Repository: EBjerrum/scikit-mol Branch: main Commit: 119cb8c00bb4 Files: 128 Total size: 3.7 MB Directory structure: gitextract_o126_axo/ ├── .github/ │ ├── CODEOWNERS │ └── workflows/ │ ├── code_quality.yaml │ ├── publish.yaml │ ├── pytest.yaml │ └── welcome.yaml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yaml ├── .vscode/ │ ├── extensions.json │ └── settings.json ├── CITATION.bib ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── Makefile ├── README.md ├── docs/ │ ├── api/ │ │ ├── fingerprints.base.md │ │ ├── scikit_mol.applicability.md │ │ ├── scikit_mol.conversions.md │ │ ├── scikit_mol.core.md │ │ ├── scikit_mol.descriptors.md │ │ ├── scikit_mol.fingerprints.md │ │ ├── scikit_mol.parallel.md │ │ ├── scikit_mol.plotting.md │ │ ├── scikit_mol.safeinference.md │ │ └── scikit_mol.standardizer.md │ ├── assets/ │ │ ├── css/ │ │ │ └── tweak-width.css │ │ └── js/ │ │ └── readthedocs.js │ ├── contributing.md │ ├── index.md │ ├── notebooks/ │ │ ├── 01_basic_usage.ipynb │ │ ├── 02_descriptor_transformer.ipynb │ │ ├── 03_example_pipeline.ipynb │ │ ├── 04_standardizer.ipynb │ │ ├── 05_smiles_sanitization.ipynb │ │ ├── 06_hyperparameter_tuning.ipynb │ │ ├── 07_parallel_transforms.ipynb │ │ ├── 08_external_library_skopt.ipynb │ │ ├── 09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb │ │ ├── 10_pipeline_pandas_output.ipynb │ │ ├── 11_safe_inference.ipynb │ │ ├── 12_custom_fingerprint_transformer.ipynb │ │ ├── 13_applicability_domain.ipynb │ │ ├── README.md │ │ ├── pair_notebook.sh │ │ ├── run_notebooks.sh │ │ ├── scripts/ │ │ │ ├── 01_basic_usage.py │ │ │ ├── 02_descriptor_transformer.py │ │ │ ├── 03_example_pipeline.py │ │ │ ├── 04_standardizer.py │ │ │ ├── 05_smiles_sanitization.py │ │ │ ├── 06_hyperparameter_tuning.py │ │ │ ├── 07_parallel_transforms.py │ │ │ ├── 08_external_library_skopt.py │ │ │ ├── 09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py │ │ │ ├── 10_pipeline_pandas_output.py │ │ │ ├── 11_safe_inference.py │ │ │ ├── 12_custom_fingerprint_transformer.py │ │ │ └── 13_applicability_domain.py │ │ └── sync_notebooks.sh │ └── overrides/ │ └── main.html ├── mkdocs.yml ├── pyproject.toml ├── resources/ │ └── logo/ │ ├── ScikitMol_Logo.ai │ └── ScikitMol_Logo_Hybrid.ai ├── ruff.toml ├── scikit_mol/ │ ├── __init__.py │ ├── _constants.py │ ├── applicability/ │ │ ├── LICENSE.MIT │ │ ├── README.md │ │ ├── __init__.py │ │ ├── base.py │ │ ├── bounding_box.py │ │ ├── convex_hull.py │ │ ├── hotelling.py │ │ ├── isolation_forest.py │ │ ├── kernel_density.py │ │ ├── knn.py │ │ ├── leverage.py │ │ ├── local_outlier.py │ │ ├── mahalanobis.py │ │ ├── standardization.py │ │ └── topkat.py │ ├── conversions.py │ ├── core.py │ ├── descriptors.py │ ├── fingerprints/ │ │ ├── __init__.py │ │ ├── atompair.py │ │ ├── avalon.py │ │ ├── baseclasses.py │ │ ├── maccs.py │ │ ├── minhash.py │ │ ├── morgan.py │ │ ├── rdkitfp.py │ │ └── topologicaltorsion.py │ ├── parallel.py │ ├── plotting.py │ ├── safeinference.py │ ├── standardizer.py │ └── utilities.py ├── setup.cfg ├── tests/ │ ├── __init__.py │ ├── applicability/ │ │ ├── __init__.py │ │ ├── conftest.py │ │ ├── test_base.py │ │ ├── test_bounding_box.py │ │ ├── test_convex_hull.py │ │ ├── test_hotelling.py │ │ ├── test_isolation_forest.py │ │ ├── test_kernel_density.py │ │ ├── test_knn.py │ │ ├── test_leverage.py │ │ ├── test_local_outlier.py │ │ ├── test_mahalanobis.py │ │ ├── test_standardization.py │ │ └── test_topkat.py │ ├── conftest.py │ ├── fixtures.py │ ├── test_desctransformer.py │ ├── test_fptransformers.py │ ├── test_fptransformersgenerator.py │ ├── test_parameter_types.py │ ├── test_safeinferencemode.py │ ├── test_sanitizer.py │ ├── test_scikit_mol.py │ ├── test_smilestomol.py │ └── test_transformers.py └── uv.toml ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/CODEOWNERS ================================================ * @EBjerrum scikit_mol/parallel.py @asiomchen scikit_mol/plotting.py @asiomchen ================================================ FILE: .github/workflows/code_quality.yaml ================================================ name: Code Quality Checks on: [ push, pull_request ] jobs: ruff-checks: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Check code formatting uses: astral-sh/ruff-action@v3 with: version: 0.8.6 args: "format --check" src: "./scikit_mol" - name: Check code style uses: astral-sh/ruff-action@v3 with: version: 0.8.6 args: "check" src: "./scikit_mol" ================================================ FILE: .github/workflows/publish.yaml ================================================ name: Publish Python🐍 distribution📦 with uv🌈 # after releasing a new version, build the distribution and uploads signed artifacts to GitHub Release on: workflow_call: inputs: python-version: type: string description: "Python version to use" required: true default: "3.9" is-draft: type: boolean description: "Is this a draft release?" required: false default: false dist-artifact-name: type: string description: "Name of the created distribution artifact" required: false default: "python-package-distributions" jobs: build-and-publish: name: Build distribution📦 runs-on: ubuntu-latest permissions: contents: write # id-token: write # IMPORTANT: mandatory for sigstore steps: - uses: actions/checkout@v4 with: persist-credentials: false - name: Set up Python🐍 uses: astral-sh/setup-uv@v5 with: python-version: ${{ inputs.python-version }} - name: Build a binary wheel and a source tarball run: uv build - name: Store the distribution packages uses: actions/upload-artifact@v4 with: name: ${{ inputs.dist-artifact-name }} path: dist/ github-release: name: >- Sign the distribution📦 with Sigstore and upload them to GitHub Release needs: - build-and-publish runs-on: ubuntu-latest permissions: contents: write # IMPORTANT: mandatory for making GitHub Releases id-token: write # IMPORTANT: mandatory for sigstore steps: - name: Download all the dists uses: actions/download-artifact@v4 with: name: ${{ inputs.dist-artifact-name }} path: dist/ - name: Sign the dists with Sigstore🔏 uses: sigstore/gh-action-sigstore-python@v3.0.0 with: inputs: >- ./dist/*.tar.gz ./dist/*.whl - name: Create GitHub Release env: GITHUB_TOKEN: ${{ github.token }} run: >- gh release create "$GITHUB_REF_NAME" --repo "$GITHUB_REPOSITORY" --generate-notes ${{ inputs.is-draft && '--draft' || '' }} - name: Upload artifact signatures to GitHub Release env: GITHUB_TOKEN: ${{ github.token }} run: >- gh release upload "$GITHUB_REF_NAME" dist/** --repo "$GITHUB_REPOSITORY" ================================================ FILE: .github/workflows/pytest.yaml ================================================ name: scikit_mol ci on: push: branches: [main] tags: ['v*'] pull_request: branches: [main] # cancel previously running tests if new commits are made # https://docs.github.com/en/actions/examples/using-concurrency-expressions-and-a-test-matrix concurrency: group: actions-id-${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} cancel-in-progress: true jobs: # run pytests for scikit_mol tests: name: pytest ${{ matrix.os }}::py${{ matrix.python-version }} runs-on: ${{ matrix.os }} strategy: max-parallel: 6 fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.10"] include: # test python version compatibility on linux only - os: ubuntu-latest python-version: 3.13 - os: ubuntu-latest python-version: 3.12 - os: ubuntu-latest python-version: 3.11 - os: ubuntu-latest python-version: 3.10 - os: ubuntu-latest python-version: 3.9 steps: - name: Checkout scikit_mol uses: actions/checkout@v4 - name: Install uv and set the python version uses: astral-sh/setup-uv@v5 with: python-version: ${{ matrix.python-version }} - name: Install scikit_mol run: uv sync --dev - name: Cache tests/data uses: actions/cache@v4 with: path: tests/data key: ${{ runner.os }}-${{ hashFiles('tests/conftest.py') }} - name: Run Tests run: uv run pytest --cov=./scikit_mol . build-and-create-signed-release: name: Build distribution📦 & create Github Release if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') needs: tests uses: ./.github/workflows/publish.yaml with: python-version: "3.9" is-draft: true publish: name: Publish to PyPI needs: build-and-create-signed-release runs-on: ubuntu-latest # will be enabled in the future # environment: # name: pypi # url: https://pypi.org/p/scikit-mol permissions: id-token: write # IMPORTANT: mandatory for trusted publishing steps: - name: Download all the dists uses: actions/download-artifact@v4 with: name: python-package-distributions path: dist/ - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@release/v1 ================================================ FILE: .github/workflows/welcome.yaml ================================================ name: Welcome WorkFlow on: issues: types: [opened] pull_request_target: types: [opened] jobs: build: name: 👋 Welcome permissions: write-all runs-on: ubuntu-latest steps: - uses: actions/first-interaction@v1.3.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} issue-message: "🎉 Welcome to scikit-mol! 🧪✨ Thank you for opening your first issue! 🚀 Your feedback helps improve the project and makes a difference. 💡 If you have any questions or need guidance, don't hesitate to ask. We're here to help! 🤝" pr-message: "🎉 Welcome to scikit-mol! 🧪✨ Thank you for submitting your first pull request! 🔧 Your effort and contributions mean a lot to us. 🙌 We'll review it as soon as possible. 🚀" ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ .scikit-mol.code-workspace.swp scikit-mol.code-workspace # test data tests/data/ # setuptools_scm version scikit_mol/_version.py notebooks/SLC6A4_active_excape_export.csv sandbox/ # PyCharm settings .idea ================================================ FILE: .pre-commit-config.yaml ================================================ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 hooks: - id: requirements-txt-fixer - id: mixed-line-ending - id: check-yaml - id: check-json - id: pretty-format-json args: ['--autofix'] exclude: .ipynb - id: check-added-large-files - id: check-merge-conflict - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. rev: v0.8.6 hooks: # Run the linter. - id: ruff args: [ --fix ] types_or: [ python, pyi ] # Run the formatter. - id: ruff-format ================================================ FILE: .readthedocs.yaml ================================================ # Read the Docs configuration file for MkDocs projects # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-22.04 tools: python: "3.12" jobs: # Use uv to speed up the build # https://docs.readthedocs.io/en/stable/build-customization.html#install-dependencies-with-uv pre_create_environment: - asdf plugin add uv - asdf install uv 0.5.24 - asdf global uv 0.5.24 create_environment: - uv venv install: - uv sync --group docs build: html: - uv run mkdocs build -d $READTHEDOCS_OUTPUT/html mkdocs: configuration: mkdocs.yml ================================================ FILE: .vscode/extensions.json ================================================ { "recommendations": [ "njpwerner.autodocstring" ] } ================================================ FILE: .vscode/settings.json ================================================ { "autoDocstring.docstringFormat": "numpy" } ================================================ FILE: CITATION.bib ================================================ @article{bjerrum_scikit-mol_2023, title = {Scikit-{Mol} brings cheminformatics to {Scikit}-{Learn}}, author = {Bjerrum, Esben Jannik and Bachorz, Rafał Adam and Bitton, Adrien and Choung, Oh-hyeon and Chen, Ya and Esposito, Carmen and Ha, Son Viet and Poehlmann, Andreas}, year = {2023}, month = dec, journal = {ChemRxiv}, url = {https://chemrxiv.org/engage/chemrxiv/article-details/60ef0fc58825826143a82cc0}, doi = {10.26434/chemrxiv-2023-fzqwd}, abstract = {Scikit-Mol is a open-source toolkit that aims to bridge the gap between two well-established toolkits, RDKit and Scikit-Learn, in order to provide a simple interface for building cheminformatics models. By leveraging the strengths of both RDKit and Scikit-Learn, Scikit-Mol provides a powerful platform for creating predictive modeling in drug discovery and materials design. Unlike other toolkits that often integrate both chemistry and machine learning, Scikit-Mol rather aims to be a simple bridge between the two, reducing the maintenance effort required to keep up with changes and new features in e.g. Scikit-Learn. A simple example of Scikit-Mol's functionality is provided, demonstrating its compatibility with Scikit-Learn pipelines. Overall, Scikit-Mol provides a useful and flexible package for building self-contained and self-documented cheminformatics models with minimal maintenance required.}, language = {en}, urldate = {2023-12-06}, keywords = {Cheminformatics, Descriptors, Fingerprints, Machine Learning, RDKit, Scikit-Learn}, note = {preprint} } ================================================ FILE: CONTRIBUTING.md ================================================ # Contribution For up-to-date information, see [docs/contribution.md](docs/contributing.md) or [https://scikit-mol.readthedocs.io/en/latest/contributing/](https://scikit-mol.readthedocs.io/en/latest/contributing/) ================================================ FILE: LICENSE ================================================ GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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If the Library as you received it specifies that a proxy can decide whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the Library. ================================================ FILE: MANIFEST.in ================================================ prune .github exclude .git* ================================================ FILE: Makefile ================================================ sync-notebooks: uv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/*.ipynb uv run ruff format "docs/notebooks/" run-notebooks: uv run jupytext --execute docs/notebooks/*ipynb ================================================ FILE: README.md ================================================ # scikit-mol ![Scikit-Mol Logo](https://raw.githubusercontent.com/EBjerrum/scikit-mol/029036fed8575705eaa80f6e3b08e70463b9a0c4/resources/logo/ScikitMol_Logo_Hybrid_300.png) [![python versions](https://shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)]() [![pypi version](https://img.shields.io/pypi/v/scikit-mol.svg)](https://pypi.org/project/scikit-mol/) [![conda version](https://img.shields.io/conda/vn/conda-forge/scikit-mol.svg)](https://anaconda.org/conda-forge/scikit-mol) [![license](https://img.shields.io/github/license/EBjerrum/scikit-mol)](#) [![powered by rdkit](https://img.shields.io/badge/Powered%20by-RDKit-3838ff.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAFVBMVEXc3NwUFP8UPP9kZP+MjP+0tP////9ZXZotAAAAAXRSTlMAQObYZgAAAAFiS0dEBmFmuH0AAAAHdElNRQfmAwsPGi+MyC9RAAAAQElEQVQI12NgQABGQUEBMENISUkRLKBsbGwEEhIyBgJFsICLC0iIUdnExcUZwnANQWfApKCK4doRBsKtQFgKAQC5Ww1JEHSEkAAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMi0wMy0xMVQxNToyNjo0NyswMDowMDzr2J4AAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjItMDMtMTFUMTU6MjY6NDcrMDA6MDBNtmAiAAAAAElFTkSuQmCC)](https://www.rdkit.org/) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) ## Scikit-Learn classes for molecular vectorization using RDKit The intended usage is to be able to add molecular vectorization directly into scikit-learn pipelines, so that the final model directly predict on RDKit molecules or SMILES strings As example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and \_test lists: pipe = Pipeline([('mol_transformer', MorganFingerprintTransformer()), ('Regressor', Ridge())]) pipe.fit(mol_list_train, y_train) pipe.score(mol_list_test, y_test) pipe.predict([Chem.MolFromSmiles('c1ccccc1C(=O)C')]) >>> array([4.93858815]) The scikit-learn compatibility should also make it easier to include the fingerprinting step in hyperparameter tuning with scikit-learns utilities The first draft for the project was created at the [RDKIT UGM 2022 hackathon](https://github.com/rdkit/UGM_2022) 2022-October-14 ## Installation Users can install latest tagged release from pip ```sh pip install scikit-mol ``` or from conda-forge ```sh conda install -c conda-forge scikit-mol ``` The conda forge package should get updated shortly after a new tagged release on pypi. Bleeding edge ```sh pip install git+https://github.com/EBjerrum/scikit-mol.git ``` ## Documentation Example notebooks and API documentation are now hosted on [https://scikit-mol.readthedocs.io](https://scikit-mol.readthedocs.io/en/latest/) - [Basic Usage and fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/01_basic_usage/) - [Descriptor transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/02_descriptor_transformer/) - [Pipelining with Scikit-Learn classes](https://scikit-mol.readthedocs.io/en/latest/notebooks/03_example_pipeline/) - [Molecular standardization](https://scikit-mol.readthedocs.io/en/latest/notebooks/04_standardizer/) - [Sanitizing SMILES input](https://scikit-mol.readthedocs.io/en/latest/notebooks/05_smiles_sanitization/) - [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/06_hyperparameter_tuning/) - [Using parallel execution to speed up descriptor and fingerprint calculations](https://scikit-mol.readthedocs.io/en/latest/notebooks/07_parallel_transforms/) - [Using skopt for hyperparameter tuning](https://scikit-mol.readthedocs.io/en/latest/notebooks/08_external_library_skopt/) - [Testing different fingerprints as part of the hyperparameter optimization](https://scikit-mol.readthedocs.io/en/latest/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers/) - [Using pandas output for easy feature importance analysis and combine pre-existing values with new computations](https://scikit-mol.readthedocs.io/en/latest/notebooks/10_pipeline_pandas_output/) - [Working with pipelines and estimators in safe inference mode for handling prediction on batches with invalid smiles or molecules](https://scikit-mol.readthedocs.io/en/latest/notebooks/11_safe_inference/) - [Creating custom fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/12_custom_fingerprint_transformer/) - [Estimating applicability domain using feature based estimators](https://scikit-mol.readthedocs.io/en/latest/notebooks/13_applicability_domain/) We also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd) ## Other use-examples Scikit-Mol has been featured in blog-posts or used in research, some examples which are listed below: - [Useful ML package for cheminformatics iwatobipen.wordpress.com](https://iwatobipen.wordpress.com/2023/11/12/useful-ml-package-for-cheminformatics-rdkit-cheminformatics-ml/) - [Boosted trees Data_in_life_blog](https://jhylin.github.io/Data_in_life_blog/posts/19_ML2-3_Boosted_trees/1_adaboost_xgb.html) - [Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.4c01710) - [Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations](https://doi.org/10.1186/s13321-025-01022-3) - [RandomNets Improve Neural Network Regression Performance via Implicit Ensembling](https://chemrxiv.org/engage/chemrxiv/article-details/67656cfa81d2151a02603f48) - [WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings](https://www.sciencedirect.com/science/article/pii/S2352914824001618) - [Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting](https://chemrxiv.org/engage/chemrxiv/article-details/661402ee21291e5d1d646651) - [AUTONOMOUS DRUG DISCOVERY](https://www.proquest.com/openview/3e830e36bc618f263905a99e787c66c6/1?pq-origsite=gscholar&cbl=18750&diss=y) - [DrugGym: A testbed for the economics of autonomous drug discovery](https://www.biorxiv.org/content/10.1101/2024.05.28.596296v1.abstract) ## Roadmap and Contributing _Help wanted!_ Are you a PhD student that want a "side-quest" to procrastinate your thesis writing or are you interested in computational chemistry, cheminformatics or simply with an interest in QSAR modelling, Python Programming open-source software? Do you want to learn more about machine learning with Scikit-Learn? Or do you use scikit-mol for your current work and would like to pay a little back to the project and see it improved as well? With a little bit of help, this project can be improved much faster! Reach to me (Esben), for a discussion about how we can proceed. Currently, we are working on fixing some deprecation warnings, it's not the most exciting work, but it's important to maintain a little. Later on we need to go over the scikit-learn compatibility and update to some of their newer features on their estimator classes. We're also brewing on some feature enhancements and tests, such as new fingerprints and a more versatile standardizer. There are more information about how to contribute to the project in [CONTRIBUTING](https://scikit-mol.readthedocs.io/en/latest/contributing/) ## BUGS Probably still, please check issues at GitHub and report there ## Contributors Scikit-Mol has been developed as a community effort with contributions from people from many different companies, consortia, foundations and academic institutions. [Cheminformania Consulting](https://www.cheminformania.com), [Aptuit](https://www.linkedin.com/company/aptuit/), [BASF](https://www.basf.com), [Bayer AG](https://www.bayer.com), [Boehringer Ingelheim](https://www.boehringer-ingelheim.com/), [Chodera Lab (MSKCC)](https://www.choderalab.org/), [EPAM Systems](https://www.epam.com/),[ETH Zürich](https://ethz.ch/en.html), [Evotec](https://www.evotec.com/), [Johannes Gutenberg University](https://www.uni-mainz.de/en/), [Martin Luther University](https://www.uni-halle.de/?lang=en), [Odyssey Therapeutics](https://odysseytx.com/), [Open Molecular Software Foundation](https://omsf.io/), [Openfree.energy](https://openfree.energy/), [Polish Academy of Sciences](https://pasific.pan.pl/polish-academy-of-sciences/), [Productivista](https://www.productivista.com), [Simulations-Plus Inc.](https://www.simulations-plus.com/), [University of Vienna](https://www.univie.ac.at/en/) - Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), esbenbjerrum+scikit_mol@gmail.com - Carmen Esposito [@cespos](https://github.com/cespos) - Son Ha [@son-ha-264](https://github.com/son-ha-264) - Oh-hyeon Choung [@Ohyeon5](https://github.com/Ohyeon5) - Andreas Poehlmann [@ap--](https://github.com/ap--) - Ya Chen [@anya-chen](https://github.com/anya-chen) - Anton Siomchen [@asiomchen](https://github.com/asiomchen) - Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz) - Adrien Chaton [@adrienchaton](https://github.com/adrienchaton) - [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz) - [@RiesBen](https://github.com/RiesBen) - [@enricogandini](https://github.com/enricogandini) - [@mikemhenry](https://github.com/mikemhenry) - [@c-feldmann](https://github.com/c-feldmann) - Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw) - Kyle Barbary [@kbarbary](https://github.com/kbarbary) ================================================ FILE: docs/api/fingerprints.base.md ================================================ `scikit_mol.fingerprints.baseclasses` ::: scikit_mol.fingerprints.baseclasses options: filters: [] ================================================ FILE: docs/api/scikit_mol.applicability.md ================================================ # `scikit-mol.applicability` ::: scikit_mol.applicability ================================================ FILE: docs/api/scikit_mol.conversions.md ================================================ # `scikit-mol.conversions` ::: scikit_mol.conversions ================================================ FILE: docs/api/scikit_mol.core.md ================================================ # `scikit-mol.core` ::: scikit_mol.core ================================================ FILE: docs/api/scikit_mol.descriptors.md ================================================ # `scikit_mol.descriptors` ::: scikit_mol.descriptors ================================================ FILE: docs/api/scikit_mol.fingerprints.md ================================================ ::: scikit_mol.fingerprints options: filters: ["!Fps"] inherited_members: - transform ================================================ FILE: docs/api/scikit_mol.parallel.md ================================================ # `scikit-mol.parallel` ::: scikit_mol.parallel ================================================ FILE: docs/api/scikit_mol.plotting.md ================================================ # `scikit-mol.plotting` ::: scikit_mol.plotting ================================================ FILE: docs/api/scikit_mol.safeinference.md ================================================ # `scikit-mol.safeinference` ::: scikit_mol.safeinference ================================================ FILE: docs/api/scikit_mol.standardizer.md ================================================ # `scikit-mol.standardizer` ::: scikit_mol.standardizer ================================================ FILE: docs/assets/css/tweak-width.css ================================================ /* snippet from datamol.io */ @media only screen and (min-width: 76.25em) { .md-main__inner { max-width: none; padding-left: 2em; padding-left: 2em; } .md-sidebar--primary { left: 0; } .md-sidebar--secondary { right: 0; margin-left: 0; -webkit-transform: none; transform: none; } } ================================================ FILE: docs/assets/js/readthedocs.js ================================================ // Add server-side search document.addEventListener("DOMContentLoaded", function(event) { // Trigger Read the Docs' search addon instead of Material MkDocs default document.querySelector(".md-search__input").addEventListener("focus", (e) => { const event = new CustomEvent("readthedocs-search-show"); document.dispatchEvent(event); }); }); // Use CustomEvent to generate the version selector document.addEventListener( "readthedocs-addons-data-ready", function (event) { const config = event.detail.data(); const versioning = `
`; document.querySelector(".md-header__topic").insertAdjacentHTML("beforeend", versioning); }); ================================================ FILE: docs/contributing.md ================================================ # Contribution Thanks for your interest in contributing to the project. Please read on in the sections that apply. ## Discord Server We have a discord server for chats and discussion, ask for an invitation: esbenbjerrum+scikit_mol@gmail.com ## Installation We use [uv] for managing the virtual environment. You can install it with: ```sh curl -LsSf https://astral.sh/uv/install.sh | sh ``` For more information and other installation methods see [documentation](https://docs.astral.sh/uv/) Clone and install in editable more like this ```sh git clone git@github.com:EBjerrum/scikit-mol.git uv sync --dev ``` After that you could either activate venv and run commands as usual: ```sh source .venv/bin/activate pytest -v --cov=scikit_mol ``` or use `uv run` to run commands in the venv (automatically check that environment is up to date): ```sh uv run pytest -v --cov=scikit_mol ``` `uv.lock` contains the pinned dependencies and is used to recreate the environment. Make sure to update it when adding new dependencies. (handled automatically when using `uv run` or manually with `uv lock`) ## Code Quality We use [ruff](https://github.com/astral-sh/ruff) to lint and format the code. The configuration is in the [ruff.toml](https://github.com/EBjerrum/scikit-mol/blob/main/ruff.toml) file. The CI will fail if the code is not formatted correctly. You can run the linter and formatter locally with: ```sh ruff format scikit_mol ruff check --fix scikit_mol ``` We also have pre-commit hooks that will run the linter and formatter before you commit, and we highly recommend you to use them. You can install them with: ```sh pre-commit install ``` For more information on pre-commit see [documentation](https://pre-commit.com/). ## Adding transformers The projects transformers subclasses the BaseEstimator and Transformer mixin classes from sklearn. Their documentation page contains information on what requisites are necessary [https://scikit-learn.org/stable/developers/develop.html](https://scikit-learn.org/stable/developers/develop.html). Most notably: - The arguments accepted by **init** should all be keyword arguments with a default value. - Every keyword argument accepted by **init** should correspond to an attribute on the instance. - - There should be no logic, not even input validation, and the parameters should not be changed inside the **init** function. Scikit-learn classes depends on this in order to for e.g. the `.get_params()`, `.set_params()`, cloning abilities and representation rendering to work. - With the new error handling, falsy objects need to return masked arrays or arrays with `np.nan` (for float dtype) ### Tips - We have observed that some external tools used "exotic" types such at `np.int64` when doing hyperparameter tuning. It is thus necessary do defensive programming to cast parameters to standard types before making calls to rdkit functions. This behaviour is tested in the `test_parameter_types` test - `@property` getters and setters can be used if additional logic are needed when setting the attributes from the keywords while at the same time adhering to the sklearn requisites. - Some RDKit features uses objects as generators which may not be picklable. If instantiated and added to the object as an attribute rather than instantiated at each function call for individual molecules, these should thus be removed and recreated via overloading the `_get_state()` and `_set_state()` methods. See [MHFingerprintTransformer](https://github.com/EBjerrum/scikit-mol/blob/main/scikit_mol/fingerprints/minhash.py#L11) for an example. ## Module organisation Currently, we have multiple classes in the same file, if they are the same type. This may change in the future. ## Docstrings We should ultimately consolidate on the NumPy docstring format [https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard) which is also used by SciPy and other scikits. ## Typehints parameters and output of methods should preferably be using typehints ## Testing New transformer classes should be added to the pytest tests in the tests directory. A lot of tests are made general, and tests aspects of the transformers that are needed for sklearn compliance or other features. The transformer is then added to a fixture and can be added to the lists of transformer objects that are run by these test. Specific tests may also be necessary to set up. As example the assert_transformer_set_params needs a list of non-default parameters in order to set the set_params functionality of the object. Scikit-Learn has a check_estimator that we should strive to get to work, some classes of scikit-mol currently does not pass all tests. ## Notebooks Another way of contributing is by providing notebooks with examples on how to use the project to build models together with Scikit-Learn and other tools. There are .ipynb files in the `docs/notebooks` and .py files in the `script` subfolder as the first are useful for online rendering in the documentation, whereas the latter is useful for sub version control. If you want to create new notebook you can first create .ipynb file, and then you run `make sync-notebooks` to create the corresponding .py file for the commit. If you updated any of the existing py/ipynb files, you can run `make sync-notebooks` to update the outdated file in the pair. The .py files are used for nice diffs, and the .ipynb files are used for rendering in the documentation. `make sync-notebooks` will sync all the notebooks with the .py files in the `scripts` folder. `make run-notebooks` will sync, run and save the notebooks, expects an ipython kernel with scikit-mol installed. If you only want to sync and run a single notebook if you are working on updating one you can adapt the commands from the MakeFile ```bash uv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/XX_YourNotebook.ipynb uv run ruff format "docs/notebooks/XX_YourNotebook.ipynb" uv run jupytext --execute docs/notebooks/XX_YourNotebook.ipynb ``` ## Documentation We use [MkDocs](https://www.mkdocs.org/) to host scikit-mol documentation on ReadTheDocs. If you're making some changes to the documentation or just what to see live preview of your docstring you can take a look at rendered documentation. Install documentation dependencies: ```sh uv sync --group docs ``` Start server: ```sh uv run mkdocs serve ``` Go to http://127.0.0.1:8000 to see the documentation ## Release ### PyPi To release a new version on PyPi, you need to create and push new tag in v0.0.0 format then workflow will automatically build and upload the package to PyPi. Additionally, the release draft with autogenerated notes and signed distribution files will be added to the GitHub release page. What is left is to publish the release, after checking that the notes are correct. ### Conda When you make a release on PyPi the conda-forge bot will automatically make a PR that updates the Conda feedstock to the new version. If new main package dependencies or pins are changed on dependencies, those changes will need to be added to the PR in the feedstocks [https://github.com/conda-forge/scikit-mol-feedstock/blob/main/recipe/meta.yaml](https://github.com/conda-forge/scikit-mol-feedstock/blob/main/recipe/meta.yaml). I.e. the run section needs to correspond to the `dependencies = [` section in pyproject.toml. If there is just a pure code change then all we have do to is merge in the PR and that will update the package on conda-forge. See https://conda-forge.org/docs/maintainer/updating_pkgs/ for more information ================================================ FILE: docs/index.md ================================================ # scikit-mol ![Scikit-Mol Logo](https://raw.githubusercontent.com/EBjerrum/scikit-mol/029036fed8575705eaa80f6e3b08e70463b9a0c4/resources/logo/ScikitMol_Logo_Hybrid_300.png) [![python versions](https://shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)]() [![pypi version](https://img.shields.io/pypi/v/scikit-mol.svg)](https://pypi.org/project/scikit-mol/) [![conda version](https://img.shields.io/conda/vn/conda-forge/scikit-mol.svg)](https://anaconda.org/conda-forge/scikit-mol) [![license](https://img.shields.io/github/license/EBjerrum/scikit-mol)](#) [![powered by rdkit](https://img.shields.io/badge/Powered%20by-RDKit-3838ff.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAFVBMVEXc3NwUFP8UPP9kZP+MjP+0tP////9ZXZotAAAAAXRSTlMAQObYZgAAAAFiS0dEBmFmuH0AAAAHdElNRQfmAwsPGi+MyC9RAAAAQElEQVQI12NgQABGQUEBMENISUkRLKBsbGwEEhIyBgJFsICLC0iIUdnExcUZwnANQWfApKCK4doRBsKtQFgKAQC5Ww1JEHSEkAAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMi0wMy0xMVQxNToyNjo0NyswMDowMDzr2J4AAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjItMDMtMTFUMTU6MjY6NDcrMDA6MDBNtmAiAAAAAElFTkSuQmCC)](https://www.rdkit.org/) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) ## Scikit-Learn classes for molecular vectorization using RDKit The intended usage is to be able to add molecular vectorization directly into scikit-learn pipelines, so that the final model directly predict on RDKit molecules or SMILES strings As example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and \_test lists: pipe = Pipeline([('mol_transformer', MorganFingerprintTransformer()), ('Regressor', Ridge())]) pipe.fit(mol_list_train, y_train) pipe.score(mol_list_test, y_test) pipe.predict([Chem.MolFromSmiles('c1ccccc1C(=O)C')]) >>> array([4.93858815]) The scikit-learn compatibility should also make it easier to include the fingerprinting step in hyperparameter tuning with scikit-learns utilities The first draft for the project was created at the [RDKIT UGM 2022 hackathon](https://github.com/rdkit/UGM_2022) 2022-October-14 ## Installation Users can install latest tagged release from pip ```sh pip install scikit-mol ``` or from conda-forge ```sh conda install -c conda-forge scikit-mol ``` The conda forge package should get updated shortly after a new tagged release on pypi. Bleeding edge ```sh pip install git+https://github.com/EBjerrum/scikit-mol.git ``` ## Documentation Example notebooks and API documentation are now hosted on [https://scikit-mol.readthedocs.io](https://scikit-mol.readthedocs.io/en/latest/) - [Basic Usage and fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/01_basic_usage/) - [Descriptor transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/02_descriptor_transformer/) - [Pipelining with Scikit-Learn classes](https://scikit-mol.readthedocs.io/en/latest/notebooks/03_example_pipeline/) - [Molecular standardization](https://scikit-mol.readthedocs.io/en/latest/notebooks/04_standardizer/) - [Sanitizing SMILES input](https://scikit-mol.readthedocs.io/en/latest/notebooks/05_smiles_sanitization/) - [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/06_hyperparameter_tuning/) - [Using parallel execution to speed up descriptor and fingerprint calculations](https://scikit-mol.readthedocs.io/en/latest/notebooks/07_parallel_transforms/) - [Using skopt for hyperparameter tuning](https://scikit-mol.readthedocs.io/en/latest/notebooks/08_external_library_skopt/) - [Testing different fingerprints as part of the hyperparameter optimization](https://scikit-mol.readthedocs.io/en/latest/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers/) - [Using pandas output for easy feature importance analysis and combine pre-existing values with new computations](https://scikit-mol.readthedocs.io/en/latest/notebooks/10_pipeline_pandas_output/) - [Working with pipelines and estimators in safe inference mode for handling prediction on batches with invalid smiles or molecules](https://scikit-mol.readthedocs.io/en/latest/notebooks/11_safe_inference/) - [Creating custom fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/12_custom_fingerprint_transformer/) - [Estimating applicability domain using feature based estimators](https://scikit-mol.readthedocs.io/en/latest/notebooks/13_applicability_domain/) We also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd) ## Other use-examples Scikit-Mol has been featured in blog-posts or used in research, some examples which are listed below: - [Useful ML package for cheminformatics iwatobipen.wordpress.com](https://iwatobipen.wordpress.com/2023/11/12/useful-ml-package-for-cheminformatics-rdkit-cheminformatics-ml/) - [Boosted trees Data_in_life_blog](https://jhylin.github.io/Data_in_life_blog/posts/19_ML2-3_Boosted_trees/1_adaboost_xgb.html) - [Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.4c01710) - [Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations](https://doi.org/10.1186/s13321-025-01022-3) - [RandomNets Improve Neural Network Regression Performance via Implicit Ensembling](https://chemrxiv.org/engage/chemrxiv/article-details/67656cfa81d2151a02603f48) - [WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings](https://www.sciencedirect.com/science/article/pii/S2352914824001618) - [Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting](https://chemrxiv.org/engage/chemrxiv/article-details/661402ee21291e5d1d646651) - [AUTONOMOUS DRUG DISCOVERY](https://www.proquest.com/openview/3e830e36bc618f263905a99e787c66c6/1?pq-origsite=gscholar&cbl=18750&diss=y) - [DrugGym: A testbed for the economics of autonomous drug discovery](https://www.biorxiv.org/content/10.1101/2024.05.28.596296v1.abstract) ## Roadmap and Contributing _Help wanted!_ Are you a PhD student that want a "side-quest" to procrastinate your thesis writing or are you interested in computational chemistry, cheminformatics or simply with an interest in QSAR modelling, Python Programming open-source software? Do you want to learn more about machine learning with Scikit-Learn? Or do you use scikit-mol for your current work and would like to pay a little back to the project and see it improved as well? With a little bit of help, this project can be improved much faster! Reach to me (Esben), for a discussion about how we can proceed. Currently, we are working on fixing some deprecation warnings, it's not the most exciting work, but it's important to maintain a little. Later on we need to go over the scikit-learn compatibility and update to some of their newer features on their estimator classes. We're also brewing on some feature enhancements and tests, such as new fingerprints and a more versatile standardizer. There are more information about how to contribute to the project in [CONTRIBUTING](https://scikit-mol.readthedocs.io/en/latest/contributing/) ## BUGS Probably still, please check issues at GitHub and report there ## Contributors Scikit-Mol has been developed as a community effort with contributions from people from many different companies, consortia, foundations and academic institutions. [Cheminformania Consulting](https://www.cheminformania.com), [Aptuit](https://www.linkedin.com/company/aptuit/), [BASF](https://www.basf.com), [Bayer AG](https://www.bayer.com), [Boehringer Ingelheim](https://www.boehringer-ingelheim.com/), [Chodera Lab (MSKCC)](https://www.choderalab.org/), [EPAM Systems](https://www.epam.com/),[ETH Zürich](https://ethz.ch/en.html), [Evotec](https://www.evotec.com/), [Johannes Gutenberg University](https://www.uni-mainz.de/en/), [Martin Luther University](https://www.uni-halle.de/?lang=en), [Odyssey Therapeutics](https://odysseytx.com/), [Open Molecular Software Foundation](https://omsf.io/), [Openfree.energy](https://openfree.energy/), [Polish Academy of Sciences](https://pasific.pan.pl/polish-academy-of-sciences/), [Productivista](https://www.productivista.com), [Simulations-Plus Inc.](https://www.simulations-plus.com/), [University of Vienna](https://www.univie.ac.at/en/) - Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), esbenbjerrum+scikit_mol@gmail.com - Carmen Esposito [@cespos](https://github.com/cespos) - Son Ha [@son-ha-264](https://github.com/son-ha-264) - Oh-hyeon Choung [@Ohyeon5](https://github.com/Ohyeon5) - Andreas Poehlmann [@ap--](https://github.com/ap--) - Ya Chen [@anya-chen](https://github.com/anya-chen) - Anton Siomchen [@asiomchen](https://github.com/asiomchen) - Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz) - Adrien Chaton [@adrienchaton](https://github.com/adrienchaton) - [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz) - [@RiesBen](https://github.com/RiesBen) - [@enricogandini](https://github.com/enricogandini) - [@mikemhenry](https://github.com/mikemhenry) - [@c-feldmann](https://github.com/c-feldmann) - Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw) - Kyle Barbary [@kbarbary](https://github.com/kbarbary) ================================================ FILE: docs/notebooks/01_basic_usage.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "aa079ac3", "metadata": {}, "source": [ "# Scikit-Mol\n", "## scikit-learn compatible RDKit transformers\n", "\n", "Scikit-mol is a collection of scikit-learn compatible transformer classes that integrate into the scikit-learn framework and thus bridge between the molecular information in form of RDKit molecules or SMILES and the machine learning framework from scikit-learn\n" ] }, { "cell_type": "markdown", "id": "76d24789", "metadata": {}, "source": [ "The transformer classes are easy to load, configure and use to process molecular information into vectorized formats using fingerprinters or collections of descriptors. For demonstration purposes, let's load a MorganTransformer, that can convert a list of RDKit molecular objects into a numpy array of morgan fingerprints. First create some molecules from SMILES strings." ] }, { "cell_type": "code", "execution_count": 1, "id": "2c8cad03", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:29.627872Z", "iopub.status.busy": "2025-05-08T16:22:29.627571Z", "iopub.status.idle": "2025-05-08T16:22:29.632065Z", "shell.execute_reply": "2025-05-08T16:22:29.631373Z" } }, "outputs": [], "source": [ "from IPython.core.display import HTML" ] }, { "cell_type": "code", "execution_count": 2, "id": "8d5b2333", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:29.634712Z", "iopub.status.busy": "2025-05-08T16:22:29.634423Z", "iopub.status.idle": "2025-05-08T16:22:29.845389Z", "shell.execute_reply": "2025-05-08T16:22:29.844169Z" } }, "outputs": [], "source": [ "from rdkit import Chem\n", "\n", "smiles_strings = [\n", " \"C12C([C@@H](OC(C=3C=CC(=CC3)F)C=4C=CC(=CC4)F)CC(N1CCCCCC5=CC=CC=C5)CC2)C(=O)OC\",\n", " \"O(C1=NC=C2C(CN(CC2=C1)C)C3=CC=C(OC)C=C3)CCCN(CC)CC\",\n", " \"O=S(=O)(N(CC=1C=CC2=CC=CC=C2C1)[C@@H]3CCNC3)C\",\n", " \"C1(=C2C(CCCC2O)=NC=3C1=CC=CC3)NCC=4C=CC(=CC4)Cl\",\n", " \"C1NC[C@@H](C1)[C@H](OC=2C=CC(=NC2C)OC)CC(C)C\",\n", " \"FC(F)(F)C=1C(CN(C2CCNCC2)CC(CC)CC)=CC=CC1\",\n", "]\n", "\n", "mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_strings]" ] }, { "cell_type": "markdown", "id": "b9a588c7", "metadata": {}, "source": [ "Next we import the Morgan fingerprint transformer" ] }, { "cell_type": "code", "execution_count": 3, "id": "0a625dda", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:29.850211Z", "iopub.status.busy": "2025-05-08T16:22:29.848822Z", "iopub.status.idle": "2025-05-08T16:22:30.986417Z", "shell.execute_reply": "2025-05-08T16:22:30.984810Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MorganFingerprintTransformer(radius=3)\n" ] } ], "source": [ "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "\n", "transformer = MorganFingerprintTransformer(radius=3)\n", "print(transformer)" ] }, { "cell_type": "markdown", "id": "355610d1", "metadata": {}, "source": [ "It actually renders as a cute little interactive block in the Jupyter notebook and lists the options that are not the default values. If we print it, it also gives the information on the settings.\n", "\n", "![An image of the interactive transformer widget](images/Transformer_Widget.jpg \"Transformer object rendering in Jupyter\")\n", "\n", "The graphical representation is probably nice when working with complex pipelines. However, the graphical representation doesn't work when previewing the notebook on GitHub and sometimes nbviewer.org, so for the rest of these scikit-mol notebook examples, we'll use the print() output." ] }, { "cell_type": "code", "execution_count": 4, "id": "9a801d0f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:30.991850Z", "iopub.status.busy": "2025-05-08T16:22:30.990911Z", "iopub.status.idle": "2025-05-08T16:22:31.011512Z", "shell.execute_reply": "2025-05-08T16:22:31.010309Z" } }, "outputs": [ { "data": { "text/html": [ "
MorganFingerprintTransformer(radius=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "MorganFingerprintTransformer(radius=3)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transformer" ] }, { "cell_type": "markdown", "id": "556858b4", "metadata": {}, "source": [ "If we want to get all the settings explicitly, we can use the .get_params() method." ] }, { "cell_type": "code", "execution_count": 5, "id": "500dc6f7", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.015226Z", "iopub.status.busy": "2025-05-08T16:22:31.014511Z", "iopub.status.idle": "2025-05-08T16:22:31.022448Z", "shell.execute_reply": "2025-05-08T16:22:31.021051Z" } }, "outputs": [ { "data": { "text/plain": [ "{'fpSize': 2048,\n", " 'n_jobs': None,\n", " 'radius': 3,\n", " 'safe_inference_mode': False,\n", " 'useBondTypes': True,\n", " 'useChirality': False,\n", " 'useCounts': False,\n", " 'useFeatures': False}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parameters = transformer.get_params()\n", "parameters" ] }, { "cell_type": "markdown", "id": "d453fa33", "metadata": {}, "source": [ "The corresponding .set_params() method can be used to update the settings from options or from a dictionary (via ** unpackaging). The get_params and set_params methods are sometimes used by sklearn, as example hyperparameter search objects." ] }, { "cell_type": "code", "execution_count": 6, "id": "3a27b07a", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.026293Z", "iopub.status.busy": "2025-05-08T16:22:31.025546Z", "iopub.status.idle": "2025-05-08T16:22:31.032975Z", "shell.execute_reply": "2025-05-08T16:22:31.031700Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MorganFingerprintTransformer(fpSize=256)\n" ] } ], "source": [ "parameters[\"radius\"] = 2\n", "parameters[\"fpSize\"] = 256\n", "transformer.set_params(**parameters)\n", "print(transformer)" ] }, { "cell_type": "markdown", "id": "3dd372d3", "metadata": {}, "source": [ "Transformation is easy, simply use the .transform() method. For sklearn compatibility the scikit-learn transformers also have a .fit_transform() method, but it is usually not fitting anything." ] }, { "cell_type": "code", "execution_count": 7, "id": "0f141920", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.036752Z", "iopub.status.busy": "2025-05-08T16:22:31.036118Z", "iopub.status.idle": "2025-05-08T16:22:31.043904Z", "shell.execute_reply": "2025-05-08T16:22:31.042561Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fps is a with shape (6, 256) and data type uint8\n" ] } ], "source": [ "fps = transformer.transform(mols)\n", "print(f\"fps is a {type(fps)} with shape {fps.shape} and data type {fps.dtype}\")" ] }, { "cell_type": "markdown", "id": "9cb75226", "metadata": {}, "source": [ "For sklearn compatibility, the transform function can be given a second parameter, usually representing the targets in the machine learning, but it is simply ignored most of the time" ] }, { "cell_type": "code", "execution_count": 8, "id": "481e527f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.047228Z", "iopub.status.busy": "2025-05-08T16:22:31.046700Z", "iopub.status.idle": "2025-05-08T16:22:31.054569Z", "shell.execute_reply": "2025-05-08T16:22:31.053275Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 0, ..., 0, 0, 0],\n", " [1, 0, 0, ..., 0, 0, 1],\n", " [1, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 1],\n", " [1, 1, 0, ..., 0, 0, 0],\n", " [1, 1, 0, ..., 0, 0, 0]], shape=(6, 256), dtype=uint8)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = range(len(mols))\n", "transformer.transform(mols, y)" ] }, { "cell_type": "markdown", "id": "500cec09", "metadata": {}, "source": [ "Sometimes we may want to transform SMILES into molecules, and scikit-mol also has a transformer for that. It simply takes a list of SMILES and produces a list of RDKit molecules, this may come in handy when building pipelines for machine learning models, as we will demo in another notebook." ] }, { "cell_type": "code", "execution_count": 9, "id": "7773a5a0", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.057901Z", "iopub.status.busy": "2025-05-08T16:22:31.057134Z", "iopub.status.idle": "2025-05-08T16:22:31.064119Z", "shell.execute_reply": "2025-05-08T16:22:31.063046Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SmilesToMolTransformer()\n" ] } ], "source": [ "from scikit_mol.conversions import SmilesToMolTransformer\n", "\n", "smi2mol = SmilesToMolTransformer()\n", "print(smi2mol)" ] }, { "cell_type": "code", "execution_count": 10, "id": "fa484453", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:31.067178Z", "iopub.status.busy": "2025-05-08T16:22:31.066755Z", "iopub.status.idle": "2025-05-08T16:22:31.074756Z", "shell.execute_reply": "2025-05-08T16:22:31.073587Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[]\n", " []\n", " []\n", " []\n", " []\n", " []]\n" ] } ], "source": [ "print(smi2mol.transform(smiles_strings))" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": "Python 3.9.4 ('rdkit')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/02_descriptor_transformer.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "e3cf34ca", "metadata": {}, "source": [ "# Desc2DTransformer: RDKit descriptors transformer\n", "\n", "The descriptors transformer can convert molecules into a list of RDKit descriptors. It largely follows the API of the other transformers, but has a few extra methods and properties to manage the descriptors." ] }, { "cell_type": "code", "execution_count": 1, "id": "81745b1f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:32.631647Z", "iopub.status.busy": "2025-05-08T16:22:32.631311Z", "iopub.status.idle": "2025-05-08T16:22:34.194489Z", "shell.execute_reply": "2025-05-08T16:22:34.193202Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "from rdkit import Chem\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scikit_mol.descriptors import MolecularDescriptorTransformer" ] }, { "cell_type": "markdown", "id": "2293e9e6", "metadata": {}, "source": [ "After instantiation of the descriptor transformer, we can query which descriptors it found available in the RDKit framework." ] }, { "cell_type": "code", "execution_count": 2, "id": "dd9a2ad0", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:34.198567Z", "iopub.status.busy": "2025-05-08T16:22:34.197421Z", "iopub.status.idle": "2025-05-08T16:22:34.206453Z", "shell.execute_reply": "2025-05-08T16:22:34.205342Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 217 available descriptors\n", "The first five descriptor names: ['MaxAbsEStateIndex', 'MaxEStateIndex', 'MinAbsEStateIndex', 'MinEStateIndex', 'qed']\n" ] } ], "source": [ "descriptor = MolecularDescriptorTransformer()\n", "available_descriptors = descriptor.available_descriptors\n", "print(f\"There are {len(available_descriptors)} available descriptors\")\n", "print(f\"The first five descriptor names: {available_descriptors[:5]}\")" ] }, { "cell_type": "markdown", "id": "110c00c0", "metadata": {}, "source": [ "We can transform molecules to their descriptor profiles" ] }, { "cell_type": "code", "execution_count": 3, "id": "4431a910", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:34.210875Z", "iopub.status.busy": "2025-05-08T16:22:34.210262Z", "iopub.status.idle": "2025-05-08T16:22:34.353857Z", "shell.execute_reply": "2025-05-08T16:22:34.352652Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "smiles_list = [\"CCCC\", \"c1ccccc1\"]\n", "mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\n", "\n", "features = descriptor.transform(mols)\n", "_ = plt.plot(np.array(features).T)" ] }, { "cell_type": "markdown", "id": "fdcb0698", "metadata": {}, "source": [ "If we only want some of them, this can be specified at object instantiation." ] }, { "cell_type": "code", "execution_count": 4, "id": "6caa9a54", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:34.357638Z", "iopub.status.busy": "2025-05-08T16:22:34.356566Z", "iopub.status.idle": "2025-05-08T16:22:34.363282Z", "shell.execute_reply": "2025-05-08T16:22:34.362201Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected descriptors are ['HeavyAtomCount', 'FractionCSP3', 'RingCount', 'MolLogP', 'MolWt']\n" ] } ], "source": [ "some_descriptors = MolecularDescriptorTransformer(\n", " desc_list=[\"HeavyAtomCount\", \"FractionCSP3\", \"RingCount\", \"MolLogP\", \"MolWt\"]\n", ")\n", "print(f\"Selected descriptors are {some_descriptors.selected_descriptors}\")\n", "features = some_descriptors.transform(mols)" ] }, { "cell_type": "markdown", "id": "52eaef77", "metadata": {}, "source": [ "If we want to update the selected descriptors on an already existing object, this can be done via the .set_params() method" ] }, { "cell_type": "code", "execution_count": 5, "id": "78fc5691", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:34.366550Z", "iopub.status.busy": "2025-05-08T16:22:34.366050Z", "iopub.status.idle": "2025-05-08T16:22:34.372939Z", "shell.execute_reply": "2025-05-08T16:22:34.371839Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MolecularDescriptorTransformer(desc_list=['HeavyAtomCount', 'FractionCSP3',\n", " 'RingCount'])\n" ] } ], "source": [ "print(\n", " some_descriptors.set_params(\n", " desc_list=[\"HeavyAtomCount\", \"FractionCSP3\", \"RingCount\"]\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "310a2a0d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/03_example_pipeline.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "e7c43298", "metadata": {}, "source": [ "# Pipelining the scikit-mol transformer\n", "\n", "One of the very usable things with scikit-learn are their pipelines. With pipelines different scikit-learn transformers can be stacked and operated on just as a single model object. In this example we will build a simple model that can predict directly on RDKit molecules and then expand it to one that predicts directly on SMILES strings\n", "\n", "First some needed imports and a dataset" ] }, { "cell_type": "code", "execution_count": 1, "id": "79139b10", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:35.876773Z", "iopub.status.busy": "2025-05-08T16:22:35.876261Z", "iopub.status.idle": "2025-05-08T16:22:36.754601Z", "shell.execute_reply": "2025-05-08T16:22:36.753459Z" } }, "outputs": [], "source": [ "import os\n", "import rdkit\n", "from rdkit import Chem\n", "from rdkit.Chem import PandasTools\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from time import time\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "17a9cdd7", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:36.758840Z", "iopub.status.busy": "2025-05-08T16:22:36.758015Z", "iopub.status.idle": "2025-05-08T16:22:36.767668Z", "shell.execute_reply": "2025-05-08T16:22:36.766504Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\" # Hmm, maybe better to download directly\n", "data = pd.read_csv(csv_file)" ] }, { "cell_type": "markdown", "id": "066131b8", "metadata": {}, "source": [ "The dataset is a subset of the SLC6A4 actives from ExcapeDB. They are hand selected to give test set performance despite the small size, and are provided as example data only and should not be used to build serious QSAR models.\n", "\n", "We add RDKit mol objects to the dataframe with pandastools and check that all conversions went well." ] }, { "cell_type": "code", "execution_count": 3, "id": "a3ec0a23", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:36.770992Z", "iopub.status.busy": "2025-05-08T16:22:36.770360Z", "iopub.status.idle": "2025-05-08T16:22:36.828093Z", "shell.execute_reply": "2025-05-08T16:22:36.826677Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 out of 200 SMILES failed in conversion\n" ] } ], "source": [ "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n", "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")" ] }, { "cell_type": "markdown", "id": "eccaf4af", "metadata": {}, "source": [ "Then, let's import some tools from scikit-learn and two transformers from scikit-mol" ] }, { "cell_type": "code", "execution_count": 4, "id": "4eb8f0fa", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:36.830959Z", "iopub.status.busy": "2025-05-08T16:22:36.830663Z", "iopub.status.idle": "2025-05-08T16:22:37.516946Z", "shell.execute_reply": "2025-05-08T16:22:37.515550Z" } }, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import train_test_split\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "from scikit_mol.conversions import SmilesToMolTransformer" ] }, { "cell_type": "code", "execution_count": 5, "id": "99edec0f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.521222Z", "iopub.status.busy": "2025-05-08T16:22:37.520115Z", "iopub.status.idle": "2025-05-08T16:22:37.527537Z", "shell.execute_reply": "2025-05-08T16:22:37.526440Z" } }, "outputs": [], "source": [ "mol_list_train, mol_list_test, y_train, y_test = train_test_split(\n", " data.ROMol, data.pXC50, random_state=0\n", ")" ] }, { "cell_type": "markdown", "id": "b8380817", "metadata": {}, "source": [ "After a split into train and test, we'll build the first pipeline" ] }, { "cell_type": "code", "execution_count": 6, "id": "a27d6ff9", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.531062Z", "iopub.status.busy": "2025-05-08T16:22:37.530349Z", "iopub.status.idle": "2025-05-08T16:22:37.539115Z", "shell.execute_reply": "2025-05-08T16:22:37.538026Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\n", " ('Regressor', Ridge())])\n" ] } ], "source": [ "pipe = Pipeline(\n", " [(\"mol_transformer\", MorganFingerprintTransformer()), (\"Regressor\", Ridge())]\n", ")\n", "print(pipe)" ] }, { "cell_type": "markdown", "id": "6c12f9a8", "metadata": {}, "source": [ "We can do the fit by simply providing the list of RDKit molecule objects" ] }, { "cell_type": "code", "execution_count": 7, "id": "634ca919", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.542129Z", "iopub.status.busy": "2025-05-08T16:22:37.541844Z", "iopub.status.idle": "2025-05-08T16:22:37.609556Z", "shell.execute_reply": "2025-05-08T16:22:37.608271Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score is :1.00\n", "Test score is :0.55\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } ], "source": [ "pipe.fit(mol_list_train, y_train)\n", "print(f\"Train score is :{pipe.score(mol_list_train,y_train):0.2F}\")\n", "print(f\"Test score is :{pipe.score(mol_list_test, y_test):0.2F}\")" ] }, { "cell_type": "markdown", "id": "8440cc5a", "metadata": {}, "source": [ "Nevermind the performance, or the exact value of the prediction, this is for demonstration purpures. We can easily predict on lists of molecules" ] }, { "cell_type": "code", "execution_count": 8, "id": "f4431aab", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.613501Z", "iopub.status.busy": "2025-05-08T16:22:37.613074Z", "iopub.status.idle": "2025-05-08T16:22:37.625937Z", "shell.execute_reply": "2025-05-08T16:22:37.624623Z" } }, "outputs": [ { "data": { "text/plain": [ "array([6.00400299])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe.predict([Chem.MolFromSmiles(\"c1ccccc1C(=O)[OH]\")])" ] }, { "cell_type": "markdown", "id": "a60e242b", "metadata": {}, "source": [ "We can also expand the already fitted pipeline, how about creating a pipeline that can predict directly from SMILES? With scikit-mol that is easy!" ] }, { "cell_type": "code", "execution_count": 9, "id": "a908097d", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.630016Z", "iopub.status.busy": "2025-05-08T16:22:37.629320Z", "iopub.status.idle": "2025-05-08T16:22:37.640274Z", "shell.execute_reply": "2025-05-08T16:22:37.639075Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline(steps=[('smiles_transformer', SmilesToMolTransformer()),\n", " ('pipe',\n", " Pipeline(steps=[('mol_transformer',\n", " MorganFingerprintTransformer()),\n", " ('Regressor', Ridge())]))])\n" ] } ], "source": [ "smiles_pipe = Pipeline(\n", " [(\"smiles_transformer\", SmilesToMolTransformer()), (\"pipe\", pipe)]\n", ")\n", "print(smiles_pipe)" ] }, { "cell_type": "code", "execution_count": 10, "id": "0124653c", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.643282Z", "iopub.status.busy": "2025-05-08T16:22:37.642781Z", "iopub.status.idle": "2025-05-08T16:22:37.655561Z", "shell.execute_reply": "2025-05-08T16:22:37.652513Z" } }, "outputs": [ { "data": { "text/plain": [ "array([6.00400299])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smiles_pipe.predict([\"c1ccccc1C(=O)[OH]\"])" ] }, { "cell_type": "markdown", "id": "069e2d01", "metadata": {}, "source": [ "From here, the pipelines could be pickled, and later loaded for easy prediction on RDKit molecule objects or SMILES in other scripts. The transformation with the MorganTransformer will be the same as during fitting, so no need to remember if radius 2 or 3 was used for this or that model, as it is already in the pipeline itself. If we need to see the parameters for a particular pipeline of model, we can always get the non default settings via print or all settings with .get_params()." ] }, { "cell_type": "code", "execution_count": 11, "id": "63c8ef60", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:37.659747Z", "iopub.status.busy": "2025-05-08T16:22:37.658755Z", "iopub.status.idle": "2025-05-08T16:22:37.669836Z", "shell.execute_reply": "2025-05-08T16:22:37.668406Z" } }, "outputs": [ { "data": { "text/plain": [ "{'memory': None,\n", " 'steps': [('smiles_transformer', SmilesToMolTransformer()),\n", " ('pipe',\n", " Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\n", " ('Regressor', Ridge())]))],\n", " 'transform_input': None,\n", " 'verbose': False,\n", " 'smiles_transformer': SmilesToMolTransformer(),\n", " 'pipe': Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\n", " ('Regressor', Ridge())]),\n", " 'smiles_transformer__n_jobs': None,\n", " 'smiles_transformer__safe_inference_mode': False,\n", " 'pipe__memory': None,\n", " 'pipe__steps': [('mol_transformer', MorganFingerprintTransformer()),\n", " ('Regressor', Ridge())],\n", " 'pipe__transform_input': None,\n", " 'pipe__verbose': False,\n", " 'pipe__mol_transformer': MorganFingerprintTransformer(),\n", " 'pipe__Regressor': Ridge(),\n", " 'pipe__mol_transformer__fpSize': 2048,\n", " 'pipe__mol_transformer__n_jobs': None,\n", " 'pipe__mol_transformer__radius': 2,\n", " 'pipe__mol_transformer__safe_inference_mode': False,\n", " 'pipe__mol_transformer__useBondTypes': True,\n", " 'pipe__mol_transformer__useChirality': False,\n", " 'pipe__mol_transformer__useCounts': False,\n", " 'pipe__mol_transformer__useFeatures': False,\n", " 'pipe__Regressor__alpha': 1.0,\n", " 'pipe__Regressor__copy_X': True,\n", " 'pipe__Regressor__fit_intercept': True,\n", " 'pipe__Regressor__max_iter': None,\n", " 'pipe__Regressor__positive': False,\n", " 'pipe__Regressor__random_state': None,\n", " 'pipe__Regressor__solver': 'auto',\n", " 'pipe__Regressor__tol': 0.0001}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smiles_pipe.get_params()" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/04_standardizer.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "095e3de9", "metadata": {}, "source": [ "# Molecule standardization\n", "When building machine learning models of molecules, it is important to standardize the molecules. We often don't want different predictions just because things are drawn in slightly different forms, such as protonated or deprotanted carboxylic acids. Scikit-mol provides a very basic standardize transformer based on the molvs implementation in RDKit" ] }, { "cell_type": "code", "execution_count": 1, "id": "d40bdabe", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:39.191239Z", "iopub.status.busy": "2025-05-08T16:22:39.189891Z", "iopub.status.idle": "2025-05-08T16:22:40.514182Z", "shell.execute_reply": "2025-05-08T16:22:40.512920Z" } }, "outputs": [], "source": [ "from rdkit import Chem\n", "from scikit_mol.standardizer import Standardizer\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import Ridge" ] }, { "cell_type": "markdown", "id": "1f739296", "metadata": {}, "source": [ "For demonstration let's create some molecules with different protonation states. The two first molecules are Benzoic acid and Sodium benzoate." ] }, { "cell_type": "code", "execution_count": 2, "id": "5a45dfd5", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:40.518530Z", "iopub.status.busy": "2025-05-08T16:22:40.517654Z", "iopub.status.idle": "2025-05-08T16:22:40.537037Z", "shell.execute_reply": "2025-05-08T16:22:40.535847Z" } }, "outputs": [ { "data": { "text/plain": [ "array([], dtype=object)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([], dtype=object)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "smiles_strings = (\n", " \"c1ccccc1C(=O)[OH]\",\n", " \"c1ccccc1C(=O)[O-].