[
  {
    "path": ".github/CODEOWNERS",
    "content": "* @EBjerrum\nscikit_mol/parallel.py @asiomchen\nscikit_mol/plotting.py @asiomchen"
  },
  {
    "path": ".github/workflows/code_quality.yaml",
    "content": "name: Code Quality Checks\non: [ push, pull_request ]\njobs:\n  ruff-checks:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v4\n      - name: Check code formatting\n        uses: astral-sh/ruff-action@v3\n        with:\n          version: 0.8.6\n          args: \"format --check\"\n          src: \"./scikit_mol\"\n\n      - name: Check code style\n        uses: astral-sh/ruff-action@v3\n        with:\n          version: 0.8.6\n          args: \"check\"\n          src: \"./scikit_mol\"\n\n        "
  },
  {
    "path": ".github/workflows/publish.yaml",
    "content": "name: Publish Python🐍 distribution📦 with uv🌈\n# after releasing a new version, build the distribution and uploads signed artifacts to GitHub Release\non:\n  workflow_call:\n    inputs:\n      python-version:\n        type: string\n        description: \"Python version to use\"\n        required: true\n        default: \"3.9\"\n      is-draft:\n        type: boolean\n        description: \"Is this a draft release?\"\n        required: false\n        default: false\n      dist-artifact-name:\n        type: string\n        description: \"Name of the created distribution artifact\"\n        required: false\n        default: \"python-package-distributions\"\n\njobs:\n  build-and-publish:\n    name: Build distribution📦\n    runs-on: ubuntu-latest\n\n    permissions:\n      contents: write #\n      id-token: write # IMPORTANT: mandatory for sigstore\n\n    steps:\n      - uses: actions/checkout@v4\n        with:\n          persist-credentials: false\n      - name: Set up Python🐍\n        uses: astral-sh/setup-uv@v5\n        with:\n          python-version: ${{ inputs.python-version }}\n      - name: Build a binary wheel and a source tarball\n        run: uv build\n      - name: Store the distribution packages\n        uses: actions/upload-artifact@v4\n        with:\n          name: ${{ inputs.dist-artifact-name }}\n          path: dist/\n      \n  github-release:\n    name: >-\n      Sign the distribution📦 with Sigstore\n      and upload them to GitHub Release\n    needs:\n      - build-and-publish\n    runs-on: ubuntu-latest\n\n    permissions:\n      contents: write # IMPORTANT: mandatory for making GitHub Releases\n      id-token: write # IMPORTANT: mandatory for sigstore\n\n    steps:\n      - name: Download all the dists\n        uses: actions/download-artifact@v4\n        with:\n          name: ${{ inputs.dist-artifact-name }}\n          path: dist/\n      - name: Sign the dists with Sigstore🔏\n        uses: sigstore/gh-action-sigstore-python@v3.0.0\n        with:\n          inputs: >-\n            ./dist/*.tar.gz\n            ./dist/*.whl\n      - name: Create GitHub Release\n        env:\n          GITHUB_TOKEN: ${{ github.token }}\n        run: >-\n          gh release create\n          \"$GITHUB_REF_NAME\"\n          --repo \"$GITHUB_REPOSITORY\"\n          --generate-notes ${{ inputs.is-draft && '--draft' || '' }}\n      - name: Upload artifact signatures to GitHub Release\n        env:\n          GITHUB_TOKEN: ${{ github.token }}\n        run: >-\n          gh release upload\n          \"$GITHUB_REF_NAME\" dist/**\n          --repo \"$GITHUB_REPOSITORY\"\n          "
  },
  {
    "path": ".github/workflows/pytest.yaml",
    "content": "name: scikit_mol ci\n\non:\n  push:\n    branches: [main]\n    tags: ['v*']\n  pull_request:\n    branches: [main]\n\n# cancel previously running tests if new commits are made\n# https://docs.github.com/en/actions/examples/using-concurrency-expressions-and-a-test-matrix\nconcurrency:\n  group: actions-id-${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}\n  cancel-in-progress: true\n\njobs:\n  # run pytests for scikit_mol\n  tests:\n    name: pytest ${{ matrix.os }}::py${{ matrix.python-version }}\n    runs-on: ${{ matrix.os }}\n    strategy:\n      max-parallel: 6\n      fail-fast: false\n      matrix:\n        os: [ubuntu-latest, macos-latest, windows-latest]\n        python-version: [\"3.10\"]\n        include:\n          # test python version compatibility on linux only\n          - os: ubuntu-latest\n            python-version: 3.13\n          - os: ubuntu-latest\n            python-version: 3.12\n          - os: ubuntu-latest\n            python-version: 3.11\n          - os: ubuntu-latest\n            python-version: 3.10\n          - os: ubuntu-latest\n            python-version: 3.9\n    steps:\n      - name: Checkout scikit_mol\n        uses: actions/checkout@v4\n      - name: Install uv and set the python version\n        uses: astral-sh/setup-uv@v5\n        with:\n          python-version: ${{ matrix.python-version }}\n      - name: Install scikit_mol\n        run: uv sync --dev\n      - name: Cache tests/data\n        uses: actions/cache@v4\n        with:\n          path: tests/data\n          key: ${{ runner.os }}-${{ hashFiles('tests/conftest.py') }}\n      - name: Run Tests\n        run: uv run pytest --cov=./scikit_mol .\n  build-and-create-signed-release:\n    name: Build distribution📦 & create Github Release\n    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')\n    needs: tests\n    uses: ./.github/workflows/publish.yaml\n    with:\n      python-version: \"3.9\"\n      is-draft: true\n  publish:\n    name: Publish to PyPI\n    needs: build-and-create-signed-release\n    runs-on: ubuntu-latest\n    # will be enabled in the future\n    # environment:\n    #   name: pypi\n    #   url: https://pypi.org/p/scikit-mol\n    permissions:\n      id-token: write  # IMPORTANT: mandatory for trusted publishing\n    steps:\n      - name: Download all the dists\n        uses: actions/download-artifact@v4\n        with:\n          name: python-package-distributions\n          path: dist/\n      - name: Publish to PyPI\n        uses: pypa/gh-action-pypi-publish@release/v1\n        "
  },
  {
    "path": ".github/workflows/welcome.yaml",
    "content": "name: Welcome WorkFlow\n\non:\n  issues:\n    types: [opened]\n  pull_request_target:\n    types: [opened]\n\njobs:\n  build:\n    name: 👋 Welcome\n    permissions: write-all\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/first-interaction@v1.3.0\n        with:\n          repo-token: ${{ secrets.GITHUB_TOKEN }}\n          issue-message: \"🎉 Welcome to scikit-mol! 🧪✨ Thank you for opening your first issue! 🚀 Your feedback helps improve the project and makes a difference. 💡 If you have any questions or need guidance, don't hesitate to ask. We're here to help! 🤝\"\n          pr-message:  \"🎉 Welcome to scikit-mol! 🧪✨ Thank you for submitting your first pull request! 🔧 Your effort and contributions mean a lot to us. 🙌 We'll review it as soon as possible. 🚀\""
  },
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n.scikit-mol.code-workspace.swp\nscikit-mol.code-workspace\n\n\n# test data\ntests/data/\n\n# setuptools_scm version\nscikit_mol/_version.py\nnotebooks/SLC6A4_active_excape_export.csv\nsandbox/\n\n# PyCharm settings\n.idea\n"
  },
  {
    "path": ".pre-commit-config.yaml",
    "content": "repos:\n-   repo: https://github.com/pre-commit/pre-commit-hooks\n    rev: v4.5.0\n    hooks:\n    -   id: requirements-txt-fixer\n    -   id: mixed-line-ending\n    -   id: check-yaml\n    -   id: check-json\n    -   id: pretty-format-json\n        args: ['--autofix']\n        exclude: .ipynb\n    -   id: check-added-large-files\n    -   id: check-merge-conflict\n- repo: https://github.com/astral-sh/ruff-pre-commit\n  # Ruff version.\n  rev: v0.8.6\n  hooks:\n    # Run the linter.\n    - id: ruff\n      args: [ --fix ]\n      types_or: [ python, pyi ]\n    # Run the formatter.\n    - id: ruff-format\n    "
  },
  {
    "path": ".readthedocs.yaml",
    "content": "# Read the Docs configuration file for MkDocs projects\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Set the version of Python and other tools you might need\nbuild:\n  os: ubuntu-22.04\n  tools:\n    python: \"3.12\"\n  jobs:\n    # Use uv to speed up the build\n    # https://docs.readthedocs.io/en/stable/build-customization.html#install-dependencies-with-uv\n    pre_create_environment:\n    - asdf plugin add uv\n    - asdf install uv 0.5.24\n    - asdf global uv 0.5.24\n    create_environment:\n    - uv venv\n    install:\n    - uv sync --group docs\n    build:\n      html:\n      - uv run mkdocs build -d $READTHEDOCS_OUTPUT/html\nmkdocs:\n  configuration: mkdocs.yml\n\n"
  },
  {
    "path": ".vscode/extensions.json",
    "content": "{\n  \"recommendations\": [\n    \"njpwerner.autodocstring\"\n  ]\n}\n"
  },
  {
    "path": ".vscode/settings.json",
    "content": "{\n  \"autoDocstring.docstringFormat\": \"numpy\"\n}\n"
  },
  {
    "path": "CITATION.bib",
    "content": "@article{bjerrum_scikit-mol_2023,\n\ttitle = {Scikit-{Mol} brings cheminformatics to {Scikit}-{Learn}},\n\tauthor = {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},\n\tyear = {2023},\n\tmonth = dec,\n\tjournal = {ChemRxiv},\n\turl = {https://chemrxiv.org/engage/chemrxiv/article-details/60ef0fc58825826143a82cc0},\n\tdoi = {10.26434/chemrxiv-2023-fzqwd},\n\tabstract = {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.},\n\tlanguage = {en},\n\turldate = {2023-12-06},\n\tkeywords = {Cheminformatics, Descriptors, Fingerprints, Machine Learning, RDKit, Scikit-Learn},\n\tnote = {preprint}\n}"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "# Contribution\n\nFor up-to-date information, see\n\n[docs/contribution.md](docs/contributing.md)\n\nor\n\n[https://scikit-mol.readthedocs.io/en/latest/contributing/](https://scikit-mol.readthedocs.io/en/latest/contributing/)\n"
  },
  {
    "path": "LICENSE",
    "content": "                   GNU LESSER GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\nCopyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>\nEveryone is permitted to copy and distribute verbatim copies\nof this license document, but changing it is not allowed.\n\nThis version of the GNU Lesser General Public License incorporates\nthe terms and conditions of version 3 of the GNU General Public\nLicense, supplemented by the additional permissions listed below.\n\n0. Additional Definitions.\n\nAs used herein, \"this License\" refers to version 3 of the GNU Lesser\nGeneral Public License, and the \"GNU GPL\" refers to version 3 of the GNU\nGeneral Public License.\n\n\"The Library\" refers to a covered work governed by this License,\nother than an Application or a Combined Work as defined below.\n\nAn \"Application\" is any work that makes use of an interface provided\nby the Library, but which is not otherwise based on the Library.\nDefining a subclass of a class defined by the Library is deemed a mode\nof using an interface provided by the Library.\n\nA \"Combined Work\" is a work produced by combining or linking an\nApplication with the Library. The particular version of the Library\nwith which the Combined Work was made is also called the \"Linked\nVersion\".\n\nThe \"Minimal Corresponding Source\" for a Combined Work means the\nCorresponding Source for the Combined Work, excluding any source code\nfor portions of the Combined Work that, considered in isolation, are\nbased on the Application, and not on the Linked Version.\n\nThe \"Corresponding Application Code\" for a Combined Work means the\nobject code and/or source code for the Application, including any data\nand utility programs needed for reproducing the Combined Work from the\nApplication, but excluding the System Libraries of the Combined Work.