to each data item.\n :type map_function: callable\n :param data: the data to use in the calculation\n :type data: iterable\n :param function_kwargs: parameters for the map function\n :type function_kwargs: dict of string to parameter\n :param chunk_size: If given, chunk the data according to this size. If not given, use an empirical value.\n :type chunk_size: int\n :param data_length: If the data is a generator, you have to set the length here. If it is none, the\n length is deduced from the len of the data.\n :type data_length: int\n\n :return: the calculated results\n :rtype: list\n \"\"\"\n if not isinstance(data, Iterable):\n raise ValueError(\n \"You passed data, which can not be handled by this distributor!\"\n )\n\n if data_length is None:\n data_length = len(data)\n\n if not chunk_size:\n chunk_size = self.calculate_best_chunk_size(data_length)\n\n chunk_generator = self.partition(data, chunk_size=chunk_size)\n\n map_kwargs = {\"map_function\": map_function, \"kwargs\": function_kwargs}\n\n if hasattr(self, \"progressbar_title\"):\n total_number_of_expected_results = math.ceil(data_length / chunk_size)\n result = tqdm(\n self.distribute(\n _function_with_partly_reduce, chunk_generator, map_kwargs\n ),\n total=total_number_of_expected_results,\n desc=self.progressbar_title,\n disable=self.disable_progressbar,\n )\n else:\n result = (\n self.distribute(\n _function_with_partly_reduce, chunk_generator, map_kwargs\n ),\n )\n\n result = list(itertools.chain.from_iterable(result))\n\n self.close()\n\n return result\n\n def distribute(self, func, partitioned_chunks, kwargs):\n \"\"\"\n This abstract base function distributes the work among workers, which can be threads or nodes in a cluster.\n Must be implemented in the derived classes.\n\n :param func: the function to send to each worker.\n :type func: callable\n :param partitioned_chunks: The list of data chunks - each element is again\n a list of chunks - and should be processed by one worker.\n :type partitioned_chunks: iterable\n :param kwargs: parameters for the map function\n :type kwargs: dict of string to parameter\n\n :return: The result of the calculation as a list - each item should be the result of the application of func\n to a single element.\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"\n Abstract base function to clean the DistributorBaseClass after use, e.g. close the connection to a DaskScheduler\n \"\"\"\n pass\n\n\nclass MapDistributor(IterableDistributorBaseClass):\n \"\"\"\n Distributor using the python build-in map, which calculates each job sequentially one after the other.\n \"\"\"\n\n def __init__(\n self, disable_progressbar=False, progressbar_title=\"Feature Extraction\"\n ):\n \"\"\"\n Creates a new MapDistributor instance\n\n :param disable_progressbar: whether to show a progressbar or not.\n :type disable_progressbar: bool\n :param progressbar_title: the title of the progressbar\n :type progressbar_title: basestring\n \"\"\"\n self.disable_progressbar = disable_progressbar\n self.progressbar_title = progressbar_title\n\n def distribute(self, func, partitioned_chunks, kwargs):\n \"\"\"\n Calculates the features in a sequential fashion by pythons map command\n\n :param func: the function to send to each worker.\n :type func: callable\n :param partitioned_chunks: The list of data chunks - each element is again\n a list of chunks - and should be processed by one worker.\n :type partitioned_chunks: iterable\n :param kwargs: parameters for the map function\n :type kwargs: dict of string to parameter\n\n :return: The result of the calculation as a list - each item should be the result of the application of func\n to a single element.\n \"\"\"\n return map(partial(func, **kwargs), partitioned_chunks)\n\n def calculate_best_chunk_size(self, data_length):\n \"\"\"\n For the map command, which calculates the features sequentially, a the chunk_size of 1 will be used.\n\n :param data_length: A length which defines how many calculations there need to be.\n :type data_length: int\n \"\"\"\n return 1\n\n\nclass LocalDaskDistributor(IterableDistributorBaseClass):\n \"\"\"\n Distributor using a local dask cluster and inproc communication.\n \"\"\"\n\n def __init__(self, n_workers):\n \"\"\"\n\n Initiates a LocalDaskDistributor instance.\n\n :param n_workers: How many workers should the local dask cluster have?\n :type n_workers: int\n \"\"\"\n\n import tempfile\n\n from distributed import Client, LocalCluster\n\n # attribute .local_dir_ is the path where the local dask workers store temporary files\n self.local_dir_ = tempfile.mkdtemp()\n cluster = LocalCluster(\n n_workers=n_workers, processes=False, local_directory=self.local_dir_\n )\n\n self.client = Client(cluster)\n self.n_workers = n_workers\n\n def distribute(self, func, partitioned_chunks, kwargs):\n \"\"\"\n Calculates the features in a parallel fashion by distributing the map command to the dask workers on a local\n machine\n\n :param func: the function to send to each worker.