[Na+]\",\n", " \"CC[NH+](C)C\",\n", " \"CC[N+](C)(C)C\",\n", " \"[O-]CC(C(=O)[O-])C[NH+](C)C\",\n", " \"[O-]CC(C(=O)[O-])C[N+](C)(C)C\",\n", ")\n", "\n", "smi2mol = SmilesToMolTransformer()\n", "\n", "mols = smi2mol.transform(smiles_strings)\n", "for mol in mols[0:2]:\n", " display(mol)" ] }, { "cell_type": "markdown", "id": "1974e56a", "metadata": {}, "source": [ "We can simply use the transformer directly and get a list of standardized molecules" ] }, { "cell_type": "code", "execution_count": 3, "id": "d13141c6", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:40.540032Z", "iopub.status.busy": "2025-05-08T16:22:40.539703Z", "iopub.status.idle": "2025-05-08T16:22:40.560979Z", "shell.execute_reply": "2025-05-08T16:22:40.560007Z" } }, "outputs": [ { "data": { "text/plain": [ "array([['O=C(O)c1ccccc1'],\n", " ['O=C(O)c1ccccc1'],\n", " ['CCN(C)C'],\n", " ['CC[N+](C)(C)C'],\n", " ['CN(C)CC(CO)C(=O)O'],\n", " ['C[N+](C)(C)CC(CO)C(=O)[O-]']], dtype='\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " SMILES\n", "0 CN(C)(C)(C)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smiles_list_valid, X_errors = smileschecker.sanitize(list(data.SMILES))\n", "smileschecker.errors" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": "Python 3.9.4 ('rdkit')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/06_hyperparameter_tuning.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "f0b0cc54", "metadata": {}, "source": [ "# Full example: Hyperparameter tuning\n", "\n", "first some imports of the usual suspects: RDKit, pandas, matplotlib, numpy and sklearn. New kid on the block is scikit-mol" ] }, { "cell_type": "code", "execution_count": 1, "id": "51aa3d62", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:44.604531Z", "iopub.status.busy": "2025-05-08T16:22:44.604218Z", "iopub.status.idle": "2025-05-08T16:22:46.163842Z", "shell.execute_reply": "2025-05-08T16:22:46.162418Z" } }, "outputs": [], "source": [ "import os\n", "import rdkit\n", "from rdkit import Chem\n", "from rdkit.Chem import PandasTools\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from time import time\n", "import numpy as np\n", "from sklearn.pipeline import Pipeline, make_pipeline\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import train_test_split\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "from scikit_mol.conversions import SmilesToMolTransformer" ] }, { "cell_type": "markdown", "id": "e07990d0", "metadata": {}, "source": [ "We will need some data. There is a dataset with the SLC6A4 active compounds from ExcapeDB on Zenodo. The scikit-mol project uses a subset of this for testing, and the samples there has been specially selected to give good results in testing (it should therefore be used for any production modelling). If full_set is false, the fast subset will be used, and otherwise the full dataset will be downloaded if needed." ] }, { "cell_type": "code", "execution_count": 2, "id": "adbc1868", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.167928Z", "iopub.status.busy": "2025-05-08T16:22:46.166905Z", "iopub.status.idle": "2025-05-08T16:22:46.173404Z", "shell.execute_reply": "2025-05-08T16:22:46.172138Z" } }, "outputs": [], "source": [ "full_set = False\n", "\n", "if full_set:\n", " csv_file = \"SLC6A4_active_excape_export.csv\"\n", " if not os.path.exists(csv_file):\n", " import urllib.request\n", "\n", " url = \"https://ndownloader.figshare.com/files/25747817\"\n", " urllib.request.urlretrieve(url, csv_file)\n", "else:\n", " csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"" ] }, { "cell_type": "markdown", "id": "d2ce3c7f", "metadata": {}, "source": [ "The CSV data is loaded into a Pandas dataframe and the PandasTools utility from RDKit is used to add a column with RDKit molecules" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a283f12", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.177164Z", "iopub.status.busy": "2025-05-08T16:22:46.176440Z", "iopub.status.idle": "2025-05-08T16:22:46.233488Z", "shell.execute_reply": "2025-05-08T16:22:46.232374Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 out of 200 SMILES failed in conversion\n" ] } ], "source": [ "data = pd.read_csv(csv_file)\n", "\n", "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n", "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")" ] }, { "cell_type": "markdown", "id": "e245e989", "metadata": {}, "source": [ "We use the train_test_split to, well, split the dataframe's molecule columns and pXC50 column into lists for train and testing" ] }, { "cell_type": "code", "execution_count": 4, "id": "303b83de", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.236917Z", "iopub.status.busy": "2025-05-08T16:22:46.236251Z", "iopub.status.idle": "2025-05-08T16:22:46.243264Z", "shell.execute_reply": "2025-05-08T16:22:46.242175Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "mol_list_train, mol_list_test, y_train, y_test = train_test_split(\n", " data.ROMol, data.pXC50, random_state=42\n", ")" ] }, { "cell_type": "markdown", "id": "56247c3b", "metadata": {}, "source": [ "We will standardize the molecules before modelling. This is best done before the hyperparameter optimization of the featurization with the scikit-mol transformer and regression modelling, as the standardization is otherwise done for every loop in the hyperparameter optimization, which will make it take longer time." ] }, { "cell_type": "code", "execution_count": 5, "id": "1383d0fc", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.247777Z", "iopub.status.busy": "2025-05-08T16:22:46.246787Z", "iopub.status.idle": "2025-05-08T16:22:46.614634Z", "shell.execute_reply": "2025-05-08T16:22:46.613399Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } ], "source": [ "# Probably the recommended way would be to prestandardize the data if there's no changes to the transformer,\n", "# and then add the standardizer in the inference pipeline.\n", "\n", "from scikit_mol.standardizer import Standardizer\n", "\n", "standardizer = Standardizer()\n", "mol_list_std_train = standardizer.transform(mol_list_train)" ] }, { "cell_type": "markdown", "id": "0775d395", "metadata": {}, "source": [ "A simple pipeline with a MorganTransformer and a Ridge() regression for demonstration." ] }, { "cell_type": "code", "execution_count": 6, "id": "51c74711", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.618057Z", "iopub.status.busy": "2025-05-08T16:22:46.617207Z", "iopub.status.idle": "2025-05-08T16:22:46.622371Z", "shell.execute_reply": "2025-05-08T16:22:46.621207Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "moltransformer = MorganFingerprintTransformer()\n", "regressor = Ridge()\n", "\n", "optimization_pipe = make_pipeline(moltransformer, regressor)" ] }, { "cell_type": "markdown", "id": "8221a682", "metadata": {}, "source": [ "For hyperparameter optimization we import the RandomizedSearchCV class from Scikit-Learn. It will try different random combinations of settings and use internal cross-validation to find the best model. In the end, it will fit the best found parameters on the full set. We also import loguniform, to get a better sampling of some of the parameters." ] }, { "cell_type": "code", "execution_count": 7, "id": "4c6b833f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.625354Z", "iopub.status.busy": "2025-05-08T16:22:46.625058Z", "iopub.status.idle": "2025-05-08T16:22:46.629915Z", "shell.execute_reply": "2025-05-08T16:22:46.628829Z" }, "title": "Now hyperparameter tuning" }, "outputs": [], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "\n", "# from sklearn.utils.fixes import loguniform\n", "from scipy.stats import loguniform" ] }, { "cell_type": "markdown", "id": "6b9d4576", "metadata": {}, "source": [ "With the pipelines, getting the names of the parameters to tune is a bit more tricky, as they are concatenations of the name of the step and the parameter with double underscores in between. We can get the available parameters from the pipeline with the get_params() method, and select the parameters we want to change from there." ] }, { "cell_type": "code", "execution_count": 8, "id": "0af1003b", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.633716Z", "iopub.status.busy": "2025-05-08T16:22:46.632911Z", "iopub.status.idle": "2025-05-08T16:22:46.641844Z", "shell.execute_reply": "2025-05-08T16:22:46.640881Z" }, "title": "Which keys do we have?" }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['memory', 'steps', 'transform_input', 'verbose', 'morganfingerprinttransformer', 'ridge', 'morganfingerprinttransformer__fpSize', 'morganfingerprinttransformer__n_jobs', 'morganfingerprinttransformer__radius', 'morganfingerprinttransformer__safe_inference_mode', 'morganfingerprinttransformer__useBondTypes', 'morganfingerprinttransformer__useChirality', 'morganfingerprinttransformer__useCounts', 'morganfingerprinttransformer__useFeatures', 'ridge__alpha', 'ridge__copy_X', 'ridge__fit_intercept', 'ridge__max_iter', 'ridge__positive', 'ridge__random_state', 'ridge__solver', 'ridge__tol'])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optimization_pipe.get_params().keys()" ] }, { "cell_type": "markdown", "id": "cb0db6a5", "metadata": {}, "source": [ "We will tune the regularization strength of the Ridge regressor, and try out different parameters for the Morgan fingerprint, namely the number of bits, the radius of the fingerprint, wheter to use counts or bits and features." ] }, { "cell_type": "code", "execution_count": 9, "id": "c2d541b3", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.645249Z", "iopub.status.busy": "2025-05-08T16:22:46.644964Z", "iopub.status.idle": "2025-05-08T16:22:46.651276Z", "shell.execute_reply": "2025-05-08T16:22:46.650210Z" } }, "outputs": [], "source": [ "param_dist = {\n", " \"ridge__alpha\": loguniform(1e-2, 1e3),\n", " \"morganfingerprinttransformer__fpSize\": [256, 512, 1024, 2048, 4096],\n", " \"morganfingerprinttransformer__radius\": [1, 2, 3, 4],\n", " \"morganfingerprinttransformer__useCounts\": [True, False],\n", " \"morganfingerprinttransformer__useFeatures\": [True, False],\n", "}" ] }, { "cell_type": "markdown", "id": "2157d154", "metadata": { "lines_to_next_cell": 2 }, "source": [ "The report function was taken from [this example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py) from the scikit learn documentation." ] }, { "cell_type": "code", "execution_count": 10, "id": "f2c91783", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.655212Z", "iopub.status.busy": "2025-05-08T16:22:46.654913Z", "iopub.status.idle": "2025-05-08T16:22:46.662196Z", "shell.execute_reply": "2025-05-08T16:22:46.661226Z" }, "title": "From https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py" }, "outputs": [], "source": [ "# Utility function to report best scores\n", "def report(results, n_top=3):\n", " for i in range(1, n_top + 1):\n", " candidates = np.flatnonzero(results[\"rank_test_score\"] == i)\n", " for candidate in candidates:\n", " print(\"Model with rank: {0}\".format(i))\n", " print(\n", " \"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n", " results[\"mean_test_score\"][candidate],\n", " results[\"std_test_score\"][candidate],\n", " )\n", " )\n", " print(\"Parameters: {0}\".format(results[\"params\"][candidate]))\n", " print(\"\")" ] }, { "cell_type": "markdown", "id": "469691f4", "metadata": {}, "source": [ "We will do 25 tries of random parameter sets, and see what comes out as the best one. If you are using the small example dataset, this should take some second, but may take some minutes with the full set." ] }, { "cell_type": "code", "execution_count": 11, "id": "79a70a0f", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:46.665390Z", "iopub.status.busy": "2025-05-08T16:22:46.665100Z", "iopub.status.idle": "2025-05-08T16:22:49.120359Z", "shell.execute_reply": "2025-05-08T16:22:49.119269Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Runtime: 2.45 for 25 iterations)\n" ] } ], "source": [ "n_iter_search = 25\n", "random_search = RandomizedSearchCV(\n", " optimization_pipe, param_distributions=param_dist, n_iter=n_iter_search, cv=3\n", ")\n", "t0 = time()\n", "random_search.fit(mol_list_std_train, y_train.values)\n", "t1 = time()\n", "\n", "print(f\"Runtime: {t1-t0:0.2F} for {n_iter_search} iterations)\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "b6160cb3", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:49.124324Z", "iopub.status.busy": "2025-05-08T16:22:49.123579Z", "iopub.status.idle": "2025-05-08T16:22:49.130023Z", "shell.execute_reply": "2025-05-08T16:22:49.128965Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model with rank: 1\n", "Mean validation score: 0.459 (std: 0.117)\n", "Parameters: {'morganfingerprinttransformer__fpSize': 1024, 'morganfingerprinttransformer__radius': 3, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': True, 'ridge__alpha': np.float64(11.211371939288233)}\n", "\n", "Model with rank: 2\n", "Mean validation score: 0.427 (std: 0.130)\n", "Parameters: {'morganfingerprinttransformer__fpSize': 512, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(22.96332964984786)}\n", "\n", "Model with rank: 3\n", "Mean validation score: 0.426 (std: 0.166)\n", "Parameters: {'morganfingerprinttransformer__fpSize': 4096, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(23.874114087368742)}\n", "\n" ] } ], "source": [ "report(random_search.cv_results_)" ] }, { "cell_type": "markdown", "id": "9a2ea219", "metadata": {}, "source": [ "It can be interesting to see what combinations of hyperparameters gave good results for the cross-validation. Usually the number of bits are in the high end and radius is 2 to 4. But this can vary a bit, as we do a small number of tries for this demo. More extended search with more iterations could maybe find even better and more consistent. solutions" ] }, { "cell_type": "markdown", "id": "6cf91582", "metadata": {}, "source": [ "Let's see if standardization had any influence on this dataset. We build an inference pipeline that includes the standardization object and the best estimator, and run the best estimator directly on the list of test molecules" ] }, { "cell_type": "code", "execution_count": 13, "id": "4daaf106", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:49.133255Z", "iopub.status.busy": "2025-05-08T16:22:49.132589Z", "iopub.status.idle": "2025-05-08T16:22:49.294436Z", "shell.execute_reply": "2025-05-08T16:22:49.293304Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Standardization 0.4921\n", "With Standardization 0.4921\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } ], "source": [ "inference_pipe = make_pipeline(standardizer, random_search.best_estimator_)\n", "\n", "print(\n", " f\"No Standardization {random_search.best_estimator_.score(mol_list_test, y_test):0.4F}\"\n", ")\n", "print(f\"With Standardization {inference_pipe.score(mol_list_test, y_test):0.4F}\")" ] }, { "cell_type": "markdown", "id": "2d31c059", "metadata": { "lines_to_next_cell": 0, "title": "Building an inference pipeline, it appears our test-data was pretty standard" }, "source": [ "We see that the dataset already appeared to be in forms that are similar to the ones coming from the standardization.\n", "\n", "Interestingly the test-set performance often seem to be better than the CV performance during the hyperparameter search. This may be due to the model being refit at the end of the search to the whole training dataset, as the refit parameter on the randomized_search object by default is true. The final model is thus fitted on more data than the individual models during training.\n", "\n", "To demonstrate the effect of standartization we can see the difference if we challenge the predictor with different forms of benzoic acid and benzoates." ] }, { "cell_type": "code", "execution_count": 14, "id": "92105568", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:49.297625Z", "iopub.status.busy": "2025-05-08T16:22:49.297285Z", "iopub.status.idle": "2025-05-08T16:22:49.318086Z", "shell.execute_reply": "2025-05-08T16:22:49.316957Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions with no standardization: [6.36710496 6.49711427 6.49711427 6.28330625 6.72697401]\n", "Predictions with standardization: [6.36710496 6.36710496 6.36710496 6.36710496 6.36710496]\n" ] } ], "source": [ "# Intergrating the Standardizer and challenge it with some different forms and salts of benzoic acid\n", "smiles_list = [\n", " \"c1ccccc1C(=O)[OH]\",\n", " \"c1ccccc1C(=O)[O-]\",\n", " \"c1ccccc1C(=O)[O-].[Na+]\",\n", " \"c1ccccc1C(=O)[O][Na]\",\n", " \"c1ccccc1C(=O)[O-].C[N+](C)C\",\n", "]\n", "mols_list = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\n", "\n", "print(\n", " f\"Predictions with no standardization: {random_search.best_estimator_.predict(mols_list)}\"\n", ")\n", "print(f\"Predictions with standardization: {inference_pipe.predict(mols_list)}\")" ] }, { "cell_type": "markdown", "id": "9d196197", "metadata": {}, "source": [ "Without standardization we get variation in the predictions, but with the standardization object in place, we get the same results. If you want a model that gives different predictions for the different forms, either the standardization need to be removed or the settings changed.\n", "\n", "From here it should be easy to save the model using pickle, so that it can be loaded and used in other python projects. The pipeline carries both the standardization, the featurization and the prediction in one, easy to reuse object. If you want the model to be able to predict directly from SMILES strings, check out the SmilesToMol class, which is also available in Scikit-Mol :-)\n" ] }, { "cell_type": "markdown", "id": "824ebc99", "metadata": {}, "source": [] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": "Python 3.9.4 ('rdkit')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/07_parallel_transforms.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "id": "6f68fb8e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "87ed8373", "metadata": {}, "source": [ "# Parallel calculations of transforms\n", "\n", "Scikit-mol supports parallel calculations of fingerprints and descriptors. This feature can be activated and configured using the `n_jobs` parameter or the `.n_jobs` attribute after object instantiation.\n", "\n", "To begin, let's import the necessary libraries: RDKit and pandas. And of course, we'll also need to import scikit-mol, which is the new kid on the block." ] }, { "cell_type": "code", "execution_count": 1, "id": "dac6956a", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:38.302600Z", "iopub.status.busy": "2024-11-24T09:27:38.302116Z", "iopub.status.idle": "2024-11-24T09:27:39.171522Z", "shell.execute_reply": "2024-11-24T09:27:39.170882Z" } }, "outputs": [], "source": [ "import pathlib\n", "import time\n", "\n", "import pandas as pd\n", "from rdkit.Chem import PandasTools\n", "\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "from scikit_mol.descriptors import MolecularDescriptorTransformer\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer" ] }, { "cell_type": "markdown", "id": "7c2a81f2", "metadata": {}, "source": [ "## Obtaining the Data\n", "\n", "We'll need some data to work with, so we'll use a dataset of SLC6A4 active compounds from ExcapeDB that is available on Zenodo. Scikit-mol uses a subset of this dataset for testing purposes, and the samples have been specially selected to provide good results in testing. Note: This dataset should never be used for production modeling.\n", "\n", "In the code below, you can set full_set to True to download the full dataset. Otherwise, the smaller dataset will be used." ] }, { "cell_type": "code", "execution_count": 2, "id": "f64c418f", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:39.174368Z", "iopub.status.busy": "2024-11-24T09:27:39.174075Z", "iopub.status.idle": "2024-11-24T09:27:39.177863Z", "shell.execute_reply": "2024-11-24T09:27:39.177305Z" } }, "outputs": [], "source": [ "full_set = False\n", "\n", "if full_set:\n", " csv_file = \"SLC6A4_active_excape_export.csv\"\n", " if not pathlib.Path(csv_file).exists():\n", " import urllib.request\n", "\n", " url = \"https://ndownloader.figshare.com/files/25747817\"\n", " urllib.request.urlretrieve(url, csv_file)\n", "else:\n", " csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "0eabd800", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:39.180191Z", "iopub.status.busy": "2024-11-24T09:27:39.179937Z", "iopub.status.idle": "2024-11-24T09:27:39.221096Z", "shell.execute_reply": "2024-11-24T09:27:39.220386Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 out of 200 SMILES failed in conversion\n" ] } ], "source": [ "data = pd.read_csv(csv_file)\n", "\n", "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n", "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")" ] }, { "cell_type": "markdown", "id": "4144946e", "metadata": {}, "source": [ "## Evaluating the Impact of Parallelism on Transformations\n", "\n", "Let's start by creating a baseline for our calculations without using parallelism." ] }, { "cell_type": "code", "execution_count": 4, "id": "a7f66af7", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:39.223702Z", "iopub.status.busy": "2024-11-24T09:27:39.223459Z", "iopub.status.idle": "2024-11-24T09:27:39.228461Z", "shell.execute_reply": "2024-11-24T09:27:39.227977Z" }, "title": "A demonstration of the speedup that can be had for the descriptor transformer" }, "outputs": [], "source": [ "transformer = MolecularDescriptorTransformer(n_jobs=1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "a03bc824", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:39.230911Z", "iopub.status.busy": "2024-11-24T09:27:39.230692Z", "iopub.status.idle": "2024-11-24T09:27:41.368180Z", "shell.execute_reply": "2024-11-24T09:27:41.367438Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/scikit-mol/.venv/lib/python3.9/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Calculation time on dataset of size 200 with parallel=1:\t2.10 seconds\n" ] } ], "source": [ "def test_transformer(transformer):\n", " t0 = time.time()\n", " transformer.transform(data.ROMol)\n", " t = time.time() - t0\n", " print(\n", " f\"Calculation time on dataset of size {len(data)} with n_jobs={transformer.n_jobs}:\\t{t:0.2F} seconds\"\n", " )\n", "\n", "\n", "test_transformer(transformer)" ] }, { "cell_type": "markdown", "id": "d304d675", "metadata": {}, "source": [ "\n", "Let's see if parallelism can help us speed up our transformations." ] }, { "cell_type": "code", "execution_count": 8, "id": "c80388e6", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:41.370886Z", "iopub.status.busy": "2024-11-24T09:27:41.370638Z", "iopub.status.idle": "2024-11-24T09:27:42.384085Z", "shell.execute_reply": "2024-11-24T09:27:42.383188Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/scikit-mol/.venv/lib/python3.9/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Calculation time on dataset of size 200 with parallel=2:\t2.19 seconds\n" ] } ], "source": [ "transformer = MolecularDescriptorTransformer(n_jobs=2)\n", "test_transformer(transformer)" ] }, { "cell_type": "markdown", "id": "731bd13a", "metadata": {}, "source": [ "We've seen that parallelism can help speed up our transformations, with the degree of speedup depending on the number of CPU cores available. However, it's worth noting that there may be some overhead associated with the process of splitting the dataset, pickling objects and functions, and passing them to the parallel child processes. As a result, it may not always be worthwhile to use parallelism, particularly for smaller datasets or certain types of fingerprints.\n", "\n", "It's also worth noting that there are different methods for creating the child processes, with the default method on Linux being 'fork', while on Mac and Windows it's 'spawn'. The code we're using has been tested on Linux using the 'fork' method.\n", "\n", "Now, let's see how parallelism impacts another type of transformer." ] }, { "cell_type": "code", "execution_count": 11, "id": "ef6d2b0c", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:42.387160Z", "iopub.status.busy": "2024-11-24T09:27:42.386886Z", "iopub.status.idle": "2024-11-24T09:27:42.484867Z", "shell.execute_reply": "2024-11-24T09:27:42.484222Z" }, "lines_to_next_cell": 2, "title": "Some of the benchmarking plots" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculation time on dataset of size 200 with parallel=1:\t0.03 seconds\n", "Calculation time on dataset of size 200 with parallel=2:\t0.08 seconds\n" ] } ], "source": [ "transformer = MorganFingerprintTransformer(n_jobs=1)\n", "test_transformer(transformer)\n", "transformer.n_jobs = 2\n", "test_transformer(transformer)" ] }, { "cell_type": "markdown", "id": "2aac85b1", "metadata": {}, "source": [ "Interestingly, we observed that parallelism actually took longer to calculate the fingerprints in some cases, which is a perfect illustration of the overhead issue associated with parallelism. Generally, the faster the fingerprint calculation in itself, the larger the dataset needs to be for parallelism to be worthwhile. For example, the Descriptor transformer, which is one of the slowest, can benefit even for smaller datasets, while for faster fingerprint types like Morgan, Atompairs, and Topological Torsion fingerprints, the dataset needs to be larger.\n", "\n", "![ Relation ship between throughput and parallel speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/max_speedup_vs_throughput.png \"Not all fingerprints are equally fast and benefit the same from parallelism\")\n", "\n", "We've also included a series of plots below, showing the speedup over serial for different numbers of cores used for different dataset sizes. These timings were taken on a 16 core machine (32 Hyperthreads). Only the largest datasets (>10,000 samples) would make it worthwhile to parallelize Morgan, Atompairs, and Topological Torsions. SECfingerprint, MACCS keys, and RDKitFP are intermediate and would benefit from parallelism when the dataset size is larger, say >500. Descriptors, on the other hand, benefit almost immediately even for the smallest datasets (>100 samples).\n", "\n", "![Atompairs fingerprint](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/AtomPairFingerprintTransformer_par.png \"Atompairs fingerprint speedup\")\n", "\n", "![Descriptors calculation speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/Desc2DTransformer_par.png \"Descriptors calculation speedup\")\n", "\n", "![MACCS keys speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MACCSTransformer_par.png \"MACCS keys speedup\")\n", "\n", "![Morgan fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MorganTransformer_par.png \"Morgan fingerprint speedup\")\n", "\n", "![RDKit fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/RDKitFPTransformer_par.png \"RDKit fingerprint speedup\")\n", "\n", "![SEC fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/SECFingerprintTransformer_par.png \"SEC fingerprint speedup\")\n", "\n", "![TopologicalTorsion fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/TopologicalTorsionFingerprintTransformer_par.png \"TopologicalTorsion fingerprint speedup\")\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "0913a9f4", "metadata": {}, "source": [ "## Performance heatmaps\n", "\n", "Multiprocessing performance is highly dependent on CPU performance, type of the function and the size of the dataset. To help users understand the performance of their system, we have created a series of heatmaps showing the speedup of different transformers for different dataset sizes and number of cores. The heatmaps are based on the same data as the plots above.\n", "If you what to test the performance of your system, you can run the code below." ] }, { "cell_type": "code", "execution_count": 12, "id": "0b353728", "metadata": {}, "outputs": [], "source": [ "from rdkit import Chem\n", "\n", "from scikit_mol.fingerprints import (\n", " AtomPairFingerprintTransformer,\n", " AvalonFingerprintTransformer,\n", " MHFingerprintTransformer,\n", " MorganFingerprintTransformer,\n", " RDKitFingerprintTransformer,\n", " TopologicalTorsionFingerprintTransformer,\n", ")\n", "from scikit_mol.plotting import ParallelTester, plot_heatmap\n", "from scikit_mol.standardizer import Standardizer\n", "\n", "mols = [Chem.MolFromSmiles(\"CCCCCCCCBr\")] * 100000\n", "transformers = [\n", " Standardizer(),\n", " MorganFingerprintTransformer(),\n", " MolecularDescriptorTransformer(),\n", " MHFingerprintTransformer(),\n", " AtomPairFingerprintTransformer(),\n", " AvalonFingerprintTransformer(),\n", " RDKitFingerprintTransformer(),\n", " TopologicalTorsionFingerprintTransformer(),\n", "]" ] }, { "cell_type": "markdown", "id": "47991a4f", "metadata": {}, "source": [ "`ParallelTester` accept the following parameters:\n", "- `transformer` - the transformer to test\n", "- `mols` - the dataset to test\n", "- `n_mols` - the number of molecules to test on (the largest number should be less than or equal to the number of molecules in `mols`)\n", "- `n_cores` - the number of cores to test on (the largest number should be less than or equal to the number of cores in your system)\n", "- `backend` - the backend to use for multiprocessing (default is `loky`, see [joblib documentation](https://joblib.readthedocs.io/en/latest/parallel.html) for more options)" ] }, { "cell_type": "code", "execution_count": 13, "id": "fa00f0eb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 10 100 100 1000 10000 100000\n", "1 0.002394 0.003997 0.003997 0.031515 0.479826 3.228737\n", "2 0.012707 1.278691 1.278691 1.246285 1.507089 3.614131\n", "4 1.238397 1.330802 1.330802 1.20337 1.389253 2.891467\n", "8 1.657188 1.669887 1.669887 1.601371 1.635777 2.541485" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transformer = MorganFingerprintTransformer()\n", "df = ParallelTester(transformer, mols).test()\n", "df" ] }, { "cell_type": "markdown", "id": "71725395", "metadata": {}, "source": [ "Resulting df have one row for each `n_jobs` and one row for each `n_mols`. Results can be plotted as a heatmap using the `plot_heatmap` method." ] }, { "cell_type": "code", "execution_count": 14, "id": "6e352da5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_heatmap(df, \"Morgan Fingerprint\")" ] }, { "cell_type": "markdown", "id": "0972c254", "metadata": {}, "source": [ "By default the time is normalized to the time of the serial calculation. Such as a value of 2 means that the calculation took twice as long as the serial calculation and values below 1 means that the calculation was faster than the serial calculation.\n", "If you want to see the absolute time, you can set `normalize=False`." ] }, { "cell_type": "code", "execution_count": 15, "id": "c99487eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_heatmap(df, \"Morgan Fingerprint\", normalize=False)" ] }, { "cell_type": "markdown", "id": "a98d12af", "metadata": {}, "source": [ "Below is example heatmaps for the native `scikit-mol` transformers." ] }, { "cell_type": "code", "execution_count": 16, "id": "6111704c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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H0aJFMXLkSMTHx3+qS/kkvL29MXDgwPyuBlGBxdXT9J/o6+sjODgYtWvXlqT//fffePr0KeRyeT7VTLPExETo6Hx+3wLBwcG4du0ahg8fnt9VoQJuwYIFePz4cZZ5unbtimbNmknSatSoIXk/duxYTJ8+HR06dMCwYcNw48YNLFiwANevX8e+fftyvd754cWLF7h06RImTZqU31UhKrA+v9+YVKA0a9YMmzZtwvz58yUdsODgYHh7eyMyMjIfa5e19x+rR0BCQgIMDQ3zuxqUS8LDwzFp0iSMHTs2y8c6VqpUCT169Mj08xcvXmD27Nno2bMnfv/9d2V6mTJlMGTIEOzatQstW7bM1brnh71790JfXx/169fP76oQFVgcnqb/pGvXroiKisKBAweUaSkpKdi8eTO6deum9piZM2eiZs2asLKygoGBAby9vVXm4a1atQoymQwrV66UpE+ZMgUymQx//fVXlvUKDQ2Fn58frK2tYWBgAGdnZ5VnB384p3HChAmQyWS4d+8e/P39YW5uDjMzMwQEBCAhIUFybGJiIoYOHQpra2uYmJigVatWePbsWbbnSe7duxc+Pj4wMjKCiYkJmjdvjuvXr2s8rm7dutizZw8ePXqkHEr8cA6XQqHAzz//jGLFikFfXx8NGjTAvXv3VMrx8PDAhQsXUKdOHRgaGmLcuHEAgOTkZAQFBaFUqVKQy+UoXrw4xowZg+TkZEkZq1atQv369WFrawu5XA53d3csWbJEpc5CCEyePBnFihWDoaEh6tWrl+m1xsTEYPjw4ShevDjkcjlKlSqFadOmQaFQqOTz9/eHmZkZzM3N0atXL8TExGhsP+DdkO3EiRNRunRp6Ovrw8rKCrVr15bcw/7+/jA2NsaDBw/g5+cHIyMjFClSBJMmTcKHu5QpFArMnTsX5cqVg76+Puzs7NC/f39ER0ernDu7X/ft27fDw8MD+vr68PDwwLZt27J1be/77rvv4OrqmmWHMEN8fDxSUlLUfnb69GmkpaWhS5cukvSM9+qGst+XMQw+c+ZMLFq0CCVLloShoSEaN26MJ0+eQAiBn376CcWKFYOBgQFat26N169fq5SzePFilCtXDnK5HEWKFMGgQYOy9TXfsGEDvL29YWJiAlNTU5QvXx7z5s1Tybdnzx7Uq1cPBgYGAIC7d++iffv2sLe3h76+PooVK4YuXbogNjZW4zmJCi1B9BFWrVolAIjz58+LmjVrip49eyo/2759u9DS0hLPnj0TTk5Oonnz5pJjixUrJgYOHCgWLlwoZs+eLapWrSoAiN27d0vytWjRQpiZmYnHjx8LIYS4cuWK0NPTE19//XWWdXv16pWwsLAQZcqUETNmzBC//fab+P7770XZsmUl+QCIoKAg5fugoCABQFSsWFG0a9dOLF68WPTp00cAEGPGjJEc26lTJwFA9OzZUyxatEh06tRJeHp6qpSZ0U4PHz5Upv3+++9CJpOJJk2aiAULFohp06aJEiVKCHNzc0k+dfbv3y+8vLyEtbW1+OOPP8Qff/whtm3bJoQQ4siRI8r6e3t7izlz5ogJEyYIQ0NDUbVqVUk5vr6+wt7eXtjY2IghQ4aIZcuWie3bt4v09HTRuHFjYWhoKIYPHy6WLVsmBg8eLHR0dETr1q0lZVSpUkX4+/uLOXPmiAULFojGjRsLAGLhwoWSfD/88IMAIJo1ayYWLlwoevfuLYoUKSKsra1Fr169lPni4+NFhQoVhJWVlRg3bpxYunSp+Oqrr4RMJhPDhg1T5lMoFKJOnTpCS0tLDBw4UCxYsEDUr19fVKhQQQAQq1atyrINx40bJ2Qymejbt6/47bffxKxZs0TXrl3FL7/8oszTq1cvoa+vL0qXLi169uwpFi5cKFq0aCEAiB9//FFSXp8+fYSOjo7o27evWLp0qRg7dqwwMjISVapUESkpKcp82f2679u3T2hpaQkPDw8xe/Zs8f333wszMzNRrlw54eTklOW1ZTh79qzQ0tISp06dEg8fPhQAxIwZMyR5MtKNjY0FACGTyUTlypXFvn37JPmCg4MFAHH48GFJenx8vAAgXF1ds6xLxnm8vLyEu7u7mD17tvjhhx+Enp6eqF69uhg3bpyoWbOmmD9/vhg6dKiQyWQiICBAUkbG92bDhg3FggULxODBg4W2trZKG/fq1UvSRvv37xcARIMGDcSiRYvEokWLxODBg0XHjh0l5aekpAhTU1PlvZucnCycnZ1FkSJFxOTJk8Xy5cvFxIkTRZUqVURYWFjWjU9UiLHTSB/l/U7jwoULhYmJiUhISBBCCNGxY0dRr149IYRQ22nMyJchJSVFeHh4iPr160vSX7x4ISwtLUWjRo1EcnKyqFixonB0dBSxsbFZ1m3btm3KumUls05j7969Jfnatm0rrKyslO8vXLggAIjhw4dL8vn7+2vsNL59+1aYm5uLvn37So59+fKlMDMzU0lXp3nz5mo7DxmdxrJly4rk5GRl+rx58wQAcfXqVWWar6+vACCWLl0qKeOPP/4QWlpa4vjx45L0pUuXCgDi5MmTyrQPv45CCOHn5ydKliypfB8eHi709PRE8+bNhUKhUKaPGzdOAJB0Gn/66SdhZGQk7ty5Iynzu+++E9ra2so/HrZv3y4AiOnTpyvzpKWlCR8fn2x1Gj09PVXuyQ/16tVLABBDhgxRpikUCtG8eXOhp6cnIiIihBBCHD9+XAAQ69atkxwfEhIiSc/J193Ly0s4ODiImJgYZVpG5yc7nUaFQiGqVq0qunbtKoQQmXYaHz16JBo3biyWLFkidu7cKebOnSscHR2FlpaW5A+4jPv9p59+UnuNxsbGWdYn4/w2NjaSawoMDBQAhKenp0hNTVWmd+3aVejp6YmkpCQhxL/3UOPGjUV6eroy38KFCwUAsXLlSmXah53GYcOGCVNTU5GWlpZlHQ8dOiT5Pr106ZIAIDZt2pTlcURfGg5P03/WqVMnJCYmYvfu3Xj79i12796d6dA0AOXwDwBER0cjNjYWPj4+uHjxoiSfvb09Fi1ahAMHDsDHxweXL1/GypUrYWpqmmV9zM3NAQC7d+9Gampqjq/nm2++kbz38fFBVFQU3rx5AwAICQkBAJVVlkOGDNFY9oEDBxATE4OuXbsiMjJS+dLW1ka1atVw5MiRHNf3QwEBAdDT05PUHwAePHggySeXyxEQECBJ27RpE8qWLQs3NzdJ/TLmeb1fv/e/jrGxsYiMjISvry8ePHigHMI7ePAgUlJSMGTIEMhkMmV+dYt4Nm3aBB8fH1hYWEjO3bBhQ6Snp+PYsWMAgL/++gs6OjoYMGCA8lhtbe1stT/w7v64fv067t69qzHv4MGDlf+WyWQYPHgwUlJScPDgQWWdzczM0KhRI0mdvb29YWxsrGyv7H7dX7x4gcuXL6NXr14wMzNTnrtRo0Zwd3fP1vWtXr0aV69exbRp07LM5+joiH379uGbb75By5YtMWzYMFy6dAk2Njb49ttvlfkqVaqEatWqYdq0aVi1ahXCwsKwd+9e9O/fH7q6ukhMTMxWvTp27Ci5pmrVqgEAevToIZkPXa1aNaSkpODZs2cA/r2Hhg8fDi2tf39l9e3bF6amptizZ0+m5zQ3N0d8fLxk6oE6f/31F9zd3ZVTPTLquW/fPpWpKURfMi6Eof/MxsYGDRs2RHBwMBISEpCeno4OHTpkmn/37t2YPHkyLl++LJkn936nIkOXLl2wdu1a7NmzB/369UODBg001sfX1xft27fHxIkTMWfOHNStWxdt2rRBt27dsrWa29HRUfLewsICwLsOrqmpKR49egQtLS04OztL8pUqVUpj2Rkdlcwm22d0iBMTE1XmTtnb22ssX1P931e0aFFJ5zKjfjdv3oSNjY3assPDw5X/PnnyJIKCgnD69GmVX6yxsbEwMzPDo0ePAAClS5eWfG5jY6Os1/vnvnLlisZzP3r0CA4ODjA2NpZ87urqqva4D02aNAmtW7dGmTJl4OHhgSZNmqBnz56oUKGCJJ+WlhZKliwpSStTpgwAKPfdvHv3LmJjY2Fra5tlnbP7dc+svYB31/fhH1YfevPmDQIDAzF69GgUL148y7zqWFpaIiAgAL/88guePn2KYsWKAQC2bNmCzp07K+cFa2trY+TIkfj7779x+/btbJX94X2Z0TH7sJ4Z6Rn3a0abfPj11dPTQ8mSJZWfqzNw4ED8+eefaNq0KYoWLYrGjRujU6dOaNKkiSTfnj17JIt5nJ2dMXLkSMyePRvr1q2Dj48PWrVqhR49ekg6vkRfGnYaKVd069YNffv2xcuXL9G0aVNltO9Dx48fR6tWrVCnTh0sXrwYDg4O0NXVxapVqxAcHKySPyoqCqGhoQCAGzduQKFQSKIN6mRscH3mzBns2rUL+/btQ+/evTFr1iycOXNGpbPxIW1tbbXpIhce056xoOOPP/5Q2wnMiLhs3LhRJQqY3fNnt/7vRwrfr1/58uUxe/ZstWVk/IK/f/8+GjRoADc3N8yePRvFixeHnp4e/vrrL8yZM0dl4Up2KBQKNGrUCGPGjFH7eUaH7b+qU6cO7t+/jx07dmD//v1Yvnw55syZg6VLl6JPnz45KkuhUMDW1hbr1q1T+3lGBzi7X/f/aubMmUhJSUHnzp2VHdunT58CeNcJCwsLQ5EiRVT+WHhfxtf49evXyk5j0aJFceLECdy9excvX75E6dKlYW9vjyJFimT765LZfZmX32+2tra4fPky9u3bh71792Lv3r1YtWoVvvrqK6xZswYA8PDhQ9y6dUtlEdesWbPg7++vvE+GDh2KqVOn4syZM8p2IfrSsNNIuaJt27bo378/zpw5g40bN2aab8uWLdDX18e+ffskUb9Vq1apzT9o0CC8ffsWU6dORWBgIObOnYuRI0dmq07Vq1dH9erV8fPPPyM4OBjdu3fHhg0bctwx+JCTkxMUCgUePnwoiQh9uEJZHRcXFwDvfpk1bNgw03x+fn6ZDqmpi8jmFhcXF/zzzz9o0KBBlufZtWsXkpOTsXPnTkkE6cPhdScnJwDvIm3vR+0iIiJUIp8uLi6Ii4vLsl0yyjx06BDi4uIkfwBkN+IF/BtRCwgIQFxcHOrUqYMJEyZI7g2FQoEHDx5IOkV37twBAOUwpouLCw4ePIhatWqp7YS/f22A5q/7++31oexc3+PHjxEdHY1y5cqpfDZlyhRMmTIFly5dgpeXV6ZlZExjUBfxLV26tPKev3HjBl68eJGrT/VRJ6NNbt++LbmHUlJS8PDhQ433i56eHlq2bImWLVtCoVBg4MCBWLZsGX788UeUKlUKe/bsgZmZmcpeswBQvnx5lC9fHj/88ANOnTqFWrVqYenSpZg8eXLuXiTRZ4JzGilXGBsbY8mSJZgwYUKWe7Zpa2tDJpMhPT1dmRYWFobt27er5N28eTM2btyIX375Bd999x26dOmCH374QfmLOzPR0dEqUYqMX5IfbhvzMfz8/AC82wLkfQsWLMjWsaamppgyZYra+ZYREREAAAcHBzRs2FDyymBkZJRn23506tQJz549w2+//abyWWJiovIJIBnRoffbOTY2VqXz37BhQ+jq6mLBggWSvOqeaNOpUyecPn1a7WbRMTExSEtLA/Bub9C0tDRJZCg9PT1b7Q+8i16/z9jYGKVKlVJ7byxcuFD5byEEFi5cCF1dXeU0iU6dOiE9PR0//fSTyrFpaWnKLWFy8nX38vLCmjVrJF/jAwcO4MaNGxqvbejQodi2bZvktWzZMgDvthHatm2bclpFxjnf9+zZM6xcuRIVKlSAg4NDpudRKBQYM2YMDA0NVeYA57aGDRtCT08P8+fPl9xDK1asQGxsLJo3b57psR9+rbW0tJTTEDK+3n/99RcaN24sifa+efNGeb9lKF++PLS0tHLlZwjR54qRRso1vXr10pinefPmmD17Npo0aYJu3bohPDwcixYtQqlSpXDlyhVlvvDwcAwYMAD16tVTLkZYuHAhjhw5An9/f5w4cSLTYeo1a9Zg8eLFaNu2LVxcXPD27Vv89ttvMDU1VXnyxcfw9vZG+/btMXfuXERFRaF69er4+++/lZ3ZrCJ0pqamWLJkCXr27IlKlSqhS5cusLGxwePHj7Fnzx7UqlVL0lHJ7PwbN27EyJEjUaVKFRgbG+fa5so9e/bEn3/+iW+++QZHjhxBrVq1kJ6ejlu3buHPP//Evn37ULlyZTRu3FgZwenfvz/i4uLw22+/wdbWFi9evFCWZ2Njg1GjRmHq1Klo0aIFmjVrhkuXLmHv3r2wtraWnHv06NHYuXMnWrRoAX9/f3h7eyM+Ph5Xr17F5s2bERYWBmtra7Rs2RK1atXCd999h7CwMLi7u2Pr1q3Z7ki7u7ujbt268Pb2hqWlJUJDQ7F582bJohfg3ebvISEh6NWrF6pVq4a9e/diz549GDdunDIK5+vri/79+2Pq1Km4fPkyGjduDF1dXdy9exebNm3CvHnz0KFDhxx93adOnYrmzZujdu3a6N27N16/fo0FCxagXLlyiIuLy/LaKlWqhEqVKknSMoapy5UrhzZt2ijTx4wZo5xmUKRIEYSFhWHZsmWIj49X2cdw2LBhSEpKgpeXF1JTUxEcHIxz585hzZo1KnMVc5uNjQ0CAwMxceJENGnSBK1atcLt27exePFiVKlSJct9KPv06YPXr1+jfv36KFasGB49eoQFCxbAy8sLZcuWRWJiIo4cOYKlS5dKjjt8+DAGDx6Mjh07okyZMkhLS8Mff/wBbW1ttG/fPk+vl6hAy7d12/RZe3/Lnayo23JnxYoVonTp0kIulws3NzexatUq5XY3Gdq1aydMTExU9kTbsWOHACCmTZuW6TkvXrwounbtKhwdHYVcLhe2traiRYsWIjQ0VJIPmWy5k7GdyofX+v5eevHx8WLQoEHC0tJSGBsbizZt2ojbt28LAJL9/tQdK8S77XH8/PyEmZmZ0NfXFy4uLsLf31+ljurExcWJbt26CXNzc8k2LBlb7ny4TUjGlifvb0Xj6+srypUrp7b8lJQUMW3aNFGuXDkhl8uFhYWF8Pb2FhMnTpRsd7Rz505RoUIFoa+vL0qUKCGmTZsmVq5cqXK96enpYuLEicLBwUEYGBiIunXrimvXrgknJyfJljtCvNuaJjAwUJQqVUro6ekJa2trUbNmTTFz5kzJfnxRUVGiZ8+ewtTUVJiZmYmePXsqt0nRtOXO5MmTRdWqVYW5ubkwMDAQbm5u4ueff1bZ78/IyEjcv39fuW+lnZ2dCAoKkmz7kuHXX38V3t7ewsDAQJiYmIjy5cuLMWPGiOfPn0vyZffrvmXLFlG2bFkhl8uFu7u72Lp1q8p2MtmV2ZY7wcHBok6dOsLGxkbo6OgIa2tr0bZtW3HhwgWVMlatWiU8PT2FkZGRMDExEQ0aNFDZtzGn58/sfs3sZ8vChQuFm5ub0NXVFXZ2dmLAgAEiOjpakufDNtq8ebNo3LixsLW1FXp6esLR0VH0799fvHjxQgghxO7du4VMJhOvXr2SlPPgwQPRu3dv4eLiIvT19YWlpaWoV6+eOHjwYLaumaiwkgmRC7ONiQiXL19GxYoVsXbtWnTv3j2/q0P/gb+/PzZv3qwxskeft4EDByI0NBTnzp3L76oQfRY4PE30ERITE1UWPsydOxdaWlqoU6dOPtWKiHLCy8urUDw3m+hTYaeR6CNMnz4dFy5cQL169aCjo6PczqNfv34ftT8eEX16/fr1y+8qEH1W2Gkk+gg1a9bEgQMH8NNPPyEuLg6Ojo6YMGECvv/++/yuGhERUZ7gnEYiIiIi0oj7NBIRERGRRuw0EhEREZFG7DQSEeUSf39/5SMGs5NX03PQC4oJEybk6eMrvwQZbRgZGakxb4kSJfL88YxEH4OdxgLi/v376N+/P0qWLAl9fX2YmpqiVq1amDdvHhITE5X5SpQoAZlMpnzZ2trCx8cH27Ztk5RXokQJtGjRQu25QkNDIZPJsHr16v9c7zFjxkAmk6Fz585qPw8LC1PWNbPntXbv3h0ymUzlF2jdunWVx2ppacHU1BSurq7o2bNnps9lVsff31/SZnK5HGXKlMH48eORlJSU/YstoO7du4cOHTrAwsIChoaGqF27tsozoLOrb9++kMlkau+duLg4DB8+HMWKFYNcLkfZsmUlj/J7X0xMDPr16wcbGxsYGRmhXr16uHjxotq8O3fuRKVKlaCvrw9HR0cEBQWpPMJN0y9cdfd7XFwcgoKC4OHhASMjI1hZWcHLywvDhg3D8+fPs9Mc/1lCQgImTJiAo0eP5nrZ739/yGQyGBgYoEKFCpg7dy4UCkWun+9zsWvXLrRs2RJ2dnbQ09ODpaUl6tSpg1mzZuHNmzf5XT2izxpXTxcAe/bsQceOHSGXy/HVV1/Bw8MDKSkpOHHiBEaPHo3r16/j119/Veb38vLCt99+CwB4/vw5li1bhnbt2mHJkiV5/hzY9wkhsH79epQoUQK7du3C27dvYWJiojavvr4+1q9fjx9++EGSHh8fjx07dkBfX1/tccWKFcPUqVOVee/du4etW7di7dq16NSpE9auXQtdXV2NdZXL5Vi+fDmAd89I3rFjB3766Sfcv38f69aty8llFyhPnjxBjRo1oK2tjdGjR8PIyAirVq1C48aNcejQoRztGRkaGorVq1er/Vqkp6fDz88PoaGhGDRoEEqXLo19+/Zh4MCBiI6Oxrhx45R5FQoFmjdvjn/++QejR4+GtbU1Fi9ejLp16+LChQsoXbq0Mu/evXvRpk0b1K1bFwsWLMDVq1cxefJkhIeHZ9ohzY7U1FTUqVMHt27dQq9evTBkyBDExcXh+vXrCA4ORtu2bVGkSJGPLj8zv/32m6TDlpCQgIkTJwJ418nLbe9/f0RGRiI4OBgjRoxAREQEfv7551w/X0GmUCjw9ddfY/Xq1ShfvjwGDhyI4sWL4+3btzh9+jR++OEH/PXXXzh06FB+V1Wj27dvZ/qYVKJ8la/PoyHx4MEDYWxsLNzc3FQeOSaEEHfv3hVz585Vvlf3WL4XL14IIyMjUaZMmSzzZTh//ny2HremyeHDhwUAcfjwYaGrqytWr16tkifjEWLt2rUTAMTly5cln69bt07o6uqKli1bCiMjI8lnmT3qLi0tTQwcOFAAEGPGjNFYz4xHwr1PoVCI6tWrC5lMJl6+fJmdyy2QBg4cKHR0dMStW7eUafHx8aJ48eKiUqVK2S5HoVCIGjVqiN69e6u9d/78808BQKxYsUKS3r59e6Gvry95DNvGjRtVHg8XHh4uzM3NRdeuXSXHu7u7C09PT5GamqpM+/7774VMJhM3b95UpmX2iMcMH9Y5o77r1q1TyZuYmCh5HGJeioiIUHlcZQZ192VOqPv+SExMFE5OTsLExESkpaV9dNkf+vAxnwXR1KlTBQAxYsQIoVAoVD5//vy55BGf6qSnp4vExMQ8qZ+me5joc8A/ZfLZ9OnTERcXhxUrVsDBwUHl81KlSmHYsGFZlmFvb4+yZcvi4cOHH12P1NRU3Lp1Cy9evMj2MevWrYO7uzvq1auHhg0bZhmxq1GjBpydnREcHKxSRpMmTWBpaZnt82pra2P+/Plwd3fHwoULERsbm+1jM8hkMtSuXRtCCDx48AAA4OvrC09PT7X5XV1d4efnB0B1WPD91/tD/jExMRg+fDiKFy8OuVyOUqVKYdq0aZJIVMbw/cyZM/Hrr7/CxcUFcrkcVapUwfnz5zVex/Hjx1GxYkW4uroq0wwNDdGqVStcvHgRd+/ezVZ7/PHHH7h27Vqm0anjx48DALp06SJJ79KlC5KSkrBjxw5l2ubNm2FnZ4d27dop02xsbNCpUyfs2LEDycnJAIAbN27gxo0b6NevH3R0/h30GDhwIIQQ2Lx5c7bqrs79+/cBALVq1VL5LGP6R2ZiYmKU91iGyMhIaGlpwcrKCuK9XcoGDBgAe3t75fv35zSGhYXBxsYGADBx4kTlPTJhwgTJ+Z49e4Y2bdrA2NgYNjY2GDVqFNLT03N8zRnXVqVKFbx9+xbh4eHK9CtXrsDf3185/cXe3h69e/dGVFSUShknTpxAlSpVoK+vDxcXFyxbtizT861duxbe3t4wMDCApaUlunTpgidPnqjk27RpkzKftbU1evTogWfPnknyZMzx/Jj2SEhIwLRp01CuXDnMmDFD7fxLBwcHjB07VpImk8kwePBgrFu3DuXKlYNcLkdISAgAYObMmahZsyasrKxgYGAAb29vtffk+2W4urpCX18f3t7eOHbsmNq6xsTEwN/fH+bm5jAzM0NAQAASEhIkedTNaYyJicGIESNQokQJyOVyFCtWDF999ZVkysaCBQtQrlw5GBoawsLCApUrV1b5mUv0X7DTmM927dqFkiVLombNmh9dRmpqKp48eQIrK6uPLuPZs2coW7YsAgMDs5U/OTkZW7ZsQdeuXQEAXbt2xeHDh/Hy5ctMj+natSs2bNig/KUbGRmJ/fv3o1u3bjmur7a2Nrp27YqEhAScOHEix8cD736pA4CFhQUAoGfPnrhy5QquXbsmyXf+/HncuXMHPXr0AAB8//33+OOPPySvjA6lra0tgHe/xHx9fbF27Vp89dVXmD9/PmrVqoXAwECMHDlSpS7BwcGYMWMG+vfvj8mTJyMsLAzt2rVDampqlteQnJys8jhD4F3HEQAuXLigsR3evn2LsWPHYty4cZIO0Ifn0dbWhp6ensbzXLp0CZUqVVIZXqtatSoSEhJw584dZT4AqFy5siRfkSJFUKxYMeXn73v9+jUiIyNVXh/O4XNycgIA/P7775JOXnaYm5vDw8ND8kv/xIkTkMlkeP36NW7cuKFMP378OHx8fNSWY2Njoxxib9u2rfJeeb8znTHsb2VlhZkzZ8LX1xezZs2STEfJqYw/RMzNzZVpBw4cwIMHDxAQEIAFCxagS5cu2LBhA5o1ayZpn6tXr6Jx48YIDw/HhAkTEBAQgKCgIJU50wDw888/46uvvkLp0qUxe/ZsDB8+XDklIiYmRplv9erV6NSpE7S1tTF16lT07dsXW7duRe3atSX5/kt7nDhxAjExMejatSu0tbVz1F6HDx/GiBEj0LlzZ8ybN0/Z6Z83bx4qVqyISZMmYcqUKdDR0UHHjh2xZ88elTL+/vtvDB8+HD169MCkSZMQFRWFJk2aqPwsAYBOnTrh7du3mDp1Kjp16oTVq1crpzBkJi4uDj4+PliwYAEaN26MefPm4ZtvvsGtW7fw9OlTAO+mRgwdOhTu7u6YO3cuJk6cCC8vL5w9ezZH7UGUpfwMc37pYmNjBQDRunXrbB/j5OQkGjduLCIiIkRERIT4559/RJcuXQQAMWTIEEm+nAxPZwwj9+rVK1v12Lx5swAg7t69K4QQ4s2bN0JfX1/MmTNHki+j3BkzZohr164JAOL48eNCCCEWLVokjI2NRXx8vNqhusyGpzNs27ZNABDz5s3Lsq4ZZWe02b1798TMmTOFTCYTHh4eyqGsmJgYoa+vL8aOHSs5fujQocLIyEjExcWpLf/kyZNCV1dX9O7dW5n2008/CSMjI3Hnzh1J3u+++05oa2uLx48fS9rHyspKvH79Wplvx44dAoDYtWtXltfWsmVLYW5uLt68eSNJr1GjhgAgZs6cmeXxQggxatQo4ezsLJKSkoQQ6u+dWbNmSb52718PANGiRQtlmpGRkaQtMuzZs0cAECEhIUIIIWbMmCEAKNvifVWqVBHVq1dXvs8Y2svq9X6dExIShKurqwAgnJychL+/v1ixYoVkGD0rgwYNEnZ2dsr3I0eOFHXq1BG2trZiyZIlQgghoqKihEwmk9x/vXr1Ek5OTsr3moanAYhJkyZJ0itWrCi8vb011tHX11e4ubkp7+tbt26J0aNHq7SFEO/a40Pr168XAMSxY8eUaW3atBH6+vri0aNHyrQbN24IbW1tyfB0WFiY0NbWFj///LOkzKtXrwodHR1lekpKirC1tRUeHh6SYd/du3cLAGL8+PG50h7z5s0TAMT27dsl6Wlpacr2yXi9P3QNQGhpaYnr16+rlPlhm6WkpAgPDw9Rv359SXrG/RcaGqpMe/TokdDX1xdt27ZVpmXcwx9+b7Rt21ZYWVlJ0pycnCQ/i8ePHy8AiK1bt6rUM+N6WrduneXPS6LcwEhjPspYyZfZ4pHM7N+/HzY2NrCxsYGnpyc2bdqEnj17Ytq0aR9dlxIlSkAIke0V1evWrUPlypVRqlQpAO+uoXnz5lkOUZcrVw4VKlTA+vXrAbyLrrVu3VoZrcqpjNXWb9++1Zg3Pj5e2WalSpXCqFGjUKtWLezYsUM5lGVmZobWrVtj/fr1yuhLeno6Nm7ciDZt2sDIyEil3JcvX6JDhw7w8vLC4sWLlembNm2Cj48PLCwsJBGxhg0bIj09XWXoqnPnzsqIJwBl9Cpj6DwzAwYMQExMDDp37oxLly7hzp07GD58OEJDQwFAsvJenTt37mDevHmYMWMG5HJ5pvm6desGMzMz9O7dGwcOHEBYWBh+/fVX5TW/f57ExES1ZWUssMnIm/H/zPKqq/uWLVtw4MABlZednZ0kn4GBAc6ePYvRo0cDeBft+vrrr+Hg4IAhQ4Yoh8gz4+Pjg1evXuH27dsA3kUU69SpAx8fH+VQ/YkTJyCEyDTSmF0fLl7z8fHR+HXPcOvWLeV97ebmhhkzZqBVq1Yq38fvR6OTkpIQGRmJ6tWrA4ByVXt6ejr27duHNm3awNHRUZm/bNmyykh6hq1bt0KhUKBTp06S+9ve3h6lS5dWrt4PDQ1FeHg4Bg4cKFlg1bx5c7i5uamN2n1Me2T8LP1wB4arV68q2yfj9eGQvK+vL9zd3VXKfL/NoqOjERsbCx8fH7W7ANSoUQPe3t7K946OjmjdujX27dunMrSu7vqioqKyXNm9ZcsWeHp6om3btiqfZfz8Mjc3x9OnT7M1rYXoY7HTmI8y5lVlp9PzvmrVquHAgQM4ePAgTp06hcjISPz+++9qhymz8rH7rsXExOCvv/6Cr68v7t27p3zVqlULoaGhyuFHdbp164ZNmzbh3r17OHXq1EcNTWeIi4sDkL1Ot76+vrKDsWrVKpQtWxbh4eEqbfbVV1/h8ePHyo7BwYMH8erVK/Ts2VOlzLS0NHTq1Anp6enYunWrpPNz9+5dhISEqPzCatiwIQBI5psBkPySBv4dMo+Ojs7yupo2bYoFCxbg2LFjqFSpElxdXbFnzx7l3ERN+wAOGzYMNWvWRPv27bPMZ29vj507dyI5ORmNGzeGs7MzRo8ejQULFqicx8DAQG2nLGN7o4w2z/h/ZnnV3c916tRBw4YNVV7qVnybmZlh+vTpCAsLQ1hYGFasWAFXV1csXLgQP/30U5bXm9ERPH78OOLj43Hp0iX4+PigTp06ynvj+PHjMDU1zXQebHbo6+sr5z1msLCw0Ph1z1CiRAkcOHAA+/btw+LFi1G0aFFERESotMfr168xbNgw2NnZwcDAADY2NnB2dgYA5ZzgiIgIJCYmSla3Z3h/zizw7v4WQqB06dIq9/jNmzeV9/ejR4/UHg8Abm5uys//a3tk/AzI+JmQoVSpUsrve3XfwwCU7fCh3bt3o3r16tDX14elpaVyuoG6OdTq2qxMmTJISEhARESEJP1jvtfv378PDw+PTD8HgLFjx8LY2BhVq1ZF6dKlMWjQIJw8eTLLY4hyilvu5CNTU1MUKVJE7byXrFhbWys7H5nJLFIDQDnpOrNtbjTZtGkTkpOTMWvWLMyaNUvl83Xr1mU6R6dr164IDAxE3759YWVlhcaNG39UHQAo2y0j2pkVbW1tSZv5+fnBzc0N/fv3x86dOyXpdnZ2WLt2LerUqYO1a9fC3t5ebXuPHj0ap0+fxsGDB1GsWDHJZwqFAo0aNcKYMWPU1qdMmTIq9VNHZGM+3uDBgxEQEIArV65AT08PXl5eWLFihdrzvO/w4cMICQnB1q1blfM7gXed4cTERISFhcHS0lL5x02dOnXw4MEDXL16FfHx8fD09FTud/j+eRwcHNQuqMpIy9jqJmPh14sXL1C8eHGVvFWrVtV47dnl5OSE3r17o23btihZsiTWrVuX6b6hGXV0dnbGsWPHlFH4GjVqwMbGBsOGDcOjR49w/Phx1KxZ8z9tjZLT+XcfMjIyktybtWrVQqVKlTBu3DjJQp5OnTrh1KlTGD16NLy8vGBsbAyFQoEmTZp81J6OCoUCMpkMe/fuVXsNH7tp+ce2h5ubG4B3PxNat24tqUdG+2Q291ndHyfHjx9Hq1atUKdOHSxevBgODg7Q1dXFqlWr/vPCkv/yvZ6VsmXL4vbt29i9ezdCQkKwZcsWLF68GOPHj9c4Z5Iou9hpzGctWrTAr7/+itOnT6NGjRq5Vq6Tk5Nkwv77MobcMhYL5NS6devg4eGBoKAglc+WLVuG4ODgTH9IOTo6olatWjh69CgGDBggWTWbE+np6QgODlZuZp1TDg4OGDFiBCZOnIgzZ84oh+q0tbXRrVs3rF69GtOmTcP27dvRt29flR/0GzZswNy5czF37lz4+vqqlO/i4oK4uDiNnfvcYmRkJLl/Dh48CAMDA7WrhzM8fvwYACQLMzI8e/YMzs7OmDNnDoYPH65M19bWhpeXl+Q8ACTX6eXlhePHj0OhUEg6VGfPnoWhoaGyg5lRTmhoqKSD+Pz5czx9+hT9+vXLxpXnjIWFBVxcXLL1h5qPjw+OHTsGZ2dneHl5wcTEBJ6enjAzM0NISAguXryo8Zfxp36KSoUKFdCjRw8sW7YMo0aNgqOjI6Kjo3Ho0CFMnDgR48ePV+b9cGW9jY0NDAwM1K64z/iZkcHFxQVCCDg7O2f5h0nGz5jbt2+jfv36KmV+7M+gD/n4+MDMzAwbNmxAYGDgf97jcMuWLdDX18e+ffskIwirVq1Sm19dm925cweGhoYqkdOPkd171sjICJ07d0bnzp2RkpKCdu3a4eeff0ZgYOBHBwmI3sfh6Xw2ZswYGBkZoU+fPnj16pXK5/fv38e8efNyXG6zZs3w9OlTbN++XZKenJyM5cuXw9bWFpUqVVKmZ3fLnSdPnuDYsWPo1KkTOnTooPIKCAjAvXv3slyxN3nyZAQFBWHIkCE5vi7gXYdx6NChuHnzJoYOHZrl9ilZGTJkCAwNDfHLL79I0nv27Ino6Gj0798fcXFxylXTGa5du4Y+ffqgR48emW6H1KlTJ5w+fRr79u1T+SwmJkbliSe56dSpU9i6dSu+/vprmJmZZZqvfv362LZtm8rLxsYGlStXxrZt29CyZctMj4+IiMC0adNQoUIFSaexQ4cOePXqFbZu3apMi4yMxKZNm9CyZUvlL+Fy5crBzc0Nv/76q2Te15IlSyCTydChQ4ePboN//vlH7dNjHj16hBs3bqgdLv2Qj48PwsLCsHHjRuVwtZaWFmrWrInZs2cjNTVV43zGjPm6H64SzktjxoxBamoqZs+eDeDfyNaHkay5c+dK3mtra8PPzw/bt29X/kEBADdv3lS5j9u1awdtbW1MnDhRpVwhhHLeYOXKlWFra4ulS5dKpiHs3bsXN2/eRPPmzf/bxf6foaEhxowZg2vXruG7775TG7XLSSRPW1sbMplMcl+GhYWp/DzNcPr0aclcxydPnmDHjh1o3Ljxf44mA0D79u3xzz//qF3FnnFdH87V1NPTg7u7O4QQGndhIMouRhrzmYuLC4KDg9G5c2eULVtW8kSYU6dOYdOmTR/1DNJ+/fph5cqV6NixI3r37o2KFSsiKioKGzduxLVr1/D7779Ltk/J2HKnV69eWS6GCQ4OhhACrVq1Uvt5s2bNoKOjg3Xr1qFatWpq8/j6+qqNzqkTGxuLtWvXAng3rJ7xRJj79++jS5cuGuemZcXKygoBAQFYvHgxbt68ibJlywIAKlasCA8PD2zatAlly5aVdK4BICAgAACUw9fvq1mzJkqWLInRo0dj586daNGiBfz9/eHt7Y34+HhcvXoVmzdvRlhYGKytrT+67hkePXqETp06oVWrVrC3t8f169exdOlSVKhQAVOmTMnyWEdHR5X5VQAwfPhw2NnZoU2bNpJ0X19f1KhRA6VKlcLLly/x66+/Ii4uDrt375ZEdjp06IDq1asjICAAN27cUD4RJj09XSUyl7Fwo3HjxujSpQuuXbuGhQsXok+fPsqvx8c4cOAAgoKC0KpVK1SvXh3GxsZ48OABVq5cieTkZJW9EtXJ6BDevn1b0pZ16tTB3r17lftpZsXAwADu7u7YuHEjypQpA0tLS3h4eGicn/ZfuLu7o1mzZli+fDl+/PFHWFlZoU6dOpg+fTpSU1NRtGhR7N+/X+2+rhMnTkRISAh8fHwwcOBApKWlKff+u3LlijKfi4sLJk+ejMDAQISFhaFNmzYwMTHBw4cPsW3bNvTr1w+jRo2Crq4upk2bhoCAAPj6+qJr16549eqVcmubESNG5Np1f/fdd7h58yZmzJiB/fv3o3379ihWrBiio6Nx8eJFbNq0Cba2ttmKuDVv3hyzZ89GkyZN0K1bN4SHh2PRokUoVaqUpB0yeHh4wM/PD0OHDoVcLlcuEMutYeHRo0dj8+bNyp/n3t7eeP36NXbu3ImlS5fC09MTjRs3hr29PWrVqgU7OzvcvHkTCxcuRPPmzXO82JIoU59+wTapc+fOHdG3b19RokQJoaenJ0xMTEStWrXEggULlFuhCJH1Vjofio6OFiNGjBDOzs5CV1dXmJqainr16om9e/eq5M3uljvly5cXjo6OWeapW7eusLW1FampqZItd7KS2ZY7eG9LFWNjY1G6dGnRo0cPsX///izL01R2hvv37wttbW2V654+fboAIKZMmaJyjJOTU6bbvry/jdHbt29FYGCgKFWqlNDT0xPW1taiZs2aYubMmSIlJUUIIbJsH2SyVcv7Xr9+LVq3bi3s7e2Fnp6ecHZ2FmPHjlXZgicnMrvHRowYIUqWLCnkcrmwsbER3bp1E/fv38+0Xl9//bWwsrIShoaGwtfXV5w/f15t3m3btgkvLy8hl8tFsWLFxA8//KBsnww5fSLMgwcPxPjx40X16tWFra2t0NHRETY2NqJ58+bi8OHD2W0KYWtrKwBItuo5ceKEACB8fHxU8n+45Y4QQpw6dUp4e3sLPT09ydc0s/syu09fyWpLqqNHj0rO9fTpU9G2bVthbm4uzMzMRMeOHcXz58/V3mN///23sr4lS5YUS5cuzbROW7ZsEbVr1xZGRkbCyMhIuLm5iUGDBonbt29L8m3cuFFUrFhRyOVyYWlpKbp37y6ePn0qyfNf2yPDtm3bRLNmzYSNjY3Q0dER5ubmonbt2mLGjBkiJiZGkheAGDRokNpyVqxYIUqXLi3kcrlwc3MTq1atUluXjDLWrl2rzF+xYkVx5MgRtdfx4T28atUqAUA8fPhQmfbhljtCvNviafDgwaJo0aJCT09PFCtWTPTq1UtERkYKIYRYtmyZqFOnjrCyshJyuVy4uLiI0aNHf7KnH9GXQSbEf5x9S1QIzZs3DyNGjEBYWJjaaBwREfBu3uqgQYOwcOHC/K4KUZ7jnEaiDwghsGLFCvj6+rLDSERE9H+c00j0f/Hx8di5cyeOHDmCq1evSp6nTERE9KVjpJHo/yIiIpSbj48bNy7TxT5ERER5ZerUqahSpQpMTExga2uLNm3aqGx7pc6mTZvg5uYGfX19lC9fHn/99ZfkcyEExo8fDwcHBxgYGKBhw4Zqt4vKCjuNRP+XsYlzdHS08okqRERZEUJwPiPlqr///huDBg3CmTNncODAAaSmpqJx48aIj4/P9JhTp06ha9eu+Prrr3Hp0iW0adMGbdq0kezvOX36dMyfPx9Lly7F2bNnYWRkBD8/P+XTurKDC2GIiIiICqiIiAjY2tri77//Rp06ddTm6dy5M+Lj47F7925lWvXq1eHl5YWlS5dCCIEiRYrg22+/xahRowC829LOzs4Oq1evRpcuXbJVF0YaiYiIiAqojOedW1paZprn9OnTKk8g8/Pzw+nTpwEADx8+xMuXLyV5zMzMUK1aNWWe7OBCGCIiIqI8lJycLHkqEgDI5XLJYyrVUSgUGD58OGrVqpXlQwFevnwJOzs7SZqdnR1evnyp/DwjLbM82VEoO405GZ8nIiKigiE/n5E9R6t9npUdO768yhOCgoKCND6datCgQbh27RpOnDiRZ3XLiULZaSQiIiIqKAIDAzFy5EhJmqYo4+DBg7F7924cO3YMxYoVyzKvvb09Xr16JUl79eoV7O3tlZ9npDk4OEjyeHl5ZfcyOKeRiIiISEumlWcvuVwOU1NTySuzTqMQAoMHD8a2bdtw+PBhODs7a6x7jRo1cOjQIUnagQMHUKNGDQCAs7Mz7O3tJXnevHmDs2fPKvNkByONRERE9MXTksnyuwoA3g1JBwcHY8eOHTAxMVHOOTQzM4OBgQEA4KuvvkLRokUxdepUAMCwYcPg6+uLWbNmoXnz5tiwYQNCQ0Px66+/Anj3uMvhw4dj8uTJKF26NJydnfHjjz+iSJEiaNOmTbbrxk4jERERUQGxZMkSAEDdunUl6atWrYK/vz8A4PHjx9DS+newuGbNmggODsYPP/yAcePGoXTp0ti+fbtk8cyYMWMQHx+Pfv36ISYmBrVr10ZISEiO5pEWyn0auRCGiIjo85OfC2EW6WZvr8KPMSh1Q56V/SlxTiMRERERacThaSIiIvriacsYR9OELUREREREGjHSSERERF+8grJ6uiBjpJGIiIiINGKkkYiIiL54WpzTqBE7jURERPTFY6dRM7YQEREREWnESCMRERF98bgQRjNGGomIiIhII0YaiYiI6IvHOY2asYWIiIiISCNGGomIiOiLx8cIasYWIiIiIiKNGGkkIiKiLx7nNGrGTiMRERF98bjljmbsVhMRERGRRow0EhER0RePw9OasYWIiIiISCNGGomIiOiLx0ijZmwhIiIiItKIkUYiIiL64nH1tGaMNBIRERGRRow0EhER0RePjxHUjJ1GIiIi+uJxIYxmbCEiIiIi0oiRRiIiIvricSGMZow0EhEREZFGjDQSERHRF49zGjVjCxERERGRRow0EhER0RePkUbN2EJEREREpBEjjURERPTF4+ppzdhpJCIioi8eh6c1YwsRERERkUaMNBIREdEXj8+e1owtREREREQaMdJIREREXzwuhNGMkUYiIiIi0oiRRiIiIvricfW0ZmwhIiIiItKIkUYiIiL64sm0GUfThJ1GIiIi+uKx06gZW4iIiIiINGKkkYiIiL54Mh3G0TRhCxERERGRRuw05oILFy5gyJAhaNiwITw9PXH48GGNx5w/fx6dO3dG5cqV0aJFC+zYsUMlz4YNG9C0aVNUqVIF3bt3x9WrV/Oi+gUG2zH3sC1zD9syd7Adcw/bMm/IdLTy7FVYFJ4ryUeJiYlwdXVFYGBgtvI/ffoUgwcPRpUqVfDnn3+ie/fumDhxIk6ePKnMExISgpkzZ6J///7YsGEDXF1dMWDAAERFReXVZeQ7tmPuYVvmHrZl7mA75h62ZeF37NgxtGzZEkWKFIFMJsP27duzzO/v7w+ZTKbyKleunDLPhAkTVD53c3PLUb04pzEX1K5dG7Vr1852/k2bNqFo0aIYNWoUAKBkyZK4dOkS1q5di1q1agEA/vjjD7Rr1w5t2rQBAPzwww84duwYtm/fjq+//jrXr6EgYDvmHrZl7mFb5g62Y+5hW+aNgrR6Oj4+Hp6enujduzfatWunMf+8efPwyy+/KN+npaXB09MTHTt2lOQrV64cDh48qHyvo5OzbmDBaaEvyJUrV1C9enVJWs2aNXHlyhUAQGpqKm7evCnJo6WlherVqyvzENsxN7Etcw/bMnewHXMP2/Lz07RpU0yePBlt27bNVn4zMzPY29srX6GhoYiOjkZAQIAkn46OjiSftbV1jupVoDuNT548Qe/evbPMk5ycjDdv3kheycnJn6iGHycyMhJWVlaSNCsrK8TFxSEpKQnR0dFIT09XmycyMvJTVrVAYzvmHrZl7mFb5g62Y+5hW2ZPXs5p/NR9lRUrVqBhw4ZwcnKSpN+9exdFihRByZIl0b17dzx+/DhH5RboTuPr16+xZs2aLPNMnToVZmZmkteMGTM+UQ2JiIioMJBpy/Lspa6vMnXq1Dy5jufPn2Pv3r3o06ePJL1atWpYvXo1QkJCsGTJEjx8+BA+Pj54+/ZttsvO1zmNO3fuzPLzBw8eaCwjMDAQI0eOlKQJIf5TvfKatbW1yuTiqKgoGBsbQ19fH9ra2tDW1labJ6eh5MKM7Zh72Ja5h22ZO9iOuYdtmf/U9VXkcnmenGvNmjUwNzdXzk/N0LRpU+W/K1SogGrVqsHJyQl//vlntuet5munsU2bNpDJZFl28mQyWZZlyOVylYZPSkrKlfrllQoVKuDEiROStDNnzqBChQoAAF1dXZQtWxZnz55F/fr1AQAKhQJnz55Fly5dPnl9Cyq2Y+5hW+YetmXuYDvmHrZl9uTl1jjq+ip5QQiBlStXomfPntDT08syr7m5OcqUKYN79+5lu/x8HZ52cHDA1q1boVAo1L4uXryYn9XLtoSEBNy6dQu3bt0CADx79gy3bt3CixcvALxb1fT9998r83fs2BFPnz7FnDlz8PDhQ2zcuBH79+9Hjx49lHl69uyJrVu3YufOnXjw4AEmT56MxMRElb8cChO2Y+5hW+YetmXuYDvmHrYlZebvv//GvXv3shU5jIuLw/379+Hg4JDt8vM10ujt7Y0LFy6gdevWaj/XFIUsKK5fvy6ZOzBz5kwAQKtWrfDTTz8hMjISL1++VH5erFgxLFy4EDNmzMC6detgZ2eHoKAg5dYHANCkSRNER0dj8eLFiIyMhKurKxYvXqwyUbkwYTvmHrZl7mFb5g62Y+5hW+aNgrQJd1xcnCQC+PDhQ1y+fBmWlpZwdHREYGAgnj17ht9//11y3IoVK1CtWjV4eHiolDlq1Ci0bNkSTk5OeP78OYKCgqCtrY2uXbtmu14ykY+9suPHjyM+Ph5NmjRR+3l8fDxCQ0Ph6+ubo3IL+vA0ERERqdLX18+3c1+sNTvPyq50cqTmTO85evQo6tWrp5Leq1cvrF69Gv7+/ggLC8PRo0eVn8XGxsLBwQHz5s1D3759VY7t0qULjh07hqioKNjY2KB27dr4+eef4eLiku165WunMa+w00hERPT5yc9O46U6c/Os7IrHhudZ2Z9SwYnFEhEREVGBxccIEhER0RevIM1pLKjYaSQiIqIvXkF69nRBxRYiIiIiIo0YaSQiIqIvHoenNWMLEREREZFGjDQSERHRF4+RRs3YQkRERESkESONRERE9MXj6mnN2EJEREREpBEjjURERPTF45xGzdhCRERERKQRI41ERET0xWOkUTN2GomIiOiLx4UwmrGFiIiIiEgjRhqJiIjoi8fhac3YQkRERESkESONRERE9MWTacvyuwoFHiONRERERKQRI41ERET0xeOcRs3YQkRERESkESONRERE9MVjpFEzdhqJiIjoi8fNvTVjCxERERGRRow0EhER0RePw9OasYWIiIiISCNGGomIiOiLxzmNmrGFiIiIiEgjRhqJiIjoi8c5jZqxhYiIiIhII0YaiYiI6Isn02IcTRN2GomIiIi0ZfldgwKP3WoiIiIi0oiRRiIiIvricXhaM7YQEREREWnESCMRERF98bi5t2ZsISIiIiLSiJFGIiIi+uJxTqNmbCEiIiIi0oiRRiIiIiLu06gRO41ERET0xePwtGZsISIiIiLSiJFGIiIi+uJxyx3N2EJEREREpBEjjURERPTF45xGzdhCRERERKQRI41ERERE3HJHI0YaiYiIiAqQY8eOoWXLlihSpAhkMhm2b9+eZf6jR49CJpOpvF6+fCnJt2jRIpQoUQL6+vqoVq0azp07l6N6sdNIREREXzyZllaevXIqPj4enp6eWLRoUY6Ou337Nl68eKF82draKj/buHEjRo4ciaCgIFy8eBGenp7w8/NDeHh4tsvn8DQRERF98QrSljtNmzZF06ZNc3ycra0tzM3N1X42e/Zs9O3bFwEBAQCApUuXYs+ePVi5ciW+++67bJVfcFqIiIiIqBBKTk7GmzdvJK/k5ORcP4+XlxccHBzQqFEjnDx5UpmekpKCCxcuoGHDhso0LS0tNGzYEKdPn852+YUy0rjEsHt+V4FIxcC3a/O7CoXCjqfj87sKhUZaemp+V6HQSE5Nyu8qFAoBnkvz7dwyrbxbCDN16lRMnDhRkhYUFIQJEybkSvkODg5YunQpKleujOTkZCxfvhx169bF2bNnUalSJURGRiI9PR12dnaS4+zs7HDr1q1sn6dQdhqJiIiICorAwECMHDlSkiaXy3OtfFdXV7i6uirf16xZE/fv38ecOXPwxx9/5Np52GkkIiIiysM5jXK5PFc7idlRtWpVnDhxAgBgbW0NbW1tvHr1SpLn1atXsLe3z3aZnNNIREREVMhcvnwZDg4OAAA9PT14e3vj0KFDys8VCgUOHTqEGjVqZLtMRhqJiIjoi1eQHiMYFxeHe/fuKd8/fPgQly9fhqWlJRwdHREYGIhnz57h999/BwDMnTsXzs7OKFeuHJKSkrB8+XIcPnwY+/fvV5YxcuRI9OrVC5UrV0bVqlUxd+5cxMfHK1dTZwc7jUREREQFSGhoKOrVq6d8nzEfslevXli9ejVevHiBx48fKz9PSUnBt99+i2fPnsHQ0BAVKlTAwYMHJWV07twZERERGD9+PF6+fAkvLy+EhISoLI7JikwIIXLh+gqUOVrt87sKRCq4ejp3cPV07uHq6dzD1dO5Iz9XT0esPp9nZdv4V8mzsj8lRhqJiIjoi5eXW+4UFgVnAJ+IiIiICixGGomIiIgK0EKYgootREREREQaMdJIREREXzxZHm7uXViwhYiIiIhII0YaiYiI6ItXkDb3LqjYQkRERESkESONRERE9MWTaXOfRk3YaSQiIiLi8LRGbCEiIiIi0oiRRiIiIvriccsdzdhCRERERKQRI41ERET0xZNpcSGMJow0EhEREZFGjDQSERHRF49zGjVjCxERERGRRow0EhEREXFOo0bsNBIREdEXTyZjp1ETDk8TERERkUaMNBIRERFxeFojRhqJiIiISCNGGomIiOiLx829NWOkkYiIiIg0YqSRiIiIiKunNWKkkYiIiIg0YqSRiIiIvnic06gZI41EREREpBEjjUREREQMo2nETiMRERF98fgYQc3YryYiIiIijRhpJCIiIuJCGI0YaSQiIiIijRhpJCIiImKkUSNGGomIiIhII0YaiYiI6IvH1dOaMdJIRERERBox0khERETEMJpG7DQSERHRF4/PntaM/WoiIiIi0oiRRiIiIiIuhNGIkUYiIiIi0oiRRiIiIvricU6jZow0EhEREZFGjDQSERERMdKoETuN/1GVsW1Re2oPXJy3G3+PWAW5hTFqTOwMp0aeMHW0RkLEG9zfcQ6nftyAlDcJmZYzQrFFbfqxMb/jwswdyvfOzSqh2o8dYVPBCWlJqXj69w3sajct168rP1QP6oQaQZ0laa9vPcMa96Fq82vpaKNKYDu4f1UXxkUtEX37OY5/9wce7bssyec5sAm8R7WGkb05Iv4Jw5GhK/Dq/L28uowCIfTCBaz+fQ1u3ryJiMgIzJ01G/Xr1c/ymPOh5zFj9izcv38f9nb26NenD1q3aq38fOOmP/Hnpk14/uI5AMClpAv69+sHn1q18/RaCoKzex7ixLZ7iItOhr2zKZr3K49iZSzU5r146DG2zbssSdPR1ULQlhbK91vnXsKlw08keUpVtEGviTVyve4Fzbm/HuHU9oeIi0mGfQkTNO3jjqJlzNXmvXz4KXYsuCpJ09bVwg9/+qnNv3vJNVzY/wR+vd1QvaVzble9QLkQ8hRndz1BfEwKbJ2M0Kh3GRQpZao275WjL/DX4luSNG1dLYxe56t8f/tsBC4deIaXD94iKS4NAdMrw66ESZ5eA31+2Gn8D+wqu6B8v0aI+CdMmWZcxALGDpY4Pvp3RN14AlMnGzRY0h/GDpbY3WlmpmUtc/ha8r5E04povHwg7m05o0wr1a46Gv36DU5+H4zHh69CS0cb1h6OuX5d+Sny2mNsaTRR+V6Rlp5p3pqTu6Js9zo40G8pom89g5OfF1ptHYMNtb5HxOWHAIAynWqizix/HBqwDC/P3kWl4S3QLuRHrHYbgsSIN3l+PfklMSkRrmXKoG3rNhgxaqTG/E+fPcOgoUPQqUNH/DJ5Cs6eO4cJP02CtbUNatWsCQCws7XD8KFD4ejoCCGAnbt2YtiI4fhz/QaUcimV15eUb64ef4a9K66j1cAKKFbGAqd3PsCaoDMYtqQ+jM3lao+RG+pg2JJ/O+nq4helK9mi7TAv5Xsd3cI/W+jaiRfYv+ommn/jgWJlzHBm1yOsnXQegxfWgVEWbTl4YZ1/EzIJBt088xJP78TAxFJ9OYXJzVOvcPj3e/Dr64oipU1xfs8TbPz5H/SbWw1GZnpqj5EbaKPvvGrK97IPGjI1OR3F3MxQtoYt9i67naf1L6j4GEHNCv9PqTyia6SPpmuH42C/pUiKjlOmR11/gt0dZ+DB7lDEPniFJ0eu4eQPwXBuWRky7cybO+FVjOTl0qoqnhy5htiHrwAAMm0t1J3bG8fG/IEry/Yj5u4LvL75FHc2ncrza/2UFGnpknZIinqbad6yPXxxbupWhO29iNiHr3Bl6T48/OsSvEe2VOapNKIlri0/iBurj+D1zac4+M0ypCUkw6N3g09xOfnGp1ZtDBk0GA3qZx1dzLBp8yYULVoUo0Z+i5IlS6Jrly5o1KAh/li3Vpmnrq8vfGr7wMnRCSWcnDB08BAYGhriytWrWZT8+Tu14z4qN3ZEpYaOsHU0QcuBFaAr18bFg48zPUYmA0ws9JUvYwt9lTzaulqSPAbG6n/ZFyZndj5EpUbFUbFBMdgUN0GLb8pBV66NS4eeZnmcsYX835eazuWbqCTsXX4D7UZ4QiuLn7OFxbndT+DZoAgq1HOAdTEjNOnrCl09LVw58iLzg2QyGJvLlS8jc+n95lHHHrU7OMOpvPoI+hdBKw9fOXTs2DG0bNkSRYoUgUwmw/bt27PMv3XrVjRq1Ag2NjYwNTVFjRo1sG/fPkmeCRMmQCaTSV5ubm45qlfh/+7KI/UX9sHDvy7g8aErGvPKzQyR8iYBIl2RrbINbc3g3LwSrq08pEyzrVQSJsWsIBQKdL8wA/2eLUebPd/Dqlzxj76GgsiitAP6Pv0Nve8tRpM/hsGkuHWmebXlukhLSpWkpSUmo0jtsgAALV0d2Hm74PHB975GQuDxwStwqF4mT+r/ufrnyhVUr1pNklazRg1cuar+/k5PT8fefSFITEyEZ4UKn6KK+SItVYHn92JR0stGmaalJYOLpzWe3IrO9LiUxHTM/PoAZvTej3WTz+HVY9Wodti1SPzSMwRzBxzCzsX/IOFNSp5cQ0GRnqrA8/tvUNLz3+9pmZYMJStY4+ntmEyPS0lKx9x+RzCnzxFsmHIB4Y+lf0gKhcC2uf+gZuuSsHUs/MOp6WkKvHwQhxLvde5kWjKUKG+JZ3cyHz1JSUrH4oGnsGjAKWyefhURT+I/RXXpI8XHx8PT0xOLFi3KVv5jx46hUaNG+Ouvv3DhwgXUq1cPLVu2xKVLlyT5ypUrhxcvXihfJ06cyFG9ODz9Ecp0rgXbSiURXHWsxrz6Viao9kNHXP3tYLbLd+9VF6lvE3Fv61llmllJOwBAjaDO+Pvb1XgTFg7vka3Q8cgkrHIdguT3op2fq5dn72JfwEJE334OIwcLVB/fEZ2OTcbv5YcjNS5JJf+jfZfhPaIlnh27gZj7L+HYoDxKtauujOgaWJtAS0cbCa9iJMclhMfCwq3op7ikz0ZUVCSsrKwkaVZWVoiLi0NSUhL09d9Fyu7cvYue/l8hJSUFhgYGmDtrNlxKuuRHlT+JhDcpUCiESnTL2FyOyGfqv+esixqjzVAv2JcwRVJCKk5uu4/fxpzAkIX1YGZtAAAoVckWZWs4wMLOEK9fxuPgHzfx+8Qz6DfdB1rahXOILOFtCoRCqAyfGpnrZdqWVkWM0HpwediVMEFSfCpO73iIlYFnMHBebZj+vy1PbHsALW0ZqrVwyvNrKAgS3qS+a0fzD9tRF1HP1XcErYoYotkAN9g6GSE5IQ1ndz7B2h8u4OvZVWFqpRoF/1IVpOHppk2bomnTptnOP3fuXMn7KVOmYMeOHdi1axcqVqyoTNfR0YG9vf1H1yvfI42JiYk4ceIEbty4ofJZUlISfv/99yyPT05Oxps3bySvNJH5PLj/yriYFerO7Y29PeYhPTk1y7x6JgZos3scom48wZkJG7N9jnIBDXAz+Lik/Iz9o85N2YJ7W88g/OID7O+9EEIIlOlYOCbPh4Vcwt3NpxF59REe7b+M7c1/htzcEGU61VKb/+jwlYi++wK9bs7DsOSNqLegD66vPgwoshfRpZxzLlECm9ZvxLo1f6BTx074Yfx43H9wP7+rVaA4ulmiYv3icChpBmcPa3QNrAIjUz2cD3mkzFOhTlGUrWYP+xKmcK/ugB4/VsOzuzF4eC0yH2te8BR3s4BnvaKwdzZFCQ8rdBpbCYamegjd/24R0fP7sTi7OwxthlYoUL/wC5qiZcxQ3tcediVM4OhugXajPGBgqofLB57nd9W+GOr6KsnJyXl2PoVCgbdv38LS0lKSfvfuXRQpUgQlS5ZE9+7d8fhx5tNs1MnXTuOdO3dQtmxZ1KlTB+XLl4evry9evPh3TkZsbCwCAgKyLGPq1KkwMzOTvA4i7ybx2nm7wMjOHN0vzMCwlD8xLOVPFK/rgYpDmmFYyp+Qab1rUl1jfbTd+wNS3yZhV7vpWS7oeF/R2mVh6VYU15ZLI5PxL2IAAFE3/l1xmZ6ShtgHr2DiaIPCKDk2AdF3XsC8lPq/ihIj32BXu2lYaNwdy0t8gzVlhyI1LgmxD8L///lbKNLSYWhnLjnO0NYMCS9j8rj2nxcrK2tERUVJ0qKiomBsbKyMMgKArq4uHB0d4e7ujmFDhqJMmTJYFxz8qav7yRia6kFLS4a4GOkP97iYZBibZy9Co62jBYeSZnj9IvPhQEt7Ixia6mWZ53NnaKIHmZYM8bHSYfj4mJRMFxR9SFtHCw7Opoh+8W4nisc3XiM+NgVz+h7FpPYhmNQ+BLERidi/+hbm9jua25dQIBia6r5rx5gP2zE108VEH9LW0YKdszGiXybmRRU/X1qyPHup66tMnTo1zy5l5syZiIuLQ6dOnZRp1apVw+rVqxESEoIlS5bg4cOH8PHxwdu3ma8d+FC+dhrHjh0LDw8PhIeH4/bt2zAxMUGtWrVy1PMNDAxEbGys5NUQrnlW58eHruD38sOxtuK3ytfL8/dwa91xrK34LYRCAT0TA7TbNx7pKWnY0Xqqxojk+8r1boBXofcQeeWRJD38wn2kJaXAwvXfYVUtHW2YlrDF20cRuXZ9BYmukT7MXewQ/yLzuWMAkJ6civjnr6Glo43S7arj/s5zAABFahpeXbiP4g3K/5tZJkPxBhXw4sydvKz6Z8ezQgWcPX9Oknb67BlUKJ/1fEWFQoGU1MI7F09HVwtFSpnhwT//RgAVCoEHVyJR3C17CwYU6QKvHr3NclVvbGQiEt+mqF0wU1ho62qhiIspHlz5948ToRB4cDUSxVzNs1WGIl3g1eO3MLZ415YVfItiwJza+GZ2LeXLxFKOmq1LokdQ5by4jHynraMF+5LGCLv2789FoRB4dC0aRcuo33LnQwqFQMTjeBhbFP7FVwWFur5KYGBgnpwrODgYEydOxJ9//glbW1tletOmTdGxY0dUqFABfn5++OuvvxATE4M///wz22Xn65zGU6dO4eDBg7C2toa1tTV27dqFgQMHwsfHB0eOHIGRkZHGMuRyOeRy6Q9jHZl2XlUZqXFJiLou3V8tNT4Jia/fIur6E2WHUcdQjpCe86Bnagg9U0MAQGLEG4j/D532ujEfJ8atxf3t//6i1jMxQJmONXBs1BqV86a8TcSVZftRY0JnvH0SibePIuA9+t0eeoVlBbXPjK/wYFco3j6KgFERS9SY0BmKdAVur383Uddv9RDEPX+Nk+PWAQDsq5aGcVFLRFwOg3FRS1QP6gSZlhZCp29Xlnlxzi74rR6C8ND7eHnuLioObwFdIzmurzqcH5f4ySQkJODxk3//+Hr27Blu3b4FM1MzODg4YN6C+XgVHo4pP00GAHTs0BHrN27A7Llz0LZ1G5w9fw77DxzAwnkLlGXMWzAftWrWgoODPeLjE7A3ZC9CL4Ri6aLFn/z6PqWarV2wde4lFC1lhqL/33InJSkdlRq8W4S2ec5FmFrqo3EvdwDAkQ23UdzVApYORkiKT8WJrfcRE5EA70bv5twlJ6bhyIbbKFfDAcYW+nj9Mh77V9+ApYMRSlcqnKMGGaq3csb2+VdQxMUURUub48zuMKQmpcOrQTEAwLZ5/8DEUh8Ne777w//vjXdRzNUclvbv2vLU9oeIjUhEpUbv8hua6sHQVNrx0dLWgrGFHqyLGn/ai/uEqrYojt2LbsGhpAkcSpki9K+nSElOR4W6DgCAXQtvwMRSjrrd3s03PrH5IYqWNoOFvQGS4tNwdudjvIlIgmeDIsoyE+NS8SYyCXGv3/0R+Pr5u2iukbletiPBn7u8fIygur5KXtiwYQP69OmDTZs2oWHDhlnmNTc3R5kyZXDvXvb3Lc7XTmNiYiJ0dP6tgkwmw5IlSzB48GD4+voi+DMc9rKtVFK5Mrf3Pekv0xXO3+DN/6OClm5FITeTdopdu9QGZDLcWq9+NdPx0b9DkZaOJr8PhY6BHl6evYstDSYgOaZwDGmZFLVCs+AR0LcyQWLEGzw/cRMbagQiMfLdikATR2sIhVDm19bXRc2fusKspB1S45Lw8K+LCPlqPpJj/91E/c6fp2BgY4YaE7vA0N4cEZcfYlvTyUgIj/3k1/cpXb9xHV/366t8P2P2LABAq5YtMXniT4iIjMDLl/9OBSlWtCgWzV+AGbNmYt36YNjZ2WHCj+OVezQCwOvXr/HD+B8QERkJY2NjlCldBksXLUaN6oVjTm1myvsURXxsCg4F30ZcdDIcSpriqwnVlVHB2IhEaL03ny4xLhXbF/6DuOhkGBjrokgpM/Sd5qNc2aulJcOrsDe4fPgJkuJTYWKpj1JetmjQ3RU6unn3B29B4FHbAQlvUnB0w13lRundx1dRdkpiI5IkcxMT49Owa/E1xEUnQ99YF0VczNB7anXYFC/8q6SzUramHRLepOL4nw/fbe5dwhidx1VQLo55E5ksacekuDTsXXYL8TEp0DfShX1JY/SYXAnWxf79HXQ3NFKyAfiOue/WGdTqUAI+nQr3RumFxfr169G7d29s2LABzZs315g/Li4O9+/fR8+ePbN9DpkQQmjOljeqVq2KIUOGqK3w4MGDsW7dOrx58wbp6Tlb2DJHq31uVZEo1wx8u1ZzJtJox9Px+V2FQiMtPftTZyhryamqOzxQzgV4Ls23c6fdidKc6SPplLHSnOk9cXFxyghgxYoVMXv2bNSrVw+WlpZwdHREYGAgnj17plwsHBwcjF69emHevHlo166dshwDAwOYmZkBAEaNGoWWLVvCyckJz58/R1BQEC5fvowbN27AxiZ7oxz5Oqexbdu2WL9+vdrPFi5ciK5duyIf+7RERET0hZBpyfLslVOhoaGoWLGicruckSNHomLFihg//t0fzS9evJCs//j111+RlpaGQYMGwcHBQfkaNmyYMs/Tp0/RtWtXuLq6olOnTrCyssKZM2ey3WEEPiLSGBISAmNjY9Su/e55s4sWLcJvv/0Gd3d3LFq0CBYW+b+bPCONVBAx0pg7GGnMPYw05h5GGnNHfkYa0++9zrOytUtZas70GchxpHH06NF48+bdHLOrV6/i22+/RbNmzfDw4UOMHKn5GbdEREREBU4BeoxgQZXjhTAPHz6Eu/u7VYJbtmxBixYtMGXKFFy8eBHNmjXL9QoSERERUf7Lcf9XT08PCQnvVqcePHgQjRs3BgBYWloqI5BEREREnxOZTJZnr8Iix5HG2rVrY+TIkahVqxbOnTuHjRvfPR7vzp07KFasWK5XkIiIiIjyX44jjQsXLoSOjg42b96MJUuWoGjRd08o2bt3L5o0aZLrFSQiIiLKc3n4GMHCIseRRkdHR+zevVslfc6cOblSISIiIiIqeD7qiTDp6enYtm0bbt68CQAoW7Ys2rRpI3m6CxEREdFno/AEBPNMjnt5169fR8uWLfHq1Su4ur57Pui0adNgY2ODXbt2wcPDI9crSURERJSnCtGClbyS4zmNffr0gYeHB54+fYqLFy/i4sWLePLkCSpUqIB+/frlRR2JiIiIKJ/lONJ4+fJlhIaGSp78YmFhgZ9//hlVqlTJ1coRERERfQqCgUaNchxpLFOmDF69eqWSHh4ejlKlSuVKpYiIiIioYMlWpPH9TbunTp2KoUOHYsKECahevToA4MyZM5g0aRKmTZuWN7UkIiIiykuMNGqUrU6jubm5ZEdzIQQ6deqkTBNCAABatmyJ9PT0PKgmEREREeWnbHUajxw5ktf1ICIiIso/XD2tUbY6jb6+vnldDyIiIiIqwD5qN+6YmBisWLFCubl3uXLl0Lt3b5iZmeVq5YiIiIioYMjx6unQ0FC4uLhgzpw5eP36NV6/fo3Zs2fDxcUFFy9ezIs6EhEREeUpIcu7V2GR40jjiBEj0KpVK/z222/KxwampaWhT58+GD58OI4dO5brlSQiIiKi/JXjTmNoaKikwwgAOjo6GDNmDCpXrpyrlSMiIiL6JLgQRqMcD0+bmpri8ePHKulPnjyBiYlJrlSKiIiIiAqWHHcaO3fujK+//hobN27EkydP8OTJE2zYsAF9+vRB165d86KORERERHlLloevQiLHw9MzZ86ETCbDV199hbS0NACArq4uBgwYgF9++SXXK0hERERE+S/HnUY9PT3MmzcPU6dOxf379wEALi4uMDQ0zPXKEREREX0ShSgimFc+ap9GADA0NET58uVzsy5EREREVEBlq9PYrl07rF69GqampmjXrl2WeY2NjVGuXDl888033OybiIiIPguFaT/FvJKtTqOZmRlk/1+KrqkjmJycjKVLl+LkyZPYuXPnf68hERERUV7jljsaZavTuGrVKrX/zsyNGzdQpUqVj68VERERERUoHz2nMSuurq44depUXhRNRERElPsYaNQox/s0Zoe2tjY8PT3zomgiIiIiygd5EmkkIiIi+qww0qhRnkQaiYiIiKhwyVansVKlSoiOjgYATJo0CQkJCXlaKSIiIqJPSchkefYqLLLVabx58ybi4+MBABMnTkRcXFyeVoqIiIiICpZszWn08vJCQEAAateuDSEEZs6cCWNjY7V5x48fn6sVJCIiIqL8l61O4+rVqxEUFITdu3dDJpNh79690NFRPVQmk7HTSERERJ+fwjOKnGey1Wl0dXXFhg0bAABaWlo4dOgQbG1t87RiRERERFRw5HjLHYVCkRf1ICIiIso/hWjBSl75qH0a79+/j7lz5+LmzZsAAHd3dwwbNgwuLi65WjkiIiIiKhhyvE/jvn374O7ujnPnzqFChQqoUKECzp49i3LlyuHAgQN5UUciIiKivCXLw1chkeNI43fffYcRI0bgl19+UUkfO3YsGjVqlGuVIyIiIqKCIceRxps3b+Lrr79WSe/duzdu3LiRK5UiIiIi+pSELO9ehUWOO402Nja4fPmySvrly5e5opqIiIiokMrx8HTfvn3Rr18/PHjwADVr1gQAnDx5EtOmTcPIkSNzvYJEREREeY6rpzXKcafxxx9/hImJCWbNmoXAwEAAQJEiRTBhwgQMHTo01ytIRERERPkvx51GmUyGESNGYMSIEXj79i0AwMTEJNcrRkRERPTJMNCo0Uft05iBnUUiIiIqFNhp1CjHC2GIiIiIKO8cO3YMLVu2RJEiRSCTybB9+3aNxxw9ehSVKlWCXC5HqVKlsHr1apU8ixYtQokSJaCvr49q1arh3LlzOaoXO41ERET0xRMyWZ69cio+Ph6enp5YtGhRtvI/fPgQzZs3R7169XD58mUMHz4cffr0wb59+5R5Nm7ciJEjRyIoKAgXL16Ep6cn/Pz8EB4enu16yYQQIsdXU8DN0Wqf31UgUjHw7dr8rkKhsOPp+PyuQqGRlp6a31UoNJJTk/K7CoVCgOfSfDt3UmLefQ31DfQ/+liZTIZt27ahTZs2meYZO3Ys9uzZg2vXrinTunTpgpiYGISEhAAAqlWrhipVqmDhwoUAAIVCgeLFi2PIkCH47rvvslWXHEUaU1NT0aBBA9y9ezcnhxEREREVbJ/xYwRPnz6Nhg0bStL8/Pxw+vRpAEBKSgouXLggyaOlpYWGDRsq82RHjhbC6Orq4sqVKzk5hIiIiOiLlpycjOTkZEmaXC6HXC7PlfJfvnwJOzs7SZqdnR3evHmDxMREREdHIz09XW2eW7duZfs8OV493aNHD6xYsULl2dNElLVErdf5XYVC4dydk/ldhUIjISklv6tQaKSmpuV3FQqFAM/8PHvezdabOvUXTJw4UZIWFBSECRMm5Nk580KOO41paWlYuXIlDh48CG9vbxgZGUk+nz17dq5VjoiIiOhzFxgYqPLUvNyKMgKAvb09Xr16JUl79eoVTE1NYWBgAG1tbWhra6vNY29vn+3z5LjTeO3aNVSqVAkAcOfOHclnMj6Ch4iIiD5DIg8jjfq5OBStTo0aNfDXX39J0g4cOIAaNWoAAPT09ODt7Y1Dhw4pF9QoFAocOnQIgwcPzvZ5ctxpPHLkSE4PISIiIirQBBT5XQWluLg43Lt3T/n+4cOHuHz5MiwtLeHo6IjAwEA8e/YMv//+OwDgm2++wcKFCzFmzBj07t0bhw8fxp9//ok9e/Yoyxg5ciR69eqFypUro2rVqpg7dy7i4+MREBCQ7Xp99BNh7t27h/v376NOnTowMDCAEIKRRiIiIqL/KDQ0FPXq1VO+zxja7tWrF1avXo0XL17g8ePHys+dnZ2xZ88ejBgxAvPmzUOxYsWwfPly+Pn5KfN07twZERERGD9+PF6+fAkvLy+EhISoLI7JSo73aYyKikKnTp1w5MgRyGQy3L17FyVLlkTv3r1hYWGBWbNm5aS4PMF9GqkgCoifn99VKBQmH+yY31UoNLgQJvdwIUzu+K3b5Xw7d0LS2zwr21C/cDx2OcdPhBkxYgR0dXXx+PFjGBoaKtM7d+6s3ECSiIiIiAqXHA9P79+/H/v27UOxYsUk6aVLl8ajR49yrWJEREREn0pBmtNYUOU40hgfHy+JMGZ4/fp1nq4MIiIiIqL8k+NOo4+Pj3K1DvBumx2FQoHp06dLJm0SERERfS5EHv5XWOR4eHr69Olo0KABQkNDkZKSgjFjxuD69et4/fo1Tp7kkxqIiIiICqMcRxo9PDxw584d1K5dG61bt0Z8fDzatWuHS5cuwcXFJS/qSERERJS3hCLvXoXER+3TaGZmhu+//z6360JERESULwrTMHJe+ahOY3R0NFasWIGbN28CANzd3REQEABLS8tcrRwRERERFQw5Hp4+duwYSpQogfnz5yM6OhrR0dGYP38+nJ2dcezYsbyoIxEREVGeElDk2auwyHGkcdCgQejcuTOWLFkCbW1tAEB6ejoGDhyIQYMG4erVq7leSSIiIiLKXzmONN67dw/ffvutssMIANra2hg5cqTk4dpEREREnw9FHr4Khxx3GitVqqScy/i+mzdvwtPTM1cqRUREREQFS7aGp69cuaL899ChQzFs2DDcu3cP1atXBwCcOXMGixYtwi+//JI3tSQiIiLKQ1w9rVm2Oo1eXl6QyWQQ4t8GHTNmjEq+bt26oXPnzrlXOyIiIiIqELLVaXz48GFe14OIiIgo3xSmVc55JVudRicnp7yuBxEREVE+4vC0Jh+1uffz589x4sQJhIeHQ6GQ9syHDh2aKxUjIiIiooIjx53G1atXo3///tDT04OVlRVkMpnyM5lMxk4jERERfXY4PK1ZjjuNP/74I8aPH4/AwEBoaeV4xx4iIiIi+gzluNOYkJCALl26sMNIREREhQa33NEsxz2/r7/+Gps2bcqLuhARERFRAZXjSOPUqVPRokULhISEoHz58tDV1ZV8Pnv27FyrHBEREdGnwTmNmnxUp3Hfvn1wdXUFAJWFMERERERU+OS40zhr1iysXLkS/v7+eVAdIiIiok+Pcxo1y3GnUS6Xo1atWnlRFyIiIqJ8wS13NMvxQphhw4ZhwYIFeVEXIiIiIiqgchxpPHfuHA4fPozdu3ejXLlyKgthtm7dmmuVIyIiIvo0ODytSY47jebm5mjXrl1e1IWIiIiICqgcdxpXrVqVF/UgIiIiyjdCcE6jJnysCxERERFplONIo7Ozc5b7MT548OA/VYiIiIjoU+OWO5rluNM4fPhwyfvU1FRcunQJISEhGD16dG7Vi4iIiIgKkBx3GocNG6Y2fdGiRQgNDf3PFSIiIiL69DinUZNcm9PYtGlTbNmyJbeKIyIiIvpkRB7+V1jkWqdx8+bNsLS0zK3iiIiIiKgAyfHwdMWKFSULYYQQePnyJSIiIrB48eJcrRwRERHRp8DHCGqW405jmzZtJO+1tLRgY2ODunXrws3NLbfqRUREREQFSI47jUFBQXlRDyIiIqJ8VHjmHuYVbu5NRERERBplO9KopaWV5abeACCTyZCWlvafK0VERET0KXFOo2bZ7jRu27Yt089Onz6N+fPnQ6FggxMREREVRtnuNLZu3Vol7fbt2/juu++wa9cudO/eHZMmTcrVyhERERF9CgqRnt9VKPA+ak7j8+fP0bdvX5QvXx5paWm4fPky1qxZAycnp9yuHxEREVGeU4j0PHsVFjnqNMbGxmLs2LEoVaoUrl+/jkOHDmHXrl3w8PDIq/oRERERUQGQ7eHp6dOnY9q0abC3t8f69evVDlcTERERfY4ECk9EMK9ku9P43XffwcDAAKVKlcKaNWuwZs0atfm2bt2aa5UjIiIiooIh253Gr776SuOWO1+aKmPbovbUHrg4bzf+HrEKANDh8EQUrysdrr+ybB8ODfg103JKta2GCv0bw9bbBQZWJlhb8VtE/BMmyWNW0g51ZvRCkdpu0Jbr4lHIZRwZuhwJ4bG5fl35yaiIJXx+6YESTStB11APMfdeYn/vRXh14X6mxxTzLYc6s/xhVa444p5E4uzPW3BjzRHl57rG+qj5U1eUalMNhramCL/0EEeHr8Sr0MzLLAw2bdiOdWs2IirqNUqXccG3Y4egXPmyavOmpaZh9cpg/LVrHyLCI+FYojgGD+uHGrWqfnSZhUnNEu1Q16U7TOSWePHmHrZdm40nMTfV5vWw90WD0l/B2qgYtGU6iIh/gr8fbMDFpyHKPJ29vkeV4s0lx90KP4PlZ0fm6XUUBHVcOqKRa0+Y6lvhacxd/HlpBh5FX1eb16toPfi5BcDGuDi0tXQQHvcYh26vw7nHfwEAtGTaaOUxEOUcasHaqCgSU+Nw+9U5bL+6ALFJkZ/ysj65uqU7w69sL5gZWOFJ9B2svzANYVHX1Ob1cWmHGs4tUMS8FADg0esb2PbPQkl+E31LdPAaDnf76jDQM8Hd8ItYf2Eawt8+/iTXUxAUprmHeSXbncbVq1fnYTU+P3aVXVC+XyOVzh0AXP3tAE6N36B8n5aQnGVZukb6eHbyFu5sOoVGvw1U+VzHUI52+8Yj4p8wbG4wAQBQc1JXtN4ZiPU1AgFROHaxl5sbofOJn/H0yDVsazYZiRFvYF7aAUnRcZkeY1rCFm12j8OVZfsR0mMuijeogEa/DUD8i2g82n8ZANDot4Gw9nBEyFfzEff8Ncr2qIP2B4KwptxwxD9//Ymu7tM6sO8I5s1agrHfD0e58mWxYd0WDBs4Fn/uWANLSwuV/EsXrUTIngMIHP8tSjg74syp8xg7cjx+W7MArm6lP6rMwsKzSAO0ch+KLVdn4HH0dfiU7Iy+1eZg+pGuiEuJVsmfmPoGh+6uQXjcI6Qr0lDWrhY6e45DXHI07kScVea7FX4aGy//rHyfpkj9JNeTn7yLNUJ7zxFYf3EqwqKuoX6ZrhhSZwEmhLRHXLJqW8anvEHIzZV49TYMaYpUlHfwQc8q4/E2+TVuvjoDPW19FLdww94by/E05i4M9UzQ0WsUvqk1G9MOfZUPV/hpVHZsjE6VvsXa8z/jYeRVNHTrjuH1FuPHXa3xVk07utpVxrlHIbgf+g9SFcloUjYAI+otQdCe9ohJDAcADKozB+mKNCw6NgKJqXFo5NYTI+svxfjd7ZCSnvSpL5EALFq0CDNmzMDLly/h6emJBQsWoGrVqmrz1q1bF3///bdKerNmzbBnzx4AgL+/v8oosZ+fH0JCQlSOy0yBeyKM+Aw6QLpG+mi6djgO9luqtkOTmpCMhFcxylfK28Qsy7u59m+c/WkTHh+8ovbzIrXcYFrCBvsDFiLq2mNEXXuMff4LYFfZBY71y+fKNRUEVca2RdyTSOz/ehFenb+HN2HheHzgH8Q+eJXpMRW+aYzYh+E4NmoNXt96hn8W7cXdzadRaXgLAIC2vh5Kt6+O42N/x7PjNxB7/yXOTPwTMfdewnOA36e6tE9u/