\n\n1. Exception to Section 3 of the GNU GPL.\n\nYou may convey a covered work under sections 3 and 4 of this License\nwithout being bound by section 3 of the GNU GPL.\n\n2. Conveying Modified Versions.\n\nIf you modify a copy of the Library, and, in your modifications, a\nfacility refers to a function or data to be supplied by an Application\nthat uses the facility (other than as an argument passed when the\nfacility is invoked), then you may convey a copy of the modified\nversion:\n\na) under this License, provided that you make a good faith effort to\nensure that, in the event an Application does not supply the\nfunction or data, the facility still operates, and performs\nwhatever part of its purpose remains meaningful, or\n\nb) under the GNU GPL, with none of the additional permissions of\nthis License applicable to that copy.\n\n3. Object Code Incorporating Material from Library Header Files.\n\nThe object code form of an Application may incorporate material from\na header file that is part of the Library. You may convey such object\ncode under terms of your choice, provided that, if the incorporated\nmaterial is not limited to numerical parameters, data structure\nlayouts and accessors, or small macros, inline functions and templates\n(ten or fewer lines in length), you do both of the following:\n\na) Give prominent notice with each copy of the object code that the\nLibrary is used in it and that the Library and its use are\ncovered by this License.\n\nb) Accompany the object code with a copy of the GNU GPL and this license\ndocument.\n\n4. Combined Works.\n\nYou may convey a Combined Work under terms of your choice that,\ntaken together, effectively do not restrict modification of the\nportions of the Library contained in the Combined Work and reverse\nengineering for debugging such modifications, if you also do each of\nthe following:\n\na) Give prominent notice with each copy of the Combined Work that\nthe Library is used in it and that the Library and its use are\ncovered by this License.\n\nb) Accompany the Combined Work with a copy of the GNU GPL and this license\ndocument.\n\nc) For a Combined Work that displays copyright notices during\nexecution, include the copyright notice for the Library among\nthese notices, as well as a reference directing the user to the\ncopies of the GNU GPL and this license document.\n\nd) Do one of the following:\n\n       0) Convey the Minimal Corresponding Source under the terms of this\n       License, and the Corresponding Application Code in a form\n       suitable for, and under terms that permit, the user to\n       recombine or relink the Application with a modified version of\n       the Linked Version to produce a modified Combined Work, in the\n       manner specified by section 6 of the GNU GPL for conveying\n       Corresponding Source.\n\n       1) Use a suitable shared library mechanism for linking with the\n       Library.  A suitable mechanism is one that (a) uses at run time\n       a copy of the Library already present on the user's computer\n       system, and (b) will operate properly with a modified version\n       of the Library that is interface-compatible with the Linked\n       Version.\n\ne) Provide Installation Information, but only if you would otherwise\nbe required to provide such information under section 6 of the\nGNU GPL, and only to the extent that such information is\nnecessary to install and execute a modified version of the\nCombined Work produced by recombining or relinking the\nApplication with a modified version of the Linked Version. (If\nyou use option 4d0, the Installation Information must accompany\nthe Minimal Corresponding Source and Corresponding Application\nCode. If you use option 4d1, you must provide the Installation\nInformation in the manner specified by section 6 of the GNU GPL\nfor conveying Corresponding Source.)\n\n5. Combined Libraries.\n\nYou may place library facilities that are a work based on the\nLibrary side by side in a single library together with other library\nfacilities that are not Applications and are not covered by this\nLicense, and convey such a combined library under terms of your\nchoice, if you do both of the following:\n\na) Accompany the combined library with a copy of the same work based\non the Library, uncombined with any other library facilities,\nconveyed under the terms of this License.\n\nb) Give prominent notice with the combined library that part of it\nis a work based on the Library, and explaining where to find the\naccompanying uncombined form of the same work.\n\n6. Revised Versions of the GNU Lesser General Public License.\n\nThe Free Software Foundation may publish revised and/or new versions\nof the GNU Lesser General Public License from time to time. Such new\nversions will be similar in spirit to the present version, but may\ndiffer in detail to address new problems or concerns.\n\nEach version is given a distinguishing version number. If the\nLibrary as you received it specifies that a certain numbered version\nof the GNU Lesser General Public License \"or any later version\"\napplies to it, you have the option of following the terms and\nconditions either of that published version or of any later version\npublished by the Free Software Foundation. If the Library as you\nreceived it does not specify a version number of the GNU Lesser\nGeneral Public License, you may choose any version of the GNU Lesser\nGeneral Public License ever published by the Free Software Foundation.\n\nIf the Library as you received it specifies that a proxy can decide\nwhether future versions of the GNU Lesser General Public License shall\napply, that proxy's public statement of acceptance of any version is\npermanent authorization for you to choose that version for the\nLibrary.\n"
  },
  {
    "path": "MANIFEST.in",
    "content": "prune .github\nexclude .git*\n\n"
  },
  {
    "path": "Makefile",
    "content": "sync-notebooks:\n\tuv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/*.ipynb\n\tuv run ruff format \"docs/notebooks/\"\n\nrun-notebooks:\n\tuv run jupytext --execute docs/notebooks/*ipynb"
  },
  {
    "path": "README.md",
    "content": "# scikit-mol\n\n![Scikit-Mol Logo](https://raw.githubusercontent.com/EBjerrum/scikit-mol/029036fed8575705eaa80f6e3b08e70463b9a0c4/resources/logo/ScikitMol_Logo_Hybrid_300.png)\n\n[![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)]()\n\n[![pypi version](https://img.shields.io/pypi/v/scikit-mol.svg)](https://pypi.org/project/scikit-mol/)\n[![conda version](https://img.shields.io/conda/vn/conda-forge/scikit-mol.svg)](https://anaconda.org/conda-forge/scikit-mol)\n[![license](https://img.shields.io/github/license/EBjerrum/scikit-mol)](#)\n\n[![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/)\n[![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)\n\n## Scikit-Learn classes for molecular vectorization using RDKit\n\nThe 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\n\nAs example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and \\_test lists:\n\n    pipe = Pipeline([('mol_transformer', MorganFingerprintTransformer()), ('Regressor', Ridge())])\n    pipe.fit(mol_list_train, y_train)\n    pipe.score(mol_list_test, y_test)\n    pipe.predict([Chem.MolFromSmiles('c1ccccc1C(=O)C')])\n\n    >>> array([4.93858815])\n\nThe scikit-learn compatibility should also make it easier to include the fingerprinting step in hyperparameter tuning with scikit-learns utilities\n\nThe first draft for the project was created at the [RDKIT UGM 2022 hackathon](https://github.com/rdkit/UGM_2022) 2022-October-14\n\n## Installation\n\nUsers can install latest tagged release from pip\n\n```sh\npip install scikit-mol\n```\n\nor from conda-forge\n\n```sh\nconda install -c conda-forge scikit-mol\n```\n\nThe conda forge package should get updated shortly after a new tagged release on pypi.\n\nBleeding edge\n\n```sh\npip install git+https://github.com/EBjerrum/scikit-mol.git\n```\n\n## Documentation\n\nExample notebooks and API documentation are now hosted on [https://scikit-mol.readthedocs.io](https://scikit-mol.readthedocs.io/en/latest/)\n\n- [Basic Usage and fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/01_basic_usage/)\n- [Descriptor transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/02_descriptor_transformer/)\n- [Pipelining with Scikit-Learn classes](https://scikit-mol.readthedocs.io/en/latest/notebooks/03_example_pipeline/)\n- [Molecular standardization](https://scikit-mol.readthedocs.io/en/latest/notebooks/04_standardizer/)\n- [Sanitizing SMILES input](https://scikit-mol.readthedocs.io/en/latest/notebooks/05_smiles_sanitization/)\n- [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/06_hyperparameter_tuning/)\n- [Using parallel execution to speed up descriptor and fingerprint calculations](https://scikit-mol.readthedocs.io/en/latest/notebooks/07_parallel_transforms/)\n- [Using skopt for hyperparameter tuning](https://scikit-mol.readthedocs.io/en/latest/notebooks/08_external_library_skopt/)\n- [Testing different fingerprints as part of the hyperparameter optimization](https://scikit-mol.readthedocs.io/en/latest/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers/)\n- [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/)\n- [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/)\n- [Creating custom fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/12_custom_fingerprint_transformer/)\n- [Estimating applicability domain using feature based estimators](https://scikit-mol.readthedocs.io/en/latest/notebooks/13_applicability_domain/)\n\nWe also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd)\n\n## Other use-examples\n\nScikit-Mol has been featured in blog-posts or used in research, some examples which are listed below:\n\n- [Useful ML package for cheminformatics iwatobipen.wordpress.com](https://iwatobipen.wordpress.com/2023/11/12/useful-ml-package-for-cheminformatics-rdkit-cheminformatics-ml/)\n- [Boosted trees Data_in_life_blog](https://jhylin.github.io/Data_in_life_blog/posts/19_ML2-3_Boosted_trees/1_adaboost_xgb.html)\n- [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)\n- [Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations](https://doi.org/10.1186/s13321-025-01022-3)\n- [RandomNets Improve Neural Network Regression Performance via Implicit Ensembling](https://chemrxiv.org/engage/chemrxiv/article-details/67656cfa81d2151a02603f48)\n- [WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings](https://www.sciencedirect.com/science/article/pii/S2352914824001618)\n- [Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting](https://chemrxiv.org/engage/chemrxiv/article-details/661402ee21291e5d1d646651)\n- [AUTONOMOUS DRUG DISCOVERY](https://www.proquest.com/openview/3e830e36bc618f263905a99e787c66c6/1?pq-origsite=gscholar&cbl=18750&diss=y)\n- [DrugGym: A testbed for the economics of autonomous drug discovery](https://www.biorxiv.org/content/10.1101/2024.05.28.596296v1.abstract)\n\n## Roadmap and Contributing\n\n_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?\nWith a little bit of help, this project can be improved much faster! Reach to me (Esben), for a discussion about how we can proceed.\n\nCurrently, 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.\n\nThere are more information about how to contribute to the project in [CONTRIBUTING](https://scikit-mol.readthedocs.io/en/latest/contributing/)\n\n## BUGS\n\nProbably still, please check issues at GitHub and report there\n\n## Contributors\n\nScikit-Mol has been developed as a community effort with contributions from people from many different companies, consortia, foundations and academic institutions.\n\n[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/)\n\n- Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), esbenbjerrum+scikit_mol@gmail.com\n- Carmen Esposito [@cespos](https://github.com/cespos)\n- Son Ha [@son-ha-264](https://github.com/son-ha-264)\n- Oh-hyeon Choung [@Ohyeon5](https://github.com/Ohyeon5)\n- Andreas Poehlmann [@ap--](https://github.com/ap--)\n- Ya Chen [@anya-chen](https://github.com/anya-chen)\n- Anton Siomchen [@asiomchen](https://github.com/asiomchen)\n- Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz)\n- Adrien Chaton [@adrienchaton](https://github.com/adrienchaton)\n- [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz)\n- [@RiesBen](https://github.com/RiesBen)\n- [@enricogandini](https://github.com/enricogandini)\n- [@mikemhenry](https://github.com/mikemhenry)\n- [@c-feldmann](https://github.com/c-feldmann)\n- Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw)\n- Kyle Barbary [@kbarbary](https://github.com/kbarbary)\n"