\n :type func: callable\n :param partitioned_chunks: The list of data chunks - each element is again\n a list of chunks - and should be processed by one worker.\n :type partitioned_chunks: iterable\n :param kwargs: parameters for the map function\n :type kwargs: dict of string to parameter\n\n :return: The result of the calculation as a list - each item should be the result of the application of func\n to a single element.\n \"\"\"\n\n if isinstance(partitioned_chunks, Iterable):\n # since dask 2.0.0 client map no longer accepts iterables\n partitioned_chunks = list(partitioned_chunks)\n result = self.client.gather(\n self.client.map(partial(func, **kwargs), partitioned_chunks)\n )\n return [item for sublist in result for item in sublist]\n\n def close(self):\n \"\"\"\n Closes the connection to the local Dask Scheduler\n \"\"\"\n self.client.close()\n\n\nclass ClusterDaskDistributor(IterableDistributorBaseClass):\n \"\"\"\n Distributor using a dask cluster, meaning that the calculation is spread over a cluster\n \"\"\"\n\n def __init__(self, address):\n \"\"\"\n Sets up a distributor that connects to a Dask Scheduler to distribute the calculation of the features\n\n :param address: the ip address and port number of the Dask Scheduler\n :type address: str\n \"\"\"\n\n from distributed import Client\n\n self.client = Client(address=address)\n\n def calculate_best_chunk_size(self, data_length):\n \"\"\"\n Uses the number of dask workers in the cluster (during execution time, meaning when you start the extraction)\n to find the optimal chunk_size.\n\n :param data_length: A length which defines how many calculations there need to be.\n :type data_length: int\n \"\"\"\n n_workers = len(self.client.scheduler_info()[\"workers\"])\n chunk_size, extra = divmod(data_length, n_workers * 5)\n if extra:\n chunk_size += 1\n return chunk_size\n\n def distribute(self, func, partitioned_chunks, kwargs):\n \"\"\"\n Calculates the features in a parallel fashion by distributing the map command to the dask workers on a cluster\n\n :param func: the function to send to each worker.\n :type func: callable\n :param partitioned_chunks: The list of data chunks - each element is again\n a list of chunks - and should be processed by one worker.\n :type partitioned_chunks: iterable\n :param kwargs: parameters for the map function\n :type kwargs: dict of string to parameter\n\n :return: The result of the calculation as a list - each item should be the result of the application of func\n to a single element.\n \"\"\"\n if isinstance(partitioned_chunks, Iterable):\n # since dask 2.0.0 client map no longer accepts iterables\n partitioned_chunks = list(partitioned_chunks)\n result = self.client.gather(\n self.client.map(partial(func, **kwargs), partitioned_chunks)\n )\n return [item for sublist in result for item in sublist]\n\n def close(self):\n \"\"\"\n Closes the connection to the Dask Scheduler\n \"\"\"\n self.client.close()\n\n\nclass MultiprocessingDistributor(IterableDistributorBaseClass):\n \"\"\"\n Distributor using a multiprocessing Pool to calculate the jobs in parallel on the local machine.\n \"\"\"\n\n def __init__(\n self,\n n_workers,\n disable_progressbar=False,\n progressbar_title=\"Feature Extraction\",\n show_warnings=True,\n ):\n \"\"\"\n Creates a new MultiprocessingDistributor instance\n\n :param n_workers: How many workers should the multiprocessing pool have?\n :type n_workers: int\n :param disable_progressbar: whether to show a progressbar or not.\n :type disable_progressbar: bool\n :param progressbar_title: the title of the progressbar\n :type progressbar_title: basestring\n :param show_warnings: whether to show warnings or not.\n :type show_warnings: bool\n \"\"\"\n self.pool = Pool(\n processes=n_workers,\n initializer=initialize_warnings_in_workers,\n initargs=(show_warnings,),\n )\n self.n_workers = n_workers\n self.disable_progressbar = disable_progressbar\n self.progressbar_title = progressbar_title\n\n def distribute(self, func, partitioned_chunks, kwargs):\n \"\"\"\n Calculates the features in a parallel fashion by distributing the map command to a thread pool\n\n :param func: the function to send to each worker.\n :type func: callable\n :param partitioned_chunks: The list of data chunks - each element is again\n a list of chunks - and should be processed by one worker.\n :type partitioned_chunks: iterable\n :param kwargs: parameters for the map function\n :type kwargs: dict of string to parameter\n\n :return: The result of the calculation as a list - each item should be the result of the application of func\n to a single element.\n \"\"\"\n return self.pool.imap_unordered(partial(func, **kwargs), partitioned_chunks)\n\n def close(self):\n \"\"\"\n Collects the result from the workers and closes the thread pool.