R+b0LpdM7Rs0xQlXUrgux9GQF9fjl3b96rNv3fPAfT6ujtq+VRH0WJF0L5Ta9SoXQ3Bv2/66DILC9+SXXD28U6cf7IHr+LCsOXKdKSmJ6OKYwu1+e9HXcK1l8cQHvcIUQnPcOLhn3jx9j6cLStI8qUpUvE2+bXylZj69lNcTr6qX6Y7Tj7cjjNhu/Dy7UOsvzAVKelJqFmildr8dyMu4J/nR/HybRgi45/hyL0NeBZ7Dy7WXgCApLR4LDg2CBefHkR43COEvb6GPy9Nh5OlOywM7D7hlX1ajdx64vj9rTj1YAdevHmAtecmIyUtCbVc2qjNv/zUOBy9+yeexNzGyzdhWHNuImQyGcrav+uA2Jk4wsXaE+vOT0HY6+t49fYR1p3/Gbra+qhaouknvLL8VZBWT2/cuBEjR45EUFAQLl68CE9PT/j5+SE8PFxt/q1bt+LFixfK17Vr16CtrY2OHTtK8jVp0kSSb/369TmqV4HrNMrlcty8qX7Yp6Cov7APHv51AY8Pqe/kuXXzwTfhq9DzyhzUmtIdOgZ6/+l8OnJdQADpyf9GItKTUiAUAkVqu/2nsguSki0r49WF+2i+8Vv0f7kS3S/MgEefhlke41DdVeXrELb/MhxqlAEAaOloQUtHG2lJ0ihOWmIKitQqPG33vtTUVNy6eQdVq3kr07S0tFClmjeuXrmh9piUlFTI5dL7VF8uxz+Xrn50mYWBtkwHRc1ccScyVJkmIHA38jycLLK3a0Qpa2/YGjniQdRlSbqLVUVMaLwHY+qtR7vyo2Coa5qbVS9wtGU6cLRww+1X/0ZbBQRuvToHZ6sKWRz5L1fbKrAzccK9iEuZ5tHXNYZCKJCYmvkIxedMW0sHTpZlcfOltB1vvjwLF+vstaOetj60ZTqIT343vUlH6933fmr6v6NiAgJp6SkobVMxF2tP2TV79mz07dsXAQEBcHd3x9KlS2FoaIiVK1eqzW9paQl7e3vl68CBAzA0NFTpNMrlckk+C4ucjRJle3g6t40cqX7uTnp6On755RdYWVkBeNdwWUlOTkZysnT4N02kQ0emnTsV/UCZzrVgW6kkgquOVfv57fUn8OZRBOKev4ZNBSfU/qUnLMoUwe4OMz76nC/O3EFqfBJqT+uJk+PWATIZav/SA1o62jByKDzDgmYl7VDhGz9cnLML56ZuhX2VUqg3rzcUKWm48ftRtccY2Zsj4VWMJC3hVSzkZkbQ1tdDalwSnp+6hWo/dMDrm0+R8CoWrl1rw6FGGcTce5n3F5UPYqJjkZ6ugKWV9N6wtLLAozD185Oq16iM4D82watSBRQrXgTnz17EkcPHoUhXfHSZhYGRnjm0tXQQlyydxvA2+TVsjTPfl1Zfxwg/NtoBHS09KEQ6tl6dibuR55Wf3w4/i6sv/sbrhOewMiqGZm790afabCw40a/QPsrMWP6uLd8kfdCWSa9hZ1Ii0+P0dYwwpeVe6P6/LTdcnIZb4WfV5tXR0kPbCkMQ+ngfktLic7P6BYax3OL/7RglSX+TFAV70xLZKqO913DEJEbgxv87ni/fhCEq/jnaeQ7FH+d+QnJ6Ihq59oClkT3MDKxz+xIKLIXIu+89dX0VuVwOuVyukjclJQUXLlxAYGCgMk1LSwsNGzbE6dOns3W+FStWoEuXLjAyMpKkHz16FLa2trCwsED9+vUxefJkZX8rO/Kt0zh37lx4enrC3Nxcki6EwM2bN2FkZJSthTdTp07FxIkTJWmN4YYmcM/N6gIAjItZoe7c3tjaeJIk6ve+q78dUP476tpjxL+IRodDE2FW0i7LYdasJEa+we5Os9BgcT9UHNIMQiFwe/0JvLpwH0JR8Ifzs0umJcOr0Ps4+X0wACDi8kNYeRRH+f6NM+00ZkfIV/PReMUg9Hu2HIq0dIRffIDb60/A1tsll2r++Rs5ZjCmTJqFzm39IZMBRYsVQYtWTbB7R+Eees4ryWkJmP13L8h1DFHaujJalRuK1wnPcT/qXYTs8vODyrwv3z7Aizf3MK7BZrhYV8S9yAv5Ve0CKTktAVP3d4NcxxCudlXQ3nMEIuOf4W6EtJ20ZNroU+MXADJsuPhL/lT2M9DEPQBVnfww41AfpClSAADpIg2Lj30L/+oTMK/jcaQr0nDz5VlcfX4in2v7aSnycMsddX2VoKAgTJgwQSVvZGQk0tPTYWcnnWJhZ2eHW7duaTzXuXPncO3aNaxYsUKS3qRJE7Rr1w7Ozs64f/8+xo0bh6ZNm+L06dPQ1s5eoC3fOo1TpkzBr7/+ilmzZqF+/frKdF1dXaxevRru7tnr9AUGBqpELZeZ5c0EaDtvFxjZmaP7hX+jhlo62ihWxx1eg5pivn4XiA+ev/3i7F0AgHkph4/uNALA4wP/YFXpQdC3MoFIS0dybAL6PV+O2I0fX2ZBE/8iBlE3n0rSXt98htLtqmd+zMsYGNqZS9IM7cyQHBuP9KR3PxBjH7zCpnrjoWMoh9zUAPEvY9Bs/cj/9PUoyMwtzKCtrYXXUdIJ8a+jomFpban2GAtLc8yY+xOSk1MQGxMLG1trLJr3G4oUdfjoMguD+JQYpCvSYCyXXqOJ3BJvkjNfRCUgEJXwDADw/M1d2Bo7oX6pr5Sdxg+9TniOuORoWBsVK7Sdxrjkd21pqv9BW+pbqkTN3icgEBH/7ufC09g7sDdxhp+bv6TTmNFhtDS0x7y/BxTaKCMAxCVH/78dpdEhU30rvNGwYryx21do6t4bsw/3x7OYu5LPHkffxKS9nWGgawxtLV3EJUcjsPEfePS68E4/+ZTU9VXURRlzw4oVK1C+fHmVRTNdunRR/rt8+fKoUKECXFxccPToUTRo0CBbZefbnMbvvvsOGzduxIABAzBq1Cikpn7cykG5XA5TU1PJK6+Gph8fuoLfyw/H2orfKl8vz9/DrXXHsbbityodRgCw9SoBAIh/obqi7WMkRb1FcmwCitfzgKGtGR7sPK/5oM/E85O3YFmmiCTNoowD3jyKyPSYF2duqywGcmroiRen76jkTUtIRvzLGMjNjeDk51Wo2u59urq6cCtbBufPXVSmKRQKnD93EeUrZP3HmFyuB1s7G6SnpePIoWOoU7fWfy7zc5Yu0vAs9jZKW/87l1MGGUpZV8ajaPXbm6gjk2lBR0s308/N9G1gqGeWZefpc5cu0vA4+hZcbf/9RSaDDK62VfAwSv38cHXeteW/828zOoy2xo6Y//dAxKcUrm3IPpSuSMOj1zdR1k7ajmXtq+J+ZObt6FfWH809+mLekYFZdgQTU+MQlxwNWxNHlLB0x+WnR3Oz+gWaEOl59lLXV8ms02htbQ1tbW28eiUNbLx69Qr29vZZXkN8fDw2bNiAr7/+WuP1lixZEtbW1rh371622yhfF8JUqVIFFy5cQEREBCpXroxr164V6L0gU+OSEHX9ieSVGp+ExNdvEXX9CcxK2qHaDx1gW6kkTJ1sULJlZfitGYqnf19H5NVHynJ63ZgPlzb/fsPLLYxh41kClu7FAQAWrkVg41lCEkFz968H+2qlYVbSDm7d66D5n6Nwce5uRN95/smuP69dnLsL9tXLoEpgO5i52MO1a22U79sI/yz+dzuAWlO6w2/1EOX7K0v3w6ykHXym9YSFa1FUGOCHMp1q4uLc3co8To294OTnBdMStnBsWAEdDk9E9K1nuL7q8Ce9vk+pa8+O2LF1D/bs3IeHDx5h2s9zkZSYhBatmwAAJvwwFYvm/6bMf+3qTRw5dAzPnj7HpYtXMGzQWCgUAj39u2S7zMLq7wcbUM2xFSoXawpbYye0qzAaetr6OP/43T3WxetHNHX7Rpm/fqmeKG1dBZaGRWBr7ATfkl3hXawJLjzbBwDQ0zZAi7KD4GheDhYG9ihl7Y2AKtMQFf8UtyPUz9UrLA7fWYdaJdugmlNz2JuUQJdKgZDrGOB02C4AQK8qE9HaY5Ayv5+bP9xsq8HKqCjsTUqgQZnuqObUTLJPY98a0+FkURarzv4ALZk2TOVWMJVbQVuWbwNpee7ArT/gU6odaji3hL2pM7pX+R56OgY4+WAHAKB3jZ/Q1vPfn5NNyvqjdYWBWHN2AiLjn8NU3wqm+laQ6xgo83gXb4QytpVhbVQUnkXrYkS9pbj09AhuvMzeHDrKPXp6evD29sahQ4eUaQqFAocOHUKNGjWyPHbTpk1ITk5Gjx49NJ7n6dOniIqKgoODQ7brlu/fVcbGxlizZg02bNiAhg0bIj39891cMz0lDY4NKqDisBbQNZLj7ZMo3Nt6Bmcnb5bks3QrCrnZv5NTXVpVgd+qwcr3zTd8CwA4PXEjzkz8890xrkVRe0p36Fsa401YBM5N2YKLc3Z9gqv6dF6F3seudtNRe0p3VP+xI2IfhuPoiFW4FXxcmcfI3gImjv9OzH4TFo7tLabAd7Y/vIY2R9zTKBzou0S5RyMAyM0MUWtKdxgXs0Ly6zjc3XoGJ78PhiLt873XNGnkVw8x0TH4dckqREVGo4yrC+YungYrq3dDg69ehENL9u/fjCnJKVi6aBWeP30OA0MD1KxdDRMmB8LE1DjbZRZW/zw/BGM9c/i59oWJ3BLP39zF8rMjlXs0WhjYSRav6GkboF35UTA3sEVqejLC4x4h+NJE/PP83S8AhUiHg2kpVC7eDPq6xniTFIk7EecQcutXpBfyvRovPD0AY7kFWpT75v+be9/BwuND8Pb/Q/0WhvZQfNCWXSqNhbnhu7Z89SYMq8/+iAtP380dNzewhWdRXwDA942lW4fMOdpfZd5jYRH6eD9M9C3QusIAmOpb40n0bcw7MhBv/7/IyNLQQbJ9nW/pTtDV1sMAn1mScnZeXYpdV5cC/2vvzuOiqt4/gH9m2FGRTdncUEkWEdxA3DMEl0pS08yfC5pWamWYpn1V3Ao1MzPXFlNb1PyWVmpYoWYqgSIugBAi4sa+yiLLzPn9QU1NDF70OzDIfN6v17ySe5975pzjaI/PvecMgJZmthjXYx4sTG1QeC8bkamHcCiu9i+laIoa0+beISEhmDJlCnr16gUfHx9s2LABJSUlCA4OBlD9hStOTk4ICwtTu+7TTz9FUFBQjcUtxcXFWL58OcaMGQN7e3ukpKRgwYIF6Ny5MwID6779nEw0oo0Rb926hZiYGPj7+9dY8fMg3peP0WKviLQjuGSjrrvQJKz65VnpIKqT0j+f+6X/XWVlla670CR8/PwFnb33lfz6W/jnZvXg+11u2rRJtbm3t7c3Nm7cCF9fXwDVm3l36NBB7YtXkpKS4Orqip9++glDhw5Va6usrAxBQUGIjY1FQUEBHB0dERAQgJUrV9ZYcHM/jSpp1BYmjdQYMWnUDiaN2sOkUXuYNGqHLpPG+LxD0kEPycNa85cBPGoa3ebeRERERNT46PyZRiIiIiJda0zPNDZWTBqJiIhI79Xn5t5NBW9PExEREZEkVhqJiIhI7wnenpbESiMRERERSWKlkYiIiPQeF8JIY6WRiIiIiCSx0khERER6j5VGaaw0EhEREZEkVhqJiIhI7ymFUtddaPRYaSQiIiIiSaw0EhERkd7jN8JIY9JIREREeo+be0vj7WkiIiIiksRKIxEREek9brkjjZVGIiIiIpLESiMRERHpPVYapbHSSERERESSWGkkIiIivcctd6Sx0khEREREklhpJCIiIr3HZxqlMWkkIiIivcfNvaXx9jQRERERSWKlkYiIiPQeb09LY6WRiIiIiCSx0khERER6j5VGaaw0EhEREZEkVhqJiIhI7ymFUtddaPRYaSQiIiIiSaw0EhERkd7jM43SmDQSERGR3lMomTRK4e1pIiIiIpLESiMRERHpPS6EkcZKIxERERFJYqWRiIiI9J6SzzRKYqWRiIiIiCSx0khERER6j1vuSGOlkYiIiIgksdJIREREek/BSqMkJo1ERESk95RKbrkjhbeniYiIiEgSK41ERESk97gQRhorjUREREQkiZVGIiIi0nvc3FsaK41EREREJImVRiIiItJ7SsHV01JYaSQiIiJqZDZv3owOHTrA1NQUvr6+iI6OrjV2586dkMlkai9TU1O1GCEEli5dCgcHB5iZmcHf3x/JyckP1CcmjURERKT3FEpFvb0e1L59+xASEoLQ0FCcP38eXl5eCAwMRFZWVq3XWFhYID09XfVKS0tTO7927Vps3LgR27ZtQ1RUFJo1a4bAwEDcu3evzv1i0khERER6TykU9fZ6UOvXr8eMGTMQHBwMd3d3bNu2Debm5tixY0et18hkMtjb26tednZ2qnNCCGzYsAGLFy/GqFGj0K1bN+zevRt37tzBwYMH69wvJo1ERERE9ai8vBxFRUVqr/Lyco2xFRUViImJgb+/v+qYXC6Hv78/IiMja32P4uJitG/fHm3btsWoUaMQHx+vOpeamoqMjAy1Nlu2bAlfX9/7tvlvXAhD1ECUqNR1F5qEwruluu5Ck1FUXPfbUnR/d4s1JwD06KjPLXfCwsKwfPlytWOhoaFYtmxZjdicnBwoFAq1SiEA2NnZITExUWP7Xbp0wY4dO9CtWzcUFhZi3bp16Nu3L+Lj49GmTRtkZGSo2vh3m3+dqwsmjURERET1aNGiRQgJCVE7ZmJiorX2/fz84Ofnp/q5b9++cHNzw/bt27Fy5UqtvQ+TRiIiItJ79bnljomJSZ2TRFtbWxgYGCAzM1PteGZmJuzt7evUhpGREbp3746rV68CgOq6zMxMODg4qLXp7e1dpzYBPtNIRERE1GgYGxujZ8+eiIiIUB1TKpWIiIhQqybej0KhwOXLl1UJorOzM+zt7dXaLCoqQlRUVJ3bBFhpJCIiIoLiIVY515eQkBBMmTIFvXr1go+PDzZs2ICSkhIEBwcDACZPngwnJyeEhYUBAFasWIE+ffqgc+fOKCgowLvvvou0tDS88MILAKpXVs+dOxerVq2Ci4sLnJ2dsWTJEjg6OiIoKKjO/WLSSERERNSIjB8/HtnZ2Vi6dCkyMjLg7e2N8PBw1UKWGzduQC7/+2Zxfn4+ZsyYgYyMDFhZWaFnz544c+YM3N3dVTELFixASUkJZs6ciYKCAvTv3x/h4eE1NgG/H5kQQmhvmI3D+/Ixuu4CUQ1TSt7TdReahDcPBOm6C00GV09rD1dPa8eRkFSdvfem6Ofrre05Pl/VW9sNiZVGIiIi0nv87mlpXAhDRERERJJYaSQiIiK9V5+bezcVrDQSERERkSRWGomIiEjvNaYtdxorVhqJiIiISBIrjURERKT3lEqunpbCSiMRERERSWKlkYiIiPSeks80SmLSSERERHqPW+5I4+1pIiIiIpLESiMRERHpPW65I42VRiIiIiKSxEojERER6T1uuSONlUYiIiIiksRKIxEREek9brkjjZVGIiIiIpLESiMRERHpPaVS6LoLjR6TRiIiItJ7SsGFMFJ4e5qIiIiIJLHSSERERHqPt6elsdJIRERERJJYaSQiIiK9x0qjNFYaiYiIiEgSK41ERESk95SClUYprDQSERERkSRWGomIiEjvKZXcp1EKK41EREREJImVRiIiItJ7XD0tjUkjERER6T0uhJHG29NEREREJImVRiIiItJ7vD0tjZVGIiIiIpLESiMRERHpPaXgljtSWGkkIiIiIkmsNBIREZHe4zON0lhpJCIiIiJJrDQSERGR3mOlURqTRiIiItJ7gpt7S+LtaSIiIiKSxEojERER6T2lklvuSGGlkYiIiIgksdJIREREeo8LYaSx0khEREREklhpJCIiIr2n5OppSUwaH1Cf0HHwCx2vdiwv8TZ2ub8Ki/atMD11m8brDo1bh+T/RtbarrWrE/qvnoQ2g9whNzRAbsItHBr7Lu7ezAEAeM4Yii4T+qN1j44wsTDHFqtJKC8s1d7AGoFp17aiZYfWNY5f2PIjjs/5pMbxzs/4wmfRaLTs7AADIwPkJ6fj/PofcOWLX1Uxryu/0fheJxfsRsy677TX+Ubov3u/x5e7/ou83Dx0fqwjQt6cBQ9P11rj937xLQ7sP4yMjCxYWlrgcf8BePnVaTAxMQYAKBQKfLLtCxw9HIHc3Hy0amWDEU8PRfCM5yGTyRpqWDox2GU8At2moKWZDW7m/4E9MWtwPTdOY+yATqPh5/wkHC07AwDS8hJw4OImtfgWptYY6z0X7vZ9YGbcAslZ57EnZg2y7t5okPHoUoD783jKazoszWyRlpeIz06vQkr2ZY2xQ1yfxUCXUWhr7QIASM2Ox56z79ca/0L/ZRjq/hx2nXkHR+J219sYGoMnvSZhTK+ZsGrWCqnZV7D1+DL8kXFRY2w7GxdM6vs6Orf2hF3LNth+fAW+i/1MLUYuk2Oi31w87hYEq2atkFeciV/iv8GeqA8bYjj0iGDS+BBy4m7gm6HLVT8rqxQAgLs3c7HdYbparOfMoej1xihc/zG21vZadrTDuN/eRvyOCEQu24eKolLYeLRF1b0KVYyhuTHSjl5A2tEL6B/2f1oeUeOwx+dNyAz+fmLCtms7jPk5FMn7NSfb9/KKEfXON8hPvA1FRRU6PtkLATtmozSrEGk/XQCAGr8fHYZ3R8Ans3D1m9/rbRyNwS9HT2Djex9hwX9egYenK/Z9eQCvz/oP9n73KaytLWvEHz1yDFs37sBby0LQzcsdN9JuY1XoOshkMrz2xosAgM8/+xoH9h/CkhVvoGOn9riSkIy3Q99D8+bNMO75oIYdYAPq1S4A43rMwxdn30ZqzmX4u07E3Me3YMkPo3C3PL9GfBe7XohOC0fKuYuoVJZjmFswXn98K0IPj0FBWRYAYPbA96FQVmHzyddRVlmMoa6TEDJkG5YeGo0Kxb2GHmKD8es4HJP9FuKT35YhOesiRnhOwVsjPsHr+4aj6F5ejXgPBx+cSTmMpDOxqKwqxyjvGfjPiE8xb/+TyC/NUovt3cEfLq29kFeS2VDD0ZmBj43EjEH/waaIxUhMv4CgHtOwcvQuzPzsCRSW5daINzE0Q3rhTfz2xxHMHLREY5tje7+EEV4TsT78DaTl/gEXu254PXAtSiru4vvYnfU8osaBzzRK4zOND0FZpUBpZoHqdS/3LgBAKJVqx0szC9A5yAd/7D+DypLa/0fQb9XzuH7kPH5783NkX0hF4bVMXPvhHMqyi1QxsR8cxtk1B5D++x/1Pj5dKcspUps75yd7ouBqOm79Gq8x/tav8Ug5GI28xNsovJaJ2I2HkX0pDY79/66m/fv3o9PTPrh5PA6FqU37fyx7Pv8WT48ehieDAuHcqT0WLH4VJqYmOHTwqMb4yxcT4OntgcARQ+DgZA/fvj0xdNhgJMQlqcUMGOyHfgN94eBkjyFDB8DHr4daTFM01HUSfkv5FmeufYf0omv4InoVKqruoV+nII3xn5x5CyeSv8bNgiRkFF3HrujlkMlkcLP3AQDYtWiHTrZe+PLsO7ieF4/Mu2n48uzbMDIwhU+H4Q04soY3sttURCTux4k/vsXtghR88lsoKqru4fEuYzTGf3h8Pn5K2IO03ETcKUzFtpOLIZPJ4enkpxZnZd4awX0X48Pj81GlrGqIoejUMz1fQHjcPvwc/1/czLuKTb/8B+VVZQjo+qzG+OTMS9hxMgwnkw6hUlGhMcbdsQd+T/kZZ1OPI6voNk4n/4jYtN/wmL1XfQ6lUVEqlfX2ehibN29Ghw4dYGpqCl9fX0RHR9ca+/HHH2PAgAGwsrKClZUV/P39a8RPnToVMplM7TVs2LAH6hOTxodg5eKAGbc+xrSrWzDs89fQoq2txrjWPTqidfeOiPs0ovbGZDI4j+yJ/OQ7eObHJXgxYweeiwxDp1E+9dT7R4PcyBBuEwci7rNjdb6m7RBPWHdxxO2TCRrPm7duCeeRPRC34z6/H01AZWUlkq4ko7dvD9UxuVyO3r7dEXdJ89x4erkjKSEZ8ZcTAQC3b6XjzKmz8OvfWy3mXNQF3Ei7BQBITkrBxdh4+PXrrbHNpsBAboj21m64khGlOiYgcCUjCp1su9WpDWMDUxjIDFFSXggAMJRX3+6vVJSrtVmlqIBLq+5a7H3jYiA3QkdbD1y+dUZ1TEDg8u1IuNh516kNE0MzGMoNUfznXAKADDLMeXwtfrj0KW7lX9V2txsdQ7kROtt1xYW0U6pjAgIX0k7D1aHHfa68v4Q75+Hdth+cLJ0BAM62bnB37I1zqSf+1y7TQ9i3bx9CQkIQGhqK8+fPw8vLC4GBgcjKytIYf+LECUyYMAHHjx9HZGQk2rZti4CAANy+fVstbtiwYUhPT1e99uzZ80D94u3pB5QRlYyjwZuQn3QHzRys0Gfpsxh3chV2e85FZbF6NbHr9CeQm3AT6ZG1V2LMW7eEcQsz9H7zGZxesgenFn6ODsO646lv5mP/kNBaE6CmrnOQD0wsmyFh5/H7xhlbmGPGrY9gYGIEoVDi2OyPceOXSxpj3acMRuXdMlz9Nkrj+aaiIL8ICoUS1jaWasetbayQdv2mxmsCRwxBYUERXgqeBwEBRZUCzzw7ElNfmKCKmTxtPEpLSvFc0AuQG8ihVCjx4pypCBw5pD6Ho1PNTaxgIDdE0T31W35F93Jhb9GhTm2M8Z6LgrJsJPyZeGYUXUduyR2M9noVn0evRLmiDEO7/B+sm9mjpZnmf4A2BRam1XP579unhWU5cPwzUZEy0Wce8kqzcPn234nnKO8ZUAgFfoz7XKv9bawszKrnMb80R+14QWkO2lp3euh290dvhblxc2wP/gVKpQJyuQF2n1qHE4lN+9nvf2pMC2HWr1+PGTNmIDg4GACwbds2HD58GDt27MDChQtrxH/55ZdqP3/yySf45ptvEBERgcmTJ6uOm5iYwN7e/qH7pfOkcdOmTYiOjsaIESPw3HPP4fPPP0dYWBiUSiVGjx6NFStWwNCw9m6Wl5ejvLxc7ViVUMBQZlAv/b0e/veziTmX05AR9QemX9+Gx8b1Q/w/KlgGpsboMmEAolbtv297Mnn1AoKU784idsMhAED2xetw8OuCbi8G6m3S6DHtCVz/MRYl6TWfGfunirtl+KL7GzBuboq2T3hi4HtTUXgtU+MtbY/gJ3Dlq9+gKK+sr24/ss6fvYhdn+7F/LfmwN3TFbdu3sGGtVux46MvMW3mRABAxE8ncfTIMSwPWwjnTu2RnJSCDe9ug20rG4x8eqiOR9A4DXMPhk/7QLwb8QKqlNW3BRWiCltOzsPUPsvwwbO/QaGswpWMKFy+c0qiNf02ymsG+nYageWHJqtusTrbemB410lY+K3m29tUdwO6jMTjbqOw9shruJGbjI6t3DFz8BLklmQiIuFbXXfvkacpVzExMYGJiUmN2IqKCsTExGDRokWqY3K5HP7+/oiMrH1B7T+VlpaisrIS1tbWasdPnDiB1q1bw8rKCkOGDMGqVatgY2NT53HoNGlctWoV1q5di4CAALz++utIS0vDu+++i9dffx1yuRzvv/8+jIyMsHz58lrbCAsLq3E+AK4YBvf67j4AoLywFPl/pMOys3rm/thYPxiZG+PK7l9rubJaWc5dKCqrkHtFvQKUl3gLTv3ctN7fR0GLdq3Qzt8TP4x5VzpYCBSmZACoTrat3dqg98LRNZJGp/5usHZ1wuHn3quPLjcqllYWMDCQIy+3QO14Xm4+bGytNF7z0ZZdGDbyCTw9uvqZus4uzrhXdg+rV36AqS9MgFwux6b3P8ak4PEYOmywKiYjPQu7d+xtskljcXk+FMoqWJiq/6VqYWqDons5tVxVLcB1Moa7T8P6Yy/idkGy2rkb+Vew4sfxMDNqDgO5EYrL87Eo4HOk5TXdfyQW3auey5Zm6nPZ0swWBaX3n8snu03DKO8ZWHV4Gm7k/f1ct5t9T1iY2WDz838/xmIgN8SkPm9iuOcUvLLnCe0OohEoKqueRytz9aq0pbkt8kqyH7rd6QMXYX/0NpxMqi5eXM9JQmsLJ4zzmaU3SWN9LoTRlKuEhoZi2bJlNWJzcnKgUChgZ2endtzOzg6JiYl1er8333wTjo6O8Pf3Vx0bNmwYRo8eDWdnZ6SkpOCtt97C8OHDERkZCQODuhXadJo07ty5Ezt37sTo0aNx8eJF9OzZE7t27cLEidWVDVdXVyxYsOC+SeOiRYsQEhKidmx7y8m1RGufUTNTWHayw5Uv1CtiHtOG4Nr351CWU1TLldWUlVXIPHsV1o85qR23cnFEUdrD/wXwKPMIfhxlWUVIPRzzwNfK5DIYmNT8WHtMewKZ564i51KaNrrYqBkZGaGLmwvORcdi0JC+AKof8D4XfQFjn3ta4zX37pVDLlffNkcur37kWfx5y6a2GNGEVxwqlFVIy7sCNzsfXLhV/aiEDNWLWo79sbfW6wLdpmKEx3R8cHzWfRPBsspiAEDrFu3Qwdod313aot0BNCIKZSWu5cTD08kP59Kq78rIIENXxz44Gv9lrdc97TUdz3R/Ce8ceQHXctS3OTqZ/D0u31avvLw14hOcTP4OJ5IOaH8QjUCVshJXM+Pg1a4fIlN+BlA9j97t+uKHCw+/zZCJoRmUQn3BhlKpgFzGpQ/aoClX0VRl1IbVq1dj7969OHHiBExNTVXHn3vuOdWvPT090a1bN3Tq1AknTpzAE0/U7R9YOk0a79y5g169egEAvLy8IJfL4e3trTrfo0cP3Llz575taCrv1tetaQAY8O5kXPvhHO6mZaOZozX8lo2HUqFE0p6/by217GSPNgPdcWDk2xrbmJKwEafe+gIpB6tXNp1b9x1G7g3Brd8ScPN4HDoM646OT/XC/seXqq4xt7NEM3tLVUXT1rM9Ku6WoehGDsrzi+ttvA1OJoPH1CFI2H0CQqH+F1jgzldQfCcPp9+q/h9M74XPIPNcCgpTMmFgYogOI3rA7f8G4disj9SuM25hhsee9cPJN3Y12DB0bcKk0Vi5ZB1c3R+DR9cu2PvlAdwru4cnRwUAAJYvXotWrW0x69VpAID+A/tgzxff4jHXzvDwdMWtG7fx0ZZd6D/QV/Uv0P4D+2DnJ3thZ98aHTu1R1JSCvZ+8a2qzabq58TPMc1vJa7nJSA1Nw7+XSbC2NAMp69VP+s1zW8l8kuzcOBi9X52w9ym4ulus/DJmUXIKbmjqlKWV5WivKoMANCz7VDcLc9HXkk6nCxd8FzPBYi9dRwJGXW79fSoOnxpJ2YNXo2U7DikZF/CCM8pMDEyw4k/qitZswevRl5JFvacXQ8AeNrrBYzr9So2HnsDWXdvq575vFdZivKqUhSXF6C4vEDtPaqUVSgszUF6YWqDjq0hHYj5BCHD3kNy5iX8kXERo3pMg4mROX6O/y8AYN6w95BbnIGdp6rv1hjKjdDOpnrfUEMDI9i0sEfHVm4oqyxFekH1P6SjrkXgOd/ZyL57B2m5f6BTaw8803M6foq//yNWTUl9VhpruxWtia2tLQwMDJCZqb7LR2ZmpuTziOvWrcPq1avxyy+/oFu3+y/W69ixI2xtbXH16tVHI2m0t7dHQkIC2rVrh+TkZCgUCiQkJMDDwwMAEB8fj9ata272rEstnGww4qvXYWrTAmXZRbhz6gr2+i1Sqyh2nTYEd2/lIu0nzRutWrs6waRlM9XPKQejEfHyR+i9cDQe/2Aa8pLu4Iex7+LO6b/L0N1eClDbVHzcyVUAgKPBm5Cw6/6LRR4l7fy7waJ9K40rnFu0s1Wrahk1M8WQzTPRoo01qsoqkJd4G+GTPsAfX59Ru67Lc/0BmQyJe/TnmTH/wMHIzy/EJ1t3IzcnHy5dOuL9LW/D2qb69nRmerZaBWHqnxt0b9+8E9lZubCyaol+A/vgpTlTVTEhC2fho827sC5sE/LyCtCqlQ2CxozAtBcnNvTwGtS5Gz+hhakVRnV7GRamtriZn4QPjs/C3T/3FbQ2d1BVYwFgkMs4GBkY4+UB6o9CfH95G364XL35f0szW4zrMQ8WpjYovJeNyNRDOBSn/o+dpijy2o+wMLPGuF6vwNK8Fa7nXkHYkRmqxTE2zR3VFiMMdZ8AIwNjzBu6Ua2d/TGb8N+YTQ3a98bk5B+HYWFug0l9Q2Blbotr2Vew9Nupqtv8rVo4qlUNrZu3xqZJR1Q/j+01E2N7zcSlm79j4f7qxW7bji3DpH4hmP3ESrQ0t0FecSZ+vLQHX/2uPvdU/4yNjdGzZ09EREQgKCgIQPXdooiICMyZM6fW69auXYu3334bR48eVRXk7ufWrVvIzc2Fg4NDnfsmE0J3y4WWLFmC7du3Y9SoUYiIiMD48ePx1VdfYdGiRZDJZHj77bcxduxYrF+//oHafV/Oh6Kp8ZlS0vSfp2wIbx4I0nUXmoyi4qa7kXhDu1tcLh1Eko6E6K5C/Oz2x+qt7f0vPtgey/v27cOUKVOwfft2+Pj4YMOGDfj666+RmJgIOzs7TJ48GU5OTggLCwMArFmzBkuXLsVXX32Ffv36qdpp3rw5mjdvjuLiYixfvhxjxoyBvb09UlJSsGDBAty9exeXL1+ucxVUp5XG5cuXw8zMDJGRkZgxYwYWLlwILy8vLFiwAKWlpXjqqaewcuVKXXaRiIiI9EBj+kaY8ePHIzs7G0uXLkVGRga8vb0RHh6uWhxz48YN1XPnALB161ZUVFRg7Nixau38tdjGwMAAly5dwq5du1BQUABHR0cEBARg5cqVD/RspU4rjfWFlUZqjFhp1A5WGrWHlUbtYaVRO3RZaRyz1aXe2v7m5WTpoEeAzvdpJCIiItK1xlRpbKy4lp6IiIiIJLHSSERERHqvMX2NYGPFSiMRERERSWKlkYiIiPQen2mUxkojEREREUlipZGIiIj03r++eps0YNJIREREeo+3p6Xx9jQRERERSWKlkYiIiPQed9yRxkojEREREUlipZGIiIj0npILYSSx0khEREREklhpJCIiIr0nuHpaEiuNRERERCSJlUYiIiLSe3ymURqTRiIiItJ7/EYYabw9TURERESSWGkkIiIivafk7t6SWGkkIiIiIkmsNBIREZHe4zON0lhpJCIiIiJJrDQSERGR3uOWO9JYaSQiIiIiSaw0EhERkd7j1whKY9JIREREeo+3p6Xx9jQRERERSWKlkYiIiPQe9/aWxkojEREREUlipZGIiIj0npILYSSx0khEREREklhpJCIiIr3HrxGUxkojEREREUlipZGIiIj0HvdplMakkYiIiPQeb09L4+1pIiIiIpLESiMRERHpPSV395bESiMRERERSWKlkYiIiPQen2mUxkojEREREUlipZGIiIj0HrfckcZKIxERERFJYqWRiIiI9J5QcvW0FFYaiYiIiEgSK41ERESk9/hMozQmjURERKT3uLe3NN6eJiIiImpkNm/ejA4dOsDU1BS+vr6Ijo6+b/z+/fvh6uoKU1NTeHp64siRI2rnhRBYunQpHBwcYGZmBn9/fyQnJz9Qn5g0EhERkd5TKkW9vR7Uvn37EBISgtDQUJw/fx5eXl4IDAxEVlaWxvgzZ85gwoQJmD59OmJjYxEUFISgoCDExcWpYtauXYuNGzdi27ZtiIqKQrNmzRAYGIh79+7VuV9MGomIiIgakfXr12PGjBkIDg6Gu7s7tm3bBnNzc+zYsUNj/AcffIBhw4Zh/vz5cHNzw8qVK9GjRw9s2rQJQHWVccOGDVi8eDFGjRqFbt26Yffu3bhz5w4OHjxY534xaSQiIiK9J5T193oQFRUViImJgb+/v+qYXC6Hv78/IiMjNV4TGRmpFg8AgYGBqvjU1FRkZGSoxbRs2RK+vr61tqkJF8IQERER1aPy8nKUl5erHTMxMYGJiUmN2JycHCgUCtjZ2akdt7OzQ2Jiosb2MzIyNMZnZGSozv91rLaYumiSSePrym903QVJ5eXlCAsLw6JFizR+aKhuOI/a86jM5cfPX9B1FyQ9KnPZ2HEetYdzKS3x4zv11vayZcuwfPlytWOhoaFYtmxZvb1nfeDtaR0pLy/H8uXLa/zLgx4M51F7OJfaw7nUDs6j9nAudWvRokUoLCxUey1atEhjrK2tLQwMDJCZmal2PDMzE/b29hqvsbe3v2/8X/99kDY1YdJIREREVI9MTExgYWGh9qqt4mtsbIyePXsiIiJCdUypVCIiIgJ+fn4ar/Hz81OLB4Cff/5ZFe/s7Ax7e3u1mKKiIkRFRdXapiZN8vY0ERER0aMqJCQEU6ZMQa9eveDj44MNGzagpKQEwcHBAIDJkyfDyckJYWFhAIDXXnsNgwYNwnvvvYeRI0di7969OHfuHD766CMAgEwmw9y5c7Fq1Sq4uLjA2dkZS5YsgaOjI4KCgurcLyaNRERERI3I+PHjkZ2djaVLlyIjIwPe3t4IDw9XLWS5ceMG5PK/bxb37dsXX331FRYvXoy33noLLi4uOHjwILp27aqKWbBgAUpKSjBz5kwUFBSgf//+CA8Ph6mpaZ37JROCX5yjC3woWTs4j9rDudQezqV2cB61h3NJ2sCkkYiIiIgkcSEMEREREUli0khEREREkpg0EhEREZEkJo1EREREJIlJYz06efIknnrqKTg6OkImk+HgwYNq54UQWLp0KRwcHGBmZgZ/f38kJyfrprONjDbmLi8vDxMnToSFhQUsLS0xffp0FBcXN+AoGl5DzdulS5cwYMAAmJqaom3btli7dm19D61BLVu2DDKZTO3l6uqqOn/v3j3Mnj0bNjY2aN68OcaMGVPjmxZu3LiBkSNHwtzcHK1bt8b8+fNRVVWlFnPixAn06NEDJiYm6Ny5M3bu3NkQw6tXjekzuH//fri6usLU1BSenp44cuSI1sdbX8LCwtC7d2+0aNECrVu3RlBQEJKSktRiGvJzuHnzZnTo0AGmpqbw9fVFdHS01sdMjwBB9ebIkSPiP//5j/j2228FAHHgwAG186tXrxYtW7YUBw8eFBcvXhRPP/20cHZ2FmVlZbrpcCOijbkbNmyY8PLyEr///rv47bffROfOncWECRMaeCQNqyHmrbCwUNjZ2YmJEyeKuLg4sWfPHmFmZia2b9/eUMOsd6GhocLDw0Okp6erXtnZ2arzL730kmjbtq2IiIgQ586dE3369BF9+/ZVna+qqhJdu3YV/v7+IjY2Vhw5ckTY2tqKRYsWqWKuXbsmzM3NRUhIiEhISBAffvihMDAwEOHh4Q06Vm1rLJ/B06dPCwMDA7F27VqRkJAgFi9eLIyMjMTly5frfQ60ITAwUHz22WciLi5OXLhwQYwYMUK0a9dOFBcXq2Ia6nO4d+9eYWxsLHbs2CHi4+PFjBkzhKWlpcjMzGyYyaBGg0ljA/n3X55KpVLY29uLd999V3WsoKBAmJiYiD179uigh43Xw8xdQkKCACDOnj2rivnxxx+FTCYTt2/fbrC+61J9zduWLVuElZWVKC8vV8W8+eabokuXLvU8ooYTGhoqvLy8NJ4rKCgQRkZGYv/+/apjV65cEQBEZGSkEKI6cZLL5SIjI0MVs3XrVmFhYaGatwULFggPDw+1tsePHy8CAwO1PBrd0eVncNy4cWLkyJFq/fH19RUvvviiVsfYULKysgQA8euvvwohGvZz6OPjI2bPnq36WaFQCEdHRxEWFqb9gVKjxtvTOpKamoqMjAz4+/urjrVs2RK+vr6IjIzUYc8av7rMXWRkJCwtLdGrVy9VjL+/P+RyOaKiohq8z42BtuYtMjISAwcOhLGxsSomMDAQSUlJyM/Pb6DR1L/k5GQ4OjqiY8eOmDhxIm7cuAEAiImJQWVlpdo8urq6ol27dmrz6Onpqfr2BqB6joqKihAfH6+K+Wcbf8U05T//DfkZbGrzW1hYCACwtrYG0HCfw4qKCsTExKjFyOVy+Pv7P7JzSQ+PSaOOZGRkAIDaH+a/fv7rHGlWl7nLyMhA69at1c4bGhrC2tpab+dXW/OWkZGhsY1/vsejztfXFzt37kR4eDi2bt2K1NRUDBgwAHfv3kVGRgaMjY1haWmpds2/51FqjmqLKSoqQllZWT2NTLca8jNYW8yj+BlVKpWYO3cu+vXrp/pauIb6HObk5EChUDSZuaT/Db97mojoX4YPH676dbdu3eDr64v27dvj66+/hpmZmQ57Rvpo9uzZiIuLw6lTp3TdFdJzrDTqiL29PQDUWOmWmZmpOkea1WXu7O3tkZWVpXa+qqoKeXl5eju/2po3e3t7jW388z2aGktLSzz22GO4evUq7O3tUVFRgYKCArWYf8+j1BzVFmNhYdFkE9OG/AzWFvOofUbnzJmDQ4cO4fjx42jTpo3qeEN9Dm1tbWFgYNAk5pL+d0wadcTZ2Rn29vaIiIhQHSsqKkJUVBT8/Px02LPGry5z5+fnh4KCAsTExKhijh07BqVSCV9f3wbvc2OgrXnz8/PDyZMnUVlZqYr5+eef0aVLF1hZWTXQaBpWcXExUlJS4ODggJ49e8LIyEhtHpOSknDjxg21ebx8+bJa8vPzzz/DwsIC7u7uqph/tvFXTFP+89+Qn8FHfX6FEJgzZw4OHDiAY8eOwdnZWe18Q30OjY2N0bNnT7UYpVKJiIiIR2YuSYt0vRKnKbt7966IjY0VsbGxAoBYv369iI2NFWlpaUKI6q0nLC0txXfffScuXbokRo0axS13/qSNuRs2bJjo3r27iIqKEqdOnRIuLi5Nfsudhpi3goICYWdnJyZNmiTi4uLE3r17hbm5eZPacmfevHnixIkTIjU1VZw+fVr4+/sLW1tbkZWVJYSo3uqkXbt24tixY+LcuXPCz89P+Pn5qa7/a6uTgIAAceHCBREeHi5atWqlcauT+fPniytXrojNmzc3iS13Gstn8PTp08LQ0FCsW7dOXLlyRYSGhj5SW+68/PLLomXLluLEiRNqWz+VlpaqYhrqc7h3715hYmIidu7cKRISEsTMmTOFpaWl2qps0g9MGuvR8ePHBYAarylTpgghqrefWLJkibCzsxMmJibiiSeeEElJSbrtdCOhjbnLzc0VEyZMEM2bNxcWFhYiODhY3L17VwejaTgNNW8XL14U/fv3FyYmJsLJyUmsXr26oYbYIMaPHy8cHByEsbGxcHJyEuPHjxdXr15VnS8rKxOzZs0SVlZWwtzcXDzzzDMiPT1drY3r16+L4cOHCzMzM2FrayvmzZsnKisr1WKOHz8uvL29hbGxsejYsaP47LPPGmJ49aoxfQa//vpr8dhjjwljY2Ph4eEhDh8+XG/j1jZNcwhA7TPSkJ/DDz/8ULRr104YGxsLHx8f8fvvv9fHsKmRkwkhRENUNImIiIjo0cVnGomIiIhIEpNGIiIiIpLEpJGIiIiIJDFpJCIiIiJJTBqJiIiISBKTRiIiIiKSxKSRiIiIiCQxaSSienH9+nXIZDJcuHBB111RSUxMRJ8+fWBqagpvb+8Gfe/Bgwdj7ty5DfqeRETaxKSRqImaOnUqZDIZVq9erXb84MGDkMlkOuqVboWGhqJZs2ZISkqq8X27RER0f0waiZowU1NTrFmzBvn5+bruitZUVFQ89LUpKSno378/2rdvDxsbGy32ioio6WPSSNSE+fv7w97eHmFhYbXGLFu2rMat2g0bNqBDhw6qn6dOnYqgoCC88847sLOzg6WlJVasWIGqqirMnz8f1tbWaNOmDT777LMa7ScmJqJv374wNTVF165d8euvv6qdj4uLw/Dhw9G8eXPY2dlh0qRJyMnJUZ0fPHgw5syZg7lz58LW1haBgYEax6FUKrFixQq0adMGJiYm8Pb2Rnh4uOq8TCZDTEwMVqxYAZlMhmXLlmlsZ/DgwXjllVcwd+5cWFlZwc7ODh9//DFKSkoQHByMFi1aoHPnzvjxxx/Vrvv111/h4+MDExMTODg4YOHChaiqqtL4HgCwZcsWuLi4wNTUFHZ2dhg7dmytsUREjQGTRqImzMDAAO+88w4+/PBD3Lp1639q69ixY7hz5w5OnjyJ9evXIzQ0FE8++SSsrKwQFRWFl156CS+++GKN95k/fz7mzZuH2NhY+Pn54amnnkJubi4AoKCgAEOGDEH37t1x7tw5hIeHIzMzE+PGjVNrY9euXTA2Nsbp06exbds2jf374IMP8N5772HdunW4dOkSAgMD8fTTTyM5ORkAkJ6eDg8PD8ybNw/p6el44403ah3rrl27YGtri+joaLzyyit4+eWX8eyzz6Jv3744f/48AgICMGnSJJSWlgIAbt++jREjRqB37964ePEitm7dik8//RSrVq3S2P65c+fw6quvYsWKFUhKSkJ4eDgGDhxYt98IIiJdEUTUJE2ZMkWMGjVKCCFEnz59xLRp04QQQhw4cED8849+aGio8PLyUrv2/fffF+3bt1drq3379kKhUKiOdenSRQwYMED1c1VVlWjWrJnYs2ePEEKI1NRUAUCsXr1aFVNZWSnatGkj1qxZI4QQYuXKlSIgIEDtvW/evCkAiKSkJCGEEIMGDRLdu3eXHK+jo6N4++231Y717t1bzJo1S/Wzl5eXCA0NvW87gwYNEv37968xrkmTJqmOpaenCwAiMjJSCCHEW2+9Jbp06SKUSqUqZvPmzaJ58+aqORs0aJB47bXXhBBCfPPNN8LCwkIUFRVJjouIqLFgpZFID6xZswa7du3ClStXHroNDw8PyOV//5VhZ2cHT09P1c8GBgawsbFBVlaW2nV+fn6qXxsaGqJXr16qfly8eBHHjx9H8+bNVS9XV1cA1c8f/qVnz5737VtRURHu3LmDfv36qR3v16/fQ425W7duNcb1z7Ha2dkBgGqsV65cgZ+fn9oCo379+qG4uFhjhXfo0KFo3749OnbsiEmTJuHLL79UVS2JiBorJo1EemDgwIEIDAzEokWLapyTy+UQQqgdq6ysrBFnZGSk9rNMJtN4TKlU1rlfxcXFeOqpp3DhwgW1V3Jystrt2mbNmtW5TW2QGutfyeGDjPWfWrRogfPnz2PPnj1wcHDA0qVL4eXlhYKCgofuMxFRfWPSSKQnVq9ejR9++AGRkZFqx1u1aoWMjAy1xFGbeyv+/vvvql9XVVUhJiYGbm5uAIAePXogPj4eHTp0QOfOndVeD5IoWlhYwNHREadPn1Y7fvr0abi7u2tnIPfh5uaGyMhItTk8ffo0WrRogTZt2mi8xtDQEP7+/li7di0uXbqE69ev49ixY/XeVyKih8WkkUhPeHp6YuLEidi4caPa8cGDByM7Oxtr165FSkoKNm/eXGNl8P9i8+bNOHDgABITEzF79mzk5+dj2rRpAIDZs2cjLy8PEyZMwNmzZ5GSkoKjR48iODgYCoXigd5n/vz5WLNmDfbt24ekpCQsXLgQFy5cwGuvvaa1sdRm1qxZuHnzJl555RUkJibiu+++Q2hoKEJCQtRu6f/l0KFD2LhxIy5cuIC0tDTs3r0bSqUSXbp0qfe+EhE9LCaNRHpkxYoVNW6purm5YcuWLdi8eTO8vLwQHR1935XFD2r16tVYvXo1vLy8cOrUKXz//fewtbUFAFV1UKFQICAgAJ6enpg7dy4sLS01Jlv38+qrryIkJATz5s2Dp6cnwsPD8f3338PFxUVrY6mNk5MTjhw5gujoaHh5eeGll17C9OnTsXjxYo3xlpaW+PbbbzFkyBC4ublh27Zt2LNnDzw8POq9r0RED0sm/v0wExERERHRv7DSSERERESSmDQSERERkSQmjUREREQkiUkjEREREUli0khEREREkpg0EhEREZEkJo1EREREJIlJIxERERFJYtJIRERERJKYNBIRERGRJCaNRERERCSJSSMRERERSfp/wZByWwved18AAAAASUVORK5CYII=", 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", 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", 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", 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gyxGW/0JdpKVYsWK4desWtm/fjt27d2PDhg2YP38+Ro0ahTFjxmTLcdObeS0+mRyVG8pPTU1F3bp1MXToULXbixQpInufXvQqK7JahrbbE1BfJ19fX5w8eRJDhgxB6dKlYWpqitTUVDRo0EDtdaypnkZGRjh69CgOHTqEHTt2YPfu3QgODkatWrWwd+/ebJ2xn14bp3eMzLRxTlwrRLkdO42UaU5OTti/fz/evn0rizbevHlT2v6xO3fuqJRx+/ZtGBsbS3/9Gxsb49atWyr5bt68CR0dHRQoUCDdugAf1varVauWbNutW7ek7Wn//Xj2aRp1aR+zsbGBmZkZrl69mmG+9evXo2bNmliyZIksPTo6GtbW1hnumxEnJydcvnwZqampsmhjeu2dHhMTE7Rp0wZt2rRBUlISWrVqhfHjxyMgIEDtEkMft+3HwwaSkpLw4MEDKdKSW3z8M/442vPq1SuV6JyrqytiY2Oz5Rw+vb6FELh79+4XWQfx0yjZfxUVFYUDBw5gzJgxGDVqlJSu7jOcFTo6OqhduzZq166NGTNmYMKECRg5ciQOHTqU4c/Ayckp3e+FtO3alp3XCtG3grenKdMaNWqElJQUzJ07V5Y+c+ZMKBQKlZmyp06dko2Fevz4MbZs2YJ69epJawPWq1cPW7Zskd3SCw8PR1BQEKpVqwYzMzO1dSlXrhxsbW2xcOFCJCYmSum7du3CjRs30LhxYwCAo6MjPD09sXLlSsTGxkr5jhw5gitXrmR4vjo6OmjRogW2bduG0NBQle1pUQldXV2VKFBISIjapX+yolGjRnjx4oVslnBycjLmzJkDU1NT+Pj4aCzj0+VSDAwM4OHhASEE3r9/r3afOnXqwMDAAH/++afsvJYsWYKYmBipbXOL2rVrQ09PDwsWLJClf3qdAh+iaadOncKePXtUtkVHRyM5OTnTx125ciXevn0rvV+/fj2eP3+u8jnQBhMTk/809OFTaZG3T6/j//JIv9evX6uklS5dGgBkn1l1GjVqhH///RenTp2S0uLi4vD333/D2dkZHh4en12vzMrOa4XoW8FII2Va06ZNUbNmTYwcORJhYWEoVaoU9u7diy1btmDAgAFwdXWV5ff09ET9+vVlS+4AkN0WHTdunLSWW69evaCnp4e//voLiYmJGa71p6+vj8mTJ8Pf3x8+Pj5o166dtOSOs7MzBg4cKOWdMGECmjdvjqpVq8Lf3x9RUVGYO3cuPD09ZR1JdSZMmIC9e/fCx8cHPXr0QLFixfD8+XOEhITg+PHjsLCwQJMmTTB27Fj4+/ujSpUquHLlCtasWSOL0n2OHj164K+//oKfnx/OnTsHZ2dnrF+/HidOnMCsWbNUxpaqU69ePdjb26Nq1aqws7PDjRs3MHfuXDRu3Djd/W1sbBAQEIAxY8agQYMGaNasGW7duoX58+ejfPnyuW6xdzs7O/Tv3x/Tp09Hs2bN0KBBA1y6dAm7du2CtbW1LCo3ZMgQbN26FU2aNIGfnx+8vLwQFxeHK1euYP369QgLC8t0dNjS0hLVqlWDv78/wsPDMWvWLLi5uaF79+7aOlWJl5cXgoODMWjQIJQvXx6mpqZo2rTpZ5dnZmYmLUvz/v175MuXD3v37sWDBw8+u8yxY8fi6NGjaNy4MZycnBAREYH58+cjf/78sgku6gwfPhxr165Fw4YN0a9fP1haWmLFihV48OABNmzY8EUW7s7Oa4XoW8FOI2Wajo4Otm7dilGjRiE4OBjLli2Ds7Mzpk6dil9//VUlv4+PDypXrowxY8bg0aNH8PDwwPLly2W374oXL45jx44hICAAEydORGpqKipWrIjVq1dnuEYjAPj5+cHY2BiTJk3CsGHDpIWfJ0+eLHvCRNOmTbF27VqMHj0aw4cPR+HChbF8+XKsWLEC165dy/AY+fLlw5kzZ/D7779jzZo1ePPmDfLly4eGDRvC2NgYADBixAjExcUhKCgIwcHBKFu2LHbs2IHhw4dnoXVVGRkZ4fDhwxg+fDhWrFiBN2/ewN3dHcuWLVNZsDo9PXv2xJo1azBjxgzExsYif/786NevH3777bcM9xs9ejRsbGwwd+5cDBw4EJaWlujRowcmTJiQ4ZqKOWXy5MkwNjbGokWLsH//flSuXBl79+5FtWrVZLfgjY2NceTIEUyYMAEhISFYuXIlzMzMUKRIEYwZMwbm5uaZPuaIESNw+fJlTJw4EW/fvkXt2rUxf/586brQpl69euHixYtYtmwZZs6cCScnp//UaQQ+PJqwb9++mDdvHoQQqFevHnbt2vXZ64w2a9YMYWFhWLp0KV6+fAlra2v4+Phkqp3t7Oxw8uRJDBs2DHPmzMG7d+9QsmRJbNu27YtFurPzWiH6VihEdo6uJvofhUKB3r17q71FmFuULl0aNjY22LdvX05XhbQgOjoaefPmxbhx4zBy5MhsK/fw4cOoWbMmQkJC0Lp162wrl4got+OYRvrmvX//XmX80eHDh3Hp0qUMn8lLX4+EhASVtLTxePwZExFlD96epm/e06dPUadOHXTo0AGOjo64efMmFi5cCHt7e7WLHNPXJzg4GMuXL0ejRo1gamqK48ePY+3atahXrx6qVq2a09UjIvomsNNI37y8efPCy8sLixcvRmRkJExMTNC4cWNMmjQJVlZWOV09ygYlS5aEnp4epkyZgjdv3kiTY8aNG5fTVSMi+mZwTCMRERERacQxjURERESkETuNRERERKQRO430TVEoFBg9erRWj7F8+XIoFArZU2y05fDhw1AoFFi/fr3Wj5UdnJ2dM72GZGaEhYVBoVBg+fLl2VZmbuLn5wdnZ+ecrsZ3Z/To0dn+KEai7wE7jSRJ6wwpFAocP35cZbsQAgUKFIBCoUCTJk1yoIbfrqCgoP/0yDb6ti1atAg+Pj6ws7ODUqmEi4sL/P39Nf7hcvz4cekz/fLlS7V5goODUblyZZiYmMDCwgJVqlTBwYMHpe0ffy+oe61Zs0bK6+zsnG6+woULZ0tb5AY//PADGjVqlNPVIPriOHuaVBgaGkrPfv7YkSNH8OTJEyiVyhyqmWYJCQnQ0/v6LuugoCBcvXoVAwYMyOmqUC504cIFuLi4oFmzZsibNy8ePHiARYsWYfv27bh06ZLap7akpqaib9++MDExQVxcnNpyR48ejbFjx6J169bw8/PD+/fvcfXqVdlz06tXr45Vq1ap7Dtz5kxcunQJtWvXltJmzZql8mjOhw8f4rfffkO9evU+9/Rzlffv32Pfvn2YOHFiTleF6Iv7+n67ktY1atQIISEh+PPPP2UdsKCgIHh5eaUbscgNPn5kHAHx8fFf5LF2pF1pz23/WIsWLVCuXDmsXLlS7SMr//77bzx+/BjdunXD7NmzVbafPn0aY8eOxfTp02XPav9UoUKFVJ6jnpCQgF69eqFWrVqwt7eX1elTacsetW/fPt1jfE2OHTuGt2/ffrHHGRLlJrw9TSratWuHV69eyR6vl5SUhPXr1+Onn35Su8+0adNQpUoVWFlZwcjICF5eXirj8JYtWwaFQoGlS5fK0idMmACFQoGdO3dmWK/Q0FDUr18f1tbWMDIygouLC7p06SLL8+mYxrSxS3fv3oWfnx8sLCxgbm4Of39/xMfHy/ZNSEhAv379YG1tjTx58qBZs2Z4+vRppsdJ7tq1C97e3jAxMUGePHnQuHFjjc+2Bj48sWTHjh14+PChdCvv03FuqampGD9+PPLnzw9DQ0PUrl0bd+/eVSnH09MT586dQ/Xq1WFsbIwRI0YAABITExEYGAg3NzcolUoUKFAAQ4cORWJioqyMZcuWoVatWrC1tYVSqYSHhwcWLFigUmchBMaNG4f8+fPD2NgYNWvWTPdco6OjMWDAABQoUABKpRJubm6YPHkyUlNTVfL5+fnB3NwcFhYW6Ny5M6KjozW2H/Ah+jNmzBgULlwYhoaGsLKyQrVq1WTXsJ+fH0xNTXH//n3Ur18fJiYmcHR0xNixY/HpymOpqamYNWsWihcvDkNDQ9jZ2aFnz56IiopSOXZmf+6bN2+Gp6cnDA0N4enpiU2bNmXq3NKTdo2oa6PXr1/jt99+w9ixY2XPYf/YrFmzYG9vj/79+0MIoRIhzMi2bdvw9u3bTHUEg4KC4OLigipVqmjMq1Ao0KdPH4SEhMDDwwNGRkaoXLkyrly5AgD466+/4ObmBkNDQ9SoUUPt7fmQkBB4eXnByMgI1tbW6NChgyxymp59+/ahWrVqsLCwgKmpKdzd3aXPz8d27NgBDw8Pqf1fvHgBf39/5M+fH0qlEg4ODmjevPkXGfNM9MUJov9ZtmyZACDOnj0rqlSpIjp27Cht27x5s9DR0RFPnz4VTk5OonHjxrJ98+fPL3r16iXmzp0rZsyYISpUqCAAiO3bt8vyNWnSRJibm4tHjx4JIYS4fPmyMDAwEF27ds2wbuHh4SJv3ryiSJEiYurUqWLRokVi5MiRolixYrJ8AERgYKD0PjAwUAAQZcqUEa1atRLz588X3bp1EwDE0KFDZfv6+voKAKJjx45i3rx5wtfXV5QqVUqlzLR2evDggZS2cuVKoVAoRIMGDcScOXPE5MmThbOzs7CwsJDlU2fv3r2idOnSwtraWqxatUqsWrVKbNq0SQghxKFDh6T6e3l5iZkzZ4rRo0cLY2NjUaFCBVk5Pj4+wt7eXtjY2Ii+ffuKv/76S2zevFmkpKSIevXqCWNjYzFgwADx119/iT59+gg9PT3RvHlzWRnly5cXfn5+YubMmWLOnDmiXr16AoCYO3euLN9vv/0mAIhGjRqJuXPnii5dughHR0dhbW0tOnfuLOWLi4sTJUuWFFZWVmLEiBFi4cKFolOnTkKhUIj+/ftL+VJTU0X16tWFjo6O6NWrl5gzZ46oVauWKFmypAAgli1blmEbjhgxQigUCtG9e3exaNEiMX36dNGuXTsxadIkKU/nzp2FoaGhKFy4sOjYsaOYO3euaNKkiQAgfv/9d1l53bp1E3p6eqJ79+5i4cKFYtiwYcLExESUL19eJCUlSfky+3Pfs2eP0NHREZ6enmLGjBli5MiRwtzcXBQvXlw4OTlleG4fe/nypQgPDxdnz54VTZs2FQDE3r17VfL16tVLFC9eXCQnJ0ufgcjISFkea2tr0axZMzFz5kxhZWUlAAh7e3sxZ84cjfVo1qyZMDIyEm/evMkw3/nz5wUAMXLkyEydHwBRsmRJUaBAATFp0iQxadIkYW5uLgoWLCjmzp0rPDw8xPTp08Vvv/0mDAwMRM2aNWX7p302y5cvL2bOnCmGDx8ujIyMhLOzs4iKipLypbVJmqtXrwoDAwNRrlw5MXv2bLFw4UIxePBgUb16dZU6Fi1aVAwePFh6X6VKFWFubi5+++03sXjxYjFhwgRRs2ZNceTIkUydM9HXhJ1GknzcaZw7d67IkyePiI+PF0II8eOPP0pf0Oo6jWn50iQlJQlPT09Rq1YtWfrz58+FpaWlqFu3rkhMTBRlypQRBQsWFDExMRnWbdOmTVLdMpJep7FLly6yfC1bthRWVlbS+3PnzgkAYsCAAbJ8fn5+GjuNb9++FRYWFqJ79+6yfV+8eCHMzc1V0tVp3Lix2s5DWqexWLFiIjExUUqfPXu2ACCuXLkipfn4+AgAYuHChbIyVq1aJXR0dMSxY8dk6QsXLhQAxIkTJ6S0T3+OQghRv359UahQIel9RESEMDAwEI0bNxapqalS+ogRIwQAWafxjz/+ECYmJuL27duyMocPHy50dXWlPx42b94sAIgpU6ZIeZKTk4W3t3emOo2lSpVSuSY/1blzZwFA9O3bV0pLTU0VjRs3FgYGBlKn6tixYwKAWLNmjWz/3bt3y9Kz8nMvXbq0cHBwENHR0VLa3r17BYAsdRqVSqUAIAAIKysr8eeff6rkuXTpktDV1RV79uwRQgi1ncbXr19LZZiamoqpU6eK4OBg0aBBA7XX0MdevXolDAwMhK+vr8b6/vrrrwKAuH79eqbOD4BQKpWyDvdff/0ldWg/7qQGBATIPodJSUnC1tZWeHp6ioSEBCnf9u3bBQAxatQoKe3TTuPMmTPVdqw/df/+fQFAHDp0SAghRFRUlAAgpk6dmqnzI/ra8fY0qeXr64uEhARs374db9++xfbt29O9NQ0ARkZG0r+joqIQExMDb29vnD9/XpbP3t4e8+bNw759++Dt7Y2LFy9i6dKlMDMzy7A+abfYtm/fjvfv32f5fD59xrS3tzdevXqFN2/eAAB2794NAOjVq5csX9++fTWWvW/fPkRHR6Ndu3Z4+fKl9NLV1UXFihVx6NChLNf3U/7+/jAwMJDVHwDu378vy6dUKuHv7y9LCwkJQbFixVC0aFFZ/WrVqgUAsvp9/HOMiYnBy5cv4ePjg/v37yMmJgYAsH//fiQlJaFv376yZUvUTeIJCQmBt7c38ubNKzt2nTp1kJKSgqNHjwIAdu7cCT09Pfzyyy/Svrq6uplqf+DD9XHt2jXcuXNHY94+ffpI/067HZqUlIT9+/dLdTY3N0fdunVldfby8oKpqanUXpn9uT9//hwXL15E586dYW5uLh27bt268PDwyNT5pdm1axd27tyJ6dOno2DBgmonuPTr1w8NGzbMcOJJ2q3oV69eYfHixRg8eDB8fX2lW68ZPX5x/fr1SEpK0nhrOjU1FevWrUOZMmVQrFixTJ4hULt2bdnwjIoVKwL4MGM5T548Kulpn4HQ0FBERESgV69esrHNjRs3RtGiRbFjx450j5n2/bJlyxaVYRMf27FjB8zNzaVJgkZGRjAwMMDhw4fVDl0g+tZwIgypZWNjgzp16iAoKAjx8fFISUlB69at082/fft2jBs3DhcvXpSNk1O3Flrbtm2xevVq7NixAz169JDNvkyPj48PfvjhB4wZMwYzZ85EjRo10KJFC/z000+Zms1dsGBB2fu8efMC+NDBNTMzw8OHD6GjowMXFxdZPjc3N41lp3VU0jphn0rrECckJEgdrzQfTyL43Pp/LF++fLLOZVr9bty4ARsbG7VlR0RESP8+ceIEAgMDcerUKZUxnzExMTA3N8fDhw8BQGUJFRsbG6leHx/78uXLGo/98OFDODg4wNTUVLbd3d1d7X6fGjt2LJo3b44iRYrA09MTDRo0QMeOHVGyZElZPh0dHZVJHUWKFAEAaQzanTt3EBMTA1tb2wzrnNmfe3rtBXw4v0//sMpIzZo1AQANGzZE8+bN4enpCVNTU6kjHBwcjJMnT+Lq1asZlpP2x4G+vr7sc62jo4M2bdogMDAQjx49UrnuAGDNmjWwtLREw4YNMzzGkSNH8PTp0wwn2ajz6THTOtoFChRQm572GUhrZ3XXTNGiRdUuI5amTZs2WLx4Mbp164bhw4ejdu3aaNWqFVq3bg0dnf+PrezYsQP16tWTJggqlUpMnjwZv/76K+zs7FCpUiU0adIEnTp1yvRnm+hrwk4jpeunn35C9+7d8eLFCzRs2DDdAfXHjh1Ds2bNUL16dcyfPx8ODg7Q19fHsmXLEBQUpJL/1atXCA0NBQBcv34dqampsi9mddIWuD59+jS2bduGPXv2oEuXLpg+fTpOnz6t0tn4lK6urtp0kQ2PXk+LTKxatUrtL4q0XzDBwcEqUcDMHj+z9f84Uvhx/UqUKIEZM2aoLSPtl/G9e/dQu3ZtFC1aFDNmzECBAgVgYGCAnTt3YubMmRlGYNKTmpqKunXrYujQoWq3p3XY/qvq1avj3r172LJlC/bu3YvFixdj5syZWLhwIbp165alslJTU2Fraytbf/BjaR3gzP7ctcXV1RVlypTBmjVrpE7jkCFD8OOPP8LAwEDqBKdNlHn8+DGSkpLg6OgIS0tLGBoawsLCQuXaSussR0VFqXTgHj16hGPHjqFHjx7Q19fPsH5r1qyBjo4O2rVrl6XzSu9a1+Zn2MjICEePHsWhQ4ewY8cO7N69G8HBwahVqxb27t0LXV1dxMfH4/DhwyoTwwYMGICmTZti8+bN2LNnD37//XdMnDgRBw8eRJkyZf5z3YhyE3YaKV0tW7ZEz549cfr0aQQHB6ebb8OGDTA0NMSePXtkUb9ly5apzd+7d2+8ffsWEydOREBAAGbNmoVBgwZlqk6VKlVCpUqVMH78eAQFBaF9+/ZYt25dljsGn3JyckJqaioePHggiwh9OkNZHVdXVwAfftnWqVMn3Xz169eXzeb9mDafTuHq6iqtp5fRcbZt24bExERs3bpV1ln49Pa6k5MTgA+Rto+jdpGRkSqRT1dXV8TGxmbYLmllHjhwALGxsbI/AG7duqX5BP/H0tIS/v7+8Pf3R2xsLKpXr47Ro0fLro3U1FTcv39f1lm9ffs2gP+fjezq6or9+/ejatWqajvhH58boPnn/nF7fSor56dOQkKCLLL/+PFjBAUFqf1jrWzZsihVqhQuXrwIHR0dlC5dGmfPnkVSUpIsOv3s2TMAUBsdXrt2LYQQGm9NJyYmYsOGDahRo4baNSS1Ia2db926pRL9vXXrlrQ9PTo6OqhduzZq166NGTNmYMKECRg5ciQOHTqEOnXq4ODBg0hMTFQbYXV1dcWvv/6KX3/9FXfu3EHp0qUxffp0rF69OvtOkCgX4JhGSpepqSkWLFiA0aNHo2nTpunm09XVhUKhQEpKipQWFhaGzZs3q+Rdv349goODMWnSJAwfPhxt27bFb7/9Jv3iTk9UVJRKRKF06dIAoLJszOeoX78+ANX18ObMmZOpfc3MzDBhwgS14y0jIyMBAA4ODqhTp47slcbExETl1nV28fX1xdOnT7Fo0SKVbQkJCdK4uLRIzsftHBMTo9L5r1OnDvT19TFnzhxZXnVPtPH19cWpU6ewZ88elW3R0dFITk4G8GFt0OTkZFkUJyUlJVPtD3yIXn/M1NQUbm5uaq+NuXPnSv8WQmDu3LnQ19eXhkn4+voiJSUFf/zxh8q+ycnJUuQuKz/30qVLY8WKFbKf8b59+3D9+nWN55acnKx2vNy///6LK1euoFy5clLapk2bVF5t2rQBAKxcuRIzZ86U8rZp0wYpKSlYsWKFlPbu3TusWbMGHh4eajt7QUFBKFiwoMrC/5/auXMnoqOjv+jajOXKlYOtrS0WLlwo+7nv2rULN27cyHBdxdevX6ukffr9snPnTpQrVw52dnZSnvj4eLx79062n6urK/LkyZMt30tEuQ0jjZShzp07a8zTuHFjzJgxAw0aNMBPP/2EiIgIzJs3D25ubrh8+bKULyIiAr/88gtq1qwp3U6bO3cuDh06BD8/Pxw/fjzd29QrVqzA/Pnz0bJlS7i6uuLt27dYtGgRzMzMsuVxXl5eXvjhhx8wa9YsvHr1CpUqVcKRI0ekzmxGETozMzMsWLAAHTt2RNmyZdG2bVvY2Njg0aNH2LFjB6pWrSrrqKR3/ODgYAwaNAjly5eHqalphh31rOjYsSP++ecf/Pzzzzh06BCqVq2KlJQU3Lx5E//88w/27NmDcuXKoV69ejAwMEDTpk3Rs2dPxMbGYtGiRbC1tcXz58+l8mxsbDB48GBMnDgRTZo0QaNGjXDhwgXs2rUL1tbWsmMPGTIEW7duRZMmTeDn5wcvLy/ExcXhypUrWL9+PcLCwmBtbY2mTZuiatWqGD58OMLCwuDh4YGNGzdmuiPt4eGBGjVqwMvLC5aWlggNDcX69etlk16AD4u/7969G507d0bFihWxa9cu7NixAyNGjJAiaz4+PujZsycmTpyIixcvol69etDX18edO3cQEhKC2bNno3Xr1ln6uU+cOBGNGzdGtWrV0KVLF7x+/Rpz5sxB8eLFNa6PGBsbiwIFCqBNmzYoXrw4TExMcOXKFSxbtgzm5ub4/fffpbzqFte+ePEigA/jID/++fTs2ROLFy9G7969cfv2bRQsWBCrVq3Cw4cPsW3bNpVyrl69isuXL2P48OEaI+Nr1qyBUqnEDz/8kGG+7KSvr4/JkyfD398fPj4+aNeuHcLDwzF79mw4OztnOLZy7NixOHr0KBo3bgwnJydERERg/vz5yJ8/v9RB3rlzp8rwktu3b6N27drw9fWFh4cH9PT0sGnTJoSHh6Nt27ZaPV+iHJFj87Yp1/l4yZ2MqFtyZ8mSJaJw4cJCqVSKokWLimXLlqksa9GqVSuRJ08eERYWJtt3y5YtAoCYPHlyusc8f/68aNeunShYsKBQKpXC1tZWNGnSRISGhsryIZ0ldz5dSkPdWotxcXGid+/ewtLSUpiamooWLVqIW7duCQCy9f7U7SvEh+Vx6tevL8zNzYWhoaFwdXUVfn5+KnVUJzY2Vvz000/CwsJCtgxL2pI7ISEhsvwPHjxQWYrGx8dHFC9eXG35SUlJYvLkyaJ48eJCqVSKvHnzCi8vLzFmzBjZckdbt24VJUuWFIaGhsLZ2VlMnjxZLF26VOV8U1JSxJgxY4SDg4MwMjISNWrUEFevXhVOTk6yJXeE+LA0TUBAgHBzcxMGBgbC2tpaVKlSRUybNk225uGrV69Ex44dhZmZmTA3NxcdO3YUFy5cyNSSO+PGjRMVKlQQFhYWwsjISBQtWlSMHz9eVn7nzp2FiYmJuHfvnrRupZ2dnQgMDBQpKSkqZf7999/Cy8tLGBkZiTx58ogSJUqIoUOHimfPnsnyZfbnvmHDBlGsWDGhVCqFh4eH2Lhxo+jcubPGJXcSExNF//79RcmSJYWZmZnQ19cXTk5OomvXrhrXABUi/c+AEB/WP+3cubOwtLQUSqVSVKxYUezevVttOcOHDxcAxOXLlzM8XkxMjDA0NBStWrXSWLdPARC9e/eWpaVd658ua5PeZyM4OFiUKVNGKJVKYWlpKdq3by+ePHkiy/Ppd9OBAwdE8+bNhaOjozAwMBCOjo6iXbt20lJRV69eFQDEv//+Kyvn5cuXonfv3qJo0aLCxMREmJubi4oVK4p//vkny+dO9DVQCJENo4iJvlEXL15EmTJlsHr16m/mMWjfKz8/P6xfvz5LTz4hAoApU6ZgxowZeP78uVbHHxPldhzTSPQ/CQkJKmmzZs2Cjo4OqlevngM1IqLcwNnZGTNnzmSHkb57HNNI9D9TpkzBuXPnULNmTejp6WHXrl3YtWsXevToobJGHBF9P3x9fXO6CkS5AjuNRP9TpUoV7Nu3D3/88QdiY2NRsGBBjB49GiNHjszpqhEREeU4jmkkIiIiIo04ppGIiIiINGKnkYiIiIg0YqeRiCib+Pn5SY8jzExeTc9Mzy1Gjx7NmcP/UVobvnz5UmNeZ2dn+Pn5ab9SRFnETmMuce/ePfTs2ROFChWCoaEhzMzMULVqVcyePVu2FIyzszMUCoX0srW1hbe3NzZt2iQrz9nZGU2aNFF7rNDQUCgUCixfvvw/13vo0KFQKBTSo8o+FRYWJtV13LhxavO0b98eCoVC5RdojRo1pH11dHRgZmYGd3d3dOzYMd1nOKvj5+cnazOlUokiRYpg1KhRKo8A+xrdvXsXrVu3Rt68eWFsbIxq1aqpPC86s7p37w6FQqH22omNjcWAAQOQP39+KJVKFCtWTPbYv49FR0ejR48esLGxgYmJCWrWrInz58+rzbt161aULVsWhoaGKFiwIAIDA6XHC6bR9AtX3fUeGxuLwMBAeHp6wsTEBFZWVihdujT69+8vPV9Z2+Lj4zF69GgcPnw428v++POhUChgZGSEkiVLYtasWUhNTc32430ttm3bhqZNm8LOzg4GBgawtLRE9erVMX36dLx58yanq0f0VePs6Vxgx44d+PHHH6FUKtGpUyd4enoiKSkJx48fx5AhQ3Dt2jX8/fffUv7SpUvj119/BQA8e/YMf/31F1q1aoUFCxbg559//mL1FkJg7dq1cHZ2xrZt2/D27VvkyZNHbV5DQ0OsXbsWv/32myw9Li4OW7ZsgaGhodr98ufPj4kTJ0p57969i40bN2L16tXw9fXF6tWroa+vr7GuSqUSixcvBvDhecpbtmzBH3/8gXv37mHNmjVZOe1c5fHjx6hcuTJ0dXUxZMgQmJiYYNmyZahXrx4OHDiQpfUlQ0NDsXz5crU/i5SUFNSvXx+hoaHo3bs3ChcujD179qBXr16IiorCiBEjpLypqalo3LgxLl26hCFDhsDa2hrz589HjRo1cO7cORQuXFjKu2vXLrRo0QI1atTAnDlzcOXKFYwbNw4RERHpdkgz4/3796hevTpu3ryJzp07o2/fvoiNjcW1a9cQFBSEli1bqn228n+1aNEiWYctPj4eY8aMAfChk5fdPv58vHz5EkFBQRg4cCAiIyMxfvz4bD9ebpaamoquXbti+fLlKFGiBHr16oUCBQrg7du3OHXqFH777Tfs3LkTBw4cyOmqanTr1q10H6lKlKNy9Hk0JO7fvy9MTU1F0aJFVR5PJoQQd+7cEbNmzZLeq3uE3/Pnz4WJiYkoUqRIhvnSnD17NlOPZtPk4MGDAoA4ePCg0NfXF8uXL1fJk/YIsFatWgkA4uLFi7Lta9asEfr6+qJp06bCxMREti29x+IlJyeLXr16CQBi6NChGuuZ9vi4j6WmpopKlSoJhUIhXrx4kZnTzZV69eol9PT0xM2bN6W0uLg4UaBAAVG2bNlMl5OamioqV64sunTpovba+eeffwQAsWTJEln6Dz/8IAwNDUV4eLiUFhwcrPJ4t4iICGFhYSHatWsn29/Dw0OUKlVKvH//XkobOXKkUCgU4saNG1JaRo/CE0L1ek+r75o1a1TyJiQkyB6dqE2RkZEqj7ZMo+66zAp1n4+EhATh5OQk8uTJI5KTkz+77E99+ti93GjixIkCgBg4cKBITU1V2f7s2TPZ40DVSUlJEQkJCVqpn6ZrmOhrwD9lctiUKVMQGxuLJUuWwMHBQWW7m5sb+vfvn2EZ9vb2KFasGB48ePDZ9Xj//j1u3ryJ58+fZ3qfNWvWwMPDAzVr1kSdOnUyjNhVrlwZLi4uCAoKUimjQYMGsLS0zPRxdXV18eeff8LDwwNz585FTExMpvdNo1AoUK1aNQghcP/+fQCAj48PSpUqpTa/u7s76tevD0D1tuDHr49v+UdHR2PAgAEoUKAAlEol3NzcMHnyZFkkKu32/bRp0/D333/D1dUVSqUS5cuXx9mzZzWex7Fjx1CmTBm4u7tLacbGxmjWrBnOnz+PO3fuZKo9Vq1ahatXr6YbnTp27BgAoG3btrL0tm3b4t27d9iyZYuUtn79etjZ2aFVq1ZSmo2NDXx9fbFlyxYkJiYCAK5fv47r16+jR48e0NP7/5sevXr1ghAC69evz1Td1bl37x4AoGrVqirb0oZ/pCc6Olq6xtK8fPkSOjo6sLKygvholbJffvkF9vb20vuPxzSGhYXBxsYGADBmzBjpGhk9erTseE+fPkWLFi1gamoKGxsbDB48GCkpKVk+57RzK1++PN6+fYuIiAgp/fLly/Dz85OGv9jb26NLly549eqVShnHjx9H+fLlYWhoCFdXV/z111/pHm/16tXw8vKCkZERLC0t0bZtWzx+/FglX0hIiJTP2toaHTp0wNOnT2V50sZ4fk57xMfHY/LkyShevDimTp2qdvylg4MDhg0bJktTKBTo06cP1qxZg+LFi0OpVGL37t0AgGnTpqFKlSqwsrKCkZERvLy81F6TH5fh7u4OQ0NDeHl54ejRo2rrGh0dDT8/P1hYWMDc3Bz+/v6Ij4+X5VE3pjE6OhoDBw6Es7MzlEol8ufPj06dOsmGbMyZMwfFixeHsbEx8ubNi3Llyql85xL9F+w05rBt27ahUKFCqFKlymeX8f79ezx+/BhWVlafXcbTp09RrFgxBAQEZCp/YmIiNmzYgHbt2gEA2rVrh4MHD+LFixfp7tOuXTusW7dO+qX78uVL7N27Fz/99FOW66urq4t27dohPj4ex48fz/L+wIdf6gCQN29eAEDHjh1x+fJlXL16VZbv7NmzuH37Njp06AAAGDlyJFatWiV7pXUobW1tAXz4Jebj44PVq1ejU6dO+PPPP1G1alUEBARg0KBBKnUJCgrC1KlT0bNnT4wbNw5hYWFo1aoV3r9/n+E5JCYmwsjISCXd2NgYAHDu3DmN7fD27VsMGzYMI0aMkHWAPj2Orq4uDAwMNB7nwoULKFu2rMrttQoVKiA+Ph63b9+W8gFAuXLlZPkcHR2RP39+afvHXr9+jZcvX6q8Ph3D5+TkBABYuXKlrJOXGRYWFvD09JT90j9+/DgUCgVev36N69evS+nHjh2Dt7e32nJsbGykW+wtW7aUrpWPO9Npt/2trKwwbdo0+Pj4YPr06bLhKFmV9oeIhYWFlLZv3z7cv38f/v7+mDNnDtq2bYt169ahUaNGsva5cuUK6tWrh4iICIwePRr+/v4IDAxUGTMNAOPHj0enTp1QuHBhzJgxAwMGDJCGRERHR0v5li9fDl9fX+jq6mLixIno3r07Nm7ciGrVqsny/Zf2OH78OKKjo9GuXTvo6upmqb0OHjyIgQMHok2bNpg9e7bU6Z89ezbKlCmDsWPHYsKECdDT08OPP/6IHTt2qJRx5MgRDBgwAB06dMDYsWPx6tUrNGjQQOW7BPjwdJm3b99i4sSJ8PX1xfLly6UhDOmJjY2Ft7c35syZg3r16mH27Nn4+eefcfPmTTx58gTAh6ER/fr1g4eHB2bNmoUxY8agdOnSOHPmTJbagyhDORnm/N7FxMQIAKJ58+aZ3sfJyUnUq1dPREZGisjISHHp0iXRtm1bAUD07dtXli8rt6fTbiN37tw5U/VYv369ACDu3LkjhBDizZs3wtDQUMycOVOWL63cqVOniqtXrwoA4tixY0IIIebNmydMTU1FXFyc2lt16d2eTrNp0yYBQMyePTvDuqaVndZmd+/eFdOmTRMKhUJ4enpKt7Kio6OFoaGhGDZsmGz/fv36CRMTExEbG6u2/BMnTgh9fX3RpUsXKe2PP/4QJiYm4vbt27K8w4cPF7q6uuLRo0ey9rGyshKvX7+W8m3ZskUAENu2bcvw3Jo2bSosLCzEmzdvZOmVK1cWAMS0adMy3F8IIQYPHixcXFzEu3fvhBDqr53p06fLfnYfnw8A0aRJEynNxMRE1hZpduzYIQCI3bt3CyGEmDp1qgAgtcXHypcvLypVqiS9T7u1l9Hr4zrHx8cLd3d3AUA4OTkJPz8/sWTJEtlt9Iz07t1b2NnZSe8HDRokqlevLmxtbcWCBQuEEEK8evVKKBQK2fXXuXNn4eTkJL3XdHsagBg7dqwsvUyZMsLLy0tjHX18fETRokWl6/rmzZtiyJAhKm0hxIf2+NTatWsFAHH06FEprUWLFsLQ0FA8fPhQSrt+/brQ1dWV3Z4OCwsTurq6Yvz48bIyr1y5IvT09KT0pKQkYWtrKzw9PWW3fbdv3y4AiFGjRmVLe8yePVsAEJs3b5alJycnS+2T9vr41jUAoaOjI65du6ZS5qdtlpSUJDw9PUWtWrVk6WnXX2hoqJT28OFDYWhoKFq2bCmlpV3Dn342WrZsKaysrGRpTk5Osu/iUaNGCQBi48aNKvVMO5/mzZtn+H1JlB0YacxBaTP50ps8kp69e/fCxsYGNjY2KFWqFEJCQtCxY0dMnjz5s+vi7OwMIUSmZ1SvWbMG5cqVg5ubG4AP59C4ceMMb1EXL14cJUuWxNq1awF8iK41b95cilZlVdps67dv32rMGxcXJ7WZm5sbBg8ejKpVq2LLli3SrSxzc3M0b94ca9eulaIvKSkpCA4ORosWLWBiYqJS7osXL9C6dWuULl0a8+fPl9JDQkLg7e2NvHnzyiJiderUQUpKisqtqzZt2kgRTwBS9Crt1nl6fvnlF0RHR6NNmza4cOECbt++jQEDBiA0NBQAZDPv1bl9+zZmz56NqVOnQqlUppvvp59+grm5Obp06YJ9+/YhLCwMf//9t3TOHx8nISFBbVlpE2zS8qb9N7286uq+YcMG7Nu3T+VlZ2cny2dkZIQzZ85gyJAhAD5Eu7p27QoHBwf07dtXukWeHm9vb4SHh+PWrVsAPkQUq1evDm9vb+lW/fHjxyGESDfSmFmfTl7z9vbW+HNPc/PmTem6Llq0KKZOnYpmzZqpfI4/jka/e/cOL1++RKVKlQBAmtWekpKCPXv2oEWLFihYsKCUv1ixYlIkPc3GjRuRmpoKX19f2fVtb2+PwoULS7P3Q0NDERERgV69eskmWDVu3BhFixZVG7X7nPZI+y79dAWGK1euSO2T9vr0lryPjw88PDxUyvy4zaKiohATEwNvb2+1qwBUrlwZXl5e0vuCBQuiefPm2LNnj8qtdXXn9+rVqwxndm/YsAGlSpVCy5YtVbalfX9ZWFjgyZMnmRrWQvS52GnMQWnjqjLT6flYxYoVsW/fPuzfvx8nT57Ey5cvsXLlSrW3KTPyueuuRUdHY+fOnfDx8cHdu3elV9WqVREaGirdflTnp59+QkhICO7evYuTJ09+1q3pNLGxsQAy1+k2NDSUOhjLli1DsWLFEBERodJmnTp1wqNHj6SOwf79+xEeHo6OHTuqlJmcnAxfX1+kpKRg48aNss7PnTt3sHv3bpVfWHXq1AEA2XgzALJf0sD/3zKPiorK8LwaNmyIOXPm4OjRoyhbtizc3d2xY8cOaWyipnUA+/fvjypVquCHH37IMJ+9vT22bt2KxMRE1KtXDy4uLhgyZAjmzJmjchwjIyO1nbK05Y3S2jztv+nlVXc9V69eHXXq1FF5qZvxbW5ujilTpiAsLAxhYWFYsmQJ3N3dMXfuXPzxxx8Znm9aR/DYsWOIi4vDhQsX4O3tjerVq0vXxrFjx2BmZpbuONjMMDQ0lMY9psmbN6/Gn3saZ2dn7Nu3D3v27MH8+fORL18+REZGqrTH69ev0b9/f9jZ2cHIyAg2NjZwcXEBAGlMcGRkJBISEmSz29N8PGYW+HB9CyFQuHBhlWv8xo0b0vX98OFDtfsDQNGiRaXt/7U90r4D0r4T0ri5uUmfe3WfYQBSO3xq+/btqFSpEgwNDWFpaSkNN1A3hlpdmxUpUgTx8fGIjIyUpX/OZ/3evXvw9PRMdzsADBs2DKampqhQoQIKFy6M3r1748SJExnuQ5RVXHInB5mZmcHR0VHtuJeMWFtbS52P9KQXqQEgDbpOb5kbTUJCQpCYmIjp06dj+vTpKtvXrFmT7hiddu3aISAgAN27d4eVlRXq1av3WXUAILVbWrQzI7q6urI2q1+/PooWLYqePXti69atsnQ7OzusXr0a1atXx+rVq2Fvb6+2vYcMGYJTp05h//79yJ8/v2xbamoq6tati6FDh6qtT5EiRVTqp47IxHi8Pn36wN/fH5cvX4aBgQFKly6NJUuWqD3Oxw4ePIjdu3dj48aN0vhO4ENnOCEhAWFhYbC0tJT+uKlevTru37+PK1euIC4uDqVKlZLWO/z4OA4ODmonVKWlpS11kzbx6/nz5yhQoIBK3goVKmg898xycnJCly5d0LJlSxQqVAhr1qxJd93QtDq6uLjg6NGjUhS+cuXKsLGxQf/+/fHw4UMcO3YMVapU+U9Lo2R1/N2nTExMZNdm1apVUbZsWYwYMUI2kcfX1xcnT57EkCFDULp0aZiamiI1NRUNGjT4rDUdU1NToVAosGvXLrXn8LmLln9uexQtWhTAh++E5s2by+qR1j7pjX1W98fJsWPH0KxZM1SvXh3z58+Hg4MD9PX1sWzZsv88seS/fNYzUqxYMdy6dQvbt2/H7t27sWHDBsyfPx+jRo3SOGaSKLPYacxhTZo0wd9//41Tp06hcuXK2Vauk5OTbMD+x9JuuaVNFsiqNWvWwNPTE4GBgSrb/vrrLwQFBaX7JVWwYEFUrVoVhw8fxi+//CKbNZsVKSkpCAoKkhazzioHBwcMHDgQY8aMwenTp6Vbdbq6uvjpp5+wfPlyTJ48GZs3b0b37t1VvujXrVuHWbNmYdasWfDx8VEp39XVFbGxsRo799nFxMREdv3s378fRkZGamcPp3n06BEAyCZmpHn69ClcXFwwc+ZMDBgwQErX1dVF6dKlZccBIDvP0qVL49ixY0hNTZV1qM6cOQNjY2Opg5lWTmhoqKyD+OzZMzx58gQ9evTIxJlnTd68eeHq6pqpP9S8vb1x9OhRuLi4oHTp0siTJw9KlSoFc3Nz7N69G+fPn9f4y/hLP0WlZMmS6NChA/766y8MHjwYBQsWRFRUFA4cOIAxY8Zg1KhRUt5PZ9bb2NjAyMhI7Yz7tO+MNK6urhBCwMXFJcM/TNK+Y27duoVatWqplPm530Gf8vb2hrm5OdatW4eAgID/vMbhhg0bYGhoiD179sjuICxbtkxtfnVtdvv2bRgbG6tETj9HZq9ZExMTtGnTBm3atEFSUhJatWqF8ePHIyAg4LODBEQf4+3pHDZ06FCYmJigW7duCA8PV9l+7949zJ49O8vlNmrUCE+ePMHmzZtl6YmJiVi8eDFsbW1RtmxZKT2zS+48fvwYR48eha+vL1q3bq3y8vf3x927dzOcsTdu3DgEBgaib9++WT4v4EOHsV+/frhx4wb69euX4fIpGenbty+MjY0xadIkWXrHjh0RFRWFnj17IjY2Vpo1nebq1avo1q0bOnTokO5ySL6+vjh16hT27Nmjsi06OlrliSfZ6eTJk9i4cSO6du0Kc3PzdPPVqlULmzZtUnnZ2NigXLly2LRpE5o2bZru/pGRkZg8eTJKliwp6zS2bt0a4eHh2Lhxo5T28uVLhISEoGnTptIv4eLFi6No0aL4+++/ZeO+FixYAIVCgdatW392G1y6dEnt02MePnyI69evq71d+ilvb2+EhYUhODhYul2to6ODKlWqYMaMGXj//r3G8Yxp43U/nSWsTUOHDsX79+8xY8YMAP8f2fo0kjVr1izZe11dXdSvXx+bN2+W/qAAgBs3bqhcx61atYKuri7GjBmjUq4QQho3WK5cOdja2mLhwoWyYQi7du3CjRs30Lhx4/92sv9jbGyMoUOH4urVqxg+fLjaqF1WInm6urpQKBSy6zIsLEzl+zTNqVOnZGMdHz9+jC1btqBevXr/OZoMAD/88AMuXbqkdhZ72nl9OlbTwMAAHh4eEEJoXIWBKLMYacxhrq6uCAoKQps2bVCsWDHZE2FOnjyJkJCQz3oGaY8ePbB06VL8+OOP6NKlC8qUKYNXr14hODgYV69excqVK2XLp6QtudO5c+cMJ8MEBQVBCIFmzZqp3d6oUSPo6elhzZo1qFixoto8Pj4+aqNz6sTExGD16tUAPtxWT3sizL1799C2bVuNY9MyYmVlBX9/f8yfPx83btxAsWLFAABlypSBp6cnQkJCUKxYMVnnGgD8/f0BQLp9/bEqVaqgUKFCGDJkCLZu3YomTZrAz88PXl5eiIuLw5UrV7B+/XqEhYXB2tr6s+ue5uHDh/D19UWzZs1gb2+Pa9euYeHChShZsiQmTJiQ4b4FCxZUGV8FAAMGDICdnR1atGghS/fx8UHlypXh5uaGFy9e4O+//0ZsbCy2b98ui+y0bt0alSpVgr+/P65fvy49ESYlJUUlMpc2caNevXpo27Ytrl69irlz56Jbt27Sz+Nz7Nu3D4GBgWjWrBkqVaoEU1NT3L9/H0uXLkViYqLKWonqpHUIb926JWvL6tWrY9euXdJ6mhkxMjKCh4cHgoODUaRIEVhaWsLT01Pj+LT/wsPDA40aNcLixYvx+++/w8rKCtWrV8eUKVPw/v175MuXD3v37lW7ruuYMWOwe/dueHt7o1evXkhOTpbW/rt8+bKUz9XVFePGjUNAQADCwsLQokUL5MmTBw8ePMCmTZvQo0cPDB48GPr6+pg8eTL8/f3h4+ODdu3aITw8XFraZuDAgdl23sOHD8eNGzcwdepU7N27Fz/88APy58+PqKgonD9/HiEhIbC1tc1UxK1x48aYMWMGGjRogJ9++gkRERGYN28e3NzcZO2QxtPTE/Xr10e/fv2gVCqlCWLZdVt4yJAhWL9+vfR97uXlhdevX2Pr1q1YuHAhSpUqhXr16sHe3h5Vq1aFnZ0dbty4gblz56Jx48ZZnmxJlK4vP2Gb1Ll9+7bo3r27cHZ2FgYGBiJPnjyiatWqYs6cOdJSKEJkvJTOp6KiosTAgQOFi4uL0NfXF2ZmZqJmzZpi165dKnkzu+ROiRIlRMGCBTPMU6NGDWFrayvev38vW3InI+ktuYOPllQxNTUVhQsXFh06dBB79+7NsDxNZae5d++e0NXVVTnvKVOmCABiwoQJKvs4OTmlu+zLx8sYvX37VgQEBAg3NzdhYGAgrK2tRZUqVcS0adNEUlKSEEJk2D5IZ6mWj71+/Vo0b95c2NvbCwMDA+Hi4iKGDRumsgRPVqR3jQ0cOFAUKlRIKJVKYWNjI3766Sdx7969dOvVtWtXYWVlJYyNjYWPj484e/as2rybNm0SpUuXFkqlUuTPn1/89ttvUvukyeoTYe7fvy9GjRolKlWqJGxtbYWenp6wsbERjRs3FgcPHsxsUwhbW1sBQLZUz/HjxwUA4e3trZL/0yV3hBDi5MmTwsvLSxgYGMh+puldl5l9+kpGS1IdPnxYdqwnT56Ili1bCgsLC2Fubi5+/PFH8ezZM7XX2JEjR6T6FipUSCxcuDDdOm3YsEFUq1ZNmJiYCBMTE1G0aFHRu3dvcevWLVm+4OBgUaZMGaFUKoWlpaVo3769ePLkiSzPf22PNJs2bRKNGjUSNjY2Qk9PT1hYWIhq1aqJqVOniujoaFleAKJ3795qy1myZIkoXLiwUCqVomjRomLZsmVq65JWxurVq6X8ZcqUEYcOHVJ7Hp9ew8uWLRMAxIMHD6S0T5fcEeLDEk99+vQR+fLlEwYGBiJ//vyic+fO4uXLl0IIIf766y9RvXp1YWVlJZRKpXB1dRVDhgz5Yk8/ou+DQoj/OPqW6Bs0e/ZsDBw4EGFhYWqjcUREwIdxq71798bcuXNzuipEWscxjUSfEEJgyZIl8PHxYYeRiIjofzimkeh/4uLisHXrVhw6dAhXrlyRPU+ZiIjoe8dII9H/REZGSouPjxgxIt3JPkRERNoyceJElC9fHnny5IGtrS1atGihsuyVOiEhIShatCgMDQ1RokQJ7Ny5U7ZdCIFRo0bBwcEBRkZGqFOnjtrlojLCTiPR/6Qt4hwVFSU9UYWIKCNCCI5npGx15MgR9O7dG6dPn8a+ffvw/v171KtXD3Fxcenuc/LkSbRr1w5du3bFhQsX0KJFC7Ro0UK2vueUKVPw559/YuHChThz5gxMTExQv3596WldmcGJMERERES5VGRkJGxtbXHkyBFUr15dbZ42bdogLi4O27dvl9IqVaqE0qVLY+HChRBCwNHREb/++isGDx4M4MOSdnZ2dli+fDnatm2bqbow0khERESUS6U979zS0jLdPKdOnVJ5Aln9+vVx6tQpAMCDBw/w4sULWR5zc3NUrFhRypMZnAhDREREpEWJiYmypyIBgFKplD2mUp3U1FQMGDAAVatWzfChAC9evICdnZ0szc7ODi9evJC2p6WllyczvslOY1buzxMREVHukJPPyJ6p84PWyo4ZVULlCUGBgYEan07Vu3dvXL16FcePH9da3bLim+w0EhEREeUWAQEBGDRokCxNU5SxT58+2L59O44ePYr8+fNnmNfe3h7h4eGytPDwcNjb20vb09IcHBxkeUqXLp3Z0+CYRiIiIiIdhY7WXkqlEmZmZrJXep1GIQT69OmDTZs24eDBg3BxcdFY98qVK+PAgQOytH379qFy5coAABcXF9jb28vyvHnzBmfOnJHyZAYjjURERPTd01EocroKAD7ckg4KCsKWLVuQJ08eacyhubk5jIyMAACdOnVCvnz5MHHiRABA//794ePjg+nTp6Nx48ZYt24dQkND8ffffwP48LjLAQMGYNy4cShcuDBcXFzw+++/w9HRES1atMh03dhpJCIiIsolFixYAACoUaOGLH3ZsmXw8/MDADx69Ag6Ov9/s7hKlSoICgrCb7/9hhEjRqBw4cLYvHmzbPLM0KFDERcXhx49eiA6OhrVqlXD7t27szSO9Jtcp5ETYYiIiL4+OTkRZp5+5tYq/By936/TWtlfEsc0EhEREZFGvD1NRERE3z1dBeNomrCFiIiIiEgjRhqJiIjou5dbZk/nZow0EhEREZFGjDQSERHRd0+HYxo1YqeRiIiIvnvsNGrGFiIiIiIijRhpJCIiou8eJ8JoxkgjEREREWnESCMRERF99zimUTO2EBERERFpxEgjERERfff4GEHN2EJEREREpBEjjURERPTd45hGzdhpJCIiou8el9zRjN1qIiIiItKIkUYiIiL67vH2tGZsISIiIiLSiJFGIiIi+u4x0qgZW4iIiIiINGKkkYiIiL57nD2tGSONRERERKQRI41ERET03eNjBDVjp5GIiIi+e5wIoxlbiIiIiIg0YqSRiIiIvnucCKMZI41EREREpBEjjURERPTd45hGzdhCRERERKQRI41ERET03WOkUTO2EBERERFpxEgjERERffc4e1ozdhqJiIjou8fb05qxhYiIiIhII0YaiYiI6LvHZ09rxhYiIiIiIo0YaSQiIqLvHifCaMZIIxERERFpxEgjERERffc4e1ozthARERERacRIIxEREX33FLqMo2nCTiMRERF999hp1IwtREREREQaMdJIRERE3z2FHuNomrCFiIiIiEgjdhqzwblz59C3b1/UqVMHpUqVwsGDBzXuc/bsWbRp0wblypVDkyZNsGXLFpU869atQ8OGDVG+fHm0b98eV65c0Ub1cw22Y/ZhW2YftmX2YDtmH7aldij0dLT2+lZ8O2eSgxISEuDu7o6AgIBM5X/y5An69OmD8uXL459//kH79u0xZswYnDhxQsqze/duTJs2DT179sS6devg7u6OX375Ba9evdLWaeQ4tmP2YVtmH7Zl9mA7Zh+25bfv6NGjaNq0KRwdHaFQKLB58+YM8/v5+UGhUKi8ihcvLuUZPXq0yvaiRYtmqV4c05gNqlWrhmrVqmU6f0hICPLly4fBgwcDAAoVKoQLFy5g9erVqFq1KgBg1apVaNWqFVq0aAEA+O2333D06FFs3rwZXbt2zfZzyA3YjtmHbZl92JbZg+2YfdiW2pGbZk/HxcWhVKlS6NKlC1q1aqUx/+zZszFp0iTpfXJyMkqVKoUff/xRlq948eLYv3+/9F5PL2vdwNzTQt+Ry5cvo1KlSrK0KlWq4PLlywCA9+/f48aNG7I8Ojo6qFSpkpSH2I7ZiW2ZfdiW2YPtmH3Yll+fhg0bYty4cWjZsmWm8pubm8Pe3l56hYaGIioqCv7+/rJ8enp6snzW1tZZqleu7jQ+fvwYXbp0yTBPYmIi3rx5I3slJiZ+oRp+npcvX8LKykqWZmVlhdjYWLx79w5RUVFISUlRm+fly5dfsqq5Gtsx+7Atsw/bMnuwHbMP2zJztDmm8Uv3VZYsWYI6derAyclJln7nzh04OjqiUKFCaN++PR49epSlcnN1p/H169dYsWJFhnkmTpwIc3Nz2Wvq1KlfqIZERET0LVDoKrT2UtdXmThxolbO49mzZ9i1axe6desmS69YsSKWL1+O3bt3Y8GCBXjw4AG8vb3x9u3bTJedo2Mat27dmuH2+/fvaywjICAAgwYNkqUJIf5TvbTN2tpaZXDxq1evYGpqCkNDQ+jq6kJXV1dtnqyGkr9lbMfsw7bMPmzL7MF2zD5sy5ynrq+iVCq1cqwVK1bAwsJCGp+apmHDhtK/S5YsiYoVK8LJyQn//PNPpset5minsUWLFlAoFBl28hQKRYZlKJVKlYZ/9+5dttRPW0qWLInjx4/L0k6fPo2SJUsCAPT19VGsWDGcOXMGtWrVAgCkpqbizJkzaNu27Revb27Fdsw+bMvsw7bMHmzH7MO2zBxtLo2jrq+iDUIILF26FB07doSBgUGGeS0sLFCkSBHcvXs30+Xn6O1pBwcHbNy4EampqWpf58+fz8nqZVp8fDxu3ryJmzdvAgCePn2Kmzdv4vnz5wA+zGoaOXKklP/HH3/EkydPMHPmTDx48ADBwcHYu3cvOnToIOXp2LEjNm7ciK1bt+L+/fsYN24cEhISVP5y+JawHbMP2zL7sC2zB9sx+7AtKT1HjhzB3bt3MxU5jI2Nxb179+Dg4JDp8nM00ujl5YVz586hefPmardrikLmFteuXZONHZg2bRoAoFmzZvjjjz/w8uVLvHjxQtqeP39+zJ07F1OnTsWaNWtgZ2eHwMBAaekDAGjQoAGioqIwf/58vHz5Eu7u7pg/f77KQOVvCdsx+7Atsw/bMnuwHbMP21I7ctMi3LGxsbII4IMHD3Dx4kVYWlqiYMGCCAgIwNOnT7Fy5UrZfkuWLEHFihXh6empUubgwYPRtGlTODk54dmzZwgMDISuri7atWuX6XopRA72yo4dO4a4uDg0aNBA7fa4uDiEhobCx8cnS+Xm9tvTREREpMrQ0DDHjn2+6gytlV32xCDNmT5y+PBh1KxZUyW9c+fOWL58Ofz8/BAWFobDhw9L22JiYuDg4IDZs2eje/fuKvu2bdsWR48exatXr2BjY4Nq1aph/PjxcHV1zXS9crTTqC3sNBIREX19crLTeKH6LK2VXeboAK2V/SXlnlgsEREREeVafIwgERERffdy05jG3IqdRiIiIvru5aZnT+dWbCEiIiIi0oiRRiIiIvru8fa0ZmwhIiIiItKIkUYiIiL67jHSqBlbiIiIiIg0YqSRiIiIvnucPa0ZW4iIiIiINGKkkYiIiL57HNOoGVuIiIiIiDRipJGIiIi+e4w0asZOIxEREX33OBFGM7YQEREREWnESCMRERF993h7WjO2EBERERFpxEgjERERffcUuoqcrkKux0gjEREREWnESCMRERF99zimUTO2EBERERFpxEgjERERffcYadSMnUYiIiL67nFxb83YQkRERESkESONRERE9N3j7WnN2EJEREREpBEjjURERPTd45hGzdhCRERERKQRI41ERET03eOYRs3YQkRERESkESONRERE9N1T6DCOpgk7jURERES6ipyuQa7HbjURERERacRIIxEREX33eHtaM7YQEREREWnESCMRERF997i4t2ZsISIiIiLSiJFGIiIi+u5xTKNmbCEiIiIi0oiRRiIiIiKu06gRO41ERET03ePtac3YQkRERESkESONRERE9N3jkjuasYWIiIiISCNGGomIiOi7xzGNmrGFiIiIiEgjRhqJiIiIuOSORow0EhEREeUiR48eRdOmTeHo6AiFQoHNmzdnmP/w4cNQKBQqrxcvXsjyzZs3D87OzjA0NETFihXx77//Zqle7DQSERHRd0+ho6O1V1bFxcWhVKlSmDdvXpb2u3XrFp4/fy69bG1tpW3BwcEYNGgQAgMDcf78eZQqVQr169dHREREpsvn7WkiIiL67uWmJXcaNmyIhg0bZnk/W1tbWFhYqN02Y8YMdO/eHf7+/gCAhQsXYseOHVi6dCmGDx+eqfJzTwsRERERfYMSExPx5s0b2SsxMTHbj1O6dGk4ODigbt26OHHihJSelJSEc+fOoU6dOlKajo4O6tSpg1OnTmW6/G8y0rjAuH1OV4GIiIiyaGDqhhw7tkJHexNhJk6ciDFjxsjSAgMDMXr06Gwp38HBAQsXLkS5cuWQmJiIxYsXo0aNGjhz5gzKli2Lly9fIiUlBXZ2drL97OzscPPmzUwf55vsNBIRERHlFgEBARg0aJAsTalUZlv57u7ucHd3l95XqVIF9+7dw8yZM7Fq1apsOw47jURERERaHNOoVCqztZOYGRUqVMDx48cBANbW1tDV1UV4eLgsT3h4OOzt7TNdJsc0EhEREX1jLl68CAcHBwCAgYEBvLy8cODAAWl7amoqDhw4gMqVK2e6TEYaiYiI6LuXmx4jGBsbi7t370rvHzx4gIsXL8LS0hIFCxZEQEAAnj59ipUrVwIAZs2aBRcXFxQvXhzv3r3D4sWLcfDgQezdu1cqY9CgQejcuTPKlSuHChUqYNasWYiLi5NmU2cGO41EREREuUhoaChq1qwpvU8bD9m5c2csX74cz58/x6NHj6TtSUlJ+PXXX/H06VMYGxujZMmS2L9/v6yMNm3aIDIyEqNGjcKLFy9QunRp7N69W2VyTEYUQgiRDeeXq8zU+SGnq0BERERZlJOzpyOXn9Va2TZ+5bVW9pfESCMRERF997S55M63IvfcwCciIiKiXIuRRiIiIqJcNBEmt2ILEREREZFGjDQSERHRd0+hxcW9vxVsISIiIiLSiJFGIiIi+u7lpsW9cyu2EBERERFpxEgjERERffcUulynURN2GomIiIh4e1ojthARERERacRIIxEREX33uOSOZmwhIiIiItKIkUYiIiL67il0OBFGE0YaiYiIiEgjRhqJiIjou8cxjZqxhYiIiIhII0YaiYiIiDimUSN2GomIiOi7p1Cw06gJb08TERERkUaMNBIRERHx9rRGjDQSERERkUaMNBIREdF3j4t7a8ZIIxERERFpxEgjEREREWdPa8RIIxERERFpxEgjERERffc4plEzRhqJiIiISCNGGomIiIgYRtOInUYiIiL67vExgpqxX01EREREGjHSSERERMSJMBox0khEREREGjHSSERERMRIo0aMNBIRERGRRow0EhER0XePs6c1Y6SRiIiIiDRipJGIiIiIYTSN2GkkIiKi7x6fPa0Z+9VEREREpBEjjUREREScCKMRI41EREREpBEjjURERPTd45hGzRhpJCIiIiKNGGkkIiIiYqRRI3YaM6DQ0UGl0b4o1r46TOwtEPssCtdXHMKZcesBADp6uqgyrh1cGpaFeSE7JMbE49H+yzgesBpxz6OkcrrcXwBzZ1tZ2ccDVuPs5E3pHrv2wp4oWLskTB3zIin2HZ6fvIVjw1cj6tZTKU+N2V3gWKUorDwL4vWNJ1hTdnA2t0D2KflzfZT8uT7MnG0AAK+uPcaZP0IQtvsCAM3na2hpioarB8C6pBMMrfIgISIG97aexYkRa5D0NiHd41oUdkD1KZ3gWLUodAz08PLyQ5wctQ5PDl+V8uQpYI1a83ugQE1PvI99h+srD+N4wGqIlFQttoj2lB/WEtUmdsD52dtxZOAyAECJ7nXh3q4abMsWgtLMGPPzdkRiTLzGskwcLeE9qQOcG5aFvrEBou++wN4u8xB+7l6mr/+vnb6pIar80Q5uLSrC2NYMERce4PCApQgPvac2v4m9BapP84NdOVdYuNnjwpyd0s8hjZVHAVQe0xa2XoVg7myLwwOX4sLsHV/idHINddfpp3T0dFE+oBU8OtWAaT5LRN16hmPDV+HhnotSnkqBvqgc2Ea23+ubT7HCo582q5+jyg9vCbeWlWBZNB+SE5Lw7OQtHB++ClG3n2W4X5n+jT98Dxe0RsLLt7iz4RSOB6xBSuJ7AFm/1un7w05jBsoNa4FSP9fHHr85eHXtMezKuaLe0j5IjInHxTk7oWeshG2ZQjgzbj0iL4VBmdcENWZ1QfMtwxFUYZisrJOj1uLKov3S+4w6OgAQce4+bq45hrePImFoaYpKgW3Qas/vWFqoF0Tq/3dmri07CPsKhWFd0il7Tz6bxT55heMBqxF95zmgADw610SzzcOwpuwQvLr+WOP5ilSBe1vP4uTvaxEf+QYWbvaoNbc7DBeYYleHWeket8W2EYi68xzra49GckISygxojBbbArDUrTfiw6Oh0NFBi+0jEPciGsFVR8DEIS/qr+iL1PfJODEy6Ms1UDaxK+eKEj3qIvJSmCxdz9gAD/dcxMM9F1FtYodMlaW0MEGb4+Px5NBVbGo0DgmRb2BR2AHvomL/V2bmr/+vWd1FvWDtWRC7O/2J2GevUaxDdfywLxArig9A3LPXKvl1lfpIePkGZ8avR9kBTdSWqWdsgJgH4bi9/iRqzPDX9inkOuldp5+qMq4dirWvjn09FiLq5lM41S+NZhuHYl3VkYi8+EDK9/LqI2yoO0Z6n5qcoq2q5wr5qxfHpfm7EX72LhR6Oqg6vj1a7RmFFcX7Izk+Ue0+7u2qodrEDtjbdR6en7wFiyKOqL+sD4QAjv66HEDWr/VvDR8jqBnHNGbAsbI77m09iwc7z+PNw0jc2XAaD/degn15NwBA0pt4bKw/FrdDTiLq9jO8OHMHh/ouhl05N+QpYC0rK+ltAuLDo6VXeh/sNFcW7cPTY9fx5mEkIi48wMnf18KsoI0UqQOAw/2X4tL83Yh5EJ79J5/N7m8PRdiu84i++xzRd57j5G9BeB/7DvaVigDQfL6J0XG4vHAPws/dw9tHkXh88AouLdiNfN7F0j2moVUe5C3iiNDJm/DyykNE332O48NXQ9/EENaeBQEATvVKwdIjP3Z3nI3IS2EI230Bp0atQ6leDaCj/3X9TaVvYoiGqwdgf4+FUscuzYXZO3B28iY8P3070+WVH9YSsY9fYm/XeQg/exdvwiLwaN8lxNz/cL1l5fr/WukaGqDwD5VwbNhKPD12HTH3XuD0mH8QffcFSv1SX+0+bx5G4vCApbix6ki60dzw0Hs4NnQlbgefQPL/ojzfi4yu008V6+CDfyduRNiu84h5EI7LC/fgwc4L8BrUVJYvNTlF9v367tVbbZ5CjtvUaByurziEV9cf4+Xlh9jrPxdmTjaw83JNdx/HKkXx7MRN3Fp7HG8eRuLRvku4te649Pvsc671b46OFl9ZdPToUTRt2hSOjo5QKBTYvHlzhvk3btyIunXrwsbGBmZmZqhcuTL27NkjyzN69GgoFArZq2jRolmqFzuNGXh26hYK1CoBi8IOAADrkk5wrFZUuqWqjtLcBCI1FYnRcbL08sNa4ufI5Wh/biq8BjeHQjfzTa9nrERx/5qIuR+Ot49ffd7J5CIKHR0UaVMVeiaGeH7qlsr2zJyviUNeuLWsiCdHrqV7nHev3uL1zaco1skHesZKKHR1ULJnPcSFRyP83IfbLQ6V3fHyyiPER8RI+4XtuQiluQmsihf4j2f6ZdWa2w0Pdp7DowOXs6W8Qk3LIfzcPTQO/hU9XyxF+3NT4dmtTob7pHf9f6109HSgo6eL5Hfyjl1yQhIcq2bty5Y+yMp1qqvUV9P2iXCsJv9jMW9hB3R/sghd7s5Hg1X9v5k/WjLLwNwYAPDudfqd5Wcnb8LWyxV2/+skmrvYwblhWTzYdR4Ar/XcJi4uDqVKlcK8efMylf/o0aOoW7cudu7ciXPnzqFmzZpo2rQpLlyQ91eKFy+O58+fS6/jx49nqV5fVyjlCzs7aROUZsbwu/EnUlNSoaOrgxO/BeFm0DG1+XWV+qg2qQNurj0uu/18cc5ORJy/j3evY+FYxR1VJ7SHiUNe6ZZAekr+Uh/ekzvCwNQIr28+xYZ6Y5D6Pjk7T/GLsvIsiLYnJ0DP0ABJse+wrdUUvL7xRNqemfNtuGYgXJuXh76xEve2nsW+7gsyPOaGuqPRbNMw9HmzGiJVID4iBpsajpM6NSb2FogPj5HtEx8eLW2LzIbz/hKKtKkK27KFsvW2sHkhO5T8uT7Oz9yGfyduhH15N9Sc3QWpScm4vvKwSv70rv+v2fvYd3h28iYq/tYar288QXx4DNzbVYND5SKIvvsip6v31cnqdfpwz0V4DWyKp0evI/reCxSsXQJurSrJ/uh+ceYO9vjPRdStZzBxyItKo36E79FxWFliAN7HvtPWqeQeCgVqzPTH0+M38Ora43Sz3Vp7HEbWZmhzbBygUEBXXw+XFu7B2YkbAfBaB3LX7emGDRuiYcOGmc4/a9Ys2fsJEyZgy5Yt2LZtG8qUKSOl6+npwd7e/rPrleORxoSEBBw/fhzXr19X2fbu3TusXLkyw/0TExPx5s0b2StZZM94liK+VVD0J2/sbD8La7yGYI/fXHj92hwenWqo5NXR00Xj4F8BhQIHe/0t23Z+5jY8OXINL688xOW/9uLo4BUo3achdA0y7rPfXHMMa8oOwT8+vyPq9jM0Dv4Vukr9bDm3nBB16xlWlxmMtZWG4/LCPai/vA8si+WXtmfmfI8MWoY1XkOwpflEWLjaw2eGX4bHrDW3O+Ij3uCf6r9hbcVhuLflXzTfGgATewstnGHOMM1vhRqzumBXh9nSgPbsoNBRIOL8fZwYGYTIiw9wZdE+XFm8HyV61lPJm9H1/7Xb3elPKBQK9Hi6GP3erUOZvo1wa+1xiFSR01X7qnzOdXp4wFJE3XmOzjdmo39iMGrO6YZryw8CH43rDtt9AXfWn8LLKw/xcO9FbG48HkoLYxTxraqtU8lVas3rDivPgtjZbkaG+fL7FEeFgFY42HsR1ngNwdZWk+HSqCwq/tZaysNrXXvU9VUSEzMepvZfpKam4u3bt7C0tJSl37lzB46OjihUqBDat2+PR48eZancHI003r59G/Xq1cOjR4+gUChQrVo1rFu3Dg4OH24Hx8TEwN/fH506dUq3jIkTJ2LMmDGytHooigbw+M/1qz6lE85O3oTbwScAAK+uPkIeJ2uUH95KFmlJ+4Vp5mSD9bUDNUZZXpy5A119PZg522Y42y3pTTyS3sQj+u5zPD99G71er4Bby4q4tS5r4eTcIvV9MmLuffiLNeL8fdiXc0OZ/o1x4Oe/AGTufNPGLEXdeop3r2PR5th4nPkjBHEvolWOV6BWCbg08cICy87Sz+Rg70UoWKcUPDrXxNnJmxD3Ilq6XZPG2M4CANSWmRvZebnCxM4C7c9NldJ09HSRv7oHSvduiD8N28omT2VW3PNovPooEgwAr288ReFWlWRpWb3+vzYx98MRUnMU9IyVUJoZIe5FNBqtHSSN7aTM+ZzrNOHlG2xrNRm6Sn0YWuVB3LPXqDapA2LuR6R7nMSYeETdfg4Lt8+Ppnwtas7phkKNvfCPz++IfZrxRJUqY9vixuqjuLrkAIAPv8/0TQxR56+fcWb8BkAIXutaXHJHXV8lMDAQo0eP1srxpk2bhtjYWPj6+kppFStWxPLly+Hu7o7nz59jzJgx8Pb2xtWrV5EnT55MlZujncZhw4bB09MToaGhiI6OxoABA1C1alUcPnwYBQsWzFQZAQEBGDRokCztL/P0O5lZoWesVPkLS6SkylaNT/uFaVHYAetrBeLd64wHdgOATWlnpKakyMbRaaJQfPi/rznSqEJHAV0D9eeTmfNV6HwIlKeXR99YCQCqP8PUVOnL4fmpW6gwohWMbMyQEPkGAOBUtxQSY+Lw+nr6t3pyk0cHLmNliQGytHpL+yDq5lOcnbLpszqMAPDsxE1YFnGUpeUt4oA3D///pv3nXP9fq+T4RCTHJ0JpYQKn+qVxfNiqnK7SV+W/XKcpie8R9+w1dPR0UbhVJdwOOZluXn0TQ1i42uHG6m9n2Sd1as7pBrcWFRBSMxBvwtLvRKf58PtM3sZpy4opFAoI8f/fk7zWs5+6vopSqdTKsYKCgjBmzBhs2bIFtrb/v9zfx7e7S5YsiYoVK8LJyQn//PMPunbtmqmyc7TTePLkSezfvx/W1tawtrbGtm3b0KtXL3h7e+PQoUMwMTHRWIZSqVRpeD2FbrbU7/62UFQY8QPePorEq2uPYVPGBWUHNsW1ZQcBfPiF2SRkMGzLFsLmphOg0NWRolTvXsci9X0yHCoVgX3Fwnh86Crev30Hh8pF4DPDHzdXH/3/cXWOlmi9fzR2d/4T4WfvwtzFDkXaVMHDvZeQEPkGpvmtUH5YSyQnJOHBznNS/cxd7WFgaggTewvoGRnAppQzAODV9Se5buxj1QntEbbrAt4+ioR+HiMU/ckbBWoUx8YGf2TqfJ0bloWxnTnCz97F+9h3sCpeAN5TOuHp8RtSJ8auvBsarOiH9XVGI+7Zazw7dQuJUXGov7wvTv/xD5ITklCie12Yu9jiwY4P5T7cewmvrz9Bg5X9cWzYSpjY50WVP9rh0vzdSEnKXW2Ynvex71TGMr2Pe4eE12+ldGM7C5jYW0jRF+sSTkh6m4A3j14i8X8zWH/YF4i7m//FpXm7AADnZ21DmxMTUD6gFW7/cxL2FdxQontd7O+5EEDmrv9vgVO90oDiw/AKCzd7eE/phKibT6XvgaoT2sPU0RJ7/OZI+6R9Fg1MDWFkbQabUs5ISUqWxvDq6OvByuPD0AxdAz2Y5rOCTSlnJMW+k6Lx35rMXKf1l/dF7LPXODFiDQDAvkJhmOazROTFMJjms0SlQF8odHQQOmWzVIb31E64vy0Ubx9GwsTREpVHt0FqSipurf0678hkRq153eHezhtbW0xC0tsE6XOXGBOPlHdJAFTb8v72UJQd2BQRFx7gxZk7sHCzR5WxbXF/W6jUmdR0rX/rtPkYQXV9FW1Yt24dunXrhpCQENSpk/HERQsLCxQpUgR3797NdPk52mlMSEiAnt7/V0GhUGDBggXo06cPfHx8EBSUs+vkHeq3GFX+aIda83rA2NYMsc+icOXvfTg9NgQAYJrPEq7NKwAAOl6UjycJqTkKT45cQ0rie7i3qYZKgW2gp9RDzIMInJ+1DednbJPy6urrwrJoPikylvwuCfmqeaBM/yYwzGuC+PAYPDl6HcFVR0jRMACou+gXFKjhKb3vcGE6AGCJy8+yaFBuYGxrjvor+sLEIS+SYuLx8vJDbGzwBx7tvwwTh7wazzc5IQklutWBzwx/6Cn18PbxK9zddAZnJ22UjqFvrIRl0XzQ1f/wR8O7V2+xqeE4VBn3E1ofGAMdfV28uvYYW1tMxsvLDwF8iDpubjoRtef3QNuTE/E+7sPi3idHrfvyjaRFJX+uJ1sA2ffoOADAHv+5uL7iEIAPf4QYWf//LYrw0HvY1moKqk1oj0q//4iYBxE4PHCZNBEsM9f/t0BpbvyhY5jfComvY3Fn42mcGBkkrQVoYp8XeQrKZ+umfRYBwK6cG4q1r46YsAgsLfQLAMDUMa8sT7nBzVFucHM8PnwV62sFfoGzyp3yFLSW3RnQNdRHlT/awbyQHd7HvsODneexu9OfsqWM8uSzQqOggR8W/Y98g2fHb2Bd5QAkvHyj7hDfhFK/NAAA+B7+Q5b+8ef507Y8M249IASq/tEOpvksER/5Bve3h+LkR+vRarrWKXdbu3YtunTpgnXr1qFx48Ya88fGxuLevXvo2LFjpo+hEB/HpL+wChUqoG/fvmor3KdPH6xZswZv3rxBSkrWLtiZOj9kVxWJiIjoCxmYuiHHjp18W3tL2ukVscpS/tjYWCkCWKZMGcyYMQM1a9aEpaUlChYsiICAADx9+lSaLBwUFITOnTtj9uzZaNWqlVSOkZERzM3NAQCDBw9G06ZN4eTkhGfPniEwMBAXL17E9evXYWNjo1oJNXJ09nTLli2xdu1atdvmzp2Ldu3aIQf7tERERPSdUOgotPbKqtDQUJQpU0ZaLmfQoEEoU6YMRo0aBQB4/vy5bObz33//jeTkZPTu3RsODg7Sq3///lKeJ0+eoF27dnB3d4evry+srKxw+vTpTHcYgc+INO7evRumpqaoVq0aAGDevHlYtGgRPDw8MG/ePOTNmzcrxWkFI41ERERfn5yMNKbc1d6jEnXdLDVn+gpkOdI4ZMgQvHnzYazIlStX8Ouvv6JRo0Z48OCByswgIiIioq9CLnqMYG6V5YkwDx48gIfHhzUQN2zYgCZNmmDChAk4f/48GjVqlO0VJCIiIqKcl+X+r4GBAeLjP8xc279/P+rV+/B0CEtLSykCSURERPQ1USgUWnt9K7IcaaxWrRoGDRqEqlWr4t9//0VwcDCAD093yZ8/v4a9iYiIiOhrlOVI49y5c6Gnp4f169djwYIFyJcvHwBg165daNCgQbZXkIiIiEjrdBTae30jcnSdRm3h7GkiIqKvT07Onk59GK21snWcLLRW9pf0WU+ESUlJwaZNm3Djxg0AQLFixdCiRQvZ012IiIiIvhrfTkBQa7Lcy7t27RqaNm2K8PBwuLu7AwAmT54MGxsbbNu2DZ6enhpKICIiIsplvqEJK9qS5TGN3bp1g6enJ548eYLz58/j/PnzePz4MUqWLIkePXpoo45ERERElMOyHGm8ePEiQkNDZU9+yZs3L8aPH4/y5ctna+WIiIiIvgTBQKNGWY40FilSBOHh4SrpERERcHNzy5ZKEREREVHukqlI48eLdk+cOBH9+vXD6NGjUalSJQDA6dOnMXbsWEyePFk7tSQiIiLSJkYaNcrUkjs6OjqyFc3TdklL+/h9SkqKNuqZJVxyh4iI6OuTk0vupDyN0VrZuvnMtVb2l5SpSOOhQ4e0XQ8iIiKinMPZ0xplqtPo4+Oj7XoQERERUS72WatxR0dHY8mSJdLi3sWLF0eXLl1gbv5thF+JiIiISC7Ls6dDQ0Ph6uqKmTNn4vXr13j9+jVmzJgBV1dXnD9/Xht1JCIiItIqodDe61uR5WdPe3t7w83NDYsWLZIeG5icnIxu3brh/v37OHr0qFYqmhWcCENERPT1ycmJMMnP32jO9Jn0HMy0VvaXlOXb06GhobIOIwDo6elh6NChKFeuXLZWjoiIiOiL4EQYjbJ8e9rMzAyPHj1SSX/8+DHy5MmTLZUiIiIiotwly53GNm3aoGvXrggODsbjx4/x+PFjrFu3Dt26dUO7du20UUciIiIi7VJo8fWNyPLt6WnTpkGhUKBTp05ITk4GAOjr6+OXX37BpEmTsr2CRERERJTzsjwRJk18fDzu3bsHAHB1dYWxsXG2Vuy/4EQYIiKir0+OToSJeKu1svVsv43he5+1TiMAGBsbo0SJEtlZFyIiIiLKpTLVaWzVqhWWL18OMzMztGrVKsO8pqamKF68OH7++Wcu9k1ERERfhW9pPUVtyVSn0dzcHIr/TUXX1BFMTEzEwoULceLECWzduvW/15CIiIhI27jkjkafPaYxI9evX0f58uURFxeX3UVnCsc0EhERfX1yckzj+5exWitb39pUa2V/SZ89pjEj7u7uOHnypDaKJiIiIsp+DDRqlOV1GjNDV1cXpUqV0kbRRERERJQDtBJpJCIiIvqqMNKokVYijURERET0bclUp7Fs2bKIiooCAIwdOxbx8fFarRQRERHRlyQUCq29vhWZ6jTeuHFDmgk9ZswYxMZqb4YREREREeU+mRrTWLp0afj7+6NatWoQQmDatGkwNVU/fXzUqFHZWkEiIiIiynmZWqfx1q1bCAwMxL1793D+/Hl4eHhAT0+1v6lQKHD+/HmtVDQruE4jERHR1ycn12lMitbe2tIGFiZaK/tLyvLi3jo6Onjx4gVsbW21Vaf/jJ1GIiKirw87jblblpfcSU1N1UY9iIiIiHLONzRhRVs+a53Ge/fuYdasWbhx4wYAwMPDA/3794erq2u2Vo6IiIiIcocsr9O4Z88eeHh44N9//0XJkiVRsmRJnDlzBsWLF8e+ffu0UUciIiIi7VJo8fWNyPKYxjJlyqB+/fqYNGmSLH348OHYu3cvJ8IQERHRZ8nRMY1vtLcGtYGZsdbK/pKyHGm8ceMGunbtqpLepUsXXL9+PVsqRURERPQlCYX2Xt+KLHcabWxscPHiRZX0ixcv5uoZ1URERET0+bI8EaZ79+7o0aMH7t+/jypVqgAATpw4gcmTJ2PQoEHZXkEiIiIirePsaY2y3Gn8/fffkSdPHkyfPh0BAQEAAEdHR4wePRr9+vXL9goSERERUc7L8kSYj719+xYAkCdPnmyrUHbgRBgiIqKvT05OhEmMS9Ba2UoTI62V/SV91jqNaXJbZ5GIiIjos/DutEZZnghDRERERNpz9OhRNG3aFI6OjlAoFNi8ebPGfQ4fPoyyZctCqVTCzc0Ny5cvV8kzb948ODs7w9DQEBUrVsS///6bpXqx00hERETfPaFQaO2VVXFxcShVqhTmzZuXqfwPHjxA48aNUbNmTVy8eBEDBgxAt27dsGfPHilPcHAwBg0ahMDAQJw/fx6lSpVC/fr1ERERkel6/acxjbkVxzQSERF9fXJyTOO7hHdaK9vQyPCz91UoFNi0aRNatGiRbp5hw4Zhx44duHr1qpTWtm1bREdHY/fu3QCAihUronz58pg7dy4AIDU1FQUKFEDfvn0xfPjwTNUlS5HG9+/fo3bt2rhz505WdiMiIiLK3b7ixwieOnUKderUkaXVr18fp06dAgAkJSXh3Llzsjw6OjqoU6eOlCczsjQRRl9fH5cvX87KLkRERETftcTERCQmJsrSlEollEpltpT/4sUL2NnZydLs7Ozw5s0bJCQkICoqCikpKWrz3Lx5M9PHyfKYxg4dOmDJkiVZ3Y2IiIgoFxNae02cOBHm5uay18SJE7/kyWWLLC+5k5ycjKVLl2L//v3w8vKCiYmJbPuMGTOyrXJEREREX7uAgACVp+ZlV5QRAOzt7REeHi5LCw8Ph5mZGYyMjKCrqwtdXV21eezt7TN9nCx3Gq9evYqyZcsCAG7fvi3bpuAjeIiIiOgrJKC9ecGG2XgrWp3KlStj586dsrR9+/ahcuXKAAADAwN4eXnhwIED0oSa1NRUHDhwAH369Mn0cbLcaTx06FBWdyEiIiLK1QRSc7oKktjYWNy9e1d6/+DBA1y8eBGWlpYoWLAgAgIC8PTpU6xcuRIA8PPPP2Pu3LkYOnQounTpgoMHD+Kff/7Bjh07pDIGDRqEzp07o1y5cqhQoQJmzZqFuLg4+Pv7Z7pen/1EmLt37+LevXuoXr06jIyMIIRgpJGIiIjoPwoNDUXNmjWl92m3tjt37ozly5fj+fPnePTokbTdxcUFO3bswMCBAzF79mzkz58fixcvRv369aU8bdq0QWRkJEaNGoUXL16gdOnS2L17t8rkmIxkeZ3GV69ewdfXF4cOHYJCocCdO3dQqFAhdOnSBXnz5sX06dOzUpxWcJ1GIiKir09OrtMY/+6t1so2Nvw2Hruc5dnTAwcOhL6+Ph49egRjY2MpvU2bNtICkkRERET0bcny7em9e/diz549yJ8/vyy9cOHCePjwYbZVjIiIiOhLyU1jGnOrLEca4+LiZBHGNK9fv9bqzCAiIiIiyjlZ7jR6e3tLs3WAD8vspKamYsqUKbJBm0RERERfC6HF/30rsnx7esqUKahduzZCQ0ORlJSEoUOH4tq1a3j9+jVOnDihjToSERERUQ7LcqTR09MTt2/fRrVq1dC8eXPExcWhVatWuHDhAlxdXbVRRyIiIiLtEqnae30jPmudRnNzc4wcOTK760JERESUI76l28ja8lmdxqioKCxZsgQ3btwAAHh4eMDf3x+WlpbZWjkiIiIiyh2yfHv66NGjcHZ2xp9//omoqChERUXhzz//hIuLC44ePaqNOhIRERFplUCq1l7fiixHGnv37o02bdpgwYIF0NXVBQCkpKSgV69e6N27N65cuZLtlSQiIiKinJXlSOPdu3fx66+/Sh1GANDV1cWgQYNkD9cmIiIi+nqkavH1bchyp7Fs2bLSWMaP3bhxA6VKlcqWShERERFR7pKp29OXL1+W/t2vXz/0798fd+/eRaVKlQAAp0+fxrx58zBp0iTt1JKIiIhIizh7WjOFEEJjK+no6EChUEBTVoVCgZSUlGyr3OeaqfNDTleBiIiIsmhg6oYcO3ZMwgutlW1uZK+1sr+kTEUaHzx4oO16EBEREeWYb2mWs7ZkqtPo5OSk7XoQERER5SDentbksxb3fvbsGY4fP46IiAikpsp75v369cuWihERERFR7pHlTuPy5cvRs2dPGBgYwMrKCgqFQtqmUCjYaSQiIqKvDm9Pa5blTuPvv/+OUaNGISAgADo6WV6xh4iIiIi+QlnuNMbHx6Nt27bsMBIREdE3g0vuaJblnl/Xrl0REhKijboQERERUS6VqXUaP5aSkoImTZogISEBJUqUgL6+vmz7jBkzsrWCn4PrNBIREX19cnKdxtcJYVor29LIWWtlf0lZvj09ceJE7NmzB+7u7gCgMhGGiIiIiL49We40Tp8+HUuXLoWfn58WqkNERET05XFMo2ZZ7jQqlUpUrVpVG3UhIiIiyhFcckezLE+E6d+/P+bMmaONuhARERFRLpXlSOO///6LgwcPYvv27ShevLjKRJiNGzdmW+WIiIiIvgzentYky51GCwsLtGrVSht1ISIiIqJcKsudxmXLlmmjHkREREQ5RgiOadSEj3UhIiIiIo2yHGl0cXHJcD3G+/fv/6cKEREREX1pXHJHsyx3GgcMGCB7//79e1y4cAG7d+/GkCFDsqteRERERJSLZLnT2L9/f7Xp8+bNQ2ho6H+uEBEREdGXxzGNmmTbmMaGDRtiw4ace2YkERER0ecSWvzftyLbOo3r16+HpaVldhVHRERERLlIlm9PlylTRjYRRgiBFy9eIDIyEvPnz8/WyhERERF9CXyMoGZZ7jS2aNFC9l5HRwc2NjaoUaMGihYtml31IiIiIqJcJMudxsDAQG3Ug4iIiCgHfTtjD7WFi3sTERERkUaZjjTq6OhkuKg3ACgUCiQnJ//nShERERF9SRzTqFmmO42bNm1Kd9upU6fw559/IjWVDU5ERET0Lcp0p7F58+Yqabdu3cLw4cOxbds2tG/fHmPHjs3WyhERERF9CakiJaerkOt91pjGZ8+eoXv37ihRogSSk5Nx8eJFrFixAk5OTtldPyIiIiKtSxUpWnt9K7LUaYyJicGwYcPg5uaGa9eu4cCBA9i2bRs8PT21VT8iIiIiygUyfXt6ypQpmDx5Muzt7bF27Vq1t6uJiIiIvkYC305EUFsUQohMLUyko6MDIyMj1KlTB7q6uunm27hxY7ZV7nPN1Pkhp6tAREREWTQwdUOOHftR7GmtlV3QtJLWyv6SMh1p7NSpk8Yld741Ch0dVBrti2Ltq8PE3gKxz6JwfcUhnBm3XsqT3gV+dOhKnJu2RXrv0qgsKv7+I2xKOiH53Xs8OXId21pNTvfYmSnXtowLqk3qCLvybhApqbi78TSODFqO93HvPud0tarkz/VR8uf6MHO2AQC8uvYYZ/4IQdjuCwCA2gt7omDtkjB1zIuk2Hd4fvIWjg1fjahbTwEAHp1rov6yPmrLXmjnj4TIN2q3ZaaN1LX1jnYzcDv4xH8655xSflhLVJvYAednb8eRgcsAaG5fdYxtzVFtUkc41SsFpYUJnh69jkP9liD67nMpT4nudeHerhpsyxaC0swY8/N2RGJMvNbP8UvJzHfAp/L7FMePh1QnBf7l0BXx4dHSexNHS3hP6gDnhmWhb2yA6LsvsLfLPISfu6eNU8l11F2nn2p9cAwK1FAd/nR/xzlsaTpBJb32gh4o2bM+Dg9ciguzd2R7nXOL8sNbwq1lJVgWzYfkhCQ8O3kLx4evQtTtZxnupzQ3RpXxP6Fwy0pQWpri7cNIHB64DGG7zgMAKgX6onJgG9k+r28+xQqPflo7l9zkWxp7qC2Z7jQuX75ci9XIncoNa4FSP9fHHr85eHXtMezKuaLe0j5IjInHxTk7AXz4RfAx54ZlUG9xL9zd8P9/sbi1qoS6f/+MEyOD8OjgFejo6cLas2CGx9ZUrolDXvywLxC3/jmJQ30Xw8DMCDVmdkH9ZX2w3Xdadpx+top98grHA1Yj+s5zQPGhE9hs8zCsKTsEr64/RsS5+7i55hjePoqEoaUpKgW2Qas9v2NpoV4Qqam4FXxC6mCmqb+sD3QN9dPtMGaljfb4z5WVnxgdl/2N8AXYlXNFiR51EXkpTJauqX3VabppGFLfp2Bri0lIepOAsoOa4od9gVhRvD+S4xMBAHrGBni45yIe7rmIahM7aPv0vrjMfAekZ5l7HyS9SZDex0fESP9WWpigzfHxeHLoKjY1GoeEyDewKOyAd1GxWjuX3CS96/RT236YCl2D//81ZWSVBx0uTsed9adU8rq2qAD7ikUQ+/RVdlc318lfvTguzd+N8LN3odDTQdXx7dFqzyjZZ/NTOvp6aLU3EPERMdj+41TEPn2NPE42Kt91L68+woa6Y6T3qcnsSOWUefPmYerUqXjx4gVKlSqFOXPmoEKFCmrz1qhRA0eOHFFJb9SoEXbs+PAHlJ+fH1asWCHbXr9+fezevTvTdcryYwS1TQiRayKajpXdcW/rWTzY+eGvsDcPI+He1hv25d2kPB9HDgDAtVkFPD50FTEPwgEACl0d1JjVBUeHrsK1pQekfK9vPMnw2JrKLdSkHFLep+Bg70XA/0YY7P/lL3S6PBPmrvaIuffis85ZW+5vD5W9P/lbEEr9XA/2lYrg1fXHuLJon7TtzcNInPx9LTpemgEzZxvE3A9HyrskxL9LkvIYWZuhQC1P7Ou2IN1jZqWNEqPjVNr8a6NvYoiGqwdgf4+FqDBSPkRDU/t+yqKwAxwru2Ol5wC8uv4YAHDgl7/R8/kSFG1XDVeXfLiW06I5+X2Ka+u0clRmvgPSkxARk27Utfywloh9/BJ7u86T0t6ERWRPpXO5jK7TTyV+0ol2b1sV7+MTcTvkpCzdxNESNf/shk0N/kDz7SOyvc65zaZG42Tv9/rPxc8Ry2Dn5Yqnx66r3cezSy0YWpoiuOoIqSP45mGkSr7U5JSv/rvwc+WmSGNwcDAGDRqEhQsXomLFipg1axbq16+PW7duwdbWViX/xo0bkZT0/78jX716hVKlSuHHH3+U5WvQoAGWLfv/yL5SqcxSvXLdYwSVSiVu3LiR09UAADw7dQsFapWARWEHAIB1SSc4ViuqEvFKY2xrDpfGZXH1o86hbdlCyJPfCiI1Fe3PTUWPp4vRYsdIWBUvkOl6qCtXV6mH1KRkqTMEAMkJHy6YfNWKZek8vzSFjg6KtKkKPRNDPD91S2W7nrESxf1rIuZ+ON4+Vh81KNbJB+/jk3BbTcQhTVbaqNbcbvg5YhnanZ6E4v61Pue0clytud3wYOc5PDpwOcN8mWlfXaU+ACD5o446hEBK4ns4Vs3d11d2yup3wMfaX5iOHk8Xo9WeUXCs4i7bVqhpOYSfu4fGwb+i54ulaH9uKjy71dHKOeQ2mb1O1fHsUhu3g0/Io2kKBRqs7Idz07ZIf+B8bwzMjQEA716/TTdPoabl8fzULdSa1x09ni9Bx8szUT6gFRQ68m5A3sIO6P5kEbrcnY8Gq/ojTwFrrdad1JsxYwa6d+8Of39/eHh4YOHChTA2NsbSpUvV5re0tIS9vb302rdvH4yNjVU6jUqlUpYvb968WapXjkUaBw0apDY9JSUFkyZNgpWVFYAPDZeRxMREJCbKw/HJIgV6ivQn62TW2UmboDQzht+NP5GakgodXR2c+C0IN4OOqc3v0bkG3r9NwN2NZ6Q080J2AIDKgW1w5NfleBMWAa9BzfDjobFY5t5X5S/pzJb7+OBVVJ/uB6/BzXFh9g7omyjh/b/bgyYOFv/hrLXHyrMg2p6cAD1DAyTFvsO2VlNkEdeSv9SH9+SOMDA1wuubT7Gh3hikvlf/WErPLrVxa+0xpHzcqflEZtvo5Ki1eHzwCt7HJ8GpXinUmtcd+qaGGm8/5iZF2lSFbdlCCKowLN08WWnfqJtP8eZhJKpN6ID9Py/E+7hElB3YBHkKWMPEIWtfMl+zrH4HAEDc8yjs/3khwkPvQVepD89utdH60FisqzQcERceAPjwvVDy5/o4P3Mb/p24Efbl3VBzdhekJiXj+srDX+jsvrzMXKfpsSvvBusSTtjbbb4svfywFhDJKbjw57c7hjFDCgVqzPTH0+M38Opa+p1m80J2KFDLEzeDjmFz4/GwcLNHrXk9oKuvi9NjQwAAL87cwR7/uYi69QwmDnlRadSP8D06DitLDMD72Nw3Vj67pQrtPdVOXV9FqVSqjfQlJSXh3LlzCAgIkNJ0dHRQp04dnDqVfqDkY0uWLEHbtm1hYmIiSz98+DBsbW2RN29e1KpVC+P+r707j4/x2v8A/pnsmyyEyWILESEiISQilBIJVRVbw1U0WqqWUkpRxFaxtOpSRXurqFqvpa1qVIPaIoidJCV2WckmCdnm/P7Iz9QjkzyJm03m83695l5znjNnznP6ZPLN9znnzMKF6nirNKosaFyxYgXc3NxgaWkpKRdCICoqCqampqW6TR0SEoJ58+ZJyvzgjJ5o+T/30entjnD+V2fsH7oCj67eQz13B3T5KghZcakaP9hdgrojassxFOTkqcsUOoXncHrRLtzYXTgf8Y+RX+P9e9/CaZA3Ln97sEg7pWn30bV7OPDuKnT58l10WjQUqgIVLqzaj6yEVAhVqRbEV7rUmDhsbvMJDC1M0GygN/w3jMfOrnPUgWP0T8dw9+AlmNpawWPKW+i9fQq2d/pMct4AYNvBCXVaNkDo8JUlvl9px+j5RQ3JF25B39QI7T7p+8oEjWb166DripHY7Te/yFg9r7TjCxTeovp1wFL0+M9YjE3ZBFV+Ae7+eanwNm31mD1SKcr6GQAAqX/HSRYkxIfHwLKJDdpO6oPQEYXXrEJHgcSzsTjx2RYAhdddnVYN4PqBX40NGkt7nRan1XvdkXzpDhLP3FCX1WvbBG0+6o2fPKaWZ1dfKd1Wj0KdVg2xo/NnJdZT6CiQnZSOP0evhVCpkHTuJszs66DdJ33VQePzGfSHl+8gIeJvvHd7LZze9pFMr6qpVBW45Y6mWCU4OBhz584tUvfhw4coKCiAUqmUlCuVSkRHR8u+1+nTp3HlyhV8//33kvKePXuif//+cHBwQGxsLGbOnIlevXohPDy8xF1xnldlQeOiRYvw7bff4ssvv0S3bv/cDtTX18eGDRvQsmXpgr4ZM2YUyVqusxheLn18belwnFmyR72K9tGVu6jVyBrtp/cv8sFu36kFajvb47fBX0rKs+LTCl/73G2Tgtx8pN9MRK2GdWX7UFy7ABCz9Thith6HST0L5GXlQAiBth+/qXGOWnWgystXzyNMOncTNu0c0WZib4SNWQcAyM3IRm5GNtJuxCP+1N8Ym7IRjv28ELPtuKSdVu/7Iun8TSSduyn7ni8zRgkRf6PD7EHQNdBDQa7mTFx1ovRoClOlJYZGLlOX6ejpov5rLeE+rhdWGg2GUKlKPb7PJJ27iZ/afgIDcxPoGujhycMMDA4P0ZrVvUDZPgNKknDmBux8nNXPs+LT8OiFec0pUQ/QrH/N2JZDk9Jep5romRiieaAPwoO3S8rtO7eAST0LvH9nnaTN174YgTYT38T6Jh9WzMlUE6+veh9NentgR5fZyHyQUmLdrPhUqPIKJGOcEnUfprZW0NHX03jXISc9G6l/x8PS0abc+65tNMUqZZ1PWFrff/89XF1diyyaGTx4sPrfrq6uaN26NZo2bYojR46ge/fupWq7yoLG6dOno3v37njnnXfQp08fhISEQF9fv8ztaErvlsetaaDwg+rFrJ0oUKmzh89zGdkdiWdv4OGlO5LypMhY5D/NhVVze8SdKPwLQUdPF+aN6+GxhknIpW33ec9WZboEdUPB0zzcPXhRtt1qQUcBXQPN/80VisL/eTa37hl9UyM4DeqI4zN/KtNblWWM6ro74GnK41ciYASAu2GXsMl1kqTMb/14pEY/wJmlezT+Ii5ufDXJzShczGHpaAtlu6Y4OWdbeXT7lVCWz4CS1HVrjKz4VPXzuBPRqO1kJ6lj5WSrcWFCTfEy1+kzToM6QtdQH1GbpatDo378C3f/lM6N7B86G1Gbj+LqD4fKre/V0eur3odjgCd2vh5cqkVUcSej0XxI58If/v+f523lZIfMuJRip6nomxrBsqkSUZtTNR6vaUQFLoQp7la0JtbW1tDV1UViojS5kZiYCBubkgP4rKwsbNu2DfPnF93260VNmjSBtbU1bty4UeqgsUoXwrRv3x6RkZFITk5Gu3btcOXKlWqzchoAbv56Fp4zB8DhjbYwb1QXTQM80fbjPrixN0JSz6CWMZwGeatXlD4v9/ETXFr3B7znBqJhDzdYOdmh25rRACBZATji2ko0DZD+VVBSuwDgNq4X6rVxgGUzW7iN7YnXV72P4zN/qpb75PksGgr7zi1h3qgu6rRqCJ9FQ9GgqwuitxyFhYMS7af3K1w01MAatt7N0XvHJ8h/kotb+yMl7TgF+kBHTwfRm4tuLaBs74gR11bC1K62ukxujJq82Q6t3uuOOi4NYNHUBq3H+MNzRn9c+Pr3ih2QcpSX+RSPrt6TPPKynuJJymM8unqv1OP74jXYbKA36ndxgYWDEk3eao/+f8xB7N4zkoDbRGmJum6N1ZkIa9dGqOvWGIZWZpU3ABWoNJ8BPouGwn/DBPXzNhN7o8lb7WHR1AZ1XBqgy1dBaNCtFS5+88+2FudW/AqbDk5oP6M/LJraoPmQTnAd1UNSp6aRu04BwH/DBPgsGlrkta1GdkPs3tN4miKdA/40JbNImwV5BchKSJXds/BV1m31KDgPfQ37h65A7uMnMFFawkRpCV0jA3WdF8fy4poDMKpthq7/HgnLZrZweKMt2s/oL7nmOi8bDvvXCj+nbb2bo8/uaVAVqBCzVfPdCKoYBgYG8PDwQFjYP7/7VSoVwsLC4O3tXeJrd+7ciZycHLzzjvwWaPfv38ejR49ga2tb6r5V+ZY7ZmZm2LhxI7Zt2wZfX18UFFSfJe+HP/oPOi4Ygm6rR8Oknjky41Jx+duD6vkfzzQf3AlQKBBdzA/WsamFc8J6bvoIesYGSIi4jl3d50r2x6rtbA9DC+mEVbl2bdo7wntuIPTNjJAa/QBhY9YV+Uu8ujCpZwH/jRNgamuF3PRsPLx0B7t7LsDdPwvn2Nl3aok2E9+EkZUpshPTcf/oNWz3mVlkD8ZWI7vh+u4IjYGxvokhajvbQ1f/n0yz3BgV5OXDbWxPdFkeBCiAtBsJ+GvKBlz+7s+KG4xKlv80t1Tj++I1aGprhS5fvgsTpQWy4tNw7ccjiFgg3dS69Rg/yWbAbx8t3ArkQNDXuLbxcMWeWCUozWeAqY0VajX8Z4WproEeunwxAmb2tZGXnYuHl+5gV4/5uH/kirpO4tlY/Np/KTotGooOswch/VYSjnz8Q4kLbLRBrYbWRTK7Vk52sO/cErv85hXzKu3j9mFPAMDbRxZIyp//uXtxLDPvP8KengvQZXkQhl1cjswHKTi/8jecXbJXXaeWfR28seVjGNWphSfJGYg7HoVt3jPw5KHmvXBrmuq05c7kyZMxYsQItGvXDp6enlixYgWysrIQFBQEoPALV+zt7RESEiJ53ffff4+AgIAii1syMzMxb948DBgwADY2NoiNjcW0adPg6OgIf3//Uver1F8jWBnu37+PyMhI+Pr6FlnxUxb8GkEiIqJXT1V+jWBUasXdYWph1avMr/n666/Vm3u7u7tj5cqV8PLyAlC4mXfjxo0lX7wSExMDZ2dn/PHHH+jRo4ekrSdPniAgIADnz59HWloa7Ozs4OfnhwULFhRZcFOSahU0lhcGjURERK+eqgwar6bsq7C2XWq/WWFtV6Zqt7k3EREREVU/VT6nkYiIiKiqVac5jdUVg0YiIiLSehW5uXdNwdvTRERERCSLmUYiIiLSehW5uXdNwUwjEREREclippGIiIi0HhfCyGOmkYiIiIhkMdNIREREWo+ZRnnMNBIRERGRLGYaiYiISOuphKqqu1DtMdNIRERERLKYaSQiIiKtx2+EkcegkYiIiLQeN/eWx9vTRERERCSLmUYiIiLSetxyRx4zjUREREQki5lGIiIi0nrMNMpjppGIiIiIZDHTSERERFqPW+7IY6aRiIiIiGQx00hERERaj3Ma5TFoJCIiIq3Hzb3l8fY0EREREclippGIiIi0Hm9Py2OmkYiIiIhkMdNIREREWo+ZRnnMNBIRERGRLGYaiYiISOuphKqqu1DtMdNIRERERLKYaSQiIiKtxzmN8hg0EhERkdYrUDFolMPb00REREQki5lGIiIi0npcCCOPmUYiIiIiksVMIxEREWk9Fec0ymKmkYiIiIhkMdNIREREWo9b7shjppGIiIiIZDHTSERERFqvgJlGWQwaiYiISOupVNxyRw5vTxMRERGRLGYaiYiISOtxIYw8ZhqJiIiISBYzjURERKT1uLm3PGYaiYiIiEgWM41ERESk9VSCq6flMNNIREREVM2sXr0ajRs3hpGREby8vHD69Oli627YsAEKhULyMDIyktQRQmDOnDmwtbWFsbExfH19cf369TL1iUEjERERab0CVUGFPcpq+/btmDx5MoKDg3Hu3Dm4ubnB398fSUlJxb7G3Nwc8fHx6sedO3ckx5cuXYqVK1di7dq1iIiIgKmpKfz9/fH06dNS94tBIxEREWk9lSiosEdZLV++HKNGjUJQUBBatmyJtWvXwsTEBOvXry/2NQqFAjY2NuqHUqlUHxNCYMWKFZg1axb69u2L1q1bY9OmTYiLi8PevXtL3S8GjUREREQVKCcnBxkZGZJHTk6Oxrq5ubmIjIyEr6+vukxHRwe+vr4IDw8v9j0yMzPRqFEjNGjQAH379sXVq1fVx27duoWEhARJmxYWFvDy8iqxzRcxaCQiIiKtp1IVVNgjJCQEFhYWkkdISIjGfjx8+BAFBQWSTCEAKJVKJCQkaHxN8+bNsX79evz888/YvHkzVCoVOnbsiPv37wOA+nVlaVMTrp4mIiIiqkAzZszA5MmTJWWGhobl1r63tze8vb3Vzzt27IgWLVpg3bp1WLBgQbm9D4NGIiIi0noVueWOoaFhqYNEa2tr6OrqIjExUVKemJgIGxubUrWhr6+PNm3a4MaNGwCgfl1iYiJsbW0lbbq7u5eqTYC3p4mIiIiqDQMDA3h4eCAsLExdplKpEBYWJskmlqSgoACXL19WB4gODg6wsbGRtJmRkYGIiIhStwkw00hERESEgpdY5VxRJk+ejBEjRqBdu3bw9PTEihUrkJWVhaCgIADA8OHDYW9vr54XOX/+fHTo0AGOjo5IS0vDsmXLcOfOHbz//vsACldWT5o0CQsXLkSzZs3g4OCA2bNnw87ODgEBAaXuF4NGIiIiomokMDAQycnJmDNnDhISEuDu7o7Q0FD1Qpa7d+9CR+efm8WpqakYNWoUEhISYGVlBQ8PD5w8eRItW7ZU15k2bRqysrIwevRopKWloVOnTggNDS2yCXhJFEIIUX6nWT18pTOgqrtAREREZfSxaleVvffXp/9VYW2P99xSYW1XJmYaiYiISOvxu6flcSEMEREREclippGIiIi0nuolviNa2zDTSERERESymGkkIiIirVedttyprphpJCIiIiJZzDQSERGR1lOpuHpaDjONRERERCSLmUYiIiLSeirOaZTFoJGIiIi0Hrfckcfb00REREQki5lGIiIi0nrcckceM41EREREJIuZRiIiItJ63HJHHjONRERERCSLmUYiIiLSetxyRx4zjUREREQki5lGIiIi0noqlajqLlR7DBqJiIhI66kEF8LI4e1pIiIiIpLFTCMRERFpPd6elsdMIxERERHJYqaRiIiItB4zjfKYaSQiIiIiWcw0EhERkdZTCWYa5TDTSERERESymGkkIiIiradScZ9GOcw0EhEREZEsZhqJiIhI63H1tDwGjURERKT1uBBGHm9PExEREZEsZhqJiIhI6/H2tDxmGomIiIhIFjONREREpPVUglvuyGGmkYiIiIhkMdNIREREWo9zGuUx00hEREREsphpJCIiIq3HTKM8Bo1ERESk9QQ395bF29NEREREJIuZRiIiItJ6KhW33JHDTCMRERERyWKmkYiIiLQeF8LIY6aRiIiIiGQx00hERERaT8XV07IYNL7AvnNLtPukL+p5NIGZXW380m8JYn8+LanjPW8wXN/3haGlCeJOxCBs7LdIuxFfpC1dAz0MPrUY9dwdsLnNFCRfvA0AsHKyQ/c1H6B2y/owtDBBVlwqorcew6l5O6DKL9DYL+vWjdD+0/6w7+QMY+taSL+djMvr/sD5lb9J6rmN7Qm3cb1g0bguMu4+xOlFuxD141/lMzjlqP2n/dAp5B2c+/c+/PXxDwCA7ms/QMPurWFmZ4XczKeIPxmDY9M3IzXmAQCg5YjX4f/DeI3trVUG4UlyRpFy80Z14TV7EBq83gqmNpbIjEtF9E9HEfH5Lqjy8tV13ru1tshrt3pPR0LE9fI65QrTIfhteAcHSspSoh9gY8uPYGhlBu95gWjUww3mDa2RnZyB2J9P4+TsbcjNyC62zY9VuzSWH522CZFf/CwpK+46fxXJ/fw79vNC6w/8UM+jKYzr1Crz+ToF+qD31sm4sfc0fu2/RHKstrM9Oi0ehvpdWkJHTxePrt3HvoHL8Pjew/I6vSpV0nVanDYTe6P1GH+YN7TGk4ePcX1XOI7P+AkFOXkAgNZj/AuPN64LAHh09R4iFuzE7dDzFXci1UD76f3g2K8DajvbI/9JLuJOxuD49B+R+ndcia+TG099MyN0XDAEjgFeMKlnjqTzt3Bk0nokno2tjNOiVwCDxhfomxoi+dJtXPkhDG/t/rTI8XbTAuA+4Q0ceHcVMm4loeP8wegfOhsbXSaqf/Ce6bx0OLLiUgF3B0m5Kq8A1348gqRzN5GTlo26bo3g++2HUOgocOKzLRr7pfRoiifJ6fh92L+Ree8RbDs2h++6MVAVqHBx9e8ACj9AfRYNxZ+j1yDxTCyUno7o8e2HyEnNws19Z8tngMqBsl1TuI7uUeSXbVLkTUT/dAyP7ybDqLYZOgQHov+B2VjfZCyESoWY7SeK/DLw/2E8dI30NQaMAGDlbA+FQoE/x6xD+o0E1GnVAL7ffgg9U0Mcm7pJUve/vnPx6Oo99fOnjx6XzwlXgodX7mJXj3nq58/++DCzs4KZbW0cm7oJj67dg3mjuui+5gOY2dbGvre/KLa9dbbvSZ437tUGfv8Zixu7ThWpW9x1/iqS+/nXNzXCgxPR+HvnSfT4bmyZ2jZvVBevLRuB+0evFTlm0USJt499jqvrwxA+dztyM7JRx6UB8p/mvvS5VEfFXaeaNB/SCZ1C3sEf761G/MkYWDrZwf+H8RACODplAwAg8/4jHJ+xGWnX4wFF4R+Wb+39FD+1nYpH1+4V2/arrv5rLrj4TSgSz9yAQk8HPp8PRf8Dc7DRZSLys3M0vqY049nju7GwbtUQocNXIjMuBS3eeQ0DDgZjo8skZMWlVOIZVg3OaZTHoPEFt0PPl/hXatuJb+L05//FzV/OAABCR6zCBwnfo2mAJ/7efkJdr3HPNmjYww37Bi6DwxttJW2k30pE+q1E9fPHd5MRveUY7Du1KPZ9r/5wqEgbtt5OcOznpQ4aW7zzGi5/exB/7ziprmPT3hHtpgVUm6BR39QIvTZPwp+j18LzswGSY5e/O6j+d8adZJycvRXDLi6HeeO6SL+ZiIKnuch+7peosbU5GnRrhYPvryn2/e4cuIA7By6on6ffSkTkl7/AbYx/kaDx6aPHyE5M+99OsIqo8gs09v3R1XvYN2iZ+nn6zUScmLUFPX+cCIWuDkSB5i0mXmyr6VueuHf4iuS6BUq+zl9Fcj//UZsLs/bmjeqWqV2Fjg56bZ6E8LnbYd+pBQwtTSXHfRb+C7f3n8OxT39Ul6XfTHyxmVdecdepJnYdnRF3IhoxW48DKPxMiNl2HDaezdR1XvxcOzlrC9zG+MGmg1ONDhr3vLFQ8vyPoK8xJukHKD2a4sGxon+UAPLjqWtkgGYDOuCXgMXqNk7N24Emb7aD24f+ODl7awWeUfVQ3bbcWb16NZYtW4aEhAS4ublh1apV8PT01Fj3u+++w6ZNm3DlyhUAgIeHBxYtWiSp/+6772Ljxo2S1/n7+yM0NLTUfeJCmDKwcFDC1NYKd/+8pC7LzchGQsR12Hk3V5eZ1LOA77cf4sDwlcX+1Sdpt6kNGvu7a8xAlMTQ3AQ5KZnq57qG+kUyE/lPcmHj6QgdPd0ytV1Run39Pm7tj8TdsEsl1tMzMYRL0OtIv5mIx/ceaazTYngX5GXn4u//hpepD4YWJniaUjSL+NbP0/FBwnq8fXQhmvRpV6Y2q5pVM1uMuv8dRt74Bj1/nIhaDayLrWtoYYLcjOxiA8YXmdSzgEPvtriyPqxIeVmuc23WYc4gZCel4+oLYwgAUCjg0NsDqdfj0O/32fggYT0Gh4egaV/NvxxeZWW5TuNORqOeR1Mo2zsCKPz8bdyrLW79fk5jfYWODpwCfaBnaoT48JgK6X91ZWBhAgAaP9eekRtPHT0d6OjpIv+p9I5Z/pNc2Pk4V1DPqTjbt2/H5MmTERwcjHPnzsHNzQ3+/v5ISkrSWP/IkSMYMmQIDh8+jPDwcDRo0AB+fn548OCBpF7Pnj0RHx+vfmzdWrY/BphpLAMTG0sARbMw2YnpMFFaqp/7/TAel9YdQGJkbIkZicDjn6Ne2ybQMzLApW//wMk520rdF1vv5nAK9MHeNxepy+78cQGu7/kidu9pJJ27CaVHU7R6rzt0DfRhbF0LWQlpxTdYCZwCfVCvbRNs8Sx62++Z1h/6o/OSYTAwM0ZK9APs8punnnv4olYjuyNm6zEUlOEWnkVTG7iP74Wjz2UZczOf4q8pGxB3IhpCpUKzAd54a8+n+KXfEtz8tXpkaEuSEHEdB4K+RmpMHExtrdBhziC8fXQhNrlOQl7mU0ldozq14DVrEC5/92ep2285oivyHj/Bjd0RkvLSXufazs7HGS4ju2Nzmykaj5vUs4BBLWO0/7QfTszeiuPTf0Tjnm3QZ9dU7OwWjAdl/GOyuirLdQoAMVuPw9jaHIHHFgIKBXT19XBx7QGcCdktqVenVUMMPrkIekYGyM18il/7L0VK1P3KOq2qp1Cg61dBeHA8SjK95kVy45mX+RRxJ6PhNWsgUqLuIzsxHc2HdIKttxPSbiRU1tlUqeq0EGb58uUYNWoUgoKCAABr167Fb7/9hvXr12P69OlF6v/000+S5//5z3+wa9cuhIWFYfjw4epyQ0ND2NjYvHS/qjxo/Prrr3H69Gm88cYbGDx4MH788UeEhIRApVKhf//+mD9/PvT0iu9mTk4OcnKkWY58UQA9RdVk1twnvAGDWsY4E7JHtu5vg5fDoJYx6ro1Ruelw9Huk7dwdtnPsq+r49IAb+39FKfm78DdgxfV5acW/BcmNpYYHB4ChUKB7MQ0XNt0BO2n9YOo4rkaZvXroOuKkdjtN7/I3M/nRf90DHcPXoKprRU8pryF3tunYHunz4q8xraDE+q0bIDQ4StL3QdTu9ro//ss/P3fcFz5zz9B09NHj3Huq1/VzxPPxsLU1grtPun7SgSNz99OfXj5DhIi/sZ7t9fC6W0fSWbLoJYxAvbNxKNr93Bq7vZSt+8S1B1RW45J/huU5TrXZvpmRui56SP8OXpNsXNkFToKAEDsz2dwfsU+AEDyxduw9W6O1h/415igsbTX6TP1u7jAc0Z/HBr3HeIjrsPS0QZdV4yE16yBiFj4X3W91Jg4bG7zCQwtTNBsoDf8N4zHzq5ztCZw7LZ6FOq0aogdnT8rsV5pxjN0+Er4fT8Oox/8B6r8AiSdu4mYrcdRz6NpZZxKjaYpVjE0NIShoWGRurm5uYiMjMSMGTPUZTo6OvD19UV4eOnurGVnZyMvLw+1a9eWlB85cgT16tWDlZUVunXrhoULF6JOnTqlPo8qDRoXLlyIpUuXws/PDx9//DHu3LmDZcuW4eOPP4aOjg6++uor6OvrY968ecW2ERISUuS4H5zREy3Lvb/Z/5+pM1FaSrJ2JkoL9aKOBq+7wtbbCR89lWYN/3VmKaJ/OooDQV+ryzLvF952TYm6D4WuDnzXjUHkl79ClDCvonaL+hjw51xc/u5PnP5cusK14GkuDr73DcI+WAcTpQWy4tPgOroHcjKykV3MQpHKovRoClOlJYZG/jO/TkdPF/Vfawn3cb2w0mgwhEqF3Ixs5GZkI+1GPOJP/Y2xKRvh2M8LMduOS9pr9b4vks7fRNK5m6V6f1NbKww6NA9xJ2Pw5+iiK6VflHD6Ohr1cCvbSVYTOenZSP07HpaO//w1qW9mhH6/z0Le48JMTEkLEJ5n36kFajvb47fBX0rKy3KdazPLpjawcFCi7y//fPg/CxIn5u7ABucJeHzvEQry8vEoSpolSom+D3uf4uc5v+o0XafP6zh/MKI2H8WV7wsDykdX7kLf1Ai+68Yg4vNdwP9nhVR5+UiPLcyEJZ27CZt2jmgzsTfCxqyrnBOpQq+veh9NentgR5fZyHxQ8kKV0oxn+s1E7Hx9DvRMDGFoboyshDS8sXVyjZxfq0lFLoTRFKsEBwdj7ty5Reo+fPgQBQUFUCqVknKlUono6OhSvd+nn34KOzs7+Pr6qst69uyJ/v37w8HBAbGxsZg5cyZ69eqF8PBw6OqWLtFWpUHjhg0bsGHDBvTv3x8XL16Eh4cHNm7ciKFDhwIAnJ2dMW3atBKDxhkzZmDy5MmSsnUWw4up/b9Jv5WIrPhUNOjuqg4SDWoZw8arGS6uPQAAODLxe5yc/c8KaFO72hhwYA5+G7wcCRF/F9u2QkcBHX1dKHQUEMXEjHVaNsCAsLmI2nQEJ2dpXmUNFE42f/YB0jzQB7f2Rao/YKvK3bBL2OQ6SVLmt348UqMf4MzSPRoDZYWi8H90DfUl5fqmRnAa1BHHZ/5U5DWamNrVxqBD85AYeRN/jFxdqrGo694YmfGppWq/utE3NYJlUyWiNhf236CWMfqFzkZBTh5+7htSYqb3RS4juyPx7A08vHRHUv6y17m2SYl+UOS677jgXzCoZYQjk9bj8b1HUOXlI/HMDdR2spfUs2pmh4w7yZXY28r14nX6Ij0TwyKfC8/m4SoUCojifo51FNA10Nd8rAZ5fdX7cAzwxM7Xg5FxW/M8t+eVZTzzs3OQn50DQ0tTNPJ3x/HnFmjRy9EUq2jKMpaHxYsXY9u2bThy5AiMjIzU5YMHD1b/29XVFa1bt0bTpk1x5MgRdO/evVRtV2nQGBcXh3btChccuLm5QUdHB+7u7urjbdu2RVxcyftOaUrv/i+3pvVNjSR/+Zo71ENdt8Z4mpKJx/ce4ty/98Hrs4FIux6P9FtJ6Dh/CLLiUhG7t3Avtxf3VHs2Vyc9NkEdyDn/qzNUeQV4ePkOCnLyoWzXFJ0WDcXf20+oM0BNAzzRadE76j3M6rg0wMCwebhz4AIil/+qnkMpClR48rAwi2jZzBY2ns2QEHEdhlam8Pi4D+q0aogD76566fEoL3mZT4vMt8nLeoonKY/x6Oo9WDgo4RTYEXf+uIgnyRkwq18H7T/th/wnubi1P1LyOqdAH+jo6SB6c9H9J5XtHdFz40f4r+9cZMWlFAaMh+fj8Z1kHJ26EcZ1zdV1n81NbTm8Kwpy85F0/hYAwLG/F1yCuuHgqOJXZVcnnZcNx81fz+LxnWSY2tWG99xAqApUiNl6HAa1jNH/QGHmIHTYv2FgbgID88JJ80+SM9S/REZcW4njMzerr2OgMNh0GuSNo59sLPKepbnOX0VyP/+GVmYwb2gNU7vCWz5Wze0AAFkJaerryX/DBGTGpeDEzML971687nPSsgBAUn72i5/Re9tk3D92DfcOX0Hjnm3QpE877Hx9TkWebqUq6ToFpOMGFK6MbvtxHySdv4WE/7+d2nH+YNz89az6uvVZNBS3fz+Px3eToV/LGM7/6owGXV2wu+eCKjvPytBt9Sg0H9IZvwQsRu7jJ+rfBznp2eo53i8zno383AFF4S1/S0cbdF46HKnRD4rs3lFTVWSmsbhb0ZpYW1tDV1cXiYnSDG9iYqLsfMQvvvgCixcvxp9//onWrVuXWLdJkyawtrbGjRs3Xo2g0cbGBteuXUPDhg1x/fp1FBQU4Nq1a3BxcQEAXL16FfXq1avUPinbNcWgw/PVz7suL5yEenXDYfwx8mucXbpXndI3tDRF3PFo7O61oEzZG1V+AdpNC4CVkx2gAB7feYgLq3/Hua/2qesYWpiitvM/mYdmA71hUs8CLYZ1QYthXdTl6beTsL7JhwAAHV0deEzuA6vm9lDl5ePe4avY7jPzlchW5D/NhX2nlmgz8U0YWZkiOzEd949ew3afmUX2YGw1shuu745ATnrRzan1TQxR29keuvqFfzg06uEGq2a2sGpmi9H3v5PU/Urnny1/vGYNhHmjulDlFyAl+gH2D16O6xr2JKyOatnXwRtbPoZRnVp4kpyBuONR2OY9A08eZqB+FxfYdnACAIy88Y3kdd87jFFfG7Wd7WFoId0GpvngToBCgeit0qkBNZncz3/Tt9pLNpjvva1wcUv4vO04NW8HAKBWQ+syzyGO3XsaYR9+i/bT++P1f49ESkwcfh24DHEnSncr6lVQ0nUKFB23iIX/BYSAz4IhMLOvjezkDNzcdxYnn9vL1qSeBfw3ToCprRVy07Px8NId7O65QLLDRU3k9mFPAMDbR6TB8YGgr3Ft42EALzeehhYm8Fk0FGb16yAnJRPXd5/Cic+2lHo6C5UPAwMDeHh4ICwsDAEBAQAKtwMKCwvD+PGav+ACAJYuXYrPP/8cBw4cUCfkSnL//n08evQItra2pe6bQhSb4694s2fPxrp169C3b1+EhYUhMDAQW7ZswYwZM6BQKPD5559j4MCBWL58eZnafT4YICIioldDcd9GVRkGrXOqsLZ3flC2aTvbt2/HiBEjsG7dOnh6emLFihXYsWMHoqOjoVQqMXz4cNjb2yMkJAQAsGTJEsyZMwdbtmyBj4+Puh0zMzOYmZkhMzMT8+bNw4ABA2BjY4PY2FhMmzYNjx8/xuXLl0udBa3STOO8efNgbGyM8PBwjBo1CtOnT4ebmxumTZuG7Oxs9OnTBwsW1OzbDERERFT1qtM3wgQGBiI5ORlz5sxBQkIC3N3dERoaql4cc/fuXejo/LPV9po1a5Cbm4uBAwdK2nm22EZXVxeXLl3Cxo0bkZaWBjs7O/j5+WHBggVlmltZpZnGisJMIxER0aunKjONA9Y0k6/0knZ9eL3C2q5MVb5PIxEREVFVq06ZxuqKXyNIRERERLKYaSQiIiKtV52+RrC6YqaRiIiIiGQx00hERERaj3Ma5THTSERERESymGkkIiIirSdU8nW0HYNGIiIi0nq8PS2Pt6eJiIiISBYzjURERKT1uOOOPGYaiYiIiEgWM41ERESk9VRcCCOLmUYiIiIiksVMIxEREWk9wdXTsphpJCIiIiJZzDQSERGR1uOcRnkMGomIiEjr8Rth5PH2NBERERHJYqaRiIiItJ6Ku3vLYqaRiIiIiGQx00hERERaj3Ma5THTSERERESymGkkIiIircctd+Qx00hEREREsphpJCIiIq3HrxGUx6CRiIiItB5vT8vj7WkiIiIiksVMIxEREWk97u0tj5lGIiIiIpLFTCMRERFpPRUXwshippGIiIiIZDHTSERERFqPXyMoj5lGIiIiIpLFTCMRERFpPe7TKI9BIxEREWk93p6Wx9vTRERERCSLmUYiIiLSeiru7i2LmUYiIiIiksVMIxEREWk9zmmUx0wjEREREclippGIiIi0HrfckcdMIxERERHJYqaRiIiItJ5QcfW0HGYaiYiIiEgWM41ERESk9TinUR6DRiIiItJ63NtbHm9PExEREVUzq1evRuPGjWFkZAQvLy+cPn26xPo7d+6Es7MzjIyM4Orqiv3790uOCyEwZ84c2NrawtjYGL6+vrh+/XqZ+sSgkYiIiLSeSiUq7FFW27dvx+TJkxEcHIxz587Bzc0N/v7+SEpK0lj/5MmTGDJkCN577z2cP38eAQEBCAgIwJUrV9R1li5dipUrV2Lt2rWIiIiAqakp/P398fTp01L3SyFEzUvIfqUzoKq7QERERGX0sWpXlb13iw/sKqztqHVxZarv5eWF9u3b4+uvvwYAqFQqNGjQABMmTMD06dOL1A8MDERWVhb27dunLuvQoQPc3d2xdu1aCCFgZ2eHKVOm4JNPPgEApKenQ6lUYsOGDRg8eHCp+sVMIxEREWk9oaq4R1nk5uYiMjISvr6+6jIdHR34+voiPDxc42vCw8Ml9QHA399fXf/WrVtISEiQ1LGwsICXl1exbWrChTBEREREFSgnJwc5OTmSMkNDQxgaGhap+/DhQxQUFECpVErKlUoloqOjNbafkJCgsX5CQoL6+LOy4uqURo0MGqsyvV1aOTk5CAkJwYwZMzReNFQ6HMfyw7EsPxzL8sFxLD8cS3nR35XtFnJZzJ07F/PmzZOUBQcHY+7cuRX2nhWBt6erSE5ODubNm1fkLw8qG45j+eFYlh+OZfngOJYfjmXVmjFjBtLT0yWPGTNmaKxrbW0NXV1dJCYmSsoTExNhY2Oj8TU2NjYl1n/2/2VpUxMGjUREREQVyNDQEObm5pJHcRlfAwMDeHh4ICwsTF2mUqkQFhYGb29vja/x9vaW1AeAgwcPqus7ODjAxsZGUicjIwMRERHFtqlJjbw9TURERPSqmjx5MkaMGIF27drB09MTK1asQFZWFoKCggAAw4cPh729PUJCQgAAEydORJcuXfDll1+id+/e2LZtG86ePYtvv/0WAKBQKDBp0iQsXLgQzZo1g4ODA2bPng07OzsEBASUul8MGomIiIiqkcDAQCQnJ2POnDlISEiAu7s7QkND1QtZ7t69Cx2df24Wd+zYEVu2bMGsWbMwc+ZMNGvWDHv37kWrVq3UdaZNm4asrCyMHj0aaWlp6NSpE0JDQ2FkZFTqftXIfRpfBZyUXD44juWHY1l+OJblg+NYfjiWVB4YNBIRERGRLC6EISIiIiJZDBqJiIiISBaDRiIiIiKSxaCRiIiIiGQxaKxAR48eRZ8+fWBnZweFQoG9e/dKjgshMGfOHNja2sLY2Bi+vr64fv161XS2mimPsUtJScHQoUNhbm4OS0tLvPfee8jMzKzEs6h8lTVuly5dQufOnWFkZIQGDRpg6dKlFX1qlWru3LlQKBSSh7Ozs/r406dPMW7cONSpUwdmZmYYMGBAkW9auHv3Lnr37g0TExPUq1cPU6dORX5+vqTOkSNH0LZtWxgaGsLR0REbNmyojNOrUNXpGty5cyecnZ1hZGQEV1dX7N+/v9zPt6KEhISgffv2qFWrFurVq4eAgADExMRI6lTmdbh69Wo0btwYRkZG8PLywunTp8v9nOkVIKjC7N+/X3z22Wdi9+7dAoDYs2eP5PjixYuFhYWF2Lt3r7h48aJ46623hIODg3jy5EnVdLgaKY+x69mzp3BzcxOnTp0Sx44dE46OjmLIkCGVfCaVqzLGLT09XSiVSjF06FBx5coVsXXrVmFsbCzWrVtXWadZ4YKDg4WLi4uIj49XP5KTk9XHx4wZIxo0aCDCwsLE2bNnRYcOHUTHjh3Vx/Pz80WrVq2Er6+vOH/+vNi/f7+wtrYWM2bMUNe5efOmMDExEZMnTxbXrl0Tq1atErq6uiI0NLRSz7W8VZdr8MSJE0JXV1csXbpUXLt2TcyaNUvo6+uLy5cvV/gYlAd/f3/xww8/iCtXrogLFy6IN954QzRs2FBkZmaq61TWdbht2zZhYGAg1q9fL65evSpGjRolLC0tRWJiYuUMBlUbDBoryYsfniqVStjY2Ihly5apy9LS0oShoaHYunVrFfSw+nqZsbt27ZoAIM6cOaOu8/vvvwuFQiEePHhQaX2vShU1bt98842wsrISOTk56jqffvqpaN68eQWfUeUJDg4Wbm5uGo+lpaUJfX19sXPnTnVZVFSUACDCw8OFEIWBk46OjkhISFDXWbNmjTA3N1eP27Rp04SLi4uk7cDAQOHv71/OZ1N1qvIafPvtt0Xv3r0l/fHy8hIffPBBuZ5jZUlKShIAxF9//SWEqNzr0NPTU4wbN079vKCgQNjZ2YmQkJDyP1Gq1nh7uorcunULCQkJ8PX1VZdZWFjAy8sL4eHhVdiz6q80YxceHg5LS0u0a9dOXcfX1xc6OjqIiIio9D5XB+U1buHh4XjttddgYGCgruPv74+YmBikpqZW0tlUvOvXr8POzg5NmjTB0KFDcffuXQBAZGQk8vLyJOPo7OyMhg0bSsbR1dVV/e0NQOEYZWRk4OrVq+o6z7fxrE5N/vmvzGuwpo1veno6AKB27doAKu86zM3NRWRkpKSOjo4OfH19X9mxpJfHoLGKJCQkAIDkh/nZ82fHSLPSjF1CQgLq1asnOa6np4fatWtr7fiW17glJCRobOP593jVeXl5YcOGDQgNDcWaNWtw69YtdO7cGY8fP0ZCQgIMDAxgaWkpec2L4yg3RsXVycjIwJMnTyrozKpWZV6DxdV5Fa9RlUqFSZMmwcfHR/21cJV1HT58+BAFBQU1Zizpf8PvniYiekGvXr3U/27dujW8vLzQqFEj7NixA8bGxlXYM9JG48aNw5UrV3D8+PGq7gppOWYaq4iNjQ0AFFnplpiYqD5GmpVm7GxsbJCUlCQ5np+fj5SUFK0d3/IaNxsbG41tPP8eNY2lpSWcnJxw48YN2NjYIDc3F2lpaZI6L46j3BgVV8fc3LzGBqaVeQ0WV+dVu0bHjx+Pffv24fDhw6hfv766vLKuQ2tra+jq6taIsaT/HYPGKuLg4AAbGxuEhYWpyzIyMhAREQFvb+8q7Fn1V5qx8/b2RlpaGiIjI9V1Dh06BJVKBS8vr0rvc3VQXuPm7e2No0ePIi8vT13n4MGDaN68OaysrCrpbCpXZmYmYmNjYWtrCw8PD+jr60vGMSYmBnfv3pWM4+XLlyXBz8GDB2Fubo6WLVuq6zzfxrM6NfnnvzKvwVd9fIUQGD9+PPbs2YNDhw7BwcFBcryyrkMDAwN4eHhI6qhUKoSFhb0yY0nlqKpX4tRkjx8/FufPnxfnz58XAMTy5cvF+fPnxZ07d4QQhVtPWFpaip9//llcunRJ9O3bl1vu/L/yGLuePXuKNm3aiIiICHH8+HHRrFmzGr/lTmWMW1pamlAqlWLYsGHiypUrYtu2bcLExKRGbbkzZcoUceTIEXHr1i1x4sQJ4evrK6ytrUVSUpIQonCrk4YNG4pDhw6Js2fPCm9vb+Ht7a1+/bOtTvz8/MSFCxdEaGioqFu3rsatTqZOnSqioqLE6tWra8SWO9XlGjxx4oTQ09MTX3zxhYiKihLBwcGv1JY7H374obCwsBBHjhyRbP2UnZ2trlNZ1+G2bduEoaGh2LBhg7h27ZoYPXq0sLS0lKzKJu3AoLECHT58WAAo8hgxYoQQonD7idmzZwulUikMDQ1F9+7dRUxMTNV2upooj7F79OiRGDJkiDAzMxPm5uYiKChIPH78uArOpvJU1rhdvHhRdOrUSRgaGgp7e3uxePHiyjrFShEYGChsbW2FgYGBsLe3F4GBgeLGjRvq40+ePBFjx44VVlZWwsTERPTr10/Ex8dL2rh9+7bo1auXMDY2FtbW1mLKlCkiLy9PUufw4cPC3d1dGBgYiCZNmogffvihMk6vQlWna3DHjh3CyclJGBgYCBcXF/Hbb79V2HmXN01jCEByjVTmdbhq1SrRsGFDYWBgIDw9PcWpU6cq4rSpmlMIIURlZDSJiIiI6NXFOY1EREREJItBIxERERHJYtBIRERERLIYNBIRERGRLAaNRERERCSLQSMRERERyWLQSERERESyGDQSUYW4ffs2FAoFLly4UNVdUYuOjkaHDh1gZGQEd3f3Sn3vrl27YtKkSZX6nkRE5YlBI1EN9e6770KhUGDx4sWS8r1790KhUFRRr6pWcHAwTE1NERMTU+T7domIqGQMGolqMCMjIyxZsgSpqalV3ZVyk5ub+9KvjY2NRadOndCoUSPUqVOnHHtFRFTzMWgkqsF8fX1hY2ODkJCQYuvMnTu3yK3aFStWoHHjxurn7777LgICArBo0SIolUpYWlpi/vz5yM/Px9SpU1G7dm3Ur18fP/zwQ5H2o6Oj0bFjRxgZGaFVq1b466+/JMevXLmCXr16wczMDEqlEsOGDcPDhw/Vx7t27Yrx48dj0qRJsLa2hr+/v8bzUKlUmD9/PurXrw9DQ0O4u7sjNDRUfVyhUCAyMhLz58+HQqHA3LlzNbbTtWtXTJgwAZMmTYKVlRWUSiW+++47ZGVlISgoCLVq1YKjoyN+//13yev++usveHp6wtDQELa2tpg+fTry8/M1vgcAfPPNN2jWrBmMjIygVCoxcODAYusSEVUHDBqJajBdXV0sWrQIq1atwv379/+ntg4dOoS4uDgcPXoUy5cvR3BwMN58801YWVkhIiICY8aMwQcffFDkfaZOnYopU6bg/Pnz8Pb2Rp8+ffDo0SMAQFpaGrp164Y2bdrg7NmzCA0NRWJiIt5++21JGxs3boSBgQFOnDiBtWvXauzfv//9b3z55Zf44osvcOnSJfj7++Ott97C9evXAQDx8fFwcXHBlClTEB8fj08++aTYc924cSOsra1x+vRpTJgwAR9++CEGDRqEjh074ty5c/Dz88OwYcOQnZ0NAHjw4AHeeOMNtG/fHhcvXsSaNWvw/fffY+HChRrbP3v2LD766CPMnz8fMTExCA0NxWuvvVa6/xBERFVFEFGNNGLECNG3b18hhBAdOnQQI0eOFEIIsWfPHvH8j35wcLBwc3OTvParr74SjRo1krTVqFEjUVBQoC5r3ry56Ny5s/p5fn6+MDU1FVu3bhVCCHHr1i0BQCxevFhdJy8vT9SvX18sWbJECCHEggULhJ+fn+S97927JwCImJgYIYQQXbp0EW3atJE9Xzs7O/H5559Lytq3by/Gjh2rfu7m5iaCg4NLbKdLly6iU6dORc5r2LBh6rL4+HgBQISHhwshhJg5c6Zo3ry5UKlU6jqrV68WZmZm6jHr0qWLmDhxohBCiF27dglzc3ORkZEhe15ERNUFM41EWmDJkiXYuHEjoqKiXroNFxcX6Oj885GhVCrh6uqqfq6rq4s6deogKSlJ8jpvb2/1v/X09NCuXTt1Py5evIjDhw/DzMxM/XB2dgZQOP/wGQ8PjxL7lpGRgbi4OPj4+EjKfXx8XuqcW7duXeS8nj9XpVIJAOpzjYqKgre3t2SBkY+PDzIzMzVmeHv06IFGjRqhSZMmGDZsGH766Sd11pKIqLpi0EikBV577TX4+/tjxowZRY7p6OhACCEpy8vLK1JPX19f8lyhUGgsU6lUpe5XZmYm+vTpgwsXLkge169fl9yuNTU1LXWb5UHuXJ8Fh2U51+fVqlUL586dw9atW2Fra4s5c+bAzc0NaWlpL91nIqKKxqCRSEssXrwYv/76K8LDwyXldevWRUJCgiRwLM+9FU+dOqX+d35+PiIjI9GiRQsAQNu2bXH16lU0btwYjo6OkkdZAkVzc3PY2dnhxIkTkvITJ06gZcuW5XMiJWjRogXCw8MlY3jixAnUqlUL9evX1/gaPT09+Pr6YunSpbh06RJu376NQ4cOVXhfiYheFoNGIi3h6uqKoUOHYuXKlZLyrl27Ijk5GUuXLkVsbCxWr15dZGXw/2L16tXYs2cPoqOjMW7cOKSmpmLkyJEAgHHjxiElJQVDhgzBmTNnEBsbiwMHDiAoKAgFBQVlep+pU6diyZIl2L59O2JiYjB9+nRcuHABEydOLLdzKc7YsWNx7949TJgwAdHR0fj5558RHByMyZMnS27pP7Nv3z6sXLkSFy5cwJ07d7Bp0yaoVCo0b968wvtKRPSyGDQSaZH58+cXuaXaokULfPPNN1i9ejXc3Nxw+vTpElcWl9XixYuxePFiuLm54fjx4/jll19gbW0NAOrsYEFBAfz8/ODq6opJkybB0tJSY7BVko8++giTJ0/GlClT4OrqitDQUPzyyy9o1qxZuZ1Lcezt7bF//36cPn0abm5uGDNmDN577z3MmjVLY31LS0vs3r0b3bp1Q4sWLbB27Vps3boVLi4uFd5XIqKXpRAvTmYiIiIiInoBM41EREREJItBIxERERHJYtBIRERERLIYNBIRERGRLAaNRERERCSLQSMRERERyWLQSERERESyGDQSERERkSwGjUREREQki0EjEREREcli0EhEREREshg0EhEREZGs/wP4+jJHuICEfgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for transformer in transformers:\n", " tester = ParallelTester(\n", " transformer,\n", " mols,\n", " n_mols=[10, 100, 1000, 5000, 10000, 20000],\n", " n_jobs=[1, 2, 4, 8],\n", " )\n", " results = tester.test()\n", " plot_heatmap(results, transformer.__class__.__name__)" ] }, { "cell_type": "code", "execution_count": 17, "id": "4fc52005", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "smiles = [\"CCCCCCCCBr\"] * 100000\n", "transformer = SmilesToMolTransformer()\n", "df = ParallelTester(\n", " transformer, smiles, n_mols=[10, 100, 1000, 5000, 10000, 20000], n_jobs=[1, 2, 4, 8]\n", ").test()\n", "plot_heatmap(df, \"SMILES to Mol\")" ] }, { "cell_type": "markdown", "id": "467c1132", "metadata": {}, "source": [ "## Modifing multiprocessing backend\n", "\n", "We use `joblib` for multiprocessing. By default, `joblib` uses the `loky` backend. As a user, you may control the backend that joblib will use by using a context manager:\n", "\n", "```python\n", "from joblib import parallel_backend\n", "from sckit_mol.transformers import MorganFingerprintTransformer\n", "transformer = MorganFingerprintTransformer(radius=2, nBits=2048)\n", "with parallel_backend('multiprocessing', n_jobs=2):\n", " transformer.transform(mols)\n", "```\n", "\n", "If you have ray or dask installed, you can also use these backends. For example, to use ray:\n", "\n", "```python\n", "from joblib import parallel_backend\n", "from ray.util.joblib import register_ray\n", "register_ray()\n", "with parallel_backend('ray', n_jobs=2):\n", " transformer.transform(mols)\n", "```\n", "\n", "And for dask:\n", "\n", "```python\n", "from joblib import parallel_backend\n", "from dask.distributed import Client\n", "client = Client()\n", "with parallel_backend('dask', n_jobs=2):\n", " transformer.transform(mols)\n", "```" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": "Python 3.9.4 ('rdkit')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.21" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/08_external_library_skopt.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c0f4155f", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:44.212745Z", "iopub.status.busy": "2024-11-24T09:27:44.212299Z", "iopub.status.idle": "2024-11-24T09:27:47.165987Z", "shell.execute_reply": "2024-11-24T09:27:47.162995Z" }, "title": "Needs scikit-optimize" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: scikit-optimize in /home/esben/python_envs/vscode/lib/python3.10/site-packages (0.10.2)\r\n", "Requirement already satisfied: scikit-learn>=1.0.0 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (1.5.2)\r\n", "Requirement already satisfied: joblib>=0.11 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (1.3.2)\r\n", "Requirement already satisfied: scipy>=1.1.0 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (1.11.3)\r\n", "Requirement already satisfied: packaging>=21.3 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (23.2)\r\n", "Requirement already satisfied: pyaml>=16.9 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (23.12.0)\r\n", "Requirement already satisfied: numpy>=1.20.3 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-optimize) (1.26.4)\r\n", "Requirement already satisfied: PyYAML in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from pyaml>=16.9->scikit-optimize) (6.0.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/esben/python_envs/vscode/lib/python3.10/site-packages (from scikit-learn>=1.0.0->scikit-optimize) (3.2.0)\r\n" ] } ], "source": [ "!pip install scikit-optimize" ] }, { "cell_type": "code", "execution_count": 2, "id": "49f80040", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:47.174431Z", "iopub.status.busy": "2024-11-24T09:27:47.173616Z", "iopub.status.idle": "2024-11-24T09:27:47.507299Z", "shell.execute_reply": "2024-11-24T09:27:47.506634Z" } }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "f1268213", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:47.510217Z", "iopub.status.busy": "2024-11-24T09:27:47.509924Z", "iopub.status.idle": "2024-11-24T09:27:48.065273Z", "shell.execute_reply": "2024-11-24T09:27:48.064596Z" } }, "outputs": [], "source": [ "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "\n", "from sklearn.pipeline import make_pipeline" ] }, { "cell_type": "code", "execution_count": 4, "id": "7239cf27", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.068114Z", "iopub.status.busy": "2024-11-24T09:27:48.067809Z", "iopub.status.idle": "2024-11-24T09:27:48.129879Z", "shell.execute_reply": "2024-11-24T09:27:48.129159Z" } }, "outputs": [], "source": [ "from skopt.space import Real, Integer, Categorical\n", "from skopt.utils import use_named_args\n", "\n", "from skopt import gp_minimize" ] }, { "cell_type": "code", "execution_count": 5, "id": "aabbba9d", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.132744Z", "iopub.status.busy": "2024-11-24T09:27:48.132424Z", "iopub.status.idle": "2024-11-24T09:27:48.173318Z", "shell.execute_reply": "2024-11-24T09:27:48.172690Z" } }, "outputs": [], "source": [ "full_set = False\n", "\n", "if full_set:\n", " csv_file = \"SLC6A4_active_excape_export.csv\"\n", " if not os.path.exists(csv_file):\n", " import urllib.request\n", "\n", " url = \"https://ndownloader.figshare.com/files/25747817\"\n", " urllib.request.urlretrieve(url, csv_file)\n", "else:\n", " csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n", "\n", "data = pd.read_csv(csv_file)\n", "trf = SmilesToMolTransformer()\n", "data[\"ROMol\"] = trf.transform(data.SMILES.values).flatten()" ] }, { "cell_type": "code", "execution_count": 6, "id": "488a3e82", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.176242Z", "iopub.status.busy": "2024-11-24T09:27:48.175854Z", "iopub.status.idle": "2024-11-24T09:27:48.188154Z", "shell.execute_reply": "2024-11-24T09:27:48.187463Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('morganfingerprinttransformer',\n",
       "                 MorganFingerprintTransformer()),\n",
       "                ('ridge', Ridge())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Pipeline(steps=[('morganfingerprinttransformer',\n", " MorganFingerprintTransformer()),\n", " ('ridge', Ridge())])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe = make_pipeline(MorganFingerprintTransformer(), Ridge())\n", "pipe" ] }, { "cell_type": "code", "execution_count": 7, "id": "44811b8e", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.190932Z", "iopub.status.busy": "2024-11-24T09:27:48.190654Z", "iopub.status.idle": "2024-11-24T09:27:48.195099Z", "shell.execute_reply": "2024-11-24T09:27:48.194411Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'memory': None, 'steps': [('morganfingerprinttransformer', MorganFingerprintTransformer()), ('ridge', Ridge())], 'verbose': False, 'morganfingerprinttransformer': MorganFingerprintTransformer(), 'ridge': Ridge(), 'morganfingerprinttransformer__fpSize': 2048, 'morganfingerprinttransformer__parallel': False, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__safe_inference_mode': False, 'morganfingerprinttransformer__useBondTypes': True, 'morganfingerprinttransformer__useChirality': False, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': 1.0, 'ridge__copy_X': True, 'ridge__fit_intercept': True, 'ridge__max_iter': None, 'ridge__positive': False, 'ridge__random_state': None, 'ridge__solver': 'auto', 'ridge__tol': 0.0001}\n" ] } ], "source": [ "print(pipe.get_params())" ] }, { "cell_type": "code", "execution_count": 8, "id": "49eb7dbe", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.197795Z", "iopub.status.busy": "2024-11-24T09:27:48.197479Z", "iopub.status.idle": "2024-11-24T09:27:48.205564Z", "shell.execute_reply": "2024-11-24T09:27:48.205022Z" } }, "outputs": [], "source": [ "max_bits = 4096\n", "\n", "morgan_space = [\n", " Categorical([True, False], name=\"morganfingerprinttransformer__useCounts\"),\n", " Categorical([True, False], name=\"morganfingerprinttransformer__useFeatures\"),\n", " Integer(512, max_bits, name=\"morganfingerprinttransformer__fpSize\"),\n", " Integer(1, 3, name=\"morganfingerprinttransformer__radius\"),\n", "]\n", "\n", "\n", "regressor_space = [Real(1e-2, 1e3, \"log-uniform\", name=\"ridge__alpha\")]\n", "\n", "search_space = morgan_space + regressor_space" ] }, { "cell_type": "code", "execution_count": 9, "id": "3818beb2", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.207944Z", "iopub.status.busy": "2024-11-24T09:27:48.207727Z", "iopub.status.idle": "2024-11-24T09:27:48.211453Z", "shell.execute_reply": "2024-11-24T09:27:48.210957Z" } }, "outputs": [], "source": [ "@use_named_args(search_space)\n", "def objective(**params):\n", " for key, value in params.items():\n", " print(f\"{key}:{value} - {type(value)}\")\n", " pipe.set_params(**params)\n", "\n", " return -np.mean(\n", " cross_val_score(\n", " pipe,\n", " data.ROMol,\n", " data.pXC50,\n", " cv=2,\n", " n_jobs=-1,\n", " scoring=\"neg_mean_absolute_error\",\n", " )\n", " )" ] }, { "cell_type": "code", "execution_count": 10, "id": "aa6b3af8", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:48.213752Z", "iopub.status.busy": "2024-11-24T09:27:48.213531Z", "iopub.status.idle": "2024-11-24T09:27:50.948132Z", "shell.execute_reply": "2024-11-24T09:27:50.947483Z" }, "lines_to_next_cell": 0, "title": "THIS takes forever on my machine with a GradientBoostingRegressor" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "morganfingerprinttransformer__useCounts:False - \n", "morganfingerprinttransformer__useFeatures:False - \n", "morganfingerprinttransformer__fpSize:3587 - \n", "morganfingerprinttransformer__radius:3 - \n", "ridge__alpha:13.116515715358098 - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "morganfingerprinttransformer__useCounts:True - \n", "morganfingerprinttransformer__useFeatures:True - \n", "morganfingerprinttransformer__fpSize:715 - \n", "morganfingerprinttransformer__radius:2 - \n", "ridge__alpha:2.445263057083992 - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "morganfingerprinttransformer__useCounts:False - \n", "morganfingerprinttransformer__useFeatures:True - \n", "morganfingerprinttransformer__fpSize:1920 - \n", "morganfingerprinttransformer__radius:3 - \n", "ridge__alpha:0.48638570461894715 - \n", "morganfingerprinttransformer__useCounts:False - \n", "morganfingerprinttransformer__useFeatures:True - \n", "morganfingerprinttransformer__fpSize:3942 - \n", "morganfingerprinttransformer__radius:1 - \n", "ridge__alpha:224.09712855921126 - \n", "morganfingerprinttransformer__useCounts:True - \n", "morganfingerprinttransformer__useFeatures:False - \n", "morganfingerprinttransformer__fpSize:2377 - \n", "morganfingerprinttransformer__radius:2 - \n", "ridge__alpha:40.10174523739503 - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "morganfingerprinttransformer__useCounts:False - \n", "morganfingerprinttransformer__useFeatures:False - \n", "morganfingerprinttransformer__fpSize:3231 - \n", "morganfingerprinttransformer__radius:1 - \n", "ridge__alpha:2.333469328026273 - \n", "morganfingerprinttransformer__useCounts:True - \n", "morganfingerprinttransformer__useFeatures:False - \n", "morganfingerprinttransformer__fpSize:1288 - \n", "morganfingerprinttransformer__radius:1 - \n", "ridge__alpha:0.41754668393896904 - \n", "morganfingerprinttransformer__useCounts:True - \n", "morganfingerprinttransformer__useFeatures:True - \n", "morganfingerprinttransformer__fpSize:1897 - \n", "morganfingerprinttransformer__radius:3 - \n", "ridge__alpha:1.777255838269662 - \n", "morganfingerprinttransformer__useCounts:False - \n", "morganfingerprinttransformer__useFeatures:False - \n", "morganfingerprinttransformer__fpSize:868 - \n", "morganfingerprinttransformer__radius:3 - \n", "ridge__alpha:18.43742127649598 - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "morganfingerprinttransformer__useCounts:True - \n", "morganfingerprinttransformer__useFeatures:True - \n", "morganfingerprinttransformer__fpSize:3202 - \n", "morganfingerprinttransformer__radius:2 - \n", "ridge__alpha:0.4219258607446576 - \n" ] }, { "data": { "text/plain": [ "'Best score=0.5968'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0)\n", "\"Best score=%.4f\" % pipe_gp.fun" ] }, { "cell_type": "code", "execution_count": 11, "id": "42d8fc82", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:50.951138Z", "iopub.status.busy": "2024-11-24T09:27:50.950922Z", "iopub.status.idle": "2024-11-24T09:27:50.956593Z", "shell.execute_reply": "2024-11-24T09:27:50.956046Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameters:\n", "{'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': False, 'morganfingerprinttransformer__fpSize': 3231, 'morganfingerprinttransformer__radius': 1, 'ridge__alpha': 2.333469328026273}\n" ] } ], "source": [ "print(\"\"\"Best parameters:\"\"\")\n", "print({param.name: value for param, value in zip(pipe_gp.space, pipe_gp.x)})" ] }, { "cell_type": "code", "execution_count": 12, "id": "c5e9fda9", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:27:50.959607Z", "iopub.status.busy": "2024-11-24T09:27:50.959088Z", "iopub.status.idle": "2024-11-24T09:27:51.588690Z", "shell.execute_reply": "2024-11-24T09:27:51.587982Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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G6yyJ+wVXrhdALm/4pYyGpNMJ+Py7ow3W4Xm0r85yLo05jw8TduPBAX685UdErYbJVAdy8vS1Wv+3fbfSsnKs23SgnSJqOzwP6eks55J74xZOnr6G/r28DR0KEXUS/F+zDuTGzVJDh0DUKfDfEhG1Jo5MdSAOdpaNqjft6SHo5u3YxtE038XMfHy6+eA96/E82k9nOZfGnkdj/y0RETUGk6kOJNDfE04OXRq81efsYIUJT4VIej7IkOD78H3iSZ6HhHSWc2nseQT6e7ZjVETU2Un3tyLVolDI8fLzjzRYZ/bzf5f0HzuA5yFFneVcOst5EFHHwt8oHczQQQ/g3deegJNDF71yZwcrvPvaEx1mDR2eh/R0lnOpPg8HW/1beR3tPIio4+Btvg5o6KAH8OAAvw6/unP1eRxPu4KkPQcx7OEhHXK17c7SH0Dn6pPBQfdh+LMfolKrg4ujFb78d2SHOw8i6hiYTHVQCoW8UzzarVDI0benJ7KuWKFvz46ZgACdpz+AztMnSqUC3h72uJiZjxuFfHqPiNpOx/wtSUTUCN7udgCAykodsvOKDRwNEXVWTKaIqNPycrcXf87MKjBgJETUmTGZIqJOy8vNTvz5atZNA0ZCRJ0Zkyki6rS83GskU2omU0TUNiSVTMXFxcHX1xdmZmYICQnB0aMNv7C0sLAQM2bMgJubG1QqFR544AFs3769SW3euXMHM2bMgIODA7p06YKxY8ciJydHr45MJqv12bx5c+ucNBG1Gb1kiiNTRNRGJJNMbdmyBdHR0ViwYAGOHz+OwMBAjBgxArm5uXXWr6iowPDhw3H58mV89dVXOHv2LD755BN4eHg0qc05c+bgxx9/xNatW7F3715kZWXhqaeeqnW89evXQ61Wi5+IiIhWvwZE1LpsrMxh1cUMAHCVc6aIqI1IJplasWIFIiMjMWXKFAQEBCA+Ph4WFhZISEios35CQgIKCgrw3XffYciQIfD19cXQoUMRGBjY6DaLiorw2WefYcWKFXjkkUcQFBSE9evX49ChQzh8+LDe8WxtbeHq6ip+zMzM2u5iEFGrkMlk4hN9Ofm3cKdcY+CIiKgzkkQyVVFRgZSUFISFhYllcrkcYWFhSE5OrnOfH374AaGhoZgxYwZcXFzQq1cvLFmyBFqtttFtpqSkQKPR6NXp0aMHvL29ax13xowZcHR0xMCBA5GQkABBEFrt/Imo7dS81XdNXWi4QIio05LEop35+fnQarVwcXHRK3dxccGZM2fq3OfixYvYtWsXnn32WWzfvh3nz5/HP//5T2g0GixYsKBRbWZnZ8PU1BS2tra16mRnZ4vfFy1ahEceeQQWFhbYuXMn/vnPf6KkpASzZs2qM7by8nKUl5eL34uLq9a30Wg00Gj4f8Z3q74mvDbS0Zn6xMPFRvz58tU8+HjYGi6YZupM/dEZsD+kpS37o7FtSiKZag6dTgdnZ2esW7cOCoUCQUFBuH79OpYvX44FCxa06rHmzZsn/tyvXz+UlpZi+fLl9SZTsbGxWLhwYa3ynTt3wsLColVj60wSExMNHQLdpTP0SZ76lvhz0t4jKC3IMGA0LdMZ+qMzYX9IS1v0R1lZWaPqSSKZcnR0hEKhqPUUXU5ODlxdXevcx83NDUqlEgqFQizz9/dHdnY2KioqGtWmq6srKioqUFhYqDc61dBxASAkJASLFy9GeXk5VCpVre0xMTGIjo4WvxcXF8PLywvh4eGwtrau/0IYKY1Gg8TERAwfPhxKpdLQ4RA6V588cCUPP+zbBACwsHLGqFHhBo6o6TpTf3QG7A9pacv+qL6zdC+SSKZMTU0RFBSEpKQk8Sk5nU6HpKQkREVF1bnPkCFDsGnTJuh0OsjlVVO/zp07Bzc3N5iamgLAPdsMCgqCUqlEUlISxo4dCwA4e/YsMjMzERoaWm+8qampsLOzqzORAgCVSlXnNqVSyX94DeD1kZ7O0Ce+no7iz9eyCzv0+XSG/uhM2B/S0hb90dj2JJFMAUB0dDQmTZqE4OBgDBw4EKtWrUJpaSmmTJkCAJg4cSI8PDwQGxsLAJg+fTo+/vhjzJ49GzNnzkRGRgaWLFmid+vtXm3a2Nhg6tSpiI6Ohr29PaytrTFz5kyEhoZi0KBBAIAff/wROTk5GDRoEMzMzJCYmIglS5bg1VdfbecrRETNYW5mCmdHK+Tm3+JaU0TUJiSTTI0fPx55eXmYP38+srOz0bdvX+zYsUOcQJ6ZmSmOQAGAl5cXfvnlF8yZMwd9+vSBh4cHZs+ejTfeeKPRbQLAypUrIZfLMXbsWJSXl2PEiBH497//LW5XKpWIi4vDnDlzIAgC/Pz8xCUXiKhj8HKzQ27+LRSX3EHRrduwsTI3dEhE1IlIJpkCgKioqHpv6+3Zs6dWWWhoaK31oJrSJgCYmZkhLi4OcXFxdW4fOXIkRo4c2eAxiEjavNztkJKWCaBqJXSb7kymiKj1SGKdKSKituTtbi/+zJXQiai1MZkiok7Py+2vhTszOW+KiFoZkyki6vT0X3jMkSkial1Mpoio03N1toGJSdWvOz7RR0StjckUEXV6Jgo5PFxsAQBXswuh0/HdmkTUephMEZFRqL7VV1FRidwbt+5Rm4io8ZhMEZFRqDkJnbf6iKg1MZkiIqPgVXN5BDUnoRNR62EyRURGwbvGE32Z1zkyRUSth8kUERkFjkwRUVthMkVERsHe1gIW5qYAOGeKiFoXkykiMgoymUx8oi87rxgVmkoDR0REnQWTKSIyGtVP9Ol0ArJyigwcDRF1FkymiMho6L9Whrf6iKh1MJkiIqPhXWMSeibf0UdErYTJFBEZDY5MEVFbYDJFREaDq6ATUVtgMkVERsPSQgUHW0sAXGuKiFoPkykiMirVt/oKCstQUlpu4GiIqDNgMkVERkVv3hRHp4ioFTCZIiKjovdaGc6bIqJWwGSKiIyKN5/oI6JWxmSKiIxKzSf6MplMEVErYDJFREbF3cUWcrkMAHBVzWSKiFqOyRQRGRWlUgE3ZxsAwNWsAgiCYOCIiKijYzJFREan+lbf7Tsa3LhZauBoiKijYzJFREbH24Pv6COi1sNkioiMDl8rQ0StickUERkd/YU7mUwRUcswmSIio+PFtaaIqBUxmSIio+NkbwWVqQkAJlNE1HJMpojI6MjlMnj+OW/qek4hKiu1Bo6IiDoySSVTcXFx8PX1hZmZGUJCQnD06NEG6xcWFmLGjBlwc3ODSqXCAw88gO3btzepzTt37mDGjBlwcHBAly5dMHbsWOTk5OjVyczMxGOPPQYLCws4OzvjtddeQ2VlZeucNBEZRPVrZbRaHdS5xQaOhog6MskkU1u2bEF0dDQWLFiA48ePIzAwECNGjEBubm6d9SsqKjB8+HBcvnwZX331Fc6ePYtPPvkEHh4eTWpzzpw5+PHHH7F161bs3bsXWVlZeOqpp8TtWq0Wjz32GCoqKnDo0CFs3LgRGzZswPz589vuYhBRm9N74bGayyMQUfNJJplasWIFIiMjMWXKFAQEBCA+Ph4WFhZISEios35CQgIKCgrw3XffYciQIfD19cXQoUMRGBjY6DaLiorw2WefYcWKFXjkkUcQFBSE9evX49ChQzh8+DAAYOfOnfjjjz/wv//9D3379sWjjz6KxYsXIy4uDhUVFW1/YYioTXASOhG1FkkkUxUVFUhJSUFYWJhYJpfLERYWhuTk5Dr3+eGHHxAaGooZM2bAxcUFvXr1wpIlS6DVahvdZkpKCjQajV6dHj16wNvbW6yTnJyM3r17w8XFRawzYsQIFBcXIz09vfUuAhG1KyZTRNRaTAwdAADk5+dDq9XqJSwA4OLigjNnztS5z8WLF7Fr1y48++yz2L59O86fP49//vOf0Gg0WLBgQaPazM7OhqmpKWxtbWvVyc7OFuvU1Ub1trqUl5ejvLxc/F5cXDUfQ6PRQKPRNHQpjFL1NeG1kQ5j6BM3Jyvx5yvXb0j6XI2hPzoS9oe0tGV/NLZNSSRTzaHT6eDs7Ix169ZBoVAgKCgI169fx/Lly7FgwQKDxhYbG4uFCxfWKt+5cycsLCwMEFHHkJiYaOgQ6C6dvU/MVXLcLtch41J2rYdXpKiz90dHw/6Qlrboj7KyskbVk0Qy5ejoCIVCUespupycHLi6uta5j5ubG5RKJRQKhVjm7++P7OxsVFRUNKpNV1dXVFRUoLCwUG906u46dz8BWN1mfbHFxMQgOjpa/F5cXAwvLy+Eh4fD2tq6oUthlDQaDRITEzF8+HAolUpDh0Mwnj7ZfvhLpJ9To6SsEn9/JAzmZqaGDqlOxtIfHQX7Q1rasj+q7yzdiySSKVNTUwQFBSEpKQkREREAqkaekpKSEBUVVec+Q4YMwaZNm6DT6SCXV039OnfuHNzc3GBqWvUL8V5tBgUFQalUIikpCWPHjgUAnD17FpmZmQgNDQUAhIaG4t1330Vubi6cnZ0BVGW/1tbWCAgIqDM2lUoFlUpVq1ypVPIfXgN4faSns/eJj4c90s+pAQA5+aW4v6ulgSNqWGfvj46G/SEtbdEfjW1PEhPQASA6OhqffPIJNm7ciNOnT2P69OkoLS3FlClTAAATJ05ETEyMWH/69OkoKCjA7Nmzce7cOfz0009YsmQJZsyY0eg2bWxsMHXqVERHR2P37t1ISUnBlClTEBoaikGDBgEAwsPDERAQgAkTJuDkyZP45Zdf8Pbbb2PGjBl1JkxE1HHUXB4hM4vLIxBR80hiZAoAxo8fj7y8PMyfPx/Z2dno27cvduzYIU72zszMFEegAMDLywu//PIL5syZgz59+sDDwwOzZ8/GG2+80eg2AWDlypWQy+UYO3YsysvLMWLECPz73/8WtysUCmzbtg3Tp09HaGgoLC0tMWnSJCxatKgdrgoRtSUvN77wmIhaTjLJFABERUXVe1tvz549tcpCQ0PF9aCa0yYAmJmZIS4uDnFxcfXW8fHx6RCTU4moabg8AhG1Bsnc5iMiam+erraQyap+5m0+ImouJlNEZLRUKiVcHKuesL2adROCIBg4IiLqiJhMEZFRq77VV1JajsLi2waOhog6IiZTRGTUOAmdiFqKyRQRGbWayyNc5bwpImoGJlNEZNRqPtGXeZ0jU0TUdEymiMio8TYfEbUUkykiMmquTtZQmlS945O3+YioOZhMEZFRUyjk8HC1BQBczy6EVqszbEBE1OEwmSIio1c9b6pCo0XujVsGjoaIOhomU0Rk9PhaGSJqCSZTRGT0vN3+Wh6Br5UhoqZiMkVERs/LgyNTRNR8TKaIyOh58zYfEbUAkykiMnq21hboYqECAFxV8zYfETUNkykiMnoymUychJ6dV4zyikoDR0REHQmTKSIi/PVEnyBUrTdFRNRYTKaIiHDXa2X4RB8RNQGTKSIiAN4eNZdH4CR0Imo8JlNEROALj4mo+ZhMEREB8HTj8ghE1DxMpoiIAFiYm8LRvgsAzpkioqZhMkVE9KfqW32FxbdRXHLHwNEQUUfBZIqI6E984TERNQeTKSKiP3m7//VEH2/1EVFjMZkiIvqT3sgUn+gjokZiMkVE9Cfe5iOi5mAyRUT0J3dnGyjkMgAcmSKixmMyRUT0JxMTBdxcbAFUzZnS6QTDBkREHQKTKSKiGrz/vNV3p7wS+TdLDBwNEXUETKaIiGrgvCkiaiomU0RENXhxeQQiaiLJJFNxcXHw9fWFmZkZQkJCcPTo0XrrbtiwATKZTO9jZmamVycnJweTJ0+Gu7s7LCwsMHLkSGRkZOjVuXDhAp588kk4OTnB2toa48aNQ05Ojl4dX1/fWsdaunRp6504EUmKN9/RR0RNJIlkasuWLYiOjsaCBQtw/PhxBAYGYsSIEcjNza13H2tra6jVavFz5coVcZsgCIiIiMDFixfx/fff48SJE/Dx8UFYWBhKS0sBAKWlpQgPD4dMJsOuXbtw8OBBVFRUYPTo0dDpdHrHWrRokd6xZs6c2TYXgogMjmtNEVFTmRg6AABYsWIFIiMjMWXKFABAfHw8fvrpJyQkJGDu3Ll17iOTyeDq6lrntoyMDBw+fBinTp1Cz549AQBr1qyBq6srvvjiC0ybNg0HDx7E5cuXceLECVhbWwMANm7cCDs7O+zatQthYWFie1ZWVvUei4g6F0f7LjBTmeBOeSUyr/M2HxHdm8FHpioqKpCSkqKXvMjlcoSFhSE5Obne/UpKSuDj4wMvLy+MGTMG6enp4rby8nIA0Lv1J5fLoVKpcODAAbGOTCaDSqUS65iZmUEul4t1qi1duhQODg7o168fli9fjsrKypadNBFJlkwmE+dNqXOLoNFoDRwREUlds0embt++DUEQYGFhAQC4cuUKvv32WwQEBCA8PLzR7eTn50Or1cLFxUWv3MXFBWfOnKlzn+7duyMhIQF9+vRBUVER3n//fQwePBjp6enw9PREjx494O3tjZiYGKxduxaWlpZYuXIlrl27BrVaDQAYNGgQLC0t8cYbb2DJkiUQBAFz586FVqsV6wDArFmz0L9/f9jb2+PQoUOIiYmBWq3GihUr6j2n8vJyMaEDgOLiYgCARqOBRqNp9LUxFtXXhNdGOoy9TzxdbZFxKRdanYDMrBvicgmGYuz9ITXsD2lpy/5obJsyQRCatSpdeHg4nnrqKbz00ksoLCxEjx49oFQqkZ+fjxUrVmD69OmNaicrKwseHh44dOgQQkNDxfLXX38de/fuxZEjR+7Zhkajgb+/P5555hksXrwYAJCSkoKpU6fi5MmTUCgUCAsLg1wuhyAI+PnnnwEAO3fuxPTp03Hp0iXI5XI888wz+OOPPzBw4ECsWbOmzmMlJCTgxRdfRElJid6oVk3vvPMOFi5cWKt806ZNYvJJRNJ1IPUGkn+vusX35N/d4OfVxcAREZEhlJWV4f/9v/+HoqIicUpQXZo9MnX8+HGsXLkSAPDVV1/BxcUFJ06cwNdff4358+c3OplydHSEQqGo9RRdTk5Oo+cpKZVK9OvXD+fPnxfLgoKCkJqaiqKiIlRUVMDJyQkhISEIDg4W64SHh+PChQvIz8+HiYkJbG1t4erqim7dutV7rJCQEFRWVuLy5cvo3r17nXViYmIQHR0tfi8uLoaXlxfCw8Mb7AxjpdFokJiYiOHDh0OpVBo6HAL7RGl1Bsm//wIAcPXww6hR/Q0aj7H3h9SwP6SlLfuj+s7SvTQ7mSorK4OVlRWAqhGep556CnK5HIMGDdJ7su5eTE1NERQUhKSkJERERAAAdDodkpKSEBUV1ag2tFot0tLSMGrUqFrbbGxsAFRNSj927Jg4clWTo6MjAGDXrl3Izc3FE088Ue+xUlNTIZfL4ezsXG8dlUpV56iVUqnkP7wG8PpIj7H2ia+Xo/jz9ZwiyVwDY+0PqWJ/SEtb9Edj22t2MuXn54fvvvsOTz75JH755RfMmTMHAJCbm9vk0Zfo6GhMmjQJwcHBGDhwIFatWoXS0lLx6b6JEyfCw8MDsbGxAKqWKhg0aBD8/PxQWFiI5cuX48qVK5g2bZrY5tatW+Hk5ARvb2+kpaVh9uzZiIiI0JvPtX79evj7+8PJyQnJycmYPXs25syZI444JScn48iRI/j73/8OKysrJCcnY86cOXjuuedgZ2fYORRE1HY8a6w1lcmFO4noHpqdTM2fPx//7//9P8yZMwfDhg0T5zvt3LkT/fr1a1Jb48ePR15eHubPn4/s7Gz07dsXO3bsECelZ2ZmQi7/68HDmzdvIjIyEtnZ2bCzs0NQUBAOHTqEgIAAsY5arUZ0dDRycnLg5uaGiRMnYt68eXrHPXv2LGJiYlBQUABfX1+89dZbYlIIVI0wbd68Ge+88w7Ky8vRtWtXzJkzR+8WHhF1PtZdzGBrbY7C4ttcuJOI7qnZE9ABIDs7G2q1GoGBgWKyc/ToUVhbW6NHjx6tFmRHV1xcDBsbm3tOYDNWGo0G27dvx6hRozhkLhHsE+Cfb3+B309fBwDs/N8sWJibGiwW9oe0sD+kpS37o7F/v1u0zpSrqyv69eunN2o0cOBAJlJE1OF5uXEldCJqnCbd5mvK7a2G1mEiIpI6vdfKZN1E924uDdQmImPWpGTqxIkTjaonk8maFQwRkVRUr4IOAFc5CZ2IGtCkZGr37t1tFQcRkaR46T3Rx9t8RFQ/g7+bj4hIijxcbVE9yM6RKSJqCOdMERHVQWVqAlcnG6hzi3BVfROCIHAKAxHViXOmiIjq4eVuB3VuEUrLKnCzqAz2tpaGDomIJIhzpoiI6uHtboejqZcBVD3Rx2SKiOrCOVNERPXQW2uKk9CJqB7Nfp1MtT/++AOZmZmoqKjQK2/oZcFERB1BzeUR+I4+IqpPs5Opixcv4sknn0RaWhpkMhmq30pTPV9Kq9W2ToRERAZy98KdRER1afZtvtmzZ6Nr167Izc2FhYUF0tPTsW/fPgQHB2PPnj2tGCIRkWG4OFrDVKkAAFxVc2SKiOrW7GQqOTkZixYtgqOjI+RyOeRyOR588EHExsZi1qxZrRkjEZFByOUyeP45b+padiG0Wp2BIyIiKWp2MqXVamFlZQUAcHR0RFZWFgDAx8cHZ8+ebZ3oiIgMrHoSemWlDtl5xQaOhoikqNlzpnr16oWTJ0+ia9euCAkJwbJly2Bqaop169ahW7durRkjEZHB1Jw3lZlVAA9XW8MFQ0SS1OyRqbfffhs6XdWQ96JFi3Dp0iU89NBD2L59O1avXt1qARIRGRInoRPRvTR7ZGrEiBHiz35+fjhz5gwKCgpgZ2fHFdCJqNPwrrE8wlU1kykiqq3ZI1OxsbFISEjQK7O3t8f69evx3nvvtTgwIiIp4MgUEd1Ls5OptWvXokePHrXKe/bsifj4+BYFRUQkFTZW5rDqYgYAuMqFO4moDs1OprKzs+Hm5lar3MnJCWq1ukVBERFJhUwmE5/oy8m/hfJyjYEjIiKpaXYy5eXlhYMHD9YqP3jwINzd3VsUFBGRlOjd6lMXGi4QIpKkZk9Aj4yMxMsvvwyNRoNHHnkEAJCUlITXX38dr7zySqsFSERkaPrJVAH8fJ0MGA0RSU2zk6nXXnsNN27cwD//+U/xJcdmZmZ44403EBMT02oBEhEZmt4TfZyETkR3aXYyJZPJ8N5772HevHk4ffo0zM3Ncf/990OlUrVmfEREBscn+oioIc1Opqp16dIFAwYMaI1YiIgkybPGqudca4qI7tbsCehERMbC3MwUzg5V7yLlyBQR3Y3JFBFRI1Tf6iu6dRtFt24bOBoikhImU0REjVC91hTA0Ski0sdkioioEbw9aj7Rx5XQiegvTKaIiBqh5shUJkemiKiGZj/N99tvv2Hu3LnIy8uDn58f+vbtK368vb1bM0YiIoPTX7iTyRQR/aXZI1MTJkyAQqHACy+8gK5du2Lv3r2YMmUKfH194eDg0JoxEhEZnKuzDRSKql+ZnDNFRDU1O5m6evUq1qxZg6ioKHzwwQdISkrCjRs3cOnSJSQkJDS5vbi4OPj6+sLMzAwhISE4evRovXU3bNgAmUym9zEzM9Ork5OTg8mTJ8Pd3R0WFhYYOXIkMjIy9OpcuHABTz75JJycnGBtbY1x48YhJydHr05BQQGeffZZWFtbw9bWFlOnTkVJSUmTz4+IOjYThRweLrYAgGvqm9DpBMMGRESS0exkKjQ0FNevX69V7uPjgzFjxjSprS1btiA6OhoLFizA8ePHERgYiBEjRiA3N7fefaytraFWq8XPlStXxG2CICAiIgIXL17E999/jxMnTsDHxwdhYWEoLS0FAJSWliI8PBwymQy7du3CwYMHUVFRgdGjR0On04ltPfvss0hPT0diYiK2bduGffv24YUXXmjS+RFR51B9q6+8ohK5N24ZOBoikopmJ1Nz5szBokWLUFDQ8qdaVqxYgcjISEyZMgUBAQGIj4+HhYVFgyNcMpkMrq6u4sfFxUXclpGRgcOHD2PNmjUYMGAAunfvjjVr1uD27dv44osvAAAHDx7E5cuXsWHDBvTu3Ru9e/fGxo0bcezYMezatQsAcPr0aezYsQOffvopQkJC8OCDD+Kjjz7C5s2bkZWV1eLzJqKOxZuvlSGiOjQ7mRo9ejR2796NBx54AFOnTsWnn36KlJQU8aXHjVVRUYGUlBSEhYX9FZRcjrCwMCQnJ9e7X0lJCXx8fODl5YUxY8YgPT1d3FZeXg4Aerf+5HI5VCoVDhw4INaRyWR67xI0MzODXC4X6yQnJ8PW1hbBwcFinbCwMMjlchw5cqRJ50lEHZ9XzRceq7k8AhFVafbTfOfPn8fJkyfFz5IlS3D58mUolUp0794dv//+e6Payc/Ph1ar1RtZAgAXFxecOXOmzn26d++OhIQE9OnTB0VFRXj//fcxePBgpKenw9PTEz169IC3tzdiYmKwdu1aWFpaYuXKlbh27RrUajUAYNCgQbC0tMQbb7yBJUuWQBAEzJ07F1qtVqyTnZ0NZ2dnvWObmJjA3t4e2dnZ9Z5TeXm5mNABQHFxMQBAo9FAo9E06roYk+prwmsjHeyTurk5W4k/X7l2o92uD/tDWtgf0tKW/dHYNpudTHXr1g3dunXDk08+KZYVFxfj5MmTjU6kmis0NBShoaHi98GDB8Pf3x9r167F4sWLoVQq8c0332Dq1Kmwt7eHQqFAWFgYHn30UQhC1aRRJycnbN26FdOnT8fq1ashl8vxzDPPoH///pDLW7b8VmxsLBYuXFirfOfOnbCwsGhR251ZYmKioUOgu7BP9JWUVYo/Hz95Dtu3l7br8dkf0sL+kJa26I+ysrJG1Wt2MlUXa2trPPTQQ3jooYcavY+joyMUCkWtp+hycnLg6uraqDaUSiX69euH8+fPi2VBQUFITU1FUVERKioq4OTkhJCQEL1bduHh4bhw4QLy8/NhYmICW1tbuLq6olu3bgAAV1fXWpPgKysrUVBQ0GBsMTExiI6OFr8XFxfDy8sL4eHhsLa2btQ5GRONRoPExEQMHz4cSqXS0OEQ2Cf1EQQBG7atwe07GpRXKjFq1Kh2OS77Q1rYH9LSlv1RfWfpXlo1mWoOU1NTBAUFISkpCREREQAAnU6HpKQkREVFNaoNrVaLtLS0On+x2djYAKialH7s2DEsXry4Vh1HR0cAwK5du5Cbm4snnngCQNUIWGFhIVJSUhAUFCTW0el0CAkJqTcelUqlNxermlKp5D+8BvD6SA/7pDYvd3ucu5iD7PxiCJDBVNl+v0bZH9LC/pCWtuiPxrZn8GQKAKKjozFp0iQEBwdj4MCBWLVqFUpLSzFlyhQAwMSJE+Hh4YHY2FgAwKJFizBo0CD4+fmhsLAQy5cvx5UrVzBt2jSxza1bt8LJyQne3t5IS0vD7NmzERERgfDwcLHO+vXr4e/vDycnJyQnJ2P27NmYM2cOunfvDgDw9/fHyJEjERkZifj4eGg0GkRFReHpp5+Gu7t7O14hIpIKb3c7nLuYA51OQFZOEXw9uUgxkbGTRDI1fvx45OXlYf78+cjOzkbfvn2xY8cOcVJ6Zmam3jymmzdvIjIyEtnZ2bCzs0NQUBAOHTqEgIAAsY5arUZ0dDRycnLg5uaGiRMnYt68eXrHPXv2LGJiYlBQUABfX1+89dZbmDNnjl6dzz//HFFRURg2bBjkcjnGjh2L1atXt+HVICIp87preQQmU0TUrGRKo9Fg5MiRiI+Px/33398qgURFRdV7W2/Pnj1631euXImVK1c22N6sWbMwa9asBussXboUS5cubbCOvb09Nm3a1GAdIjIeessjZHF5BCJq5jpTSqWyzZ/YIyKSIm+3v0amMrlwJxGhBYt2Pvfcc/jss89aMxYiIsnzdOMq6ESkr9lzpiorK5GQkIBff/0VQUFBsLS01Nu+YsWKFgdHRCQ1XSxVsLe1QEFhGVdBJyIALUimTp06hf79+wMAzp07p7dNJpO1LCoiIgnzdrdHQWEZCgrLUFJaji6WtZdCISLj0exkavfu3a0ZBxFRh+HlbofUP64BAK6pb6KHX+MWGCaizqlF703Zv38/nnvuOQwePBjXr18HAPz3v/8VXxRMRNQZedWchK7mvCkiY9fsZOrrr7/GiBEjYG5ujuPHj4sv9i0qKsKSJUtaLUAiIqnRWx7hOudNERm7ZidT//rXvxAfH49PPvlEb7n1IUOG4Pjx460SHBGRFNVcuJPLIxBRs5Ops2fP4m9/+1utchsbGxQWFrYkJiIiSXN3sYFcXvWgzVXe5iMyes1OplxdXXH+/Pla5QcOHEC3bt1aFBQRkZSZKk3g5lz1EvWrWQUQBMHAERGRITU7mYqMjMTs2bNx5MgRyGQyZGVl4fPPP8err76K6dOnt2aMRESSUz0J/fYdDW4Ulho4GiIypGYvjTB37lzodDoMGzYMZWVl+Nvf/gaVSoVXX30VM2fObM0YiYgkx8vdDodPXAJQtRK6o10XA0dERIbS7GRKJpPhrbfewmuvvYbz58+jpKQEAQEB6NKFv1CIqPPTm4R+vQD9enoZMBoiMqRmJ1OZmZnw8vKCqakpAgICam3z9vZucXBERFLl5VZjeQROQicyas2eM9W1a1fk5eXVKr9x4wa6du3aoqCIiKSu5sgUX3hMZNyanUwJglDnO/hKSkpgZmbWoqCIiKTO2cEKKtOqwX0mU0TGrcm3+aKjowFUzZmaN28eLCwsxG1arRZHjhxB3759Wy1AIiIpkstl8HSzw4UrebieU4hKrQ4miha9oYuIOqgmJ1MnTpwAUDUylZaWBlNTU3GbqakpAgMD8eqrr7ZehEREEuXlXpVMabU6qHOK9G79EZHxaFIy9fvvv+PXX3+FQqHAlClTsHr1alhZWbVVbEREklbzhcdX1QVMpoiMVJPGpPv164eCgqqXeu7duxcVFRVtEhQRUUfgzUnoRIQmJlO2tra4ePEiAODy5cvQ6XRtEhQRUUfg5VFjeQQmU0RGq0m3+caOHYuhQ4fCzc0NMpkMwcHBUCgUddatTrqIiDorb73bfEymiIxVk5KpdevW4amnnsL58+cxa9YsREZGcs4UERktaytz2FiZo+jWbY5MERmxJj/NN3LkSABASkoKZs+ezWSKiIyal7sdis7eRu6NW7h9pwLmZqb33omIOpVmv05m/fr1AIA//vgDmZmZtSajP/HEEy2LjIioA/Bys8Ops1kAgGvqQtzf1dnAERFRe2t2MnXp0iVEREQgLS0NMpkMgiAAgLgqularbZ0IiYgkTO+Fx1kFTKaIjFCzl+udNWsWunbtitzcXFhYWCA9PR379u1DcHAw9uzZ04ohEhFJl7c7X3hMZOyaPTKVnJyMXbt2wdHREXK5HHK5HA8++CBiY2Mxa9YscaV0IqLOjC88JqJmj0xptVpx8rmjoyOysqrmDPj4+ODs2bOtEx0RkcR5utqKPzOZIjJOzR6Z6tWrF06ePImuXbsiJCQEy5Ytg6mpKdatW4du3bq1ZoxERJKlUinh4miFnPxbyMwqgCAI4txRIjIOzR6Zevvtt8UV0BctWoRLly7hoYcewvbt27F69epWC5CISOq8/pw3VVJajsLi2waOhojaW7NHpkaMGCH+7OfnhzNnzqCgoAB2dnb8vzIiMipe7nY49vsVAFWT0O1sLAwcERG1p2aPTNXF3t6+2YlUXFwcfH19YWZmhpCQEBw9erTeuhs2bIBMJtP7mJmZ6dXJycnB5MmT4e7uDgsLC4wcORIZGRl6dbKzszFhwgS4urrC0tIS/fv3x9dff61Xx9fXt9axli5d2qxzJKLOSe+JvqwCA0ZCRIbQqslUc23ZsgXR0dFYsGABjh8/jsDAQIwYMQK5ubn17mNtbQ21Wi1+rly5Im4TBAERERG4ePEivv/+e5w4cQI+Pj4ICwtDaWmpWG/ixIk4e/YsfvjhB6SlpeGpp57CuHHjaj2JuGjRIr1jzZw5s/UvAhF1WHyij8i4SSKZWrFiBSIjIzFlyhQEBAQgPj4eFhYWSEhIqHcfmUwGV1dX8ePi4iJuy8jIwOHDh7FmzRoMGDAA3bt3x5o1a3D79m188cUXYr1Dhw5h5syZGDhwILp164a3334btra2SElJ0TuWlZWV3rEsLS1b/yIQUYfl5cZkisiYGTyZqqioQEpKCsLCwsQyuVyOsLAwJCcn17tfSUkJfHx84OXlhTFjxiA9PV3cVl5eDgB6t/7kcjlUKhUOHDgglg0ePBhbtmxBQUEBdDodNm/ejDt37uDhhx/WO9bSpUvh4OCAfv36Yfny5aisrGzpaRNRJ+LqZA0Tk6pfp5m8zUdkdJo9Ab215OfnQ6vV6o0sAYCLiwvOnDlT5z7du3dHQkIC+vTpg6KiIrz//vsYPHgw0tPT4enpiR49esDb2xsxMTFYu3YtLC0tsXLlSly7dg1qtVps58svv8T48ePh4OAAExMTWFhY4Ntvv4Wfn59YZ9asWejfvz/s7e1x6NAhxMTEQK1WY8WKFfWeU3l5uZjQAUBxcTEAQKPRQKPRNOs6dWbV14TXRjrYJ03n4WKLK9cLcC27EHfKy6GQt97/q7I/pIX9IS1t2R+NbbNFyZRGo0F2djbKysrg5OQEe3v7e+/UCkJDQxEaGip+Hzx4MPz9/bF27VosXrwYSqUS33zzDaZOnQp7e3soFAqEhYXh0UcfFd8hCADz5s1DYWEhfv31Vzg6OuK7777DuHHjsH//fvTu3RsAEB0dLdbv06cPTE1N8eKLLyI2NhYqlarO+GJjY7Fw4cJa5Tt37oSFBZ/yqU9iYqKhQ6C7sE8aTymv+h8ojUaLL7/6ETZdlK1+DPaHtLA/pKUt+qOsrKxR9ZqcTN26dQv/+9//sHnzZhw9ehQVFRXiInWenp4IDw/HCy+8gAEDBjSqPUdHRygUCuTk5OiV5+TkwNXVtVFtKJVK9OvXD+fPnxfLgoKCkJqaiqKiIlRUVMDJyQkhISEIDg4GAFy4cAEff/wxTp06hZ49ewIAAgMDsX//fsTFxSE+Pr7OY4WEhKCyshKXL19G9+7d66wTExOjl4QVFxfDy8sL4eHhsLa2btQ5GRONRoPExEQMHz4cSmXr/wGipmOfNN3Vmwdw/mrVfEu/7n0xINCn1dpmf0gL+0Na2rI/qu8s3UuTkqkVK1bg3XffxX333YfRo0fjzTffhLu7O8zNzVFQUIBTp05h//79CA8PR0hICD766CPcf//9DbZpamqKoKAgJCUlISIiAgCg0+mQlJSEqKioRsWl1WqRlpaGUaNG1dpmY2MDoGpS+rFjx7B48WIAf2Wb8ruG4hUKhbgYaV1SU1Mhl8vh7Fz/m+FVKlWdo1ZKpZL/8BrA6yM97JPG8/V0FH9W5xa3yXVjf0gL+0Na2qI/Gttek5Kp3377Dfv27RNHcu42cOBAPP/884iPj8f69euxf//+eyZTQNWttEmTJiE4OBgDBw7EqlWrUFpaiilTpgCoWsLAw8MDsbGxAKqWKhg0aBD8/PxQWFiI5cuX48qVK5g2bZrY5tatW+Hk5ARvb2+kpaVh9uzZiIiIQHh4OACgR48e8PPzw4svvoj3338fDg4O+O6775CYmIht27YBqHqZ85EjR/D3v/8dVlZWSE5Oxpw5c/Dcc8/Bzs4ORETV9JZHUPOJPiJj0qRkquayArdu3RJfdHw3lUqFl156qdHtjh8/Hnl5eZg/fz6ys7PRt29f7NixQ5yUnpmZqTeCdPPmTURGRiI7Oxt2dnYICgrCoUOHEBAQINZRq9WIjo5GTk4O3NzcMHHiRMybN0/crlQqsX37dsydOxejR49GSUkJ/Pz8sHHjRnGES6VSYfPmzXjnnXdQXl6Orl27Ys6cOXq38IiIAP1kKvM6kykiY9LsCegPPfQQduzY0eh5TfcSFRVV7229PXv26H1fuXIlVq5c2WB7s2bNwqxZsxqsc//999da8bym/v374/Dhww22QUQEAHY2FrC0MEVpWQWuqrk8ApExafazu/369UNISEit5QtSU1PrnLtERNSZyWQycfHO7LxilFdwPToiY9HsZGr9+vWYPHkyHnzwQRw4cADnzp3DuHHjEBQUBIVC0ZoxEhF1CN4eVcvDCAJwPbvQsMEQUbtp0TpTCxcuhEqlwvDhw6HVajFs2DAkJydj4MCBrRUfEVGHofdaGfVNdPN2bKA2EXUWzR6ZysnJwezZs/Gvf/0LAQEBUCqVmDx5MhMpIjJaXu5/LVyceZ3zpoiMRbOTqa5du2Lfvn3YunUrUlJS8PXXX+OFF17A8uXLWzM+IqIOg8sjEBmnZt/mS0hIwNNPPy1+HzlyJHbv3o3HH38cly9fRlxcXKsESETUUejd5stiMkVkLJo9MlUzkarWv39/HDp0CLt27WpRUEREHZGFuSkc7CwBAFezeJuPyFg0KZnKzMy8Zx1fX18cOnQIAHD9+vXmRUVE1EF5/zlvqrD4NopL7hg4GiJqD01KpgYMGIAXX3wRv/32W711ioqK8NVXX6FXr14NLohJRNQZ1Zw3dY3zpoiMQpPmTJ0+fRrvvvsuhg8fDjMzMwQFBcHd3R1mZma4efMm/vjjD6Snp6N///5YtmwZF+8kIqOj/1qZAgTc72bAaIioPTRpZGrp0qV49913oVarERcXh/vvvx/5+fnIyMgAADz77LNISUlBcnIyEykiMkpebn8tj8An+oiMQ5NGplatWoVXX30Vzs7O+PHHH/Hvf/8bFhYWbRUbEVGH4+3OJ/qIjE2TRqbc3d1x4sQJAMB///tflJaWtklQREQdlZuzDRRyGQCOTBEZiyYlU6+88gpGjx6Nhx56CADwv//9D0ePHsXt27fbJDgioo5GqVTAzcUWQNXyCIIgGDYgImpzTUqmZs6ciWPHjmHkyJEQBAFxcXEYPHgwrK2t4e/vj6effhpLly7Fzz//3FbxEhFJXvWtvjvllcgvKDFwNETU1pq8aGefPn3w1ltv4b777sPhw4dx69YtHDhwAC+//DLs7Ozw/fffY9y4cW0RKxFRh1BzJfRMzpsi6vSa/TqZ6if4ACAkJAQhISHidw5rE5Ex03tHX1YBgnp7GzAaImprzX6dTENkMllbNEtE1CF4uddYHoEjU0SdXrNHpgAgKSkJSUlJyM3NhU6n09uWkJDQosCIiDoqvZEpPtFH1Ok1O5lauHAhFi1ahODgYLi5uXE0iojoT072XWCmMsGd8krOmSIyAs1OpuLj47FhwwZMmDChNeMhIurwZDIZvNztkXEpF+qcQlRWamFiojB0WETURpo9Z6qiogKDBw9uzViIiDqN6if6tDoBWTlFBo6GiNpSs5OpadOmYdOmTa0ZCxFRp6H3wmPe6iPq1Jp9m+/OnTtYt24dfv31V/Tp0wdKpVJv+4oVK1ocHBFRR1Vzramr6gIA9xkuGCJqU81Opn7//Xf07dsXAHDq1Cm9bZyMTkTGjssjEBmPZidTu3fvbs04iIg6Ff2FO5lMEXVmbbJoJxGRsbPuYgZba3MAQGZWgYGjIaK21KSRqejoaCxevBiWlpaIjo5usC7nTBGRsfNyt0dh8XXcuFmKstsVsDA3NXRIRNQGmpRMnThxAhqNRvy5PpwzRURUdasv7cx1AFUroXfv5mLgiIioLTQpmao5T4pzpoiIGqb3RF8WkymizqpF7+a7c+cOfv/991rv5pPJZBg9enSLgyMi6si8PWo+0cd5U0SdVbOTqR07dmDChAm4ceNGrW0ymQxarbZFgRERdXT6a03xiT6izqrZT/PNnDkT48aNg1qthk6n0/s0J5GKi4uDr68vzMzMEBISgqNHj9Zbd8OGDZDJZHofMzMzvTo5OTmYPHky3N3dYWFhgZEjRyIjI0OvTnZ2NiZMmABXV1dYWlqif//++Prrr/XqFBQU4Nlnn4W1tTVsbW0xdepUlJSUNPn8iMj4eLjaonoKaeZ1jkwRdVbNTqZycnIQHR0NF5eWzwHYsmULoqOjsWDBAhw/fhyBgYEYMWIEcnNz693H2toaarVa/Fy5ckXcJggCIiIicPHiRXz//fc4ceIEfHx8EBYWhtLSUrHexIkTcfbsWfzwww9IS0vDU089hXHjxulNrn/22WeRnp6OxMREbNu2Dfv27cMLL7zQ4nMmos5PZWoCVydrAFUjU4IgGDgiImoLzU6m/u///g979uxplSBWrFiByMhITJkyBQEBAYiPj4eFhQUSEhLq3Ucmk8HV1VX81EzqMjIycPjwYaxZswYDBgxA9+7dsWbNGty+fRtffPGFWO/QoUOYOXMmBg4ciG7duuHtt9+Gra0tUlJSAACnT5/Gjh078OmnnyIkJAQPPvggPvroI2zevBlZWVmtcu5E1Ll5uVXNmyotq8DNojIDR0NEbaHZydTHH3+Mb775BpMnT8YHH3yA1atX630aq6KiAikpKQgLC/srKLkcYWFhSE5Orne/kpIS+Pj4wMvLC2PGjEF6erq4rby8HAD0bv3J5XKoVCocOHBALBs8eDC2bNmCgoIC6HQ6bN68GXfu3MHDDz8MAEhOToatrS2Cg4PFfcLCwiCXy3HkyJFGnyMRGS+uhE7U+TV7AvoXX3yBnTt3wszMDHv27NFbW0omk2HWrFmNaic/Px9arbbW7UIXFxecOXOmzn26d++OhIQE9OnTB0VFRXj//fcxePBgpKenw9PTEz169IC3tzdiYmKwdu1aWFpaYuXKlbh27RrUarXYzpdffonx48fDwcEBJiYmsLCwwLfffgs/Pz8AVXOqnJ2d9Y5tYmICe3t7ZGdn13tO5eXlYkIHAMXFxQAAjUYjrtNFf6m+Jrw20sE+aT0eLjbiz5eu5iHg/qZPjWB/SAv7Q1rasj8a22azk6m33noLCxcuxNy5cyGXt+9baUJDQxEaGip+Hzx4MPz9/bF27VosXrwYSqUS33zzDaZOnQp7e3soFAqEhYXh0Ucf1ZuzMG/ePBQWFuLXX3+Fo6MjvvvuO4wbNw779+9H7969mx1fbGwsFi5cWKt8586dsLCwaHa7nV1iYqKhQ6C7sE9aLivrr3ma+w4eh+xOZrPbYn9IC/tDWtqiP8rKGndrvtnJVEVFBcaPH9/iRMrR0REKhQI5OTl65Tk5OXB1dW1UG0qlEv369cP58+fFsqCgIKSmpqKoqAgVFRVwcnJCSEiIeMvuwoUL+Pjjj3Hq1Cn07NkTABAYGIj9+/cjLi4O8fHxcHV1rTUJvrKyEgUFBQ3GFhMTo/e6neLiYnh5eSE8PBzW1taNOidjotFokJiYiOHDh0OpVBo6HAL7pDVl5xbjq1/XAwCU5nYYNWpUk9tgf0gL+0Na2rI/qu8s3Uuzk6lJkyZhy5YtePPNN5vbBADA1NQUQUFBSEpKQkREBABAp9MhKSkJUVFRjWpDq9UiLS2tzl9SNjZVQ+wZGRk4duwYFi9eDOCvbPPuZFChUIgLkIaGhqKwsBApKSkICgoCAOzatQs6nQ4hISH1xqNSqaBSqWqVK5VK/sNrAK+P9LBPWs7d1Q6mSgUqNFpczy5s0fVkf0gL+0Na2qI/Gttes5MprVaLZcuW4ZdffkGfPn1qHbApLzqOjo7GpEmTEBwcjIEDB2LVqlUoLS3FlClTAFQtYeDh4YHY2FgAwKJFizBo0CD4+fmhsLAQy5cvx5UrVzBt2jSxza1bt8LJyQne3t5IS0vD7NmzERERgfDwcABAjx494OfnhxdffBHvv/8+HBwc8N1334lLIACAv78/Ro4cicjISMTHx0Oj0SAqKgpPP/003N3dm3vpiMiIKBRyeLja4tLVG7iWXQitVgeFon2nRhBR22p2MpWWloZ+/foBAE6dOqW3rakvOh4/fjzy8vIwf/58ZGdno2/fvtixY4c4KT0zM1NvBOnmzZuIjIxEdnY27OzsEBQUhEOHDiEgIECso1arER0djZycHLi5uWHixImYN2+euF2pVGL79u2YO3cuRo8ejZKSEvj5+WHjxo16I1yff/45oqKiMGzYMMjlcowdO7ZJTysSEXm52+PS1RuorNQhO68YHq62hg6JiFpRs5Op1n7RcVRUVL239e5ez2rlypVYuXJlg+3NmjXrnk8U3n///bVWPL+bvb09Nm3a1GAdIqKGeLvrv1aGyRRR58KxZiKiNsa1pog6NyZTRERtzMvdXvw5M4vv6CPqbJhMERG1MS83jkwRdWZMpoiI2pittTm6WFYtl3KVI1NEnQ6TKSKiNiaTycR5Uzn5t1BezteQEHUmTKaIiNqBd415U9eyCw0XCBG1OiZTRETtgE/0EXVeTKaIiNpBzUnofKKPqHNhMkVE1A5q3ubjyBRR58JkioioHdRc9fyqmskUUWfCZIqIqB1YmJvCyb4LAI5MEXU2TKaIiNqJt0fVrb6iW7dRdOu2gaMhotbCZIqIqJ1wJXSizonJFBFRO9FfHoFP9BF1FkymiIjaSc1kKpMjU0SdBpMpIqJ24uVWY3kEPtFH1GkwmSIiaiduztZQKKp+7XLOFFHnwWSKiKidmJgo4OFiCwC4pr4JnU4wbEBE1CqYTBERtaPqeVPlFZXIvXHLwNEQUWtgMkVE1I74wmOizofJFBFRO9Jba0rN5RGIOgMmU0RE7YgjU0SdD5MpIqJ25O1eY3kEJlNEnQKTKSKiduRgZwlzMyUArjVF1FkwmSIiakcymQxef45OqXOLUKGpNHBERNRSTKaIiNpZ9SR0nU5AVk6RgaMhopZiMkVE1M68OQmdqFNhMkVE1M70n+jj8ghEHR2TKSKidqaXTHESOlGHx2SKiKideblxeQSizoTJFBFRO+tiqYK9rQUAIJO3+Yg6PCZTREQGUD06VVBYhpLScgNHQ0QtwWSKiMgAas6busZ5U0QdmqSSqbi4OPj6+sLMzAwhISE4evRovXU3bNgAmUym9zEzM9Ork5OTg8mTJ8Pd3R0WFhYYOXIkMjIyxO2XL1+u1Ub1Z+vWrWK9urZv3ry59S8AERmNmslUJpMpog5NMsnUli1bEB0djQULFuD48eMIDAzEiBEjkJubW+8+1tbWUKvV4ufKlSviNkEQEBERgYsXL+L777/HiRMn4OPjg7CwMJSWlgIAvLy89PZXq9VYuHAhunTpgkcffVTvWOvXr9erFxER0SbXgYiMg/47+jhviqgjMzF0ANVWrFiByMhITJkyBQAQHx+Pn376CQkJCZg7d26d+8hkMri6uta5LSMjA4cPH8apU6fQs2dPAMCaNWvg6uqKL774AtOmTYNCoai1/7fffotx48ahS5cueuW2trb1HouIqKn0Rqauc2SKqCOTRDJVUVGBlJQUxMTEiGVyuRxhYWFITk6ud7+SkhL4+PhAp9Ohf//+WLJkiZg4lZdXTeiseetPLpdDpVLhwIEDmDZtWq32UlJSkJqairi4uFrbZsyYgWnTpqFbt2546aWXMGXKFMhksjrjKi8vF48PAMXFxQAAjUYDjUbT0KUwStXXhNdGOtgnbc/J3gJymQw6QUBmVkGD15r9IS3sD2lpy/5obJuSSKby8/Oh1Wrh4uKiV+7i4oIzZ87UuU/37t2RkJCAPn36oKioCO+//z4GDx6M9PR0eHp6okePHvD29kZMTAzWrl0LS0tLrFy5EteuXYNara6zzc8++wz+/v4YPHiwXvmiRYvwyCOPwMLCAjt37sQ///lPlJSUYNasWXW2Exsbi4ULF9Yq37lzJywsLBpzSYxSYmKioUOgu7BP2paVpQmKSjS4fDUPP/30U73/g1aN/SEt7A9paYv+KCsra1Q9mSAIQqsfvYmysrLg4eGBQ4cOITQ0VCx//fXXsXfvXhw5cuSebWg0Gvj7++OZZ57B4sWLAVSNNE2dOhUnT56EQqFAWFgY5HI5BEHAzz//rLf/7du34ebmhnnz5uGVV15p8Fjz58/H+vXrcfXq1Tq31zUy5eXlhfz8fFhbW9/zXIyNRqNBYmIihg8fDqVSaehwCOyT9vJG7Hc4erJqrudXa6bBwc6yznrsD2lhf0hLW/ZHcXExHB0dUVRU1ODfb0mMTDk6OkKhUCAnJ0evPCcnp9HzlJRKJfr164fz58+LZUFBQUhNTUVRUREqKirg5OSEkJAQBAcH19r/q6++QllZGSZOnHjPY4WEhGDx4sUoLy+HSqWqtV2lUtVZrlQq+Q+vAbw+0sM+aVveHg5iMqXOuwVXZ9sG67M/pIX9IS1t0R+NbU8ST/OZmpoiKCgISUlJYplOp0NSUpLeSFVDtFot0tLS4ObmVmubjY0NnJyckJGRgWPHjmHMmDG16nz22Wd44okn4OTkdM9jpaamws7Ors6EiYiosbw9ar7wmJPQiToqSYxMAUB0dDQmTZqE4OBgDBw4EKtWrUJpaan4dN/EiRPh4eGB2NhYAFXzmAYNGgQ/Pz8UFhZi+fLluHLlit7E8q1bt8LJyQne3t5IS0vD7NmzERERgfDwcL1jnz9/Hvv27cP27dtrxfXjjz8iJycHgwYNgpmZGRITE7FkyRK8+uqrbXg1iMgY1HxHH18rQ9RxSSaZGj9+PPLy8jB//nxkZ2ejb9++2LFjhzgpPTMzE3L5XwNpN2/eRGRkJLKzs2FnZ4egoCAcOnQIAQEBYh21Wo3o6Gjk5OTAzc0NEydOxLx582odOyEhAZ6enrWSLKBqiC8uLg5z5syBIAjw8/MTl3EgImqJmssjcGSKqOOSxAT0zq64uBg2Njb3nMBmrDQaDbZv345Ro0Zx/oFEsE/ah04nIOzZD1FRUQlvd3ts+uj5OuuxP6SF/SEtbdkfjf37LYk5U0RExkgul8HL1RYAcD2nEJVanWEDIqJmYTJFRGRA1bf6tFodsnOLDBwNETUHkykiIgPy0ntHH+dNEXVETKaIiAzIu+Y7+vhEH1GHxGSKiMiAODJF1PExmSIiMiC95RHUTKaIOiImU0REBmRjZQ7rLmYAODJF1FExmSIiMrDq0ancG7dw+06FgaMhoqZiMkVEZGDeNeZNXcsuNFwgRNQsTKaIiAys5rypzOt8oo+oo2EyRURkYJyETtSxMZkiIjIwLzcuj0DUkTGZIiIyME83W/FnJlNEHQ+TKSIiAzNTKeHsaAWgahV0QRAMHBERNQWTKSIiCah+oq+ktBxFt24bOBoiagomU0REEqD3RB9v9RF1KEymiIgkwMutxhN9fOExUYfCZIqISAL0lkfgyBRRh8JkiohIAmqugs5kiqhjYTJFRCQBLk7WMDGp+pXMhTuJOhYmU0REEmCikMPT1RYAcE19E1qtzrABEVGjMZkiIpKI6pXQKzRa5N64ZeBoiKixmEwREUkEJ6ETdUxMpoiIJEI/meLyCEQdBZMpIiKJ0FtripPQiToMJlNERBLhVWN5BK6CTtRxMJkiIpIIe1sLWFqYAuBtPqKOhMkUEZFEyGQy8VZfdl4xyisqDRwRETUGkykiIgmpvtUnCMD17ELDBkNEjcJkiohIQvSe6OMkdKIOgckUEZGE6D3Rx3lTRB0CkykiIgnhwp1EHY+kkqm4uDj4+vrCzMwMISEhOHr0aL11N2zYAJlMpvcxMzPTq5OTk4PJkyfD3d0dFhYWGDlyJDIyMsTtly9frtVG9Wfr1q1ivczMTDz22GOwsLCAs7MzXnvtNVRWcmIoEbU+by6PQNThSCaZ2rJlC6Kjo7FgwQIcP34cgYGBGDFiBHJzc+vdx9raGmq1WvxcuXJF3CYIAiIiInDx4kV8//33OHHiBHx8fBAWFobS0lIAgJeXl97+arUaCxcuRJcuXfDoo48CALRaLR577DFUVFTg0KFD2LhxIzZs2ID58+e37QUhIqNkYW4KBztLALzNR9RRSCaZWrFiBSIjIzFlyhQEBAQgPj4eFhYWSEhIqHcfmUwGV1dX8ePi4iJuy8jIwOHDh7FmzRoMGDAA3bt3x5o1a3D79m188cUXAACFQqG3v6urK7799luMGzcOXbp0AQDs3LkTf/zxB/73v/+hb9++ePTRR7F48WLExcWhoqKibS8KERml6lt9hcW3UVxyx8DRENG9mBg6AACoqKhASkoKYmJixDK5XI6wsDAkJyfXu19JSQl8fHyg0+nQv39/LFmyBD179gQAlJeXA4DerT+5XA6VSoUDBw5g2rRptdpLSUlBamoq4uLixLLk5GT07t1bL1EbMWIEpk+fjvT0dPTr169WO+Xl5eLxAaC4uBgAoNFooNFo7nk9jE31NeG1kQ72iWF5uNgiNf0aAODy1Tz4+TgAYH9IBf99SEtb9kdj25REMpWfnw+tVquXsACAi4sLzpw5U+c+3bt3R0JCAvr06YOioiK8//77GDx4MNLT0+Hp6YkePXrA29sbMTExWLt2LSwtLbFy5Upcu3YNarW6zjY/++wz+Pv7Y/DgwWJZdnZ2nXFVb6tLbGwsFi5cWKt8586dsLCwqP9CGLnExERDh0B3YZ8YRknRX3Oltu3Yg57drAGwP6SG/SEtbdEfZWVljaoniWSqOUJDQxEaGip+Hzx4MPz9/bF27VosXrwYSqUS33zzDaZOnQp7e3soFAqEhYXh0UcfhSAItdq7ffs2Nm3ahHnz5rU4tpiYGERHR4vfi4uL4eXlhfDwcFhbW7e4/c5Go9EgMTERw4cPh1KpNHQ4BPaJodm6XMTelB8BAA7OPhg+PJj9ISH89yEtbdkf1XeW7kUSyZSjoyMUCgVycnL0ynNycuDq6tqoNpRKJfr164fz58+LZUFBQUhNTUVRUREqKirg5OSEkJAQBAcH19r/q6++QllZGSZOnKhX7urqWuupwuo464tNpVJBpVLVGSP/4dWP10d62CeG0dXLSfz5enaR2AfsD2lhf0hLW/RHY9uTxAR0U1NTBAUFISkpSSzT6XRISkrSG31qiFarRVpaGtzc3Gpts7GxgZOTEzIyMnDs2DGMGTOmVp3PPvsMTzzxBJycnPTKQ0NDkZaWpvdUYWJiIqytrREQENDYUyQiajQ3Zxso5DIAXAWdqCOQxMgUAERHR2PSpEkIDg7GwIEDsWrVKpSWlmLKlCkAgIkTJ8LDwwOxsbEAgEWLFmHQoEHw8/NDYWEhli9fjitXruhNLN+6dSucnJzg7e2NtLQ0zJ49GxEREQgPD9c79vnz57Fv3z5s3769Vlzh4eEICAjAhAkTsGzZMmRnZ+Ptt9/GjBkz6hx9IiJqKaVSATdnG1zLLsTVrII6pyYQkXRIJpkaP3488vLyMH/+fGRnZ6Nv377YsWOHONk7MzMTcvlfA2k3b95EZGQksrOzYWdnh6CgIBw6dEhvtEitViM6Oho5OTlwc3PDxIkT65wTlZCQAE9Pz1pJFlC1fMK2bdswffp0hIaGwtLSEpMmTcKiRYva4CoQEVXxcrfDtexC3CmvRP7NUkOHQ0QNkAn8X542V1xcDBsbGxQVFXECeh00Gg22b9+OUaNGcf6BRLBPDG/1+t34clsKAGDFvKegvpLG/pAI/vuQlrbsj8b+/ZbEnCkiItLnzXf0EXUYTKaIiCTIq8Y7+q6qCw0XCBHdE5MpIiIJ8qoxMnWNT/QRSRqTKSIiCXK06wIzVdUzQtc4MkUkaUymiIgkSC6XwdOtanQqK7cIWh2fFSKSKiZTREQS5fVnMqXTCSgq4Ut1iaSKyRQRkUR5e/w1Cb2gqMKAkRBRQ5hMERFJVPXIFACcuVyC1PRr0Gp1Boyo+bRaHY6fykTi/tM4fiqzQ59Havo1nL50i/0hAVLpD8msgE5ERPryCkrEn09fuoU5i7+Gk0MXvPz8Ixg66AEDRtY0ew+fw6qEXci78df5dIbz2Laf/WFIUuoPjkwREUnQ3sPnsPbz/bXK826U4K3lP2Dv4XMGiKrp9h4+h7eW/6D3hxvgeRgKz6NtcGSKiEhitFodViXsarDOkrhfcOV6AeRyWTtF1XQ6nYDPvzvaYB2eR/sxpvP4MGE3HhzgB4WifcaMmEwREUnMydPXav0f991Ky8qxbtOBdoqo7fA8pKWznEfujVs4efoa+vfybpfj8TYfEZHE3LhZaugQiDq89vx3xJEpIiKJcbCzbFS9aU8PQTdvxzaOpvkuZubj080H71mP59E+jO08GvvvqDUwmSIikphAf084OXRp8Fafs4MVJjwV0m5zQppjSPB9+D7xJM9DIoztPAL9PdstJuleLSIiI6VQyPHy8480WGf283+X9B88gOchNTyPtiPtK0ZEZKSGDnoA7772BJwcuuiVOztY4d3Xnugw6wHxPKSF59E2ZIIg8O2Zbay4uBg2NjYoKiqCtbW1ocORHI1Gg+3bt2PUqFFQKpWGDofAPpESrVaH42lXkLTnIIY9PAT9e/tIfuSgLlqtDidPX8ONm6VwsLNEoL9nhz0P9od0tHV/NPbvN+dMERFJmEIhR9+ensi6YoW+PTvmHzyg6jza6zH1tsT+kBap9EfH/K+AiIiISCKYTBERERG1AJMpIiIiohZgMkVERETUAkymiIiIiFqAyRQRERFRCzCZIiIiImoBJlNERERELcBkioiIiKgFuAJ6O6h+Y09xcbGBI5EmjUaDsrIyFBcX89UlEsE+kRb2h7SwP6SlLfuj+u/2vd68x2SqHdy6dQsA4OXlZeBIiIiIqKlu3boFGxuberfzRcftQKfTISsrC1ZWVpDJZIYOR3KKi4vh5eWFq1ev8kXQEsE+kRb2h7SwP6SlLftDEATcunUL7u7ukMvrnxnFkal2IJfL4enpaegwJM/a2pq/mCSGfSIt7A9pYX9IS1v1R0MjUtU4AZ2IiIioBZhMEREREbUAkykyOJVKhQULFkClUhk6FPoT+0Ra2B/Swv6QFin0ByegExEREbUAR6aIiIiIWoDJFBEREVELMJkiIiIiagEmU0REREQtwGSKDCY2NhYDBgyAlZUVnJ2dERERgbNnzxo6LPrT0qVLIZPJ8PLLLxs6FKN1/fp1PPfcc3BwcIC5uTl69+6NY8eOGToso6TVajFv3jx07doV5ubmuO+++7B48eJ7vrONWs++ffswevRouLu7QyaT4bvvvtPbLggC5s+fDzc3N5ibmyMsLAwZGRntEhuTKTKYvXv3YsaMGTh8+DASExOh0WgQHh6O0tJSQ4dm9H777TesXbsWffr0MXQoRuvmzZsYMmQIlEolfv75Z/zxxx/44IMPYGdnZ+jQjNJ7772HNWvW4OOPP8bp06fx3nvvYdmyZfjoo48MHZrRKC0tRWBgIOLi4urcvmzZMqxevRrx8fE4cuQILC0tMWLECNy5c6fNY+PSCCQZeXl5cHZ2xt69e/G3v/3N0OEYrZKSEvTv3x///ve/8a9//Qt9+/bFqlWrDB2W0Zk7dy4OHjyI/fv3GzoUAvD444/DxcUFn332mVg2duxYmJub43//+58BIzNOMpkM3377LSIiIgBUjUq5u7vjlVdewauvvgoAKCoqgouLCzZs2ICnn366TePhyBRJRlFREQDA3t7ewJEYtxkzZuCxxx5DWFiYoUMxaj/88AOCg4Pxj3/8A87OzujXrx8++eQTQ4dltAYPHoykpCScO3cOAHDy5EkcOHAAjz76qIEjIwC4dOkSsrOz9X5v2djYICQkBMnJyW1+fL7omCRBp9Ph5ZdfxpAhQ9CrVy9Dh2O0Nm/ejOPHj+O3334zdChG7+LFi1izZg2io6Px5ptv4rfffsOsWbNgamqKSZMmGTo8ozN37lwUFxejR48eUCgU0Gq1ePfdd/Hss88aOjQCkJ2dDQBwcXHRK3dxcRG3tSUmUyQJM2bMwKlTp3DgwAFDh2K0rl69itmzZyMxMRFmZmaGDsfo6XQ6BAcHY8mSJQCAfv364dSpU4iPj2cyZQBffvklPv/8c2zatAk9e/ZEamoqXn75Zbi7u7M/iLf5yPCioqKwbds27N69G56enoYOx2ilpKQgNzcX/fv3h4mJCUxMTLB3716sXr0aJiYm0Gq1hg7RqLi5uSEgIECvzN/fH5mZmQaKyLi99tprmDt3Lp5++mn07t0bEyZMwJw5cxAbG2vo0AiAq6srACAnJ0evPCcnR9zWlphMkcEIgoCoqCh8++232LVrF7p27WrokIzasGHDkJaWhtTUVPETHByMZ599FqmpqVAoFIYO0agMGTKk1lIh586dg4+Pj4EiMm5lZWWQy/X/ZCoUCuh0OgNFRDV17doVrq6uSEpKEsuKi4tx5MgRhIaGtvnxeZuPDGbGjBnYtGkTvv/+e1hZWYn3tW1sbGBubm7g6IyPlZVVrflqlpaWcHBw4Dw2A5gzZw4GDx6MJUuWYNy4cTh69CjWrVuHdevWGTo0ozR69Gi8++678Pb2Rs+ePXHixAmsWLECzz//vKFDMxolJSU4f/68+P3SpUtITU2Fvb09vL298fLLL+Nf//oX7r//fnTt2hXz5s2Du7u7+MRfmxKIDARAnZ/169cbOjT609ChQ4XZs2cbOgyj9eOPPwq9evUSVCqV0KNHD2HdunWGDsloFRcXC7Nnzxa8vb0FMzMzoVu3bsJbb70llJeXGzo0o7F79+46/2ZMmjRJEARB0Ol0wrx58wQXFxdBpVIJw4YNE86ePdsusXGdKSIiIqIW4JwpIiIiohZgMkVERETUAkymiIiIiFqAyRQRERFRCzCZIiIiImoBJlNERERELcBkioiIiKgFmEwRERERtQCTKSLqdB5++GG8/PLLhg5DJAgCXnjhBdjb20MmkyE1NbVNjlPzvKV2DYg6MyZTRNTqJk+eDJlMhqVLl+qVf/fdd5DJZAaKynB27NiBDRs2YNu2bVCr1XzXIVEnw2SKiNqEmZkZ3nvvPdy8edPQobSaioqKZu134cIFuLm5YfDgwXB1dYWJCd8xT9SZMJkiojYRFhYGV1dXxMbG1lvH19cXq1at0ivr27cv3nnnHfH7ww8/jJkzZ+Lll1+GnZ0dXFxc8Mknn6C0tBRTpkyBlZUV/Pz88PPPP+u1U1lZiaioKNjY2MDR0RHz5s1DzVeR6nQ6xMbGomvXrjA3N0dgYCC++uorvTYefvhhREVF4eWXX4ajoyNGjBhR53mUl5dj1qxZcHZ2hpmZGR588EH89ttvAKpG6WbOnInMzEzIZDL4+vrW2YZOp8OyZcvg5+cHlUoFb29vvPvuu+L2HTt24MEHH4StrS0cHBzw+OOP48KFC/Ve27t99dVX6N27N8zNzeHg4ICwsDCUlpbWW//ChQuQyWTYtm0bhg0bBgsLC3Tv3h1Hjhxp9DGJjAWTKSJqEwqFAkuWLMFHH32Ea9eutaitjRs3wtHREUePHsXMmTMxffp0/OMf/8DgwYNx/PhxhIeHY8KECSgrK9Pbx8TEBEePHsWHH36IFStW4NNPPxW3x8bG4j//+Q/i4+ORnp6OOXPm4LnnnsPevXtrHdvU1BQHDx5EfHx8nfG9/vrr+Prrr7Fx40YcP34cfn5+GDFiBAoKCvDhhx9i0aJF8PT0hFqtFpOsu8XExGDp0qWYN28e/vjjD2zatAkuLi7i9tLSUkRHR+PYsWNISkqCXC7Hk08+CZ1Od8/rp1ar8cwzz+D555/H6dOnsWfPHjz11FNo6D33J0+ehEwmw4oVKzBv3jycPHkS3t7emDt37j2PR2R0BCKiVjZp0iRhzJgxgiAIwqBBg4Tnn39eEARB+Pbbb4Wav3Z8fHyElStX6u0bGBgoLFiwQPw+dOhQ4cEHHxS/V1ZWCpaWlsKECRPEMrVaLQAQkpOTxX38/f0FnU4n1nnjjTcEf39/QRAE4c6dO4KFhYVw6NAhvWNPnTpVeOaZZ/SO3a9fvwbPtaSkRFAqlcLnn38ullVUVAju7u7CsmXLBEEQhJUrVwo+Pj71tlFcXCyoVCrhk08+afBYNeXl5QkAhLS0NL14Z8+eXevnlJQUAYBw+fLlRrc/f/58wc7OTsjNzRXLVq9eLfTs2bPRbRAZC45MEVGbeu+997Bx40acPn262W306dNH/FmhUMDBwQG9e/cWy6pHcHJzc8WyQYMG6U12Dw0NRUZGBrRaLc6fP4+ysjIMHz4cXbp0ET//+c9/at06CwoKajC2CxcuQKPRYMiQIWKZUqnEwIEDG33Op0+fRnl5OYYNG1ZvnYyMDDzzzDPo1q0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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from skopt.plots import plot_convergence\n", "\n", "plot_convergence(pipe_gp)" ] }, { "cell_type": "code", "execution_count": null, "id": "aebdc43d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "0e70fca3", "metadata": {}, "source": [ "# Example: Using Multiple Different Fingerprint Transformer\n", "\n", "In this notebook we will explore how to evaluate the performance of machine learning models depending on different fingerprint transformers (Featurization techniques). This is an example, that you easily could adapt for many different combinations of featurizers, optimization and other modelling techniques.\n", "\n", "Following steps will happen:\n", "* Data Parsing\n", "* Pipeline Building\n", "* Training Phase\n", "* Analysis\n", "\n", "Authors: @VincentAlexanderScholz, @RiesBen\n", "\n", "## Imports:\n", "First we will import all the stuff that we will need for our work.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "b705b5c9", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:50.685069Z", "iopub.status.busy": "2025-05-08T16:22:50.684787Z", "iopub.status.idle": "2025-05-08T16:22:52.211375Z", "shell.execute_reply": "2025-05-08T16:22:52.210158Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "from time import time\n", "from matplotlib import pyplot as plt\n", "\n", "from rdkit.Chem import PandasTools\n", "\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.pipeline import Pipeline, make_pipeline\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import train_test_split\n", "\n", "from scikit_mol import fingerprints" ] }, { "cell_type": "markdown", "id": "2a4eb825", "metadata": {}, "source": [ "## Get Data:\n", "In this step we will check if the SLC6A4 data set is already present or needs to be downloaded.\n", "\n", "\n", "**WARNING:** The Dataset is a simple and very well selected" ] }, { "cell_type": "code", "execution_count": 2, "id": "34b2618a", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:52.215818Z", "iopub.status.busy": "2025-05-08T16:22:52.214649Z", "iopub.status.idle": "2025-05-08T16:22:52.271743Z", "shell.execute_reply": "2025-05-08T16:22:52.270511Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 out of 200 SMILES failed in conversion\n" ] } ], "source": [ "full_set = False\n", "\n", "# if not present download example data\n", "if full_set:\n", " csv_file = \"SLC6A4_active_excape_export.csv\"\n", " if not os.path.exists(csv_file):\n", " import urllib.request\n", "\n", " url = \"https://ndownloader.figshare.com/files/25747817\"\n", " urllib.request.urlretrieve(url, csv_file)\n", "else:\n", " csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n", "\n", "# Parse Database\n", "data = pd.read_csv(csv_file)\n", "\n", "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n", "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")" ] }, { "cell_type": "markdown", "id": "b8dba759", "metadata": {}, "source": [ "## Build Pipeline:\n", "In this stage we will build the Pipeline consisting of the featurization part (fingerprint transformers) and the model part (Ridge Regression).\n", "\n", "Note that the featurization in this section is a hyperparameter, living in `param_grid`, and the `\"fp_transformer\"` string is just a placeholder, being replaced during pipeline execution.\n", "\n", "This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies." ] }, { "cell_type": "code", "execution_count": 3, "id": "e06042cc", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:52.275673Z", "iopub.status.busy": "2025-05-08T16:22:52.274777Z", "iopub.status.idle": "2025-05-08T16:22:52.289345Z", "shell.execute_reply": "2025-05-08T16:22:52.288093Z" } }, "outputs": [ { "data": { "text/plain": [ "[{'fp_transformer': [MorganFingerprintTransformer(),\n", " AvalonFingerprintTransformer()],\n", " 'fp_transformer__fpSize': [256, 512, 1024, 2048, 4096],\n", " 'regressor__alpha': array([0.1 , 0.325, 0.55 , 0.775, 1. ])},\n", " {'fp_transformer': [RDKitFingerprintTransformer(),\n", " AtomPairFingerprintTransformer(),\n", " MACCSKeysFingerprintTransformer()],\n", " 'regressor__alpha': array([0.1 , 0.325, 0.55 , 0.775, 1. ])}]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regressor = Ridge()\n", "optimization_pipe = Pipeline(\n", " [\n", " (\n", " \"fp_transformer\",\n", " \"fp_transformer\",\n", " ), # this is a placeholder for different transformers\n", " (\"regressor\", regressor),\n", " ]\n", ")\n", "\n", "param_grid = [ # Here pass different Options and Approaches\n", " {\n", " \"fp_transformer\": [\n", " fingerprints.MorganFingerprintTransformer(),\n", " fingerprints.AvalonFingerprintTransformer(),\n", " ],\n", " \"fp_transformer__fpSize\": [2**x for x in range(8, 13)],\n", " },\n", " {\n", " \"fp_transformer\": [\n", " fingerprints.RDKitFingerprintTransformer(),\n", " fingerprints.AtomPairFingerprintTransformer(),\n", " fingerprints.MACCSKeysFingerprintTransformer(),\n", " ],\n", " },\n", "]\n", "\n", "global_options = {\n", " \"regressor__alpha\": np.linspace(0.1, 1, 5),\n", "}\n", "\n", "[params.update(global_options) for params in param_grid]\n", "\n", "param_grid" ] }, { "cell_type": "markdown", "id": "521aa24a", "metadata": {}, "source": [ "## Train Model\n", "In this section, the combinatorial approaches are trained." ] }, { "cell_type": "code", "execution_count": 4, "id": "f1cf66df", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:22:52.293059Z", "iopub.status.busy": "2025-05-08T16:22:52.292608Z", "iopub.status.idle": "2025-05-08T16:23:24.403914Z", "shell.execute_reply": "2025-05-08T16:23:24.402481Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Runtime: 32.10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } ], "source": [ "# Split Data\n", "mol_list_train, mol_list_test, y_train, y_test = train_test_split(\n", " data.ROMol, data.pXC50, random_state=0\n", ")\n", "\n", "# Define Search Process\n", "grid = GridSearchCV(optimization_pipe, n_jobs=1, param_grid=param_grid)\n", "\n", "# Train\n", "t0 = time()\n", "grid.fit(mol_list_train, y_train.values)\n", "t1 = time()\n", "\n", "print(f\"Runtime: {t1-t0:0.2F}\")" ] }, { "cell_type": "markdown", "id": "55aa1549", "metadata": {}, "source": [ "## Analysis\n", "\n", "Now let's investigate our results from the training stage. Which one is the best fingerprint method for this data set? Which parameters are optimal?" ] }, { "cell_type": "code", "execution_count": 5, "id": "f80006f8", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:23:24.408236Z", "iopub.status.busy": "2025-05-08T16:23:24.407494Z", "iopub.status.idle": "2025-05-08T16:23:24.478389Z", "shell.execute_reply": "2025-05-08T16:23:24.477178Z" } }, "outputs": [ { "data": { "text/html": [ "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_fp_transformerparam_fp_transformer__fpSizeparam_regressor__alphaparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoremean_test_scorestd_test_scorerank_test_score
00.0111850.0032720.0036250.000579MorganFingerprintTransformer()256.00.100{'fp_transformer': MorganFingerprintTransforme...0.0179750.3946820.5245980.5421160.3102380.3579220.19020925
10.0097650.0003830.0033770.000101MorganFingerprintTransformer()256.00.325{'fp_transformer': MorganFingerprintTransforme...0.0787580.4495480.5542410.5723630.3305430.3970900.18107124
20.0099010.0003030.0036310.000097MorganFingerprintTransformer()256.00.550{'fp_transformer': MorganFingerprintTransforme...0.1282210.4902530.5752300.5932370.3440760.4262030.17306123
30.0108760.0007100.0039380.000378MorganFingerprintTransformer()256.00.775{'fp_transformer': MorganFingerprintTransforme...0.1695850.5217230.5908900.6083800.3538660.4488890.16610022
40.0102370.0005220.0036980.000063MorganFingerprintTransformer()256.01.000{'fp_transformer': MorganFingerprintTransforme...0.2048310.5467740.6030100.6197520.3613240.4671380.16006021
...................................................