  },
  {
    "path": "docs/api/fingerprints.base.md",
    "content": "`scikit_mol.fingerprints.baseclasses`\n\n::: scikit_mol.fingerprints.baseclasses\n    options:\n      filters: []"
  },
  {
    "path": "docs/api/scikit_mol.applicability.md",
    "content": "# `scikit-mol.applicability`\n\n::: scikit_mol.applicability\n\n\n"
  },
  {
    "path": "docs/api/scikit_mol.conversions.md",
    "content": "# `scikit-mol.conversions`\n\n::: scikit_mol.conversions"
  },
  {
    "path": "docs/api/scikit_mol.core.md",
    "content": "# `scikit-mol.core`\n\n::: scikit_mol.core"
  },
  {
    "path": "docs/api/scikit_mol.descriptors.md",
    "content": "# `scikit_mol.descriptors`\n\n::: scikit_mol.descriptors"
  },
  {
    "path": "docs/api/scikit_mol.fingerprints.md",
    "content": "::: scikit_mol.fingerprints\n    options:\n      filters: [\"!Fps\"]\n      inherited_members:\n        - transform"
  },
  {
    "path": "docs/api/scikit_mol.parallel.md",
    "content": "# `scikit-mol.parallel`\n\n::: scikit_mol.parallel"
  },
  {
    "path": "docs/api/scikit_mol.plotting.md",
    "content": "# `scikit-mol.plotting`\n\n::: scikit_mol.plotting"
  },
  {
    "path": "docs/api/scikit_mol.safeinference.md",
    "content": "# `scikit-mol.safeinference`\n\n::: scikit_mol.safeinference"
  },
  {
    "path": "docs/api/scikit_mol.standardizer.md",
    "content": "# `scikit-mol.standardizer`\n\n::: scikit_mol.standardizer\n\n\n"
  },
  {
    "path": "docs/assets/css/tweak-width.css",
    "content": "/* snippet from datamol.io */\n@media only screen and (min-width: 76.25em) {\n    .md-main__inner {\n      max-width: none;\n      padding-left: 2em;\n      padding-left: 2em;\n    }\n    .md-sidebar--primary {\n      left: 0;\n    }\n    .md-sidebar--secondary {\n      right: 0;\n      margin-left: 0;\n      -webkit-transform: none;\n      transform: none;\n    }\n  }"
  },
  {
    "path": "docs/assets/js/readthedocs.js",
    "content": "// Add server-side search\ndocument.addEventListener(\"DOMContentLoaded\", function(event) {\n    // Trigger Read the Docs' search addon instead of Material MkDocs default\n    document.querySelector(\".md-search__input\").addEventListener(\"focus\", (e) => {\n            const event = new CustomEvent(\"readthedocs-search-show\");\n            document.dispatchEvent(event);\n        });\n    });\n\n// Use CustomEvent to generate the version selector\ndocument.addEventListener(\n    \"readthedocs-addons-data-ready\",\n    function (event) {\n    const config = event.detail.data();\n    const versioning = `\n<div class=\"md-version\">\n<button class=\"md-version__current\" aria-label=\"Select version\">\n${config.versions.current.slug}\n</button>\n\n<ul class=\"md-version__list\">\n${ config.versions.active.map(\n(version) => `\n<li class=\"md-version__item\">\n<a href=\"${ version.urls.documentation }\" class=\"md-version__link\">\n    ${ version.slug }\n</a>\n        </li>`).join(\"\\n\")}\n</ul>\n</div>`;\n\ndocument.querySelector(\".md-header__topic\").insertAdjacentHTML(\"beforeend\", versioning);\n});"
  },
  {
    "path": "docs/contributing.md",
    "content": "# Contribution\n\nThanks for your interest in contributing to the project. Please read on in the sections that apply.\n\n## Discord Server\n\nWe have a discord server for chats and discussion, ask for an invitation: esbenbjerrum+scikit_mol@gmail.com\n\n## Installation\n\nWe use [uv] for managing the virtual environment. You can install it with:\n\n```sh\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\nFor more information and other installation methods see [documentation](https://docs.astral.sh/uv/)\n\nClone and install in editable more like this\n\n```sh\ngit clone git@github.com:EBjerrum/scikit-mol.git\nuv sync --dev\n```\n\nAfter that you could either activate venv and run commands as usual:\n\n```sh\nsource .venv/bin/activate\npytest -v --cov=scikit_mol\n```\n\nor use `uv run` to run commands in the venv (automatically check that environment is up to date):\n\n```sh\nuv run pytest -v --cov=scikit_mol\n```\n\n`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`)\n\n## Code Quality\n\nWe 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:\n\n```sh\nruff format scikit_mol\nruff check --fix scikit_mol\n```\n\nWe 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:\n\n```sh\npre-commit install\n```\n\nFor more information on pre-commit see [documentation](https://pre-commit.com/).\n\n## Adding transformers\n\nThe 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:\n\n- The arguments accepted by **init** should all be keyword arguments with a default value.\n- Every keyword argument accepted by **init** should correspond to an attribute on the instance.\n- - There should be no logic, not even input validation, and the parameters should not be changed inside the **init** function.\n    Scikit-learn classes depends on this in order to for e.g. the `.get_params()`, `.set_params()`, cloning abilities and representation rendering to work.\n- With the new error handling, falsy objects need to return masked arrays or arrays with `np.nan` (for float dtype)\n\n### Tips\n\n- 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\n\n- `@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.\n\n- 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. \n  See [MHFingerprintTransformer](https://github.com/EBjerrum/scikit-mol/blob/main/scikit_mol/fingerprints/minhash.py#L11) for an example.\n\n## Module organisation\n\nCurrently, we have multiple classes in the same file, if they are the same type. This may change in the future.\n\n## Docstrings\n\nWe 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.\n\n## Typehints\n\nparameters and output of methods should preferably be using typehints\n\n## Testing\n\nNew 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.\nScikit-Learn has a check_estimator that we should strive to get to work, some classes of scikit-mol currently does not pass all tests.\n\n## Notebooks\n\nAnother 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.\n\nIf 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.\n\nIf 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.\n\n`make sync-notebooks` will sync all the notebooks with the .py files in the `scripts` folder.\n\n`make run-notebooks` will sync, run and save the notebooks, expects an ipython kernel with scikit-mol installed.\n\nIf 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\n\n```bash\nuv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/XX_YourNotebook.ipynb\nuv run ruff format \"docs/notebooks/XX_YourNotebook.ipynb\"\nuv run jupytext --execute docs/notebooks/XX_YourNotebook.ipynb\n```\n\n## Documentation\n\nWe 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.\n\nInstall documentation dependencies:\n\n```sh\nuv sync --group docs\n```\n\nStart server:\n\n```sh\nuv run mkdocs serve\n```\n\nGo to http://127.0.0.1:8000 to see the documentation\n\n## Release\n\n### PyPi\n\nTo 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.\n\n### Conda\n\nWhen 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\n"
  },
  {
    "path": "docs/index.md",
    "content": "# scikit-mol\n\n![Scikit-Mol Logo](https://raw.githubusercontent.com/EBjerrum/scikit-mol/029036fed8575705eaa80f6e3b08e70463b9a0c4/resources/logo/ScikitMol_Logo_Hybrid_300.png)\n\n[![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)]()\n\n[![pypi version](https://img.shields.io/pypi/v/scikit-mol.svg)](https://pypi.org/project/scikit-mol/)\n[![conda version](https://img.shields.io/conda/vn/conda-forge/scikit-mol.svg)](https://anaconda.org/conda-forge/scikit-mol)\n[![license](https://img.shields.io/github/license/EBjerrum/scikit-mol)](#)\n\n[![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/)\n[![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)\n\n## Scikit-Learn classes for molecular vectorization using RDKit\n\nThe 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\n\nAs example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and \\_test lists:\n\n    pipe = Pipeline([('mol_transformer', MorganFingerprintTransformer()), ('Regressor', Ridge())])\n    pipe.fit(mol_list_train, y_train)\n    pipe.score(mol_list_test, y_test)\n    pipe.predict([Chem.MolFromSmiles('c1ccccc1C(=O)C')])\n\n    >>> array([4.93858815])\n\nThe scikit-learn compatibility should also make it easier to include the fingerprinting step in hyperparameter tuning with scikit-learns utilities\n\nThe first draft for the project was created at the [RDKIT UGM 2022 hackathon](https://github.com/rdkit/UGM_2022) 2022-October-14\n\n## Installation\n\nUsers can install latest tagged release from pip\n\n```sh\npip install scikit-mol\n```\n\nor from conda-forge\n\n```sh\nconda install -c conda-forge scikit-mol\n```\n\nThe conda forge package should get updated shortly after a new tagged release on pypi.