\n \"\"\"\n self.pool.close()\n self.pool.terminate()\n self.pool.join()\n\n\nclass ApplyDistributor(DistributorBaseClass):\n def __init__(self, meta):\n self.meta = meta\n\n def map_reduce(\n self,\n map_function,\n data,\n function_kwargs=None,\n chunk_size=None,\n data_length=None,\n ):\n return data.apply(map_function, meta=self.meta, **function_kwargs)\n" }, { "path": "tsfresh/utilities/profiling.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\"\"\"\nContains methods to start and stop the profiler that checks the runtime of the different feature calculators\n\"\"\"\n\nimport cProfile\nimport io\nimport logging\nimport pstats\n\nfrom tsfresh import defaults\n\n_logger = logging.getLogger(__name__)\n\n\n# todo: tackle a debate about the need for this profiler\n# todo: we need unit tests for the profiling routine\n\n\ndef start_profiling():\n \"\"\"\n Helper function to start the profiling process and return the profiler (to close it later).\n\n :return: a started profiler.\n :rtype: cProfile.Profile\n\n Start and stop the profiler with:\n\n >>> profiler = start_profiling()\n >>> # Do something you want to profile\n >>> end_profiling(profiler, \"cumulative\", \"out.txt\")\n \"\"\"\n profiler = cProfile.Profile()\n profiler.enable()\n return profiler\n\n\ndef end_profiling(profiler, filename, sorting=None):\n \"\"\"\n Helper function to stop the profiling process and write out the profiled\n data into the given filename. Before this, sort the stats by the passed sorting.\n\n :param profiler: An already started profiler (probably by start_profiling).\n :type profiler: cProfile.Profile\n :param filename: The name of the output file to save the profile.\n :type filename: basestring\n :param sorting: The sorting of the statistics passed to the sort_stats function.\n :type sorting: basestring\n\n :return: None\n :rtype: None\n\n Start and stop the profiler with:\n\n >>> profiler = start_profiling()\n >>> # Do something you want to profile\n >>> end_profiling(profiler, \"out.txt\", \"cumulative\")\n \"\"\"\n profiler.disable()\n s = io.StringIO()\n ps = pstats.Stats(profiler, stream=s).sort_stats(sorting)\n ps.print_stats()\n\n with open(filename, \"w+\") as f:\n _logger.info(\n \"[calculate_ts_features] Finished profiling of time series feature extraction\"\n )\n f.write(s.getvalue())\n\n\ndef get_n_jobs():\n \"\"\"\n Get the number of jobs to use for parallel processing.\n\n :return: The number of jobs to use for parallel processing.\n :rtype: int\n \"\"\"\n return defaults.N_PROCESSES\n\n\ndef set_n_jobs(n_jobs):\n \"\"\"\n Set the number of jobs to use for parallel processing.\n\n :param n_jobs: The number of jobs to use for parallel processing.\n :type n_jobs: int\n\n :return: None\n :rtype: None\n \"\"\"\n defaults.N_PROCESSES = n_jobs\n" }, { "path": "tsfresh/utilities/string_manipulation.py", "content": "# -*- coding: utf-8 -*-\n# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)\n# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016\n\nimport ast\n\nimport numpy as np\n\n\ndef get_config_from_string(parts):\n \"\"\"\n Helper function to extract the configuration of a certain function from the column name.\n The column name parts (split by \"__\") should be passed to this function. It will skip the\n kind name and the function name and only use the parameter parts. These parts will be split up on \"_\"\n into the parameter name and the parameter value. This value is transformed into a python object\n (for example is \"(1, 2, 3)\" transformed into a tuple consisting of the ints 1, 2 and 3).\n\n Returns None of no parameters are in the column name.\n\n :param parts: The column name split up on \"__\"\n :type parts: list\n :return: a dictionary with all parameters, which are encoded in the column name.\n :rtype: dict\n \"\"\"\n relevant_parts = parts[2:]\n if not relevant_parts:\n return\n\n config_kwargs = [s.rsplit(\"_\", 1)[0] for s in relevant_parts]\n config_values = [s.rsplit(\"_\", 1)[1] for s in relevant_parts]\n\n dict_if_configs = {}\n\n for key, value in zip(config_kwargs, config_values):\n if value.lower() == \"nan\":\n dict_if_configs[key] = np.nan\n elif value.lower() == \"-inf\":\n dict_if_configs[key] = -np.inf\n elif value.lower() == \"inf\":\n dict_if_configs[key] = np.inf\n else:\n dict_if_configs[key] = ast.literal_eval(value)\n\n return dict_if_configs\n\n\ndef convert_to_output_format(param):\n \"\"\"\n Helper function to convert parameters to a valid string, that can be used in a column name.\n Does the opposite which is used in the from_columns function.\n\n The parameters are sorted by their name and written out in the form\n\n ______ ...\n\n If a is a string, this method will wrap it with parenthesis \", so \"\"\n\n :param param: The dictionary of parameters to write out\n :type param: dict\n\n :return: The string of parsed parameters\n :rtype: str\n \"\"\"\n\n def add_parenthesis_if_string_value(x):\n if isinstance(x, str):\n return '\"' + str(x) + '\"'\n else:\n return str(x)\n\n return \"__\".join(\n str(key) + \"_\" + add_parenthesis_if_string_value(param[key])\n for key in sorted(param.keys())\n )\n" } ]