600.1019760.0021680.0264310.002031MACCSKeysFingerprintTransformer()NaN0.100{'fp_transformer': MACCSKeysFingerprintTransfo...-1.649022-1.943461-0.602509-0.418328-0.752525-1.0731690.60698765
610.1018120.0030230.0265440.001390MACCSKeysFingerprintTransformer()NaN0.325{'fp_transformer': MACCSKeysFingerprintTransfo...-0.969593-0.813087-0.1886900.003831-0.314764-0.4564610.37259564
620.1026810.0012730.0269260.001608MACCSKeysFingerprintTransformer()NaN0.550{'fp_transformer': MACCSKeysFingerprintTransfo...-0.657588-0.505782-0.0459400.124510-0.171340-0.2512280.28970062
630.1007030.0040150.0261710.001515MACCSKeysFingerprintTransformer()NaN0.775{'fp_transformer': MACCSKeysFingerprintTransfo...-0.468371-0.3568250.0366420.182939-0.087318-0.1385870.24211559
640.1017800.0019390.0272330.001860MACCSKeysFingerprintTransformer()NaN1.000{'fp_transformer': MACCSKeysFingerprintTransfo...-0.339715-0.2666520.0921800.218357-0.028878-0.0649420.21091957
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65 rows × 16 columns

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" ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", "0 0.011185 0.003272 0.003625 0.000579 \n", "1 0.009765 0.000383 0.003377 0.000101 \n", "2 0.009901 0.000303 0.003631 0.000097 \n", "3 0.010876 0.000710 0.003938 0.000378 \n", "4 0.010237 0.000522 0.003698 0.000063 \n", ".. ... ... ... ... \n", "60 0.101976 0.002168 0.026431 0.002031 \n", "61 0.101812 0.003023 0.026544 0.001390 \n", "62 0.102681 0.001273 0.026926 0.001608 \n", "63 0.100703 0.004015 0.026171 0.001515 \n", "64 0.101780 0.001939 0.027233 0.001860 \n", "\n", " param_fp_transformer param_fp_transformer__fpSize \\\n", "0 MorganFingerprintTransformer() 256.0 \n", "1 MorganFingerprintTransformer() 256.0 \n", "2 MorganFingerprintTransformer() 256.0 \n", "3 MorganFingerprintTransformer() 256.0 \n", "4 MorganFingerprintTransformer() 256.0 \n", ".. ... ... \n", "60 MACCSKeysFingerprintTransformer() NaN \n", "61 MACCSKeysFingerprintTransformer() NaN \n", "62 MACCSKeysFingerprintTransformer() NaN \n", "63 MACCSKeysFingerprintTransformer() NaN \n", "64 MACCSKeysFingerprintTransformer() NaN \n", "\n", " param_regressor__alpha params \\\n", "0 0.100 {'fp_transformer': MorganFingerprintTransforme... \n", "1 0.325 {'fp_transformer': MorganFingerprintTransforme... \n", "2 0.550 {'fp_transformer': MorganFingerprintTransforme... \n", "3 0.775 {'fp_transformer': MorganFingerprintTransforme... \n", "4 1.000 {'fp_transformer': MorganFingerprintTransforme... \n", ".. ... ... \n", "60 0.100 {'fp_transformer': MACCSKeysFingerprintTransfo... \n", "61 0.325 {'fp_transformer': MACCSKeysFingerprintTransfo... \n", "62 0.550 {'fp_transformer': MACCSKeysFingerprintTransfo... \n", "63 0.775 {'fp_transformer': MACCSKeysFingerprintTransfo... \n", "64 1.000 {'fp_transformer': MACCSKeysFingerprintTransfo... \n", "\n", " split0_test_score split1_test_score split2_test_score \\\n", "0 0.017975 0.394682 0.524598 \n", "1 0.078758 0.449548 0.554241 \n", "2 0.128221 0.490253 0.575230 \n", "3 0.169585 0.521723 0.590890 \n", "4 0.204831 0.546774 0.603010 \n", ".. ... ... ... \n", "60 -1.649022 -1.943461 -0.602509 \n", "61 -0.969593 -0.813087 -0.188690 \n", "62 -0.657588 -0.505782 -0.045940 \n", "63 -0.468371 -0.356825 0.036642 \n", "64 -0.339715 -0.266652 0.092180 \n", "\n", " split3_test_score split4_test_score mean_test_score std_test_score \\\n", "0 0.542116 0.310238 0.357922 0.190209 \n", "1 0.572363 0.330543 0.397090 0.181071 \n", "2 0.593237 0.344076 0.426203 0.173061 \n", "3 0.608380 0.353866 0.448889 0.166100 \n", "4 0.619752 0.361324 0.467138 0.160060 \n", ".. ... ... ... ... \n", "60 -0.418328 -0.752525 -1.073169 0.606987 \n", "61 0.003831 -0.314764 -0.456461 0.372595 \n", "62 0.124510 -0.171340 -0.251228 0.289700 \n", "63 0.182939 -0.087318 -0.138587 0.242115 \n", "64 0.218357 -0.028878 -0.064942 0.210919 \n", "\n", " rank_test_score \n", "0 25 \n", "1 24 \n", "2 23 \n", "3 22 \n", "4 21 \n", ".. ... \n", "60 65 \n", "61 64 \n", "62 62 \n", "63 59 \n", "64 57 \n", "\n", "[65 rows x 16 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_training_stats = pd.DataFrame(grid.cv_results_)\n", "df_training_stats" ] }, { "cell_type": "code", "execution_count": 6, "id": "a6041579", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:23:24.481751Z", "iopub.status.busy": "2025-05-08T16:23:24.481189Z", "iopub.status.idle": "2025-05-08T16:23:24.710744Z", "shell.execute_reply": "2025-05-08T16:23:24.709653Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Best Fingerprint Method / Performance\n", "res_dict = {}\n", "for i, row in df_training_stats.iterrows():\n", " fp_name = row[\"param_fp_transformer\"]\n", " if (\n", " fp_name in res_dict\n", " and row[\"mean_test_score\"] > res_dict[fp_name][\"mean_test_score\"]\n", " ):\n", " res_dict[fp_name] = row.to_dict()\n", " elif not fp_name in res_dict:\n", " res_dict[fp_name] = row.to_dict()\n", "\n", "df = pd.DataFrame(list(res_dict.values()))\n", "df = df.sort_values(by=\"mean_test_score\")\n", "\n", "# plot test score vs. approach\n", "plt.figure(figsize=[14, 5])\n", "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n", "plt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14)\n", "plt.ylabel(\"mean score\", fontsize=14)\n", "plt.title(\"Best Model of Fingerprint Transformer Type\", fontsize=18)\n", "pass" ] }, { "cell_type": "code", "execution_count": 7, "id": "3ee14366", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:23:24.713891Z", "iopub.status.busy": "2025-05-08T16:23:24.713590Z", "iopub.status.idle": "2025-05-08T16:23:25.088793Z", "shell.execute_reply": "2025-05-08T16:23:25.087682Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Best Fingerprint Method / Performance\n", "from collections import defaultdict\n", "\n", "res_dict = defaultdict(list)\n", "for i, row in df_training_stats.iterrows():\n", " fp_name = row[\"param_fp_transformer\"]\n", " if \"Morgan\" in str(fp_name):\n", " res_dict[fp_name].append(row)\n", "\n", "for fp_type, rows in res_dict.items():\n", " df = pd.DataFrame(rows)\n", " df = df.sort_values(by=\"mean_test_score\")\n", "\n", " # plot test score vs. approach\n", " xlabels = map(\n", " lambda x: \"_\".join(x),\n", " zip(\n", " df.param_fp_transformer__fpSize.astype(str),\n", " df.param_regressor__alpha.astype(str),\n", " ),\n", " )\n", "\n", " plt.figure(figsize=[14, 5])\n", " plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n", " plt.xticks(range(len(df)), xlabels, rotation=90, fontsize=14)\n", " plt.ylabel(\"mean score\", fontsize=14)\n", " plt.xlabel(\"bitsize_alpha\", fontsize=14)\n", "\n", " plt.title(\n", " \"Fingerprint Transformer \" + str(fp_type).split(\"(\")[0] + \" per Bitsize\",\n", " fontsize=18,\n", " )\n", " pass" ] }, { "cell_type": "code", "execution_count": 8, "id": "20f7e139", "metadata": { "execution": { "iopub.execute_input": "2025-05-08T16:23:25.091970Z", "iopub.status.busy": "2025-05-08T16:23:25.091624Z", "iopub.status.idle": "2025-05-08T16:23:25.443209Z", "shell.execute_reply": "2025-05-08T16:23:25.441976Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot ALL test score vs. approach\n", "df = df_training_stats.sort_values(by=\"mean_test_score\")\n", "\n", "plt.figure(figsize=[16, 9])\n", "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n", "plt.ylabel(\"mean score\", fontsize=14)\n", "plt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14)\n", "plt.title(\"test score vs. approach\", fontsize=18)\n", "pass" ] }, { "cell_type": "markdown", "id": "f407671c", "metadata": {}, "source": [ "This file have the following licence:\n", "\n", " Apache License\n", " Version 2.0, January 2004\n", " http://www.apache.org/licenses/\n", "\n", " TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n", "\n", " 1. 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We also recommend that a\n", " file or class name and description of purpose be included on the\n", " same \"printed page\" as the copyright notice for easier\n", " identification within third-party archives.\n", "\n", " Copyright 2023 Esben Jannik Bjerrum\n", "\n", " Licensed under the Apache License, Version 2.0 (the \"License\");\n", " you may not use this file except in compliance with the License.\n", " You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", " Unless required by applicable law or agreed to in writing, software\n", " distributed under the License is distributed on an \"AS IS\" BASIS,\n", " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", " See the License for the specific language governing permissions and\n", " limitations under the License.\n" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/10_pipeline_pandas_output.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "454d87b5", "metadata": {}, "source": [ "# Preserving feature information in DataFrames\n", "\n", "This notebook highlights the ability of scikit-mol transformers to return data in DataFrames with meaningful column names. Some use-cases of this feature are illustrated.\n", "\n", "***NOTE***: The goal of this notebook is to highlight the advantages of storing transformer output in DataFrames with meaningful column names. This notebook should *not* be considered a set of good practices for training and evaluating QSAR pipelines. The performance metrics of the resulting pipelines are pretty bad: the dataset they have been trained on is pretty small. Tuning the hyperparameters of the Random Forest regressor model (maximum depth of the trees, maximum features to consider when splitting...) can be beneficial. Also including dimensionality reduction / feature selection techniques can be beneficial, since pipelines use a high number of features for a small number of samples. Of course, to further reduce the risk of overfitting, the best hyperparameters and preprocessing techniques should be chosen in cross validation." ] }, { "cell_type": "code", "execution_count": 14, "id": "cb457069", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:31.171627Z", "iopub.status.busy": "2024-11-24T09:28:31.171255Z", "iopub.status.idle": "2024-11-24T09:28:32.152283Z", "shell.execute_reply": "2024-11-24T09:28:32.151641Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "import pandas as pd\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", "from sklearn.preprocessing import FunctionTransformer\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "from sklearn.compose import make_column_selector, make_column_transformer\n", "from scikit_mol.standardizer import Standardizer\n", "from scikit_mol.descriptors import MolecularDescriptorTransformer\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer" ] }, { "cell_type": "code", "execution_count": 15, "id": "bc72ca09", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:32.155106Z", "iopub.status.busy": "2024-11-24T09:28:32.154793Z", "iopub.status.idle": "2024-11-24T09:28:32.161683Z", "shell.execute_reply": "2024-11-24T09:28:32.161035Z" } }, "outputs": [], "source": [ "csv_file = Path(\"../../tests/data/SLC6A4_active_excapedb_subset.csv\")\n", "assert csv_file.is_file()\n", "data = pd.read_csv(csv_file)\n", "data.drop_duplicates(subset=\"Ambit_InchiKey\", inplace=True)" ] }, { "cell_type": "markdown", "id": "f482cac3", "metadata": {}, "source": [ "Let's split the dataset in training and test, so we will be able to use the test set to evaluate the performance of models trained on the training set." ] }, { "cell_type": "code", "execution_count": 16, "id": "6019d09f", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:32.164164Z", "iopub.status.busy": "2024-11-24T09:28:32.163946Z", "iopub.status.idle": "2024-11-24T09:28:32.168382Z", "shell.execute_reply": "2024-11-24T09:28:32.167874Z" } }, "outputs": [], "source": [ "data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": 17, "id": "fe9efa0e", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:32.171037Z", "iopub.status.busy": "2024-11-24T09:28:32.170722Z", "iopub.status.idle": "2024-11-24T09:28:32.174941Z", "shell.execute_reply": "2024-11-24T09:28:32.174393Z" } }, "outputs": [], "source": [ "column_smiles = \"SMILES\"\n", "column_target = \"pXC50\"\n", "\n", "smis_train = data_train.loc[:, column_smiles]\n", "target_train = data_train.loc[:, column_target]\n", "smis_test = data_test.loc[:, column_smiles]\n", "target_test = data_test.loc[:, column_target]" ] }, { "cell_type": "markdown", "id": "7b4cca39", "metadata": {}, "source": [ "## Descriptors pipeline that returns DataFrames\n", "\n", "Define a pipeline that:\n", "\n", "- converts SMILES strings to Mol objects\n", "- standardizes the molecules\n", "- computes molecular descriptors\n", "\n", "Then, we will configure the pipeline to return output in Pandas DataFrames.\n", "The column names will correspond to the descriptor names." ] }, { "cell_type": "code", "execution_count": 18, "id": "33ce774b", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:32.177459Z", "iopub.status.busy": "2024-11-24T09:28:32.177241Z", "iopub.status.idle": "2024-11-24T09:28:32.194656Z", "shell.execute_reply": "2024-11-24T09:28:32.194079Z" } }, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
       "                ('standardizer', Standardizer()),\n",
       "                ('moleculardescriptortransformer',\n",
       "                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
       "                                                           'MaxEStateIndex',\n",
       "                                                           'MinAbsEStateIndex',\n",
       "                                                           'MinEStateIndex',\n",
       "                                                           'qed', 'SPS',\n",
       "                                                           'MolWt',\n",
       "                                                           'HeavyAtomMolWt',\n",
       "                                                           'ExactMolWt',\n",
       "                                                           'NumValenceElectrons',\n",
       "                                                           'NumRadicalElectrons',\n",
       "                                                           'MaxPartialCharge',\n",
       "                                                           'MinPartialCharge',\n",
       "                                                           'MaxAbsPartialCharge',\n",
       "                                                           'MinAbsPartialCharge',\n",
       "                                                           'FpDensityMorgan1',\n",
       "                                                           'FpDensityMorgan2',\n",
       "                                                           'FpDensityMorgan3',\n",
       "                                                           'BCUT2D_MWHI',\n",
       "                                                           'BCUT2D_MWLOW',\n",
       "                                                           'BCUT2D_CHGHI',\n",
       "                                                           'BCUT2D_CHGLO',\n",
       "                                                           'BCUT2D_LOGPHI',\n",
       "                                                           'BCUT2D_LOGPLOW',\n",
       "                                                           'BCUT2D_MRHI',\n",
       "                                                           'BCUT2D_MRLOW',\n",
       "                                                           'AvgIpc', 'BalabanJ',\n",
       "                                                           'BertzCT', 'Chi0', ...]))])
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" ], "text/plain": [ "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", " ('standardizer', Standardizer()),\n", " ('moleculardescriptortransformer',\n", " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", " 'MaxEStateIndex',\n", " 'MinAbsEStateIndex',\n", " 'MinEStateIndex',\n", " 'qed', 'SPS',\n", " 'MolWt',\n", " 'HeavyAtomMolWt',\n", " 'ExactMolWt',\n", " 'NumValenceElectrons',\n", " 'NumRadicalElectrons',\n", " 'MaxPartialCharge',\n", " 'MinPartialCharge',\n", " 'MaxAbsPartialCharge',\n", " 'MinAbsPartialCharge',\n", " 'FpDensityMorgan1',\n", " 'FpDensityMorgan2',\n", " 'FpDensityMorgan3',\n", " 'BCUT2D_MWHI',\n", " 'BCUT2D_MWLOW',\n", " 'BCUT2D_CHGHI',\n", " 'BCUT2D_CHGLO',\n", " 'BCUT2D_LOGPHI',\n", " 'BCUT2D_LOGPLOW',\n", " 'BCUT2D_MRHI',\n", " 'BCUT2D_MRLOW',\n", " 'AvgIpc', 'BalabanJ',\n", " 'BertzCT', 'Chi0', ...]))])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "descriptors_pipeline = make_pipeline(\n", " SmilesToMolTransformer(),\n", " Standardizer(),\n", " MolecularDescriptorTransformer(),\n", ")\n", "descriptors_pipeline.set_output(transform=\"pandas\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "2cb55603", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:32.196995Z", "iopub.status.busy": "2024-11-24T09:28:32.196792Z", "iopub.status.idle": "2024-11-24T09:28:34.209289Z", "shell.execute_reply": "2024-11-24T09:28:34.208617Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "data": { "text/html": [ "
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Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
       "                ('standardizer', Standardizer()),\n",
       "                ('morganfingerprinttransformer',\n",
       "                 MorganFingerprintTransformer())])
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" ], "text/plain": [ "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", " ('standardizer', Standardizer()),\n", " ('morganfingerprinttransformer',\n", " MorganFingerprintTransformer())])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fingerprints_pipeline = make_pipeline(\n", " SmilesToMolTransformer(),\n", " Standardizer(),\n", " MorganFingerprintTransformer(),\n", ")\n", "fingerprints_pipeline.set_output(transform=\"pandas\")" ] }, { "cell_type": "code", "execution_count": 21, "id": "781d1bc8", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:34.222936Z", "iopub.status.busy": "2024-11-24T09:28:34.222716Z", "iopub.status.idle": "2024-11-24T09:28:34.618391Z", "shell.execute_reply": "2024-11-24T09:28:34.617722Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " fp_morgan_1 fp_morgan_2 fp_morgan_3 fp_morgan_4 fp_morgan_5 \\\n", "0 0 1 0 0 0 \n", "1 0 1 0 0 0 \n", "2 0 1 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", ".. ... ... ... ... ... \n", "154 0 0 0 0 0 \n", "155 0 1 0 0 0 \n", "156 0 0 0 0 0 \n", "157 0 1 0 0 0 \n", "158 0 1 0 0 0 \n", "\n", " fp_morgan_6 fp_morgan_7 fp_morgan_8 fp_morgan_9 fp_morgan_10 ... \\\n", "0 0 0 0 0 0 ... \n", "1 0 0 0 0 0 ... \n", "2 0 0 0 0 0 ... \n", "3 0 0 0 1 0 ... \n", "4 0 0 0 0 0 ... \n", ".. ... ... ... ... ... ... \n", "154 0 0 0 0 0 ... \n", "155 0 0 0 0 0 ... \n", "156 0 0 0 0 0 ... \n", "157 0 0 0 0 0 ... \n", "158 0 0 0 0 0 ... \n", "\n", " fp_morgan_2039 fp_morgan_2040 fp_morgan_2041 fp_morgan_2042 \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", ".. ... ... ... ... \n", "154 0 0 0 0 \n", "155 0 0 0 0 \n", "156 0 0 0 0 \n", "157 0 0 0 0 \n", "158 0 0 0 0 \n", "\n", " fp_morgan_2043 fp_morgan_2044 fp_morgan_2045 fp_morgan_2046 \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", ".. ... ... ... ... \n", "154 0 0 0 0 \n", "155 0 0 0 0 \n", "156 0 0 0 0 \n", "157 0 0 0 0 \n", "158 0 0 0 0 \n", "\n", " fp_morgan_2047 fp_morgan_2048 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 1 0 \n", "4 0 0 \n", ".. ... ... \n", "154 0 0 \n", "155 0 0 \n", "156 0 0 \n", "157 0 0 \n", "158 0 0 \n", "\n", "[159 rows x 2048 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_fingerprints = fingerprints_pipeline.transform(smis_train)\n", "df_fingerprints" ] }, { "cell_type": "markdown", "id": "19a13ca2", "metadata": {}, "source": [ "## Analyze feature importance of regression pipeline\n", "\n", "Making the transformation steps return Pandas DataFrames instead of NumPy arrays makes it easy to analyze the feature importance of regression models.\n", "\n", "Let's define a pipeline that, starting from SMILES strings, computes descriptors and uses them to predict the target with a Random Forest (RF) regression model. Since descriptors values have very different ranges, it's better to scale them before passing them to the RF regression model." ] }, { "cell_type": "code", "execution_count": 22, "id": "4872ecab", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:34.621103Z", "iopub.status.busy": "2024-11-24T09:28:34.620854Z", "iopub.status.idle": "2024-11-24T09:28:34.640701Z", "shell.execute_reply": "2024-11-24T09:28:34.640027Z" } }, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n",
       "                ('standardizer', Standardizer()),\n",
       "                ('moleculardescriptortransformer',\n",
       "                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
       "                                                           'MaxEStateIndex',\n",
       "                                                           'MinAbsEStateIndex',\n",
       "                                                           'MinEStateIndex',\n",
       "                                                           'qed', 'SPS',\n",
       "                                                           'MolWt',\n",
       "                                                           'HeavyAtomMolWt',\n",
       "                                                           'ExactMolWt',\n",
       "                                                           'NumValenceElectrons',\n",
       "                                                           'NumRadicalElectrons',\n",
       "                                                           'MaxPa...\n",
       "                                                           'MaxAbsPartialCharge',\n",
       "                                                           'MinAbsPartialCharge',\n",
       "                                                           'FpDensityMorgan1',\n",
       "                                                           'FpDensityMorgan2',\n",
       "                                                           'FpDensityMorgan3',\n",
       "                                                           'BCUT2D_MWHI',\n",
       "                                                           'BCUT2D_MWLOW',\n",
       "                                                           'BCUT2D_CHGHI',\n",
       "                                                           'BCUT2D_CHGLO',\n",
       "                                                           'BCUT2D_LOGPHI',\n",
       "                                                           'BCUT2D_LOGPLOW',\n",
       "                                                           'BCUT2D_MRHI',\n",
       "                                                           'BCUT2D_MRLOW',\n",
       "                                                           'AvgIpc', 'BalabanJ',\n",
       "                                                           'BertzCT', 'Chi0', ...])),\n",
       "                ('standardscaler', StandardScaler()),\n",
       "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])
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" ], "text/plain": [ "Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\n", " ('standardizer', Standardizer()),\n", " ('moleculardescriptortransformer',\n", " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", " 'MaxEStateIndex',\n", " 'MinAbsEStateIndex',\n", " 'MinEStateIndex',\n", " 'qed', 'SPS',\n", " 'MolWt',\n", " 'HeavyAtomMolWt',\n", " 'ExactMolWt',\n", " 'NumValenceElectrons',\n", " 'NumRadicalElectrons',\n", " 'MaxPa...\n", " 'MaxAbsPartialCharge',\n", " 'MinAbsPartialCharge',\n", " 'FpDensityMorgan1',\n", " 'FpDensityMorgan2',\n", " 'FpDensityMorgan3',\n", " 'BCUT2D_MWHI',\n", " 'BCUT2D_MWLOW',\n", " 'BCUT2D_CHGHI',\n", " 'BCUT2D_CHGLO',\n", " 'BCUT2D_LOGPHI',\n", " 'BCUT2D_LOGPLOW',\n", " 'BCUT2D_MRHI',\n", " 'BCUT2D_MRLOW',\n", " 'AvgIpc', 'BalabanJ',\n", " 'BertzCT', 'Chi0', ...])),\n", " ('standardscaler', StandardScaler()),\n", " ('randomforestregressor', RandomForestRegressor(max_depth=5))])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params_random_forest = {\n", " \"max_depth\": 5, # Setting a low maximum depth to avoid overfitting\n", "}\n", "\n", "regression_pipeline = make_pipeline(\n", " SmilesToMolTransformer(),\n", " Standardizer(),\n", " MolecularDescriptorTransformer(),\n", " StandardScaler(), # Scale the descriptors\n", " RandomForestRegressor(**params_random_forest),\n", ")\n", "regression_pipeline.set_output(transform=\"pandas\")" ] }, { "cell_type": "code", "execution_count": 23, "id": "f0b2f44f", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:34.643396Z", "iopub.status.busy": "2024-11-24T09:28:34.643148Z", "iopub.status.idle": "2024-11-24T09:28:37.656343Z", "shell.execute_reply": "2024-11-24T09:28:37.655703Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } ], "source": [ "regression_pipeline.fit(smis_train, target_train)\n", "pred_test = regression_pipeline.predict(smis_test)" ] }, { "cell_type": "markdown", "id": "3aa6802d", "metadata": { "lines_to_next_cell": 2 }, "source": [ "Let's define a simple function to compute regression metrics, and use it to evaluate the test set performance of the pipeline." ] }, { "cell_type": "code", "execution_count": 24, "id": "8b59851a", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.658965Z", "iopub.status.busy": "2024-11-24T09:28:37.658741Z", "iopub.status.idle": "2024-11-24T09:28:37.666594Z", "shell.execute_reply": "2024-11-24T09:28:37.665979Z" } }, "outputs": [ { "data": { "text/plain": [ "{'RMSE': 0.8539077762549014,\n", " 'MAE': 0.7222501985900492,\n", " 'R2': 0.16145333000722317}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def compute_metrics(y_true, y_pred):\n", " result = {\n", " \"RMSE\": mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5,\n", " \"MAE\": mean_absolute_error(y_true=y_true, y_pred=y_pred),\n", " \"R2\": r2_score(y_true=y_true, y_pred=y_pred),\n", " }\n", " return result\n", "\n", "\n", "performance = compute_metrics(y_true=target_test, y_pred=pred_test)\n", "performance" ] }, { "cell_type": "code", "execution_count": 25, "id": "68528957", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.668899Z", "iopub.status.busy": "2024-11-24T09:28:37.668697Z", "iopub.status.idle": "2024-11-24T09:28:37.673651Z", "shell.execute_reply": "2024-11-24T09:28:37.672957Z" } }, "outputs": [ { "data": { "text/html": [ "
RandomForestRegressor(max_depth=5)
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" ], "text/plain": [ "RandomForestRegressor(max_depth=5)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regressor = regression_pipeline[-1]\n", "regressor" ] }, { "cell_type": "markdown", "id": "d8f32688", "metadata": {}, "source": [ "Since we used `set_output(transform=\"pandas\")` on the pipeline, the last step of the pipeline (the regression model) has the descriptor names in the `feature_names_in_` attribute. We can use them and the `feature_importances_` attribute to easily analyze the feature importances." ] }, { "cell_type": "code", "execution_count": 26, "id": "24011e90", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.677173Z", "iopub.status.busy": "2024-11-24T09:28:37.676324Z", "iopub.status.idle": "2024-11-24T09:28:37.690728Z", "shell.execute_reply": "2024-11-24T09:28:37.690184Z" } }, "outputs": [ { "data": { "text/html": [ "
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featureimportance
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217 rows × 2 columns

\n", "
" ], "text/plain": [ " feature importance\n", "0 MaxAbsEStateIndex 0.003240\n", "1 MaxEStateIndex 0.001821\n", "2 MinAbsEStateIndex 0.002461\n", "3 MinEStateIndex 0.004588\n", "4 qed 0.009382\n", ".. ... ...\n", "212 fr_thiazole 0.000000\n", "213 fr_thiocyan 0.000000\n", "214 fr_thiophene 0.000835\n", "215 fr_unbrch_alkane 0.000000\n", "216 fr_urea 0.000000\n", "\n", "[217 rows x 2 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_importance = pd.DataFrame(\n", " {\n", " \"feature\": regressor.feature_names_in_,\n", " \"importance\": regressor.feature_importances_,\n", " }\n", ")\n", "df_importance" ] }, { "cell_type": "markdown", "id": "64ac369d", "metadata": {}, "source": [ "Sort the features by most to least important:" ] }, { "cell_type": "code", "execution_count": 27, "id": "713d24f1", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.693255Z", "iopub.status.busy": "2024-11-24T09:28:37.693046Z", "iopub.status.idle": "2024-11-24T09:28:37.700756Z", "shell.execute_reply": "2024-11-24T09:28:37.700214Z" } }, "outputs": [ { "data": { "text/html": [ "
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featureimportance
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\n", "
" ], "text/plain": [ " feature importance\n", "0 PEOE_VSA6 0.138689\n", "1 VSA_EState5 0.062832\n", "2 MaxAbsPartialCharge 0.052241\n", "3 VSA_EState6 0.048135\n", "4 SlogP_VSA6 0.027086\n", ".. ... ...\n", "212 fr_term_acetylene 0.000000\n", "213 fr_thiazole 0.000000\n", "214 fr_thiocyan 0.000000\n", "215 fr_unbrch_alkane 0.000000\n", "216 fr_urea 0.000000\n", "\n", "[217 rows x 2 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_importance.sort_values(\n", " by=\"importance\", ascending=False, inplace=True, ignore_index=True\n", ")\n", "df_importance" ] }, { "cell_type": "code", "execution_count": 28, "id": "4b97778f", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.703238Z", "iopub.status.busy": "2024-11-24T09:28:37.703004Z", "iopub.status.idle": "2024-11-24T09:28:37.707129Z", "shell.execute_reply": "2024-11-24T09:28:37.706557Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 5 most important features are:\n", "PEOE_VSA6\n", "VSA_EState5\n", "MaxAbsPartialCharge\n", "VSA_EState6\n", "SlogP_VSA6\n" ] } ], "source": [ "n_top_features = 5\n", "top_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\n", "print(f\"The {n_top_features} most important features are:\")\n", "for feature in top_features:\n", " print(feature)" ] }, { "cell_type": "markdown", "id": "f79c93a0", "metadata": {}, "source": [ "## Including external features\n", "\n", "The ability to keep the results of scikit-learn transformers in DataFrames with meaningful column names simplifies the task of analyzing the resulting models.\n", "\n", "Another good use-case is when we want to combine cheminformatics features from some other tool (QM packages, Deep Learning embeddings...) with the traditional cheminformatics features available in scikit-mol. It will be easier to keep track of the features that come from scikit-mol and the features that come from other tools, if they are stored in DataFrames with meaningful column names.\n", "\n", "Let's include features from the popular [CDDD](https://github.com/jrwnter/cddd) tool. CDDD is a Variational AutoEncoder Deep Learning model, and the CDDD features are the inner latent space representations of the SMILES. For additional details, have a look at the original CDDD paper:\n", "\n", "> R. Winter, F. Montanari, F. Noé, and D.-A. Clevert, “Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations,” Chem. Sci., vol. 10, no. 6, pp. 1692–1701, Feb. 2019, [doi: 10.1039/C8SC04175J](https://doi.org/10.1039/C8SC04175J).\n", "\n", "We have precomputed these features and stored them in a file:" ] }, { "cell_type": "code", "execution_count": 29, "id": "bf8ddaf9", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.709630Z", "iopub.status.busy": "2024-11-24T09:28:37.709389Z", "iopub.status.idle": "2024-11-24T09:28:37.755699Z", "shell.execute_reply": "2024-11-24T09:28:37.755108Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "text/html": [ "
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Ambit_InchiKeycddd_1cddd_2cddd_3cddd_4cddd_5cddd_6cddd_7cddd_8cddd_9...cddd_503cddd_504cddd_505cddd_506cddd_507cddd_508cddd_509cddd_510cddd_511cddd_512
0RBCQCVSMIQCOMN-PCQZLOAONA-N0.0344840.288955-0.0385340.4859960.398606-0.0773400.417946-0.1143990.340925...-0.442085-0.122121-0.7550120.580011-0.999539-0.4663820.3785110.5793420.7538470.812943
1ALZTYVXVRZIERJ-UHFFFAOYNA-N-0.7847290.1463790.4424660.1878160.042911-0.0074600.0128990.170997-0.322217...-0.4348620.216556-0.687221-0.103207-0.999198-0.3354000.1364680.550440-0.019943-0.173729
2MOEMPBAHOJKXBG-MRXNPFEDNA-N-0.751528-0.5061150.4129680.3419480.822811-0.7137950.159594-0.4532310.053278...0.530237-0.131153-0.007292-0.065849-0.978371-0.6531900.404358-0.0799140.7115370.445195
3HEKGBDCRHYILPL-QWOVJGMINA-N-0.7574060.0003280.6703890.8560430.0028860.0644780.181017-0.2299660.598979...0.670772-0.372262-0.571060-0.443543-0.9863630.118407-0.077974-0.0975960.283461-0.099510
4SNNRWIBSGBMYRF-UKRRQHHQNA-N-0.477663-0.129261-0.332024-0.5911080.786510-0.007520-0.171381-0.0488440.565125...0.3425380.6803070.662410-0.493203-0.987440-0.7314360.016999-0.503085-0.066302-0.377198
..................................................................
189PIKWEFAACQLYMF-UHFFFAOYNA-N-0.3544240.0371330.2614930.1910340.203483-0.7186520.481088-0.077800-0.359651...0.4654820.1816670.0087070.374962-0.998080-0.015004-0.071801-0.205790-0.3949280.386006
190AUZWJAMWJZUPHQ-UHFFFAOYNA-N-0.647606-0.604185-0.0703980.1093050.667468-0.239701-0.332139-0.4908620.302364...0.2689340.103272-0.120228-0.133202-0.981479-0.6839750.748666-0.1710970.0531430.144776
191JCEWQICHOLLRDL-WUFINQPMNA-N-0.681951-0.3466290.387501-0.7603210.0035850.1738320.5841960.314204-0.375850...-0.4018280.1873820.6329960.507790-0.9995350.0416120.090283-0.432323-0.1912790.136006
192NGRIUVQYFBDXMT-JYAVWHMHNA-N-0.622850-0.760069-0.1751920.3067670.828635-0.2512260.0952010.029581-0.098511...-0.4281200.510929-0.1127620.072157-0.974629-0.7245490.7548210.5806990.4372760.079424
193ZWLWOTHDIGRTNE-UHFFFAOYNA-N-0.740121-0.4621450.3510890.1043310.579055-0.524488-0.763961-0.3398950.693571...-0.461685-0.5824360.4412680.113779-0.919631-0.5691330.647876-0.3484180.3951810.272382
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194 rows × 513 columns

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" ], "text/plain": [ " Ambit_InchiKey cddd_1 cddd_2 cddd_3 cddd_4 \\\n", "0 RBCQCVSMIQCOMN-PCQZLOAONA-N 0.034484 0.288955 -0.038534 0.485996 \n", "1 ALZTYVXVRZIERJ-UHFFFAOYNA-N -0.784729 0.146379 0.442466 0.187816 \n", "2 MOEMPBAHOJKXBG-MRXNPFEDNA-N -0.751528 -0.506115 0.412968 0.341948 \n", "3 HEKGBDCRHYILPL-QWOVJGMINA-N -0.757406 0.000328 0.670389 0.856043 \n", "4 SNNRWIBSGBMYRF-UKRRQHHQNA-N -0.477663 -0.129261 -0.332024 -0.591108 \n", ".. ... ... ... ... ... \n", "189 PIKWEFAACQLYMF-UHFFFAOYNA-N -0.354424 0.037133 0.261493 0.191034 \n", "190 AUZWJAMWJZUPHQ-UHFFFAOYNA-N -0.647606 -0.604185 -0.070398 0.109305 \n", "191 JCEWQICHOLLRDL-WUFINQPMNA-N -0.681951 -0.346629 0.387501 -0.760321 \n", "192 NGRIUVQYFBDXMT-JYAVWHMHNA-N -0.622850 -0.760069 -0.175192 0.306767 \n", "193 ZWLWOTHDIGRTNE-UHFFFAOYNA-N -0.740121 -0.462145 0.351089 0.104331 \n", "\n", " cddd_5 cddd_6 cddd_7 cddd_8 cddd_9 ... cddd_503 \\\n", "0 0.398606 -0.077340 0.417946 -0.114399 0.340925 ... -0.442085 \n", "1 0.042911 -0.007460 0.012899 0.170997 -0.322217 ... -0.434862 \n", "2 0.822811 -0.713795 0.159594 -0.453231 0.053278 ... 0.530237 \n", "3 0.002886 0.064478 0.181017 -0.229966 0.598979 ... 0.670772 \n", "4 0.786510 -0.007520 -0.171381 -0.048844 0.565125 ... 0.342538 \n", ".. ... ... ... ... ... ... ... \n", "189 0.203483 -0.718652 0.481088 -0.077800 -0.359651 ... 0.465482 \n", "190 0.667468 -0.239701 -0.332139 -0.490862 0.302364 ... 0.268934 \n", "191 0.003585 0.173832 0.584196 0.314204 -0.375850 ... -0.401828 \n", "192 0.828635 -0.251226 0.095201 0.029581 -0.098511 ... -0.428120 \n", "193 0.579055 -0.524488 -0.763961 -0.339895 0.693571 ... -0.461685 \n", "\n", " cddd_504 cddd_505 cddd_506 cddd_507 cddd_508 cddd_509 cddd_510 \\\n", "0 -0.122121 -0.755012 0.580011 -0.999539 -0.466382 0.378511 0.579342 \n", "1 0.216556 -0.687221 -0.103207 -0.999198 -0.335400 0.136468 0.550440 \n", "2 -0.131153 -0.007292 -0.065849 -0.978371 -0.653190 0.404358 -0.079914 \n", "3 -0.372262 -0.571060 -0.443543 -0.986363 0.118407 -0.077974 -0.097596 \n", "4 0.680307 0.662410 -0.493203 -0.987440 -0.731436 0.016999 -0.503085 \n", ".. ... ... ... ... ... ... ... \n", "189 0.181667 0.008707 0.374962 -0.998080 -0.015004 -0.071801 -0.205790 \n", "190 0.103272 -0.120228 -0.133202 -0.981479 -0.683975 0.748666 -0.171097 \n", "191 0.187382 0.632996 0.507790 -0.999535 0.041612 0.090283 -0.432323 \n", "192 0.510929 -0.112762 0.072157 -0.974629 -0.724549 0.754821 0.580699 \n", "193 -0.582436 0.441268 0.113779 -0.919631 -0.569133 0.647876 -0.348418 \n", "\n", " cddd_511 cddd_512 \n", "0 0.753847 0.812943 \n", "1 -0.019943 -0.173729 \n", "2 0.711537 0.445195 \n", "3 0.283461 -0.099510 \n", "4 -0.066302 -0.377198 \n", ".. ... ... \n", "189 -0.394928 0.386006 \n", "190 0.053143 0.144776 \n", "191 -0.191279 0.136006 \n", "192 0.437276 0.079424 \n", "193 0.395181 0.272382 \n", "\n", "[194 rows x 513 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_cddd_features = Path(\"../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\")\n", "assert file_cddd_features.is_file()\n", "df_cddd = pd.read_csv(file_cddd_features)\n", "df_cddd" ] }, { "cell_type": "markdown", "id": "92dc6bf5", "metadata": { "lines_to_next_cell": 2 }, "source": [ "The CDDD features are stored in columns `cddd_1`, `cddd_2`, ..., `cddd_512`. The file has the identifier column `Ambit_InchiKey` that we can use to combine the CDDD features with the rest of the data:" ] }, { "cell_type": "code", "execution_count": 30, "id": "db83be01", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.758200Z", "iopub.status.busy": "2024-11-24T09:28:37.757994Z", "iopub.status.idle": "2024-11-24T09:28:37.769048Z", "shell.execute_reply": "2024-11-24T09:28:37.768408Z" } }, "outputs": [], "source": [ "def combine_datasets(data, cddd):\n", " data_combined = pd.merge(\n", " left=data,\n", " right=cddd,\n", " on=\"Ambit_InchiKey\",\n", " how=\"inner\",\n", " validate=\"one_to_one\",\n", " )\n", " return data_combined\n", "\n", "\n", "data_combined_train = combine_datasets(data_train, df_cddd)\n", "data_combined_test = combine_datasets(data_test, df_cddd)" ] }, { "cell_type": "code", "execution_count": 31, "id": "dae995b7", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.771772Z", "iopub.status.busy": "2024-11-24T09:28:37.771516Z", "iopub.status.idle": "2024-11-24T09:28:37.775128Z", "shell.execute_reply": "2024-11-24T09:28:37.774567Z" } }, "outputs": [], "source": [ "# The CDDD descriptors couldn't be computed for few molecules and can be removed as commented out below. The Datafile is now prefiltered\n", "# target_train = data_train.loc[data_train[\"Ambit_InchiKey\"].isin(data_combined_train[\"Ambit_InchiKey\"]), column_target]\n", "# target_test = data_test.loc[data_test[\"Ambit_InchiKey\"].isin(data_combined_test[\"Ambit_InchiKey\"]), column_target]\n", "\n", "target_train = data_combined_train.loc[:, column_target]\n", "target_test = data_combined_test.loc[:, column_target]" ] }, { "cell_type": "markdown", "id": "2ec82fc8", "metadata": {}, "source": [ "Now we can define a pipeline that uses the original SMILES column to compute the descriptors available in scikit-mol, then concatenates them with the pre-computed CDDD features, and uses all of them to train the regression model. We will need a slightly more complex pipeline with column selectors and transformers. For more details on this technique, please refer to the [official documentation](https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html).\n", "\n", "Since we will keep everything in DataFrames, it will be easier to understand the effect of the CDDD features and the traditional descriptors available in scikit-mol." ] }, { "cell_type": "code", "execution_count": 32, "id": "dc6de049", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.777567Z", "iopub.status.busy": "2024-11-24T09:28:37.777354Z", "iopub.status.idle": "2024-11-24T09:28:37.808615Z", "shell.execute_reply": "2024-11-24T09:28:37.808024Z" } }, "outputs": [ { "data": { "text/html": [ "
ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                 Pipeline(steps=[('smilestomoltransformer',\n",
       "                                                  SmilesToMolTransformer()),\n",
       "                                                 ('standardizer',\n",
       "                                                  Standardizer()),\n",
       "                                                 ('moleculardescriptortransformer',\n",
       "                                                  MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
       "                                                                                            'MaxEStateIndex',\n",
       "                                                                                            'MinAbsEStateIndex',\n",
       "                                                                                            'MinEStateIndex',\n",
       "                                                                                            'qed',\n",
       "                                                                                            'SPS',\n",
       "                                                                                            'MolWt',\n",
       "                                                                                            'HeavyAtomMolWt',\n",
       "                                                                                            'ExactMolWt',\n",
       "                                                                                            'Num...\n",
       "                                                                                            'BCUT2D_LOGPHI',\n",
       "                                                                                            'BCUT2D_LOGPLOW',\n",
       "                                                                                            'BCUT2D_MRHI',\n",
       "                                                                                            'BCUT2D_MRLOW',\n",
       "                                                                                            'AvgIpc',\n",
       "                                                                                            'BalabanJ',\n",
       "                                                                                            'BertzCT',\n",
       "                                                                                            'Chi0', ...]))]),\n",
       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\n",
       "                                ('pipeline-2',\n",
       "                                 Pipeline(steps=[('functiontransformer',\n",
       "                                                  FunctionTransformer())]),\n",
       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])
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" ], "text/plain": [ "ColumnTransformer(transformers=[('pipeline-1',\n", " Pipeline(steps=[('smilestomoltransformer',\n", " SmilesToMolTransformer()),\n", " ('standardizer',\n", " Standardizer()),\n", " ('moleculardescriptortransformer',\n", " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", " 'MaxEStateIndex',\n", " 'MinAbsEStateIndex',\n", " 'MinEStateIndex',\n", " 'qed',\n", " 'SPS',\n", " 'MolWt',\n", " 'HeavyAtomMolWt',\n", " 'ExactMolWt',\n", " 'Num...\n", " 'BCUT2D_LOGPHI',\n", " 'BCUT2D_LOGPLOW',\n", " 'BCUT2D_MRHI',\n", " 'BCUT2D_MRLOW',\n", " 'AvgIpc',\n", " 'BalabanJ',\n", " 'BertzCT',\n", " 'Chi0', ...]))]),\n", " ),\n", " ('pipeline-2',\n", " Pipeline(steps=[('functiontransformer',\n", " FunctionTransformer())]),\n", " )])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A pipeline to compute scikit-mol descriptors\n", "descriptors_pipeline = make_pipeline(\n", " SmilesToMolTransformer(),\n", " Standardizer(),\n", " MolecularDescriptorTransformer(),\n", ")\n", "# A pipeline that just passes the input data.\n", "# We will use it to preserve the CDDD features and pass them to downstream steps.\n", "identity_pipeline = make_pipeline(\n", " FunctionTransformer(),\n", ")\n", "combined_transformer = make_column_transformer(\n", " (descriptors_pipeline, make_column_selector(pattern=\"SMILES\")),\n", " (identity_pipeline, make_column_selector(pattern=r\"^cddd_\\d+$\")),\n", " remainder=\"drop\",\n", ")\n", "combined_transformer" ] }, { "cell_type": "code", "execution_count": 33, "id": "6ee85c3c", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.811016Z", "iopub.status.busy": "2024-11-24T09:28:37.810811Z", "iopub.status.idle": "2024-11-24T09:28:37.883600Z", "shell.execute_reply": "2024-11-24T09:28:37.882894Z" } }, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                                  Pipeline(steps=[('smilestomoltransformer',\n",
       "                                                                   SmilesToMolTransformer()),\n",
       "                                                                  ('standardizer',\n",
       "                                                                   Standardizer()),\n",
       "                                                                  ('moleculardescriptortransformer',\n",
       "                                                                   MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n",
       "                                                                                                             'MaxEStateIndex',\n",
       "                                                                                                             'MinAbsEStateIndex',\n",
       "                                                                                                             'MinEStateIndex',\n",
       "                                                                                                             'qed',\n",
       "                                                                                                             'SPS',\n",
       "                                                                                                             'MolW...\n",
       "                                                                                                             'Chi0', ...]))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\n",
       "                                                 ('pipeline-2',\n",
       "                                                  Pipeline(steps=[('functiontransformer',\n",
       "                                                                   FunctionTransformer())]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])),\n",
       "                ('standardscaler', StandardScaler()),\n",
       "                ('randomforestregressor', RandomForestRegressor(max_depth=5))])
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" ], "text/plain": [ "Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(transformers=[('pipeline-1',\n", " Pipeline(steps=[('smilestomoltransformer',\n", " SmilesToMolTransformer()),\n", " ('standardizer',\n", " Standardizer()),\n", " ('moleculardescriptortransformer',\n", " MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\n", " 'MaxEStateIndex',\n", " 'MinAbsEStateIndex',\n", " 'MinEStateIndex',\n", " 'qed',\n", " 'SPS',\n", " 'MolW...\n", " 'Chi0', ...]))]),\n", " ),\n", " ('pipeline-2',\n", " Pipeline(steps=[('functiontransformer',\n", " FunctionTransformer())]),\n", " )])),\n", " ('standardscaler', StandardScaler()),\n", " ('randomforestregressor', RandomForestRegressor(max_depth=5))])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline_combined = make_pipeline(\n", " combined_transformer,\n", " StandardScaler(),\n", " RandomForestRegressor(**params_random_forest),\n", ")\n", "pipeline_combined.set_output(transform=\"pandas\")" ] }, { "cell_type": "code", "execution_count": 34, "id": "03960958", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:37.886041Z", "iopub.status.busy": "2024-11-24T09:28:37.885822Z", "iopub.status.idle": "2024-11-24T09:28:42.859220Z", "shell.execute_reply": "2024-11-24T09:28:42.858489Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] }, { "data": { "text/plain": [ "{'RMSE': 0.8008666941742995,\n", " 'MAE': 0.6900085446099777,\n", " 'R2': 0.26735673218396616}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline_combined.fit(data_combined_train, target_train)\n", "pred_combined_test = pipeline_combined.predict(data_combined_test)\n", "performance_combined = compute_metrics(y_true=target_test, y_pred=pred_combined_test)\n", "performance_combined" ] }, { "cell_type": "markdown", "id": "c49ecd90", "metadata": {}, "source": [ "Let's combine the performance metrics obtained using only the scikit-mol descriptors as input features, and the performance metrics obtained using also the CDDD features:" ] }, { "cell_type": "code", "execution_count": 35, "id": "6ce2fe53", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:42.861769Z", "iopub.status.busy": "2024-11-24T09:28:42.861505Z", "iopub.status.idle": "2024-11-24T09:28:42.869169Z", "shell.execute_reply": "2024-11-24T09:28:42.868553Z" } }, "outputs": [ { "data": { "text/html": [ "
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RMSEMAER2
descriptors0.8539080.7222500.161453
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" ], "text/plain": [ " RMSE MAE R2\n", "descriptors 0.853908 0.722250 0.161453\n", "combined 0.800867 0.690009 0.267357" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_performance = pd.DataFrame(\n", " [performance, performance_combined], index=[\"descriptors\", \"combined\"]\n", ")\n", "df_performance" ] }, { "cell_type": "markdown", "id": "83b7fd13", "metadata": {}, "source": [ "All performance metrics were improved by the inclusion of the CDDD features.\n", "Let's analyze the feature importances of the model:" ] }, { "cell_type": "code", "execution_count": 36, "id": "9c98ac71", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:42.872116Z", "iopub.status.busy": "2024-11-24T09:28:42.871685Z", "iopub.status.idle": "2024-11-24T09:28:42.886733Z", "shell.execute_reply": "2024-11-24T09:28:42.886003Z" } }, "outputs": [ { "data": { "text/html": [ "
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featureimportance
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" ], "text/plain": [ " feature importance\n", "0 pipeline-1__PEOE_VSA6 0.077408\n", "1 pipeline-2__cddd_102 0.069598\n", "2 pipeline-2__cddd_369 0.051396\n", "3 pipeline-2__cddd_378 0.044292\n", "4 pipeline-2__cddd_372 0.030435\n", ".. ... ...\n", "724 pipeline-1__PEOE_VSA3 0.000000\n", "725 pipeline-1__Ipc 0.000000\n", "726 pipeline-1__SMR_VSA8 0.000000\n", "727 pipeline-2__cddd_461 0.000000\n", "728 pipeline-1__NumRadicalElectrons 0.000000\n", "\n", "[729 rows x 2 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regressor = pipeline_combined[-1]\n", "df_importance = pd.DataFrame(\n", " {\n", " \"feature\": regressor.feature_names_in_,\n", " \"importance\": regressor.feature_importances_,\n", " }\n", ").sort_values(by=\"importance\", ascending=False, ignore_index=True)\n", "df_importance" ] }, { "cell_type": "code", "execution_count": 37, "id": "9dbd2a9e", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:42.889250Z", "iopub.status.busy": "2024-11-24T09:28:42.889020Z", "iopub.status.idle": "2024-11-24T09:28:42.893106Z", "shell.execute_reply": "2024-11-24T09:28:42.892486Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 5 most important features are:\n", "pipeline-1__PEOE_VSA6\n", "pipeline-2__cddd_102\n", "pipeline-2__cddd_369\n", "pipeline-2__cddd_378\n", "pipeline-2__cddd_372\n" ] } ], "source": [ "top_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\n", "print(f\"The {n_top_features} most important features are:\")\n", "for feature in top_features:\n", " print(feature)" ] }, { "cell_type": "markdown", "id": "7b394662", "metadata": {}, "source": [ "As we can see, some CDDD features are among the most important features for the regression model.\n", "\n", "Note that since the pipeline is a combination of two pipelines, the column names were prefixed by `pipeline-1` (the scikit-mol descriptors pipeline) and `pipeline-2` (the pipeline that selects and preserves pre-computed CDDD features)." ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/11_safe_inference.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "f34dacf0", "metadata": {}, "source": [ "# Safe inference mode\n", "\n", "I think everyone which have worked with SMILES and RDKit sooner or later come across a SMILES that doesn't parse. It can happen if the SMILES was produced with a different toolkit that are less strict with e.g. valence rules, or maybe a characher was missing in the copying from the email. During curation of the dataset for training models, these SMILES need to be identfied and eventually fixed or removed. But what happens when we are finished with our modelling? What kind of molecules and SMILES will a user of the model send for the model in the future when it's in deployment. What kind of SMILES will a generative model create that we need to predict? We don't know and we won't know. So it's kind of crucial to be able to handle these situations. Scikit-Learn models usually simply explodes the entire batch that are being predicted. This is where safe_inference_mode was introduced in Scikit-Mol. With the introduction all transformers got a safe inference mode, where they handle invalid input. How they handle it depends a bit on the transformer, so we will go through the different usual steps and see how things have changed with the introduction of the safe inference mode.\n", "\n", "NOTE! In the following demonstration I switch on the safe inference mode individually for demonstration purposes. I would not recommend to do that while building and training models, instead I would switch it on _after_ training and evaluation (more on that later). Otherwise, there's a risk to train on the 2% of a dataset that didn't fail....\n", "\n", "First some imports and test SMILES and molecules." ] }, { "cell_type": "code", "execution_count": 10, "id": "ac780f4c", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:44.417205Z", "iopub.status.busy": "2024-11-24T09:28:44.417002Z", "iopub.status.idle": "2024-11-24T09:28:45.205864Z", "shell.execute_reply": "2024-11-24T09:28:45.205244Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[],\n", " [],\n", " [],\n", " [],\n", " [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid Molecule: Explicit valence for atom # 0 N, 4, is greater than permitted')],\n", " [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid SMILES: I'm not a SMILES')]],\n", " dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from rdkit import Chem\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "\n", "# We have some deprecation warnings, we are adressing them, but they just distract from this demonstration\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", "\n", "smiles = [\"C1=CC=C(C=C1)F\", \"C1=CC=C(C=C1)O\", \"C1=CC=C(C=C1)N\", \"C1=CC=C(C=C1)Cl\"]\n", "smiles_with_invalid = smiles + [\"N(C)(C)(C)C\", \"I'm not a SMILES\"]\n", "\n", "smi2mol = SmilesToMolTransformer(safe_inference_mode=True)\n", "\n", "mols_with_invalid = smi2mol.transform(smiles_with_invalid)\n", "mols_with_invalid" ] }, { "cell_type": "markdown", "id": "bdd18682", "metadata": {}, "source": [ "Without the safe inference mode, the transformation would simply fail, but now we get the expected array back with our RDKit molecules and a last entry which is an object of the type InvalidMol. InvalidMol is simply a placeholder that tells what step failed the conversion and the error. InvalidMol evaluates to `False` in boolean contexts, so it gets easy to filter away and handle in `if`s and list comprehensions. As example:" ] }, { "cell_type": "code", "execution_count": 11, "id": "44a6019c", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.208884Z", "iopub.status.busy": "2024-11-24T09:28:45.208436Z", "iopub.status.idle": "2024-11-24T09:28:45.213259Z", "shell.execute_reply": "2024-11-24T09:28:45.212730Z" } }, "outputs": [ { "data": { "text/plain": [ "[array([], dtype=object),\n", " array([], dtype=object),\n", " array([], dtype=object),\n", " array([], dtype=object)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[mol for mol in mols_with_invalid if mol]" ] }, { "cell_type": "markdown", "id": "176a44de", "metadata": {}, "source": [ "or" ] }, { "cell_type": "code", "execution_count": 12, "id": "8286fd44", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.215847Z", "iopub.status.busy": "2024-11-24T09:28:45.215431Z", "iopub.status.idle": "2024-11-24T09:28:45.219372Z", "shell.execute_reply": "2024-11-24T09:28:45.218875Z" } }, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ,\n", " ,\n", " ], dtype=object)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask = mols_with_invalid.astype(bool)\n", "mols_with_invalid[mask]" ] }, { "cell_type": "markdown", "id": "c7be8909", "metadata": {}, "source": [ "Having a failsafe SmilesToMol conversion leads us to next step, featurization. The transformers in safe inference mode now return a NumPy masked array instead of a regular NumPy array. It simply evaluates the incoming mols in a boolean context, so e.g. `None`, `np.nan` and other Python objects that evaluates to False will also get masked (i.e. if you use a dataframe with an ROMol column produced with the PandasTools utility)" ] }, { "cell_type": "code", "execution_count": 13, "id": "9a705642", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.221712Z", "iopub.status.busy": "2024-11-24T09:28:45.221465Z", "iopub.status.idle": "2024-11-24T09:28:45.246566Z", "shell.execute_reply": "2024-11-24T09:28:45.245960Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "text/plain": [ "masked_array(\n", " data=[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0,\n", " 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0],\n", " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0,\n", " 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],\n", " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0,\n", " 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n", " [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0,\n", " 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0],\n", " [--, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n", " --, --, --, --, --, --, --, --, --],\n", " [--, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n", " --, --, --, --, --, --, --, --, --]],\n", " mask=[[False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False],\n", " [False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False],\n", " [False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False],\n", " [False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False],\n", " [ True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True],\n", " [ True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True]],\n", " fill_value=1e+20)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "\n", "mfp = MorganFingerprintTransformer(radius=2, fpSize=25, safe_inference_mode=True)\n", "fps = mfp.transform(mols_with_invalid)\n", "fps" ] }, { "cell_type": "markdown", "id": "a5e2b301", "metadata": {}, "source": [ "However, currently scikit-learn models accepts masked arrays, but they do not respect the mask! So if you fed it directly to the model to train, it would seemingly work, but the invalid samples would all have the fill_value, meaning you could get weird results. Instead, we need the last part of the puzzle, the SafeInferenceWrapper class." ] }, { "cell_type": "code", "execution_count": 14, "id": "37987dc9", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.249048Z", "iopub.status.busy": "2024-11-24T09:28:45.248844Z", "iopub.status.idle": "2024-11-24T09:28:45.318911Z", "shell.execute_reply": "2024-11-24T09:28:45.318291Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/scikit_mol/safeinference.py:64: UserWarning: SafeInferenceWrapper is in safe_inference_mode during use of fit and invalid data detected. This mode is intended for safe inference in production, not for training and evaluation.\n", " warnings.warn(\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([ 0., 1., 0., 1., nan, nan])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scikit_mol.safeinference import SafeInferenceWrapper\n", "from sklearn.linear_model import LogisticRegression\n", "import numpy as np\n", "\n", "regressor = LogisticRegression()\n", "wrapper = SafeInferenceWrapper(regressor, safe_inference_mode=True)\n", "wrapper.fit(fps, [0, 1, 0, 1, 0, 1])\n", "wrapper.predict(fps)" ] }, { "cell_type": "markdown", "id": "7aa1223f", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "f08d26d5", "metadata": {}, "source": [ "The prediction went fine both in fit and in prediction, where the result shows `nan` for the invalid entries. However, please note fit in sage_inference_mode is not recommended in a training session, but you are warned and not blocked, because maybe you know what you do and do it on purpose.\n", "The SafeInferenceMapper both handles rows that are masked in masked arrays, but also checks rows for nonfinite values and filters these away. Sometimes some descriptors may return an inf or nan, even though the molecule itself is valid. The masking of nonfinite values can be switched off, maybe you are using a model that can handle missing data and only want to filter away invalid molecules.\n", "\n", "## Setting safe_inference_mode post-training\n", "As I said before I believe in catching errors and fixing those during training, but what do we do when we need to switch on safe inference mode for all objects in a pipeline? There's of course a tool for that, so lets demo that:" ] }, { "cell_type": "code", "execution_count": 15, "id": "51436aa8", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.321557Z", "iopub.status.busy": "2024-11-24T09:28:45.321253Z", "iopub.status.idle": "2024-11-24T09:28:45.333442Z", "shell.execute_reply": "2024-11-24T09:28:45.332830Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Without safe inference mode:\n", "Prediction failed with exception: Invalid input found: [InvalidMol('SmilesToMolTransformer()', error='Invalid Molecule: Explicit valence for atom # 0 N, 4, is greater than permitted'), InvalidMol('SmilesToMolTransformer()', error='Invalid SMILES: I'm not a SMILES')].\n", "\n", "With safe inference mode:\n", "[ 1. 0. 1. 0. nan nan]\n" ] } ], "source": [ "from scikit_mol.safeinference import set_safe_inference_mode\n", "from sklearn.pipeline import Pipeline\n", "\n", "pipe = Pipeline(\n", " [\n", " (\"smi2mol\", SmilesToMolTransformer()),\n", " (\"mfp\", MorganFingerprintTransformer(radius=2, fpSize=25)),\n", " (\"safe_regressor\", SafeInferenceWrapper(LogisticRegression())),\n", " ]\n", ")\n", "\n", "pipe.fit(smiles, [1, 0, 1, 0])\n", "\n", "print(\"Without safe inference mode:\")\n", "try:\n", " pipe.predict(smiles_with_invalid)\n", "except Exception as e:\n", " print(\"Prediction failed with exception: \", e)\n", "print()\n", "\n", "set_safe_inference_mode(pipe, True)\n", "\n", "print(\"With safe inference mode:\")\n", "print(pipe.predict(smiles_with_invalid))" ] }, { "cell_type": "markdown", "id": "cf53d58f", "metadata": {}, "source": [ "We see that the prediction fail without safe inference mode, and proceeds when it's conveniently set by the `set_safe_inference_mode` utility. The model is now ready for save and reuse in a more failsafe manner :-)" ] }, { "cell_type": "markdown", "id": "685e22fd", "metadata": {}, "source": [ "## Combining safe_inference_mode with pandas output\n", "One potential issue can happen when we combine the safe_inference_mode with Pandas output mode of the transformers. It will work, but depending on the batch something surprising can happen due to the way that Pandas converts masked Numpy arrays. Let me demonstrate the issue, first we predict a batch without any errors." ] }, { "cell_type": "code", "execution_count": 16, "id": "b8dbd88c", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.336071Z", "iopub.status.busy": "2024-11-24T09:28:45.335859Z", "iopub.status.idle": "2024-11-24T09:28:45.351873Z", "shell.execute_reply": "2024-11-24T09:28:45.351251Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " fp_morgan_1 fp_morgan_2 fp_morgan_3 fp_morgan_4 fp_morgan_5 \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 1 0 0 0 0 \n", "\n", " fp_morgan_6 fp_morgan_7 fp_morgan_8 fp_morgan_9 fp_morgan_10 ... \\\n", "0 0 0 0 1 1 ... \n", "1 0 0 1 1 1 ... \n", "2 0 0 0 1 1 ... \n", "3 0 0 0 1 1 ... \n", "\n", " fp_morgan_16 fp_morgan_17 fp_morgan_18 fp_morgan_19 fp_morgan_20 \\\n", "0 0 1 0 1 1 \n", "1 0 1 0 0 1 \n", "2 0 1 0 1 1 \n", "3 0 1 0 0 1 \n", "\n", " fp_morgan_21 fp_morgan_22 fp_morgan_23 fp_morgan_24 fp_morgan_25 \n", "0 1 0 1 1 0 \n", "1 1 0 0 1 0 \n", "2 1 0 0 0 0 \n", "3 1 0 1 0 1 \n", "\n", "[4 rows x 25 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mfp.set_output(transform=\"pandas\")\n", "\n", "mols = smi2mol.transform(smiles)\n", "\n", "fps = mfp.transform(mols)\n", "fps" ] }, { "cell_type": "markdown", "id": "092ca859", "metadata": {}, "source": [ "Then lets see if we transform a batch with an invalid molecule:" ] }, { "cell_type": "code", "execution_count": 17, "id": "710ceeb0", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.354427Z", "iopub.status.busy": "2024-11-24T09:28:45.354176Z", "iopub.status.idle": "2024-11-24T09:28:45.377892Z", "shell.execute_reply": "2024-11-24T09:28:45.377253Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " fp_morgan_1 fp_morgan_2 fp_morgan_3 fp_morgan_4 fp_morgan_5 \\\n", "0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 \n", "3 1.0 0.0 0.0 0.0 0.0 \n", "4 NaN NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN NaN \n", "\n", " fp_morgan_6 fp_morgan_7 fp_morgan_8 fp_morgan_9 fp_morgan_10 ... \\\n", "0 0.0 0.0 0.0 1.0 1.0 ... \n", "1 0.0 0.0 1.0 1.0 1.0 ... \n", "2 0.0 0.0 0.0 1.0 1.0 ... \n", "3 0.0 0.0 0.0 1.0 1.0 ... \n", "4 NaN NaN NaN NaN NaN ... \n", "5 NaN NaN NaN NaN NaN ... \n", "\n", " fp_morgan_16 fp_morgan_17 fp_morgan_18 fp_morgan_19 fp_morgan_20 \\\n", "0 0.0 1.0 0.0 1.0 1.0 \n", "1 0.0 1.0 0.0 0.0 1.0 \n", "2 0.0 1.0 0.0 1.0 1.0 \n", "3 0.0 1.0 0.0 0.0 1.0 \n", "4 NaN NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN NaN \n", "\n", " fp_morgan_21 fp_morgan_22 fp_morgan_23 fp_morgan_24 fp_morgan_25 \n", "0 1.0 0.0 1.0 1.0 0.0 \n", "1 1.0 0.0 0.0 1.0 0.0 \n", "2 1.0 0.0 0.0 0.0 0.0 \n", "3 1.0 0.0 1.0 0.0 1.0 \n", "4 NaN NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN NaN \n", "\n", "[6 rows x 25 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fps = mfp.transform(mols_with_invalid)\n", "fps" ] }, { "cell_type": "markdown", "id": "b87b46b3", "metadata": {}, "source": [ "The second output is no longer integers, but floats. As most sklearn models cast input arrays to float32 internally, this difference is likely benign, but that's not guaranteed! Thus, if you want to use pandas output for your production models, do check that the final outputs are the same for the valid rows, with and without a single invalid row. Alternatively the dtype for the output of the transformer can be switched to float for consistency if it's supported by the transformer." ] }, { "cell_type": "code", "execution_count": 18, "id": "bbfe1ec0", "metadata": { "execution": { "iopub.execute_input": "2024-11-24T09:28:45.380434Z", "iopub.status.busy": "2024-11-24T09:28:45.380233Z", "iopub.status.idle": "2024-11-24T09:28:45.393639Z", "shell.execute_reply": "2024-11-24T09:28:45.393095Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " fp_morgan_1 fp_morgan_2 fp_morgan_3 fp_morgan_4 fp_morgan_5 \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 1 0 0 0 0 \n", "\n", " fp_morgan_6 fp_morgan_7 fp_morgan_8 fp_morgan_9 fp_morgan_10 ... \\\n", "0 0 0 0 1 1 ... \n", "1 0 0 1 1 1 ... \n", "2 0 0 0 1 1 ... \n", "3 0 0 0 1 1 ... \n", "\n", " fp_morgan_16 fp_morgan_17 fp_morgan_18 fp_morgan_19 fp_morgan_20 \\\n", "0 0 1 0 1 1 \n", "1 0 1 0 0 1 \n", "2 0 1 0 1 1 \n", "3 0 1 0 0 1 \n", "\n", " fp_morgan_21 fp_morgan_22 fp_morgan_23 fp_morgan_24 fp_morgan_25 \n", "0 1 0 1 1 0 \n", "1 1 0 0 1 0 \n", "2 1 0 0 0 0 \n", "3 1 0 1 0 1 \n", "\n", "[4 rows x 25 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mfp_float = MorganFingerprintTransformer(\n", " radius=2, fpSize=25, safe_inference_mode=True, dtype=np.float32\n", ")\n", "mfp_float.set_output(transform=\"pandas\")\n", "fps = mfp_float.transform(mols)\n", "fps" ] }, { "cell_type": "markdown", "id": "2c7b382c", "metadata": {}, "source": [ "I hope this new feature of Scikit-Mol will make it even easier to handle models, even when used in environments without SMILES or molecule validity guarantees." ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/12_custom_fingerprint_transformer.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "de742d6d", "metadata": {}, "source": [ "# Creating custom fingerprint transformers\n", "\n", "If the default fingerprint transformers provided by the scikit-mol library are not enough for your needs, you can create your own custom fingerprint transformers. In this notebook, we will show you how to do this.\n", "\n", "Note that base classes are partially stable and may change in the future versions of the library. We will try to keep the changes minimal and provide a migration guide if necessary. This notebook is also will be updated accordingly." ] }, { "cell_type": "markdown", "id": "0e0a8a2b", "metadata": {}, "source": [ "## Basics\n", "\n", "For now we recommend you to use the `BaseFpsTransformer` class as a base class for your custom fingerprint transformers. This class provides a simple interface to create fingerprint transformers that can be used with the scikit-mol library.\n", "\n", "To create your custom fingerprint transformer, you need to create a class that inherits from the `BaseFpsTransformer` class and implement the `_transform_mol` method. This method should take a single RDKit molecule object as input and return a fingerprint as a numpy array." ] }, { "cell_type": "code", "execution_count": 1, "id": "1ed1c7f0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n", "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n" ] }, { "data": { "text/plain": [ "array([[2., 2., 2., ..., 2., 2., 2.],\n", " [2., 2., 2., ..., 2., 2., 2.],\n", " [2., 2., 2., ..., 2., 2., 2.],\n", " ...,\n", " [2., 2., 2., ..., 2., 2., 2.],\n", " [2., 2., 2., ..., 2., 2., 2.],\n", " [2., 2., 2., ..., 2., 2., 2.]], shape=(100, 64))" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scikit_mol.fingerprints.baseclasses import BaseFpsTransformer\n", "import numpy as np\n", "from rdkit import Chem\n", "\n", "\n", "class DummyFingerprintTransformer(BaseFpsTransformer):\n", " def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode=False):\n", " self.fpSize = fpSize\n", " super().__init__(\n", " n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name=\"dummy\"\n", " )\n", "\n", " def _transform_mol(self, mol):\n", " return mol.GetNumAtoms() * np.ones(self.fpSize)\n", "\n", "\n", "trans = DummyFingerprintTransformer(n_jobs=4)\n", "mols = [Chem.MolFromSmiles(\"CC\")] * 100\n", "trans.transform(mols)" ] }, { "cell_type": "markdown", "id": "619ebaf3", "metadata": {}, "source": [ "## Non-pickable objects\n", "When working with some of the `rdkit` function and classes you will often discover that some of the are unpickable objects. This means that they cannot be serialized and deserialized using the `pickle` module. This is a problem when you want to use the parallelization (controlled by the `n_jobs` parameter).\n", "\n", "Any non-pickable object in the transformer attributes should be initialized in the `__init__` method of the transforme from the other *picklable* arguments.\n", "\n", "In the example below, we will create a custom fingerprint transformer that uses the Morgan fingerprint with radius **2** and **1024** bits. Used generator is unpickable, but it can be created during the initialization of the transformer from the picklable `maxPath` and `fpSize` arguments." ] }, { "cell_type": "code", "execution_count": 2, "id": "4e944033", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]], shape=(100, 512), dtype=uint8)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from rdkit.Chem import rdFingerprintGenerator\n", "\n", "\n", "class UnpickableFingerprintTransformer(BaseFpsTransformer):\n", " def __init__(self, fpSize=1024, n_jobs=1, safe_inference_mode=False, **kwargs):\n", " self.fpSize = fpSize\n", " super().__init__(\n", " n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs\n", " )\n", " self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(\n", " maxPath=2, fpSize=self.fpSize\n", " )\n", "\n", " def _transform_mol(self, mol):\n", " return self.fp_gen.GetFingerprintAsNumPy(mol)\n", "\n", "\n", "trans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512)\n", "trans.transform(mols)" ] }, { "cell_type": "markdown", "id": "b3f2c4c4", "metadata": {}, "source": [ "Non-pickable object should not be present among the `__init__` arguments of the transformer. Doing so will prevent them to be pickled to recreate a transformer instance in the worker processes. If you for some reason need to pass a non-pickable object to the transformer you can do so (**highly discouraged**, please [open the issue](https://github.com/EBjerrum/scikit-mol/issues), maybe we will be able to help you do it better) by using the transformer in the sequential mode (i.e. `n_jobs=1`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "e569b656", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n_jobs=1 is fine\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n_jobs=2 is not fine, because the generator passed as an argument is not picklable\n", "Error msg: Could not pickle the task to send it to the workers.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=43991) is multi-threaded, use of fork() may lead to deadlocks in the child.\n", " pid = os.fork()\n" ] } ], "source": [ "class BadTransformer(BaseFpsTransformer):\n", " def __init__(self, generator, n_jobs=1):\n", " self.generator = generator\n", " super().__init__(n_jobs=n_jobs)\n", "\n", " def _transform_mol(self, mol):\n", " return self.generator.GetFingerprint(mol)\n", "\n", "\n", "fp_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=10)\n", "BadTransformer(fp_gen, n_jobs=1).transform(mols)\n", "print(\"n_jobs=1 is fine\")\n", "try:\n", " BadTransformer(fp_gen, n_jobs=2).transform(mols)\n", "except Exception as e:\n", " print(\n", " \"n_jobs=2 is not fine, because the generator passed as an argument is not picklable\"\n", " )\n", " print(f\"Error msg: {e}\")" ] }, { "cell_type": "markdown", "id": "25517562", "metadata": {}, "source": [ "## Fingerprint name\n", "\n", "To use the fingerptint in the `pandas` output mode it needes to know the name of the fingerprint and the number of bits (features) in it to generate the columns names. The number of feature is derived from `fpSize` attribute \n", "\n", "And the name resolution works as follows (in order of priority):\n", "- if the fingerprint has a name set during the initialization of the base class, it is used\n", "- if the name of the class follows the pattern `XFingerprintTransformer`, the name (`fp_X`) is extracted from it\n", "- as a last resort, the name is set to name of the class" ] }, { "cell_type": "code", "execution_count": 4, "id": "07dbac7a", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['namedtansformer1_1' 'namedtansformer1_2' 'namedtansformer1_3' ...\n", " 'namedtansformer1_1022' 'namedtansformer1_1023' 'namedtansformer1_1024']\n", "['fp_fancy_1' 'fp_fancy_2' 'fp_fancy_3' ... 'fp_fancy_1022'\n", " 'fp_fancy_1023' 'fp_fancy_1024']\n", "['fp_fancy_1' 'fp_fancy_2' 'fp_fancy_3' ... 'fp_fancy_1022'\n", " 'fp_fancy_1023' 'fp_fancy_1024']\n" ] } ], "source": [ "class NamedTansformer1(UnpickableFingerprintTransformer):\n", " pass\n", "\n", "\n", "class NamedTansformer2(UnpickableFingerprintTransformer):\n", " def __init__(self):\n", " super().__init__(name=\"fp_fancy\")\n", "\n", "\n", "class FancyFingerprintTransformer(UnpickableFingerprintTransformer):\n", " pass\n", "\n", "\n", "print(NamedTansformer1().get_feature_names_out())\n", "print(NamedTansformer2().get_feature_names_out())\n", "print(FancyFingerprintTransformer().get_feature_names_out())" ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/13_applicability_domain.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "ee549099", "metadata": {}, "source": [ "# Applicability Domain Estimation\n", "\n", "This notebook demonstrates how to use scikit-mol's applicability domain estimators to assess whether new compounds are within the domain of applicability of a trained model.\n", "\n", "We'll explore two different approaches:\n", "1. Using Morgan binary fingerprints with a k-Nearest Neighbors based applicability domain\n", "2. Using count-based Morgan fingerprints with dimensionality reduction and a leverage-based applicability domain\n", "\n", "First, let's import the necessary libraries and load our dataset:" ] }, { "cell_type": "code", "execution_count": 1, "id": "40500fae", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:14.229954Z", "iopub.status.busy": "2025-05-11T09:39:14.229779Z", "iopub.status.idle": "2025-05-11T09:39:17.380877Z", "shell.execute_reply": "2025-05-11T09:39:17.380373Z" } }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "from rdkit import Chem\n", "from rdkit.Chem import PandasTools\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA\n", "\n", "from scikit_mol.conversions import SmilesToMolTransformer\n", "from scikit_mol.fingerprints import MorganFingerprintTransformer\n", "from scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain" ] }, { "cell_type": "markdown", "id": "e5d1277e", "metadata": {}, "source": [ "## Load and Prepare Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "79d3b853", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:17.383214Z", "iopub.status.busy": "2025-05-11T09:39:17.382749Z", "iopub.status.idle": "2025-05-11T09:39:17.445075Z", "shell.execute_reply": "2025-05-11T09:39:17.444550Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 out of 200 SMILES failed in conversion\n" ] } ], "source": [ "# Load the dataset\n", "# Results are better with the full set, but it takes longer to run, so for the notebook documentation we standard use a subset.\n", "# The subset has been filtered to only include nicely predicted compounds, and is thus artificial.\n", "\n", "full_set = False\n", "\n", "if full_set:\n", " csv_file = \"SLC6A4_active_excape_export.csv\"\n", " if not os.path.exists(csv_file):\n", " import urllib.request\n", "\n", " url = \"https://ndownloader.figshare.com/files/25747817\"\n", " urllib.request.urlretrieve(url, csv_file)\n", " percentile = 95\n", "else:\n", " csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n", " percentile = 90\n", "data = pd.read_csv(csv_file)\n", "\n", "# Add RDKit mol objects\n", "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n", "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n", "\n", "# Split into train/val/test\n", "X = data.ROMol\n", "y = data.pXC50\n", "\n", "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "X_train, X_val, y_train, y_val = train_test_split(\n", " X_temp, y_temp, test_size=0.25, random_state=42\n", ")" ] }, { "cell_type": "markdown", "id": "e2896ad5", "metadata": {}, "source": [ "## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints\n", "\n", "In this example, we'll use binary Morgan fingerprints and a k-NN based applicability domain with Tanimoto distance.\n", "This is particularly suitable for binary fingerprints as the Tanimoto coefficient is a natural similarity measure for them." ] }, { "cell_type": "code", "execution_count": 3, "id": "9c89148b", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:17.446860Z", "iopub.status.busy": "2025-05-11T09:39:17.446670Z", "iopub.status.idle": "2025-05-11T09:39:17.835548Z", "shell.execute_reply": "2025-05-11T09:39:17.835023Z" } }, "outputs": [], "source": [ "# Create pipeline for binary fingerprints\n", "binary_fp_pipe = Pipeline(\n", " [\n", " (\"fp\", MorganFingerprintTransformer(fpSize=2048, radius=2)),\n", " (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)),\n", " ]\n", ")\n", "\n", "# Train the model\n", "binary_fp_pipe.fit(X_train, y_train)\n", "\n", "# Get predictions and errors\n", "y_pred_test = binary_fp_pipe.predict(X_test)\n", "abs_errors = np.abs(y_test - y_pred_test)\n", "\n", "# Create and fit k-NN AD estimator\n", "knn_ad = KNNApplicabilityDomain(\n", " n_neighbors=3, distance_metric=\"tanimoto\", percentile=percentile\n", ")\n", "knn_ad.fit(binary_fp_pipe.named_steps[\"fp\"].transform(X_train))\n", "\n", "# Fit threshold using validation set\n", "knn_ad.fit_threshold(binary_fp_pipe.named_steps[\"fp\"].transform(X_val))\n", "\n", "# Get AD scores for test set\n", "knn_scores = knn_ad.transform(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))" ] }, { "cell_type": "markdown", "id": "a7f33a3f", "metadata": {}, "source": [ "Let's visualize the relationship between prediction errors and AD scores:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ee7b2f64", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:17.837494Z", "iopub.status.busy": "2025-05-11T09:39:17.837306Z", "iopub.status.idle": "2025-05-11T09:39:18.097264Z", "shell.execute_reply": "2025-05-11T09:39:18.096667Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "95th percentile of errors inside domain: 1.31\n", "95th percentile of errors outside domain: 1.17\n", "Fraction of samples outside domain: 0.15\n" ] } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.scatter(knn_scores, abs_errors, alpha=0.5)\n", "plt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n", "plt.xlabel(\"k-NN AD Score\")\n", "plt.ylabel(\"Absolute Prediction Error\")\n", "plt.title(\"Prediction Errors vs k-NN AD Scores\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# Calculate error statistics\n", "in_domain = knn_ad.predict(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))\n", "errors_in = abs_errors[in_domain == 1]\n", "errors_out = abs_errors[in_domain == -1]\n", "\n", "print(f\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\")\n", "print(f\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\")\n", "print(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")" ] }, { "cell_type": "markdown", "id": "09bdc3b2", "metadata": {}, "source": [ "## Example 2: Leverage-based AD with Count-based Morgan Fingerprints\n", "\n", "In this example, we'll use count-based Morgan fingerprints, reduce their dimensionality with PCA,\n", "and apply a leverage-based applicability domain estimator." ] }, { "cell_type": "code", "execution_count": 5, "id": "fe4a6819", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:18.099081Z", "iopub.status.busy": "2025-05-11T09:39:18.098853Z", "iopub.status.idle": "2025-05-11T09:39:18.569295Z", "shell.execute_reply": "2025-05-11T09:39:18.568707Z" } }, "outputs": [], "source": [ "# Create pipeline for count-based fingerprints with PCA\n", "count_fp_pipe = Pipeline(\n", " [\n", " (\"fp\", MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),\n", " (\"pca\", PCA(n_components=0.9)), # Keep 90% of variance\n", " (\"scaler\", StandardScaler()),\n", " (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42)),\n", " ]\n", ")\n", "\n", "# Train the model\n", "count_fp_pipe.fit(X_train, y_train)\n", "\n", "# Get predictions and errors\n", "y_pred_test = count_fp_pipe.predict(X_test)\n", "abs_errors = np.abs(y_test - y_pred_test)\n", "\n", "# Create and fit leverage AD estimator\n", "leverage_ad = LeverageApplicabilityDomain(percentile=percentile)\n", "X_train_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n", " count_fp_pipe.named_steps[\"pca\"].transform(\n", " count_fp_pipe.named_steps[\"fp\"].transform(X_train)\n", " )\n", ")\n", "leverage_ad.fit(X_train_transformed)\n", "\n", "# Fit threshold using validation set\n", "X_val_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n", " count_fp_pipe.named_steps[\"pca\"].transform(\n", " count_fp_pipe.named_steps[\"fp\"].transform(X_val)\n", " )\n", ")\n", "leverage_ad.fit_threshold(X_val_transformed)\n", "\n", "# Get AD scores for test set\n", "X_test_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n", " count_fp_pipe.named_steps[\"pca\"].transform(\n", " count_fp_pipe.named_steps[\"fp\"].transform(X_test)\n", " )\n", ")\n", "leverage_scores = leverage_ad.transform(X_test_transformed)" ] }, { "cell_type": "markdown", "id": "d7723e37", "metadata": {}, "source": [ "Visualize the relationship between prediction errors and leverage scores:" ] }, { "cell_type": "code", "execution_count": 6, "id": "57d73a11", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:18.571392Z", "iopub.status.busy": "2025-05-11T09:39:18.571206Z", "iopub.status.idle": "2025-05-11T09:39:18.710911Z", "shell.execute_reply": "2025-05-11T09:39:18.710436Z" } }, "outputs": [ { "data": { "image/png": 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atGl69tlnNXDgQA0fPjx0zPHHH6+vvvpKr732mlauXKkXXnhBf/nLX3THHXforrvu+l7nEwwGNXToUD388MNN7s/NzQ2tT58+XXPnztVzzz2nm2++Wc8++6ycTmdYEhnptdzU97lt2zadeeaZGjhwoB5++GHl5ubKarXqjTfe0COPPHLEASiaO8+zzjpLt956a5P7+/fv3+KyjjnmGJ177rk699xzQ/0Lv/nmm1DfrGg79DtqOP9bbrmlyVo/6UDCeeqpp2rbtm165ZVXtHr1av3tb3/TI488oiVLlujqq69ulXgBxA7JFQBI6tu3r9566y2NGzfusMNQN9y8FRYWhjXF27t37xFrf/r27StJKigo0IQJE5o9rqVNBLt166aUlBR99dVXjfZt3bpVCQkJYTfm8apnz5566623VFVVFVZ7tXXr1tD+lrBarZo6daqmTp2qYDCo66+/Xo899ph+97vfNapZOdipp56q7OxsrVixQieffLL+/e9/6ze/+U2j4+x2u6ZPn67p06fL5/PpRz/6kf7whz9o7ty532so+L59++rTTz/VmWeeecS/fe/evTV69GitWLFCN9xwg1588UVNmzZNSUlJYeW15Fo+nH/961+qra3Vq6++GlbbeWiTwoa/TVFRUVjtzrffftvo30Pfvn3ldrsPe+0fjVGjRumdd95RSUmJevbsqb59+6qgoOCw7+nZs6c+++wzBYPBsGS/pddcw7/9xMTEFp1Pw6iUM2fOlNvt1qmnnqo777yT5ArogOhzBQCSLr74YgUCAd19992N9vn9flVUVEiq79OVmJioP//5z2G1PYcb6azBD37wA/Xu3VsLFiwIldfg4LIa5n069JhDmc1mTZw4Ua+88op27NgR2l5WVqZ//vOfOvnkk5WWlnbEuGJtypQpCgQCWrhwYdj2Rx55RCaTSZMnTz5iGd9++23Y64SEhFCzq4OHM29KQkKCLrzwQv3rX//S3//+d/n9/rBmdk2Vb7VaNWjQIBmGobq6uiPGdzgXX3yx9uzZo8cff7zRvpqaGnk8nrBt06dP13/+8x898cQT2rdvX6NYW3otH05DDefB16XL5WrUv+7MM8+UxWJpNET7oX/Lhrg2btyoVatWNdpXUVEhv9/fbDylpaWh4c8P5vP5tHbtWiUkJIQS6AsuuECffvqpXnrppUbHN5zPlClTVFpaGtZ/ze/3689//rMcDodOO+20ZmOR6mtKx48fr8cee0wlJSWN9h88vcGh147D4VBeXt4Rr0sA7RM1VwCg+kESrrvuOs2fP1+bN2/WxIkTlZiYqMLCQj333HN69NFHdeGFF6pbt2665ZZbNH/+fJ1zzjmaMmWKPvnkE7355ptH7IuUkJCgxYsXa+rUqRoxYoRmzpyp7Oxsbd26VVu2bAnddI4cOVKSNHv2bOXn58tsNocNs32we+65R2vWrNHJJ5+s66+/XhaLRY899phqa2v1wAMPRPdLkvT888/L4XA02n7WWWcpKyvrqMqcOnWqTj/9dP3mN7/Rjh07NHz4cK1evVqvvPKKbr755lCN3+FcffXV2r9/v8444wwde+yx+uabb/TnP/9ZI0aMCPWjOZzp06frz3/+s+bNm6ehQ4c2es/EiRPVvXt3jRs3TllZWfryyy+1cOFCnX322Y36ijVl7dq1jYYml6Rp06bppz/9qZ599ln97Gc/09tvv61x48YpEAho69atevbZZ0NzUDW4+OKLdcstt+iWW25RRkZGo5qTll7LhzNx4sRQTeB1110nt9utxx9/XJmZmWHJRFZWlm666Sb98Y9/1LnnnqtJkybp008/Df17OLgm7le/+pVeffVVnXPOObriiis0cuRIeTweff7553r++ee1Y8eOZv8N7d69W6NHj9YZZ5yhM888U927d1d5ebmefvppffrpp7r55ptD7/3Vr36l559/XhdddJGuvPJKjRw5Uvv379err76qJUuWaPjw4br22mv12GOP6YorrtDHH3+sXr166fnnn9eGDRu0YMGCFv1NFy1apJNPPllDhw7VNddcoz59+qisrEwbN27U7t279emnn0qSBg0apPHjx2vkyJHKyMjQRx99pOeff1433HDDET8DQDsUu4EKASB6GoYa/+9//3vY42bMmGHY7fZm9//1r381Ro4caSQnJxupqanG0KFDjVtvvdUoLi4OHRMIBIy77rrLyM7ONpKTk43x48cbBQUFRs+ePQ87FHuD9evXG2eddZaRmppq2O12Y9iwYWHDa/v9fuPGG280unXrZphMprBh2XXIUOyGYRibNm0y8vPzDYfDYaSkpBinn3668f7777fo+2kuxkMdbij2g9/fUN5zzz3XqIzDffdVVVXGL37xC6NHjx5GYmKi0a9fP+PBBx8MGza+4fxnzZrV6P3PP/+8MXHiRCMzM9OwWq3GcccdZ1x33XVGSUnJYc+rQTAYNHJzc5scEt4wDOOxxx4zTj31VOOYY44xkpKSjL59+xq/+tWvDJfLddhyG4bybm75+9//bhhG/TDg999/vzF48GAjKSnJ6NKlizFy5EjjrrvuavIzxo0bZ0gyrr766mY/uyXXcs+ePZsdJvzVV181hg0bZthsNqNXr17G/fffbzzxxBOGJGP79u2h4/x+v/G73/3O6N69u5GcnGycccYZxpdffmkcc8wxxs9+9rOwMquqqoy5c+caeXl5htVqNbp27WqcdNJJxkMPPWT4fL5mz6WystJ49NFHjfz8fOPYY481EhMTjdTUVGPs2LHG448/3ug6+fbbb40bbrjByMnJMaxWq3HssccaM2bMMPbt2xc6pqyszJg5c6bRtWtXw2q1GkOHDjWWLVsWVs6RhtLftm2bcfnllxvdu3c3EhMTjZycHOOcc84xnn/++dAx99xzjzF69GgjPT3dSE5ONgYOHGj84Q9/OOz5Ami/TIYR5V7MAACgU6uoqFCXLl10zz33NNl/DQA6KvpcAQCAo1ZTU9NoW0MfxPHjx7dtMAAQY/S5AgAAR23FihVavny5pkyZIofDofXr1+vpp5/WxIkTozIPGAC0JyRXAADgqA0bNkwWi0UPPPCAKisrQ4Nc3HPPPbEODQDaHH2uAAAAACAK6HMFAAAAAFFAcgUAAAAAUUCfqyYEg0EVFxcrNTU1bAJEAAAAAJ2LYRiqqqpSjx49lJBw+LopkqsmFBcXKzc3N9ZhAAAAAIgTu3bt0rHHHnvYY0iumpCamiqp/gtMS0uLcTQAAMSJujpp2bL69ZkzpcTE2MYDAG2gsrJSubm5oRzhcBgtsAmVlZVyOp1yuVwkVwAANPB4JIejft3tluz22MYDAG0gktyAAS0AAAAAIApIrgAAAAAgCkiuAAAAACAKGNACAAAAHYphGPL7/QoEArEOBe2A2WyWxWKJyhRMJFcAAADoMHw+n0pKSlRdXR3rUNCOpKSkKDs7W1ar9XuVQ3IFAACADiEYDGr79u0ym83q0aOHrFZrVGoj0HEZhiGfz6e9e/dq+/bt6tev3xEnCj4ckisAANAySUnSa68dWAfijM/nUzAYVG5urlJSUmIdDtqJ5ORkJSYm6ptvvpHP55PNZjvqskiuAABAy1gs0tlnxzoK4Ii+T80DOqdoXTNceQAAAAAQBdRcAQCAlqmrk/7xj/r1yy6TEhNjGw8AxBlqrgAAQMv4fNLMmfWLzxfraABE2RVXXKFp06a1+eeOHz9eN9988/cq484779SIESMOe0xbnB/JFQAAABAnNm7cKLPZrLOb6N+4Y8cOmUym0JKamqrBgwdr1qxZKiwsbLbM5cuXh72vqWXHjh2teFadB8kVAAAAECeWLl2qG2+8Ue+++66Ki4ubPOatt95SSUmJPv30U91777368ssvNXz4cK1du7bJ46dPn66SkpLQMnbsWF1zzTVh23Jzc48qXh+12GFIruJYMGho1/5qbS2t1K791QoGjViHBAAA0D55PM0vXm/Lj62pOfKxR8ntdmvFihX6+c9/rrPPPlvLly9v8rhjjjlG3bt3V58+fXTeeefprbfe0pgxY3TVVVcpEAg0Oj45OVndu3cPLVarVSkpKWHbzGZz6PiHHnpI2dnZOuaYYzRr1izV1dWF9vXq1Ut33323Lr/8cqWlpenaa6+VJK1fv16nnHKKkpOTlZubq9mzZ8tz0Hfxl7/8Rf369ZPNZlNWVpYuvPDCsBiDwaBuvfVWZWRkqHv37rrzzjvD9u/cuVPnnXeeHA6H0tLSdPHFF6usrKzZ7zIQCGjOnDlKT0/XMccco1tvvVWG0fr30iRXcaqovEqL123TI2u+1p/WFuqRNV9r8bptKiqvinVoAAAA7Y/D0fxywQXhx2ZmNn/s5Mnhx/bq1fiYo/Tss89q4MCBGjBggH7yk5/oiSeeaFFCkJCQoJtuuknffPONPv7446P+fEl6++23tW3bNr399tt68skntXz58kZJ3kMPPaThw4frk08+0e9+9ztt27ZNkyZN0gUXXKDPPvtMK1as0Pr163XDDTdIkj766CPNnj1bv//97/XVV19p5cqVOvXUU8PKfPLJJ2W32/XBBx/ogQce0O9//3utWbNGUn3idd5552n//v165513tGbNGv3vf//T9OnTmz2PP/7xj1q+fLmeeOIJrV+/Xvv379dLL730vb6blmC0wDhUVF6lZRt2aL/Hp2ynTSnWZFX7/CoodqnYVaOZ43opLzM11mGGCQYN7amokcfnl91qUU56shISmBEdAACgpZYuXaqf/OQnkqRJkybJ5XLpnXfe0fjx44/43oEDB0qq75c1evToo46hS5cuWrhwocxmswYOHKizzz5ba9eu1TXXXBM65owzztAvf/nL0Ourr75al112WWhQin79+ulPf/qTTjvtNC1evFg7d+6U3W7XOeeco9TUVPXs2VMnnHBC2OcOGzZM8+bNC71/4cKFWrt2rc466yytXbtWn3/+ubZv3x5qvvjUU09p8ODB+u9//6sTTzyx0XksWLBAc+fO1Y9+9CNJ0pIlS7Rq1aqj/l5aiuQqzgSDhlYVlGm/x6d+mQ6ZTPUJSqotUY4kiwrL3Vq9pUx9ujriJnkpKq/SqoIybdvrltcfkM1iVt9uDuUPyYq7JBAAAHRSbnfz+w5qEidJKi9v/thDJ5uN0kAQX331lT788MNQ7YrFYtH06dO1dOnSFiVXDTVcDfeOR2vw4MFhTQSzs7P1+eefhx0zatSosNeffvqpPvvsM/2jYaqG/x9PMBjU9u3bddZZZ6lnz57q06ePJk2apEmTJun8889XSkpK6Phhw4aFlZmdna3y//93+PLLL5WbmxvWL2zQoEFKT0/Xl19+2Si5crlcKikp0ZgxY0LbLBaLRo0a1epNA0mu4syeihpt2+tWttPW6B+HyWRSttOmonK39lTUKDcjpZlS2k57rGUDABylpCTp2WcPrAPtid0e+2MPY+nSpfL7/erRo0dom2EYSkpK0sKFC+V0Og/7/i+//FKS1Lt37+8VR+Ih89eZTCYFg8GwbfZDztntduu6667T7NmzG5V33HHHyWq1atOmTVq3bp1Wr16tO+64Q3feeaf++9//Kj09vcWf2x7Q5yrOeHx+ef0BpVibznuTrWbV+gPy+PxtHFljh9aypdoSZU4wKdWWqH6ZDu33+LR6SxkDcQBAR2GxSBddVL9YeD4LRIvf79dTTz2lP/7xj9q8eXNo+fTTT9WjRw89/fTTh31/MBjUn/70J/Xu3btRc7u28IMf/EBffPGF8vLyGi1Wq1VSfc3RhAkT9MADD+izzz7Tjh079O9//7tF5R9//PHatWuXdu3aFdr2xRdfqKKiQoMGDWp0vNPpVHZ2tj744IPQNr/f/737o7UEv4xxxm61yGYxq9rnV6otsdH+Gl9ASRaz7M0kX22pvdWyAQAAxKPXXntN3333na666qpGNVQXXHCBli5dqp/97Gehbd9++61KS0tVXV2tgoICLViwQB9++KFef/31sCZ9beW2227TD3/4Q91www26+uqrZbfb9cUXX2jNmjVauHChXnvtNf3vf//Tqaeeqi5duuiNN95QMBjUgAEDWlT+hAkTNHToUF122WVasGCB/H6/rr/+ep122mmNmig2uOmmm3TfffepX79+GjhwoB5++GFVVFRE8aybRs1VnMlJT1bfbg6VuLyN2oQahqESl1d5mQ7lpCfHKMID2lMtGwAgCvx+6bnn6hc/v+1AtCxdulQTJkxosunfBRdcoI8++kifffZZaNuECROUnZ2toUOH6vbbb9fxxx+vzz77TKeffnpbhh0ybNgwvfPOO/r66691yimn6IQTTtAdd9wRauKYnp6uF198UWeccYaOP/54LVmyRE8//bQGDx7covJNJpNeeeUVdenSRaeeeqomTJigPn36aMWKFc2+55e//KV++tOfasaMGRo7dqxSU1N1/vnnR+V8Dxur0RYDvrczlZWVcjqdcrlcSktLa/PPP7QfU7LVrBpfQCUurzLs1rjpx7Rrf7UeWfO10lMSm6xlq/LWqaK6Tr84qz81VwDQEXg8B4aZdruj1tcEiBav16vt27erd+/estlssQ4H7cjhrp1IcgNqruJQXmaqZo7rpSE9nKqortOOfR5VVNdpaI4zbhIrqX3VsgEAAACtLfYdd9CkvMxU9RnviOu5oxISTMofkqViV40Ky91N1rJNHJwVVzEDAAAArYXkKo4lJJjivjldQy1bwzxXZZVeJVnMGprj1MTBzHMFAACAzoPkCt9be6hlAwAAAFobyRWioj3UsgEAgM6B8doQqWhdMwxoAQAAgA4hMbF+9OLq6uoYR4L2puGaabiGjhY1VwAAoGWsVmnZsgPrQJwxm81KT09XeXm5JCklJUUmE90U0DzDMFRdXa3y8nKlp6d/70mYSa4AAEDLJCZKV1wR6yiAw+revbskhRIsoCXS09ND1873EdPk6t1339WDDz6ojz/+WCUlJXrppZc0bdq0Zo+/4oor9OSTTzbaPmjQIG3ZskWSdOedd+quu+4K2z9gwABt3bo1qrEDAAAg/phMJmVnZyszM1N1dXWxDgftQGJi4veusWoQ0+TK4/Fo+PDhuvLKK/WjH/3oiMc/+uijuu+++0Kv/X6/hg8frosuuijsuMGDB+utt94KvbZYqKADAOB78/ulVavq1/PzJf7/ijhmNpujdsMMtFRMfxUnT56syZMnt/h4p9Mpp9MZev3yyy/ru+++08yZM8OOs1gsUanWAwAAB6mtlc45p37d7Sa5AoBDtOvRApcuXaoJEyaoZ8+eYdsLCwvVo0cP9enTR5dddpl27tx52HJqa2tVWVkZtgAAAABAJNptclVcXKw333xTV199ddj2MWPGaPny5Vq5cqUWL16s7du365RTTlFVVVWzZc2fPz9UK+Z0OpWbm9va4QMAAADoYNptcvXkk08qPT290QAYkydP1kUXXaRhw4YpPz9fb7zxhioqKvTss882W9bcuXPlcrlCy65du1o5egAAAAAdTbtsLG0Yhp544gn99Kc/lfUI82ykp6erf//+KioqavaYpKQkJSUlRTtMAAAAAJ1Iu6y5euedd1RUVKSrrrrqiMe63W5t27ZN2dnZbRAZAAAAgM4qpsmV2+3W5s2btXnzZknS9u3btXnz5tAAFHPnztXll1/e6H1Lly7VmDFjNGTIkEb7brnlFr3zzjvasWOH3n//fZ1//vkym8269NJLW/VcAAAAAHRuMW0W+NFHH+n0008PvZ4zZ44kacaMGVq+fLlKSkoajfTncrn0wgsv6NFHH22yzN27d+vSSy/Vt99+q27duunkk0/Wf/7zH3Xr1q31TgQAgM7AapUWLjywDgAIYzIMw4h1EPGmsrJSTqdTLpdLaWlpsQ4HAAAAQIxEkhu0yz5XAAAAABBv2uVogQAAIAYCAem99+rXTzlFMptjGw8AxBmSKwAA0DJer9TQV9rtluz22MYDAHGGZoEAAAAAEAUkVwAAAAAQBSRXAAAAABAFJFcAAAAAEAUkVwAAAAAQBSRXAAAAABAFDMUOAABaJjFReuCBA+sAgDAkVwAAoGWsVulXv4p1FAAQt2gWCAAAAABRQM0VAABomUBA2rSpfv0HP5DM5tjGAwBxhuQKAAC0jNcrjR5dv+52S3Z7bOMBgDhDs0AAAAAAiAKSKwAAAACIApIrAAAAAIgCkisAAAAAiAKSKwAAAACIApIrAAAAAIgChmIHAAAtk5gozZt3YB0AEIbkCgAAtIzVKt15Z6yjAIC4RbNAAAAAAIgCaq4AAEDLBIPSl1/Wrx9/vJTAM1oAOBjJFQAAaJmaGmnIkPp1t1uy22MbDwDEGR45AQAAAEAUkFwBAAAAQBSQXAEAAABAFJBcAQAAAEAUkFwBAAAAQBSQXAEAAABAFDAUOwAAaJnEROmWWw6sAwDCkFwBAICWsVqlBx+MdRQAELdoFggAAAAAUUDNFQAAaJlgUNq5s379uOOkBJ7RAsDBSK4AAEDL1NRIvXvXr7vdkt0e23gAIM7wyAkAAAAAooDkCgAAAACigOQKAAAAAKKA5AoAAAAAooDkCgAAAACigOQKAAAAAKKAodgBAEDLWCzS9dcfWAcAhOGXEQAAtExSkrRoUayjAIC4RbNAAAAAAIgCaq4AAEDLGIa0b1/9eteukskU23gAIM6QXAEAgJaprpYyM+vX3W7Jbo9tPAAQZ2LaLPDdd9/V1KlT1aNHD5lMJr388suHPX7dunUymUyNltLS0rDjFi1apF69eslms2nMmDH68MMPW/EsAAAAACDGyZXH49Hw4cO1KMLOsV999ZVKSkpCS2bDUzRJK1as0Jw5czRv3jxt2rRJw4cPV35+vsrLy6MdPgAAAACExLRZ4OTJkzV58uSI35eZman09PQm9z388MO65pprNHPmTEnSkiVL9Prrr+uJJ57Q7bff/n3CBQAAAIBmtcvRAkeMGKHs7GydddZZ2rBhQ2i7z+fTxx9/rAkTJoS2JSQkaMKECdq4cWOz5dXW1qqysjJsAQAAAIBItKvkKjs7W0uWLNELL7ygF154Qbm5uRo/frw2bdokSdq3b58CgYCysrLC3peVldWoX9bB5s+fL6fTGVpyc3Nb9TwAAAAAdDztarTAAQMGaMCAAaHXJ510krZt26ZHHnlEf//734+63Llz52rOnDmh15WVlSRYAAAAACLSrpKrpowePVrr16+XJHXt2lVms1llZWVhx5SVlal79+7NlpGUlKSkpKRWjRMAgHbPYpFmzDiwDgAI066aBTZl8+bNys7OliRZrVaNHDlSa9euDe0PBoNau3atxo4dG6sQAQDoGJKSpOXL6xceSgJAIzF97OR2u1VUVBR6vX37dm3evFkZGRk67rjjNHfuXO3Zs0dPPfWUJGnBggXq3bu3Bg8eLK/Xq7/97W/697//rdWrV4fKmDNnjmbMmKFRo0Zp9OjRWrBggTweT2j0QAAAAABoDTFNrj766COdfvrpodcN/Z5mzJih5cuXq6SkRDt37gzt9/l8+uUvf6k9e/YoJSVFw4YN01tvvRVWxvTp07V3717dcccdKi0t1YgRI7Ry5cpGg1wAAIAIGYZUXV2/npIimUyxjQcA4ozJMAwj1kHEm8rKSjmdTrlcLqWlpcU6HAAA4oPHIzkc9etut2S3xzYeAGgDkeQG7b7PFQAAAADEA5IrAAAAAIgCkisAAAAAiAKSKwAAAACIApIrAAAAAIgCkisAAAAAiIKYznMFAADaEbNZuvDCA+sAgDAkVwAAoGVsNum552IdBQDELZoFAgAAAEAUkFwBAAAAQBSQXAEAgJbxeCSTqX7xeGIdDQDEHZIrAAAAAIgCkisAAAAAiAKSKwAAAACIApIrAAAAAIgCkisAAAAAiAKSKwAAAACIAkusAwAAAO2E2SxNmXJgHQAQhuQKAAC0jM0mvf56rKMAgLhFs0AAAAAAiAKSKwAAAACIApIrAADQMh6PZLfXLx5PrKMBgLhDnysAANBy1dWxjgAA4hY1VwAAAAAQBSRXAAAAABAFJFcAAAAAEAUkVwAAAAAQBSRXAAAAABAFjBYIAABaJiFBOu20A+sAgDAkVwAAoGWSk6V162IdBQDELR47AQAAAEAUkFwBAAAAQBSQXAEAgJbxeKRu3eoXjyfW0QBA3KHPFQAAaLl9+2IdAQDELZIrAAAAAHEjGDS0p6JGHp9fdqtFOenJSkgwxTqsFiG5AgAAABAXisqrtKqgTNv2uuX1B2SzmNW3m0P5Q7KUl5ka6/COiOQKAAAAQMwVlVdp2YYd2u/xKdtpU4o1WdU+vwqKXSp21WjmuF5xn2AxoAUAAACAmAoGDa0qKNN+j0/9Mh1KtSXKnGBSqi1R/TId2u/xafWWMgWDRqxDPSySK7S6YNDQrv3V2lpaqV37q+P+HwUAAADa1p6KGm3b61a20yaTKbx/lclkUrbTpqJyt/ZU1MQowpahWSBaVXtvNwsAOEhCgjRq1IF1AIgSj88vrz+gFGtyk/uTrWaVVXrl8fnbOLLIkFyh1XSEdrMAgIMkJ0v//W+sowDQAdmtFtksZlX7/Eq1JTbaX+MLKMlilt0a3+kLj53QKjpKu1kAAAC0vpz0ZPXt5lCJyyvDCL8/NAxDJS6v8jIdyklvumYrXpBcoVV0lHazAAAAaH0JCSblD8lSht2qwnK3qrx18geDqvLWqbDcrQy7VRMHZ8X9fFckV2gVB9rNNl11m2w1q9YfiPt2swCAg1RXS7161S/V1bGOBkAHk5eZqpnjemlID6cqquu0Y59HFdV1GprjbDfdSSJqtOj3+3Xvvffqyiuv1LHHHttaMaED6CjtZgEABzEM6ZtvDqwDQJTlZaaqz3iH9lTUyOPzy261KCc9Oe5rrBpEVHNlsVj04IMPyu+ntgGH11HazQIAAKBtJSSYlJuRooHd05SbkdJuEivpKJoFnnHGGXrnnXdaIxZ0IB2l3SwAAADQUhG3yZo8ebJuv/12ff755xo5cqTsdnvY/nPPPTdqwaF9a2g32zDPVVmlV0kWs4bmODVxMPNcAQAAoGMxGYe22TqChMNMGmgymRQIBFpc1rvvvqsHH3xQH3/8sUpKSvTSSy9p2rRpzR7/4osvavHixdq8ebNqa2s1ePBg3XnnncrPzw8dc+edd+quu+4Ke9+AAQO0devWFsdVWVkpp9Mpl8ultLS0Fr8PTQsGjXbbbhYAcBCPR3I46tfdbumQB6wA0BFFkhtE3CwwGAw2u0SSWEmSx+PR8OHDtWjRohYd/+677+qss87SG2+8oY8//linn366pk6dqk8++STsuMGDB6ukpCS0rF+/PqK4EF3tud0sAAAA0FIxHapt8uTJmjx5couPX7BgQdjre++9V6+88or+9a9/6YQTTghtt1gs6t69e4vLra2tVW1tbeh1ZWVli98LAECnYTJJgwYdWAcAhDmqea7eeecdTZ06VXl5ecrLy9O5556r9957L9qxHVEwGFRVVZUyMjLCthcWFqpHjx7q06ePLrvsMu3cufOw5cyfP19OpzO05ObmtmbYAAC0Tykp0pYt9UtKSqyjAYC4E3Fy9X//93+aMGGCUlJSNHv2bM2ePVvJyck688wz9c9//rM1YmzWQw89JLfbrYsvvji0bcyYMVq+fLlWrlypxYsXa/v27TrllFNUVVXVbDlz586Vy+UKLbt27WqL8AEAAAB0IBEPaHH88cfr2muv1S9+8Yuw7Q8//LAef/xxffnll0cXiMl0xAEtDvbPf/5T11xzjV555RVNmDCh2eMqKirUs2dPPfzww7rqqqtaVDYDWgAAAACQWnlAi//973+aOnVqo+3nnnuutm/fHmlxR+WZZ57R1VdfrWefffawiZUkpaenq3///ioqKmqT2AAA6LCqq6XBg+uX6upYRwMAcSfi5Co3N1dr165ttP2tt95qk75KTz/9tGbOnKmnn35aZ5999hGPd7vd2rZtm7Kzs1s9NgAAOjTDkL74on6JrOELAHQKEY8W+Mtf/lKzZ8/W5s2bddJJJ0mSNmzYoOXLl+vRRx+NqCy32x1Wo7R9+3Zt3rxZGRkZOu644zR37lzt2bNHTz31lKT6poAzZszQo48+qjFjxqi0tFSSlJycLKfTKUm65ZZbNHXqVPXs2VPFxcWaN2+ezGazLr300khPFQAAAABaLOLk6uc//7m6d++uP/7xj3r22Wcl1ffDWrFihc4777yIyvroo490+umnh17PmTNHkjRjxgwtX75cJSUlYSP9/fWvf5Xf79esWbM0a9as0PaG4yVp9+7duvTSS/Xtt9+qW7duOvnkk/Wf//xH3bp1i/RUAQAAAKDFIhrQwu/3695779WVV16pY489tjXjiikGtAAAoAkej+Rw1K+73ZLdHtt4AKANtNqAFhaLRQ888ID8fv/3ChAAAAAAOpqIB7Q488wz9c4777RGLAAAAADQbkXc52ry5Mm6/fbb9fnnn2vkyJGyH9Ik4Nxzz41acAAAII6YTFLPngfWAQBhIp5EOCGh+couk8mkQCDwvYOKNfpcAQAAAJAiyw0irrkKBoNHHRgAAAAAdFQR9bmqq6uTxWJRQUFBa8UDAAAAAO1SRMlVYmKijjvuuA7R9A8AAESopkY68cT6paYm1tEAQNyJeLTA3/zmN/r1r3+t/fv3t0Y8AAAgXgWD0kcf1S90EwCARiLuc7Vw4UIVFRWpR48e6tmzZ6PRAjdt2hS14AAAAACgvYg4uZo2bVorhAEAAAAA7VvEydW8efNaIw4AAAAAaNda3Ofqww8/POxAFrW1tXr22WejEhQAAAAAtDctTq7Gjh2rb7/9NvQ6LS1N//vf/0KvKyoqdOmll0Y3OiAOBYOGdu2v1tbSSu3aX61gMKJ5uAEAANBBtbhZoGEYh33d3DagIykqr9KqgjJt2+uW1x+QzWJW324O5Q/JUl5maqzDA4DW17VrrCMAgLgVcZ+rwzGZTNEsDogrReVVWrZhh/Z7fMp22pRiTVa1z6+CYpeKXTWaOa4XCRaAjs1ul/bujXUUABC3Ip7nCuiMgkFDqwrKtN/jU79Mh1JtiTInmJRqS1S/TIf2e3xavaWMJoIAAACdWEQ1V1988YVKS0sl1TcB3Lp1q9xutyRp37590Y8OiBN7Kmq0ba9b2U5boxpak8mkbKdNReVu7amoUW5GSoyiBAAAQCxFlFydeeaZYf2qzjnnHEn1N5eGYdAsEB2Wx+eX1x9QijW5yf3JVrPKKr3y+PxtHBkAtKGaGmny5Pr1N9+Ukpv+TQSAzqrFydX27dtbMw4grtmtFtksZlX7/Eq1JTbaX+MLKMlilt0a1W6MABBfgkHpnXcOrAMAwrT4TrBnz56tGQcQ13LSk9W3m0MFxS45kixhtbSGYajE5dXQHKdy0nmKCwAA0FkxoAXQAgkJJuUPyVKG3arCcreqvHXyB4Oq8tapsNytDLtVEwdnKSGBprEAAACdFckV0EJ5mamaOa6XhvRwqqK6Tjv2eVRRXaehOU6GYQcAAEB057kCOrq8zFT1Ge/QnooaeXx+2a0W5aQnU2MFAAAAkisgUgkJJoZbBwAAQCMkVwAAoOVSeLgEAM2JuM9VWVmZfvrTn6pHjx6yWCwym81hCwAA6KDsdsnjqV/s9lhHAwBxJ+KaqyuuuEI7d+7U7373O2VnZzNxMAAAAADoKJKr9evX67333tOIESNaIRwAaF4waDCYCAAAiFsRJ1e5ubkyDKM1YgGAZhWVV2lVQZm27XXL6w/IZjGrbzeH8odkMQw+0Fa8XumCC+rXX3hBstliGw8AxJmI+1wtWLBAt99+u3bs2NEK4QBAY0XlVVq2YYcKil1KT0lUn64OpackqqDYpWUbdqiovCrWIQKdQyAgvfFG/RIIxDoaAIg7EddcTZ8+XdXV1erbt69SUlKUmJgYtn///v1RCw4AgkFDqwrKtN/jU79MR6ifZ6otUY4kiwrL3Vq9pUx9ujpoIggAAGIq4uRqwYIFrRAGADRtT0WNtu11K9tpazSAjslkUrbTpqJyt/ZU1DD/GAAAiKmIk6sZM2a0RhwA0CSPzy+vP6AUa3KT+5OtZpVVeuXx+ds4MgAAgHBHNYlwIBDQyy+/rC+//FKSNHjwYJ177rnMcwUg6uxWi2wWs6p9fqXaEhvtr/EFlGQxy25lTnQAABBbEd+NFBUVacqUKdqzZ48GDBggSZo/f75yc3P1+uuvq2/fvlEPEkDnlZOerL7dHCoodsmRZAlrGmgYhkpcXg3NcSonvemaLQAAgLYS8WiBs2fPVt++fbVr1y5t2rRJmzZt0s6dO9W7d2/Nnj27NWIE0IklJJiUPyRLGXarCsvdqvLWyR8Mqspbp8JytzLsVk0cnMVgFgAAIOZMRoSTVtntdv3nP//R0KFDw7Z/+umnGjdunNxud1QDjIXKyko5nU65XC6lpaXFOhwACp/nqtZf3xQwL9OhiYOZ5woAALSeSHKDiJsFJiUlqaqq8ZwybrdbVqs10uIAoEXyMlPVZ7xDeypq5PH5ZbdalJOeTI0VAACIGxE3CzznnHN07bXX6oMPPpBhGDIMQ//5z3/0s5/9TOeee25rxAgAkuqbCOZmpGhg9zTlZqSQWAEAgLgScXL1pz/9SX379tXYsWNls9lks9k0btw45eXl6dFHH22NGAEAQDzweqWLLqpfvN5YRwMAcSfiPlcNCgsLtXXrVknS8ccfr7y8vKgGFkv0uQIAoAkej+Rw1K+73ZLdHtt4AKANtGqfqwb9+vVTv379jvbtAAAAANChtCi5mjNnju6++27Z7XbNmTPnsMc+/PDDUQkMAAAAANqTFiVXn3zyierq6kLrAAAAAIBwLUqu3n777SbXAQCdSzBoMBw+AADNiHi0wCuvvLLJea48Ho+uvPLKqAQFAIg/ReVVWrxumx5Z87X+tLZQj6z5WovXbVNReeP/JwAA0BlFnFw9+eSTqqmpabS9pqZGTz31VFSCAgDEl6LyKi3bsEMFxS6lpySqT1eH0lMSVVDs0rINO0iwAABQBMlVZWWlXC6XDMNQVVWVKisrQ8t3332nN954Q5mZmRF9+LvvvqupU6eqR48eMplMevnll4/4nnXr1ukHP/iBkpKSlJeXp+XLlzc6ZtGiRerVq5dsNpvGjBmjDz/8MKK4AAAHBIOGVhWUab/Hp36ZDqXaEmVOMCnVlqh+mQ7t9/i0ekuZgsGjmtkD7UlKSv0Q7G53/ToAIEyLk6v09HRlZGTIZDKpf//+6tKlS2jp2rWrrrzySs2aNSuiD/d4PBo+fLgWLVrUouO3b9+us88+W6effro2b96sm2++WVdffbVWrVoVOmbFihWaM2eO5s2bp02bNmn48OHKz89XeXl5RLEBAOrtqajRtr1uZTttMpnC+1eZTCZlO20qKndrT0XjVg3oYEym+rmt7Pb6dQBAmBZPIvzOO+/IMAydccYZeuGFF5SRkRHaZ7Va1bNnT/Xo0ePoAzGZ9NJLL2natGnNHnPbbbfp9ddfV0FBQWjbJZdcooqKCq1cuVKSNGbMGJ144olauHChJCkYDCo3N1c33nijbr/99hbFwiTCAHDA1tJK/Wltofp0dcjcxOAV/mBQO/Z5dOOZ/TSwO