\n\nBleeding edge\n\n```sh\npip install git+https://github.com/EBjerrum/scikit-mol.git\n```\n\n## Documentation\n\nExample notebooks and API documentation are now hosted on [https://scikit-mol.readthedocs.io](https://scikit-mol.readthedocs.io/en/latest/)\n\n- [Basic Usage and fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/01_basic_usage/)\n- [Descriptor transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/02_descriptor_transformer/)\n- [Pipelining with Scikit-Learn classes](https://scikit-mol.readthedocs.io/en/latest/notebooks/03_example_pipeline/)\n- [Molecular standardization](https://scikit-mol.readthedocs.io/en/latest/notebooks/04_standardizer/)\n- [Sanitizing SMILES input](https://scikit-mol.readthedocs.io/en/latest/notebooks/05_smiles_sanitization/)\n- [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://scikit-mol.readthedocs.io/en/latest/notebooks/06_hyperparameter_tuning/)\n- [Using parallel execution to speed up descriptor and fingerprint calculations](https://scikit-mol.readthedocs.io/en/latest/notebooks/07_parallel_transforms/)\n- [Using skopt for hyperparameter tuning](https://scikit-mol.readthedocs.io/en/latest/notebooks/08_external_library_skopt/)\n- [Testing different fingerprints as part of the hyperparameter optimization](https://scikit-mol.readthedocs.io/en/latest/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers/)\n- [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/)\n- [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/)\n- [Creating custom fingerprint transformers](https://scikit-mol.readthedocs.io/en/latest/notebooks/12_custom_fingerprint_transformer/)\n- [Estimating applicability domain using feature based estimators](https://scikit-mol.readthedocs.io/en/latest/notebooks/13_applicability_domain/)\n\nWe also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd)\n\n## Other use-examples\n\nScikit-Mol has been featured in blog-posts or used in research, some examples which are listed below:\n\n- [Useful ML package for cheminformatics iwatobipen.wordpress.com](https://iwatobipen.wordpress.com/2023/11/12/useful-ml-package-for-cheminformatics-rdkit-cheminformatics-ml/)\n- [Boosted trees Data_in_life_blog](https://jhylin.github.io/Data_in_life_blog/posts/19_ML2-3_Boosted_trees/1_adaboost_xgb.html)\n- [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)\n- [Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations](https://doi.org/10.1186/s13321-025-01022-3)\n- [RandomNets Improve Neural Network Regression Performance via Implicit Ensembling](https://chemrxiv.org/engage/chemrxiv/article-details/67656cfa81d2151a02603f48)\n- [WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings](https://www.sciencedirect.com/science/article/pii/S2352914824001618)\n- [Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting](https://chemrxiv.org/engage/chemrxiv/article-details/661402ee21291e5d1d646651)\n- [AUTONOMOUS DRUG DISCOVERY](https://www.proquest.com/openview/3e830e36bc618f263905a99e787c66c6/1?pq-origsite=gscholar&cbl=18750&diss=y)\n- [DrugGym: A testbed for the economics of autonomous drug discovery](https://www.biorxiv.org/content/10.1101/2024.05.28.596296v1.abstract)\n\n## Roadmap and Contributing\n\n_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?\nWith a little bit of help, this project can be improved much faster! Reach to me (Esben), for a discussion about how we can proceed.\n\nCurrently, 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.\n\nThere are more information about how to contribute to the project in [CONTRIBUTING](https://scikit-mol.readthedocs.io/en/latest/contributing/)\n\n## BUGS\n\nProbably still, please check issues at GitHub and report there\n\n## Contributors\n\nScikit-Mol has been developed as a community effort with contributions from people from many different companies, consortia, foundations and academic institutions.\n\n[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/)\n\n- Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), esbenbjerrum+scikit_mol@gmail.com\n- Carmen Esposito [@cespos](https://github.com/cespos)\n- Son Ha [@son-ha-264](https://github.com/son-ha-264)\n- Oh-hyeon Choung [@Ohyeon5](https://github.com/Ohyeon5)\n- Andreas Poehlmann [@ap--](https://github.com/ap--)\n- Ya Chen [@anya-chen](https://github.com/anya-chen)\n- Anton Siomchen [@asiomchen](https://github.com/asiomchen)\n- Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz)\n- Adrien Chaton [@adrienchaton](https://github.com/adrienchaton)\n- [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz)\n- [@RiesBen](https://github.com/RiesBen)\n- [@enricogandini](https://github.com/enricogandini)\n- [@mikemhenry](https://github.com/mikemhenry)\n- [@c-feldmann](https://github.com/c-feldmann)\n- Mieczyslaw Torchala [@mieczyslaw](https://github.com/mieczyslaw)\n- Kyle Barbary [@kbarbary](https://github.com/kbarbary)\n"
  },
  {
    "path": "docs/notebooks/01_basic_usage.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aa079ac3\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Scikit-Mol\\n\",\n    \"## scikit-learn compatible RDKit transformers\\n\",\n    \"\\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\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"76d24789\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"2c8cad03\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:29.627872Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:29.627571Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:29.632065Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:29.631373Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from IPython.core.display import HTML\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"8d5b2333\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:29.634712Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:29.634423Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:29.845389Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:29.844169Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from rdkit import Chem\\n\",\n    \"\\n\",\n    \"smiles_strings = [\\n\",\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\",\n    \"    \\\"O(C1=NC=C2C(CN(CC2=C1)C)C3=CC=C(OC)C=C3)CCCN(CC)CC\\\",\\n\",\n    \"    \\\"O=S(=O)(N(CC=1C=CC2=CC=CC=C2C1)[C@@H]3CCNC3)C\\\",\\n\",\n    \"    \\\"C1(=C2C(CCCC2O)=NC=3C1=CC=CC3)NCC=4C=CC(=CC4)Cl\\\",\\n\",\n    \"    \\\"C1NC[C@@H](C1)[C@H](OC=2C=CC(=NC2C)OC)CC(C)C\\\",\\n\",\n    \"    \\\"FC(F)(F)C=1C(CN(C2CCNCC2)CC(CC)CC)=CC=CC1\\\",\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_strings]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b9a588c7\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next we import the Morgan fingerprint transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"0a625dda\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:29.850211Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:29.848822Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:30.986417Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:30.984810Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MorganFingerprintTransformer(radius=3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"\\n\",\n    \"transformer = MorganFingerprintTransformer(radius=3)\\n\",\n    \"print(transformer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"355610d1\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\n\",\n    \"![An image of the interactive transformer widget](images/Transformer_Widget.jpg \\\"Transformer object rendering in Jupyter\\\")\\n\",\n    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"9a801d0f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:30.991850Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:30.990911Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.011512Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.010309Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-1 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-1 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-1 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-1 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-1\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>MorganFingerprintTransformer(radius=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-1\\\" type=\\\"checkbox\\\" checked><label for=\\\"sk-estimator-id-1\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\\\">?<span>Documentation for MorganFingerprintTransformer</span></a><span class=\\\"sk-estimator-doc-link fitted\\\">i<span>Fitted</span></span></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>MorganFingerprintTransformer(radius=3)</pre></div> </div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"MorganFingerprintTransformer(radius=3)\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"556858b4\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we want to get all the settings explicitly, we can use the .get_params() method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"500dc6f7\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.015226Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.014511Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.022448Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.021051Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'fpSize': 2048,\\n\",\n       \" 'n_jobs': None,\\n\",\n       \" 'radius': 3,\\n\",\n       \" 'safe_inference_mode': False,\\n\",\n       \" 'useBondTypes': True,\\n\",\n       \" 'useChirality': False,\\n\",\n       \" 'useCounts': False,\\n\",\n       \" 'useFeatures': False}\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"parameters = transformer.get_params()\\n\",\n    \"parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d453fa33\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"3a27b07a\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.026293Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.025546Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.032975Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.031700Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MorganFingerprintTransformer(fpSize=256)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"parameters[\\\"radius\\\"] = 2\\n\",\n    \"parameters[\\\"fpSize\\\"] = 256\\n\",\n    \"transformer.set_params(**parameters)\\n\",\n    \"print(transformer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3dd372d3\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"0f141920\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.036752Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.036118Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.043904Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.042561Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fps is a <class 'numpy.ndarray'> with shape (6, 256) and data type uint8\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fps = transformer.transform(mols)\\n\",\n    \"print(f\\\"fps is a {type(fps)} with shape {fps.shape} and data type {fps.dtype}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9cb75226\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"481e527f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.047228Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.046700Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.054569Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.053275Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 1, 0, ..., 0, 0, 0],\\n\",\n       \"       [1, 0, 0, ..., 0, 0, 1],\\n\",\n       \"       [1, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 1],\\n\",\n       \"       [1, 1, 0, ..., 0, 0, 0],\\n\",\n       \"       [1, 1, 0, ..., 0, 0, 0]], shape=(6, 256), dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y = range(len(mols))\\n\",\n    \"transformer.transform(mols, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"500cec09\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"7773a5a0\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.057901Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.057134Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.064119Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.063046Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"SmilesToMolTransformer()\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"\\n\",\n    \"smi2mol = SmilesToMolTransformer()\\n\",\n    \"print(smi2mol)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"fa484453\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:31.067178Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:31.066755Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:31.074756Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:31.073587Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[<rdkit.Chem.rdchem.Mol object at 0x7feda5e663b0>]\\n\",\n      \" [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66420>]\\n\",\n      \" [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66490>]\\n\",\n      \" [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66500>]\\n\",\n      \" [<rdkit.Chem.rdchem.Mol object at 0x7feda5e66570>]\\n\",\n      \" [<rdkit.Chem.rdchem.Mol object at 0x7feda5e665e0>]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(smi2mol.transform(smiles_strings))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.9.4 ('rdkit')\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/02_descriptor_transformer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e3cf34ca\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Desc2DTransformer: RDKit descriptors transformer\\n\",\n    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"81745b1f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:32.631647Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:32.631311Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:34.194489Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:34.193202Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from rdkit import Chem\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from scikit_mol.descriptors import MolecularDescriptorTransformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2293e9e6\",\n   \"metadata\": {},\n   \"source\": [\n    \"After instantiation of the descriptor transformer, we can query which descriptors it found available in the RDKit framework.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"dd9a2ad0\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:34.198567Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:34.197421Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:34.206453Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:34.205342Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"There are 217 available descriptors\\n\",\n      \"The first five descriptor names: ['MaxAbsEStateIndex', 'MaxEStateIndex', 'MinAbsEStateIndex', 'MinEStateIndex', 'qed']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"descriptor = MolecularDescriptorTransformer()\\n\",\n    \"available_descriptors = descriptor.available_descriptors\\n\",\n    \"print(f\\\"There are {len(available_descriptors)} available descriptors\\\")\\n\",\n    \"print(f\\\"The first five descriptor names: {available_descriptors[:5]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"110c00c0\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can transform molecules to their descriptor profiles\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"4431a910\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:34.210875Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:34.210262Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:34.353857Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:34.352652Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"smiles_list = [\\\"CCCC\\\", \\\"c1ccccc1\\\"]\\n\",\n    \"mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\\n\",\n    \"\\n\",\n    \"features = descriptor.transform(mols)\\n\",\n    \"_ = plt.plot(np.array(features).T)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fdcb0698\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we only want some of them, this can be specified at object instantiation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"6caa9a54\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:34.357638Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:34.356566Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:34.363282Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:34.362201Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Selected descriptors are ['HeavyAtomCount', 'FractionCSP3', 'RingCount', 'MolLogP', 'MolWt']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"some_descriptors = MolecularDescriptorTransformer(\\n\",\n    \"    desc_list=[\\\"HeavyAtomCount\\\", \\\"FractionCSP3\\\", \\\"RingCount\\\", \\\"MolLogP\\\", \\\"MolWt\\\"]\\n\",\n    \")\\n\",\n    \"print(f\\\"Selected descriptors are {some_descriptors.selected_descriptors}\\\")\\n\",\n    \"features = some_descriptors.transform(mols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"52eaef77\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we want to update the selected descriptors on an already existing object, this can be done via the .set_params() method\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"78fc5691\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:34.366550Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:34.366050Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:34.372939Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:34.371839Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MolecularDescriptorTransformer(desc_list=['HeavyAtomCount', 'FractionCSP3',\\n\",\n      \"                                          'RingCount'])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\n\",\n    \"    some_descriptors.set_params(\\n\",\n    \"        desc_list=[\\\"HeavyAtomCount\\\", \\\"FractionCSP3\\\", \\\"RingCount\\\"]\\n\",\n    \"    )\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"310a2a0d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/03_example_pipeline.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e7c43298\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Pipelining the scikit-mol transformer\\n\",\n    \"\\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    \"\\n\",\n    \"First some needed imports and a dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"79139b10\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:35.876773Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:35.876261Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:36.754601Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:36.753459Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import rdkit\\n\",\n    \"from rdkit import Chem\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from time import time\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"17a9cdd7\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:36.758840Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:36.758015Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:36.767668Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:36.766504Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"csv_file = \\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\"  # Hmm, maybe better to download directly\\n\",\n    \"data = pd.read_csv(csv_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"066131b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\n\",\n    \"We add RDKit mol objects to the dataframe with pandastools and check that all conversions went well.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"a3ec0a23\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:36.770992Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:36.770360Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:36.828093Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:36.826677Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 out of 200 SMILES failed in conversion\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eccaf4af\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then, let's import some tools from scikit-learn and two transformers from scikit-mol\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"4eb8f0fa\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:36.830959Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:36.830663Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.516946Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.515550Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"99edec0f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.521222Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.520115Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.527537Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.526440Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mol_list_train, mol_list_test, y_train, y_test = train_test_split(\\n\",\n    \"    data.ROMol, data.pXC50, random_state=0\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8380817\",\n   \"metadata\": {},\n   \"source\": [\n    \"After a split into train and test, we'll build the first pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"a27d6ff9\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.531062Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.530349Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.539115Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.538026Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\\n\",\n      \"                ('Regressor', Ridge())])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pipe = Pipeline(\\n\",\n    \"    [(\\\"mol_transformer\\\", MorganFingerprintTransformer()), (\\\"Regressor\\\", Ridge())]\\n\",\n    \")\\n\",\n    \"print(pipe)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c12f9a8\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can do the fit by simply providing the list of RDKit molecule objects\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"634ca919\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.542129Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.541844Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.609556Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.608271Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train score is :1.00\\n\",\n      \"Test score is  :0.55\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pipe.fit(mol_list_train, y_train)\\n\",\n    \"print(f\\\"Train score is :{pipe.score(mol_list_train,y_train):0.2F}\\\")\\n\",\n    \"print(f\\\"Test score is  :{pipe.score(mol_list_test, y_test):0.2F}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8440cc5a\",\n   \"metadata\": {},\n   \"source\": [\n    \"Nevermind the performance, or the exact value of the prediction, this is for demonstration purpures. We can easily predict on lists of molecules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"f4431aab\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.613501Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.613074Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.625937Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.624623Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6.00400299])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.predict([Chem.MolFromSmiles(\\\"c1ccccc1C(=O)[OH]\\\")])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a60e242b\",\n   \"metadata\": {},\n   \"source\": [\n    \"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!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"a908097d\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.630016Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.629320Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.640274Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.639075Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Pipeline(steps=[('smiles_transformer', SmilesToMolTransformer()),\\n\",\n      \"                ('pipe',\\n\",\n      \"                 Pipeline(steps=[('mol_transformer',\\n\",\n      \"                                  MorganFingerprintTransformer()),\\n\",\n      \"                                 ('Regressor', Ridge())]))])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"smiles_pipe = Pipeline(\\n\",\n    \"    [(\\\"smiles_transformer\\\", SmilesToMolTransformer()), (\\\"pipe\\\", pipe)]\\n\",\n    \")\\n\",\n    \"print(smiles_pipe)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"0124653c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.643282Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.642781Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.655561Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.652513Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6.00400299])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"smiles_pipe.predict([\\\"c1ccccc1C(=O)[OH]\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"069e2d01\",\n   \"metadata\": {},\n   \"source\": [\n    \"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().