7+ZAICOpVUmET7ttNMk1dceHXfccY2eXraFjRs3asKECWHb8vPzdfPNN0uSfD6fPv74Y82dOze0PyEhQRMmTNDGjRubLbe2tla1tbWh15WVldENHADaMbvVIpvFrGqfX6m2xEb7a3wBJVnMsluPel56tBe1tdJ119WvP/aYlJQU23gAIM5EPKDFv//9bz3//PONtj/33HN68sknoxJUc0pLS5WVlRW2LSsrS5WVlaqpqdG+ffsUCASaPKa0tLTZcufPny+n0xlacnNzWyV+AGiPctKT1bebQyUurw5t7GAYhkpcXuVlOpSTnhyjCNFm/H7pySfrF78/1tEAQNyJOLmaP3++unbt2mh7Zmam7r333qgE1dbmzp0rl8sVWnbt2hXrkAAgbiQkmJQ/JEsZdqsKy92q8tbJHwyqylunwnK3MuxWTRycxXxXAIBOL+I2HDt37lTv3r0bbe/Zs6d27twZlaCa0717d5WVlYVtKysrU1pampKTk2U2m2U2m5s8pnv37s2Wm5SUpCSaNgBAs/IyUzVzXC+tKijTtr1ulVV6lWQxa2iOUxMHZykvMzXWIQIAEHMRJ1eZmZn67LPP1KtXr7Dtn376qY455phoxdWksWPH6o033gjbtmbNGo0dO1ZS/cAaI0eO1Nq1a0MDYwSDQa1du1Y33HBDq8YGAB1dXmaq+ox3aE9FjTw+v+xWi3LSk6mxAgDg/4s4ubr00ks1e/Zspaam6tRTT5VUP5LgTTfdpEsuuSSistxut4qKikKvt2/frs2bNysjI0PHHXec5s6dqz179oQmJ/7Zz36mhQsX6tZbb9WVV16pf//733r22Wf1+uuvh8qYM2eOZsyYoVGjRmn06NFasGCBPB6PZs6cGempAgAOkZBgUm4G8xsBANCUiJOru+++Wzt27NCZZ54pi6X+7cFgUJdffnnEfa4++ugjnX766aHXc+bMkSTNmDFDy5cvV0lJSVhTw969e+v111/XL37xCz366KM69thj9be//U35+fmhY6ZPn669e/fqjjvuUGlpqUaMGKGVK1c2GuQCAAAAAKKpxfNcHerrr7/Wp59+quTkZA0dOlQ9e/aMdmwxwzxXAAA0weORHI76dbe7fjJhAOjgWmWeq0P1799f/fv3P9q3A0AjwaBBfx4gnqWkSOXlB9YBAGFalFzNmTNHd999t+x2e6jpXnMefvjhqASGjosbaDSlqLwqNBKd1x+QzWJW324O5Q9pnyPRcZ2jQzKZpG7dYh0FAMStFiVXn3zyierq6kLrzTGZuHGIN/F2g9fRbqARHUXlVVq2YYf2e3zKdtqUYk1Wtc+vgmKXil01mjmuV7u6PrjOAQDonFqUXL399ttNriO+xdsNXke7gUZ0BIOGVhWUab/Hp36ZjtBDmlRbohxJFhWWu7V6S5n6dHW0i5ofrnN0aLW1UkMLlocflpgjEgDCJMQ6ALSOhhu8gmKX0lMS1aerQ+kpiSoodmnZhh0qKq9q03gOvYFOtSXKnGBSqi1R/TId2u/xafWWMgWDRzW+CtqxPRU12rbXrWynrVHtt8lkUrbTpqJyt/ZU1MQowpbjOkeH5/dLf/lL/eL3xzoaAIg7Laq5+tGPftTiAl988cWjDgbREY81AZHcQDOHTufi8fnl9QeUYk1ucn+y1ayySq88vvi/keM6BwCgc2tRzZXT6QwtaWlpWrt2rT766KPQ/o8//lhr166V0+lstUDRcvFYE3DgBrrpfD7ZalatP9AubqARXXarRTaLWdXN/O1rfAElWcyyN3PtxBOucwAAOrcW3a0sW7YstH7bbbfp4osv1pIlS2Q2myVJgUBA119/PXNCxYl4rAk4+AY61ZbYaH97uoFGdOWkJ6tvN4cKil1yJFnCHggYhqESl1dDc5zKSW/6eo4nXOcAAHRuEfe5euKJJ3TLLbeEEitJMpvNmjNnjp544omoBoejE481AQ030CUurw6dt7rhBjov09EubqARXQkJJuUPyVKG3arCcreqvHXyB4Oq8tapsNytDLtVEwdntYvBLLjOAQDo3CJOrvx+v7Zu3dpo+9atWxUMBqMSFL6feLzB60g30Ii+vMxUzRzXS0N6OFVRXacd+zyqqK7T0Bxnuxpdj+scAIDOLeKqi5kzZ+qqq67Stm3bNHr0aEnSBx98oPvuu08zZ86MeoCIXMMNXrGrRoXl9X2vkq1m1fgCKnF5Y3aD13AD3TA8fFmlV0kWs4bmODVxMPP/dHZ5manqM94RV/OyHQ2ucwAAOi+TcWjVxhEEg0E99NBDevTRR1VSUiJJys7O1k033aRf/vKXYc0F26vKyko5nU65XK523Y/s4Hmuav31TQHzMh0xv8GLt4mNgdbAdY4OKRiUdu6sXz/uOCmBGV0AdHyR5AYRJ1eHfpCkdp2ANKWjJFcSN3gAAADA9xFJbnBUIxr4/X6tW7dO27Zt049//GNJUnFxsdLS0uRwOI6mSLSShATTUc2nQ1IGAAAARCbi5Oqbb77RpEmTtHPnTtXW1uqss85Samqq7r//ftXW1mrJkiWtESfa0MHNCb3+gGwWs/p2cyh/CP1FAKBT8/mk3/ymfv0Pf5Cs1tjGAwBxJuLG0jfddJNGjRql7777TsnJB0abO//887V27dqoBoe2V1RepWUbdqig2KX0lET16epQekqiCopdWrZhh4rKq2IdIgAgVurqpIceql/q6mIdDQDEnYhrrt577z29//77sh7ytKpXr17as2dP1AJD2wsGDa0qKNN+j0/9Mh2hyVxTbYlyJFlUWO7W6i1l6tPVQRNBAAAA4BAR11wFg0EFAoFG23fv3q3UVJqMtWd7Kmq0bW/90O0NiVUDk8mkbKdNReVu7amoiVGEAAAAQPyKOLmaOHGiFixYEHptMpnkdrs1b948TZkyJZqxoY15fH55/QGlWJuu0Ey2mlXrD8jj87dxZAAAAED8i7hZ4EMPPaRJkyZp0KBB8nq9+vGPf6zCwkJ17dpVTz/9dGvEiDZit1pks5hV7fMr1ZbYaH+Nr36uLHszyRcAAADQmUV8l5ybm6tPP/1UK1as0Keffiq3262rrrpKl112WdgAF2h/ctKT1bebQwXFLjmSLGFNAw3DUInLq6E5TuWk83cGAAAADhVRclVXV6eBAwfqtdde02WXXabLLrusteJCDCQkmJQ/JEvFrhoVltf3vUq2mlXjC6jE5VWG3aqJg7MYzAIAAABoQkTJVWJiorxeb2vFgjiQl5mqmeN6hea5Kqv0Ksli1tAcpyYOZp4rAOjUkpOlgoID6wCAMCbDMIxI3nDvvffq66+/1t/+9jdZLB2z701lZaWcTqdcLpfS0tJiHU5MBIOG9lTUyOPzy261KCc9mRorAAAAdDqR5AYRZ0f//e9/tXbtWq1evVpDhw6V3W4P2//iiy9GWiTiUEKCSbkZKbEOAwAAAGg3Ik6u0tPTdcEFF7RGLECnR40hgLjm80n33lu//utfS1ZrbOMBgDgTcbPAzoBmgYiFovKqUF83rz8gm8Wsvt0cyh9CXzcAccLjkRyO+nW3Wzqk9QoAdESR5AYtnkQ4GAzq/vvv17hx43TiiSfq9ttvV01NzfcOFkB9YrVsww4VFLuUnpKoPl0dSk9JVEGxS8s27FBReVWsQwQAAMARtDi5+sMf/qBf//rXcjgcysnJ0aOPPqpZs2a1ZmxApxAMGlpVUKb9Hp/6ZTqUakuUOcGkVFui+mU6tN/j0+otZQoGqWQGAACIZy1Orp566in95S9/0apVq/Tyyy/rX//6l/7xj38oGAy2ZnxAh7enokbb9tbPK3bwxM2SZDKZlO20qajcrT0V1BQDAADEsxYnVzt37tSUKVNCrydMmCCTyaTi4uJWCQzoLDw+v7z+gFKsTY8vk2w1q9YfkMfnb+PIAAAAEIkWJ1d+v182my1sW2Jiourq6qIeFNCZ2K0W2SxmVTeTPNX4AkqymGVvJvkCAABAfGjx3ZphGLriiiuUlJQU2ub1evWzn/0sbK4r5rkCIpOTnqy+3RwqKHbJkWQJaxpoGIZKXF4NzXEqJz05hlECAADgSFqcXM2YMaPRtp/85CdRDQbojBISTMofkqViV40Ky+v7XiVbzarxBVTi8irDbtXEwVnMdwUg9mw26cMPD6wDAMIwz1UTmOcKsXDwPFe1/vqmgHmZDk0czDxXAAAAsRJJbkAnDiBO5GWmqs94h/ZU1Mjj88tutSgnPZkaKwAAgHaC5AqIIwkJJuVmpMQ6DKDFgkGDBwKdic8nPfpo/fpNN0lWa2zjAYA4Q7PAJtAsEACO7OCmrF5/QDaLWX27OZQ/hKasHZbHIzkc9etut3TQgFYA0FHRLBAA0KqKyqu0bMMO7ff4lO20KcWarGqfXwXFLhW7ajRzXC8SLABAp9Piea4AAJDqmwKuKijTfo9P/TIdSrUlypxgUqotUf0yHdrv8Wn1ljIFgzSMAAB0LkeVXP3973/XuHHj1KNHD33zzTeSpAULFuiVV16JanAAgPizp6JG2/bWTxtw8LxskmQymZTttKmo3K09FTUxihAAgNiIOLlavHix5syZoylTpqiiokKBQECSlJ6ergULFkQ7PnQiwaChXfurtbW0Urv2V/PUG4hTHp9fXn9AKdamW5YnW82q9Qfk8fnbODIAAGIr4j5Xf/7zn/X4449r2rRpuu+++0LbR40apVtuuSWqwaHzoGM80H7YrRbZLGZV+/xKtSU22l/jq5+nzd5M8gUAQEcVcc3V9u3bdcIJJzTanpSUJI/HE5Wg0Lk0dIwvKHYpPSVRfbo6lJ6SqIJil5Zt2KGi8qpYhwjgIDnpyerbzaESl1eHDjhrGIZKXF7lZTqUk54cowgBAIiNiJOr3r17a/PmzY22r1y5Uscff3w0YkInQsd4oP1JSDApf0iWMuxWFZa7VeWtkz8YVJW3ToXlbmXYrZo4OIv5rjoim016++36xWaLdTQAEHcibrMxZ84czZo1S15v/RPLDz/8UE8//bTmz5+vv/3tb60RIzqwSDrGM7kuED/yMlM1c1yvUHPeskqvkixmDc1xauJgmvN2WGazNH58rKMAgLgVcXJ19dVXKzk5Wb/97W9VXV2tH//4x+rRo4ceffRRXXLJJa0RIzqwAx3jm24+lGw1q6zSS8d4IA7lZaaqz3iH9lTUyOPzy261KCc9mRorAECndVRDsV922WUqLCyU2+1WaWmpdu/erauuuuqog1i0aJF69eolm82mMWPG6MMPP2z22PHjx8tkMjVazj777NAxV1xxRaP9kyZNOur40HoO7hjfFDrGA/EtIcGk3IwUDeyeptyMFBKrjq6uTlq0qH6pq4t1NAAQdyJOrs444wxVVFRIklJSUpSZmSlJqqys1BlnnBFxACtWrNCcOXM0b948bdq0ScOHD1d+fr7Ky8ubPP7FF19USUlJaCkoKJDZbNZFF10UdtykSZPCjnv66acjjg2tj47xANCO+HzSDTfULz5frKMBgLgTcXK1bt06+Zr4QfV6vXrvvfciDuDhhx/WNddco5kzZ2rQoEFasmSJUlJS9MQTTzR5fEZGhrp37x5a1qxZo5SUlEbJVVJSUthxXbp0iTg2tD46xgMAAKCjaHFbq88++yy0/sUXX6i0tDT0OhAIaOXKlcrJyYnow30+nz7++GPNnTs3tC0hIUETJkzQxo0bW1TG0qVLdckll8hut4dtX7dunTIzM9WlSxedccYZuueee3TMMcc0WUZtba1qa2tDrysrKyM6D3w/dIwHAABAR9Di5GrEiBGh/ktNNf9LTk7Wn//854g+fN++fQoEAsrKygrbnpWVpa1btx7x/R9++KEKCgq0dOnSsO2TJk3Sj370I/Xu3Vvbtm3Tr3/9a02ePFkbN26U2WxuVM78+fN11113RRQ7oouO8QAAAGjvWpxcbd++XYZhqE+fPvrwww/VrVu30D6r1arMzMwmE5fWtHTpUg0dOlSjR48O237wqIVDhw7VsGHD1LdvX61bt05nnnlmo3Lmzp2rOXPmhF5XVlYqNze39QJHkxo6xgNAexIMGjwYAgBIiiC56tmzpyQpGAxG7cO7du0qs9mssrKysO1lZWXq3r37Yd/r8Xj0zDPP6Pe///0RP6dPnz7q2rWrioqKmkyukpKSlJSUFFnwAIBOr6i8KtSk2esPyGYxq283h/KH0KQZADqjiMe3fuqppw67//LLL29xWVarVSNHjtTatWs1bdo0SfXJ29q1a3XDDTcc9r3PPfecamtr9ZOf/OSIn7N79259++23ys7ObnFsAAAcTlF5lZZt2KH9Hp+ynTalWJNV7fOroNilYleNZo7rRYIFAJ1MxMnVTTfdFPa6rq5O1dXVslqtSklJiSi5kqQ5c+ZoxowZGjVqlEaPHq0FCxbI4/Fo5syZkuqTtZycHM2fPz/sfUuXLtW0adMaDVLhdrt111136YILLlD37t21bds23XrrrcrLy1N+fn6kpwsAQCPBoKFVBWXa7/GpX6ZDJlN9M8BUW6IcSRYVlru1ekuZ+nR1dKwmgklJ0muvHVgHAISJOLn67rvvGm0rLCzUz3/+c/3qV7+KOIDp06dr7969uuOOO1RaWqoRI0Zo5cqVoUEudu7cqYSE8BHjv/rqK61fv16rV69uVJ7ZbNZnn32mJ598UhUVFerRo4cmTpyou+++m6Z/AICo2FNRo2173cp22kKJVQOTyaRsp01F5W7tqajpWH1JLRbp7LNjHQUAxC2TcejMrUfpo48+0k9+8pMWjfIX7yorK+V0OuVyuZSWlhbrcAAAcWZraaX+tLZQfbo6ZG6iZsofDGrHPo9uPLOfBnbn/yMA0J5FkhtEPIlwcywWi4qLi6NVHAAAcctutchmMava529yf40voCSLWXZrxA1E4ltdnbR8ef1SVxfraAAg7kT8q//qq6+GvTYMQyUlJVq4cKHGjRsXtcCASDAUMoC2lJOerL7dHCoodsmRZAlrGmgYhkpcXg3NcSonPTmGUbYCn0/6/32iddFFUmJibOMBgDgTcXLVMKpfA5PJpG7duumMM87QH//4x2jFBbQYQyEDaGsJCSblD8lSsatGheX1fa+SrWbV+AIqcXmVYbdq4uAsHvIAQCcTcXIVzXmugO+LoZABxEpeZqpmjusVerhTVulVksWsoTlOTRzMwx0A6Iw6WGNwdCaddihkAHEjLzNVfcY7aJYMAJDUwuRqzpw5LS7w4YcfPupggEh02qGQAcSVhAQTvzEAAEktTK4++eSTFhV26A0u0Jo8Pr+8/oBSrE13GE+2mlVW6ZWnmdG8AAAAgGhqUXL19ttvt3YcQMQOHgo51dZ4xKoOOxQyAAAA4tL3uuvcvXu3JOnYY4+NSjBAJDrtUMgAECtJSdKzzx5YBwCEiXgS4WAwqN///vdyOp3q2bOnevbsqfT0dN19992MJIg21TAUcobdqsJyt6q8dfIHg6ry1qmw3M1QyAAQbRZL/fxWF11Uvw4ACBPxL+NvfvMbLV26VPfdd19o0uD169frzjvvlNfr1R/+8IeoBwk0h6GQAQAAEC9MhmEYkbyhR48eWrJkic4999yw7a+88oquv/567dmzJ6oBxkJlZaWcTqdcLpfS0tJiHQ5aIBg0GAoZAFqb3y+99FL9+vnnU3sFoFOIJDeI+Fdx//79GjhwYKPtAwcO1P79+yMtDogKhkIGgDZQWytdfHH9uttNcgUAh4i4z9Xw4cO1cOHCRtsXLlyo4cOHRyUoAAAAAGhvIn7k9MADD+jss8/WW2+9pbFjx0qSNm7cqF27dumNN96IeoAAAAAA0B5EXHN12mmn6euvv9b555+viooKVVRU6Ec/+pG++uornXLKKa0RIwAAAADEvYgHtOgMGNACAIAmeDySw1G/7nZLdnts4wGANhBJbhBxzdXKlSu1fv360OtFixZpxIgR+vGPf6zvvvsu8miBOBQMGtq1v1pbSyu1a3+1gkGeQQAAAODwIk6ufvWrX6myslKS9Pnnn2vOnDmaMmWKtm/frjlz5kQ9QKCtFZVXafG6bXpkzdf609pCPbLmay1et01F5VWxDg0AAABxLOIBLbZv365BgwZJkl544QVNnTpV9957rzZt2qQpU6ZEPUCgLRWVV2nZhh3a7/Ep22lTijVZ1T6/CopdKnbVaOa4XkxMDKDzslqlZcsOrAMAwkScXFmtVlVXV0uS3nrrLV1++eWSpIyMjFCNFtAeBYOGVhWUab/Hp36ZDplM9ZMQp9oS5UiyqLDcrdVbytSnq4MJigF0TomJ0hVXxDoKAIhbESdXJ598subMmaNx48bpww8/1IoVKyRJX3/9tY499tioBwi0lT0VNdq2161spy2UWDUwmUzKdtpUVO7WnooaJiwGAABAIxH3uVq4cKEsFouef/55LV68WDk5OZKkN998U5MmTYp6gEBb8fj88voDSrE2/cwh2WpWrT8gj8/fxpEBQJzw+6XXX69f/PwWAsChIq65Ou644/Taa6812v7II49EJSAgVuxWi2wWs6p9fqXaEhvtr/EFlGQxy95M8gUAHV5trXTOOfXrbrdk4fcQAA52VL+KgUBAL730kr788ktJ0vHHH69p06bJwo8s2rGc9GT17eZQQbFLjiRLWNNAwzBU4vJqaI5TOenJMYwSAAAA8SribGjLli2aOnWqysrKNGDAAEnS/fffr27duulf//qXhgwZEvUggbaQkGBS/pAsFbtqVFhe3/cq2WpWjS+gEpdXGXarJg7OapPBLIJBQ3sqauTx+WW3WpSTnswgGgAAAHHOZBhGRLOjjh07Vt26ddOTTz6pLl26SJK+++47XXHFFdq7d6/ef//9Vgm0LUUyCzM6nqLyKq0qKNO2vW7V+uubAuZlOjRxcFabDMN+8Od7/QHZLGb17eZQ/pC2+XwAaJbHIzkc9etut2S3xzYeAGgDkeQGESdXycnJ+uijjzR48OCw7QUFBTrxxBNVU1MTecRxhuQKsao5ajzPlkXVPn+o5qy15tmipgxAi5BcAeiEIskNIm4W2L9/f5WVlTVKrsrLy5WXlxdpcUBcSkgwtflw67GaZ4uaMgAAgOho0VDslZWVoWX+/PmaPXu2nn/+ee3evVu7d+/W888/r5tvvln3339/a8cLdFiRzLMVLQ01ZQXFLqWnJKpPV4fSUxJVUOzSsg07VFReFbXPAgAA6OhaVHOVnp7eaOS0iy++OLStoWXh1KlTFQgEWiFMoOM7MM9W06MRJlvNKqv0Rm2erVjVlAFtgaaurcRqlRYuPLAOAAjTouTq7bffbu04gE6vrefZiqSmrK2bSALfB01dW1FiojRrVqyjAIC41aK7tNNOO61FhRUUFHyvYIDOrK3n2WrrmjKgLTQeFCZZ1T6/CopdKnbVtNqgMAAASC3sc3U4VVVV+utf/6rRo0dr+PDh0YgJ6JQa5tnKsFtVWO5WlbdO/mBQVd46FZa7oz7P1sE1ZU2Jdk0Z0NoObeqaakuUOcGkVFui+mU6tN/j0+otZQoGIxokFwcLBKR16+oXugEAQCNHnVy9++67mjFjhrKzs/XQQw/pjDPO0H/+859oxgZ0OnmZqZo5rpeG9HCqorpOO/Z5VFFdp6E5zqg/cW+oKStxeXXojAwNNWV5mY6o1ZQBrS0Wg8J0Ol6vdPrp9YvXG+toACDuRPRIurS0VMuXL9fSpUtVWVmpiy++WLW1tXr55Zc1aNCg1ooR6FTyMlPVZ7yj1TvjN9SUFbtqVFhef0OabDWrxhcIzasVzZoyfH8M0nB4NHUFAMRai5OrqVOn6t1339XZZ5+tBQsWaNKkSTKbzVqyZElrxgd0Sm01z1ZDTVlD5/+ySq+SLGYNzXFq4mA6/8cTBmk4srYeFAYAgEO1+P8wb775pmbPnq2f//zn6tevX2vGBKANtVVNGY4egzS0TFsPCgMAwKFa3Odq/fr1qqqq0siRIzVmzBgtXLhQ+/bta83YALSRhpqygd3TlJuRQmIVRxikoeXaelAYAAAO1eLk6oc//KEef/xxlZSU6LrrrtMzzzyjHj16KBgMas2aNaqqqmrNOAGgU2KQhsi05aAwAAAcymQcOkxYBL766istXbpUf//731VRUaGzzjpLr776ajTji4nKyko5nU65XC6lpaXFOhwAndjW0kr9aW2h+nR1yNxEjYs/GNSOfR7deGY/DezO71UDBv9oJR6P5HDUr7vdkt0e23gAoA1Ekht8r3muBgwYoAceeEC7d+/W008//X2KAgA0gfnIjg5NXVtJYqL0wAP1S2LjQUMAoLP7XjVXHRU1VwDiRTBoaPG6bSoodqlfpqPRIA2F5W4NzXHqZ6f1JYEAAKAVtFnNFQCgdTFIAwAA7QftSAAgzjEfGeJGICBt2lS//oMfSGZzbOMBgDhDcgUA7QDzkSEueL3S6NH16wxoAQCNkFwBQDvRMEgDAACIT3HR52rRokXq1auXbDabxowZow8//LDZY5cvXy6TyRS22Gy2sGMMw9Add9yh7OxsJScna8KECSosLGzt0wAAoNPYvb9aW0srtWt/NZNYA8D/F/PkasWKFZozZ47mzZunTZs2afjw4crPz1d5eXmz70lLS1NJSUlo+eabb8L2P/DAA/rTn/6kJUuW6IMPPpDdbld+fr68Xm9rnw4AAJ3Cwn8X6U9rC/XImq+1eN02FZVXxTokAIi5mCdXDz/8sK655hrNnDlTgwYN0pIlS5SSkqInnnii2feYTCZ17949tGRlZYX2GYahBQsW6Le//a3OO+88DRs2TE899ZSKi4v18ssvt8EZAQDQMW07KIFypljUp6tD6SmJKih2admGHSRYADq9mCZXPp9PH3/8sSZMmBDalpCQoAkTJmjjxo3Nvs/tdqtnz57Kzc3Veeedpy1btoT2bd++XaWlpWFlOp1OjRkzptkya2trVVlZGbYAAIADgkFDb31xoFWJIylR5gSTUm2J6pfp0H6PT6u3lNFEEECnFtPkat++fQoEAmE1T5KUlZWl0tLSJt8zYMAAPfHEE3rllVf0f//3fwoGgzrppJO0e/duSQq9L5Iy58+fL6fTGVpyc3O/76kBANCh7Kmo0fZ9nib3mUwmZTttKip3a09FTRtHBgDxo92NFjh27FiNHTs29Pqkk07S8ccfr8cee0x33333UZU5d+5czZkzJ/S6srKSBAsAgIN4fH55ZNL7l82SyWRS0BJ+C5FsNaus0iuPzx+jCAEg9mKaXHXt2lVms1llZWVh28vKytS9e/cWlZGYmKgTTjhBRUVFkhR6X1lZmbKzs8PKHDFiRJNlJCUlKSkp6SjOAACAzsFutSjRZtNb03+uVFtio/01voCSLGbZre3uuS0ARE1MmwVarVaNHDlSa9euDW0LBoNau3ZtWO3U4QQCAX3++eehRKp3797q3r17WJmVlZX64IMPWlwmAAAIl5OerL7dHCpxeWUY4f2qDMNQicurvEyHctKTYxQhAMRezB8vzZkzRzNmzNCoUaM0evRoLViwQB6PRzNnzpQkXX755crJydH8+fMlSb///e/1wx/+UHl5eaqoqNCDDz6ob775RldffbWk+nbfN998s+655x7169dPvXv31u9+9zv16NFD06ZNi9VpAgDQriUkmJQ/qJv8n38u1+466fjjlWxLVI0voBKXVxl2qyYOzlJCginWoQJAzMQ8uZo+fbr27t2rO+64Q6WlpRoxYoRWrlwZGpBi586dSkg4UMH23Xff6ZprrlFpaam6dOmikSNH6v3339egQYNCx9x6663yeDy69tprVVFRoZNPPlkrV65sNNkwgM4jGDS0p6JGHp9fdqtFOenJ3AQCEcpLteimm38kSbp9+fsqs1iVZDFraI5TEwdnKS8zNcYRAkBsmYxD6/ahyspKOZ1OuVwupaWlxTocAN9TUXmVVhWUadtet7z+gGwWs/p2cyh/CDeDQEQ8HsnhkCTt3lkud2ISDysAdHiR5AYxr7kCgNZUVF6lZRt2aL/Hp2ynTSnWZFX7/CoodqnYVaOZ43qRYAFH4diMFMluj3UYABBXYjqgBQC0pmDQ0KqCMu33+NQv06FUG5OeAgCA1kNyBaDD2lNRo2173cp22mQyhTdZYtJTAAAQbSRXADosj88vrz+glGbm3Um2mlXrDzDpKQAAiAqSKwAdlt1qkc1iVnUzyROTngIAgGjijgJAh9Uw6WlBsUuOJEtY08CGSU+H5jiZ9BRoqcRE6ZZbDqwDAMKQXKHFWjpPEPMJIV4kJJiUPyRLxa4aFZbX971KtpqZ9BQ4Wlar9OCDsY4CAOIWyRVapKXzBDGfEOJNXmaqZo7rFbouyyq9THoKAABaBckVjqil8wS11XxC1IwhUnmZqeoz3sF1A3xfwaC0c2f9+nHHSQl03QaAg5Fc4bAOnSeooc9Kqi1RjiSLCsvdWr2lTL0y7C06rk9XR0Q3tIcmUjW+gNZ8Qc0YIpeQYFJuRkqswwDat5oaqXfv+nW3m0mEAeAQJFc4rJbOE7Rp13ctnk+opTe4hzYx9PmD2ltVq7Tk+glgW6tmDAAAADga1OfjsFo6T9C3Hl9U5xNqaGJYUOxSekqieh9j13cen0orvfrOU6u6QFDmBJNSbfWJ1n6PT6u3lCkYNI76XAEAAIDvg+QKh9XSeYKOsVujNp/QoU0RU22JqvYF5PEFlJ1mk7cuqG17PTKM+kTq0JoxAAAAIBZIrnBYDfMElbi8oWSmQcM8QXmZDv0gt0uLjmvJfEJNNUX0BYLyB4JKtCTIYbNov8enKu+BRC7SmjEAAAAg2kiucFgN8wRl2K0qLHerylsnfzCoKm+dCsvdoXmCLJaEFh3XksEsmmqKaDUnyGJOUF3AUKI5QYFgUL5AMLQ/kpoxAAAAoDWQXOGIGuYJGtLDqYrqOu3Y51FFdZ2G5jjDBpFo6XFH0lRTxFSbRV1SrHJ76+TzB2ROSJDVXH/5RlozBgAAALQGHvOjRVo6T1A05hNqaIpYUOySI8kik8kkk8mkvEyHqrx1Kq2s1bFdkpVsNavKW6cSlzeimjEAwFGyWKTrrz+wDgAIwy8jWqyl8wR93/mEGpoiFrtqVFhe3/cq2WpWotmkLilWmRPq//vNtx4lWcwamuPUxMHMcwUArS4pSVq0KNZRAEDcIrlCXGpoYtgwz1VZpVdJFrPG9j1GEwZlKjnRctQ1YwAAAEBrILlC3IpGE0MAQBQZhrRvX/16166Sid9jADgYyRXi2vdtYggAiKLqaikzs37d7Zbs9tjGAwBxhtECAQAAACAKSK4AAAAAIApIrgAAAAAgCkiuAAAAACAKSK4AAAAAIApIrgAAAAAgChiKHQAAtIzFIs2YcWAdABCGX0YAANAySUnS8uWxjgIA4hbJFY5KMGhoT0WNPD6/7FaLctKTlZBginVYAAAAQMyQXCFiReVVWlVQpm173fL6A7JZzOrbzaH8IVnKy0yNdXgAgNZiGFJ1df16Sopk4qEaAByM5AoRKSqv0rINO7Tf41O206YUa7KqfX4VFLtU7KrRzHG9SLAAoKOqrpYcjvp1t1uy22MbDwDEGUYLRIsFg4ZWFZRpv8enfpkOpdoSZU4wKdWWqH6ZDu33+LR6S5mCQSPWoQIAAABtjuQKLbanokbb9rqV7bTJdEhTEJPJpGynTUXlbu2pqInq5waDhnbtr9bW0krt2l9N8gYAAIC4RLNAtJjH55fXH1CKNbnJ/clWs8oqvfL4/FH7TPp3AfGHAW0AAGgayRVazG61yGYxq9rnV6otsdH+Gl9ASRaz7NboXFb07wK+v2gnQjzwAACgeSRXaLGc9GT17eZQQbFLjiRLWNNAwzBU4vJqaI5TOelN12xF4tD+XQ2flWpLlCPJosJyt1ZvKVOfrg6emAPNiHYixAMPAAAOjz5XaLGEBJPyh2Qpw25VYblbVd46+YNBVXnrVFjuVobdqomDs6KS7MSqfxfQUTQkQgXFLqWnJKpPV4fSUxJVUOzSsg07VFReFVF5DGgDAMCRUXOFiORlpmrmuF6hp+FllV4lWcwamuPUxMHRaxYUi/5dQEfRGjW/kTzwyM1Iifo5IU6YzdKFFx5YBwCEIblCxPIyU9VnvKNVO7S3df8uoCNpjUSIBx6QJNls0nPPxToKAIhb3JniqCQkmFr16XRb9u8COprWSIR44AEAwJHR5wpxqS37dwEdzcGJUFOOJhFqeOBR4vLKMML7VTU88MjLdPDAAwDQqZFcIW419O8a0sOpiuo67djnUUV1nYbmOBmVDDiM1kiEeOABSZLHI5lM9YvHE+toACDu0H4Dca0t+ncBHU1DIlTsqlFheX3fq2SrWTW+gEpc3qNOhNpqQBsAANork3HoY02osrJSTqdTLpdLaWlpsQ4HAI7KwfNc1frrmwLmZTq+dyIU7YmJ0Y54PJLDUb/udkt2e2zjAYA2EEluQM0V0ElwQ9z5tFbNb2sPaAMAQHtFcgV0AgfXYHj9AdksZvXt5lD+EJpydXQkQgAAtJ24GNBi0aJF6tWrl2w2m8aMGaMPP/yw2WMff/xxnXLKKerSpYu6dOmiCRMmNDr+iiuukMlkClsmTZrU2qcBxKWi8iot27BDBcUupackqk9Xh9JTElVQ7NKyDTtUVF4V6xABAAA6hJgnVytWrNCcOXM0b948bdq0ScOHD1d+fr7Ky8ubPH7dunW69NJL9fbbb2vjxo3Kzc3VxIkTtWfPnrDjJk2apJKSktDy9NNPt8XpxLVg0NCu/dXaWlqpXfurFQzS3a6jCwYNrSoo036PT/0yHUq1JcqcYFKqLVH9Mh3a7/Fp9ZYyrgUAAIAoiPmAFmPGjNGJJ56ohQsXSpKCwaByc3N144036vbbbz/i+wOBgLp06aKFCxfq8ssvl1Rfc1VRUaGXX375qGLqiANa0Cysc9q1v1qPrPla6SmJTU78WuWtU0V1nX5xVn+ajgE4Mq9XuuCC+vUXXpBsttjGAwBtIJLcIKY1Vz6fTx9//LEmTJgQ2paQkKAJEyZo48aNLSqjurpadXV1ysjICNu+bt06ZWZmasCAAfr5z3+ub7/9ttkyamtrVVlZGbZ0JDQL67w8Pr+8/oBSmpksNtlqVq0/IE8zk80CQBibTXr99fqFxAoAGolpcrVv3z4FAgFlZWWFbc/KylJpaWmLyrjtttvUo0ePsARt0qRJeuqpp7R27Vrdf//9eueddzR58mQFAoEmy5g/f76cTmdoyc3NPfqTijM0C+vc7FaLbBazqptJnmp89cNz25tJvgAAANBy7fqO6r777tMzzzyjdevWyXbQE7RLLrkktD506FANGzZMffv21bp163TmmWc2Kmfu3LmaM2dO6HVlZWWHSbD2VNRo2976SURNpvDhl00mk7KdNhWVu7WnooZmYR1QTnqy+nZzqKDYJUeSJewaMAxDJS6vhuY4lZOeHMMoAQAAOoaY1lx17dpVZrNZZWVlYdvLysrUvXv3w773oYce0n333afVq1dr2LBhhz22T58+6tq1q4qKiprcn5SUpLS0tLClo6BZWOeWkGBS/pAsZditKix3q8pbJ38wqCpvnQrL3cqwWzVxcBbzXQFoGY+nfuJgu71+HQAQJqbJldVq1ciRI7V27drQtmAwqLVr12rs2LHNvu+BBx7Q3XffrZUrV2rUqFFH/Jzdu3fr22+/VXZ2dlTibk9oFoa8zFTNHNdLQ3o4VVFdpx37PKqortPQHKdmjuvFgCYAIlNdXb8AABqJ+R31nDlzNGPGDI0aNUqjR4/WggUL5PF4NHPmTEnS5ZdfrpycHM2fP1+SdP/99+uOO+7QP//5T/Xq1SvUN8vhcMjhcMjtduuuu+7SBRdcoO7du2vbtm269dZblZeXp/z8/JidZ6zQLAxSfYLVZ7xDeypq5PH5ZbdalJOeTI0VAABAFMU8uZo+fbr27t2rO+64Q6WlpRoxYoRWrlwZGuRi586dSkg4UMG2ePFi+Xw+XXjhhWHlzJs3T3feeafMZrM+++wzPfnkk6qoqFCPHj00ceJE3X333UpKSmrTc4sHDc3Cil01Kiyv73uVbDWrxhdQictLs7BOJCHBRL86AACAVhTzea7iUUef56rWX98UMC/ToYmDmecKANBCHo/kcNSvu931fa8AoIOLJDeIec0V2gbNwgAAAIDWRXLVidAsDAAAAGg9JFcAAKBlEhKk0047sA4ACENyBQAAWiY5WVq3LtZRAEDc4rETAAAAAEQBNVcAOoxg0GDQFgAAEDMkVwA6hIOnG/D6A7JZzOrbzaH8IUw3AESNxyP16lW/vmMHQ7EDwCFIrgC0e0XlVVq2YYf2e3zKdtqUYk1Wtc+vgmKXil01mjmuFwkWEC379sU6AgCIW/S5AtCuBYOGVhWUab/Hp36ZDqXaEmVOMCnVlqh+mQ7t9/i0ekuZgkHmSwcAAK2L5ApAu7anokbb9rqV7bTJZArvX2UymZTttKmo3K09FTUxihAAAHQWJFcA2jWPzy+vP6AUa9OtnJOtZtX6A/L4/G0cGQAA6GxIrgC0a3arRTaLWdXNJE81voCSLGbZm0m+AAAAooXkCkC7lpOerL7dHCpxeWUY4f2qDMNQicurvEyHctKTYxQhAADoLHiUC6BdS0gwKX9IlopdNSosr+97lWw1q8YXUInLqwy7VRMHZzHfFRANCQnSqFEH1gEAYUzGoY96ocrKSjmdTrlcLqWlpcU6HAAtcPA8V7X++qaAeZkOTRzMPFcAAODoRZIbUHOFdi8YNLSnokYen192q0U56cnUUnRCeZmp6jPewbUAAABihuQK7drBtRVef0A2i1l9uzmUP4Tais4oIcGk3IyUWIcBAAA6KRpMo90qKq/Ssg07VFDsUnpKovp0dSg9JVEFxS4t27BDReVVsQ4RADqW6mqpV6/6pbo61tEAQNyh5grtUjBoaFVBmfZ7fOqX6QhNHptqS5QjyaLCcrdWbylTn64OmoUBQLQYhvTNNwfWAQBhqLlCu7Snokbb9taPDNeQWDUwmUzKdtpUVO7WnoqaGEUIAACAzobkCu2Sx+eX1x9QSjMTwyZbzar1B+RpZmJZAAAAINpoFoh2yW61yGYxq9rnV6otUVL9hLFVXr98gaB8/oCs5gTZm0m+AAAAgGjjzhPtUk56svp2c6ig2CVHkkXfVdepqNyt76p98geCqvYF1DfToRpfINahAgAAoJOgWSDapYQEk/KHZCnDbtUnOyv04fZvVV7pVYKpvgYrLbn+ucGTGxk1EAAAAG2D5ArtVl5mqmac1FOSVOX1y2QyFDSkLGeyTuyVoRNy07Xf49PqLWUKBhnVCgC+N5NJGjSofjExEisAHIpmgWjXkhMt6uqwKtvZVYmWBFnNCUq1WUIjCB48aiCTywLA95SSIm3ZEusoACBukVyhXfP4/KoNBJXTJUXmJuazSraaVVbpZdRAAAAAtDqaBaJdO3jUwKbU+AJKspgZNRAAAACtjuQK7VrDqIElLq8MI7xflWEYKnF5lZfpUE56cowiBIAOpLpaGjy4fqmujnU0ABB3eJyPdq1h1MBiV40Ky93KdtqUbDWrxhdQicurDLtVEwdnKaGJJoMAgAgZhvTFFwfWAQBhqLlCu5eXmaqZ43ppSA+nKqrrtGOfRxXVdRqa49TMcb2Ul5ka6xABAADQCVBzhQ4hLzNVfcY7tKeiRh6fX3arRTnpydRYAQAAoM2QXKHDSEgwMdw6AAAAYoZmgQAAAAAQBSRXAAAAABAFNAsEAAAtYzJJPXseWAfasWDQoK82oo7kCgCAGGl3N3cpKdKOHbGOAvjeisqrtKqgTNv2uuX1B2SzmNW3m0P5Q7IYZRjfC8kVAAAxwM0dEBtF5VVatmGH9nt8ynbalGJNVrXPr4Jil4pdNUzjgu+F5AoAgDbGzR0QG8GgoVUFZdrv8alfpkOm/9+8NdWWKEeSRYXlbq3eUqY+XR3xXYuMuMWAFgAAtKFDb+5SbYkyJ5iUaktUv0yH9nt8Wr2lTMGgEetQG6upkU48sX6pqYl1NEDE9lTUaNtet7KdtlBi1cBkMinbaVNRuVt7Kri+cXSouQIAoA1FcnMXd3P3BYPSRx8dWAfaGY/PL68/oBRrcpP7k61mlVV65fH52zgydBTUXAEA0IYO3Nw1/Xwz2WpWrT/AzR3QCuxWi2wWs6qb+fdV4wsoyWKWvZl/n8CRkFwBANCGuLkDYicnPVl9uzlU4vLKMMKb3hqGoRKXV3mZDuWkN12zBRwJyRUQB4JBQ7v2V2traaV27a+Oz74WAKKCmzsgdhISTMofkqUMu1WF5W5VeevkDwZV5a1TYblbGXarJg7OYjALHDUeiwExxnDMQOfScHNX7KpRYXl936tkq1k1voBKXF5u7oBWlpeZqpnjeoX+31tW6VWSxayhOU5NHMz/e/H9mIxDH5tBlZWVcjqdcrlcSktLi3U46MAaD8dsUbXPH7rBYjhmoOM6+MFKrb++KWBepiO+b+48HsnhqF93uyW7PbbxAN9Du5vEGzETSW5AzRUQI8y1AXRueZmp6jPe0f5u7rp2jXUEQFQkJJjib0ROtHtx0edq0aJF6tWrl2w2m8aMGaMPP/zwsMc/99xzGjhwoGw2m4YOHao33ngjbL9hGLrjjjuUnZ2t5ORkTZgwQYWFha15CkDEmGsDQMPN3cDuacrNSIn/xMpul/burV+otQKARmKeXK1YsUJz5szRvHnztGnTJg0fPlz5+fkqLy9v8vj3339fl156qa666ip98sknmjZtmqZNm6aCgoLQMQ888ID+9Kc/acmSJfrggw9kt9uVn58vr9fbVqcFHBHDMQMAAHQsMe9zNWbMGJ144olauHChJCkYDCo3N1c33nijbr/99kbHT58+XR6PR6+99lpo2w9/+EONGDFCS5YskWEY6tGjh375y1/qlltukSS5XC5lZWVp+fLluuSSS44YE32u0BZ27a/WI2u+VnpKolJtiY32V3nrVFFdp1+c1Z9mCwAAADESSW4Q05orn8+njz/+WBMmTAhtS0hI0IQJE7Rx48Ym37Nx48aw4yUpPz8/dPz27dtVWloadozT6dSYMWOaLbO2tlaVlZVhC9DaGI4ZQLtTUyONH1+/1NBkGQAOFdPkat++fQoEAsrKygrbnpWVpdLS0ibfU1paetjjG/4bSZnz58+X0+kMLbm5uUd1PkAkmGsDQLsTDErvvFO/BIOxjgYA4k7M+1zFg7lz58rlcoWWXbt2xTokdBINc20M6eFURXWdduzzqKK6TkNznAzDDgAA0M7EdCj2rl27ymw2q6ysLGx7WVmZunfv3uR7unfvftjjG/5bVlam7OzssGNGjBjRZJlJSUlKSko62tMAvpd2OxwzAAAAwsS05spqtWrkyJFau3ZtaFswGNTatWs1duzYJt8zduzYsOMlac2aNaHje/fure7du4cdU1lZqQ8++KDZMoFYa3fDMQMAAKCRmE8iPGfOHM2YMUOjRo3S6NGjtWDBAnk8Hs2cOVOSdPnllysnJ0fz58+XJN1000067bTT9Mc//lFnn322nnnmGX300Uf661//Kql+fqCbb75Z99xzj/r166fevXvrd7/7nXr06KFp06bF6jQBAAAAdHAxT66mT5+uvXv36o477lBpaalGjBihlStXhgak2LlzpxISDlSwnXTSSfrnP/+p3/72t/r1r3+tfv366eWXX9aQIUNCx9x6663yeDy69tprVVFRoZNPPlkrV66UzWZr8/MDAAAA0DnEfJ6reMQ8VwAANMHjkTIz69fLyyW7PbbxAEAbiCQ3iHnNFQAAaCfs9voECwDQJIZiBwAAAIAoILkCAAAAgCgguQIAAC3j9Upnn12/eL2xjgYA4g59rgAAQMsEAtIbbxxYBwCEoeYKAAAAAKKA5AoAAAAAooDkCgAAAACigOQKAAAAAKKA5AoAAAAAooDRAptgGIYkqbKyMsaRAAAQRzyeA+uVlYwYCKBTaMgJGnKEwyG5akJVVZUkKTc3N8aRAAAQp3r0iHUEANCmqqqq5HQ6D3uMyWhJCtbJBINBFRcXKzU1VSaTKdbhQPVPDHJzc7Vr1y6lpaXFOhzEGa4PHAnXCI6EawRHwjXSeRmGoaqqKvXo0UMJCYfvVUXNVRMSEhJ07LHHxjoMNCEtLY0fNDSL6wNHwjWCI+EawZFwjXROR6qxasCAFgAAAAAQBSRXAAAAABAFJFdoF5KSkjRv3jwlJSXFOhTEIa4PHAnXCI6EawRHwjWClmBACwAAAACIAmquAAAAACAKSK4AAAAAIApIrgAAAAAgCkiuAAAAACAKSK4QNxYtWqRevXrJZrNpzJgx+vDDD5s99vHHH9cpp5yiLl26qEuXLpowYcJhj0f7F8n18eKLL2rUqFFKT0+X3W7XiBEj9Pe//70No0UsRHKNHOyZZ56RyWTStGnTWjdAxFwk18jy5ctlMpnCFpvN1obRoq1F+htSUVGhWbNmKTs7W0lJSerfv7/eeOONNooW8YrkCnFhxYoVmjNnjubNm6dNmzZp+PDhys/PV3l5eZPHr1u3Tpdeeqnefvttbdy4Ubm5uZo4caL27NnTxpGjLUR6fWRkZOg3v/mNNm7cqM8++0wzZ87UzJkztWrVqjaOHG0l0mukwY4dO3TLLbfolFNOaaNIEStHc42kpaWppKQktHzzzTdtGDHaUqTXh8/n01lnnaUdO3bo+eef11dffaXHH39cOTk5bRw54o4BxIHRo0cbs2bNCr0OBAJGjx49jPnz57fo/X6/30hNTTWefPLJ1goRMfR9rw/DMIwTTjjB+O1vf9sa4SEOHM014vf7jZNOOsn429/+ZsyYMcM477zz2iBSxEqk18iyZcsMp9PZRtEh1iK9PhYvXmz06dPH8Pl8bRUi2glqrhBzPp9PH3/8sSZMmBDalpCQoAkTJmjjxo0tKqO6ulp1dXXKyMhorTARI9/3+jAMQ2vXrtVXX32lU089tTVDRYwc7TXy+9//XpmZmbrqqqvaIkzE0NFeI263Wz179lRubq7OO+88bdmypS3CRRs7muvj1Vdf1dixYzVr1ixlZWVpyJAhuvfeexUIBNoqbMQpS6wDAPbt26dAIKCsrKyw7VlZWdq6dWuLyrjtttvUo0ePsB9GdAxHe324XC7l5OSotrZWZrNZf/nLX3TWWWe1driIgaO5RtavX6+lS5dq8+bNbRAhYu1orpEBAwboiSee0LBhw+RyufTQQw/ppJNO0pYtW3Tssce2RdhoI0dzffzvf//Tv//9b1122WV64403VFRUpOuvv151dXWaN29eW4SNOEVyhXbvvvvu0zPPPKN169bR2Rghqamp2rx5s9xut9auXas5c+aoT58+Gj9+fKxDQ4xVVVXppz/9qR5//HF17do11uEgTo0dO1Zjx44NvT7ppJN0/PHH67HHHtPdd98dw8gQD4LBoDIzM/XXv/5VZrNZI0eO1J49e/Tggw+SXHVyJFeIua5du8psNqusrCxse1lZmbp3737Y9z700EO677779NZbb2nYsGGtGSZi5Givj4SEBOXl5UmSRowYoS+//FLz588nueqAIr1Gtm3bph07dmjq1KmhbcFgUJJksVj01VdfqW/fvq0bNNrU9/n/TIPExESdcMIJKioqao0QEUNHc31kZ2crMTFRZrM5tO34449XaWmpfD6frFZrq8aM+EWfK8Sc1WrVyJEjtXbt2tC2YDCotWvXhj01PNQDDzygu+++WytXrtSoUaPaIlTEwNFeH4cKBoOqra1tjRARY5FeIwMHDtTnn3+uzZs3h5Zzzz1Xp59+ujZv3qzc3Ny2DB9tIBq/I4FAQJ9//rmys7NbK0zEyNFcH+PGjVNRUVHowYwkff3118rOziax6uxiPaIGYBiG8cwzzxhJSUnG8uXLjS+++MK49tprjfT0dKO0tNQwDMP46U9/atx+++2h4++77z7DarUazz//vFFSUhJaqqqqYnUKaEWRXh/33nuvsXr1amPbtm3GF198YTz00EOGxWIxHn/88VidAlpZpNfIoRgtsOOL9Bq56667jFWrVhnbtm0zPv74Y+OSSy4xbDabsWXLllidAlpRpNfHzp07jdTUVOOGG24wvvrqK+O1114zMjMzjXvuuSdWp4A4QbNAxIXp06dr7969uuOOO1RaWqoRI0Zo5cqVoc6lO3fuVELCgYrWxYsXy+fz6cILLwwrZ968ebrzzjvbMnS0gUivD4/Ho+uvv167d+9WcnKyBg4cqP/7v//T9OnTY3UKaGWRXiPofCK9Rr777jtdc801Ki0tVZcuXTRy5Ei9//77GjRoUKxOAa0o0usjNzdXq1at0i9+8QsNGzZMOTk5uummm3TbbbfF6hQQJ0yGYRixDgIAAAAA2jse4wEAAABAFJBcAQAAAEAUkFwBAAAAQBSQXAEAAABAFJBcAQAAAEAUkFwBAAAAQBSQXAEAAABAFJBcAQAAAEAUkFwBAAAAQBSQXAEAmnTFFVdo2rRpsQ4jLj399NMym82aNWtWo33r1q2TyWSSyWRSQkKCnE6nTjjhBN16660qKSk5YtkvvfSSfvjDH8rpdCo1NVWDBw/WzTff3ApnAQCINpIrAEC7UVdXF+sQJElLly7Vrbfeqqefflper7fJY7766isVFxfrv//9r2677Ta99dZbGjJkiD7//PNmy127dq2mT5+uCy64QB9++KE+/vhj/eEPf2jV8w4EAgoGg61WPgB0JiRXAICjUlBQoMmTJ8vhcCgrK0s//elPtW/fPknSX//6V/Xo0aPRTft5552nK6+8MvT6lVde0Q9+8APZbDb16dNHd911l/x+f2i/yWTS4sWLde6558put+sPf/iDAoGArrrqKvXu3VvJyckaMGCAHn300bDP8fv9mj17ttLT03XMMcfotttu04wZM8Jq4oLBoObPnx8qZ/jw4Xr++eePeN7bt2/X+++/r9tvv139+/fXiy++2ORxmZmZ6t69u/r3769LLrlEGzZsULdu3fTzn/+82bL/9a9/ady4cfrVr36lAQMGqH///po2bZoWLVrU6LgTTzxRNptNXbt21fnnnx/a99133+nyyy9Xly5dlJKSosmTJ6uwsDC0f/ny5UpPT9err76qQYMGKSkpSTt37lRtba1uueUW5eTkyG63a8yYMVq3bt0Rvw8AwAEkVwCAiFVUVOiMM87QCSecoI8++kgrV65UWVmZLr74YknSRRddpG+//VZvv/126D379+/XypUrddlll0mS3nvvPV1++eW66aab9MUXX+ixxx7T8uXL9Yc//CHss+68806df/75+vzzz3XllVcqGAzq2GOP1XPPPacvvvhCd9xxh37961/r2WefDb3n/vvv1z/+8Q8tW7ZMGzZsUGVlpV5++eWwcufPn6+nnnpKS5Ys0ZYtW/SLX/xCP/nJT/TOO+8c9tyXLVums88+W06nUz/5yU+0dOnSFn1nycnJ+tnPfqYNGzaovLy8yWO6d++uLVu2qKCgoNlyXn/9dZ1//vmaMmWKPvnkE61du1ajR48O7b/iiiv00Ucf6dVXX9XGjRtlGIamTJkSVvtVXV2t+++/X3/729+0ZcsWZWZm6oYbbtDGjRv1zDPP6LPPPtNFF12kSZMmhSVmAIAjMAAAaMKMGTOM8847r8l9d999tzFx4sSwbbt27TL+Xzv3FhL1m8YB/JuHyVHHsVTERJ3wXOQxC830YpBiSkQt06zs5EVaYGRlFEYHihIpOlxkmZEIGkh1YQfLRMRAxNAOiDIVBRGpaZLamM48e9Hub/+D2tau0K77/cCAv/fwPO+8N/Lw/uYFIN3d3SIikpKSItu3b1f6r1y5IgsWLBCz2SwiInq9Xk6dOmUVo7KyUry8vJRnAFJQUPAv15qfny/p6enKs6enp5SUlCjPExMT4uvrq3wfk8kkjo6O8vTpU6s4O3bskKysrGnzmM1m8fHxkTt37oiISF9fn6hUKnnz5o0yprGxUQDI4ODgpPn3798XANLa2jpl/OHhYTEYDAJA/Pz8ZMOGDVJeXi4mk0kZExsbK9nZ2VPO7+npEQDS0tKitPX394tarZZbt26JiEhFRYUAkI6ODmXMu3fvxNbWVj58+GAVT6/Xy6FDh6bdDyIissaTKyIi+m2dnZ1obGyEs7Oz8gkJCQEAvH79GgCQnZ2N2tpajI2NAQCqqqqQmZkJGxsbJcbx48etYuTm5uLjx48YHR1Vci1dunRS/suXLyM6OhoeHh5wdnZGWVkZ3r9/DwAYGhrCp0+frE5zbG1tER0drTwbjUaMjo4iKSnJKv/NmzeV9U/l0aNHGBkZgcFgAAC4u7sjKSkJ169f/6V9ExEAP153nIqTkxPq6upgNBpx5MgRODs7Y9++fVi2bJmyJx0dHdDr9VPO7+rqgp2dHZYvX660ubm5ITg4GF1dXUqbSqVCWFiY8vzixQuYzWY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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "95th percentile of errors inside domain: 1.39\n", "95th percentile of errors outside domain: 1.22\n", "Fraction of samples outside domain: 0.07\n" ] } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.scatter(leverage_scores, abs_errors, alpha=0.5)\n", "plt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n", "plt.xlabel(\"Leverage AD Score\")\n", "plt.ylabel(\"Absolute Prediction Error\")\n", "plt.title(\"Prediction Errors vs Leverage Scores\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# Calculate error statistics\n", "in_domain = leverage_ad.predict(X_test_transformed)\n", "errors_in = abs_errors[in_domain == 1]\n", "errors_out = abs_errors[in_domain == -1]\n", "\n", "print(f\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\")\n", "print(f\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\")\n", "print(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")" ] }, { "cell_type": "markdown", "id": "e22b19f0", "metadata": {}, "source": [ "## Testing Famous Drugs\n", "\n", "Let's test some well-known drugs to see if they fall within our model's applicability domain:" ] }, { "cell_type": "code", "execution_count": 7, "id": "1d33100d", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:18.712701Z", "iopub.status.busy": "2025-05-11T09:39:18.712517Z", "iopub.status.idle": "2025-05-11T09:39:18.747175Z", "shell.execute_reply": "2025-05-11T09:39:18.746652Z" } }, "outputs": [ { "data": { "text/html": [ "
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k-NN Scorek-NN StatusLeverage ScoreLeverage Status
Drug
Aspirin0.813836Outside0.154726Inside
Viagra0.829481Outside0.374886Inside
Heroin0.852061Outside0.107878Inside
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" ], "text/plain": [ " k-NN Score k-NN Status Leverage Score Leverage Status\n", "Drug \n", "Aspirin 0.813836 Outside 0.154726 Inside\n", "Viagra 0.829481 Outside 0.374886 Inside\n", "Heroin 0.852061 Outside 0.107878 Inside" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define famous drugs\n", "famous_drugs = {\n", " \"Aspirin\": \"CC(=O)OC1=CC=CC=C1C(=O)O\",\n", " \"Viagra\": \"CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4\",\n", " \"Heroin\": \"CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5\",\n", "}\n", "\n", "\n", "# Function to process a drug through both AD pipelines\n", "def check_drug_applicability(smiles, name):\n", " mol = Chem.MolFromSmiles(smiles)\n", "\n", " # k-NN AD\n", " fp_binary = binary_fp_pipe.named_steps[\"fp\"].transform([mol])\n", " knn_score = knn_ad.transform(fp_binary)[0][0]\n", " knn_status = \"Inside\" if knn_ad.predict(fp_binary)[0] == 1 else \"Outside\"\n", "\n", " # Leverage AD\n", " fp_count = count_fp_pipe.named_steps[\"fp\"].transform([mol])\n", " fp_pca = count_fp_pipe.named_steps[\"pca\"].transform(fp_count)\n", " fp_scaled = count_fp_pipe.named_steps[\"scaler\"].transform(fp_pca)\n", " leverage_score = leverage_ad.transform(fp_scaled)[0][0]\n", " leverage_status = \"Inside\" if leverage_ad.predict(fp_scaled)[0] == 1 else \"Outside\"\n", "\n", " return {\n", " \"knn_score\": knn_score,\n", " \"knn_status\": knn_status,\n", " \"leverage_score\": leverage_score,\n", " \"leverage_status\": leverage_status,\n", " }\n", "\n", "\n", "# Process each drug\n", "results = []\n", "for name, smiles in famous_drugs.items():\n", " result = check_drug_applicability(smiles, name)\n", " results.append(\n", " {\n", " \"Drug\": name,\n", " \"k-NN Score\": result[\"knn_score\"],\n", " \"k-NN Status\": result[\"knn_status\"],\n", " \"Leverage Score\": result[\"leverage_score\"],\n", " \"Leverage Status\": result[\"leverage_status\"],\n", " }\n", " )\n", "\n", "# Display results\n", "pd.DataFrame(results).set_index(\"Drug\")" ] }, { "cell_type": "markdown", "id": "a5241345", "metadata": {}, "source": [ "Let's visualize where these drugs fall in our AD plots:" ] }, { "cell_type": "code", "execution_count": 8, "id": "3aaf4485", "metadata": { "execution": { "iopub.execute_input": "2025-05-11T09:39:18.748976Z", "iopub.status.busy": "2025-05-11T09:39:18.748794Z", "iopub.status.idle": "2025-05-11T09:39:19.086510Z", "shell.execute_reply": "2025-05-11T09:39:19.086029Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot for k-NN AD\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(1, 2, 1)\n", "plt.scatter(knn_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\n", "plt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n", "\n", "for result in results:\n", " plt.axvline(x=result[\"k-NN Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\")\n", "\n", "plt.xlabel(\"k-NN AD Score\")\n", "plt.ylabel(\"Absolute Prediction Error\")\n", "plt.title(\"k-NN AD Scores\")\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Plot for Leverage AD\n", "plt.subplot(1, 2, 2)\n", "plt.scatter(leverage_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\n", "plt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n", "\n", "for result in results:\n", " plt.axvline(\n", " x=result[\"Leverage Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\"\n", " )\n", "\n", "plt.xlabel(\"Leverage AD Score\")\n", "plt.ylabel(\"Absolute Prediction Error\")\n", "plt.title(\"Leverage AD Scores\")\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3c018e68", "metadata": {}, "source": [ "## Conclusions\n", "\n", "This notebook demonstrated two different approaches to applicability domain estimation:\n", "\n", "1. The k-NN based approach with binary fingerprints and Tanimoto distance provides a chemical similarity-based assessment\n", "of whether new compounds are similar enough to the training set.\n", "\n", "2. The leverage-based approach with count-based fingerprints and dimensionality reduction focuses on the statistical\n", "novelty of compounds in the reduced feature space.\n", "\n", "The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given\n", "that our training set is focused on SLC6A4 actives, while these drugs have different primary targets.\n", "\n", "The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors (when using the full set),\n", "validating the usefulness of these approaches for identifying potentially unreliable predictions." ] } ], "metadata": { "jupytext": { "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent" }, "kernelspec": { "display_name": "vscode", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/notebooks/README.md ================================================ # Scikit-Mol notebooks with examples ## Documentation This is a collection of notebooks in the notebooks directory which demonstrates some different aspects and use cases - [Basic Usage and fingerprint transformers](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/01_basic_usage.ipynb) - [Descriptor transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/02_descriptor_transformer.ipynb) - [Pipelining with Scikit-Learn classes](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/03_example_pipeline.ipynb) - [Molecular standardization](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/04_standardizer.ipynb) - [Sanitizing SMILES input](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/05_smiles_sanitization.ipynb) - [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/06_hyperparameter_tuning.ipynb) - [Using parallel execution to speed up descriptor and fingerprint calculations](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/07_parallel_transforms.ipynb) - [Using skopt for hyperparameter tuning](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/08_external_library_skopt.ipynb) - [Testing different fingerprints as part of the hyperparameter optimization](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb) - [Using pandas output for easy feature importance analysis and combine pre-existing values with new computations](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/10_pipeline_pandas_output.ipynb) - [Working with pipelines and estimators in safe inference mode](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/11_safe_inference.ipynb) ================================================ FILE: docs/notebooks/pair_notebook.sh ================================================ jupytext --set-formats ipynb,py:percent $1 ================================================ FILE: docs/notebooks/run_notebooks.sh ================================================ # Sync the .py and .ipynb source ./sync_notebooks.sh # Execute the notebooks, gives a .nbconvert.ipynb extension jupyter nbconvert --to notebook --execute *ipynb # move the .nbconvert.ipynb to the original .ipynb for file in *.nbconvert.ipynb; do fname=${file/.nbconvert.ipynb/}; rm $fname.ipynb mv $file $fname.ipynb done ================================================ FILE: docs/notebooks/scripts/01_basic_usage.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: Python 3.9.4 ('rdkit') # language: python # name: python3 # --- # %% [markdown] # # Scikit-Mol # ## scikit-learn compatible RDKit transformers # # Scikit-mol is a collection of scikit-learn compatible transformer classes that integrate into the scikit-learn framework and thus bridge between the molecular information in form of RDKit molecules or SMILES and the machine learning framework from scikit-learn # # %% [markdown] # The transformer classes are easy to load, configure and use to process molecular information into vectorized formats using fingerprinters or collections of descriptors. For demonstration purposes, let's load a MorganTransformer, that can convert a list of RDKit molecular objects into a numpy array of morgan fingerprints. First create some molecules from SMILES strings. # %% from IPython.core.display import HTML # %% from rdkit import Chem smiles_strings = [ "C12C([C@@H](OC(C=3C=CC(=CC3)F)C=4C=CC(=CC4)F)CC(N1CCCCCC5=CC=CC=C5)CC2)C(=O)OC", "O(C1=NC=C2C(CN(CC2=C1)C)C3=CC=C(OC)C=C3)CCCN(CC)CC", "O=S(=O)(N(CC=1C=CC2=CC=CC=C2C1)[C@@H]3CCNC3)C", "C1(=C2C(CCCC2O)=NC=3C1=CC=CC3)NCC=4C=CC(=CC4)Cl", "C1NC[C@@H](C1)[C@H](OC=2C=CC(=NC2C)OC)CC(C)C", "FC(F)(F)C=1C(CN(C2CCNCC2)CC(CC)CC)=CC=CC1", ] mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_strings] # %% [markdown] # Next we import the Morgan fingerprint transformer # %% from scikit_mol.fingerprints import MorganFingerprintTransformer transformer = MorganFingerprintTransformer(radius=3) print(transformer) # %% [markdown] # It actually renders as a cute little interactive block in the Jupyter notebook and lists the options that are not the default values. If we print it, it also gives the information on the settings. # # ![An image of the interactive transformer widget](images/Transformer_Widget.jpg "Transformer object rendering in Jupyter") # # The graphical representation is probably nice when working with complex pipelines. However, the graphical representation doesn't work when previewing the notebook on GitHub and sometimes nbviewer.org, so for the rest of these scikit-mol notebook examples, we'll use the print() output. # %% transformer # %% [markdown] # If we want to get all the settings explicitly, we can use the .get_params() method. # %% parameters = transformer.get_params() parameters # %% [markdown] # The corresponding .set_params() method can be used to update the settings from options or from a dictionary (via ** unpackaging). The get_params and set_params methods are sometimes used by sklearn, as example hyperparameter search objects. # %% parameters["radius"] = 2 parameters["fpSize"] = 256 transformer.set_params(**parameters) print(transformer) # %% [markdown] # Transformation is easy, simply use the .transform() method. For sklearn compatibility the scikit-learn transformers also have a .fit_transform() method, but it is usually not fitting anything. # %% fps = transformer.transform(mols) print(f"fps is a {type(fps)} with shape {fps.shape} and data type {fps.dtype}") # %% [markdown] # For sklearn compatibility, the transform function can be given a second parameter, usually representing the targets in the machine learning, but it is simply ignored most of the time # %% y = range(len(mols)) transformer.transform(mols, y) # %% [markdown] # Sometimes we may want to transform SMILES into molecules, and scikit-mol also has a transformer for that. It simply takes a list of SMILES and produces a list of RDKit molecules, this may come in handy when building pipelines for machine learning models, as we will demo in another notebook. # %% from scikit_mol.conversions import SmilesToMolTransformer smi2mol = SmilesToMolTransformer() print(smi2mol) # %% print(smi2mol.transform(smiles_strings)) ================================================ FILE: docs/notebooks/scripts/02_descriptor_transformer.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Desc2DTransformer: RDKit descriptors transformer # # The descriptors transformer can convert molecules into a list of RDKit descriptors. It largely follows the API of the other transformers, but has a few extra methods and properties to manage the descriptors. # %% from rdkit import Chem import numpy as np import matplotlib.pyplot as plt from scikit_mol.descriptors import MolecularDescriptorTransformer # %% [markdown] # After instantiation of the descriptor transformer, we can query which descriptors it found available in the RDKit framework. # %% descriptor = MolecularDescriptorTransformer() available_descriptors = descriptor.available_descriptors print(f"There are {len(available_descriptors)} available descriptors") print(f"The first five descriptor names: {available_descriptors[:5]}") # %% [markdown] # We can transform molecules to their descriptor profiles # %% smiles_list = ["CCCC", "c1ccccc1"] mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list] features = descriptor.transform(mols) _ = plt.plot(np.array(features).T) # %% [markdown] # If we only want some of them, this can be specified at object instantiation. # %% some_descriptors = MolecularDescriptorTransformer( desc_list=["HeavyAtomCount", "FractionCSP3", "RingCount", "MolLogP", "MolWt"] ) print(f"Selected descriptors are {some_descriptors.selected_descriptors}") features = some_descriptors.transform(mols) # %% [markdown] # If we want to update the selected descriptors on an already existing object, this can be done via the .set_params() method # %% print( some_descriptors.set_params( desc_list=["HeavyAtomCount", "FractionCSP3", "RingCount"] ) ) # %% ================================================ FILE: docs/notebooks/scripts/03_example_pipeline.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Pipelining the scikit-mol transformer # # One of the very usable things with scikit-learn are their pipelines. With pipelines different scikit-learn transformers can be stacked and operated on just as a single model object. In this example we will build a simple model that can predict directly on RDKit molecules and then expand it to one that predicts directly on SMILES strings # # First some needed imports and a dataset # %% import os import rdkit from rdkit import Chem from rdkit.Chem import PandasTools import pandas as pd import matplotlib.pyplot as plt from time import time import numpy as np # %% csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv" # Hmm, maybe better to download directly data = pd.read_csv(csv_file) # %% [markdown] # The dataset is a subset of the SLC6A4 actives from ExcapeDB. They are hand selected to give test set performance despite the small size, and are provided as example data only and should not be used to build serious QSAR models. # # We add RDKit mol objects to the dataframe with pandastools and check that all conversions went well. # %% PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion") # %% [markdown] # Then, let's import some tools from scikit-learn and two transformers from scikit-mol # %% from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.conversions import SmilesToMolTransformer # %% mol_list_train, mol_list_test, y_train, y_test = train_test_split( data.ROMol, data.pXC50, random_state=0 ) # %% [markdown] # After a split into train and test, we'll build the first pipeline # %% pipe = Pipeline( [("mol_transformer", MorganFingerprintTransformer()), ("Regressor", Ridge())] ) print(pipe) # %% [markdown] # We can do the fit by simply providing the list of RDKit molecule objects # %% pipe.fit(mol_list_train, y_train) print(f"Train score is :{pipe.score(mol_list_train,y_train):0.2F}") print(f"Test score is :{pipe.score(mol_list_test, y_test):0.2F}") # %% [markdown] # Nevermind the performance, or the exact value of the prediction, this is for demonstration purpures. We can easily predict on lists of molecules # %% pipe.predict([Chem.MolFromSmiles("c1ccccc1C(=O)[OH]")]) # %% [markdown] # We can also expand the already fitted pipeline, how about creating a pipeline that can predict directly from SMILES? With scikit-mol that is easy! # %% smiles_pipe = Pipeline( [("smiles_transformer", SmilesToMolTransformer()), ("pipe", pipe)] ) print(smiles_pipe) # %% smiles_pipe.predict(["c1ccccc1C(=O)[OH]"]) # %% [markdown] # From here, the pipelines could be pickled, and later loaded for easy prediction on RDKit molecule objects or SMILES in other scripts. The transformation with the MorganTransformer will be the same as during fitting, so no need to remember if radius 2 or 3 was used for this or that model, as it is already in the pipeline itself. If we need to see the parameters for a particular pipeline of model, we can always get the non default settings via print or all settings with .get_params(). # %% smiles_pipe.get_params() ================================================ FILE: docs/notebooks/scripts/04_standardizer.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: Python 3.9.4 ('rdkit') # language: python # name: python3 # --- # %% [markdown] # # Molecule standardization # When building machine learning models of molecules, it is important to standardize the molecules. We often don't want different predictions just because things are drawn in slightly different forms, such as protonated or deprotanted carboxylic acids. Scikit-mol provides a very basic standardize transformer based on the molvs implementation in RDKit # %% from rdkit import Chem from scikit_mol.standardizer import Standardizer from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.conversions import SmilesToMolTransformer from sklearn.pipeline import make_pipeline from sklearn.linear_model import Ridge # %% [markdown] # For demonstration let's create some molecules with different protonation states. The two first molecules are Benzoic acid and Sodium benzoate. # %% smiles_strings = ( "c1ccccc1C(=O)[OH]", "c1ccccc1C(=O)[O-].[Na+]", "CC[NH+](C)C", "CC[N+](C)(C)C", "[O-]CC(C(=O)[O-])C[NH+](C)C", "[O-]CC(C(=O)[O-])C[N+](C)(C)C", ) smi2mol = SmilesToMolTransformer() mols = smi2mol.transform(smiles_strings) for mol in mols[0:2]: display(mol) # %% [markdown] # We can simply use the transformer directly and get a list of standardized molecules # %% # You can just run straight up like this. Note that neutralising is optional standardizer = Standardizer() standard_mols = standardizer.transform(mols) standard_smiles = smi2mol.inverse_transform(standard_mols) standard_smiles # %% [markdown] # Some of the molecules were desalted and neutralized. # # A typical use case would be to add the standardizer to a pipeline for prediction # %% # Typical use case is to use it in an sklearn pipeline, like below predictor = Ridge() std_pipe = make_pipeline( SmilesToMolTransformer(), Standardizer(), MorganFingerprintTransformer(useCounts=True), predictor, ) nonstd_pipe = make_pipeline( SmilesToMolTransformer(), MorganFingerprintTransformer(useCounts=True), predictor ) fake_y = range(len(smiles_strings)) std_pipe.fit(smiles_strings, fake_y) print(f"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}") print(f"Predictions with standardization: {std_pipe.predict(smiles_strings)}") # %% [markdown] # As we can see, the predictions with the standardizer and without are different. The two first molecules were benzoic acid and sodium benzoate, which with the standardized pipeline is predicted as the same, but differently with the nonstandardized pipeline. Whether we want to make the prediction on the parent compound, or predict the exact form, will of course depend on the use-case, but now there is at least a way to handle it easily in pipelined predictors. # # The example also demonstrate another feature. We created the ridge regressor before creating the two pipelines. Fitting one of the pipelines thus also updated the object in the other pipeline. This can be useful for building inference pipelines that takes in SMILES molecules, but rather do the fitting on already converted and standardized molecules. However, be aware that the crossvalidation classes of scikit-learn may clone the estimators internally when doing the search loop, which would break this interdependence, and necessitate the rebuilding of the inference pipeline. # # If we had fitted the non standardizing pipeline, the model would have been different as shown below, as some of the molecules would be perceived different by the Ridge regressor. # %% nonstd_pipe.fit(smiles_strings, fake_y) print(f"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}") print(f"Predictions with standardization: {std_pipe.predict(smiles_strings)}") ================================================ FILE: docs/notebooks/scripts/05_smiles_sanitization.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: Python 3.9.4 ('rdkit') # language: python # name: python3 # --- # %% [markdown] # # SMILES sanitation # Sometimes we are faced with datasets which has SMILES that rdkit doesn't want to sanitize. This can be human entry errors, or differences between RDKits more strict sanitazion and other toolkits implementations of the parser. e.g. RDKit will not handle a tetravalent nitrogen when it has no charge, where other toolkits may simply build the graph anyway, disregarding the issues with the valence rules or guessing that the nitrogen should have a charge, where it could also by accident instead have a methyl group too many. # %% import pandas as pd from rdkit.Chem import PandasTools csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv" # Hmm, maybe better to download directly data = pd.read_csv(csv_file) # %% [markdown] # Now, this example dataset contain all sanitizable SMILES, so for demonstration purposes, we will corrupt one of them # %% data.loc[1, "SMILES"] = "CN(C)(C)(C)" # %% PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"Dataset contains {data.ROMol.isna().sum()} unparsable mols") # %% [markdown] # If we use these SMILES for the scikit-learn pipeline, we would face an error, so we need to check and clean the dataset first. The CheckSmilesSanitation can help us with that. # %% from scikit_mol.utilities import CheckSmilesSanitization smileschecker = CheckSmilesSanitization() smiles_list_valid, y_valid, smiles_errors, y_errors = smileschecker.sanitize( list(data.SMILES), list(data.pXC50) ) # %% [markdown] # Now the smiles_list_valid should be all valid and the y_values filtered as well. Errors are returned, but also accessible after the call to .sanitize() in the .errors property # %% smileschecker.errors # %% [markdown] # The checker can also be used only on X # %% smiles_list_valid, X_errors = smileschecker.sanitize(list(data.SMILES)) smileschecker.errors ================================================ FILE: docs/notebooks/scripts/06_hyperparameter_tuning.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: Python 3.9.4 ('rdkit') # language: python # name: python3 # --- # %% [markdown] # # Full example: Hyperparameter tuning # # first some imports of the usual suspects: RDKit, pandas, matplotlib, numpy and sklearn. New kid on the block is scikit-mol # %% import os import rdkit from rdkit import Chem from rdkit.Chem import PandasTools import pandas as pd import matplotlib.pyplot as plt from time import time import numpy as np from sklearn.pipeline import Pipeline, make_pipeline from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.conversions import SmilesToMolTransformer # %% [markdown] # We will need some data. There is a dataset with the SLC6A4 active compounds from ExcapeDB on Zenodo. The scikit-mol project uses a subset of this for testing, and the samples there has been specially selected to give good results in testing (it should therefore be used for any production modelling). If full_set is false, the fast subset will be used, and otherwise the full dataset will be downloaded if needed. # %% full_set = False if full_set: csv_file = "SLC6A4_active_excape_export.csv" if not os.path.exists(csv_file): import urllib.request url = "https://ndownloader.figshare.com/files/25747817" urllib.request.urlretrieve(url, csv_file) else: csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv" # %% [markdown] # The CSV data is loaded into a Pandas dataframe and the PandasTools utility from RDKit is used to add a column with RDKit molecules # %% data = pd.read_csv(csv_file) PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion") # %% [markdown] # We use the train_test_split to, well, split the dataframe's molecule columns and pXC50 column into lists for train and testing # %% mol_list_train, mol_list_test, y_train, y_test = train_test_split( data.ROMol, data.pXC50, random_state=42 ) # %% [markdown] # We will standardize the molecules before modelling. This is best done before the hyperparameter optimization of the featurization with the scikit-mol transformer and regression modelling, as the standardization is otherwise done for every loop in the hyperparameter optimization, which will make it take longer time. # %% # Probably the recommended way would be to prestandardize the data if there's no changes to the transformer, # and then add the standardizer in the inference pipeline. from scikit_mol.standardizer import Standardizer standardizer = Standardizer() mol_list_std_train = standardizer.transform(mol_list_train) # %% [markdown] # A simple pipeline with a MorganTransformer and a Ridge() regression for demonstration. # %% moltransformer = MorganFingerprintTransformer() regressor = Ridge() optimization_pipe = make_pipeline(moltransformer, regressor) # %% [markdown] # For hyperparameter optimization we import the RandomizedSearchCV class from Scikit-Learn. It will try different random combinations of settings and use internal cross-validation to find the best model. In the end, it will fit the best found parameters on the full set. We also import loguniform, to get a better sampling of some of the parameters. # %% Now hyperparameter tuning from sklearn.model_selection import RandomizedSearchCV # from sklearn.utils.fixes import loguniform from scipy.stats import loguniform # %% [markdown] # With the pipelines, getting the names of the parameters to tune is a bit more tricky, as they are concatenations of the name of the step and the parameter with double underscores in between. We can get the available parameters from the pipeline with the get_params() method, and select the parameters we want to change from there. # %% Which keys do we have? optimization_pipe.get_params().keys() # %% [markdown] # We will tune the regularization strength of the Ridge regressor, and try out different parameters for the Morgan fingerprint, namely the number of bits, the radius of the fingerprint, wheter to use counts or bits and features. # %% param_dist = { "ridge__alpha": loguniform(1e-2, 1e3), "morganfingerprinttransformer__fpSize": [256, 512, 1024, 2048, 4096], "morganfingerprinttransformer__radius": [1, 2, 3, 4], "morganfingerprinttransformer__useCounts": [True, False], "morganfingerprinttransformer__useFeatures": [True, False], } # %% [markdown] # The report function was taken from [this example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py) from the scikit learn documentation. # %% From https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py # Utility function to report best scores def report(results, n_top=3): for i in range(1, n_top + 1): candidates = np.flatnonzero(results["rank_test_score"] == i) for candidate in candidates: print("Model with rank: {0}".format(i)) print( "Mean validation score: {0:.3f} (std: {1:.3f})".format( results["mean_test_score"][candidate], results["std_test_score"][candidate], ) ) print("Parameters: {0}".format(results["params"][candidate])) print("") # %% [markdown] # We will do 25 tries of random parameter sets, and see what comes out as the best one. If you are using the small example dataset, this should take some second, but may take some minutes with the full set. # %% n_iter_search = 25 random_search = RandomizedSearchCV( optimization_pipe, param_distributions=param_dist, n_iter=n_iter_search, cv=3 ) t0 = time() random_search.fit(mol_list_std_train, y_train.values) t1 = time() print(f"Runtime: {t1-t0:0.2F} for {n_iter_search} iterations)") # %% report(random_search.cv_results_) # %% [markdown] # It can be interesting to see what combinations of hyperparameters gave good results for the cross-validation. Usually the number of bits are in the high end and radius is 2 to 4. But this can vary a bit, as we do a small number of tries for this demo. More extended search with more iterations could maybe find even better and more consistent. solutions # %% [markdown] # Let's see if standardization had any influence on this dataset. We build an inference pipeline that includes the standardization object and the best estimator, and run the best estimator directly on the list of test molecules # %% inference_pipe = make_pipeline(standardizer, random_search.best_estimator_) print( f"No Standardization {random_search.best_estimator_.score(mol_list_test, y_test):0.4F}" ) print(f"With Standardization {inference_pipe.score(mol_list_test, y_test):0.4F}") # %% Building an inference pipeline, it appears our test-data was pretty standard [markdown] # We see that the dataset already appeared to be in forms that are similar to the ones coming from the standardization. # # Interestingly the test-set performance often seem to be better than the CV performance during the hyperparameter search. This may be due to the model being refit at the end of the search to the whole training dataset, as the refit parameter on the randomized_search object by default is true. The final model is thus fitted on more data than the individual models during training. # # To demonstrate the effect of standartization we can see the difference if we challenge the predictor with different forms of benzoic acid and benzoates. # %% # Intergrating the Standardizer and challenge it with some different forms and salts of benzoic acid smiles_list = [ "c1ccccc1C(=O)[OH]", "c1ccccc1C(=O)[O-]", "c1ccccc1C(=O)[O-].[Na+]", "c1ccccc1C(=O)[O][Na]", "c1ccccc1C(=O)[O-].C[N+](C)C", ] mols_list = [Chem.MolFromSmiles(smiles) for smiles in smiles_list] print( f"Predictions with no standardization: {random_search.best_estimator_.predict(mols_list)}" ) print(f"Predictions with standardization: {inference_pipe.predict(mols_list)}") # %% [markdown] # Without standardization we get variation in the predictions, but with the standardization object in place, we get the same results. If you want a model that gives different predictions for the different forms, either the standardization need to be removed or the settings changed. # # From here it should be easy to save the model using pickle, so that it can be loaded and used in other python projects. The pipeline carries both the standardization, the featurization and the prediction in one, easy to reuse object. If you want the model to be able to predict directly from SMILES strings, check out the SmilesToMol class, which is also available in Scikit-Mol :-) # # %% [markdown] # ================================================ FILE: docs/notebooks/scripts/07_parallel_transforms.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: Python 3.9.4 ('rdkit') # language: python # name: python3 # --- # %% # %% [markdown] # # Parallel calculations of transforms # # Scikit-mol supports parallel calculations of fingerprints and descriptors. This feature can be activated and configured using the `n_jobs` parameter or the `.n_jobs` attribute after object instantiation. # # To begin, let's import the necessary libraries: RDKit and pandas. And of course, we'll also need to import scikit-mol, which is the new kid on the block. # %% import pathlib import time import pandas as pd from rdkit.Chem import PandasTools from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.fingerprints import MorganFingerprintTransformer # %% [markdown] # ## Obtaining the Data # # We'll need some data to work with, so we'll use a dataset of SLC6A4 active compounds from ExcapeDB that is available on Zenodo. Scikit-mol uses a subset of this dataset for testing purposes, and the samples have been specially selected to provide good results in testing. Note: This dataset should never be used for production modeling. # # In the code below, you can set full_set to True to download the full dataset. Otherwise, the smaller dataset will be used. # %% full_set = False if full_set: csv_file = "SLC6A4_active_excape_export.csv" if not pathlib.Path(csv_file).exists(): import urllib.request url = "https://ndownloader.figshare.com/files/25747817" urllib.request.urlretrieve(url, csv_file) else: csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv" # %% data = pd.read_csv(csv_file) PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion") # %% [markdown] # ## Evaluating the Impact of Parallelism on Transformations # # Let's start by creating a baseline for our calculations without using parallelism. # %% A demonstration of the speedup that can be had for the descriptor transformer transformer = MolecularDescriptorTransformer(n_jobs=1) # %% def test_transformer(transformer): t0 = time.time() transformer.transform(data.ROMol) t = time.time() - t0 print( f"Calculation time on dataset of size {len(data)} with n_jobs={transformer.n_jobs}:\t{t:0.2F} seconds" ) test_transformer(transformer) # %% [markdown] # # Let's see if parallelism can help us speed up our transformations. # %% transformer = MolecularDescriptorTransformer(n_jobs=2) test_transformer(transformer) # %% [markdown] # We've seen that parallelism can help speed up our transformations, with the degree of speedup depending on the number of CPU cores available. However, it's worth noting that there may be some overhead associated with the process of splitting the dataset, pickling objects and functions, and passing them to the parallel child processes. As a result, it may not always be worthwhile to use parallelism, particularly for smaller datasets or certain types of fingerprints. # # It's also worth noting that there are different methods for creating the child processes, with the default method on Linux being 'fork', while on Mac and Windows it's 'spawn'. The code we're using has been tested on Linux using the 'fork' method. # # Now, let's see how parallelism impacts another type of transformer. # %% Some of the benchmarking plots transformer = MorganFingerprintTransformer(n_jobs=1) test_transformer(transformer) transformer.n_jobs = 2 test_transformer(transformer) # %% [markdown] # Interestingly, we observed that parallelism actually took longer to calculate the fingerprints in some cases, which is a perfect illustration of the overhead issue associated with parallelism. Generally, the faster the fingerprint calculation in itself, the larger the dataset needs to be for parallelism to be worthwhile. For example, the Descriptor transformer, which is one of the slowest, can benefit even for smaller datasets, while for faster fingerprint types like Morgan, Atompairs, and Topological Torsion fingerprints, the dataset needs to be larger. # # ![ Relation ship between throughput and parallel speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/max_speedup_vs_throughput.png "Not all fingerprints are equally fast and benefit the same from parallelism") # # We've also included a series of plots below, showing the speedup over serial for different numbers of cores used for different dataset sizes. These timings were taken on a 16 core machine (32 Hyperthreads). Only the largest datasets (>10,000 samples) would make it worthwhile to parallelize Morgan, Atompairs, and Topological Torsions. SECfingerprint, MACCS keys, and RDKitFP are intermediate and would benefit from parallelism when the dataset size is larger, say >500. Descriptors, on the other hand, benefit almost immediately even for the smallest datasets (>100 samples). # # ![Atompairs fingerprint](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/AtomPairFingerprintTransformer_par.png "Atompairs fingerprint speedup") # # ![Descriptors calculation speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/Desc2DTransformer_par.png "Descriptors calculation speedup") # # ![MACCS keys speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MACCSTransformer_par.png "MACCS keys speedup") # # ![Morgan fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MorganTransformer_par.png "Morgan fingerprint speedup") # # ![RDKit fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/RDKitFPTransformer_par.png "RDKit fingerprint speedup") # # ![SEC fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/SECFingerprintTransformer_par.png "SEC fingerprint speedup") # # ![TopologicalTorsion fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/TopologicalTorsionFingerprintTransformer_par.png "TopologicalTorsion fingerprint speedup") # # # # # %% [markdown] # ## Performance heatmaps # # Multiprocessing performance is highly dependent on CPU performance, type of the function and the size of the dataset. To help users understand the performance of their system, we have created a series of heatmaps showing the speedup of different transformers for different dataset sizes and number of cores. The heatmaps are based on the same data as the plots above. # If you what to test the performance of your system, you can run the code below. # %% from rdkit import Chem from scikit_mol.fingerprints import ( AtomPairFingerprintTransformer, AvalonFingerprintTransformer, MHFingerprintTransformer, MorganFingerprintTransformer, RDKitFingerprintTransformer, TopologicalTorsionFingerprintTransformer, ) from scikit_mol.plotting import ParallelTester, plot_heatmap from scikit_mol.standardizer import Standardizer mols = [Chem.MolFromSmiles("CCCCCCCCBr")] * 100000 transformers = [ Standardizer(), MorganFingerprintTransformer(), MolecularDescriptorTransformer(), MHFingerprintTransformer(), AtomPairFingerprintTransformer(), AvalonFingerprintTransformer(), RDKitFingerprintTransformer(), TopologicalTorsionFingerprintTransformer(), ] # %% [markdown] # `ParallelTester` accept the following parameters: # - `transformer` - the transformer to test # - `mols` - the dataset to test # - `n_mols` - the number of molecules to test on (the largest number should be less than or equal to the number of molecules in `mols`) # - `n_cores` - the number of cores to test on (the largest number should be less than or equal to the number of cores in your system) # - `backend` - the backend to use for multiprocessing (default is `loky`, see [joblib documentation](https://joblib.readthedocs.io/en/latest/parallel.html) for more options) # %% transformer = MorganFingerprintTransformer() df = ParallelTester(transformer, mols).test() df # %% [markdown] # Resulting df have one row for each `n_jobs` and one row for each `n_mols`. Results can be plotted as a heatmap using the `plot_heatmap` method. # %% plot_heatmap(df, "Morgan Fingerprint") # %% [markdown] # By default the time is normalized to the time of the serial calculation. Such as a value of 2 means that the calculation took twice as long as the serial calculation and values below 1 means that the calculation was faster than the serial calculation. # If you want to see the absolute time, you can set `normalize=False`. # %% plot_heatmap(df, "Morgan Fingerprint", normalize=False) # %% [markdown] # Below is example heatmaps for the native `scikit-mol` transformers. # %% for transformer in transformers: tester = ParallelTester( transformer, mols, n_mols=[10, 100, 1000, 5000, 10000, 20000], n_jobs=[1, 2, 4, 8], ) results = tester.test() plot_heatmap(results, transformer.__class__.__name__) # %% smiles = ["CCCCCCCCBr"] * 100000 transformer = SmilesToMolTransformer() df = ParallelTester( transformer, smiles, n_mols=[10, 100, 1000, 5000, 10000, 20000], n_jobs=[1, 2, 4, 8] ).test() plot_heatmap(df, "SMILES to Mol") # %% [markdown] # ## Modifing multiprocessing backend # # We use `joblib` for multiprocessing. By default, `joblib` uses the `loky` backend. As a user, you may control the backend that joblib will use by using a context manager: # # ```python # from joblib import parallel_backend # from sckit_mol.transformers import MorganFingerprintTransformer # transformer = MorganFingerprintTransformer(radius=2, nBits=2048) # with parallel_backend('multiprocessing', n_jobs=2): # transformer.transform(mols) # ``` # # If you have ray or dask installed, you can also use these backends. For example, to use ray: # # ```python # from joblib import parallel_backend # from ray.util.joblib import register_ray # register_ray() # with parallel_backend('ray', n_jobs=2): # transformer.transform(mols) # ``` # # And for dask: # # ```python # from joblib import parallel_backend # from dask.distributed import Client # client = Client() # with parallel_backend('dask', n_jobs=2): # transformer.transform(mols) # ``` ================================================ FILE: docs/notebooks/scripts/08_external_library_skopt.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% Needs scikit-optimize # !pip install scikit-optimize # %% import os import numpy as np import pandas as pd # %% from sklearn.linear_model import Ridge from sklearn.model_selection import cross_val_score from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.conversions import SmilesToMolTransformer from sklearn.pipeline import make_pipeline # %% from skopt.space import Real, Integer, Categorical from skopt.utils import use_named_args from skopt import gp_minimize # %% full_set = False if full_set: csv_file = "SLC6A4_active_excape_export.csv" if not os.path.exists(csv_file): import urllib.request url = "https://ndownloader.figshare.com/files/25747817" urllib.request.urlretrieve(url, csv_file) else: csv_file = "../tests/data/SLC6A4_active_excapedb_subset.csv" data = pd.read_csv(csv_file) trf = SmilesToMolTransformer() data["ROMol"] = trf.transform(data.SMILES.values).flatten() # %% pipe = make_pipeline(MorganFingerprintTransformer(), Ridge()) pipe # %% print(pipe.get_params()) # %% max_bits = 4096 morgan_space = [ Categorical([True, False], name="morganfingerprinttransformer__useCounts"), Categorical([True, False], name="morganfingerprinttransformer__useFeatures"), Integer(512, max_bits, name="morganfingerprinttransformer__fpSize"), Integer(1, 3, name="morganfingerprinttransformer__radius"), ] regressor_space = [Real(1e-2, 1e3, "log-uniform", name="ridge__alpha")] search_space = morgan_space + regressor_space # %% @use_named_args(search_space) def objective(**params): for key, value in params.items(): print(f"{key}:{value} - {type(value)}") pipe.set_params(**params) return -np.mean( cross_val_score( pipe, data.ROMol, data.pXC50, cv=2, n_jobs=-1, scoring="neg_mean_absolute_error", ) ) # %% THIS takes forever on my machine with a GradientBoostingRegressor pipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0) "Best score=%.4f" % pipe_gp.fun # %% print("""Best parameters:""") print({param.name: value for param, value in zip(pipe_gp.space, pipe_gp.x)}) # %% from skopt.plots import plot_convergence plot_convergence(pipe_gp) # %% ================================================ FILE: docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Example: Using Multiple Different Fingerprint Transformer # # In this notebook we will explore how to evaluate the performance of machine learning models depending on different fingerprint transformers (Featurization techniques). This is an example, that you easily could adapt for many different combinations of featurizers, optimization and other modelling techniques. # # Following steps will happen: # * Data Parsing # * Pipeline Building # * Training Phase # * Analysis # # Authors: @VincentAlexanderScholz, @RiesBen # # ## Imports: # First we will import all the stuff that we will need for our work. # # %% import os import numpy as np import pandas as pd from time import time from matplotlib import pyplot as plt from rdkit.Chem import PandasTools from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline, make_pipeline from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from scikit_mol import fingerprints # %% [markdown] # ## Get Data: # In this step we will check if the SLC6A4 data set is already present or needs to be downloaded. # # # **WARNING:** The Dataset is a simple and very well selected # %% full_set = False # if not present download example data if full_set: csv_file = "SLC6A4_active_excape_export.csv" if not os.path.exists(csv_file): import urllib.request url = "https://ndownloader.figshare.com/files/25747817" urllib.request.urlretrieve(url, csv_file) else: csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv" # Parse Database data = pd.read_csv(csv_file) PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion") # %% [markdown] # ## Build Pipeline: # In this stage we will build the Pipeline consisting of the featurization part (fingerprint transformers) and the model part (Ridge Regression). # # Note that the featurization in this section is a hyperparameter, living in `param_grid`, and the `"fp_transformer"` string is just a placeholder, being replaced during pipeline execution. # # This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies. # %% regressor = Ridge() optimization_pipe = Pipeline( [ ( "fp_transformer", "fp_transformer", ), # this is a placeholder for different transformers ("regressor", regressor), ] ) param_grid = [ # Here pass different Options and Approaches { "fp_transformer": [ fingerprints.MorganFingerprintTransformer(), fingerprints.AvalonFingerprintTransformer(), ], "fp_transformer__fpSize": [2**x for x in range(8, 13)], }, { "fp_transformer": [ fingerprints.RDKitFingerprintTransformer(), fingerprints.AtomPairFingerprintTransformer(), fingerprints.MACCSKeysFingerprintTransformer(), ], }, ] global_options = { "regressor__alpha": np.linspace(0.1, 1, 5), } [params.update(global_options) for params in param_grid] param_grid # %% [markdown] # ## Train Model # In this section, the combinatorial approaches are trained. # %% # Split Data mol_list_train, mol_list_test, y_train, y_test = train_test_split( data.ROMol, data.pXC50, random_state=0 ) # Define Search Process grid = GridSearchCV(optimization_pipe, n_jobs=1, param_grid=param_grid) # Train t0 = time() grid.fit(mol_list_train, y_train.values) t1 = time() print(f"Runtime: {t1-t0:0.2F}") # %% [markdown] # ## Analysis # # Now let's investigate our results from the training stage. Which one is the best fingerprint method for this data set? Which parameters are optimal? # %% df_training_stats = pd.DataFrame(grid.cv_results_) df_training_stats # %% # Best Fingerprint Method / Performance res_dict = {} for i, row in df_training_stats.iterrows(): fp_name = row["param_fp_transformer"] if ( fp_name in res_dict and row["mean_test_score"] > res_dict[fp_name]["mean_test_score"] ): res_dict[fp_name] = row.to_dict() elif not fp_name in res_dict: res_dict[fp_name] = row.to_dict() df = pd.DataFrame(list(res_dict.values())) df = df.sort_values(by="mean_test_score") # plot test score vs. approach plt.figure(figsize=[14, 5]) plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score) plt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14) plt.ylabel("mean score", fontsize=14) plt.title("Best Model of Fingerprint Transformer Type", fontsize=18) pass # %% # Best Fingerprint Method / Performance from collections import defaultdict res_dict = defaultdict(list) for i, row in df_training_stats.iterrows(): fp_name = row["param_fp_transformer"] if "Morgan" in str(fp_name): res_dict[fp_name].append(row) for fp_type, rows in res_dict.items(): df = pd.DataFrame(rows) df = df.sort_values(by="mean_test_score") # plot test score vs. approach xlabels = map( lambda x: "_".join(x), zip( df.param_fp_transformer__fpSize.astype(str), df.param_regressor__alpha.astype(str), ), ) plt.figure(figsize=[14, 5]) plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score) plt.xticks(range(len(df)), xlabels, rotation=90, fontsize=14) plt.ylabel("mean score", fontsize=14) plt.xlabel("bitsize_alpha", fontsize=14) plt.title( "Fingerprint Transformer " + str(fp_type).split("(")[0] + " per Bitsize", fontsize=18, ) pass # %% # plot ALL test score vs. approach df = df_training_stats.sort_values(by="mean_test_score") plt.figure(figsize=[16, 9]) plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score) plt.ylabel("mean score", fontsize=14) plt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14) plt.title("test score vs. approach", fontsize=18) pass # %% [markdown] # This file have the following licence: # # Apache License # Version 2.0, January 2004 # http://www.apache.org/licenses/ # # TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION # # 1. 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We also recommend that a # file or class name and description of purpose be included on the # same "printed page" as the copyright notice for easier # identification within third-party archives. # # Copyright 2023 Esben Jannik Bjerrum # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ================================================ FILE: docs/notebooks/scripts/10_pipeline_pandas_output.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Preserving feature information in DataFrames # # This notebook highlights the ability of scikit-mol transformers to return data in DataFrames with meaningful column names. Some use-cases of this feature are illustrated. # # ***NOTE***: The goal of this notebook is to highlight the advantages of storing transformer output in DataFrames with meaningful column names. This notebook should *not* be considered a set of good practices for training and evaluating QSAR pipelines. The performance metrics of the resulting pipelines are pretty bad: the dataset they have been trained on is pretty small. Tuning the hyperparameters of the Random Forest regressor model (maximum depth of the trees, maximum features to consider when splitting...) can be beneficial. Also including dimensionality reduction / feature selection techniques can be beneficial, since pipelines use a high number of features for a small number of samples. Of course, to further reduce the risk of overfitting, the best hyperparameters and preprocessing techniques should be chosen in cross validation. # %% from pathlib import Path import pandas as pd from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from sklearn.preprocessing import FunctionTransformer from scikit_mol.conversions import SmilesToMolTransformer from sklearn.compose import make_column_selector, make_column_transformer from scikit_mol.standardizer import Standardizer from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.fingerprints import MorganFingerprintTransformer # %% csv_file = Path("../../tests/data/SLC6A4_active_excapedb_subset.csv") assert csv_file.is_file() data = pd.read_csv(csv_file) data.drop_duplicates(subset="Ambit_InchiKey", inplace=True) # %% [markdown] # Let's split the dataset in training and test, so we will be able to use the test set to evaluate the performance of models trained on the training set. # %% data_train, data_test = train_test_split(data, test_size=0.2, random_state=42) # %% column_smiles = "SMILES" column_target = "pXC50" smis_train = data_train.loc[:, column_smiles] target_train = data_train.loc[:, column_target] smis_test = data_test.loc[:, column_smiles] target_test = data_test.loc[:, column_target] # %% [markdown] # ## Descriptors pipeline that returns DataFrames # # Define a pipeline that: # # - converts SMILES strings to Mol objects # - standardizes the molecules # - computes molecular descriptors # # Then, we will configure the pipeline to return output in Pandas DataFrames. # The column names will correspond to the descriptor names. # %% descriptors_pipeline = make_pipeline( SmilesToMolTransformer(), Standardizer(), MolecularDescriptorTransformer(), ) descriptors_pipeline.set_output(transform="pandas") # %% df_descriptors = descriptors_pipeline.transform(smis_train) df_descriptors # %% [markdown] # All scikit-mol transformers are now compatible with the scikit-learn [set_output API](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html). # # Let's define a pipeline that returns Morgan fingerprints in a DataFrame. # Columns will be named with the pattern `fp_morgan_1`, `fp_morgan_2`, ...,`fp_morgan_N`. # %% fingerprints_pipeline = make_pipeline( SmilesToMolTransformer(), Standardizer(), MorganFingerprintTransformer(), ) fingerprints_pipeline.set_output(transform="pandas") # %% df_fingerprints = fingerprints_pipeline.transform(smis_train) df_fingerprints # %% [markdown] # ## Analyze feature importance of regression pipeline # # Making the transformation steps return Pandas DataFrames instead of NumPy arrays makes it easy to analyze the feature importance of regression models. # # Let's define a pipeline that, starting from SMILES strings, computes descriptors and uses them to predict the target with a Random Forest (RF) regression model. Since descriptors values have very different ranges, it's better to scale them before passing them to the RF regression model. # %% params_random_forest = { "max_depth": 5, # Setting a low maximum depth to avoid overfitting } regression_pipeline = make_pipeline( SmilesToMolTransformer(), Standardizer(), MolecularDescriptorTransformer(), StandardScaler(), # Scale the descriptors RandomForestRegressor(**params_random_forest), ) regression_pipeline.set_output(transform="pandas") # %% regression_pipeline.fit(smis_train, target_train) pred_test = regression_pipeline.predict(smis_test) # %% [markdown] # Let's define a simple function to compute regression metrics, and use it to evaluate the test set performance of the pipeline. # %% def compute_metrics(y_true, y_pred): result = { "RMSE": mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5, "MAE": mean_absolute_error(y_true=y_true, y_pred=y_pred), "R2": r2_score(y_true=y_true, y_pred=y_pred), } return result performance = compute_metrics(y_true=target_test, y_pred=pred_test) performance # %% regressor = regression_pipeline[-1] regressor # %% [markdown] # Since we used `set_output(transform="pandas")` on the pipeline, the last step of the pipeline (the regression model) has the descriptor names in the `feature_names_in_` attribute. We can use them and the `feature_importances_` attribute to easily analyze the feature importances. # %% df_importance = pd.DataFrame( { "feature": regressor.feature_names_in_, "importance": regressor.feature_importances_, } ) df_importance # %% [markdown] # Sort the features by most to least important: # %% df_importance.sort_values( by="importance", ascending=False, inplace=True, ignore_index=True ) df_importance # %% n_top_features = 5 top_features = df_importance.head(n_top_features).loc[:, "feature"].tolist() print(f"The {n_top_features} most important features are:") for feature in top_features: print(feature) # %% [markdown] # ## Including external features # # The ability to keep the results of scikit-learn transformers in DataFrames with meaningful column names simplifies the task of analyzing the resulting models. # # Another good use-case is when we want to combine cheminformatics features from some other tool (QM packages, Deep Learning embeddings...) with the traditional cheminformatics features available in scikit-mol. It will be easier to keep track of the features that come from scikit-mol and the features that come from other tools, if they are stored in DataFrames with meaningful column names. # # Let's include features from the popular [CDDD](https://github.com/jrwnter/cddd) tool. CDDD is a Variational AutoEncoder Deep Learning model, and the CDDD features are the inner latent space representations of the SMILES. For additional details, have a look at the original CDDD paper: # # > R. Winter, F. Montanari, F. Noé, and D.-A. Clevert, “Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations,” Chem. Sci., vol. 10, no. 6, pp. 1692–1701, Feb. 2019, [doi: 10.1039/C8SC04175J](https://doi.org/10.1039/C8SC04175J). # # We have precomputed these features and stored them in a file: # %% file_cddd_features = Path("../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz") assert file_cddd_features.is_file() df_cddd = pd.read_csv(file_cddd_features) df_cddd # %% [markdown] # The CDDD features are stored in columns `cddd_1`, `cddd_2`, ..., `cddd_512`. The file has the identifier column `Ambit_InchiKey` that we can use to combine the CDDD features with the rest of the data: # %% def combine_datasets(data, cddd): data_combined = pd.merge( left=data, right=cddd, on="Ambit_InchiKey", how="inner", validate="one_to_one", ) return data_combined data_combined_train = combine_datasets(data_train, df_cddd) data_combined_test = combine_datasets(data_test, df_cddd) # %% # The CDDD descriptors couldn't be computed for few molecules and can be removed as commented out below. The Datafile is now prefiltered # target_train = data_train.loc[data_train["Ambit_InchiKey"].isin(data_combined_train["Ambit_InchiKey"]), column_target] # target_test = data_test.loc[data_test["Ambit_InchiKey"].isin(data_combined_test["Ambit_InchiKey"]), column_target] target_train = data_combined_train.loc[:, column_target] target_test = data_combined_test.loc[:, column_target] # %% [markdown] # Now we can define a pipeline that uses the original SMILES column to compute the descriptors available in scikit-mol, then concatenates them with the pre-computed CDDD features, and uses all of them to train the regression model. We will need a slightly more complex pipeline with column selectors and transformers. For more details on this technique, please refer to the [official documentation](https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html). # # Since we will keep everything in DataFrames, it will be easier to understand the effect of the CDDD features and the traditional descriptors available in scikit-mol. # %% # A pipeline to compute scikit-mol descriptors descriptors_pipeline = make_pipeline( SmilesToMolTransformer(), Standardizer(), MolecularDescriptorTransformer(), ) # A pipeline that just passes the input data. # We will use it to preserve the CDDD features and pass them to downstream steps. identity_pipeline = make_pipeline( FunctionTransformer(), ) combined_transformer = make_column_transformer( (descriptors_pipeline, make_column_selector(pattern="SMILES")), (identity_pipeline, make_column_selector(pattern=r"^cddd_\d+$")), remainder="drop", ) combined_transformer # %% pipeline_combined = make_pipeline( combined_transformer, StandardScaler(), RandomForestRegressor(**params_random_forest), ) pipeline_combined.set_output(transform="pandas") # %% pipeline_combined.fit(data_combined_train, target_train) pred_combined_test = pipeline_combined.predict(data_combined_test) performance_combined = compute_metrics(y_true=target_test, y_pred=pred_combined_test) performance_combined # %% [markdown] # Let's combine the performance metrics obtained using only the scikit-mol descriptors as input features, and the performance metrics obtained using also the CDDD features: # %% df_performance = pd.DataFrame( [performance, performance_combined], index=["descriptors", "combined"] ) df_performance # %% [markdown] # All performance metrics were improved by the inclusion of the CDDD features. # Let's analyze the feature importances of the model: # %% regressor = pipeline_combined[-1] df_importance = pd.DataFrame( { "feature": regressor.feature_names_in_, "importance": regressor.feature_importances_, } ).sort_values(by="importance", ascending=False, ignore_index=True) df_importance # %% top_features = df_importance.head(n_top_features).loc[:, "feature"].tolist() print(f"The {n_top_features} most important features are:") for feature in top_features: print(feature) # %% [markdown] # As we can see, some CDDD features are among the most important features for the regression model. # # Note that since the pipeline is a combination of two pipelines, the column names were prefixed by `pipeline-1` (the scikit-mol descriptors pipeline) and `pipeline-2` (the pipeline that selects and preserves pre-computed CDDD features). ================================================ FILE: docs/notebooks/scripts/11_safe_inference.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Safe inference mode # # I think everyone which have worked with SMILES and RDKit sooner or later come across a SMILES that doesn't parse. It can happen if the SMILES was produced with a different toolkit that are less strict with e.g. valence rules, or maybe a characher was missing in the copying from the email. During curation of the dataset for training models, these SMILES need to be identfied and eventually fixed or removed. But what happens when we are finished with our modelling? What kind of molecules and SMILES will a user of the model send for the model in the future when it's in deployment. What kind of SMILES will a generative model create that we need to predict? We don't know and we won't know. So it's kind of crucial to be able to handle these situations. Scikit-Learn models usually simply explodes the entire batch that are being predicted. This is where safe_inference_mode was introduced in Scikit-Mol. With the introduction all transformers got a safe inference mode, where they handle invalid input. How they handle it depends a bit on the transformer, so we will go through the different usual steps and see how things have changed with the introduction of the safe inference mode. # # NOTE! In the following demonstration I switch on the safe inference mode individually for demonstration purposes. I would not recommend to do that while building and training models, instead I would switch it on _after_ training and evaluation (more on that later). Otherwise, there's a risk to train on the 2% of a dataset that didn't fail.... # # First some imports and test SMILES and molecules. # %% from rdkit import Chem from scikit_mol.conversions import SmilesToMolTransformer # We have some deprecation warnings, we are adressing them, but they just distract from this demonstration import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) smiles = ["C1=CC=C(C=C1)F", "C1=CC=C(C=C1)O", "C1=CC=C(C=C1)N", "C1=CC=C(C=C1)Cl"] smiles_with_invalid = smiles + ["N(C)(C)(C)C", "I'm not a SMILES"] smi2mol = SmilesToMolTransformer(safe_inference_mode=True) mols_with_invalid = smi2mol.transform(smiles_with_invalid) mols_with_invalid # %% [markdown] # Without the safe inference mode, the transformation would simply fail, but now we get the expected array back with our RDKit molecules and a last entry which is an object of the type InvalidMol. InvalidMol is simply a placeholder that tells what step failed the conversion and the error. InvalidMol evaluates to `False` in boolean contexts, so it gets easy to filter away and handle in `if`s and list comprehensions. As example: # %% [mol for mol in mols_with_invalid if mol] # %% [markdown] # or # %% mask = mols_with_invalid.astype(bool) mols_with_invalid[mask] # %% [markdown] # Having a failsafe SmilesToMol conversion leads us to next step, featurization. The transformers in safe inference mode now return a NumPy masked array instead of a regular NumPy array. It simply evaluates the incoming mols in a boolean context, so e.g. `None`, `np.nan` and other Python objects that evaluates to False will also get masked (i.e. if you use a dataframe with an ROMol column produced with the PandasTools utility) # %% from scikit_mol.fingerprints import MorganFingerprintTransformer mfp = MorganFingerprintTransformer(radius=2, fpSize=25, safe_inference_mode=True) fps = mfp.transform(mols_with_invalid) fps # %% [markdown] # However, currently scikit-learn models accepts masked arrays, but they do not respect the mask! So if you fed it directly to the model to train, it would seemingly work, but the invalid samples would all have the fill_value, meaning you could get weird results. Instead, we need the last part of the puzzle, the SafeInferenceWrapper class. # %% from scikit_mol.safeinference import SafeInferenceWrapper from sklearn.linear_model import LogisticRegression import numpy as np regressor = LogisticRegression() wrapper = SafeInferenceWrapper(regressor, safe_inference_mode=True) wrapper.fit(fps, [0, 1, 0, 1, 0, 1]) wrapper.predict(fps) # %% [markdown] # # %% [markdown] # The prediction went fine both in fit and in prediction, where the result shows `nan` for the invalid entries. However, please note fit in sage_inference_mode is not recommended in a training session, but you are warned and not blocked, because maybe you know what you do and do it on purpose. # The SafeInferenceMapper both handles rows that are masked in masked arrays, but also checks rows for nonfinite values and filters these away. Sometimes some descriptors may return an inf or nan, even though the molecule itself is valid. The masking of nonfinite values can be switched off, maybe you are using a model that can handle missing data and only want to filter away invalid molecules. # # ## Setting safe_inference_mode post-training # As I said before I believe in catching errors and fixing those during training, but what do we do when we need to switch on safe inference mode for all objects in a pipeline? There's of course a tool for that, so lets demo that: # %% from scikit_mol.safeinference import set_safe_inference_mode from sklearn.pipeline import Pipeline pipe = Pipeline( [ ("smi2mol", SmilesToMolTransformer()), ("mfp", MorganFingerprintTransformer(radius=2, fpSize=25)), ("safe_regressor", SafeInferenceWrapper(LogisticRegression())), ] ) pipe.fit(smiles, [1, 0, 1, 0]) print("Without safe inference mode:") try: pipe.predict(smiles_with_invalid) except Exception as e: print("Prediction failed with exception: ", e) print() set_safe_inference_mode(pipe, True) print("With safe inference mode:") print(pipe.predict(smiles_with_invalid)) # %% [markdown] # We see that the prediction fail without safe inference mode, and proceeds when it's conveniently set by the `set_safe_inference_mode` utility. The model is now ready for save and reuse in a more failsafe manner :-) # %% [markdown] # ## Combining safe_inference_mode with pandas output # One potential issue can happen when we combine the safe_inference_mode with Pandas output mode of the transformers. It will work, but depending on the batch something surprising can happen due to the way that Pandas converts masked Numpy arrays. Let me demonstrate the issue, first we predict a batch without any errors. # %% mfp.set_output(transform="pandas") mols = smi2mol.transform(smiles) fps = mfp.transform(mols) fps # %% [markdown] # Then lets see if we transform a batch with an invalid molecule: # %% fps = mfp.transform(mols_with_invalid) fps # %% [markdown] # The second output is no longer integers, but floats. As most sklearn models cast input arrays to float32 internally, this difference is likely benign, but that's not guaranteed! Thus, if you want to use pandas output for your production models, do check that the final outputs are the same for the valid rows, with and without a single invalid row. Alternatively the dtype for the output of the transformer can be switched to float for consistency if it's supported by the transformer. # %% mfp_float = MorganFingerprintTransformer( radius=2, fpSize=25, safe_inference_mode=True, dtype=np.float32 ) mfp_float.set_output(transform="pandas") fps = mfp_float.transform(mols) fps # %% [markdown] # I hope this new feature of Scikit-Mol will make it even easier to handle models, even when used in environments without SMILES or molecule validity guarantees. ================================================ FILE: docs/notebooks/scripts/12_custom_fingerprint_transformer.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: .venv # language: python # name: python3 # --- # %% [markdown] # # Creating custom fingerprint transformers # # If the default fingerprint transformers provided by the scikit-mol library are not enough for your needs, you can create your own custom fingerprint transformers. In this notebook, we will show you how to do this. # # Note that base classes are partially stable and may change in the future versions of the library. We will try to keep the changes minimal and provide a migration guide if necessary. This notebook is also will be updated accordingly. # %% [markdown] # ## Basics # # For now we recommend you to use the `BaseFpsTransformer` class as a base class for your custom fingerprint transformers. This class provides a simple interface to create fingerprint transformers that can be used with the scikit-mol library. # # To create your custom fingerprint transformer, you need to create a class that inherits from the `BaseFpsTransformer` class and implement the `_transform_mol` method. This method should take a single RDKit molecule object as input and return a fingerprint as a numpy array. # %% from scikit_mol.fingerprints.baseclasses import BaseFpsTransformer import numpy as np from rdkit import Chem class DummyFingerprintTransformer(BaseFpsTransformer): def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode=False): self.fpSize = fpSize super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name="dummy" ) def _transform_mol(self, mol): return mol.GetNumAtoms() * np.ones(self.fpSize) trans = DummyFingerprintTransformer(n_jobs=4) mols = [Chem.MolFromSmiles("CC")] * 100 trans.transform(mols) # %% [markdown] # ## Non-pickable objects # When working with some of the `rdkit` function and classes you will often discover that some of the are unpickable objects. This means that they cannot be serialized and deserialized using the `pickle` module. This is a problem when you want to use the parallelization (controlled by the `n_jobs` parameter). # # Any non-pickable object in the transformer attributes should be initialized in the `__init__` method of the transforme from the other *picklable* arguments. # # In the example below, we will create a custom fingerprint transformer that uses the Morgan fingerprint with radius **2** and **1024** bits. Used generator is unpickable, but it can be created during the initialization of the transformer from the picklable `maxPath` and `fpSize` arguments. # %% from rdkit.Chem import rdFingerprintGenerator class UnpickableFingerprintTransformer(BaseFpsTransformer): def __init__(self, fpSize=1024, n_jobs=1, safe_inference_mode=False, **kwargs): self.fpSize = fpSize super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs ) self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator( maxPath=2, fpSize=self.fpSize ) def _transform_mol(self, mol): return self.fp_gen.GetFingerprintAsNumPy(mol) trans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512) trans.transform(mols) # %% [markdown] # Non-pickable object should not be present among the `__init__` arguments of the transformer. Doing so will prevent them to be pickled to recreate a transformer instance in the worker processes. If you for some reason need to pass a non-pickable object to the transformer you can do so (**highly discouraged**, please [open the issue](https://github.com/EBjerrum/scikit-mol/issues), maybe we will be able to help you do it better) by using the transformer in the sequential mode (i.e. `n_jobs=1`). # %% class BadTransformer(BaseFpsTransformer): def __init__(self, generator, n_jobs=1): self.generator = generator super().__init__(n_jobs=n_jobs) def _transform_mol(self, mol): return self.generator.GetFingerprint(mol) fp_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=10) BadTransformer(fp_gen, n_jobs=1).transform(mols) print("n_jobs=1 is fine") try: BadTransformer(fp_gen, n_jobs=2).transform(mols) except Exception as e: print( "n_jobs=2 is not fine, because the generator passed as an argument is not picklable" ) print(f"Error msg: {e}") # %% [markdown] # ## Fingerprint name # # To use the fingerptint in the `pandas` output mode it needes to know the name of the fingerprint and the number of bits (features) in it to generate the columns names. The number of feature is derived from `fpSize` attribute # # And the name resolution works as follows (in order of priority): # - if the fingerprint has a name set during the initialization of the base class, it is used # - if the name of the class follows the pattern `XFingerprintTransformer`, the name (`fp_X`) is extracted from it # - as a last resort, the name is set to name of the class # %% class NamedTansformer1(UnpickableFingerprintTransformer): pass class NamedTansformer2(UnpickableFingerprintTransformer): def __init__(self): super().__init__(name="fp_fancy") class FancyFingerprintTransformer(UnpickableFingerprintTransformer): pass print(NamedTansformer1().get_feature_names_out()) print(NamedTansformer2().get_feature_names_out()) print(FancyFingerprintTransformer().get_feature_names_out()) ================================================ FILE: docs/notebooks/scripts/13_applicability_domain.py ================================================ # --- # jupyter: # jupytext: # formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.16.6 # kernelspec: # display_name: vscode # language: python # name: python3 # --- # %% [markdown] # # Applicability Domain Estimation # # This notebook demonstrates how to use scikit-mol's applicability domain estimators to assess whether new compounds are within the domain of applicability of a trained model. # # We'll explore two different approaches: # 1. Using Morgan binary fingerprints with a k-Nearest Neighbors based applicability domain # 2. Using count-based Morgan fingerprints with dimensionality reduction and a leverage-based applicability domain # # First, let's import the necessary libraries and load our dataset: # %% import os import numpy as np import pandas as pd from rdkit import Chem from rdkit.Chem import PandasTools import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain # %% [markdown] # ## Load and Prepare Data # %% # Load the dataset # Results are better with the full set, but it takes longer to run, so for the notebook documentation we standard use a subset. # The subset has been filtered to only include nicely predicted compounds, and is thus artificial. full_set = False if full_set: csv_file = "SLC6A4_active_excape_export.csv" if not os.path.exists(csv_file): import urllib.request url = "https://ndownloader.figshare.com/files/25747817" urllib.request.urlretrieve(url, csv_file) percentile = 95 else: csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv" percentile = 90 data = pd.read_csv(csv_file) # Add RDKit mol objects PandasTools.AddMoleculeColumnToFrame(data, smilesCol="SMILES") print(f"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion") # Split into train/val/test X = data.ROMol y = data.pXC50 X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split( X_temp, y_temp, test_size=0.25, random_state=42 ) # %% [markdown] # ## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints # # In this example, we'll use binary Morgan fingerprints and a k-NN based applicability domain with Tanimoto distance. # This is particularly suitable for binary fingerprints as the Tanimoto coefficient is a natural similarity measure for them. # %% # Create pipeline for binary fingerprints binary_fp_pipe = Pipeline( [ ("fp", MorganFingerprintTransformer(fpSize=2048, radius=2)), ("rf", RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)), ] ) # Train the model binary_fp_pipe.fit(X_train, y_train) # Get predictions and errors y_pred_test = binary_fp_pipe.predict(X_test) abs_errors = np.abs(y_test - y_pred_test) # Create and fit k-NN AD estimator knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric="tanimoto", percentile=percentile) knn_ad.fit(binary_fp_pipe.named_steps["fp"].transform(X_train)) # Fit threshold using validation set knn_ad.fit_threshold(binary_fp_pipe.named_steps["fp"].transform(X_val)) # Get AD scores for test set knn_scores = knn_ad.transform(binary_fp_pipe.named_steps["fp"].transform(X_test)) # %% [markdown] # Let's visualize the relationship between prediction errors and AD scores: # %% plt.figure(figsize=(10, 6)) plt.scatter(knn_scores, abs_errors, alpha=0.5) plt.axvline(x=knn_ad.threshold_, color="r", linestyle="--", label="AD Threshold") plt.xlabel("k-NN AD Score") plt.ylabel("Absolute Prediction Error") plt.title("Prediction Errors vs k-NN AD Scores") plt.legend() plt.show() # Calculate error statistics in_domain = knn_ad.predict(binary_fp_pipe.named_steps["fp"].transform(X_test)) errors_in = abs_errors[in_domain == 1] errors_out = abs_errors[in_domain == -1] print(f"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}") print(f"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}") print(f"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}") # %% [markdown] # ## Example 2: Leverage-based AD with Count-based Morgan Fingerprints # # In this example, we'll use count-based Morgan fingerprints, reduce their dimensionality with PCA, # and apply a leverage-based applicability domain estimator. # %% # Create pipeline for count-based fingerprints with PCA count_fp_pipe = Pipeline( [ ("fp", MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)), ("pca", PCA(n_components=0.9)), # Keep 90% of variance ("scaler", StandardScaler()), ("rf", RandomForestRegressor(n_estimators=100, random_state=42)), ] ) # Train the model count_fp_pipe.fit(X_train, y_train) # Get predictions and errors y_pred_test = count_fp_pipe.predict(X_test) abs_errors = np.abs(y_test - y_pred_test) # Create and fit leverage AD estimator leverage_ad = LeverageApplicabilityDomain(percentile=percentile) X_train_transformed = count_fp_pipe.named_steps["scaler"].transform( count_fp_pipe.named_steps["pca"].transform( count_fp_pipe.named_steps["fp"].transform(X_train) ) ) leverage_ad.fit(X_train_transformed) # Fit threshold using validation set X_val_transformed = count_fp_pipe.named_steps["scaler"].transform( count_fp_pipe.named_steps["pca"].transform( count_fp_pipe.named_steps["fp"].transform(X_val) ) ) leverage_ad.fit_threshold(X_val_transformed) # Get AD scores for test set X_test_transformed = count_fp_pipe.named_steps["scaler"].transform( count_fp_pipe.named_steps["pca"].transform( count_fp_pipe.named_steps["fp"].transform(X_test) ) ) leverage_scores = leverage_ad.transform(X_test_transformed) # %% [markdown] # Visualize the relationship between prediction errors and leverage scores: # %% plt.figure(figsize=(10, 6)) plt.scatter(leverage_scores, abs_errors, alpha=0.5) plt.axvline(x=leverage_ad.threshold_, color="r", linestyle="--", label="AD Threshold") plt.xlabel("Leverage AD Score") plt.ylabel("Absolute Prediction Error") plt.title("Prediction Errors vs Leverage Scores") plt.legend() plt.show() # Calculate error statistics in_domain = leverage_ad.predict(X_test_transformed) errors_in = abs_errors[in_domain == 1] errors_out = abs_errors[in_domain == -1] print(f"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}") print(f"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}") print(f"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}") # %% [markdown] # ## Testing Famous Drugs # # Let's test some well-known drugs to see if they fall within our model's applicability domain: # %% # Define famous drugs famous_drugs = { "Aspirin": "CC(=O)OC1=CC=CC=C1C(=O)O", "Viagra": "CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4", "Heroin": "CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5", } # Function to process a drug through both AD pipelines def check_drug_applicability(smiles, name): mol = Chem.MolFromSmiles(smiles) # k-NN AD fp_binary = binary_fp_pipe.named_steps["fp"].transform([mol]) knn_score = knn_ad.transform(fp_binary)[0][0] knn_status = "Inside" if knn_ad.predict(fp_binary)[0] == 1 else "Outside" # Leverage AD fp_count = count_fp_pipe.named_steps["fp"].transform([mol]) fp_pca = count_fp_pipe.named_steps["pca"].transform(fp_count) fp_scaled = count_fp_pipe.named_steps["scaler"].transform(fp_pca) leverage_score = leverage_ad.transform(fp_scaled)[0][0] leverage_status = "Inside" if leverage_ad.predict(fp_scaled)[0] == 1 else "Outside" return { "knn_score": knn_score, "knn_status": knn_status, "leverage_score": leverage_score, "leverage_status": leverage_status, } # Process each drug results = [] for name, smiles in famous_drugs.items(): result = check_drug_applicability(smiles, name) results.append( { "Drug": name, "k-NN Score": result["knn_score"], "k-NN Status": result["knn_status"], "Leverage Score": result["leverage_score"], "Leverage Status": result["leverage_status"], } ) # Display results pd.DataFrame(results).set_index("Drug") # %% [markdown] # Let's visualize where these drugs fall in our AD plots: # %% # Plot for k-NN AD plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) plt.scatter(knn_scores, abs_errors, alpha=0.2, label="Test compounds") plt.axvline(x=knn_ad.threshold_, color="r", linestyle="--", label="AD Threshold") for result in results: plt.axvline(x=result["k-NN Score"], color="g", alpha=0.5, label=f"{result['Drug']}") plt.xlabel("k-NN AD Score") plt.ylabel("Absolute Prediction Error") plt.title("k-NN AD Scores") plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left") # Plot for Leverage AD plt.subplot(1, 2, 2) plt.scatter(leverage_scores, abs_errors, alpha=0.2, label="Test compounds") plt.axvline(x=leverage_ad.threshold_, color="r", linestyle="--", label="AD Threshold") for result in results: plt.axvline( x=result["Leverage Score"], color="g", alpha=0.5, label=f"{result['Drug']}" ) plt.xlabel("Leverage AD Score") plt.ylabel("Absolute Prediction Error") plt.title("Leverage AD Scores") plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left") plt.tight_layout() plt.show() # %% [markdown] # ## Conclusions # # This notebook demonstrated two different approaches to applicability domain estimation: # # 1. The k-NN based approach with binary fingerprints and Tanimoto distance provides a chemical similarity-based assessment # of whether new compounds are similar enough to the training set. # # 2. The leverage-based approach with count-based fingerprints and dimensionality reduction focuses on the statistical # novelty of compounds in the reduced feature space. # # The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given # that our training set is focused on SLC6A4 actives, while these drugs have different primary targets. # # The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors (when using the full set), # validating the usefulness of these approaches for identifying potentially unreliable predictions. ================================================ FILE: docs/notebooks/sync_notebooks.sh ================================================ jupytext --sync [0-9]*.py ================================================ FILE: docs/overrides/main.html ================================================ {% extends "base.html" %} {% block outdated %} You're not viewing the latest version. Click here to go to latest. {% endblock %} {% block site_meta %} {{ super() }} {% endblock %} ================================================ FILE: mkdocs.yml ================================================ site_name: "scikit-mol" site_description: "scikit-learn classes for molecular vectorization using RDKit" repo_url: "https://github.com/EBjerrum/scikit-mol" repo_name: "EBjerrum/scikit-mol" copyright: Copyright 2022 - 2025 use_directory_urls: true docs_dir: "docs" theme: name: material features: - navigation.tabs - navigation.expand extra_javascript: - assets/js/readthedocs.js extra_css: - assets/css/tweak-width.css watch: - "scikit_mol" plugins: - search - autorefs - mkdocstrings: handlers: python: options: docstring_style: numpy inventories: - https://scikit-learn.org/stable/objects.inv - https://docs.python.org/objects.inv - https://www.rdkit.org/docs/objects.inv - mkdocs-jupyter: execute: false include: ["*.ipynb"] nav: - Overview: index.md - API: - scikit-mol.applicability: api/scikit_mol.applicability.md - scikit-mol.core: api/scikit_mol.core.md - scikit-mol.conversion: api/scikit_mol.conversions.md - scikit-mol.descriptors: api/scikit_mol.descriptors.md - scikit-mol.fingerprints: api/scikit_mol.fingerprints.md - scikit_mol.fingerprints.baseclasses: api/fingerprints.base.md - scikit-mol.parallel: api/scikit_mol.parallel.md - scikit-mol.plotting: api/scikit_mol.plotting.md - scikit-mol.safeinference: api/scikit_mol.safeinference.md - scikit-mol.standardizer: api/scikit_mol.standardizer.md - Notebooks: - Basic Usage and fingerprint transformers: notebooks/01_basic_usage.ipynb - Descriptor transformer: notebooks/02_descriptor_transformer.ipynb - Pipelining with Scikit-Learn classes: notebooks/03_example_pipeline.ipynb - Molecular standardization: notebooks/04_standardizer.ipynb - Sanitizing SMILES input: notebooks/05_smiles_sanitization.ipynb - Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer: notebooks/06_hyperparameter_tuning.ipynb - Using parallel execution to speed up descriptor and fingerprint calculations: notebooks/07_parallel_transforms.ipynb - Using skopt for hyperparameter tuning: notebooks/08_external_library_skopt.ipynb - Testing different fingerprints as part of the hyperparameter optimization: notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb - Using pandas output for easy feature importance analysis and combine pre-existing values with new computations: notebooks/10_pipeline_pandas_output.ipynb - Working with pipelines and estimators in safe inference mode: notebooks/11_safe_inference.ipynb - Creating custom fingerptint transformers: notebooks/12_custom_fingerprint_transformer.ipynb - Estimating applicability domain using feature based estimators: notebooks/13_applicability_domain.ipynb - Contributing: contributing.md ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [project] name = "scikit-mol" dynamic = ["version"] description = "scikit-learn classes for molecule transformation" readme = "README.md" license = "LGPL-3.0" requires-python = ">=3.9,<3.14" authors = [ { name = "Esben Jannik Bjerrum", email = "esben@cheminformania.com" }, ] classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Topic :: Scientific/Engineering", "Topic :: Utilities", ] dependencies = [ "numpy", "packaging", "pandas", "rdkit", "scikit-learn", ] [project.urls] Download = "https://github.com/EBjerrum/scikit-mol" Homepage = "https://github.com/EBjerrum/scikit-mol" [tool.hatch] build.hooks.vcs.version-file = "scikit_mol/_version.py" version.source = "vcs" # Get the version from git, eg: 0.0.6.dev0+g1234567 # Drop the local version part (eg: +g1234567) or pypi will reject package version.raw-options.local_scheme = "no-local-version" # A manually triggered GitHub release workflow may generate a new tag # with .devN suffix. 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"tests/**" = ["B", "E4", "E7", "E9", "F", "S", "PTH", "RUF"] [format] # Like Black, use double quotes for strings. quote-style = "double" # Like Black, indent with spaces, rather than tabs. indent-style = "space" # Like Black, respect magic trailing commas. skip-magic-trailing-comma = false # Like Black, automatically detect the appropriate line ending. line-ending = "auto" # Enable auto-formatting of code examples in docstrings. Markdown, # reStructuredText code/literal blocks and doctests are all supported. # # This is currently disabled by default, but it is planned for this # to be opt-out in the future. docstring-code-format = false # Set the line length limit used when formatting code snippets in # docstrings. # # This only has an effect when the `docstring-code-format` setting is # enabled. docstring-code-line-length = "dynamic" ================================================ FILE: scikit_mol/__init__.py ================================================ try: from scikit_mol._version import __version__ except ImportError: # pragma: no cover __version__ = "not-installed" ================================================ FILE: scikit_mol/_constants.py ================================================ DOCS_VERSION = "latest" DOCS_BASE_URL = f"https://scikit-mol.readthedocs.org/en/{DOCS_VERSION}/api/" ================================================ FILE: scikit_mol/applicability/LICENSE.MIT ================================================ Copyright (c) 2023 Olivier J. M. Béquignon Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: scikit_mol/applicability/README.md ================================================ # Applicability Domain Estimators This module contains applicability domain estimators for chemical modeling. ## License Information Files in this module are licensed under LGPL as part of scikit-mol, with some files containing code adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD). - Files containing the following header are adapted from MLChemAD (originally MIT licensed): ```python """ This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ ``` - All other files are original implementations under scikit-mol's GPL/LGPL license. The original MLChemAD MIT license is preserved in LICENSE.MIT for reference. ================================================ FILE: scikit_mol/applicability/__init__.py ================================================ from .base import BaseApplicabilityDomain from .bounding_box import BoundingBoxApplicabilityDomain from .convex_hull import ConvexHullApplicabilityDomain from .hotelling import HotellingT2ApplicabilityDomain from .isolation_forest import IsolationForestApplicabilityDomain from .kernel_density import KernelDensityApplicabilityDomain from .knn import KNNApplicabilityDomain from .leverage import LeverageApplicabilityDomain from .local_outlier import LocalOutlierFactorApplicabilityDomain from .mahalanobis import MahalanobisApplicabilityDomain from .standardization import StandardizationApplicabilityDomain from .topkat import TopkatApplicabilityDomain __all__ = [ "BaseApplicabilityDomain", "BoundingBoxApplicabilityDomain", "ConvexHullApplicabilityDomain", "HotellingT2ApplicabilityDomain", "IsolationForestApplicabilityDomain", "KNNApplicabilityDomain", "KernelDensityApplicabilityDomain", "LeverageApplicabilityDomain", "LocalOutlierFactorApplicabilityDomain", "MahalanobisApplicabilityDomain", "StandardizationApplicabilityDomain", "TopkatApplicabilityDomain", ] ================================================ FILE: scikit_mol/applicability/base.py ================================================ """Base class for applicability domain estimators.""" from abc import ABC, abstractmethod from typing import Any, ClassVar, Optional, Union import numpy as np import pandas as pd from numpy.typing import ArrayLike, NDArray from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils import check_array from sklearn.utils._set_output import _SetOutputMixin, _wrap_method_output from sklearn.utils.validation import check_is_fitted class _ADOutputMixin(_SetOutputMixin): """Extends sklearn's _SetOutputMixin to handle predict and score_transform methods.""" def __init_subclass__(cls, **kwargs): # First handle transform/fit_transform via parent super().__init_subclass__(auto_wrap_output_keys=("transform",), **kwargs) # Add our additional methods for method in ["predict", "score_transform"]: if method not in cls.__dict__: continue wrapped_method = _wrap_method_output(getattr(cls, method), "transform") setattr(cls, method, wrapped_method) def _safe_flatten(X: Union[ArrayLike, pd.DataFrame]) -> NDArray[np.float64]: """Safely flatten numpy arrays or pandas DataFrames to 1D array. Parameters ---------- X : array-like or DataFrame of shape (n_samples, n_features) Input data to flatten Returns ------- flattened : ndarray of shape (n_samples,) Flattened 1D array """ if hasattr(X, "to_numpy"): # pandas DataFrame return X.to_numpy().ravel() return np.asarray(X).ravel() class BaseApplicabilityDomain(BaseEstimator, TransformerMixin, _ADOutputMixin, ABC): """Base class for applicability domain estimators. Parameters ---------- percentile : float or None, default=None Percentile of samples to consider within domain (0-100). If None: - For methods with statistical thresholds: use statistical method - For percentile-only methods: use 99.0 (include 99% of training samples) feature_name : str, default="AD_estimator" Name for the output feature column. Notes ----- Subclasses must define `_scoring_convention` as either: - 'high_outside': Higher scores indicate samples outside domain (e.g., distances) - 'high_inside': Higher scores indicate samples inside domain (e.g., likelihoods) The raw scores from `.transform()` should maintain their natural interpretation, while `.predict()` will handle the conversion to ensure consistent output (1 = inside domain, -1 = outside domain). Attributes ---------- n_features_in_ : int Number of features seen during fit. threshold_ : float Current threshold for domain membership. """ _supports_threshold_fitting: ClassVar[bool] = True _scoring_convention: ClassVar[str] # Must be set by subclasses def __init__( self, percentile: Optional[float] = None, feature_name: str = "AD_estimator" ) -> None: if not hasattr(self, "_scoring_convention"): raise TypeError( f"Class {self.__class__.__name__} must define _scoring_convention " "as either 'high_outside' or 'high_inside'" ) if self._scoring_convention not in ["high_outside", "high_inside"]: raise ValueError( f"Invalid _scoring_convention '{self._scoring_convention}'. " "Must be either 'high_outside' or 'high_inside'" ) if percentile is not None and not 0 <= percentile <= 100: raise ValueError("percentile must be between 0 and 100") self.percentile = percentile self.feature_name = feature_name self._check_params = { "estimator": self, "accept_sparse": False, "dtype": None, "ensure_all_finite": True, "ensure_2d": True, } @abstractmethod def fit(self, X: ArrayLike, y: Optional[Any] = None) -> "BaseApplicabilityDomain": """Fit the applicability domain estimator. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : BaseApplicabilityDomain Returns the instance itself. """ raise NotImplementedError("Subclasses should implement fit") def fit_threshold( self, X: Union[ArrayLike, pd.DataFrame], target_percentile: Optional[float] = None, ) -> "BaseApplicabilityDomain": """Update threshold estimation using new data.""" check_is_fitted(self) X = check_array(X, **self._check_params) if target_percentile is not None: if not 0 <= target_percentile <= 100: raise ValueError("target_percentile must be between 0 and 100") self.percentile = target_percentile # Use statistical threshold if available and percentile is None if self.percentile is None and hasattr(self, "_set_statistical_threshold"): self._set_statistical_threshold(X) return self # Otherwise use percentile-based threshold scores = _safe_flatten(self.transform(X)) if self.percentile is None: # Default percentile for methods without statistical thresholds if self._scoring_convention == "high_outside": self.threshold_ = np.percentile(scores, 99.0) else: # high_inside self.threshold_ = np.percentile(scores, 1.0) else: if self._scoring_convention == "high_outside": self.threshold_ = np.percentile(scores, self.percentile) else: # high_inside self.threshold_ = np.percentile(scores, 100 - self.percentile) return self def transform( self, X: Union[ArrayLike, pd.DataFrame], y: Optional[Any] = None ) -> Union[NDArray[np.float64], pd.DataFrame]: """Calculate applicability domain scores. Parameters ---------- X : array-like or pandas DataFrame The data to transform. Returns ------- scores : ndarray or pandas DataFrame Method-specific scores. Interpretation depends on `_scoring_convention`: - 'high_outside': Higher scores indicate samples further from training data - 'high_inside': Higher scores indicate samples closer to training data Shape (n_samples, 1). """ check_is_fitted(self) X = check_array(X, **self._check_params) # Calculate scores scores = self._transform(X) return scores @abstractmethod def _transform(self, X: NDArray) -> NDArray[np.float64]: """Implementation of the transform method. Parameters ---------- X : ndarray of shape (n_samples, n_features) Validated input data. Returns ------- scores : ndarray of shape (n_samples, 1) Method-specific scores. """ raise NotImplementedError("Subclasses should implement _transform") def predict( self, X: Union[ArrayLike, pd.DataFrame] ) -> Union[NDArray[np.int_], pd.DataFrame]: """Predict whether samples are within the applicability domain. Returns ------- predictions : ndarray of shape (n_samples,) Returns 1 for inside and -1 for outside. """ check_is_fitted(self) X = check_array(X, **self._check_params) # Calculate predictions scores = _safe_flatten(self.transform(X)) if self._scoring_convention == "high_outside": predictions = np.where(scores <= self.threshold_, 1, -1) else: # high_inside predictions = np.where(scores >= self.threshold_, 1, -1) return predictions.ravel() def score_transform( self, X: Union[ArrayLike, pd.DataFrame] ) -> Union[NDArray[np.float64], pd.DataFrame]: """Transform raw scores to [0,1] range using sigmoid. Parameters ---------- X : array-like or DataFrame of shape (n_samples, n_features) The samples to transform. Returns ------- scores : ndarray or DataFrame of shape (n_samples, 1) Transformed scores in [0,1] range. Higher values indicate samples more likely to be within domain, regardless of the method's raw score convention. """ check_is_fitted(self) scores = _safe_flatten(self.transform(X)) # TODO: the sharpness ought to somehow be fitted to the range of the raw_scores if self._scoring_convention == "high_outside": # Flip sign for sigmoid so higher output = more likely inside return (1 / (1 + np.exp(scores - self.threshold_))).reshape(-1, 1) else: # high_inside # No sign flip needed return (1 / (1 + np.exp(self.threshold_ - scores))).reshape(-1, 1) def get_feature_names_out(self, input_features=None) -> NDArray[np.str_]: """Get feature name for output column.""" # TODO: what is the mechanism around input_features? return np.array([f"{self.feature_name}"]) ================================================ FILE: scikit_mol/applicability/bounding_box.py ================================================ """ Bounding box applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional, Tuple, Union import numpy as np from numpy.typing import ArrayLike, NDArray from .base import BaseApplicabilityDomain class BoundingBoxApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain defined by feature value ranges. Samples falling outside the allowed range for any feature are considered outside the domain. The range for each feature is defined by percentiles of the training set distribution. Parameters ---------- percentile : float or tuple of float, default=(0.1, 99.9) Percentile(s) of the training set distribution used to define the bounding box. If float, uses (percentile, 100-percentile). feature_name : str, default="BoundingBox" Prefix for feature names in output. Attributes ---------- n_features_in_ : int Number of features seen during fit. min_ : ndarray of shape (n_features,) Minimum allowed value for each feature. max_ : ndarray of shape (n_features,) Maximum allowed value for each feature. threshold_ : float Current threshold for domain membership (always 0.5). Notes ----- The bounding box method is simple but effective, especially for chemical descriptors with clear physical interpretations. For high-dimensional or correlated features, other methods may be more appropriate. Examples -------- >>> from sklearn.pipeline import make_pipeline >>> from sklearn.preprocessing import StandardScaler >>> from scikit_mol.applicability import BoundingBoxApplicabilityDomain >>> >>> # Basic usage >>> ad = BoundingBoxApplicabilityDomain(percentile=1) >>> ad.fit(X_train) >>> predictions = ad.predict(X_test) >>> >>> # With preprocessing >>> pipe = make_pipeline( ... StandardScaler(), ... BoundingBoxApplicabilityDomain(percentile=1) ... ) >>> pipe.fit(X_train) >>> predictions = pipe.predict(X_test) """ _scoring_convention = "high_outside" _supports_threshold_fitting = False def __init__( self, percentile: Union[float, Tuple[float, float]] = (0.1, 99.9), feature_name: str = "BoundingBox", ) -> None: super().__init__(percentile=None, feature_name=feature_name) if isinstance(percentile, (int, float)): if not 0 <= percentile <= 100: raise ValueError("percentile must be between 0 and 100") self.box_percentile = (percentile, 100 - percentile) else: if not all(0 <= p <= 100 for p in percentile): raise ValueError("percentiles must be between 0 and 100") if len(percentile) != 2: raise ValueError("percentile must be a float or tuple of 2 floats") if percentile[0] >= percentile[1]: raise ValueError("first percentile must be less than second") self.box_percentile = percentile def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "BoundingBoxApplicabilityDomain": """Fit the bounding box applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : BoundingBoxApplicabilityDomain Returns the instance itself. """ X = self._validate_data(X) self.n_features_in_ = X.shape[1] # Calculate bounds self.min_ = np.percentile(X, self.box_percentile[0], axis=0) self.max_ = np.percentile(X, self.box_percentile[1], axis=0) # Fixed threshold since we count violations self.threshold_ = 0.5 return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate the number of features outside their bounds. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- violations : ndarray of shape (n_samples, 1) Number of features outside their bounds for each sample. Zero indicates all features within bounds. """ violations = np.sum((X < self.min_) | (X > self.max_), axis=1) return violations.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/convex_hull.py ================================================ """ Convex hull applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from scipy import optimize from .base import BaseApplicabilityDomain class ConvexHullApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain defined as the convex hull of the training data. The convex hull approach determines if a point belongs to the convex hull of the training set by checking if it can be represented as a convex combination of training points. Parameters ---------- percentile : float or None, default=None Not used, present for API consistency. feature_name : str, default="ConvexHull" Prefix for feature names in output. Notes ----- The method is based on the `highs` solver from `scipy.optimize`. Note that this method can be computationally expensive for high-dimensional data or large training sets, as it requires solving a linear programming problem for each test point. For high-dimensional data (e.g., fingerprints), consider using dimensionality reduction before applying this method. Attributes ---------- n_features_in_ : int Number of features seen during fit. points_ : ndarray of shape (n_features + 1, n_samples) Transformed training points used for convex hull calculations. threshold_ : float Fixed at 0.5 since output is binary (inside/outside hull). """ _scoring_convention = "high_outside" _supports_threshold_fitting = False def __init__( self, percentile: Optional[float] = None, feature_name: str = "ConvexHull" ) -> None: super().__init__(percentile=None, feature_name=feature_name) self.threshold_ = 0.5 # Fixed threshold since output is binary def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "ConvexHullApplicabilityDomain": """Fit the convex hull applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : ConvexHullApplicabilityDomain Returns the instance itself. """ X = self._validate_data(X) self.n_features_in_ = X.shape[1] # Add ones column and transpose for convex hull calculations self.points_ = np.r_[X.T, np.ones((1, X.shape[0]))].astype(np.float32) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate distance from convex hull for each sample. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- distances : ndarray of shape (n_samples, 1) Distance from convex hull. Zero for points inside the hull, positive for points outside. """ distances = [] for sample in X: # Append 1 to sample vector sample_ext = np.r_[sample, 1].astype(np.float32) # Try to solve the linear programming problem result = optimize.linprog( np.ones(self.points_.shape[1], dtype=np.float32), A_eq=self.points_, b_eq=sample_ext, method="highs", ) # Distance is positive if no solution found, 0 if solution exists distances.append(0.0 if result.success else 1.0) return np.array(distances).reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/hotelling.py ================================================ """ Hotelling T² applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from scipy.stats import f as f_dist from sklearn.utils import check_array from .base import BaseApplicabilityDomain class HotellingT2ApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on Hotelling's T² statistic. Uses Hotelling's T² statistic to define an elliptical confidence region around the training data. The threshold can be set using either the F-distribution (statistical approach) or adjusted using a validation set. Parameters ---------- significance : float, default=0.05 Significance level for F-distribution threshold. percentile : float or None, default=None If not None, overrides significance-based threshold. Must be between 0 and 100. feature_name : str, default="HotellingT2" Prefix for feature names in output. Notes ----- Lower volume protrusion scores indicate samples closer to the training data center. By default, the threshold is set using the F-distribution with a significance level of 0.05 (95% confidence). Attributes ---------- n_features_in_ : int Number of features seen during fit. t2_ : ndarray of shape (n_features,) Hotelling T² ellipse parameters. threshold_ : float Current threshold for volume protrusions. References ---------- .. [1] Hotelling, H. (1931). The generalization of Student's ratio. The Annals of Mathematical Statistics, 2(3), 360-378. """ _scoring_convention = "high_outside" _supports_threshold_fitting = True def __init__( self, significance: float = 0.05, percentile: Optional[float] = None, feature_name: str = "HotellingT2", ) -> None: if not 0 < significance < 1: raise ValueError("significance must be between 0 and 1") super().__init__(percentile=percentile, feature_name=feature_name) self.significance = significance def _set_statistical_threshold(self, X: NDArray) -> None: """Set threshold using F-distribution.""" n_samples = X.shape[0] f_stat = ( (n_samples - 1) / n_samples * self.n_features_in_ * (n_samples**2 - 1) / (n_samples * (n_samples - self.n_features_in_)) ) f_stat *= f_dist.ppf( 1 - self.significance, self.n_features_in_, n_samples - self.n_features_in_ ) self.threshold_ = f_stat def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "HotellingT2ApplicabilityDomain": """Fit the Hotelling T² applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : HotellingT2ApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] # Determine the Hotelling T² ellipse self.t2_ = np.sqrt((1 / X.shape[0]) * (X**2).sum(axis=0)) # Set initial threshold if self.percentile is not None: self.fit_threshold(X) else: self._set_statistical_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate volume protrusion scores for samples. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- scores : ndarray of shape (n_samples, 1) The volume protrusion scores. Higher values indicate samples further from the training data center. """ protrusions = (X**2 / self.t2_**2).sum(axis=1) return protrusions.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/isolation_forest.py ================================================ """ Isolation Forest applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from sklearn.ensemble import IsolationForest from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class IsolationForestApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on Isolation Forest. Uses Isolation Forest to identify outliers based on the isolation depth of samples in random decision trees. Parameters ---------- n_estimators : int, default=100 Number of trees in the forest. contamination : float, default=0.01 Expected proportion of outliers in the training data. random_state : Optional[int], default=None Controls the randomness of the forest. percentile : float or None, default=None Percentile of training set scores to use as threshold (0-100). If None, uses contamination-based threshold from IsolationForest. feature_name : str, default="IsolationForest" Name for feature names in output. Attributes ---------- n_features_in_ : int Number of features seen during fit. iforest_ : IsolationForest Fitted isolation forest model. threshold_ : float Current threshold for domain membership. Notes ----- The scoring convention is 'high_inside' because higher scores from IsolationForest indicate samples more similar to the training data. References ---------- .. [1] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422). """ _scoring_convention = "high_inside" _supports_threshold_fitting = True def __init__( self, n_estimators: int = 100, contamination: float = 0.01, random_state: Optional[int] = None, percentile: Optional[float] = None, feature_name: str = "IsolationForest", ) -> None: if not 0 < contamination < 1: raise ValueError("contamination must be between 0 and 1") super().__init__(percentile=percentile, feature_name=feature_name) self.n_estimators = n_estimators self.contamination = contamination self.random_state = random_state def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "IsolationForestApplicabilityDomain": """Fit the isolation forest applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : IsolationForestApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] self.iforest_ = IsolationForest( n_estimators=self.n_estimators, contamination=self.contamination, random_state=self.random_state, ) self.iforest_.fit(X) # Set initial threshold if self.percentile is not None: self.fit_threshold(X) else: # Use IsolationForest's default threshold self.threshold_ = self.iforest_.offset_ return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate anomaly scores for samples. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- scores : ndarray of shape (n_samples, 1) The anomaly scores of the samples. Higher scores indicate samples more similar to training data. """ scores = self.iforest_.score_samples(X) return scores.reshape(-1, 1) # def fit_threshold(self, X, target_percentile=95): # """Update the threshold using new data without refitting the model. # Parameters # ---------- # X : array-like of shape (n_samples, n_features) # Data to compute threshold from. # target_percentile : float, default=95 # Target percentile of samples to include within domain. # Returns # ------- # self : object # Returns the instance itself. # """ # check_is_fitted(self) # X = check_array(X) # if not 0 <= target_percentile <= 100: # raise ValueError("target_percentile must be between 0 and 100") # # Get decision function scores # scores = self.iforest_.score_samples(X) # # Set threshold to achieve desired percentile # self.threshold_ = np.percentile(scores, 100 - target_percentile) # return self # def predict(self, X): # """Predict whether samples are within the applicability domain. # Parameters # ---------- # X : array-like of shape (n_samples, n_features) # The samples to predict. # Returns # ------- # y_pred : ndarray of shape (n_samples,) # Returns 1 for samples inside the domain and -1 for samples outside # (following scikit-learn's convention for outlier detection). # """ # scores = self._transform(X).ravel() # if hasattr(self, "threshold_"): # return np.where(scores > self.threshold_, 1, -1) # return self.iforest_.predict(X) ================================================ FILE: scikit_mol/applicability/kernel_density.py ================================================ """ Kernel Density applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)Chem Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from sklearn.neighbors import KernelDensity from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class KernelDensityApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on kernel density estimation. Uses kernel density estimation to model the distribution of the training data. Samples with density below a threshold (determined by percentile of training data densities) are considered outside the domain. Parameters ---------- bandwidth : float, default=1.0 The bandwidth of the kernel. kernel : str, default='gaussian' The kernel to use. Options: ['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine']. percentile : float or None, default=None The percentile of training set densities to use as threshold (0-100). If None, uses 99.0 (exclude bottom 1% of training samples). feature_name : str, default="KernelDensity" Name for the output feature column. Attributes ---------- n_features_in_ : int Number of features seen during fit. kde_ : KernelDensity Fitted kernel density estimator. threshold_ : float Density threshold for domain membership. Notes ----- The scoring convention is 'high_inside' because higher density scores indicate samples more similar to the training data. Examples -------- >>> from scikit_mol.applicability import KernelDensityApplicabilityDomain >>> ad = KernelDensityApplicabilityDomain(bandwidth=1.0) >>> ad.fit(X_train) >>> predictions = ad.predict(X_test) """ _scoring_convention = "high_inside" def __init__( self, bandwidth: float = 1.0, kernel: str = "gaussian", percentile: Optional[float] = None, feature_name: str = "KernelDensity", ) -> None: super().__init__(percentile=percentile or 99.0, feature_name=feature_name) self.bandwidth = bandwidth self.kernel = kernel def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "KernelDensityApplicabilityDomain": """Fit the kernel density applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : KernelDensityApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] # Fit KDE self.kde_ = KernelDensity(bandwidth=self.bandwidth, kernel=self.kernel) self.kde_.fit(X) # Set initial threshold based on training data self.fit_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate log density scores for samples. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- scores : ndarray of shape (n_samples, 1) The log density scores of the samples. Higher scores indicate samples more similar to the training data. """ scores = self.kde_.score_samples(X) return scores.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/knn.py ================================================ """ K-Nearest Neighbors applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Callable, ClassVar, Literal, Optional, Union import numpy as np from numpy.typing import ArrayLike from sklearn.neighbors import NearestNeighbors from sklearn.utils import check_array from .base import BaseApplicabilityDomain class KNNApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain defined using K-nearest neighbors. Determines domain membership based on the mean distance to k nearest neighbors in the training set. Higher distances indicate samples further from the training distribution. Parameters ---------- n_neighbors : int, default=5 Number of neighbors to use for distance calculation. percentile : float or None, default=None Percentile of training set distances to use as threshold (0-100). If None, uses 99.0 (include 99% of training samples). distance_metric : str or callable, default='euclidean' Distance metric to use. As examples: - 'euclidean': Euclidean distance (default) - 'manhattan': Manhattan distance - 'cosine': Cosine distance - 'tanimoto': Tanimoto distance for binary fingerprints (same as 'jaccard') - 'jaccard': Jaccard distance for binary fingerprints - callable: Custom distance metric function(X, Y) -> array-like Any distance metric supported by sklearn.neighbors.NearestNeighbors can also be used. Note: Only distance metrics are supported (higher values = more distant) currently. n_jobs : int, default=None Number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. feature_name : str, default='KNN' Prefix for feature names in output. Notes ----- For binary fingerprints, the Tanimoto distance is equivalent to the Jaccard distance. Both 'tanimoto' and 'jaccard' options use scipy's implementation of the Jaccard distance metric. Attributes ---------- n_features_in_ : int Number of features seen during fit. threshold_ : float Distance threshold for domain membership. nn_ : NearestNeighbors Fitted nearest neighbors model. Examples -------- >>> import numpy as np >>> from scikit_mol.applicability import KNNApplicabilityDomain >>> >>> # Generate example data >>> rng = np.random.RandomState(0) >>> X_train = rng.normal(0, 1, (100, 5)) >>> X_test = rng.normal(0, 2, (20, 5)) # More spread out than training >>> >>> # Fit AD model >>> ad = KNNApplicabilityDomain(n_neighbors=5, percentile=95) >>> ad.fit(X_train) >>> >>> # Get raw distance scores (higher = more distant) >>> distances = ad.transform(X_test) >>> >>> # Get domain membership predictions >>> predictions = ad.predict(X_test) # 1 = inside, -1 = outside >>> >>> # Get probability-like scores >>> scores = ad.score_transform(X_test) # Higher = more likely inside """ _scoring_convention: ClassVar[str] = ( "high_outside" # Higher distance = outside domain ) def __init__( self, n_neighbors: int = 5, percentile: Optional[float] = None, distance_metric: Union[ Literal["euclidean", "manhattan", "cosine", "tanimoto", "jaccard"], Callable ] = "euclidean", n_jobs: Optional[int] = None, feature_name: str = "KNN", ) -> None: super().__init__(percentile=percentile, feature_name=feature_name) self.n_neighbors = n_neighbors self.distance_metric = distance_metric self.n_jobs = n_jobs @property def distance_metric(self) -> Union[Callable, str]: return self._distance_metric @distance_metric.setter def distance_metric(self, value: Union[str, Callable]) -> None: if not isinstance(value, (str, Callable)): raise ValueError("distance_metric must be a string or callable") if value == "tanimoto": self._distance_metric = "jaccard" # Use scipy's jaccard metric else: self._distance_metric = value def fit(self, X: ArrayLike, y=None) -> "KNNApplicabilityDomain": """Fit the KNN applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : KNNApplicabilityDomain Returns the instance itself. """ if not isinstance(self.n_neighbors, int) or self.n_neighbors < 1: raise ValueError("n_neighbors must be a positive integer") X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] # Fit nearest neighbors model self.nn_ = NearestNeighbors( n_neighbors=self.n_neighbors + 1, # +1 because point is its own neighbor metric=self.distance_metric, n_jobs=self.n_jobs, ) if self.distance_metric == "jaccard": X = X.astype(bool) self.nn_.fit(X) # Set initial threshold based on training data self.fit_threshold(X) return self def _transform(self, X: np.ndarray) -> np.ndarray: """Calculate mean distance to k nearest neighbors in training set. Parameters ---------- X : ndarray of shape (n_samples, n_features) Validated input data. Returns ------- distances : ndarray of shape (n_samples, 1) Mean distance to k nearest neighbors. Higher values indicate samples further from the training set. """ if self.distance_metric == "jaccard": X = X.astype(bool) distances, _ = self.nn_.kneighbors(X) mean_distances = distances[:, 1:].mean(axis=1) # Skip first (self) neighbor return mean_distances.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/leverage.py ================================================ """ Leverage-based applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class LeverageApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain defined using the leverage approach. The leverage approach measures how far a sample is from the center of the feature space using the diagonal elements of the hat matrix H = X(X'X)^(-1)X'. Higher leverage values indicate samples further from the center of the training data. Parameters ---------- threshold_factor : float, default=3 Factor used in calculating the leverage threshold h* = threshold_factor * (p+1)/n where p is the number of features and n is the number of samples. percentile : float or None, default=None If not None, overrides the statistical threshold with a percentile-based one. See BaseApplicabilityDomain for details. Attributes ---------- n_features_in_ : int Number of features seen during fit. threshold_ : float Calculated leverage threshold. var_covar_ : ndarray of shape (n_features, n_features) Variance-covariance matrix of the training data. Notes ----- The statistical threshold h* = 3 * (p+1)/n is a commonly used rule of thumb in regression diagnostics, where p is the number of features and n is the number of training samples. Input data should be scaled (e.g., using StandardScaler) to ensure all features contribute equally. For high-dimensional data like fingerprints, dimensionality reduction (e.g., PCA) is strongly recommended to avoid computational issues with the variance-covariance matrix inversion. Examples -------- >>> from sklearn.pipeline import Pipeline >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.decomposition import PCA >>> from scikit_mol.applicability import LeverageApplicabilityDomain >>> >>> # Create pipeline with scaling and dimensionality reduction >>> pipe = Pipeline([ ... ('scaler', StandardScaler()), ... ('pca', PCA(n_components=0.95)), # Keep 95% of variance ... ('ad', LeverageApplicabilityDomain()) ... ]) >>> >>> # Fit pipeline >>> X_train = [[0, 1, 2], [1, 2, 3], [2, 3, 4]] # Example data >>> pipe.fit(X_train) >>> >>> # Predict domain membership for new samples >>> X_test = [[0, 1, 2], [10, 20, 30]] >>> pipe.predict(X_test) # Returns [1, -1] (in/out of domain) """ _scoring_convention = "high_outside" _supports_threshold_fitting = True def __init__( self, threshold_factor: float = 3, percentile: Optional[float] = None, feature_name: str = "Leverage", ) -> None: super().__init__(percentile=percentile, feature_name=feature_name) self.threshold_factor = threshold_factor def _set_statistical_threshold(self, X: NDArray) -> None: """Set the statistical threshold h* = threshold_factor * (p+1)/n.""" n_samples = X.shape[0] self.threshold_ = self.threshold_factor * (self.n_features_in_ + 1) / n_samples def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "LeverageApplicabilityDomain": """Fit the leverage applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Ignored Not used, present for API consistency. Returns ------- self : LeverageApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] # Calculate variance-covariance matrix self.var_covar_ = np.linalg.inv(X.T.dot(X)) # Set initial threshold self._set_statistical_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate leverage values. Higher values indicate samples further from the center of the training data. """ h = np.sum(X.dot(self.var_covar_) * X, axis=1) return h.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/local_outlier.py ================================================ """ Local Outlier Factor applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from sklearn.neighbors import LocalOutlierFactor from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class LocalOutlierFactorApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on Local Outlier Factor (LOF). LOF measures the local deviation of density of a sample with respect to its neighbors, identifying samples that have substantially lower density than their neighbors. Parameters ---------- n_neighbors : int, default=20 Number of neighbors to use for LOF calculation. contamination : float, default=0.1 Expected proportion of outliers in the data set. metric : str, default='euclidean' Metric to use for distance computation. percentile : float or None, default=None Percentile of training set scores to use as threshold (0-100). If None, uses contamination-based threshold from LOF. feature_name : str, default="LOF" Name for the output feature column. Attributes ---------- n_features_in_ : int Number of features seen during fit. lof_ : LocalOutlierFactor Fitted LOF estimator. threshold_ : float Current threshold for domain membership. Notes ----- The scoring convention is 'high_outside' because higher LOF scores indicate samples that are more likely to be outliers. References ---------- .. [1] Breunig et al. (2000). LOF: Identifying Density-Based Local Outliers. In: Proc. 2000 ACM SIGMOD Int. Conf. Manag. Data, ACM, pp. 93-104. """ _scoring_convention = "high_outside" def __init__( self, n_neighbors: int = 20, contamination: float = 0.1, metric: str = "euclidean", percentile: Optional[float] = None, feature_name: str = "LOF", ) -> None: super().__init__(percentile=percentile, feature_name=feature_name) self.n_neighbors = n_neighbors self.contamination = contamination self.metric = metric def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "LocalOutlierFactorApplicabilityDomain": """Fit the LOF applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : LocalOutlierFactorApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] self.lof_ = LocalOutlierFactor( n_neighbors=self.n_neighbors, metric=self.metric, contamination=self.contamination, novelty=True, ) self.lof_.fit(X) # Set initial threshold based on training data self.fit_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate LOF scores for samples. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- scores : ndarray of shape (n_samples, 1) The LOF scores of the samples. Higher scores indicate samples that are more likely to be outliers. """ # Get negative LOF scores (higher means more likely to be outlier) scores = -self.lof_.score_samples(X) return scores.reshape(-1, 1) def _set_statistical_threshold(self, X): """Set the statistical threshold for the LOF scores.""" self.threshold_ = -self.lof_.offset_ # def predict(self, X): # """Predict whether samples are within the applicability domain. # Parameters # ---------- # X : array-like of shape (n_samples, n_features) # The samples to predict. # Returns # ------- # y_pred : ndarray of shape (n_samples,) # Returns 1 for samples inside the domain and -1 for samples outside # (following scikit-learn's convention for outlier detection). # """ # return self.lof_.predict(X) ================================================ FILE: scikit_mol/applicability/mahalanobis.py ================================================ """ Mahalanobis distance applicability domain. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from scipy import linalg, stats from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class MahalanobisApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on Mahalanobis distance. Uses Mahalanobis distance to measure how many standard deviations a sample is from the training set mean, taking into account the covariance structure of the data. For multivariate normal data, the squared Mahalanobis distances follow a chi-square distribution. Parameters ---------- percentile : float or None, default=None Percentile of training set scores to use as threshold (0-100). If None, uses 95.0 (exclude top 5% of training samples). feature_name : str, default="Mahalanobis" Name for the output feature column. Attributes ---------- n_features_in_ : int Number of features seen during fit. mean_ : ndarray of shape (n_features,) Mean of training data. covariance_ : ndarray of shape (n_features, n_features) Covariance matrix of training data. threshold_ : float Current threshold for domain membership. Notes ----- The scoring convention is 'high_outside' because higher Mahalanobis distances indicate samples further from the training data mean. """ _scoring_convention = "high_outside" def __init__( self, percentile: Optional[float] = None, feature_name: str = "Mahalanobis", ) -> None: super().__init__(percentile=percentile or 95.0, feature_name=feature_name) def _set_statistical_threshold(self, X: NDArray) -> None: """Set threshold based on chi-square distribution. For multivariate normal data, squared Mahalanobis distances follow a chi-square distribution with degrees of freedom equal to the number of features. """ df = self.n_features_in_ self.threshold_ = np.sqrt(stats.chi2.ppf(0.95, df)) def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "MahalanobisApplicabilityDomain": """Fit the Mahalanobis distance applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : MahalanobisApplicabilityDomain Returns the instance itself. Raises ------ ValueError If X has fewer samples than features, making covariance estimation unstable. """ X = check_array(X, **self._check_params) n_samples, n_features = X.shape self.n_features_in_ = n_features if n_samples <= n_features: raise ValueError( f"n_samples ({n_samples}) must be greater than n_features ({n_features}) " "for stable covariance estimation." ) # Calculate mean and covariance self.mean_ = np.mean(X, axis=0) self.covariance_ = np.cov(X, rowvar=False, ddof=1) # Add small regularization to ensure positive definiteness min_eig = np.min(linalg.eigvalsh(self.covariance_)) if min_eig < 1e-6: self.covariance_ += (abs(min_eig) + 1e-6) * np.eye(n_features) # Set initial threshold based on training data self.fit_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate Mahalanobis distances. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- distances : ndarray of shape (n_samples, 1) The Mahalanobis distances of the samples. Higher distances indicate samples further from the training data mean. """ # Calculate Mahalanobis distances using stable computation diff = X - self.mean_ try: # Try Cholesky decomposition first (more stable) L = linalg.cholesky(self.covariance_, lower=True) mahal_dist = np.sqrt( np.sum(linalg.solve_triangular(L, diff.T, lower=True) ** 2, axis=0) ) except linalg.LinAlgError: # Fallback to standard computation if Cholesky fails inv_covariance = linalg.pinv( self.covariance_ ) # Use pseudo-inverse for stability mahal_dist = np.sqrt(np.sum(diff @ inv_covariance * diff, axis=1)) return mahal_dist.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/standardization.py ================================================ """ Standardization approach applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from scipy import stats from sklearn.preprocessing import StandardScaler from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class StandardizationApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain based on standardized feature values. Samples are considered within the domain if their standardized features fall within a certain number of standard deviations from the mean. The maximum absolute standardized value across all features is used as the score. Parameters ---------- percentile : float or None, default=None Percentile of training set scores to use as threshold (0-100). If None, uses 95.0 (exclude top 5% of training samples). feature_name : str, default="Standardization" Name for the output feature column. Attributes ---------- n_features_in_ : int Number of features seen during fit. scaler_ : StandardScaler Fitted standard scaler. threshold_ : float Current threshold for domain membership. Notes ----- The scoring convention is 'high_outside' because higher standardized values indicate samples further from the training data mean. """ _scoring_convention = "high_outside" def __init__( self, percentile: Optional[float] = None, feature_name: str = "Standardization", ) -> None: super().__init__(percentile=percentile or 95.0, feature_name=feature_name) def _set_statistical_threshold(self, X: NDArray) -> None: """Set threshold based on normal distribution. For normally distributed data, ~95% of values fall within 2 standard deviations of the mean. """ self.threshold_ = stats.norm.ppf(0.975) # 2 standard deviations def fit( self, X: ArrayLike, y: Optional[Any] = None ) -> "StandardizationApplicabilityDomain": """Fit the standardization applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : StandardizationApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] # Fit standard scaler self.scaler_ = StandardScaler() self.scaler_.fit(X) # Set initial threshold based on training data self.fit_threshold(X) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate maximum absolute standardized values. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- scores : ndarray of shape (n_samples, 1) The maximum absolute standardized values. Higher values indicate samples further from the training data mean. """ # Calculate standardized values and take max absolute value per sample X_std = self.scaler_.transform(X) scores = np.max(np.abs(X_std), axis=1) return scores.reshape(-1, 1) ================================================ FILE: scikit_mol/applicability/topkat.py ================================================ """ TOPKAT's Optimal Prediction Space (OPS) applicability domain. This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD) Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License) Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License) See LICENSE.MIT in this directory for the original MIT license. """ from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, NDArray from sklearn.utils.validation import check_array from .base import BaseApplicabilityDomain class TopkatApplicabilityDomain(BaseApplicabilityDomain): """Applicability domain defined using TOPKAT's Optimal Prediction Space (OPS). The method transforms the input space (P-space) to a normalized space (S-space), then projects it to the Optimal Prediction Space using eigendecomposition. Parameters ---------- percentile : float or None, default=None Not used, present for API consistency. feature_name : str, default="TOPKAT" Name for the output feature column. Attributes ---------- n_features_in_ : int Number of features seen during fit. X_min_ : ndarray of shape (n_features,) Minimum values of training features. X_max_ : ndarray of shape (n_features,) Maximum values of training features. eigen_val_ : ndarray of shape (n_features + 1,) Eigenvalues of the S-space transformation. eigen_vec_ : ndarray of shape (n_features + 1, n_features + 1) Eigenvectors of the S-space transformation. threshold_ : float Fixed threshold based on dimensionality. Notes ----- The scoring convention is 'high_outside' because higher OPS distances indicate samples further from the training data. References ---------- .. [1] Gombar, Vijay K. (1996). Method and apparatus for validation of model-based predictions (US Patent No. 6-036-349) USPTO. """ _scoring_convention = "high_outside" _supports_threshold_fitting = False def __init__( self, percentile: Optional[float] = None, feature_name: str = "TOPKAT", ) -> None: super().__init__(percentile=None, feature_name=feature_name) def fit(self, X: ArrayLike, y: Optional[Any] = None) -> "TopkatApplicabilityDomain": """Fit the TOPKAT applicability domain. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : Any, optional (default=None) Not used, present for API consistency. Returns ------- self : TopkatApplicabilityDomain Returns the instance itself. """ X = check_array(X, **self._check_params) self.n_features_in_ = X.shape[1] n_samples = X.shape[0] # Store scaling factors self.X_min_ = X.min(axis=0) self.X_max_ = X.max(axis=0) # Transform P-space to S-space denom = np.where( (self.X_max_ - self.X_min_) != 0, (self.X_max_ - self.X_min_), 1 ) S = (2 * X - self.X_max_ - self.X_min_) / denom # Add column of ones S = np.c_[np.ones(n_samples), S] # Calculate eigendecomposition self.eigen_val_, self.eigen_vec_ = np.linalg.eigh(S.T.dot(S)) # Ensure real values (numerical stability) self.eigen_val_ = np.real(self.eigen_val_) self.eigen_vec_ = np.real(self.eigen_vec_) # Set fixed threshold based on dimensionality self.threshold_ = 5 * (self.n_features_in_ + 1) / (2 * self.n_features_in_) return self def _transform(self, X: NDArray) -> NDArray[np.float64]: """Calculate OPS distance scores for samples. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data to transform. Returns ------- distances : ndarray of shape (n_samples, 1) OPS distance scores. Higher values indicate samples further from the training data. """ # Transform to S-space denom = np.where( (self.X_max_ - self.X_min_) != 0, (self.X_max_ - self.X_min_), 1 ) S = (2 * X - self.X_max_ - self.X_min_) / denom # Add column of ones if X.ndim == 1: S = np.r_[1, S].reshape(1, -1) else: S = np.c_[np.ones(X.shape[0]), S] # Project to OPS OPS = S.dot(self.eigen_vec_) # Calculate OPS distances - matching MLChemAD's approach denom = np.divide( np.ones_like(self.eigen_val_, dtype=float), self.eigen_val_, out=np.zeros_like(self.eigen_val_), where=self.eigen_val_ != 0, ) distances = (OPS * OPS).dot(denom) return distances.reshape(-1, 1) # def predict(self, X): # """Predict whether samples are within the applicability domain. # Parameters # ---------- # X : array-like of shape (n_samples, n_features) # The samples to predict. # Returns # ------- # y_pred : ndarray of shape (n_samples,) # Returns 1 for samples inside the domain and -1 for samples outside # (following scikit-learn's convention for outlier detection). # """ # scores = self._transform(X).ravel() # threshold = self.threshold_ # return np.where(scores < threshold, 1, -1) ================================================ FILE: scikit_mol/conversions.py ================================================ from collections.abc import Sequence from typing import Optional, Union import numpy as np from numpy.typing import NDArray from rdkit import Chem from rdkit.rdBase import BlockLogs from sklearn.base import BaseEstimator, TransformerMixin from scikit_mol._constants import DOCS_BASE_URL from scikit_mol.core import ( InvalidMol, NoFitNeededMixin, check_transform_input, feature_names_default_mol, ) from scikit_mol.parallel import parallelized_with_batches # from scikit_mol._invalid import InvalidMol class SmilesToMolTransformer(TransformerMixin, NoFitNeededMixin, BaseEstimator): """ Transformer for converting SMILES strings to RDKit mol objects. This transformer can be included in pipelines during development and training, but the safe inference mode should only be enabled when deploying models for inference in production environments. """ _doc_link_module = "scikit_mol" _doc_link_template = ( DOCS_BASE_URL + "{estimator_module}/#{estimator_module}.{estimator_name}" ) def __init__( self, n_jobs: Optional[None] = None, safe_inference_mode: bool = False ): """ Parameters ----------- n_jobs : int, optional default=None The maximum number of concurrently running jobs. `None` is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, default=False If `True`, enables safeguards for handling invalid data during inference. This should only be set to `True` when deploying models to production. """ self.n_jobs = n_jobs self.safe_inference_mode = safe_inference_mode @feature_names_default_mol def get_feature_names_out(self, input_features=None): return input_features def fit(self, X=None, y=None): """Included for scikit-learn compatibility, does nothing""" return self def transform( self, X_smiles_list: Sequence[str], y=None ) -> NDArray[Union[Chem.Mol, InvalidMol]]: """Converts SMILES into RDKit mols Parameters ---------- X_smiles_list : Sequence[str] sequence of SMILES strings to transform Returns ------- NDArray[Union[Chem.Mol, InvalidMol]] Array of RDKit mol objects or InvalidMol objects if a SMILES string is invalid and `safe_inference_mode=True` Raises ------ ValueError Raises ValueError if a SMILES string is unparsable by RDKit and `safe_inference_mode=False` """ arrays = parallelized_with_batches(self._transform, X_smiles_list, self.n_jobs) arr = np.concatenate(arrays) return arr @check_transform_input def _transform(self, X): X_out = [] with BlockLogs(): for smiles in X: mol = Chem.MolFromSmiles(smiles, sanitize=False) if mol: errors = Chem.DetectChemistryProblems(mol) if errors: error_message = "\n".join(error.Message() for error in errors) message = f"Invalid Molecule: {error_message}" X_out.append(InvalidMol(str(self), message)) else: Chem.SanitizeMol(mol) X_out.append(mol) else: message = f"Invalid SMILES: {smiles}" X_out.append(InvalidMol(str(self), message)) if not self.safe_inference_mode and not all(X_out): fails = [x for x in X_out if not x] raise ValueError( f"Invalid input found: {fails}." ) # TODO with this approach we get all errors, but we do process ALL the smiles first which could be slow return np.array(X_out).reshape(-1, 1) @check_transform_input def inverse_transform(self, X_mols_list, y=None): X_out = [] for mol in X_mols_list: if isinstance(mol, Chem.Mol): try: smiles = Chem.MolToSmiles(mol) X_out.append(smiles) except Exception as e: X_out.append( InvalidMol(str(self), f"Error converting Mol to SMILES: {e}") ) else: X_out.append(InvalidMol(str(self), f"Not a Mol: {mol}")) if not self.safe_inference_mode and not all(isinstance(x, str) for x in X_out): fails = [x for x in X_out if not isinstance(x, str)] raise ValueError(f"Invalid Mols found: {fails}.") return np.array(X_out).reshape(-1, 1) ================================================ FILE: scikit_mol/core.py ================================================ """ Core functionality for scikit-mol. Users of scikit-mol should not need to use this module directly. Users who want to create their own transformers should use this module. """ import functools from dataclasses import dataclass import numpy as np from packaging.version import Version SKLEARN_VERSION_PANDAS_OUT = Version("1.2") DEFAULT_MOL_COLUMN_NAME = "ROMol" class NoFitNeededMixin: """ Mixin class to add a `__sklearn_is_fitted__` method to a transformer, which does not need to be fitted. """ def __sklearn_is_fitted__(self): return True @dataclass class InvalidMol: """Represents molecules which raised an error during a pipeline step. Evaluates to `False` in boolean contexts. Parameters ----------- pipeline_step : str The name of the pipeline step where the error occurred. error : str The error message. """ pipeline_step: str error: str def __bool__(self): return False def __repr__(self): return f"InvalidMol('{self.pipeline_step}', error='{self.error}')" def _validate_transform_input(X): """Validate and adapt the input of the _transform method""" try: shape = X.shape except AttributeError: # If X is not array-like or dataframe-like, # we just return it as is, so users can use simple lists and sequences. return X # If X is an array-like or dataframe-like, we make sure it is compatible with # the scikit-learn API, and that it contains a single column: # scikit-mol transformers need a single column with smiles or mols. if len(shape) == 1: return X # Flatt Arrays and list-like data are also supported #TODO, add a warning about non-2D data if logging is implemented if shape[1] != 1: raise ValueError( "Only one column supported. You may want to use a ColumnTransformer https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html " ) return np.array(X).flatten() def check_transform_input(method): """ Decorator to check the input of the _transform method and make it compatible with the scikit-learn API and with downstream methods. """ @functools.wraps(method) def wrapper(obj, X): X = _validate_transform_input(X) result = method(obj, X) # If the output of the _transform method # must be changed depending on the initial type of X, do it here. return result return wrapper def feature_names_default_mol(method): """ Decorator that returns the default feature names for the mol object """ @functools.wraps(method) def wrapper(obj, input_features=None): prefix = DEFAULT_MOL_COLUMN_NAME if input_features is not None: return np.array([f"{prefix}_{name}" for name in input_features]) else: return np.array([prefix]) return wrapper ================================================ FILE: scikit_mol/descriptors.py ================================================ import functools from typing import List, Optional, Union import numpy as np from rdkit.Chem import Descriptors from rdkit.Chem.rdchem import Mol from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator from sklearn.base import BaseEstimator, TransformerMixin from scikit_mol._constants import DOCS_BASE_URL from scikit_mol.core import NoFitNeededMixin, check_transform_input from scikit_mol.parallel import parallelized_with_batches class MolecularDescriptorTransformer(TransformerMixin, NoFitNeededMixin, BaseEstimator): """Descriptor calculation transformer Parameters ---------- desc_list : (List of descriptor names) A list of RDKit descriptors to include in the calculation n_jobs : int optional default: None The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as n_jobs=1 unless the call is performed under a parallel_config() context manager that sets another value for n_jobs. safe_inference_mode : bool If True, enables safeguards for handling invalid data during inference. This should only be set to True when deploying models to production. Returns ------- np.array Descriptor values, shape (samples, len(descriptor list)) """ _doc_link_module = "scikit_mol" _doc_link_template = ( DOCS_BASE_URL + "{estimator_module}/#{estimator_module}.{estimator_name}" ) def __init__( self, desc_list: Optional[str] = None, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.float32, ): self.desc_list = desc_list self.n_jobs = n_jobs self.safe_inference_mode = safe_inference_mode self.dtype = dtype def _get_desc_calculator(self) -> MolecularDescriptorCalculator: if self.desc_list: unknown_descriptors = [ desc_name for desc_name in self.desc_list if desc_name not in self.available_descriptors ] assert not unknown_descriptors, f"Unknown descriptor names {unknown_descriptors} specified, please check available_descriptors property\nPlease check available list {self.available_descriptors}" else: self.desc_list = self.available_descriptors return MolecularDescriptorCalculator(self.desc_list) @property def desc_list(self): """Descriptor names of currently selected descriptors""" return self._desc_list def get_feature_names_out(self, input_features=None): return np.array(self.selected_descriptors) @desc_list.setter def desc_list(self, desc_list): self._desc_list = desc_list self.calculators = self._get_desc_calculator() @property def available_descriptors(self) -> List[str]: """List of names of all available descriptor""" return [descriptor[0] for descriptor in Descriptors._descList] @property def selected_descriptors(self) -> List[str]: """List of the names of the descriptors in the descriptor calculator""" return list(self.calculators.GetDescriptorNames()) @property def start_method(self): return self._start_method @start_method.setter def start_method(self, start_method): """Allowed methods are spawn, fork and forkserver on macOS and Linux, only spawn is possible on Windows. None will choose the default for the OS and version of Python.""" allowed_start_methods = ["spawn", "fork", "forkserver", None] assert ( start_method in allowed_start_methods ), f"start_method not in allowed methods {allowed_start_methods}" self._start_method = start_method def _transform_mol(self, mol: Mol) -> Union[np.ndarray, np.ma.MaskedArray]: if not mol: if self.safe_inference_mode: return np.ma.masked_all(len(self.desc_list)) else: raise ValueError(f"Invalid molecule provided: {mol}") try: return np.array(list(self.calculators.CalcDescriptors(mol))) except Exception as e: if self.safe_inference_mode: return np.ma.masked_all(len(self.desc_list)) else: raise e def fit(self, x, y=None): """Included for scikit-learn compatibility, does nothing""" return self @check_transform_input def _transform(self, x: List[Mol]) -> Union[np.ndarray, np.ma.MaskedArray]: if self.safe_inference_mode: arrays = [self._transform_mol(mol) for mol in x] return np.ma.array(arrays, dtype=self.dtype) else: arr = np.zeros((len(x), len(self.desc_list)), dtype=self.dtype) for i, mol in enumerate(x): arr[i, :] = self._transform_mol(mol) return arr def transform(self, x: List[Mol], y=None) -> Union[np.ndarray, np.ma.MaskedArray]: """Transform a list of molecules into an array of descriptor values Parameters ---------- x : (List, np.array, pd.Series) A list of RDKit molecules y : NoneType, optional Target values for scikit-learn compatibility, not used, by default None Returns ------- Union[np.ndarray, np.ma.MaskedArray] Descriptors, shape (samples, length of .selected_descriptors) """ fn = functools.partial(parallel_helper, self.get_params()) arrays = parallelized_with_batches(fn, x, self.n_jobs) if self.safe_inference_mode: arrays = np.ma.concatenate(arrays) else: arrays = np.concatenate(arrays) return arrays # May be safer to instantiate the transformer object in the child process, and only transfer the parameters # There were issues with freezing when using RDKit 2022.3 def parallel_helper(params, mols): """Will get a tuple with Desc2DTransformer parameters and mols to transform. Will then instantiate the transformer and transform the molecules""" from scikit_mol.descriptors import MolecularDescriptorTransformer transformer = MolecularDescriptorTransformer(**params) y = transformer._transform(mols) return y ================================================ FILE: scikit_mol/fingerprints/__init__.py ================================================ from scikit_mol._constants import DOCS_BASE_URL from .atompair import AtomPairFingerprintTransformer from .avalon import AvalonFingerprintTransformer # TODO, these baseclasses needed for backwards compatibility with tests, needs to be removed when tests updated from .baseclasses import ( FpsGeneratorTransformer, FpsTransformer, ) from .maccs import MACCSKeysFingerprintTransformer from .minhash import MHFingerprintTransformer, SECFingerprintTransformer from .morgan import MorganFingerprintTransformer from .rdkitfp import RDKitFingerprintTransformer from .topologicaltorsion import ( TopologicalTorsionFingerprintTransformer, ) __all__ = [ "AtomPairFingerprintTransformer", "AvalonFingerprintTransformer", "FpsGeneratorTransformer", "FpsTransformer", "MACCSKeysFingerprintTransformer", "MHFingerprintTransformer", "MorganFingerprintTransformer", "RDKitFingerprintTransformer", "SECFingerprintTransformer", "TopologicalTorsionFingerprintTransformer", ] for name in __all__: if name.startswith("Fps") or name.startswith("_"): continue cls = locals()[name] cls._doc_link_module = "scikit_mol" cls._doc_link_template = ( DOCS_BASE_URL + "scikit_mol.fingerprints/#scikit_mol.fingerprints.{estimator_name}" ) ================================================ FILE: scikit_mol/fingerprints/atompair.py ================================================ from typing import Optional, Sequence import numpy as np from rdkit.Chem.rdFingerprintGenerator import GetAtomPairGenerator from .baseclasses import FpsGeneratorTransformer class AtomPairFingerprintTransformer(FpsGeneratorTransformer): """ AtomPair fingerprints encode pairs of atoms at various topological or 3D distances in a molecule. They are useful for capturing structural relationships and connectivity patterns. """ _regenerate_on_properties = ( "fpSize", "includeChirality", "use2D", "minLength", "maxLength", ) def __init__( self, minLength: int = 1, maxLength: int = 30, fromAtoms: Optional[Sequence] = None, ignoreAtoms: Optional[Sequence] = None, atomInvariants: Optional[Sequence] = None, includeChirality: bool = False, use2D: bool = True, confId: int = -1, fpSize: int = 2048, useCounts: bool = False, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, ): """Transform RDKit mols into Count or bit-based hashed AtomPair Fingerprints Parameters ---------- minLength : int, optional Minimum distance between atom pairs, by default 1 maxLength : int, optional Maximum distance between atom pairs, by default 30 fromAtoms : Sequence, optional Atom indices to use as starting points, by default None ignoreAtoms : array-like, optional Atom indices to exclude, by default None atomInvariants : array-like, optional Atom invariants to use, by default None includeChirality : bool, optional Include chirality in calculation of the fingerprint keys, by default False use2D : bool, optional Use 2D distances (topological) instead of 3D, by default True confId : int, optional Which conformer to use for 3D distance calculations, by default -1 fpSize : int, optional Size of the hashed fingerprint, by default 2048 useCounts : bool, optional If toggled will create the count and not bit-based fingerprint, by default False n_jobs : int, optional The maximum number of concurrently running jobs. `None` is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `joblib.parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional If `True`, will return masked arrays for invalid mols, by default `False` """ self._initializing = True super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode) self.fpSize = fpSize self.use2D = use2D self.includeChirality = includeChirality self.minLength = minLength self.maxLength = maxLength self.useCounts = useCounts self.confId = confId self.fromAtoms = fromAtoms self.ignoreAtoms = ignoreAtoms self.atomInvariants = atomInvariants self._generate_fp_generator() delattr(self, "_initializing") def _generate_fp_generator(self): self._fpgen = GetAtomPairGenerator( minDistance=int(self.minLength), maxDistance=int(self.maxLength), includeChirality=bool(self.includeChirality), use2D=bool(self.use2D), fpSize=int(self.fpSize), ) def _transform_mol(self, mol) -> np.array: if self.useCounts: return self._fpgen.GetCountFingerprintAsNumPy( mol, fromAtoms=self.fromAtoms, ignoreAtoms=self.ignoreAtoms, customAtomInvariants=self.atomInvariants, ) else: return self._fpgen.GetFingerprintAsNumPy( mol, fromAtoms=self.fromAtoms, ignoreAtoms=self.ignoreAtoms, customAtomInvariants=self.atomInvariants, ) ================================================ FILE: scikit_mol/fingerprints/avalon.py ================================================ from typing import Optional import numpy as np from rdkit.Avalon import pyAvalonTools from .baseclasses import FpsTransformer class AvalonFingerprintTransformer(FpsTransformer): "Fingerprint from the Avalon toolkit, https://doi.org/10.1021/ci050413p" def __init__( self, fpSize: int = 512, isQuery: bool = False, resetVect: bool = False, bitFlags: int = 15761407, useCounts: bool = False, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.int8, ): """Transform RDKit mols into Count or bit-based Avalon Fingerprints Parameters ---------- fpSize : int, optional Size of the fingerprint, by default 512 isQuery : bool, optional Use the fingerprint for a query structure, by default False resetVect : bool, optional Reset vector, by default False. NB: only used in GetAvalonFP (not for GetAvalonCountFP) bitFlags : int, optional Substructure fingerprint (32767) or similarity fingerprint (15761407), by default 15761407 useCounts : bool, optional If toggled will create the count and not bit-based fingerprint, by default False n_jobs : int, optional The number of jobs to run in parallel, by default None safe_inference_mode : bool, optional If True, enables safe inference mode, by default False dtype : numpy.dtype, optional Data type of the fingerprint array, by default numpy.int8 """ super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype ) self.fpSize = fpSize self.isQuery = isQuery self.resetVect = resetVect self.bitFlags = bitFlags self.useCounts = useCounts def _mol2fp(self, mol): if self.useCounts: return pyAvalonTools.GetAvalonCountFP( mol, nBits=int(self.fpSize), isQuery=bool(self.isQuery), bitFlags=int(self.bitFlags), ) else: return pyAvalonTools.GetAvalonFP( mol, nBits=int(self.fpSize), isQuery=bool(self.isQuery), resetVect=bool(self.resetVect), bitFlags=int(self.bitFlags), ) ================================================ FILE: scikit_mol/fingerprints/baseclasses.py ================================================ import functools import inspect import re from abc import ABC, abstractmethod from typing import Optional, Type from warnings import simplefilter, warn # from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect import numpy as np from rdkit import DataStructs from scipy.sparse import lil_matrix from sklearn.base import BaseEstimator, TransformerMixin from scikit_mol.core import NoFitNeededMixin, check_transform_input from scikit_mol.parallel import parallelized_with_batches simplefilter("always", DeprecationWarning) _PATTERN_FINGERPRINT_TRANSFORMER = re.compile( r"^(?P\w+)FingerprintTransformer$" ) class BaseFpsTransformer(TransformerMixin, NoFitNeededMixin, ABC, BaseEstimator): """Base class for fingerprint transformers Parameters ---------- name : Optional[str], optional name of the fingerprint, used for column prefix in when the output mode is set to `pandas`, by default None n_jobs : Optional[str], optional The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool If `True`, enables safeguards for handling invalid data during inference. This should only be set to `True` when deploying models to production. """ def __init__( self, name: Optional[str] = None, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, ): self.n_jobs = n_jobs self.safe_inference_mode = safe_inference_mode self._fp_name = name # TODO, remove when finally deprecating nBits and dtype @property def nBits(self): warn( "nBits will be replaced by fpSize, due to changes harmonization!", DeprecationWarning, stacklevel=2, ) return self.fpSize # TODO, remove when finally deprecating nBits and dtype @nBits.setter def nBits(self, nBits): if nBits is not None: warn( "nBits will be replaced by fpSize, due to changes harmonization!", DeprecationWarning, stacklevel=3, ) self.fpSize = nBits def _get_column_prefix(self) -> str: if hasattr(self, "_fp_name") and self._fp_name: return str(self._fp_name).lower() cls_name = type(self).__name__ matched = _PATTERN_FINGERPRINT_TRANSFORMER.match(cls_name) if matched: fingerprint_name = matched.group("fingerprint_name") return f"fp_{fingerprint_name.lower()}" else: return cls_name.lower() def _get_n_digits_column_suffix(self) -> int: return len(str(self.fpSize)) def get_display_feature_names_out(self, input_features=None): """Get feature names for display purposes All feature names will have the same length, since the different elements will be prefixed with zeros depending on the number of bits. """ prefix = self._get_column_prefix() n_digits = self._get_n_digits_column_suffix() return np.array( [f"{prefix}_{str(i).zfill(n_digits)}" for i in range(1, self.fpSize + 1)] ) def get_feature_names_out(self, input_features=None): """Get feature names for fingerprint transformers This method is used by the scikit-learn set_output API to get the column names of the transformed dataframe. """ prefix = self._get_column_prefix() return np.array([f"{prefix}_{i}" for i in range(1, self.fpSize + 1)]) def _safe_transform_mol(self, mol): """Handle safe inference mode with masked arrays""" if not mol and self.safe_inference_mode: return np.ma.masked_all(self.fpSize) try: result = self._transform_mol(mol) return result except Exception as e: if self.safe_inference_mode: return np.ma.masked_all(self.fpSize) else: raise e @abstractmethod def _transform_mol(self, mol): """Transform a single molecule to numpy array""" raise NotImplementedError def fit(self, X, y=None): """Included for scikit-learn compatibility Also sets the column prefix for use by the transform method with dataframe output. """ return self @check_transform_input def _transform(self, X): if self.safe_inference_mode: arrays = [self._safe_transform_mol(mol) for mol in X] return np.ma.stack(arrays) else: arrays = [self._transform_mol(mol) for mol in X] return np.stack(arrays) def _transform_sparse(self, X): arr = np.zeros((len(X), self.fpSize), dtype=self.dtype) for i, mol in enumerate(X): arr[i, :] = self._transform_mol(mol) return lil_matrix(arr) def transform(self, X, y=None): """Transform a list of RDKit molecule objects into a fingerprint array Parameters ---------- X : (List, np.array, pd.Series) A list of RDKit molecules y : NoneType, optional Target values for scikit-learn compatibility, not used, by default None Returns ------- np.array Fingerprints, shape (samples, fingerprint size) """ func = functools.partial( parallel_helper, cls=self.__class__, parameters=self.get_params(), ) arrays = parallelized_with_batches(func, X, self.n_jobs) if self.safe_inference_mode: arr = np.ma.concatenate(arrays) return arr else: return np.concatenate(arrays) class FpsTransformer(BaseFpsTransformer): """Classic fingerprint transformer using mol2fp pattern""" def __init__( self, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.int8, ): super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode) self.dtype = dtype def _transform_mol(self, mol): """Implements the mol -> rdkit fingerprint data structure -> numpy array pattern""" fp = self._mol2fp(mol) return self._fp2array(fp) @abstractmethod def _mol2fp(self, mol): """Generate fingerprint from mol MUST BE OVERWRITTEN """ raise NotImplementedError("_mol2fp not implemented") def _fp2array(self, fp): """Convert RDKit fingerprint data structure to numpy array""" if fp: arr = np.zeros((self.fpSize,), dtype=self.dtype) DataStructs.ConvertToNumpyArray(fp, arr) return arr else: return np.ma.masked_all((self.fpSize,), dtype=self.dtype) @check_transform_input def _transform(self, X): if self.safe_inference_mode: arrays = [self._safe_transform_mol(mol) for mol in X] return np.ma.stack(arrays) else: arr = np.zeros((len(X), self.fpSize), dtype=self.dtype) for i, mol in enumerate(X): arr[i, :] = self._transform_mol(mol) return arr # TODO, remove when finally deprecating nBits def _get_param_names(self): """Get parameter names excluding deprecated parameters""" params = super()._get_param_names() # Remove deprecated parameters before they're accessed return [p for p in params if p not in ("nBits")] class FpsGeneratorTransformer(BaseFpsTransformer): """Abstract base class for fingerprint transformers based on (unpicklable)fingerprint generators""" _regenerate_on_properties = () def __getstate__(self): # Get the state of the parent class state = super().__getstate__() state.update(self.get_params()) # Remove the potentiallyunpicklable property from the state state.pop("_fpgen", None) # fpgen is not picklable return state def __setstate__(self, state): # Restore the state of the parent class super().__setstate__(state) # Re-create the unpicklable property # Do we need this part of code? params variable is not used and tests pass without it generatort_keys = inspect.signature( self._generate_fp_generator ).parameters.keys() params = [ # noqa: F841 setattr(self, k, state["_" + k]) if "_" + k in state else setattr(self, k, state[k]) for k in generatort_keys ] self._generate_fp_generator() # TODO: overload set_params in order to not make multiple calls to _generate_fp_generator def __setattr__(self, name: str, value): super().__setattr__(name, value) if ( not hasattr(self, "_initializing") and name in self._regenerate_on_properties ): self._generate_fp_generator() @abstractmethod def _generate_fp_generator(self): raise NotImplementedError("_generate_fp_generator not implemented") @abstractmethod def _transform_mol(self, mol) -> np.array: """Generate numpy array descriptor from RDKit molecule MUST BE OVERWRITTEN """ raise NotImplementedError("_transform_mol not implemented") # TODO, remove when finally deprecating nBits and dtype @property def dtype(self): warn( "dtype is no longer supported, due to move to generator based fingerprints", DeprecationWarning, stacklevel=2, ) return None # TODO, remove when finally deprecating nBits and dtype @dtype.setter def dtype(self, dtype): if dtype is not None: warn( "dtype is no longer supported, due to move to generator based fingerprints", DeprecationWarning, stacklevel=3, ) pass # TODO, remove when finally deprecating nBits and dtype def _get_param_names(self): """Get parameter names excluding deprecated parameters""" params = super()._get_param_names() # Remove deprecated parameters before they're accessed return [p for p in params if p not in ("dtype", "nBits")] def parallel_helper(X_mols, cls: Type[BaseFpsTransformer], parameters: dict): """Parallel_helper takes a tuple with class, the objects parameters and the mols to process. Then instantiates the class with the parameters and processes the mol. Intention is to be able to do this in child processes as some classes can't be pickled. This is a workaround for the fact that joblib doesn't support initialiers like multiprocessing does. """ transformer = cls(**parameters) return transformer._transform(X_mols) ================================================ FILE: scikit_mol/fingerprints/maccs.py ================================================ from typing import Optional import numpy as np from rdkit.Chem import rdMolDescriptors from .baseclasses import FpsTransformer class MACCSKeysFingerprintTransformer(FpsTransformer): """MACCS keys fingerprinter calculates the 167 fixed MACCS keys""" def __init__( self, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.int8, fpSize=167, ): """ Parameters ---------- n_jobs : int, optional default=None The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `joblib.parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional If `True`, will return masked arrays for invalid mols, by default `False` dtype : np.dtype, optional Data type of the fingerprint array, by default np.int8 fpSize : int, optional Size of the fingerprint, by default 167 Raises ------ ValueError _description_ """ super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype ) if fpSize != 167: raise ValueError( "fpSize can only be 167, matching the number of defined MACCS keys!" ) self._fpSize = fpSize @property def fpSize(self): return self._fpSize @fpSize.setter def fpSize(self, fpSize): if fpSize != 167: raise ValueError( "fpSize can only be 167, matching the number of defined MACCS keys!" ) self._fpSize = fpSize def _mol2fp(self, mol): return rdMolDescriptors.GetMACCSKeysFingerprint(mol) ================================================ FILE: scikit_mol/fingerprints/minhash.py ================================================ from typing import Optional from warnings import warn import numpy as np from rdkit.Chem import rdMHFPFingerprint from .baseclasses import FpsTransformer # TODO move to use FpsGeneratorTransformer class MHFingerprintTransformer(FpsTransformer): "Transforms the RDKit mol into the [MinHash fingerprint (MHFP)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0321-8)." def __init__( self, radius: int = 3, rings: bool = True, isomeric: bool = False, kekulize: bool = False, min_radius: int = 1, fpSize: int = 2048, seed: int = 42, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.int32, ): """ Parameters ---------- radius : int, optional The MHFP radius. rings : bool, optional Whether to include rings in the shingling. isomeric : bool, optional Whether the isomeric SMILES to be considered kekulize : bool, optional Whether to kekulize the extracted SMILES min_radius : int, optional The minimum radius that is used to extract n-gram. fpSize : int, optional The number of permutations used for hashing. Defaults to `2048`, this is effectively the length of the FP. seed : int, optional The value used to seed numpy.random. n_jobs : int or None, optional The maximum number of concurrently running jobs. `None` is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional Whether to use safe inference mode. dtype : numpy.dtype, optional The data type of the fingerprint. Defaults to `numpy.int32`. """ super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype ) self.radius = radius self.rings = rings self.isomeric = isomeric self.kekulize = kekulize self.min_radius = min_radius # Set the .n_permutations and .seed without creating the encoder twice self.fpSize = fpSize self._seed = seed # create the encoder instance self._recreate_encoder() def __getstate__(self): # Get the state of the parent class state = super().__getstate__() # Remove the unpicklable property from the state state.pop("mhfp_encoder", None) # mhfp_encoder is not picklable return state def __setstate__(self, state): # Restore the state of the parent class super().__setstate__(state) # Re-create the unpicklable property self._recreate_encoder() def _mol2fp(self, mol): fp = self.mhfp_encoder.EncodeMol( mol, self.radius, self.rings, self.isomeric, self.kekulize, self.min_radius ) return fp def _fp2array(self, fp): return np.array(fp) def _recreate_encoder(self): self.mhfp_encoder = rdMHFPFingerprint.MHFPEncoder( int(self.fpSize), int(self._seed) ) @property def seed(self): return self._seed @seed.setter def seed(self, seed): self._seed = seed # each time the seed parameter is modified refresh an instance of the encoder self._recreate_encoder() @property def n_permutations(self): warn( "n_permutations will be replace by fpSize, due to changes harmonization!", DeprecationWarning, ) return self.fpSize @n_permutations.setter def n_permutations(self, n_permutations): warn( "n_permutations will be replace by fpSize, due to changes harmonization!", DeprecationWarning, ) self.fpSize = n_permutations # each time the n_permutations parameter is modified refresh an instance of the encoder self._recreate_encoder() # TODO use FpsGeneratorTransformer instead class SECFingerprintTransformer(FpsTransformer): # https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0321-8 def __init__( self, radius: int = 3, rings: bool = True, isomeric: bool = False, kekulize: bool = False, min_radius: int = 1, fpSize: int = 2048, n_permutations: int = 0, seed: int = 0, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = np.int8, ): """Transforms the RDKit mol into the SMILES extended connectivity fingerprint (SECFP) Args: radius (int, optional): The MHFP radius. Defaults to 3. rings (bool, optional): Whether to include rings in the shingling. Defaults to True. isomeric (bool, optional): Whether the isomeric SMILES to be considered. Defaults to False. kekulize (bool, optional): Whether to kekulize the extracted SMILES. Defaults to False. min_radius (int, optional): The minimum radius that is used to extract n-gram. Defaults to 1. fpSize (int, optional): The length of the folded fingerprint. Defaults to 2048. n_permutations (int, optional): The number of permutations used for hashing. Defaults to 0. seed (int, optional): The value used to seed numpy.random. Defaults to 0. """ super().__init__( n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype ) self.radius = radius self.rings = rings self.isomeric = isomeric self.kekulize = kekulize self.min_radius = min_radius self.fpSize = fpSize # Set the .n_permutations and seed without creating the encoder twice self._n_permutations = n_permutations self._seed = seed # create the encoder instance self._recreate_encoder() def __getstate__(self): # Get the state of the parent class state = super().__getstate__() # Remove the unpicklable property from the state state.pop("mhfp_encoder", None) # mhfp_encoder is not picklable return state def __setstate__(self, state): # Restore the state of the parent class super().