\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"63c8ef60\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:37.659747Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:37.658755Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:37.669836Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:37.668406Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'memory': None,\\n\",\n       \" 'steps': [('smiles_transformer', SmilesToMolTransformer()),\\n\",\n       \"  ('pipe',\\n\",\n       \"   Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\\n\",\n       \"                   ('Regressor', Ridge())]))],\\n\",\n       \" 'transform_input': None,\\n\",\n       \" 'verbose': False,\\n\",\n       \" 'smiles_transformer': SmilesToMolTransformer(),\\n\",\n       \" 'pipe': Pipeline(steps=[('mol_transformer', MorganFingerprintTransformer()),\\n\",\n       \"                 ('Regressor', Ridge())]),\\n\",\n       \" 'smiles_transformer__n_jobs': None,\\n\",\n       \" 'smiles_transformer__safe_inference_mode': False,\\n\",\n       \" 'pipe__memory': None,\\n\",\n       \" 'pipe__steps': [('mol_transformer', MorganFingerprintTransformer()),\\n\",\n       \"  ('Regressor', Ridge())],\\n\",\n       \" 'pipe__transform_input': None,\\n\",\n       \" 'pipe__verbose': False,\\n\",\n       \" 'pipe__mol_transformer': MorganFingerprintTransformer(),\\n\",\n       \" 'pipe__Regressor': Ridge(),\\n\",\n       \" 'pipe__mol_transformer__fpSize': 2048,\\n\",\n       \" 'pipe__mol_transformer__n_jobs': None,\\n\",\n       \" 'pipe__mol_transformer__radius': 2,\\n\",\n       \" 'pipe__mol_transformer__safe_inference_mode': False,\\n\",\n       \" 'pipe__mol_transformer__useBondTypes': True,\\n\",\n       \" 'pipe__mol_transformer__useChirality': False,\\n\",\n       \" 'pipe__mol_transformer__useCounts': False,\\n\",\n       \" 'pipe__mol_transformer__useFeatures': False,\\n\",\n       \" 'pipe__Regressor__alpha': 1.0,\\n\",\n       \" 'pipe__Regressor__copy_X': True,\\n\",\n       \" 'pipe__Regressor__fit_intercept': True,\\n\",\n       \" 'pipe__Regressor__max_iter': None,\\n\",\n       \" 'pipe__Regressor__positive': False,\\n\",\n       \" 'pipe__Regressor__random_state': None,\\n\",\n       \" 'pipe__Regressor__solver': 'auto',\\n\",\n       \" 'pipe__Regressor__tol': 0.0001}\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"smiles_pipe.get_params()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/04_standardizer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"095e3de9\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Molecule standardization\\n\",\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\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"d40bdabe\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:39.191239Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:39.189891Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:40.514182Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:40.512920Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from rdkit import Chem\\n\",\n    \"from scikit_mol.standardizer import Standardizer\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.linear_model import Ridge\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1f739296\",\n   \"metadata\": {},\n   \"source\": [\n    \"For demonstration let's create some molecules with different protonation states. The two first molecules are Benzoic acid and Sodium benzoate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"5a45dfd5\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:40.518530Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:40.517654Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:40.537037Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:40.535847Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([<rdkit.Chem.rdchem.Mol object at 0x7f3682927ca0>], dtype=object)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([<rdkit.Chem.rdchem.Mol object at 0x7f3682927bc0>], dtype=object)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"smiles_strings = (\\n\",\n    \"    \\\"c1ccccc1C(=O)[OH]\\\",\\n\",\n    \"    \\\"c1ccccc1C(=O)[O-].[Na+]\\\",\\n\",\n    \"    \\\"CC[NH+](C)C\\\",\\n\",\n    \"    \\\"CC[N+](C)(C)C\\\",\\n\",\n    \"    \\\"[O-]CC(C(=O)[O-])C[NH+](C)C\\\",\\n\",\n    \"    \\\"[O-]CC(C(=O)[O-])C[N+](C)(C)C\\\",\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"smi2mol = SmilesToMolTransformer()\\n\",\n    \"\\n\",\n    \"mols = smi2mol.transform(smiles_strings)\\n\",\n    \"for mol in mols[0:2]:\\n\",\n    \"    display(mol)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1974e56a\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can simply use the transformer directly and get a list of standardized molecules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"d13141c6\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:40.540032Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:40.539703Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:40.560979Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:40.560007Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([['O=C(O)c1ccccc1'],\\n\",\n       \"       ['O=C(O)c1ccccc1'],\\n\",\n       \"       ['CCN(C)C'],\\n\",\n       \"       ['CC[N+](C)(C)C'],\\n\",\n       \"       ['CN(C)CC(CO)C(=O)O'],\\n\",\n       \"       ['C[N+](C)(C)CC(CO)C(=O)[O-]']], dtype='<U26')\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# You can just run straight up like this. Note that neutralising is optional\\n\",\n    \"standardizer = Standardizer()\\n\",\n    \"standard_mols = standardizer.transform(mols)\\n\",\n    \"standard_smiles = smi2mol.inverse_transform(standard_mols)\\n\",\n    \"standard_smiles\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d268d331\",\n   \"metadata\": {},\n   \"source\": [\n    \"Some of the molecules were desalted and neutralized.\\n\",\n    \"\\n\",\n    \"A typical use case would be to add the standardizer to a pipeline for prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"a376a759\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:40.565099Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:40.564746Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:40.603278Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:40.602109Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predictions with no standardization: [0.51983795 0.61543701 2.31738354 3.01206795 3.44085399 4.37516731]\\n\",\n      \"Predictions with standardization:    [0.51983795 0.51983795 2.06562022 3.01206795 3.95446692 4.92816899]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Typical use case is to use it in an sklearn pipeline, like below\\n\",\n    \"predictor = Ridge()\\n\",\n    \"\\n\",\n    \"std_pipe = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(),\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MorganFingerprintTransformer(useCounts=True),\\n\",\n    \"    predictor,\\n\",\n    \")\\n\",\n    \"nonstd_pipe = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(), MorganFingerprintTransformer(useCounts=True), predictor\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"fake_y = range(len(smiles_strings))\\n\",\n    \"\\n\",\n    \"std_pipe.fit(smiles_strings, fake_y)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(f\\\"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}\\\")\\n\",\n    \"print(f\\\"Predictions with standardization:    {std_pipe.predict(smiles_strings)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f0d071fb\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\\n\",\n    \"\\n\",\n    \"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.\\n\",\n    \"\\n\",\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"50f71bca\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:40.606383Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:40.605699Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:40.631525Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:40.630106Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predictions with no standardization: [0.07445775 0.96053374 2.05993278 3.00857908 3.96365443 4.93284221]\\n\",\n      \"Predictions with standardization:    [0.07445775 0.07445775 2.32132164 3.00857908 2.68502208 4.30275549]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"nonstd_pipe.fit(smiles_strings, fake_y)\\n\",\n    \"print(f\\\"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}\\\")\\n\",\n    \"print(f\\\"Predictions with standardization:    {std_pipe.predict(smiles_strings)}\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.9.4 ('rdkit')\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/05_smiles_sanitization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9b787560\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SMILES sanitation\\n\",\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"612aa974\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:41.856567Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:41.856271Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:42.443540Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:42.442130Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"\\n\",\n    \"csv_file = \\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\"  # Hmm, maybe better to download directly\\n\",\n    \"data = pd.read_csv(csv_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0f957a69\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, this example dataset contain all sanitizable SMILES, so for demonstration purposes, we will corrupt one of them\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"b09cfd6b\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:42.448114Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:42.447423Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:42.454532Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:42.453410Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data.loc[1, \\\"SMILES\\\"] = \\\"CN(C)(C)(C)\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"e20fb5cc\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:42.458752Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:42.457870Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:42.522970Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:42.521865Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset contains 1 unparsable mols\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[18:22:42] Explicit valence for atom # 1 N, 4, is greater than permitted\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"Dataset contains {data.ROMol.isna().sum()} unparsable mols\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f8dccd93\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"3dbd50b3\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:42.526969Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:42.526369Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:43.317088Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:43.316227Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Error