__setstate__(state) # Re-create the unpicklable property self._recreate_encoder() def _mol2fp(self, mol): return self.mhfp_encoder.EncodeSECFPMol( mol, int(self.radius), bool(self.rings), bool(self.isomeric), bool(self.kekulize), int(self.min_radius), int(self.fpSize), ) def _recreate_encoder(self): self.mhfp_encoder = rdMHFPFingerprint.MHFPEncoder( self._n_permutations, self._seed ) @property def seed(self): return self._seed @seed.setter def seed(self, seed): self._seed = seed # each time the seed parameter is modified refresh an instance of the encoder self._recreate_encoder() @property def n_permutations(self): return self._n_permutations @n_permutations.setter def n_permutations(self, n_permutations): self._n_permutations = n_permutations # each time the n_permutations parameter is modified refresh an instance of the encoder self._recreate_encoder() @property def length(self): warn( "length will be replace by fpSize, due to changes harmonization!", DeprecationWarning, ) return self.fpSize ================================================ FILE: scikit_mol/fingerprints/morgan.py ================================================ from typing import Optional import numpy as np from rdkit.Chem.rdFingerprintGenerator import ( GetMorganFeatureAtomInvGen, GetMorganGenerator, ) from .baseclasses import FpsGeneratorTransformer class MorganFingerprintTransformer(FpsGeneratorTransformer): """RDKit-based Morgan fingerprints transformer for molecular feature generation. Generates fingerprints equivalent to Extended Connectivity Fingerprints (ECFP) from Pipeline Pilot. Features are created by hashing atomic environments within specified radius. Supports optional inclusion of chirality and bond types for enhanced structural representation. """ _regenerate_on_properties = ( "radius", "fpSize", "useChirality", "useFeatures", "useBondTypes", ) def __init__( self, fpSize=2048, radius: int = 2, useChirality: bool = False, useBondTypes: bool = True, useFeatures: bool = False, useCounts: bool = False, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, dtype: np.dtype = None, nBits: Optional[int] = None, ): """Transform RDKit mols into Count or bit-based hashed MorganFingerprints Parameters ---------- fpSize : int, optional Size of the hashed fingerprint, by default 2048 radius : int, optional Radius of the fingerprint, by default 2 useChirality : bool, optional Include chirality in calculation of the fingerprint keys, by default `False` useBondTypes : bool, optional Include bondtypes in calculation of the fingerprint keys, by default `True` useFeatures : bool, optional use chemical features, rather than atom-type in calculation of the fingerprint keys, by default `False` useCounts : bool, optional If toggled will create the count and not bit-based fingerprint, by default `False` n_jobs : int, optional default=None The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `joblib.parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional If `True`, will return masked arrays for invalid mols, by default `False` """ self._initializing = True super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode) self.fpSize = fpSize self.radius = radius self.useChirality = useChirality self.useFeatures = useFeatures self.useCounts = useCounts self.useBondTypes = useBondTypes self.dtype = dtype self.nBits = nBits self._generate_fp_generator() delattr(self, "_initializing") def _generate_fp_generator(self): if self.useFeatures: atomInvariantsGenerator = GetMorganFeatureAtomInvGen() else: atomInvariantsGenerator = None self._fpgen = GetMorganGenerator( radius=int(self.radius), fpSize=int(self.fpSize), includeChirality=bool(self.useChirality), useBondTypes=bool(self.useBondTypes), atomInvariantsGenerator=atomInvariantsGenerator, ) def _transform_mol(self, mol) -> np.array: if self.useCounts: return self._fpgen.GetCountFingerprintAsNumPy(mol) else: return self._fpgen.GetFingerprintAsNumPy(mol) ================================================ FILE: scikit_mol/fingerprints/rdkitfp.py ================================================ from typing import Optional import numpy as np from rdkit.Chem.rdFingerprintGenerator import GetRDKitFPGenerator from .baseclasses import FpsGeneratorTransformer class RDKitFingerprintTransformer(FpsGeneratorTransformer): _regenerate_on_properties = ( "minPath", "maxPath", "useHs", "branchedPaths", "useBondOrder", "countSimulation", "fpSize", "countBounds", "numBitsPerFeature", ) def __init__( self, minPath: int = 1, maxPath: int = 7, useHs: bool = True, branchedPaths: bool = True, useBondOrder: bool = True, countSimulation: bool = False, countBounds=None, fpSize: int = 2048, numBitsPerFeature: int = 2, useCounts: bool = False, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, ): """Calculates the RDKit fingerprints Parameters ---------- minPath : int, optional the minimum path length (in bonds) to be included, by default 1 maxPath : int, optional the maximum path length (in bonds) to be included, by default 7 useHs : bool, optional toggles inclusion of Hs in paths (if the molecule has explicit Hs), by default True branchedPaths : bool, optional toggles generation of branched subgraphs, not just linear paths, by default True useBondOrder : bool, optional toggles inclusion of bond orders in the path hashes, by default True countSimulation : bool, optional if set, use count simulation while generating the fingerprint, by default False countBounds : _type_, optional boundaries for count simulation, corresponding bit will be set if the count is higher than the number provided for that spot, by default None fpSize : int, optional size of the generated fingerprint, does not affect the sparse versions, by default 2048 numBitsPerFeature : int, optional the number of bits set per path/subgraph found, by default 2 n_jobs : int, optional default=None The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `joblib.parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional If `True`, will return masked arrays for invalid mols, by default `False` """ self._initializing = True super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode) self.minPath = minPath self.maxPath = maxPath self.useHs = useHs self.branchedPaths = branchedPaths self.useBondOrder = useBondOrder self.countSimulation = countSimulation self.fpSize = fpSize self.numBitsPerFeature = numBitsPerFeature self.countBounds = countBounds self.useCounts = useCounts self._generate_fp_generator() delattr(self, "_initializing") def _transform_mol(self, mol) -> np.array: if self.useCounts: return self._fpgen.GetCountFingerprintAsNumPy(mol) else: return self._fpgen.GetFingerprintAsNumPy(mol) def _generate_fp_generator(self): self._fpgen = GetRDKitFPGenerator( minPath=int(self.minPath), maxPath=int(self.maxPath), useHs=bool(self.useHs), branchedPaths=bool(self.branchedPaths), useBondOrder=bool(self.useBondOrder), countSimulation=bool(self.countSimulation), fpSize=int(self.fpSize), countBounds=bool(self.countBounds), numBitsPerFeature=int(self.numBitsPerFeature), ) ================================================ FILE: scikit_mol/fingerprints/topologicaltorsion.py ================================================ from typing import Optional, Sequence import numpy as np from rdkit.Chem.rdFingerprintGenerator import GetTopologicalTorsionGenerator from .baseclasses import FpsGeneratorTransformer class TopologicalTorsionFingerprintTransformer(FpsGeneratorTransformer): """ Transformer for generating topological torsion fingerprints. """ _regenerate_on_properties = ("fpSize", "includeChirality", "targetSize") def __init__( self, targetSize: int = 4, fromAtoms: Optional[Sequence] = None, ignoreAtoms: Optional[Sequence] = None, atomInvariants: Optional[Sequence] = None, confId: int = -1, includeChirality: bool = False, fpSize: int = 2048, useCounts: bool = False, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, ): """ Parameters ---------- targetSize : int, optional The number of atoms to include in the torsion, by default 4. fromAtoms : list, optional Atom indices to include in the fingerprint generation, by default None. ignoreAtoms : list, optional Atom indices to exclude in the fingerprint generation, by default None. atomInvariants : list, optional Custom atom invariants to be used, by default None. confId : int, optional Conformation ID to use, by default -1. includeChirality : bool, optional Whether to include chirality in the fingerprint, by default False. fpSize : int, optional Size of the fingerprint, by default 2048. useCounts : bool, optional Whether to use counts in the fingerprint, by default False. n_jobs : int, optional default=None The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a `joblib.parallel_config()` context manager that sets another value for `n_jobs`. safe_inference_mode : bool, optional If `True`, will return masked arrays for invalid mols, by default `False` """ self._initializing = True super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode) self.fpSize = fpSize self.includeChirality = includeChirality self.targetSize = targetSize self.fromAtoms = fromAtoms self.ignoreAtoms = ignoreAtoms self.atomInvariants = atomInvariants self.confId = confId self.useCounts = useCounts self._generate_fp_generator() delattr(self, "_initializing") def _generate_fp_generator(self): """ Generate the fingerprint generator. """ self._fpgen = GetTopologicalTorsionGenerator( torsionAtomCount=int(self.targetSize), includeChirality=bool(self.includeChirality), fpSize=int(self.fpSize), ) def _transform_mol(self, mol) -> np.array: """ Transform a molecule into its fingerprint representation. Parameters ---------- mol : RDKit Mol The molecule to transform. Returns ------- np.array The fingerprint of the molecule as a NumPy array. """ if self.useCounts: return self._fpgen.GetCountFingerprintAsNumPy( mol, fromAtoms=self.fromAtoms, ignoreAtoms=self.ignoreAtoms, customAtomInvariants=self.atomInvariants, ) else: return self._fpgen.GetFingerprintAsNumPy( mol, fromAtoms=self.fromAtoms, ignoreAtoms=self.ignoreAtoms, customAtomInvariants=self.atomInvariants, ) ================================================ FILE: scikit_mol/parallel.py ================================================ from collections.abc import Sequence from typing import Any, Callable, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed, effective_n_jobs def parallelized_with_batches( fn: Callable[[Sequence[Any]], Any], inputs_list: Union[np.ndarray, pd.DataFrame], n_jobs: Optional[int] = None, **job_kwargs: Any, ) -> Sequence[Optional[Any]]: """ Execute a function in parallel with batches of inputs. Parameters ---------- fn : Callable[[Sequence[Any]], Any] The function to be executed in parallel. It should accept a sequence of inputs. inputs_list : np.ndarray The list of inputs to be processed in parallel. n_jobs : Optional[int], default=None The number of jobs to run in parallel. If `None`, it will be determined automatically. **job_kwargs : Any Additional keyword arguments to pass to the `joblib.Parallel` object. Returns ------- Sequence[Optional[Any]] A sequence of results from the function executed in parallel. """ n_jobs = effective_n_jobs(n_jobs) if n_jobs > len(inputs_list): n_jobs = len(inputs_list) pool = Parallel(n_jobs=n_jobs, **job_kwargs) if isinstance(inputs_list, (pd.DataFrame, pd.Series)): indexes = np.array_split(range(len(inputs_list)), n_jobs) input_chunks = [inputs_list.iloc[idx] for idx in indexes] else: input_chunks = np.array_split(inputs_list, n_jobs) results = pool(delayed(fn)(chunk) for chunk in input_chunks) return results ================================================ FILE: scikit_mol/plotting.py ================================================ import os import platform import re import subprocess import time from typing import TYPE_CHECKING, Optional, Sequence import joblib import matplotlib.pyplot as plt import pandas as pd import seaborn as sns if TYPE_CHECKING: from rdkit import Chem class ParallelTester: """ A class to test the performance of a transformer on a set of molecules using parallel processing.\ """ def __init__( self, transformer: object, mols: list["Chem.Mol"], n_mols: Sequence[int] = (10, 100, 100, 1000, 10000, 100000), n_jobs: Sequence[int] = (1, 2, 4, 8), backend: str = "loky", ): """ Parameters ---------- transformer : object The transformer object that has a `transform` method to apply to the molecules mols : Sequence[Chem.Mol] A list of molecules to be transformed n_mols : Sequence[int], optional A tuple of integers specifying the number of molecules to test with n_jobs : Sequence[int], optional A tuple of integers specifying the number of parallel jobs to test with backend : str, optional The parallel backend to use """ self.mols = mols n_mols = sorted(n_mols) if max(n_mols) > len(mols): raise ValueError( f"Maximum number of molecules {max(n_mols)} is greater than the number of molecules {len(mols)}" ) self.n_mols = n_mols n_jobs = sorted(n_jobs) if max(n_jobs) > os.cpu_count(): raise ValueError( f"Maximum number of jobs {max(n_jobs)} is greater than the number of CPUs {os.cpu_count()}" ) self.n_jobs = n_jobs self.transformer = transformer self.backend = backend def _test_single(self, mols, n_jobs): start = time.perf_counter() with joblib.parallel_backend(self.backend, n_jobs=n_jobs): self.transformer.transform(mols) return time.perf_counter() - start def test(self) -> pd.DataFrame: """Tests the transformer on various subsets of molecules with different numbers of parallel jobs and returns the results as a DataFrame. Returns ------- pandas.DataFrame A DataFrame containing the time taken to transform the molecules with different numbers of molecules and parallel jobs. """ results = pd.DataFrame(columns=self.n_mols, index=self.n_jobs) for n_mol in self.n_mols: for n_job in self.n_jobs: results.at[n_job, n_mol] = self._test_single(self.mols[:n_mol], n_job) return results def get_processor_name() -> str: """ Retrieves the name of the processor on the current system. Returns ------- str The name of the processor. Returns an empty string if the processor name cannot be determined. Notes ----- - On Windows, it uses `platform.processor()`. - On macOS (Darwin), it uses the `sysctl` command to get the CPU brand string. - On Linux, it reads `/proc/cpuinfo` to find the model name. """ if platform.system() == "Windows": return platform.processor() elif platform.system() == "Darwin": os.environ["PATH"] = os.environ["PATH"] + os.pathsep + "/usr/sbin" command = "sysctl -n machdep.cpu.brand_string" return subprocess.check_output(command).strip() # noqa: S603 elif platform.system() == "Linux": command = "cat /proc/cpuinfo" all_info = subprocess.check_output(command, shell=True).decode().strip() # noqa: S602 for line in all_info.split("\n"): if "model name" in line: return re.sub( pattern=".*model name.*:", repl="", string=line, count=1 ).strip() return "" def plot_heatmap( df: pd.DataFrame, name: Optional[str] = None, normalize: bool = True ) -> plt.Axes: """ Plots a heatmap of the given DataFrame from [ParallelTester][scikit_mol.plotting.ParallelTester]. Parameters ---------- df : pandas.DataFrame The DataFrame containing the data to be plotted name : str, optional The name to be used in the title of the plot normalize : bool, optional If True, normalize the DataFrame by the first row Returns ------- matplotlib.axes.Axes The Axes object of the plot Notes ----- The function normalizes the DataFrame by dividing by the first row if `normalize` is True. The colormap used is "PiYG_r". The title of the plot includes the maximum single-threaded speed and the CPU name. """ df = df.astype(float) max_speed = (df.columns / df.loc[1]).max() v_min, v_max = None, None if normalize: df = df / df.loc[1] cmap = sns.color_palette("PiYG_r", as_cmap=True) v_min = 0.0 v_max = 2.0 else: cmap = sns.color_palette("PiYG_r", as_cmap=True) plt.figure(figsize=(8, 6)) g = sns.heatmap(df, annot=True, fmt=".2f", cmap=cmap, vmin=v_min, vmax=v_max) title = "" if name: title = name + "\n" title += f"Max single-threaded speed {max_speed:.0f} mols/s" title += "\nCPU: " + get_processor_name() plt.title(title) plt.xlabel("Number of mols") plt.ylabel("Number of jobs") return g ================================================ FILE: scikit_mol/safeinference.py ================================================ """Wrapper for sklearn estimators and pipelines to handle errors.""" import warnings from functools import wraps from typing import Union import numpy as np import pandas as pd from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils import check_array from sklearn.utils.metaestimators import available_if from sklearn.utils.validation import NotFittedError, check_is_fitted from scikit_mol._constants import DOCS_BASE_URL from .utilities import set_safe_inference_mode __all__ = ["MaskedArrayError", "SafeInferenceWrapper", "set_safe_inference_mode"] class MaskedArrayError(ValueError): """Raised when a masked array is passed but safe_inference_mode is False.""" pass def filter_invalid_rows(warn_on_invalid=False, replace_value=np.nan): def decorator(func): @wraps(func) def wrapper(obj, X, y=None, *args, **kwargs): if not getattr(obj, "safe_inference_mode", True): if isinstance(X, np.ma.MaskedArray) and X.mask.any(): raise MaskedArrayError( f"Masked array detected with safe_inference_mode=False and {X.mask.any(axis=1).sum()} filtered rows. " "Set safe_inference_mode=True to process masked arrays for inference of production models." ) return func(obj, X, y, *args, **kwargs) if not hasattr(obj, "replace_value"): raise ValueError( "replace_value must be set in the SafeInferenceWrapper" ) else: replace_value = obj.replace_value # Initialize valid_mask as all True valid_mask = np.ones(X.shape[0], dtype=bool) # Handle masked arrays if isinstance(X, np.ma.MaskedArray): try: valid_mask &= ~X.mask.any(axis=1) # workaround for situation where mask is single boolean (all masked/ all unmasked) and no axis present except np.exceptions.AxisError: valid_mask &= ~X.mask # Handle non-finite values if required if getattr(obj, "mask_nonfinite", True): if isinstance(X, np.ma.MaskedArray): valid_mask &= np.isfinite(X.data).all(axis=1) else: valid_mask &= np.isfinite(X).all(axis=1) if warn_on_invalid and not np.all(valid_mask): warnings.warn( f"SafeInferenceWrapper is in safe_inference_mode during use of {func.__name__} and invalid data detected. " "This mode is intended for safe inference in production, not for training and evaluation.", UserWarning, ) valid_indices = np.where(valid_mask)[0] reduced_X = X[valid_mask] if y is not None: # TODO, how can we check y in the same way as the estimator? y = check_array( y, ensure_all_finite=False, # accept_sparse="csr", ensure_2d=False, dtype=None, input_name="y", estimator=obj, ) reduced_y = y[valid_mask] else: reduced_y = None # handle case where all rows are masked e.g. single invalid input is passed if len(valid_indices) == 0: result = np.array([]) else: result = func(obj, reduced_X, reduced_y, *args, **kwargs) if result is None: return None if isinstance(result, np.ndarray): if result.ndim == 1: output = np.full(X.shape[0], replace_value) else: output = np.full((X.shape[0], result.shape[1]), replace_value) output[valid_indices] = result return output elif isinstance(result, pd.DataFrame): output = pd.DataFrame(index=range(X.shape[0]), columns=result.columns) output.iloc[valid_indices] = result return output elif isinstance(result, pd.Series): output = pd.Series(index=range(X.shape[0]), dtype=result.dtype) output.iloc[valid_indices] = result return output else: return result return wrapper return decorator class SafeInferenceWrapper(TransformerMixin, BaseEstimator): """Wrapper for sklearn estimators to ensure safe inference in production environments. This wrapper is designed to be applied to trained models for use in production settings. While it can be included during model development and training, the safe inference mode should only be enabled when deploying models for inference in production.""" _doc_link_module = "scikit_mol" _doc_link_template = ( DOCS_BASE_URL + "{estimator_module}/#{estimator_module}.{estimator_name}" ) def __init__( self, estimator: BaseEstimator, safe_inference_mode: bool = False, replace_value: Union[int, float, str] = np.nan, mask_nonfinite: bool = True, ): """ Parameters ----------- estimator : BaseEstimator The trained sklearn estimator to be wrapped. safe_inference_mode : bool, default=False If True, enables safeguards for handling invalid data during inference. This should only be set to True when deploying models to production. replace_value : any, default=np.nan The value to use for replacing invalid data points. """ self.estimator = estimator self.safe_inference_mode = safe_inference_mode self.replace_value = replace_value self.mask_nonfinite = mask_nonfinite @property def n_features_in_(self): return self.estimator.n_features_in_ @filter_invalid_rows(warn_on_invalid=True) def fit(self, X, y=None, **fit_params): return self.estimator.fit(X, y, **fit_params) @available_if(lambda self: hasattr(self.estimator, "predict")) @filter_invalid_rows() def predict(self, X, y=None): return self.estimator.predict(X) @available_if(lambda self: hasattr(self.estimator, "predict_proba")) @filter_invalid_rows() def predict_proba(self, X, y=None): return self.estimator.predict_proba(X) @available_if(lambda self: hasattr(self.estimator, "decision_function")) @filter_invalid_rows() def decision_function(self, X, y=None): return self.estimator.decision_function(X) @available_if(lambda self: hasattr(self.estimator, "transform")) @filter_invalid_rows() def transform(self, X, y=None): return self.estimator.transform(X) @available_if(lambda self: hasattr(self.estimator, "fit_transform")) @filter_invalid_rows(warn_on_invalid=True) def fit_transform(self, X, y=None, **fit_params): return self.estimator.fit_transform(X, y, **fit_params) @available_if(lambda self: hasattr(self.estimator, "score")) @filter_invalid_rows(warn_on_invalid=True) def score(self, X, y=None): return self.estimator.score(X, y) @available_if(lambda self: hasattr(self.estimator, "get_feature_names_out")) @filter_invalid_rows(warn_on_invalid=True) def get_feature_names_out(self, *args, **kwargs): return self.estimator.get_feature_names_out(*args, **kwargs) def __sklearn_is_fitted__(self): try: check_is_fitted(self.estimator) return True except NotFittedError: return False ================================================ FILE: scikit_mol/standardizer.py ================================================ # A scikit-learn compatible molecule standardizer # Author: Son Ha import functools from typing import Optional import numpy as np from rdkit import Chem from rdkit.Chem.MolStandardize import rdMolStandardize from rdkit.rdBase import BlockLogs from sklearn.base import BaseEstimator, TransformerMixin from scikit_mol._constants import DOCS_BASE_URL from scikit_mol.core import ( InvalidMol, NoFitNeededMixin, check_transform_input, feature_names_default_mol, ) from scikit_mol.parallel import parallelized_with_batches class Standardizer(TransformerMixin, NoFitNeededMixin, BaseEstimator): """Standardize molecules with RDKit""" _doc_link_module = "scikit_mol" _doc_link_template = ( DOCS_BASE_URL + "{estimator_module}/#{estimator_module}.{estimator_name}" ) def __init__( self, neutralize: bool = True, n_jobs: Optional[int] = None, safe_inference_mode: bool = False, ): """ Parameters ---------- neutralize : bool, optional If True, neutralizes the molecule n_jobs : Optional[int], optional The maximum number of concurrently running jobs. None is a marker for 'unset' that will be interpreted as n_jobs=1 unless the call is performed under a parallel_config() context manager that sets another value for n_jobs safe_inference_mode : bool, optional If True, enables safeguards for handling invalid data during inference. This should only be set to True when deploying models to production """ self.neutralize = neutralize self.n_jobs = n_jobs self.safe_inference_mode = safe_inference_mode def fit(self, X, y=None): return self def _standardize_mol(self, mol): if not mol: if self.safe_inference_mode: if isinstance(mol, InvalidMol): return mol else: return InvalidMol(str(self), f"Invalid input molecule: {mol}") else: raise ValueError(f"Invalid input molecule: {mol}") try: block = BlockLogs() # Block all RDkit logging # Normalizing functional groups clean_mol = rdMolStandardize.Cleanup(mol) # Get parents fragments parent_clean_mol = rdMolStandardize.FragmentParent(clean_mol) # Neutralise if self.neutralize: uncharger = rdMolStandardize.Uncharger() uncharged_parent_clean_mol = uncharger.uncharge(parent_clean_mol) else: uncharged_parent_clean_mol = parent_clean_mol del block # Release logging block to previous state Chem.SanitizeMol(uncharged_parent_clean_mol) return uncharged_parent_clean_mol except Exception as e: if self.safe_inference_mode: return InvalidMol(str(self), f"Standardization failed: {e}") else: raise def _transform(self, X): return np.array([self._standardize_mol(mol) for mol in X]).reshape(-1, 1) @feature_names_default_mol def get_feature_names_out(self, input_features=None): return input_features @check_transform_input def transform(self, X, y=None): parameters = self.get_params() func = functools.partial(parallel_helper, self.__class__.__name__, parameters) arrays = parallelized_with_batches(func, X, self.n_jobs) arr = np.concatenate(arrays) return arr def parallel_helper(classname, parameters, X_mols): """Parallel_helper takes a tuple with classname, the objects parameters and the mols to process. Then instantiates the class with the parameters and processes the mol. Intention is to be able to do this in child processes as some classes can't be pickled""" from scikit_mol import standardizer transformer = getattr(standardizer, classname)(**parameters) return transformer._transform(X_mols) ================================================ FILE: scikit_mol/utilities.py ================================================ # For a non-scikit-learn check smiles sanitizer class import warnings import pandas as pd from rdkit import Chem from sklearn.base import BaseEstimator from sklearn.compose import ColumnTransformer from sklearn.pipeline import FeatureUnion, Pipeline class CheckSmilesSanitization: def __init__(self, return_mol=False): self.return_mol = return_mol self.errors = pd.DataFrame() def sanitize(self, X_smiles_list, y=None): if y: y_out = [] X_out = [] y_errors = [] X_errors = [] for smiles, y_value in zip(X_smiles_list, y): mol = Chem.MolFromSmiles(smiles) if mol: if self.return_mol: X_out.append(mol) else: X_out.append(smiles) y_out.append(y_value) else: X_errors.append(smiles) y_errors.append(y_value) if X_errors: print( f"Error in parsing {len(X_errors)} SMILES. Unparsable SMILES can be found in self.errors" ) self.errors = pd.DataFrame({"SMILES": X_errors, "y": y_errors}) return X_out, y_out, X_errors, y_errors else: X_out = [] X_errors = [] for smiles in X_smiles_list: mol = Chem.MolFromSmiles(smiles) if mol: if self.return_mol: X_out.append(mol) else: X_out.append(smiles) else: X_errors.append(smiles) if X_errors: print( f"Error in parsing {len(X_errors)} SMILES. Unparsable SMILES can be found in self.errors" ) self.errors = pd.DataFrame({"SMILES": X_errors}) return X_out, X_errors """ R :param estimator: A scikit-learn estimator, pipeline, or custom wrapper :param value: Boolean value to set for safe_inference_mode """ def set_safe_inference_mode(estimator: BaseEstimator, value: bool) -> BaseEstimator: """Recursively set the safe_inference_mode parameter for all compatible estimators. Parameters ---------- estimator : A scikit-learn estimator, pipeline, or custom wrapper value : Boolean value to set for safe_inference_mode Returns ------- BaseEstimator The estimator with the `safe_inference_mode` parameter set to the specified value """ def _set_safe_inference_mode_recursive(est, val): if hasattr(est, "safe_inference_mode"): est.safe_inference_mode = val # Handle Pipeline if isinstance(est, Pipeline): for _, step in est.steps: _set_safe_inference_mode_recursive(step, val) # Handle FeatureUnion elif isinstance(est, FeatureUnion): for _, transformer in est.transformer_list: _set_safe_inference_mode_recursive(transformer, val) # Handle ColumnTransformer elif isinstance(est, ColumnTransformer): for _, transformer, _ in est.transformers: _set_safe_inference_mode_recursive(transformer, val) # Handle SafeInferenceWrapper elif hasattr(est, "estimator") and isinstance(est.estimator, BaseEstimator): _set_safe_inference_mode_recursive(est.estimator, val) # Handle other estimators with get_params elif isinstance(est, BaseEstimator): params = est.get_params(deep=False) for _, param_value in params.items(): if isinstance(param_value, BaseEstimator): _set_safe_inference_mode_recursive(param_value, val) # Apply the recursive function _set_safe_inference_mode_recursive(estimator, value) # Final check params = estimator.get_params(deep=True) mismatched_params = [ key.rstrip("__safe_inference_mode") for key, val in params.items() if key.endswith("__safe_inference_mode") and val != value ] if mismatched_params: warnings.warn( f"The following components have 'safe_inference_mode' set to a different value than requested: {mismatched_params}. " "This could be due to nested estimators that were not properly handled.", UserWarning, ) return estimator ================================================ FILE: setup.cfg ================================================ [metadata] name = scikit_mol url = https://github.com/EBjerrum/scikit-mol download_url = https://github.com/EBjerrum/scikit-mol license = LGPL-3.0 license_file = LICENSE description = scikit-learn classes for molecule transformation long_description = file: README.md long_description_content_type = text/markdown author = Esben Jannik Bjerrum author_email = esben@cheminformania.com classifiers = Development Status :: 3 - Alpha Intended Audience :: Science/Research License :: OSI Approved :: Apache Software License Programming Language :: Python Programming Language :: Python :: 3 :: Only Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 Topic :: Scientific/Engineering Topic :: Utilities Operating System :: Microsoft :: Windows Operating System :: POSIX Operating System :: Unix Operating System :: MacOS [options] packages = find: python_requires = >=3.8 install_requires = rdkit numpy pandas scikit-learn packaging [options.packages.find] exclude = tests/* [options.extras_require] dev = pytest>=6 pytest-cov jupytext pre-commit ruff==0.8.6 ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/applicability/__init__.py ================================================ ================================================ FILE: tests/applicability/conftest.py ================================================ import numpy as np import pytest from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from scikit_mol.applicability import ( BoundingBoxApplicabilityDomain, ConvexHullApplicabilityDomain, HotellingT2ApplicabilityDomain, IsolationForestApplicabilityDomain, KernelDensityApplicabilityDomain, KNNApplicabilityDomain, LeverageApplicabilityDomain, LocalOutlierFactorApplicabilityDomain, MahalanobisApplicabilityDomain, StandardizationApplicabilityDomain, TopkatApplicabilityDomain, ) from scikit_mol.fingerprints import MorganFingerprintTransformer from ..fixtures import mols_list @pytest.fixture( params=[ (KNNApplicabilityDomain, dict(n_neighbors=3)), (LeverageApplicabilityDomain, dict(threshold_factor=3)), (BoundingBoxApplicabilityDomain, dict(percentile=(1, 99))), (ConvexHullApplicabilityDomain, dict()), # No special parameters needed (HotellingT2ApplicabilityDomain, dict(significance=0.05)), ( IsolationForestApplicabilityDomain, dict( n_estimators=100, contamination=0.1, random_state=42, # Add fixed random state ), ), ( KernelDensityApplicabilityDomain, dict(bandwidth=1.0, kernel="gaussian"), ), ( LocalOutlierFactorApplicabilityDomain, dict( n_neighbors=3, contamination=0.1 ), # Reduced from 20 to 3 for small test datasets ), (MahalanobisApplicabilityDomain, dict()), # No special parameters needed (StandardizationApplicabilityDomain, dict()), # No special parameters needed (TopkatApplicabilityDomain, dict()), # No special parameters needed ] ) def ad_estimator(request): """Fixture providing fresh AD estimator instances.""" estimator_class, params = request.param return estimator_class(**params) @pytest.fixture def reduced_fingerprints(mols_list): """Create dimensionality-reduced fingerprints for AD testing.""" # Generate larger fingerprints first fps = MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list) # Reduce dimensionality while preserving ~90% variance pca = PCA(n_components=0.9) return StandardScaler().fit_transform(pca.fit_transform(fps)) @pytest.fixture def binary_fingerprints(mols_list): """Binary fingerprints for testing e.g. Tanimoto distance.""" return MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list) @pytest.fixture def ad_test_data(): """Simple 2D data with clear in/out domain regions.""" rng = np.random.RandomState(42) # Fixed seed for reproducibility X_train = rng.uniform(0, 1, (20, 2)) X_test_in = rng.uniform(0.25, 0.75, (5, 2)) X_test_out = rng.uniform(2, 3, (5, 2)) X_test = np.vstack([X_test_in, X_test_out]) y_test = np.array([1] * 5 + [-1] * 5) return X_train, X_test, y_test ================================================ FILE: tests/applicability/test_base.py ================================================ """Common tests for all applicability domain estimators.""" import numpy as np import pytest from numpy.testing import assert_array_almost_equal, assert_array_equal from sklearn.utils.estimator_checks import check_estimator def test_basic_functionality(ad_estimator, reduced_fingerprints): """Test basic fit/transform on reduced fingerprints.""" ad_estimator.fit(reduced_fingerprints) scores = ad_estimator.transform(reduced_fingerprints) assert scores.shape == (len(reduced_fingerprints), 1) assert np.isfinite(scores).all() def test_predict_functionality(ad_estimator, ad_test_data): """Test predict method returns expected values.""" X_train, X_test, expected = ad_test_data # Fit and predict ad_estimator.fit(X_train) predictions = ad_estimator.predict(X_test) # Check output format assert predictions.shape == (len(X_test),) # Should be 1D assert set(np.unique(predictions)) <= {-1, 1} # Only -1 and 1 allowed # Check predictions make sense (in/out of domain) accuracy = np.mean(predictions == expected) assert accuracy >= 0.8 # Allow some misclassification def test_score_transform(ad_estimator, ad_test_data): """Test score_transform returns valid probability-like scores.""" X_train, X_test, expected = ad_test_data # Fit and get scores ad_estimator.fit(X_train) scores = ad_estimator.score_transform(X_test) # Check output format assert scores.shape == (len(X_test), 1) assert np.all((0 <= scores) & (scores <= 1)) # Scores in [0,1] # Check scores correlate with domain membership in_domain = expected == 1 mean_in = np.mean(scores[in_domain]) mean_out = np.mean(scores[~in_domain]) assert mean_in > mean_out # Inside domain should have higher scores @pytest.mark.threshold_fitting def test_threshold_setting(ad_estimator, reduced_fingerprints): """Test threshold setting and percentile behavior.""" if not ad_estimator._supports_threshold_fitting: pytest.skip("Estimator does not support threshold fitting") # Test default threshold ad_estimator.fit(reduced_fingerprints) pred_default = ad_estimator.predict(reduced_fingerprints) # Test custom percentile ad_estimator.percentile = 90 ad_estimator.fit_threshold(reduced_fingerprints) pred_90 = ad_estimator.predict(reduced_fingerprints) # More samples should be outside with stricter threshold n_inside_default = np.sum(pred_default == 1) n_inside_90 = np.sum(pred_90 == 1) assert n_inside_90 <= n_inside_default def test_feature_names(ad_estimator, reduced_fingerprints): """Test feature names are properly handled.""" ad_estimator.fit(reduced_fingerprints) # Check feature names exist and match name feature_names = ad_estimator.get_feature_names_out() assert len(feature_names) == 1 assert feature_names[0] == ad_estimator.feature_name def test_pandas_output(ad_estimator, reduced_fingerprints): """Test pandas DataFrame output functionality.""" ad_estimator.set_output(transform="pandas") ad_estimator.fit(reduced_fingerprints) # Test transform output scores_df = ad_estimator.transform(reduced_fingerprints) assert hasattr(scores_df, "columns") assert len(scores_df.columns) == 1 assert scores_df.columns[0] == ad_estimator.feature_name # Test predict output pred_df = ad_estimator.predict(reduced_fingerprints) assert hasattr(pred_df, "columns") assert len(pred_df.columns) == 1 def test_input_validation(ad_estimator): """Test input validation and error handling.""" # Test fitting with invalid input with pytest.raises(ValueError): ad_estimator.fit([[]]) # Empty data with pytest.raises(ValueError): ad_estimator.fit([[1], [2, 3]]) # Inconsistent dimensions # Test invalid percentile only if threshold fitting is supported if ad_estimator._supports_threshold_fitting: with pytest.raises(ValueError): ad_estimator.percentile = 101 ad_estimator.fit([[1, 2]]) def test_refit_consistency(ad_estimator, reduced_fingerprints): """Test consistency when refitting with same data.""" ad_estimator.fit(reduced_fingerprints) scores1 = ad_estimator.transform(reduced_fingerprints) ad_estimator.fit(reduced_fingerprints) scores2 = ad_estimator.transform(reduced_fingerprints) assert_array_almost_equal(scores1, scores2) ================================================ FILE: tests/applicability/test_bounding_box.py ================================================ """Tests specific to Bounding Box applicability domain.""" import numpy as np import pytest from sklearn.exceptions import NotFittedError from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from scikit_mol.applicability import BoundingBoxApplicabilityDomain def test_bounding_box_bounds(ad_test_data): """Test the bounds calculation.""" X_train, _, _ = ad_test_data ad = BoundingBoxApplicabilityDomain(percentile=(1, 99)) ad.fit(X_train) # Check bounds match numpy percentile expected_min = np.percentile(X_train, 1, axis=0) expected_max = np.percentile(X_train, 99, axis=0) assert np.allclose(ad.min_, expected_min) assert np.allclose(ad.max_, expected_max) def test_bounding_box_violations(): """Test violation counting.""" X_train = np.array([[1, 1], [2, 2], [3, 3]]) X_test = np.array( [ [2, 2], # Inside bounds (0 violations) [0, 2], # One violation [0, 4], # Two violations ] ) ad = BoundingBoxApplicabilityDomain(percentile=(0, 100)) ad.fit(X_train) scores = ad.transform(X_test) assert scores[0, 0] == 0 # Inside bounds assert scores[1, 0] == 1 # One violation assert scores[2, 0] == 2 # Two violations def test_bounding_box_percentile_validation(): """Test percentile parameter validation.""" # Invalid single percentile with pytest.raises(ValueError): BoundingBoxApplicabilityDomain(percentile=101) # Invalid tuple length with pytest.raises(ValueError): BoundingBoxApplicabilityDomain(percentile=(1, 2, 3)) # Invalid order with pytest.raises(ValueError): BoundingBoxApplicabilityDomain(percentile=(99, 1)) def test_bounding_box_pipeline(): """Test bounding box works in pipeline with scaling.""" X = np.random.randn(10, 5) pipe = Pipeline( [("scaler", StandardScaler()), ("ad", BoundingBoxApplicabilityDomain())] ) # Should run without errors pipe.fit(X) scores = pipe.transform(X) assert scores.shape == (len(X), 1) ================================================ FILE: tests/applicability/test_convex_hull.py ================================================ """Tests specific to Convex Hull applicability domain.""" import numpy as np import pytest from sklearn.decomposition import PCA from sklearn.exceptions import NotFittedError from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from scikit_mol.applicability import ConvexHullApplicabilityDomain def test_convex_hull_simple(): """Test with simple 2D data where result is obvious.""" # Create a triangle of points X_train = np.array([[0, 0], [1, 0], [0, 1]]) X_test = np.array( [ [0.5, 0.25], # Inside triangle [2, 2], # Outside triangle ] ) ad = ConvexHullApplicabilityDomain() ad.fit(X_train) scores = ad.transform(X_test) assert scores[0, 0] == 0.0 # Inside point assert scores[1, 0] == 1.0 # Outside point def test_convex_hull_pipeline(): """Test convex hull works in pipeline with dimensionality reduction.""" pipe = Pipeline( [ ("scaler", StandardScaler()), ("pca", PCA(n_components=2)), # Reduce to 2D for speed ("ad", ConvexHullApplicabilityDomain()), ] ) # Generate random high-dimensional data X = np.random.randn(10, 5) # Should run without errors pipe.fit(X) scores = pipe.transform(X) assert scores.shape == (len(X), 1) assert np.all((scores == 0) | (scores == 1)) # Binary output def test_convex_hull_numerical_stability(): """Test numerical stability with nearly colinear points.""" X_train = np.array( [ [0, 0], [1, 0], [2, 1e-10], # Nearly colinear ] ) X_test = np.array([[0.5, 0]]) ad = ConvexHullApplicabilityDomain() ad.fit(X_train) # Should not raise and give consistent results scores = ad.transform(X_test) assert np.all(np.isfinite(scores)) def test_convex_hull_single_point(): """Test behavior with single point (degenerate hull).""" X_train = np.array([[1, 1]]) X_test = np.array([[1, 1], [2, 2]]) ad = ConvexHullApplicabilityDomain() ad.fit(X_train) scores = ad.transform(X_test) assert scores[0, 0] == 0.0 # Same point assert scores[1, 0] == 1.0 # Different point ================================================ FILE: tests/applicability/test_hotelling.py ================================================ """Tests specific to Hotelling T² applicability domain.""" import numpy as np import pytest from sklearn.exceptions import NotFittedError from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from scikit_mol.applicability import HotellingT2ApplicabilityDomain def test_hotelling_threshold(): """Test F-distribution threshold calculation.""" X = np.random.randn(100, 3) # 100 samples, 3 features ad = HotellingT2ApplicabilityDomain(significance=0.05) ad.fit(X) # Threshold should be positive assert ad.threshold_ > 0 # More stringent significance should give higher threshold ad_strict = HotellingT2ApplicabilityDomain(significance=0.01) ad_strict.fit(X) assert ad_strict.threshold_ > ad.threshold_ def test_hotelling_scores(): """Test score calculation with known data.""" # Create data with known center and spread X_train = np.array([[0, 0], [1, 0], [-1, 0], [0, 1], [0, -1]]) X_test = np.array( [ [0, 0], # Center point [2, 0], # Further out [10, 10], # Far out ] ) ad = HotellingT2ApplicabilityDomain() ad.fit(X_train) scores = ad.transform(X_test) # Scores should increase with distance from center assert scores[0, 0] < scores[1, 0] < scores[2, 0] def test_hotelling_significance_validation(): """Test significance parameter validation.""" with pytest.raises(ValueError): HotellingT2ApplicabilityDomain(significance=0) with pytest.raises(ValueError): HotellingT2ApplicabilityDomain(significance=1) with pytest.raises(ValueError): HotellingT2ApplicabilityDomain(significance=-0.5) def test_hotelling_pipeline(): """Test Hotelling works in pipeline with scaling.""" pipe = Pipeline( [("scaler", StandardScaler()), ("ad", HotellingT2ApplicabilityDomain())] ) X = np.random.randn(10, 5) # Should run without errors pipe.fit(X) scores = pipe.transform(X) assert scores.shape == (len(X), 1) assert np.all(scores >= 0) # Scores should be non-negative def test_hotelling_threshold_fitting(): """Test threshold fitting with percentile.""" X = np.random.randn(100, 3) ad = HotellingT2ApplicabilityDomain(percentile=90) ad.fit(X) # Get scores and check threshold matches 90th percentile scores = ad.transform(X) expected_threshold = np.percentile(scores, 90) assert np.isclose(ad.threshold_, expected_threshold) ================================================ FILE: tests/applicability/test_isolation_forest.py ================================================ """Tests specific to Isolation Forest applicability domain.""" import numpy as np from scikit_mol.applicability import IsolationForestApplicabilityDomain def test_refit_consistency(): """Test consistency when refitting with same data.""" X = np.random.RandomState(42).normal(0, 1, (100, 2)) # Use fixed random state ad = IsolationForestApplicabilityDomain( n_estimators=100, contamination=0.1, random_state=42 ) # First fit ad.fit(X) scores1 = ad.transform(X) # Second fit ad.fit(X) scores2 = ad.transform(X) assert np.allclose(scores1, scores2) ================================================ FILE: tests/applicability/test_kernel_density.py ================================================ """Tests for KernelDensityApplicabilityDomain.""" import pytest from scikit_mol.applicability import KernelDensityApplicabilityDomain @pytest.fixture def ad_estimator(): """Fixture providing a KernelDensityApplicabilityDomain instance.""" return KernelDensityApplicabilityDomain() def test_kernel_parameter(): """Test different kernel parameters.""" kernels = ["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] # Create data with clear density gradient X = [[0, 0], [0.1, 0.1], [0.2, 0.2], [2, 2]] for kernel in kernels: ad = KernelDensityApplicabilityDomain(kernel=kernel) ad.fit(X) scores = ad.transform(X) assert scores.shape == (4, 1) # First point should have higher density than last point assert scores[0, 0] > scores[-1, 0], f"Failed for kernel {kernel}" def test_bandwidth_effect(): """Test effect of bandwidth parameter on scores.""" X = [[0, 0], [1, 1], [2, 2]] test_point = [[10, 10]] # Far from training data # Larger bandwidth should give higher scores to outliers ad_small = KernelDensityApplicabilityDomain(bandwidth=0.1) ad_large = KernelDensityApplicabilityDomain(bandwidth=10.0) ad_small.fit(X) ad_large.fit(X) score_small = ad_small.transform(test_point) score_large = ad_large.transform(test_point) assert score_large[0, 0] > score_small[0, 0] ================================================ FILE: tests/applicability/test_knn.py ================================================ """Tests specific to KNN applicability domain.""" import numpy as np import pytest from scikit_mol.applicability import KNNApplicabilityDomain from scikit_mol.fingerprints import MorganFingerprintTransformer @pytest.fixture def binary_fingerprints(mols_list): """Binary fingerprints for testing Tanimoto distance.""" return MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list) def test_knn_tanimoto(binary_fingerprints): """Test KNN with Tanimoto distance on binary fingerprints.""" ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric="tanimoto") ad.fit(binary_fingerprints) scores = ad.transform(binary_fingerprints) assert scores.shape == (len(binary_fingerprints), 1) assert np.all((0 <= scores) & (scores <= 1)) # Tanimoto distances are [0,1] # ... other KNN-specific tests ... ================================================ FILE: tests/applicability/test_leverage.py ================================================ """Tests specific to Leverage applicability domain.""" import numpy as np import pytest from sklearn.decomposition import PCA from sklearn.exceptions import NotFittedError from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from scikit_mol.applicability import LeverageApplicabilityDomain def test_leverage_statistical_threshold(ad_test_data): """Test the statistical threshold calculation.""" X_train, _, _ = ad_test_data ad = LeverageApplicabilityDomain(threshold_factor=3) ad.fit(X_train) # Check threshold matches formula h* = 3 * (p+1)/n n_samples, n_features = X_train.shape expected_threshold = 3 * (n_features + 1) / n_samples assert np.isclose(ad.threshold_, expected_threshold) def test_leverage_pipeline(reduced_fingerprints): """Test leverage works in pipeline with scaling and PCA.""" pipe = Pipeline( [ ("scaler", StandardScaler()), ("pca", PCA(n_components=0.95)), ("ad", LeverageApplicabilityDomain()), ] ) # Should run without errors pipe.fit(reduced_fingerprints) scores = pipe.transform(reduced_fingerprints) assert scores.shape == (len(reduced_fingerprints), 1) def test_leverage_threshold_factor(): """Test different threshold factors.""" X = np.array([[1, 2], [3, 4], [5, 6]]) ad1 = LeverageApplicabilityDomain(threshold_factor=3) ad2 = LeverageApplicabilityDomain(threshold_factor=2) ad1.fit(X) ad2.fit(X) # Higher threshold factor should result in higher threshold assert ad1.threshold_ > ad2.threshold_ def test_leverage_var_covar_matrix(ad_test_data): """Test the variance-covariance matrix calculation.""" X_train, _, _ = ad_test_data ad = LeverageApplicabilityDomain() ad.fit(X_train) # Check matrix properties assert ad.var_covar_.shape == (X_train.shape[1], X_train.shape[1]) assert np.allclose(ad.var_covar_, ad.var_covar_.T) # Should be symmetric ================================================ FILE: tests/applicability/test_local_outlier.py ================================================ """Tests for LocalOutlierFactorApplicabilityDomain.""" import numpy as np import pytest from scikit_mol.applicability import LocalOutlierFactorApplicabilityDomain @pytest.fixture def ad_estimator(): """Fixture providing a LocalOutlierFactorApplicabilityDomain instance.""" return LocalOutlierFactorApplicabilityDomain() def test_n_neighbors_effect(): """Test effect of n_neighbors parameter on scores.""" # Create data with clear outlier X = np.vstack([np.random.randn(50, 2), [[10, 10]]]) outlier = np.array([[10, 10]]) # Compare different n_neighbors settings ad_small = LocalOutlierFactorApplicabilityDomain(n_neighbors=2) ad_large = LocalOutlierFactorApplicabilityDomain(n_neighbors=5) ad_small.fit(X) ad_large.fit(X) score_small = ad_small.transform(outlier) score_large = ad_large.transform(outlier) # Scores should be different but both should identify the point as an outlier assert score_small != score_large assert ad_small.predict(outlier) == -1 assert ad_large.predict(outlier) == -1 def test_metric_parameter(): """Test different metric parameters.""" metrics = ["euclidean", "manhattan", "cosine"] X = np.random.randn(10, 2) for metric in metrics: ad = LocalOutlierFactorApplicabilityDomain(metric=metric) ad.fit(X) scores = ad.transform(X) assert scores.shape == (10, 1) def test_contamination_effect(): """Test effect of contamination parameter on predictions.""" X = np.random.randn(100, 2) # Compare different contamination levels ad_low = LocalOutlierFactorApplicabilityDomain(contamination=0.05) ad_high = LocalOutlierFactorApplicabilityDomain(contamination=0.25) ad_low.fit(X) ad_high.fit(X) pred_low = ad_low.predict(X) pred_high = ad_high.predict(X) # Higher contamination should result in more outliers assert np.sum(pred_high == -1) > np.sum(pred_low == -1) ================================================ FILE: tests/applicability/test_mahalanobis.py ================================================ """Tests for MahalanobisApplicabilityDomain.""" import numpy as np import pytest from numpy.testing import assert_array_almost_equal from scikit_mol.applicability import MahalanobisApplicabilityDomain @pytest.fixture def ad_estimator(): """Fixture providing a MahalanobisApplicabilityDomain instance.""" return MahalanobisApplicabilityDomain() def test_statistical_threshold(): """Test chi-square based statistical threshold.""" # Create multivariate normal data n_samples = 1000 n_features = 3 mean = np.zeros(n_features) cov = np.eye(n_features) X = np.random.multivariate_normal(mean, cov, n_samples) # Fit with statistical threshold ad = MahalanobisApplicabilityDomain(percentile=None) ad.fit(X) # For standard normal data, ~95% should be within threshold predictions = ad.predict(X) inside_ratio = np.mean(predictions == 1) assert 0.93 <= inside_ratio <= 0.97 # Allow some variation def test_mean_covariance(): """Test mean and covariance computation.""" X = np.array([[1, 2], [3, 4], [5, 6]]) ad = MahalanobisApplicabilityDomain() ad.fit(X) # Check mean computation expected_mean = np.array([3, 4]) assert_array_almost_equal(ad.mean_, expected_mean) # Check covariance computation expected_cov = np.array([[4, 4], [4, 4]]) assert_array_almost_equal(ad.covariance_, expected_cov) def test_distance_properties(): """Test properties of Mahalanobis distances.""" # Create data with clear outlier X = np.vstack([np.random.randn(50, 2), [[10, 10]]]) outlier = np.array([[10, 10]]) ad = MahalanobisApplicabilityDomain() ad.fit(X) # Distance to mean should be zero mean_dist = ad.transform(ad.mean_.reshape(1, -1)) assert_array_almost_equal(mean_dist, [[0]], decimal=10) # Outlier should have large distance and be predicted outside outlier_dist = ad.transform(outlier) assert outlier_dist[0, 0] > ad.threshold_ assert ad.predict(outlier) == -1 ================================================ FILE: tests/applicability/test_standardization.py ================================================ """Tests for StandardizationApplicabilityDomain.""" import numpy as np import pytest from numpy.testing import assert_array_almost_equal from scikit_mol.applicability import StandardizationApplicabilityDomain @pytest.fixture def ad_estimator(): """Fixture providing a StandardizationApplicabilityDomain instance.""" return StandardizationApplicabilityDomain() def test_statistical_threshold(): """Test normal distribution based statistical threshold.""" # Create standard normal data n_samples = 1000 n_features = 3 X = np.random.randn(n_samples, n_features) # Fit with statistical threshold ad = StandardizationApplicabilityDomain(percentile=None) ad.fit(X) # For standard normal data, ~95% should be within threshold predictions = ad.predict(X) inside_ratio = np.mean(predictions == 1) assert 0.93 <= inside_ratio <= 0.97 # Allow some variation def test_standardization(): """Test standardization of features.""" X = np.array([[1, 2], [3, 4], [5, 6]]) ad = StandardizationApplicabilityDomain() ad.fit(X) # Transform data X_std = ad.scaler_.transform(X) # Check standardization properties assert_array_almost_equal(np.mean(X_std, axis=0), [0, 0]) assert_array_almost_equal(np.std(X_std, axis=0), [1, 1]) def test_max_absolute_score(): """Test that scores are maximum absolute standardized values.""" # Create data with known standardized values X = np.array([[0, 0], [1, 2], [3, -4]]) ad = StandardizationApplicabilityDomain() ad.fit(X) # Create test point with one extreme standardized value X_test = np.array([[0, 10]]) # Second feature will be very large when standardized scores = ad.transform(X_test) # Score should be the maximum absolute standardized value X_std = ad.scaler_.transform(X_test) expected_score = np.max(np.abs(X_std)) assert_array_almost_equal(scores, [[expected_score]]) def test_outlier_detection(): """Test outlier detection on simple dataset.""" # Create data with clear outlier X = np.vstack([np.random.randn(50, 2), [[10, 10]]]) outlier = np.array([[10, 10]]) ad = StandardizationApplicabilityDomain() ad.fit(X) # Outlier should have high score and be predicted outside outlier_score = ad.transform(outlier) assert outlier_score[0, 0] > ad.threshold_ assert ad.predict(outlier) == -1 ================================================ FILE: tests/applicability/test_topkat.py ================================================ """Tests for TopkatApplicabilityDomain.""" import numpy as np import pytest from numpy.testing import assert_array_almost_equal from scikit_mol.applicability import TopkatApplicabilityDomain @pytest.fixture def ad_estimator(): """Fixture providing a TopkatApplicabilityDomain instance.""" return TopkatApplicabilityDomain() def test_ops_transformation(): """Test OPS transformation and distance calculation.""" # Create simple test data X_train = np.array([[0, 0], [1, 1], [2, 2]]) X_test = np.array([[0.5, 0.5], [10, 10]]) # Fit AD model ad = TopkatApplicabilityDomain() ad.fit(X_train) # Check distances distances = ad.transform(X_test) assert distances.shape == (2, 1) assert distances[0] < distances[1] # Interpolated point should have lower distance def test_fixed_threshold(): """Test that threshold is based on dimensionality.""" X = np.random.randn(10, 3) ad = TopkatApplicabilityDomain() ad.fit(X) # Check threshold formula expected_threshold = 5 * (3 + 1) / (2 * 3) # n_features = 3 assert_array_almost_equal(ad.threshold_, expected_threshold) def test_eigendecomposition(): """Test eigendecomposition properties.""" X = np.random.randn(10, 2) ad = TopkatApplicabilityDomain() ad.fit(X) # Check eigenvalue/vector shapes assert ad.eigen_val_.shape == (3,) # n_features + 1 assert ad.eigen_vec_.shape == (3, 3) # (n_features + 1, n_features + 1) # Check eigenvalues are real and sorted assert np.all(np.isreal(ad.eigen_val_)) assert np.all(np.diff(ad.eigen_val_) >= 0) # Sorted in ascending order ================================================ FILE: tests/conftest.py ================================================ import hashlib import shutil from pathlib import Path, PurePath from urllib.parse import urlsplit from urllib.request import urlopen import numpy as np import pandas as pd import pytest import sklearn # Register custom marks def pytest_configure(config): config.addinivalue_line( "markers", "threshold_fitting: mark tests that verify threshold fitting functionality", ) TEST_DATA_URL = "https://ndownloader.figshare.com/files/25747817" TEST_DATA_MD5 = "1ec89bde544c3c4bc400d5b75315921e" def md5(fn): m = hashlib.md5() with open(fn, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): m.update(chunk) return m.hexdigest() @pytest.fixture(scope="session") def data_pth(tmp_path_factory) -> Path: """download the smallest aperio test image svs or use local""" filename = PurePath(urlsplit(TEST_DATA_URL).path).name data_dir = Path(__file__).parent / "data" data_dir.mkdir(parents=True, exist_ok=True) data_fn = data_dir / filename if not data_fn.is_file(): # download svs from openslide test images with urlopen(TEST_DATA_URL) as response, open(data_fn, "wb") as out_file: shutil.copyfileobj(response, out_file) if md5(data_fn) != TEST_DATA_MD5: # pragma: no cover shutil.rmtree(data_fn) pytest.fail("incorrect md5") yield data_fn.absolute() @pytest.fixture() def data(data_pth) -> pd.DataFrame: yield pd.read_csv(data_pth) @pytest.fixture(scope="module") def pandas_output(): """Set sklearn to output pandas dataframes""" sklearn.set_config(transform_output="pandas") yield sklearn.set_config(transform_output="default") # Fixed Numpy random seed in all tests automatically @pytest.fixture(autouse=True) def setup_random(): """Set fixed random seed before each test.""" np.random.seed(0xDEADFACE) ================================================ FILE: tests/fixtures.py ================================================ import os from pathlib import Path import numpy as np import pandas as pd import pytest import sklearn from packaging.version import Version from rdkit import Chem from rdkit.Chem import rdMolDescriptors from sklearn.compose import make_column_selector, make_column_transformer from sklearn.pipeline import make_pipeline from sklearn.preprocessing import FunctionTransformer from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import ( DEFAULT_MOL_COLUMN_NAME, SKLEARN_VERSION_PANDAS_OUT, InvalidMol, ) from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.fingerprints import ( AtomPairFingerprintTransformer, AvalonFingerprintTransformer, MACCSKeysFingerprintTransformer, MHFingerprintTransformer, MorganFingerprintTransformer, RDKitFingerprintTransformer, SECFingerprintTransformer, TopologicalTorsionFingerprintTransformer, ) from scikit_mol.standardizer import Standardizer # TODO these should really go into the conftest.py, so that they are automatically imported in the tests _SMILES_LIST = [ "O=C(O)c1ccccc1", "O=C([O-])c1ccccc1", "O=C([O-])c1ccccc1.[Na+]", "O=C(O[Na])c1ccccc1", "C[N+](C)C.O=C([O-])c1ccccc1", ] _CANONICAL_SMILES_LIST = [ Chem.MolToSmiles(Chem.MolFromSmiles(smiles)) for smiles in _SMILES_LIST ] @pytest.fixture def smiles_list(): return _CANONICAL_SMILES_LIST.copy() _CONTAINER_CREATORS = [ lambda x: x, lambda x: np.array(x), lambda x: np.array(x).reshape(-1, 1), ] _names_to_test = [ "molecule", "mol", "smiles", DEFAULT_MOL_COLUMN_NAME, "hello", None, ] for name in _names_to_test: _CONTAINER_CREATORS.append(lambda x, name=name: pd.Series(x, name=name)) _CONTAINER_CREATORS.append( lambda x, name=name: pd.DataFrame({name: x}) if name else pd.DataFrame(x) ) @pytest.fixture( params=[container(_CANONICAL_SMILES_LIST) for container in _CONTAINER_CREATORS] ) def smiles_container( request, ): return request.param.copy() @pytest.fixture def chiral_smiles_list(): # Need to be a certain size, so the fingerprints reacts to different max_lenǵths and radii return [ Chem.MolToSmiles(Chem.MolFromSmiles(smiles)) for smiles in [ "N[C@@H](C)C(=O)OCCCCCCCCCCCC", "C1C[C@H]2CCCC[C@H]2CC1CCCCCCCCC", "N[C@@H](C)C(=O)Oc1ccccc1CCCCCCCCCCCCCCCCCCN[H]", ] ] @pytest.fixture def smiles_list_with_invalid(smiles_list): data = smiles_list.copy() data.extend(["invalid"]) return data @pytest.fixture def invalid_smiles_list(): return ["NOT MOL", "invalid", "C(=O)OX", "XC1CCCCC1"] _MOLS_LIST = [Chem.MolFromSmiles(smiles) for smiles in _SMILES_LIST] @pytest.fixture def mols_list(): return _MOLS_LIST.copy() @pytest.fixture(params=[container(_MOLS_LIST) for container in _CONTAINER_CREATORS]) def mols_container(request): return request.param.copy() @pytest.fixture def chiral_mols_list(chiral_smiles_list): return [Chem.MolFromSmiles(smiles) for smiles in chiral_smiles_list] @pytest.fixture def mols_with_invalid_container(smiles_list_with_invalid): mols = [] for smiles in smiles_list_with_invalid: mol = Chem.MolFromSmiles(smiles) if mol is None: mols.append(InvalidMol("TestError", f"Invalid SMILES: {smiles}")) else: mols.append(mol) return mols @pytest.fixture def fingerprint(mols_list): return rdMolDescriptors.GetHashedMorganFingerprint(mols_list[0], 2, nBits=1000) _DIR_DATA = Path(__file__).parent / "data" _FILE_SLC6A4 = _DIR_DATA / "SLC6A4_active_excapedb_subset.csv" _FILE_SLC6A4_WITH_CDDD = _DIR_DATA / "CDDD_SLC6A4_active_excapedb_subset.csv.gz" @pytest.fixture def SLC6A4_subset(): data = pd.read_csv(_FILE_SLC6A4) return data @pytest.fixture def SLC6A4_subset_with_cddd(SLC6A4_subset): data = SLC6A4_subset.copy().drop_duplicates(subset="Ambit_InchiKey") cddd = pd.read_csv(_FILE_SLC6A4_WITH_CDDD, index_col="Ambit_InchiKey") data = data.merge( cddd, left_on="Ambit_InchiKey", right_index=True, how="inner", validate="one_to_one", ) return data skip_pandas_output_test = pytest.mark.skipif( Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT, reason=f"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher", ) _FEATURIZER_CLASSES = [ MACCSKeysFingerprintTransformer, RDKitFingerprintTransformer, AtomPairFingerprintTransformer, TopologicalTorsionFingerprintTransformer, MorganFingerprintTransformer, SECFingerprintTransformer, MHFingerprintTransformer, AvalonFingerprintTransformer, MolecularDescriptorTransformer, ] @pytest.fixture(params=_FEATURIZER_CLASSES) def featurizer(request): return request.param() @pytest.fixture def combined_transformer(featurizer): descriptors_pipeline = make_pipeline( SmilesToMolTransformer(), Standardizer(), featurizer, ) # A pipeline that just passes the input data. # We will use it to preserve the CDDD features and pass them to downstream steps. identity_pipeline = make_pipeline( FunctionTransformer(), ) transformer = make_column_transformer( (descriptors_pipeline, make_column_selector(pattern="SMILES")), (identity_pipeline, make_column_selector(pattern=r"^cddd_\d+$")), remainder="drop", ) return transformer @pytest.fixture def morgan_transformer(): return MorganFingerprintTransformer() @pytest.fixture def rdkit_transformer(): return RDKitFingerprintTransformer() @pytest.fixture def atompair_transformer(): return AtomPairFingerprintTransformer() @pytest.fixture def topologicaltorsion_transformer(): return TopologicalTorsionFingerprintTransformer() ================================================ FILE: tests/test_desctransformer.py ================================================ import time import joblib import numpy as np import numpy.ma as ma import pandas as pd import pytest import sklearn from packaging.version import Version from rdkit.Chem import Descriptors from sklearn import clone from sklearn.pipeline import Pipeline from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT from scikit_mol.descriptors import MolecularDescriptorTransformer from .fixtures import ( mols_container, mols_list, mols_with_invalid_container, skip_pandas_output_test, smiles_container, smiles_list, smiles_list_with_invalid, ) @pytest.fixture def default_descriptor_transformer(): return MolecularDescriptorTransformer() @pytest.fixture def selected_descriptor_transformer(): return MolecularDescriptorTransformer( desc_list=["HeavyAtomCount", "FractionCSP3", "RingCount", "MolLogP", "MolWt"] ) def test_descriptor_transformer_clonability(default_descriptor_transformer): for t in [default_descriptor_transformer]: params = t.get_params() t2 = clone(t) params_2 = t2.get_params() # Parameters of cloned transformers should be the same assert all([params[key] == params_2[key] for key in params.keys()]) # Cloned transformers should not be the same object assert t2 != t def test_descriptor_transformer_set_params(default_descriptor_transformer): for t in [default_descriptor_transformer]: params = t.get_params() # change extracted dictionary params["desc_list"] = ["HeavyAtomCount", "FractionCSP3"] # change params in transformer t.set_params(desc_list=["HeavyAtomCount", "FractionCSP3"]) # get parameters as dictionary and assert that it is the same params_2 = t.get_params() assert all([params[key] == params_2[key] for key in params.keys()]) assert len(default_descriptor_transformer.selected_descriptors) == 2 def test_descriptor_transformer_available_descriptors( default_descriptor_transformer, selected_descriptor_transformer ): # Default have as many as in RDkit and all are selected assert len(default_descriptor_transformer.available_descriptors) == len( Descriptors._descList ) assert len(default_descriptor_transformer.selected_descriptors) == len( Descriptors._descList ) # Default have as many as in RDkit but only 5 are selected assert len(selected_descriptor_transformer.available_descriptors) == len( Descriptors._descList ) assert len(selected_descriptor_transformer.selected_descriptors) == 5 def test_descriptor_transformer_transform( mols_container, default_descriptor_transformer ): features = default_descriptor_transformer.transform(mols_container) assert len(features) == len(mols_container) assert len(features[0]) == len(Descriptors._descList) def test_descriptor_transformer_wrong_descriptors(): with pytest.raises(AssertionError): MolecularDescriptorTransformer( desc_list=[ "Color", "Icecream content", "ChokolateDarkness", "Content42", "MolWt", ] ) def test_descriptor_transformer_parallel(mols_list, default_descriptor_transformer): default_descriptor_transformer.set_params(n_jobs=2) features = default_descriptor_transformer.transform(mols_list) assert len(features) == len(mols_list) assert len(features[0]) == len(Descriptors._descList) # Now with Rdkit 2022.3 creating a second transformer and running it, froze the process transformer2 = MolecularDescriptorTransformer( **default_descriptor_transformer.get_params() ) features2 = transformer2.transform(mols_list) assert len(features2) == len(mols_list) assert len(features2[0]) == len(Descriptors._descList) # This test may fail on windows and mac (due to spawn rather than fork?) # def test_descriptor_transformer_parallel_speedup(mols_list, default_descriptor_transformer): # n_phys_cpus = joblib.cpu_count(only_physical_cores=True) # mols_list = mols_list*50 # if n_phys_cpus > 1: # t0 = time.time() # features = default_descriptor_transformer.transform(mols_list) # t_single = time.time()-t0 # default_descriptor_transformer.set_params(parallel=True) # t0 = time.time() # features = default_descriptor_transformer.transform(mols_list) # t_par = time.time()-t0 # assert(t_par < t_single/(n_phys_cpus/1.5)) # div by 1.5 as we don't assume full speedup def test_transform_with_safe_inference_mode(mols_with_invalid_container): transformer = MolecularDescriptorTransformer(safe_inference_mode=True) descriptors = transformer.transform(mols_with_invalid_container) assert isinstance(descriptors, ma.MaskedArray) assert len(descriptors) == len(mols_with_invalid_container) # Check that the last row (corresponding to the InvalidMol) is fully masked assert np.all(descriptors.mask[-1]) # Check that other rows are not masked assert not np.any(descriptors.mask[:-1]) def test_transform_without_safe_inference_mode(mols_with_invalid_container): transformer = MolecularDescriptorTransformer(safe_inference_mode=False) with pytest.raises( Exception ): # You might want to be more specific about the exception type transformer.transform(mols_with_invalid_container) def test_transform_parallel_with_safe_inference_mode(mols_with_invalid_container): transformer = MolecularDescriptorTransformer(safe_inference_mode=True, n_jobs=2) descriptors = transformer.transform(mols_with_invalid_container) assert isinstance(descriptors, ma.MaskedArray) assert len(descriptors) == len(mols_with_invalid_container) # Check that the last row (corresponding to the InvalidMol) is fully masked assert np.all(descriptors.mask[-1]) # Check that other rows are not masked assert not np.any(descriptors.mask[:-1]) def test_transform_parallel_without_safe_inference_mode(mols_with_invalid_container): transformer = MolecularDescriptorTransformer(safe_inference_mode=False, n_jobs=2) with pytest.raises( Exception ): # You might want to be more specific about the exception type transformer.transform(mols_with_invalid_container) def test_safe_inference_mode_setting(): transformer = MolecularDescriptorTransformer() assert not transformer.safe_inference_mode # Default should be False transformer.set_params(safe_inference_mode=True) assert transformer.safe_inference_mode transformer.set_params(safe_inference_mode=False) assert not transformer.safe_inference_mode # TODO, if these tests are run before the others, these tests will fail, probably due to pandas output? @skip_pandas_output_test def test_descriptor_transformer_pandas_output( mols_container, default_descriptor_transformer, selected_descriptor_transformer, pandas_output, ): for transformer in [ default_descriptor_transformer, selected_descriptor_transformer, ]: features = transformer.transform(mols_container) assert isinstance(features, pd.DataFrame) assert features.shape[0] == len(mols_container) assert features.columns.tolist() == transformer.selected_descriptors @skip_pandas_output_test def test_descriptor_transformer_pandas_output_pipeline( smiles_container, default_descriptor_transformer, pandas_output ): pipeline = Pipeline( [("s2m", SmilesToMolTransformer()), ("desc", default_descriptor_transformer)] ) features = pipeline.fit_transform(smiles_container) assert isinstance(features, pd.DataFrame) assert features.shape[0] == len(smiles_container) assert ( features.columns.tolist() == default_descriptor_transformer.selected_descriptors ) ================================================ FILE: tests/test_fptransformers.py ================================================ import pickle import tempfile import numpy as np import pandas as pd import pytest from rdkit import Chem from sklearn import clone from scikit_mol.fingerprints import ( AvalonFingerprintTransformer, MACCSKeysFingerprintTransformer, MHFingerprintTransformer, SECFingerprintTransformer, ) from .fixtures import ( chiral_mols_list, chiral_smiles_list, fingerprint, mols_container, mols_list, mols_with_invalid_container, smiles_container, smiles_list, smiles_list_with_invalid, ) @pytest.fixture def maccs_transformer(): return MACCSKeysFingerprintTransformer() @pytest.fixture def secfp_transformer(): return SECFingerprintTransformer() @pytest.fixture def mhfp_transformer(): return MHFingerprintTransformer() @pytest.fixture def avalon_transformer(): return AvalonFingerprintTransformer() def test_clonability( maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ): for t in [ maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ]: params = t.get_params() t2 = clone(t) params_2 = t2.get_params() # Parameters of cloned transformers should be the same assert all([params[key] == params_2[key] for key in params.keys()]) # Cloned transformers should not be the same object assert t2 != t def test_set_params( secfp_transformer, mhfp_transformer, avalon_transformer, ): for t in [avalon_transformer]: params = t.get_params() # change extracted dictionary params["fpSize"] = 4242 # change params in transformer t.set_params(fpSize=4242) # get parameters as dictionary and assert that it is the same params_2 = t.get_params() assert all([params[key] == params_2[key] for key in params.keys()]) for t in [secfp_transformer, mhfp_transformer]: params = t.get_params() params["fpSize"] = 4242 t.set_params(fpSize=4242) params_2 = t.get_params() assert all([params[key] == params_2[key] for key in params.keys()]) def test_transform( mols_container, maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ): # Test the different transformers for t in [ maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ]: params = t.get_params() print(type(t), params) fps = t.transform(mols_container) # Assert that the same length of input and output assert len(fps) == len(mols_container) # assert that the size of the fingerprint is the expected size fpsize = params["fpSize"] assert len(fps[0]) == fpsize def test_transform_parallel( mols_container, maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ): # Test the different transformers for t in [ maccs_transformer, secfp_transformer, mhfp_transformer, avalon_transformer, ]: t.set_params(n_jobs=2) params = t.get_params() fps = t.transform(mols_container) # Assert that the same length of input and output assert len(fps) == len(mols_container) # assert that the size of the fingerprint is the expected size fpsize = params["fpSize"] assert len(fps[0]) == fpsize def test_picklable( maccs_transformer, secfp_transformer, avalon_transformer, ): # Test the different transformers for t in [ maccs_transformer, secfp_transformer, avalon_transformer, ]: with tempfile.NamedTemporaryFile() as f: pickle.dump(t, f) f.seek(0) t2 = pickle.load(f) assert t.get_params() == t2.get_params() def assert_transformer_set_params(tr_class, new_params, mols_list): default_params = tr_class().get_params() for key in new_params.keys(): tr = tr_class() params = tr.get_params() params[key] = new_params[key] fps_default = tr.transform(mols_list) tr.set_params(**params) new_tr = tr_class(**params) fps_reset_params = tr.transform(mols_list) fps_init_new_params = new_tr.transform(mols_list) # Now fp_default should not be the same as fp_reset_params assert ~np.all( [ np.array_equal(fp_default, fp_reset_params) for fp_default, fp_reset_params in zip(fps_default, fps_reset_params) ] ), f"Assertation error, FP appears the same, although the {key} should be changed from {default_params[key]} to {params[key]}" # fp_reset_params and fp_init_new_params should however be the same assert np.all( [ np.array_equal(fp_init_new_params, fp_reset_params) for fp_init_new_params, fp_reset_params in zip( fps_init_new_params, fps_reset_params ) ] ), f"Assertation error, FP appears to be different, although the {key} should be changed back as well as initialized to {params[key]}" def test_SECFingerprintTransformer(chiral_mols_list): new_params = { "isomeric": True, "kekulize": True, "fpSize": 1048, "min_radius": 2, #'n_permutations': 2, # The SECFp is not using this setting "radius": 2, "rings": False, #'seed': 1 # The SECFp is not using this setting } assert_transformer_set_params( SECFingerprintTransformer, new_params, chiral_mols_list ) def test_MHFingerprintTransformer(chiral_mols_list): new_params = { "radius": 2, "rings": False, "isomeric": True, "kekulize": True, "min_radius": 2, "fpSize": 4096, "seed": 44, } assert_transformer_set_params( MHFingerprintTransformer, new_params, chiral_mols_list ) def test_AvalonFingerprintTransformer(chiral_mols_list): new_params = { "fpSize": 1024, "isQuery": True, # 'resetVect': True, #TODO: this doesn't change the FP "bitFlags": 32767, } assert_transformer_set_params( AvalonFingerprintTransformer, new_params, chiral_mols_list ) def test_transform_with_safe_inference_mode( mols_with_invalid_container, maccs_transformer, secfp_transformer, avalon_transformer, ): for t in [ maccs_transformer, secfp_transformer, avalon_transformer, ]: t.set_params(safe_inference_mode=True) print(type(t)) fps = t.transform(mols_with_invalid_container) assert len(fps) == len(mols_with_invalid_container) # Check that the last row (corresponding to the InvalidMol) contains NaNs assert np.all(fps.mask[-1]) # Check that other rows don't contain NaNs assert not np.any(fps.mask[:-1]) def test_transform_without_safe_inference_mode( mols_with_invalid_container, maccs_transformer, secfp_transformer, avalon_transformer, # MHFP seem to accept invalid mols and return 0,0,0,0's ): for t in [ maccs_transformer, secfp_transformer, avalon_transformer, ]: t.set_params(safe_inference_mode=False) with pytest.raises( Exception ): # You might want to be more specific about the exception type print(f"testing {type(t)}") t.transform(mols_with_invalid_container) # Add this test to check parallel processing with error handling def test_transform_parallel_with_safe_inference_mode( mols_with_invalid_container, maccs_transformer, secfp_transformer, avalon_transformer, ): for t in [ maccs_transformer, secfp_transformer, avalon_transformer, ]: t.set_params(safe_inference_mode=True, n_jobs=2) fps = t.transform(mols_with_invalid_container) assert len(fps) == len(mols_with_invalid_container) print(fps.mask) # Check that the last row (corresponding to the InvalidMol) is masked assert np.all( fps.mask[-1] ) # Mask should be true for all elements in the last row # Check that other rows don't contain any masked values assert not np.any(fps.mask[:-1, :]) ================================================ FILE: tests/test_fptransformersgenerator.py ================================================ import pickle import tempfile import numpy as np import pytest from sklearn import clone from scikit_mol.fingerprints import ( AtomPairFingerprintTransformer, MorganFingerprintTransformer, RDKitFingerprintTransformer, TopologicalTorsionFingerprintTransformer, ) from .fixtures import ( chiral_mols_list, chiral_smiles_list, fingerprint, mols_container, mols_list, smiles_container, smiles_list, ) test_transformers = [ AtomPairFingerprintTransformer, MorganFingerprintTransformer, RDKitFingerprintTransformer, TopologicalTorsionFingerprintTransformer, ] @pytest.mark.parametrize("transformer_class", test_transformers) def test_fpstransformer_transform_mol(transformer_class, mols_list): transformer = transformer_class() fp = transformer._transform_mol(mols_list[0]) # See that fp is the correct type, shape and bit count assert type(fp) == type(np.array([0])) assert fp.shape == (2048,) if isinstance(transformer, RDKitFingerprintTransformer): assert fp.sum() == 104 elif isinstance(transformer, AtomPairFingerprintTransformer): assert fp.sum() == 32 elif isinstance(transformer, TopologicalTorsionFingerprintTransformer): assert fp.sum() == 12 elif isinstance(transformer, MorganFingerprintTransformer): assert fp.sum() == 14 else: raise NotImplementedError(f"missing Assert for {transformer_class}") @pytest.mark.parametrize("transformer_class", test_transformers) def test_clonability(transformer_class): transformer = transformer_class() params = transformer.get_params() t2 = clone(transformer) params_2 = t2.get_params() # Parameters of cloned transformers should be the same assert all([params[key] == params_2[key] for key in params.keys()]) # Cloned transformers should not be the same object assert t2 != transformer @pytest.mark.parametrize("transformer_class", test_transformers) def test_set_params(transformer_class): transformer = transformer_class() params = transformer.get_params() # change extracted dictionary params["fpSize"] = 4242 # change params in transformer transformer.set_params(fpSize=4242) # get parameters as dictionary and assert that it is the same params_2 = transformer.get_params() assert all([params[key] == params_2[key] for key in params.keys()]) @pytest.mark.parametrize("transformer_class", test_transformers) def test_transform(mols_container, transformer_class): transformer = transformer_class() # Test the different transformers params = transformer.get_params() fps = transformer.transform(mols_container) # Assert that the same length of input and output assert len(fps) == len(mols_container) fpsize = params["fpSize"] assert len(fps[0]) == fpsize @pytest.mark.parametrize("transformer_class", test_transformers) def test_transform_parallel(mols_container, transformer_class): transformer = transformer_class() # Test the different transformers transformer.set_params(n_jobs=2) params = transformer.get_params() fps = transformer.transform(mols_container) # Assert that the same length of input and output assert len(fps) == len(mols_container) fpsize = params["fpSize"] assert len(fps[0]) == fpsize @pytest.mark.parametrize("transformer_class", test_transformers) def test_picklable(transformer_class): # Test the different transformers transformer = transformer_class() p = transformer.get_params() with tempfile.NamedTemporaryFile() as f: pickle.dump(transformer, f) f.seek(0) t2 = pickle.load(f) print(p) print(vars(transformer)) print(vars(t2)) assert transformer.get_params() == t2.get_params() @pytest.mark.parametrize("transfomer", test_transformers) def assert_transformer_set_params(transfomer, new_params, mols_list): default_params = transfomer().get_params() for key in new_params.keys(): tr = transfomer() params = tr.get_params() params[key] = new_params[key] fps_default = tr.transform(mols_list) tr.set_params(**params) new_tr = transfomer(**params) fps_reset_params = tr.transform(mols_list) fps_init_new_params = new_tr.transform(mols_list) # Now fp_default should not be the same as fp_reset_params assert ~np.all( [ np.array_equal(fp_default, fp_reset_params) for fp_default, fp_reset_params in zip(fps_default, fps_reset_params) ] ), f"Assertation error, FP appears the same, although the {key} should be changed from {default_params[key]} to {params[key]}" # fp_reset_params and fp_init_new_params should however be the same assert np.all( [ np.array_equal(fp_init_new_params, fp_reset_params) for fp_init_new_params, fp_reset_params in zip( fps_init_new_params, fps_reset_params ) ] ), f"Assertation error, FP appears to be different, although the {key} should be changed back as well as initialized to {params[key]}" def test_morgan_set_params(chiral_mols_list): new_params = { "fpSize": 1024, "radius": 1, "useBondTypes": False, # TODO, why doesn't this change the FP? "useChirality": True, "useCounts": True, "useFeatures": True, } assert_transformer_set_params( MorganFingerprintTransformer, new_params, chiral_mols_list ) def test_atompairs_set_params(chiral_mols_list): new_params = { #'atomInvariants': 1, #'confId': -1, #'fromAtoms': 1, #'ignoreAtoms': 0, "includeChirality": True, "maxLength": 3, "minLength": 3, "fpSize": 1024, #'nBitsPerEntry': 3, #TODO: seem deprecated with the generators? #'use2D': True, #TODO, understand why this can't be set different "useCounts": True, } assert_transformer_set_params( AtomPairFingerprintTransformer, new_params, chiral_mols_list ) def test_topologicaltorsion_set_params(chiral_mols_list): new_params = { #'atomInvariants': 0, #'fromAtoms': 0, #'ignoreAtoms': 0, #'includeChirality': True, #TODO, figure out why this setting seems to give same FP whether toggled or not "fpSize": 1024, #'nBitsPerEntry': 3, #Todo: not setable with the generators? "targetSize": 5, "useCounts": True, } assert_transformer_set_params( TopologicalTorsionFingerprintTransformer, new_params, chiral_mols_list ) def test_RDKitFPTransformer(chiral_mols_list): new_params = { #'atomInvariantsGenerator': None, #'branchedPaths': False, #'countBounds': 0, #TODO: What does this do? "countSimulation": True, "fpSize": 1024, "maxPath": 3, "minPath": 2, "numBitsPerFeature": 3, "useBondOrder": False, # TODO, why doesn't this change the FP? #'useHs': False, #TODO, why doesn't this change the FP? } assert_transformer_set_params( RDKitFingerprintTransformer, new_params, chiral_mols_list ) ================================================ FILE: tests/test_parameter_types.py ================================================ import numpy as np import pytest from rdkit import Chem from .fixtures import ( atompair_transformer, mols_list, morgan_transformer, rdkit_transformer, smiles_list, topologicaltorsion_transformer, ) from .test_fptransformers import ( avalon_transformer, ) def test_Transformer_exotic_types( mols_list, morgan_transformer, atompair_transformer, topologicaltorsion_transformer, avalon_transformer, ): for transformer in [ morgan_transformer, atompair_transformer, topologicaltorsion_transformer, avalon_transformer, ]: params = transformer.get_params() for useCounts in [np.bool_(True), np.bool_(False)]: for key, value in params.items(): if isinstance(value, int): exotic_type_value = np.int64(value) elif isinstance(value, bool): exotic_type_value = np.bool_(value) else: print(f"{key}:{value}:{type(value)}") exotic_type_value = value exotic_params = {key: exotic_type_value, "useCounts": useCounts} print(exotic_params) transformer.set_params(**exotic_params) transformer.transform(mols_list) def test_RDKFp_exotic_types(mols_list, rdkit_transformer): transformer = rdkit_transformer params = transformer.get_params() for key, value in params.items(): if isinstance(value, int): exotic_type_value = np.int64(value) elif isinstance(value, bool): exotic_type_value = np.bool_(value) else: print(f"{key}:{value}:{type(value)}") exotic_type_value = value exotic_params = {key: exotic_type_value} print(exotic_params) transformer.set_params(**exotic_params) transformer.transform(mols_list) ================================================ FILE: tests/test_safeinferencemode.py ================================================ import numpy as np import pandas as pd import pytest from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.fingerprints import MorganFingerprintTransformer from scikit_mol.safeinference import SafeInferenceWrapper from scikit_mol.utilities import set_safe_inference_mode from .fixtures import ( SLC6A4_subset, invalid_smiles_list, skip_pandas_output_test, smiles_list, ) def equal_val(value, expected_value): try: if np.isnan(expected_value): return np.isnan(value) else: return value == expected_value except TypeError: return value == expected_value @pytest.fixture(params=[1, 2]) def transformer(request): return MorganFingerprintTransformer(fpSize=5, n_jobs=request.param) @pytest.fixture(params=[np.nan, None, np.inf, 0, -100]) def smiles_pipeline(request, transformer): return Pipeline( [ ("s2m", SmilesToMolTransformer()), ("FP", transformer), ( "RF", SafeInferenceWrapper( RandomForestRegressor(n_estimators=3, random_state=42), replace_value=request.param, ), ), ] ) @pytest.fixture def smiles_pipeline_trained(smiles_pipeline, SLC6A4_subset): X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50 X_smiles = X_smiles.to_frame() # Train the model smiles_pipeline.fit(X_smiles, Y) return smiles_pipeline def test_safeinference_wrapper_basic(smiles_pipeline, SLC6A4_subset): X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50 X_smiles = X_smiles.to_frame() # Set safe inference mode set_safe_inference_mode(smiles_pipeline, True) # Train the model smiles_pipeline.fit(X_smiles, Y) # Test prediction predictions = smiles_pipeline.predict(X_smiles) assert len(predictions) == len(X_smiles) assert not np.any( equal_val(predictions, smiles_pipeline.named_steps["RF"].replace_value) ) def test_safeinference_wrapper_with_single_invalid_smiles(smiles_pipeline_trained): set_safe_inference_mode(smiles_pipeline_trained, True) replace_value = smiles_pipeline_trained.named_steps["RF"].replace_value # Test prediction prediction = smiles_pipeline_trained.predict(["invalid_smiles"]) assert len(prediction) == 1 assert equal_val(prediction[0], replace_value) def test_safeinference_wrapper_with_invalid_smiles( smiles_pipeline, SLC6A4_subset, invalid_smiles_list ): X_smiles, Y = SLC6A4_subset.SMILES[:100], SLC6A4_subset.pXC50[:100] X_smiles = X_smiles.to_frame() # Set safe inference mode set_safe_inference_mode(smiles_pipeline, True) # Train the model smiles_pipeline.fit(X_smiles, Y) replace_value = smiles_pipeline.named_steps["RF"].replace_value # Create a test set with invalid SMILES X_test = pd.DataFrame({"SMILES": X_smiles["SMILES"].tolist() + invalid_smiles_list}) len_invalid = len(invalid_smiles_list) # Test prediction with invalid SMILES predictions = smiles_pipeline.predict(X_test) invalid_predictions = predictions[-len_invalid:] assert len(predictions) == len(X_test) assert np.all(equal_val(invalid_predictions, replace_value)) def test_safeinference_wrapper_without_safe_mode( smiles_pipeline, SLC6A4_subset, invalid_smiles_list ): X_smiles, Y = SLC6A4_subset.SMILES[:100], SLC6A4_subset.pXC50[:100] X_smiles = X_smiles.to_frame() # Ensure safe inference mode is off (default behavior) set_safe_inference_mode(smiles_pipeline, False) # Train the model smiles_pipeline.fit(X_smiles, Y) # Create a test set with invalid SMILES X_test = pd.DataFrame({"SMILES": X_smiles["SMILES"].tolist() + invalid_smiles_list}) # Test prediction with invalid SMILES with pytest.raises(Exception): smiles_pipeline.predict(X_test) @skip_pandas_output_test def test_safeinference_wrapper_pandas_output( smiles_pipeline, SLC6A4_subset, pandas_output ): X_smiles = SLC6A4_subset.SMILES[:100].to_frame() # Set safe inference mode set_safe_inference_mode(smiles_pipeline, True) # Fit and transform (up to the FP step) result = smiles_pipeline[:-1].fit_transform(X_smiles) assert isinstance(result, pd.DataFrame) assert result.shape[0] == len(X_smiles) assert result.shape[1] == smiles_pipeline.named_steps["FP"].fpSize @skip_pandas_output_test def test_safeinference_wrapper_get_feature_names_out(smiles_pipeline): # Get feature names from the FP step feature_names = smiles_pipeline.named_steps["FP"].get_feature_names_out() assert len(feature_names) == smiles_pipeline.named_steps["FP"].fpSize assert all(isinstance(name, str) for name in feature_names) ================================================ FILE: tests/test_sanitizer.py ================================================ import numpy as np import pandas as pd import pytest from rdkit import Chem from scikit_mol.utilities import CheckSmilesSanitization from .fixtures import smiles_list, smiles_list_with_invalid @pytest.fixture def sanitizer(): return CheckSmilesSanitization() @pytest.fixture def return_mol_sanitizer(): return CheckSmilesSanitization(return_mol=True) def test_checksmilessanitation(smiles_list, smiles_list_with_invalid, sanitizer): smiles_list_sanitized, errors = sanitizer.sanitize(smiles_list_with_invalid) assert len(smiles_list_with_invalid) > len(smiles_list_sanitized) assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)]) assert errors[0] == sanitizer.errors.SMILES[0] def test_checksmilessanitation_x_and_y( smiles_list, smiles_list_with_invalid, sanitizer ): smiles_list_sanitized, y_sanitized, errors, y_errors = sanitizer.sanitize( smiles_list_with_invalid, list(range(len(smiles_list_with_invalid))) ) assert len(smiles_list_with_invalid) > len(smiles_list_sanitized) assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)]) assert errors[0] == sanitizer.errors.SMILES[0] # Test that y is correctly split into y_error and the rest assert all([a == b for a, b in zip(y_sanitized, list(range(len(smiles_list) - 1)))]) assert y_errors[0] == len(smiles_list_with_invalid) - 1 # Last smiles is invalid def test_checksmilessanitation_np(smiles_list, smiles_list_with_invalid, sanitizer): smiles_list_sanitized, errors = sanitizer.sanitize( np.array(smiles_list_with_invalid) ) assert len(smiles_list_with_invalid) > len(smiles_list_sanitized) assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)]) assert errors[0] == sanitizer.errors.SMILES[0] def test_checksmilessanitation_numpy(smiles_list, smiles_list_with_invalid, sanitizer): smiles_list_sanitized, errors = sanitizer.sanitize( pd.Series(smiles_list_with_invalid) ) assert len(smiles_list_with_invalid) > len(smiles_list_sanitized) assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)]) assert errors[0] == sanitizer.errors.SMILES[0] def test_checksmilessanitation_return_mol( smiles_list, smiles_list_with_invalid, return_mol_sanitizer ): smiles_list_sanitized, errors = return_mol_sanitizer.sanitize( smiles_list_with_invalid ) assert len(smiles_list_with_invalid) > len(smiles_list_sanitized) assert all( [ a == b for a, b in zip( smiles_list, [Chem.MolToSmiles(smiles) for smiles in smiles_list_sanitized], ) ] ) assert errors[0] == return_mol_sanitizer.errors.SMILES[0] ================================================ FILE: tests/test_scikit_mol.py ================================================ def test_load_data(data): assert len(data) > 0 ================================================ FILE: tests/test_smilestomol.py ================================================ import numpy as np import pandas as pd import pytest import sklearn from packaging.version import Version from rdkit import Chem from sklearn import clone from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import ( DEFAULT_MOL_COLUMN_NAME, SKLEARN_VERSION_PANDAS_OUT, InvalidMol, ) from .fixtures import ( skip_pandas_output_test, smiles_container, smiles_list, smiles_list_with_invalid, ) @pytest.fixture def smilestomol_transformer(): return SmilesToMolTransformer() def test_smilestomol(smiles_container, smilestomol_transformer): result_mols = smilestomol_transformer.transform(smiles_container) result_smiles = [Chem.MolToSmiles(mol) for mol in result_mols.flatten()] if isinstance(smiles_container, pd.DataFrame): expected_smiles = smiles_container.iloc[:, 0].tolist() else: expected_smiles = smiles_container assert all([a == b for a, b in zip(expected_smiles, result_smiles)]) def test_smilestomol_transform(smilestomol_transformer, smiles_container): result = smilestomol_transformer.transform(smiles_container) assert len(result) == len(smiles_container) assert all(isinstance(mol, Chem.Mol) for mol in result.flatten()) def test_smilestomol_fit(smilestomol_transformer, smiles_container): result = smilestomol_transformer.fit(smiles_container) assert result == smilestomol_transformer def test_smilestomol_clone(smilestomol_transformer): t2 = clone(smilestomol_transformer) params = smilestomol_transformer.get_params() params_2 = t2.get_params() assert all([params[key] == params_2[key] for key in params.keys()]) def test_smilestomol_unsanitzable(smiles_list_with_invalid, smilestomol_transformer): with pytest.raises(ValueError): smilestomol_transformer.transform(smiles_list_with_invalid) def test_descriptor_transformer_parallel(smiles_container, smilestomol_transformer): smilestomol_transformer.set_params(n_jobs=2) mol_list = smilestomol_transformer.transform(smiles_container) if isinstance(smiles_container, pd.DataFrame): expected_smiles = smiles_container.iloc[:, 0].tolist() else: expected_smiles = smiles_container assert all( [ a == b for a, b in zip( expected_smiles, [Chem.MolToSmiles(mol) for mol in mol_list.flatten()] ) ] ) def test_smilestomol_inverse_transform(smilestomol_transformer, smiles_container): mols = smilestomol_transformer.transform(smiles_container) result = smilestomol_transformer.inverse_transform(mols) assert len(result) == len(smiles_container) assert all(isinstance(smiles, str) for smiles in result.flatten()) def test_smilestomol_inverse_transform_with_invalid( smiles_list_with_invalid, smilestomol_transformer ): smilestomol_transformer.set_params(safe_inference_mode=True) # Forward transform mols = smilestomol_transformer.transform(smiles_list_with_invalid) # Inverse transform result = smilestomol_transformer.inverse_transform(mols) assert len(result) == len(smiles_list_with_invalid) # Check that all but the last element are the same as the original SMILES for original, res in zip(smiles_list_with_invalid[:-1], result[:-1].flatten()): assert isinstance(res, str) assert original == res # Check that the last element is an InvalidMol instance assert isinstance(result[-1].item(), InvalidMol) assert "Invalid SMILES" in result[-1].item().error assert smiles_list_with_invalid[-1] in result[-1].item().error def test_smilestomol_get_feature_names_out(smilestomol_transformer): feature_names = smilestomol_transformer.get_feature_names_out() assert feature_names == [DEFAULT_MOL_COLUMN_NAME] def test_smilestomol_safe_inference(smiles_list_with_invalid, smilestomol_transformer): smilestomol_transformer.set_params(safe_inference_mode=True) result = smilestomol_transformer.transform(smiles_list_with_invalid) assert len(result) == len(smiles_list_with_invalid) assert isinstance(result, np.ndarray) # Check that all but the last element are valid RDKit Mol objects for mol in result[:-1].flatten(): assert isinstance(mol, Chem.Mol) assert mol is not None # Check that the last element is an InvalidMol instance last_mol = result[-1].item() assert isinstance(last_mol, InvalidMol) # Check if the error message is correctly set for the invalid SMILES assert "Invalid SMILES" in last_mol.error assert smiles_list_with_invalid[-1] in last_mol.error @pytest.mark.skipif( not skip_pandas_output_test, reason="Pandas output not supported in this sklearn version", ) def test_smilestomol_safe_inference_pandas_output( smiles_list_with_invalid, smilestomol_transformer, pandas_output ): smilestomol_transformer.set_params(safe_inference_mode=True) result = smilestomol_transformer.transform(smiles_list_with_invalid) assert len(result) == len(smiles_list_with_invalid) assert isinstance(result, pd.DataFrame) assert result.columns == [DEFAULT_MOL_COLUMN_NAME] # Check that all but the last element are valid RDKit Mol objects for mol in result[DEFAULT_MOL_COLUMN_NAME][:-1]: assert isinstance(mol, Chem.Mol) assert mol is not None # Check that the last element is an InvalidMol instance last_mol = result[DEFAULT_MOL_COLUMN_NAME].iloc[-1] assert isinstance(last_mol, InvalidMol) # Check if the error message is correctly set for the invalid SMILES assert "Invalid SMILES" in last_mol.error assert smiles_list_with_invalid[-1] in last_mol.error @skip_pandas_output_test def test_pandas_output(smiles_container, smilestomol_transformer, pandas_output): mols = smilestomol_transformer.transform(smiles_container) assert isinstance(mols, pd.DataFrame) assert mols.shape[0] == len(smiles_container) assert mols.columns.tolist() == [DEFAULT_MOL_COLUMN_NAME] ================================================ FILE: tests/test_transformers.py ================================================ # checking that the new transformers can work within a scikitlearn pipeline of the kind # Pipeline([("s2m", SmilesToMol()), ("FP", FPTransformer()), ("RF", RandomForestRegressor())]) # using some test data stored in ./data/SLC6A4_active_excape_subset.csv # to run as # pytest tests/test_transformers.py --> tests/test_transformers.py::test_transformer PASSED import numpy as np import pandas as pd import pytest import sklearn from packaging.version import Version from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from scikit_mol.conversions import SmilesToMolTransformer from scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT from scikit_mol.descriptors import MolecularDescriptorTransformer from scikit_mol.fingerprints import ( AtomPairFingerprintTransformer, AvalonFingerprintTransformer, MACCSKeysFingerprintTransformer, MHFingerprintTransformer, MorganFingerprintTransformer, RDKitFingerprintTransformer, SECFingerprintTransformer, TopologicalTorsionFingerprintTransformer, ) from scikit_mol.fingerprints.baseclasses import BaseFpsTransformer from .fixtures import ( SLC6A4_subset, SLC6A4_subset_with_cddd, combined_transformer, featurizer, mols_container, skip_pandas_output_test, ) def test_transformer(SLC6A4_subset): # load some toy data for quick testing on a small number of samples X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50 X_smiles = X_smiles.to_frame() X_train, X_test = X_smiles[:128], X_smiles[128:] Y_train, Y_test = Y[:128], Y[128:] # run FP with default parameters except when useCounts can be given as an argument FP_dict = { "MACCSTransformer": [MACCSKeysFingerprintTransformer, None], "RDKitFPTransformer": [RDKitFingerprintTransformer, None], "AtomPairFingerprintTransformer": [AtomPairFingerprintTransformer, False], "AtomPairFingerprintTransformer useCounts": [ AtomPairFingerprintTransformer, True, ], "TopologicalTorsionFingerprintTransformer": [ TopologicalTorsionFingerprintTransformer, False, ], "TopologicalTorsionFingerprintTransformer useCounts": [ TopologicalTorsionFingerprintTransformer, True, ], "MorganTransformer": [MorganFingerprintTransformer, False], "MorganTransformer useCounts": [MorganFingerprintTransformer, True], "SECFingerprintTransformer": [SECFingerprintTransformer, None], "MHFingerprintTransformer": [MHFingerprintTransformer, None], "AvalonFingerprintTransformer": [AvalonFingerprintTransformer, None], } # fit on toy data and print train/test score if successful or collect the failed FP failed_FP = [] for FP_name, (FP, useCounts) in FP_dict.items(): try: print( f"\nrunning pipeline fitting and scoring for {FP_name} with useCounts={useCounts}" ) if useCounts is None: pipeline = Pipeline( [ ("s2m", SmilesToMolTransformer()), ("FP", FP()), ("RF", RandomForestRegressor()), ] ) else: pipeline = Pipeline( [ ("s2m", SmilesToMolTransformer()), ("FP", FP(useCounts=useCounts)), ("RF", RandomForestRegressor()), ] ) pipeline.fit(X_train, Y_train) train_score = pipeline.score(X_train, Y_train) test_score = pipeline.score(X_test, Y_test) print( f"\nfitting and scoring completed train_score={train_score}, test_score={test_score}" ) except: print( f"\n!!!! FAILED pipeline fitting and scoring for {FP_name} with useCounts={useCounts}" ) failed_FP.append(FP_name) pass # overall result assert len(failed_FP) == 0, f"the following FP have failed {failed_FP}" @skip_pandas_output_test def test_transformer_pandas_output(SLC6A4_subset, pandas_output): # load some toy data for quick testing on a small number of samples X_smiles = SLC6A4_subset.SMILES X_smiles = X_smiles.to_frame() # run FP with default parameters except when useCounts can be given as an argument FP_dict = { "MACCSTransformer": [MACCSKeysFingerprintTransformer, None], "RDKitFPTransformer": [RDKitFingerprintTransformer, None], "AtomPairFingerprintTransformer": [AtomPairFingerprintTransformer, False], "AtomPairFingerprintTransformer useCounts": [ AtomPairFingerprintTransformer, True, ], "TopologicalTorsionFingerprintTransformer": [ TopologicalTorsionFingerprintTransformer, False, ], "TopologicalTorsionFingerprintTransformer useCounts": [ TopologicalTorsionFingerprintTransformer, True, ], "MorganTransformer": [MorganFingerprintTransformer, False], "MorganTransformer useCounts": [MorganFingerprintTransformer, True], "SECFingerprintTransformer": [SECFingerprintTransformer, None], "MHFingerprintTransformer": [MHFingerprintTransformer, None], "AvalonFingerprintTransformer": [AvalonFingerprintTransformer, None], } # fit on toy data and check that the output is a pandas dataframe failed_FP = [] for FP_name, (FP, useCounts) in FP_dict.items(): try: print( f"\nrunning pipeline fitting and scoring for {FP_name} with useCounts={useCounts}" ) if useCounts is None: pipeline = Pipeline([("s2m", SmilesToMolTransformer()), ("FP", FP())]) else: pipeline = Pipeline( [("s2m", SmilesToMolTransformer()), ("FP", FP(useCounts=useCounts))] ) pipeline.fit(X_smiles) X_transformed = pipeline.transform(X_smiles) assert isinstance( X_transformed, pd.DataFrame ), f"the output of {FP_name} is not a pandas dataframe" assert ( X_transformed.shape[0] == len(X_smiles) ), f"the number of rows in the output of {FP_name} is not equal to the number of samples" assert ( len(X_transformed.columns) == pipeline.named_steps["FP"].fpSize ), f"the number of columns in the output of {FP_name} is not equal to the number of bits" print(f"\nfitting and transforming completed") except Exception as err: print( f"\n!!!! FAILED pipeline fitting and transforming for {FP_name} with useCounts={useCounts}" ) print("\n".join(err.args)) failed_FP.append(FP_name) pass # overall result assert ( len(failed_FP) == 0 ), f"the following FP have failed pandas transformation {failed_FP}" @skip_pandas_output_test def test_pandas_out_same_values(featurizer, mols_container): featurizer_default = sklearn.base.clone(featurizer) featurizer_default.set_output(transform="default") featurizer_pandas = sklearn.base.clone(featurizer) featurizer_pandas.set_output(transform="pandas") result_default = featurizer_default.fit_transform(mols_container) result_pandas = featurizer_pandas.fit_transform(mols_container) assert isinstance(result_default, np.ndarray) assert isinstance(result_pandas, pd.DataFrame) assert result_default.shape == result_pandas.shape featurizer_class_with_nan = MolecularDescriptorTransformer if isinstance(featurizer, featurizer_class_with_nan): assert ( pd.isna(result_default) == pd.isna(result_pandas.values) ).all(), ( "NaN values are not in the same positions in the default and pandas output" ) nan_replacement = 0.0 result_default = np.nan_to_num(result_default, nan=nan_replacement) result_pandas = result_pandas.fillna(nan_replacement) else: assert (result_default == result_pandas.values).all() @skip_pandas_output_test def test_combined_transformer_pandas_out( combined_transformer, SLC6A4_subset_with_cddd, pandas_output ): result = combined_transformer.fit_transform(SLC6A4_subset_with_cddd) assert isinstance(result, pd.DataFrame) assert result.shape[0] == SLC6A4_subset_with_cddd.shape[0] n_cddd_features = SLC6A4_subset_with_cddd.columns.str.match(r"^cddd_\d+$").sum() pipeline_skmol = combined_transformer.named_transformers_["pipeline-1"] featurizer_skmol = pipeline_skmol[-1] if isinstance(featurizer_skmol, BaseFpsTransformer): n_skmol_features = featurizer_skmol.fpSize elif isinstance(featurizer_skmol, MolecularDescriptorTransformer): n_skmol_features = len(featurizer_skmol.desc_list) else: raise ValueError(f"Unexpected featurizer type {type(featurizer_skmol)}") expected_n_features = n_cddd_features + n_skmol_features assert result.shape[1] == expected_n_features ================================================ FILE: uv.toml ================================================ required-version = ">=0.5.24"