in parsing 1 SMILES. Unparsable SMILES can be found in self.errors\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[18:22:43] Explicit valence for atom # 1 N, 4, is greater than permitted\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.utilities import CheckSmilesSanitization\\n\",\n    \"\\n\",\n    \"smileschecker = CheckSmilesSanitization()\\n\",\n    \"\\n\",\n    \"smiles_list_valid, y_valid, smiles_errors, y_errors = smileschecker.sanitize(\\n\",\n    \"    list(data.SMILES), list(data.pXC50)\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c888d7da\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"5af5ea3d\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:43.320958Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:43.320157Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:43.335067Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:43.333676Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SMILES</th>\\n\",\n       \"      <th>y</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>CN(C)(C)(C)</td>\\n\",\n       \"      <td>7.18046</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        SMILES        y\\n\",\n       \"0  CN(C)(C)(C)  7.18046\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"smileschecker.errors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c2ce2677\",\n   \"metadata\": {},\n   \"source\": [\n    \"The checker can also be used only on X\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"84db07cc\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:43.339302Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:43.338668Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:43.391019Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:43.389989Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Error in parsing 1 SMILES. Unparsable SMILES can be found in self.errors\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[18:22:43] Explicit valence for atom # 1 N, 4, is greater than permitted\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SMILES</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>CN(C)(C)(C)</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        SMILES\\n\",\n       \"0  CN(C)(C)(C)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"smiles_list_valid, X_errors = smileschecker.sanitize(list(data.SMILES))\\n\",\n    \"smileschecker.errors\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.9.4 ('rdkit')\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/06_hyperparameter_tuning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f0b0cc54\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Full example: Hyperparameter tuning\\n\",\n    \"\\n\",\n    \"first some imports of the usual suspects: RDKit, pandas, matplotlib, numpy and sklearn. New kid on the block is scikit-mol\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"51aa3d62\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:44.604531Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:44.604218Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.163842Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.162418Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import rdkit\\n\",\n    \"from rdkit import Chem\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from time import time\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.pipeline import Pipeline, make_pipeline\\n\",\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e07990d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"adbc1868\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.167928Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.166905Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.173404Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.172138Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"full_set = False\\n\",\n    \"\\n\",\n    \"if full_set:\\n\",\n    \"    csv_file = \\\"SLC6A4_active_excape_export.csv\\\"\\n\",\n    \"    if not os.path.exists(csv_file):\\n\",\n    \"        import urllib.request\\n\",\n    \"\\n\",\n    \"        url = \\\"https://ndownloader.figshare.com/files/25747817\\\"\\n\",\n    \"        urllib.request.urlretrieve(url, csv_file)\\n\",\n    \"else:\\n\",\n    \"    csv_file = \\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d2ce3c7f\",\n   \"metadata\": {},\n   \"source\": [\n    \"The CSV data is loaded into a Pandas dataframe and the PandasTools utility from RDKit is used to add a column with RDKit molecules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"9a283f12\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.177164Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.176440Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.233488Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.232374Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 out of 200 SMILES failed in conversion\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"\\n\",\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e245e989\",\n   \"metadata\": {},\n   \"source\": [\n    \"We use the train_test_split to, well, split the dataframe's molecule columns and pXC50 column into lists for train and testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"303b83de\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.236917Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.236251Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.243264Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.242175Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mol_list_train, mol_list_test, y_train, y_test = train_test_split(\\n\",\n    \"    data.ROMol, data.pXC50, random_state=42\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"56247c3b\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"1383d0fc\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.247777Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.246787Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.614634Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.613399Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Probably the recommended way would be to prestandardize the data if there's no changes to the transformer,\\n\",\n    \"# and then add the standardizer in the inference pipeline.\\n\",\n    \"\\n\",\n    \"from scikit_mol.standardizer import Standardizer\\n\",\n    \"\\n\",\n    \"standardizer = Standardizer()\\n\",\n    \"mol_list_std_train = standardizer.transform(mol_list_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0775d395\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple pipeline with a MorganTransformer and a Ridge() regression for demonstration.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"51c74711\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.618057Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.617207Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.622371Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.621207Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"moltransformer = MorganFingerprintTransformer()\\n\",\n    \"regressor = Ridge()\\n\",\n    \"\\n\",\n    \"optimization_pipe = make_pipeline(moltransformer, regressor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8221a682\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"4c6b833f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.625354Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.625058Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.629915Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.628829Z\"\n    },\n    \"title\": \"Now hyperparameter tuning\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import RandomizedSearchCV\\n\",\n    \"\\n\",\n    \"# from sklearn.utils.fixes import loguniform\\n\",\n    \"from scipy.stats import loguniform\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6b9d4576\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"0af1003b\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.633716Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.632911Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.641844Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.640881Z\"\n    },\n    \"title\": \"Which keys do we have?\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"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'])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"optimization_pipe.get_params().keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cb0db6a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"c2d541b3\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.645249Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.644964Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.651276Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.650210Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"param_dist = {\\n\",\n    \"    \\\"ridge__alpha\\\": loguniform(1e-2, 1e3),\\n\",\n    \"    \\\"morganfingerprinttransformer__fpSize\\\": [256, 512, 1024, 2048, 4096],\\n\",\n    \"    \\\"morganfingerprinttransformer__radius\\\": [1, 2, 3, 4],\\n\",\n    \"    \\\"morganfingerprinttransformer__useCounts\\\": [True, False],\\n\",\n    \"    \\\"morganfingerprinttransformer__useFeatures\\\": [True, False],\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2157d154\",\n   \"metadata\": {\n    \"lines_to_next_cell\": 2\n   },\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"f2c91783\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.655212Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.654913Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:46.662196Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:46.661226Z\"\n    },\n    \"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\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Utility function to report best scores\\n\",\n    \"def report(results, n_top=3):\\n\",\n    \"    for i in range(1, n_top + 1):\\n\",\n    \"        candidates = np.flatnonzero(results[\\\"rank_test_score\\\"] == i)\\n\",\n    \"        for candidate in candidates:\\n\",\n    \"            print(\\\"Model with rank: {0}\\\".format(i))\\n\",\n    \"            print(\\n\",\n    \"                \\\"Mean validation score: {0:.3f} (std: {1:.3f})\\\".format(\\n\",\n    \"                    results[\\\"mean_test_score\\\"][candidate],\\n\",\n    \"                    results[\\\"std_test_score\\\"][candidate],\\n\",\n    \"                )\\n\",\n    \"            )\\n\",\n    \"            print(\\\"Parameters: {0}\\\".format(results[\\\"params\\\"][candidate]))\\n\",\n    \"            print(\\\"\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"469691f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"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   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"79a70a0f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:46.665390Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:46.665100Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:49.120359Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:49.119269Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Runtime: 2.45 for 25 iterations)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_iter_search = 25\\n\",\n    \"random_search = RandomizedSearchCV(\\n\",\n    \"    optimization_pipe, param_distributions=param_dist, n_iter=n_iter_search, cv=3\\n\",\n    \")\\n\",\n    \"t0 = time()\\n\",\n    \"random_search.fit(mol_list_std_train, y_train.values)\\n\",\n    \"t1 = time()\\n\",\n    \"\\n\",\n    \"print(f\\\"Runtime: {t1-t0:0.2F} for {n_iter_search} iterations)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"b6160cb3\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:49.124324Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:49.123579Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:49.130023Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:49.128965Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model with rank: 1\\n\",\n      \"Mean validation score: 0.459 (std: 0.117)\\n\",\n      \"Parameters: {'morganfingerprinttransformer__fpSize': 1024, 'morganfingerprinttransformer__radius': 3, 'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': True, 'ridge__alpha': np.float64(11.211371939288233)}\\n\",\n      \"\\n\",\n      \"Model with rank: 2\\n\",\n      \"Mean validation score: 0.427 (std: 0.130)\\n\",\n      \"Parameters: {'morganfingerprinttransformer__fpSize': 512, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(22.96332964984786)}\\n\",\n      \"\\n\",\n      \"Model with rank: 3\\n\",\n      \"Mean validation score: 0.426 (std: 0.166)\\n\",\n      \"Parameters: {'morganfingerprinttransformer__fpSize': 4096, 'morganfingerprinttransformer__radius': 2, 'morganfingerprinttransformer__useCounts': True, 'morganfingerprinttransformer__useFeatures': False, 'ridge__alpha': np.float64(23.874114087368742)}\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"report(random_search.cv_results_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9a2ea219\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6cf91582\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"4daaf106\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:49.133255Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:49.132589Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:49.294436Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:49.293304Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"No Standardization 0.4921\\n\",\n      \"With Standardization 0.4921\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"inference_pipe = make_pipeline(standardizer, random_search.best_estimator_)\\n\",\n    \"\\n\",\n    \"print(\\n\",\n    \"    f\\\"No Standardization {random_search.best_estimator_.score(mol_list_test, y_test):0.4F}\\\"\\n\",\n    \")\\n\",\n    \"print(f\\\"With Standardization {inference_pipe.score(mol_list_test, y_test):0.4F}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d31c059\",\n   \"metadata\": {\n    \"lines_to_next_cell\": 0,\n    \"title\": \"Building an inference pipeline, it appears our test-data was pretty standard\"\n   },\n   \"source\": [\n    \"We see that the dataset already appeared to be in forms that are similar to the ones coming from the standardization.\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"92105568\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:49.297625Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:49.297285Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:49.318086Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:49.316957Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predictions with no standardization: [6.36710496 6.49711427 6.49711427 6.28330625 6.72697401]\\n\",\n      \"Predictions with standardization:    [6.36710496 6.36710496 6.36710496 6.36710496 6.36710496]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Intergrating the Standardizer and challenge it with some different forms and salts of benzoic acid\\n\",\n    \"smiles_list = [\\n\",\n    \"    \\\"c1ccccc1C(=O)[OH]\\\",\\n\",\n    \"    \\\"c1ccccc1C(=O)[O-]\\\",\\n\",\n    \"    \\\"c1ccccc1C(=O)[O-].[Na+]\\\",\\n\",\n    \"    \\\"c1ccccc1C(=O)[O][Na]\\\",\\n\",\n    \"    \\\"c1ccccc1C(=O)[O-].C[N+](C)C\\\",\\n\",\n    \"]\\n\",\n    \"mols_list = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\\n\",\n    \"\\n\",\n    \"print(\\n\",\n    \"    f\\\"Predictions with no standardization: {random_search.best_estimator_.predict(mols_list)}\\\"\\n\",\n    \")\\n\",\n    \"print(f\\\"Predictions with standardization:    {inference_pipe.predict(mols_list)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9d196197\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\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\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"824ebc99\",\n   \"metadata\": {},\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.9.4 ('rdkit')\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/07_parallel_transforms.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6f68fb8e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"87ed8373\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Parallel calculations of transforms\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"dac6956a\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:38.302600Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:38.302116Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:39.171522Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:39.170882Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pathlib\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import pandas as pd\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"from scikit_mol.descriptors import MolecularDescriptorTransformer\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7c2a81f2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Obtaining the Data\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"f64c418f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:39.174368Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:39.174075Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:39.177863Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:39.177305Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"full_set = False\\n\",\n    \"\\n\",\n    \"if full_set:\\n\",\n    \"    csv_file = \\\"SLC6A4_active_excape_export.csv\\\"\\n\",\n    \"    if not pathlib.Path(csv_file).exists():\\n\",\n    \"        import urllib.request\\n\",\n    \"\\n\",\n    \"        url = \\\"https://ndownloader.figshare.com/files/25747817\\\"\\n\",\n    \"        urllib.request.urlretrieve(url, csv_file)\\n\",\n    \"else:\\n\",\n    \"    csv_file = \\\"../tests/data/SLC6A4_active_excapedb_subset.csv\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"0eabd800\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:39.180191Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:39.179937Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:39.221096Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:39.220386Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 out of 200 SMILES failed in conversion\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"\\n\",\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4144946e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the Impact of Parallelism on Transformations\\n\",\n    \"\\n\",\n    \"Let's start by creating a baseline for our calculations without using parallelism.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"a7f66af7\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:39.223702Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:39.223459Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:39.228461Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:39.227977Z\"\n    },\n    \"title\": \"A demonstration of the speedup that can be had for the descriptor transformer\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"transformer = MolecularDescriptorTransformer(n_jobs=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"a03bc824\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:39.230911Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:39.230692Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:41.368180Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:41.367438Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Calculation time on dataset of size 200  with parallel=1:\\t2.10 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def test_transformer(transformer):\\n\",\n    \"    t0 = time.time()\\n\",\n    \"    transformer.transform(data.ROMol)\\n\",\n    \"    t = time.time() - t0\\n\",\n    \"    print(\\n\",\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    \"\\n\",\n    \"\\n\",\n    \"test_transformer(transformer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d304d675\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Let's see if parallelism can help us speed up our transformations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"c80388e6\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:41.370886Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:41.370638Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:42.384085Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:42.383188Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Calculation time on dataset of size 200  with parallel=2:\\t2.19 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"transformer = MolecularDescriptorTransformer(n_jobs=2)\\n\",\n    \"test_transformer(transformer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"731bd13a\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\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    \"\\n\",\n    \"Now, let's see how parallelism impacts another type of transformer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"ef6d2b0c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:42.387160Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:42.386886Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:42.484867Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:42.484222Z\"\n    },\n    \"lines_to_next_cell\": 2,\n    \"title\": \"Some of the benchmarking plots\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Calculation time on dataset of size 200  with parallel=1:\\t0.03 seconds\\n\",\n      \"Calculation time on dataset of size 200  with parallel=2:\\t0.08 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"transformer = MorganFingerprintTransformer(n_jobs=1)\\n\",\n    \"test_transformer(transformer)\\n\",\n    \"transformer.n_jobs = 2\\n\",\n    \"test_transformer(transformer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2aac85b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\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    \"\\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    \"\\n\",\n    \"![Atompairs fingerprint](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/AtomPairFingerprintTransformer_par.png \\\"Atompairs fingerprint speedup\\\")\\n\",\n    \"\\n\",\n    \"![Descriptors calculation speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/Desc2DTransformer_par.png \\\"Descriptors calculation speedup\\\")\\n\",\n    \"\\n\",\n    \"![MACCS keys speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MACCSTransformer_par.png \\\"MACCS keys speedup\\\")\\n\",\n    \"\\n\",\n    \"![Morgan fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/MorganTransformer_par.png \\\"Morgan fingerprint speedup\\\")\\n\",\n    \"\\n\",\n    \"![RDKit fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/RDKitFPTransformer_par.png \\\"RDKit fingerprint speedup\\\")\\n\",\n    \"\\n\",\n    \"![SEC fingerprint speedup](https://github.com/EBjerrum/scikit-mol/raw/main/notebooks/images/SECFingerprintTransformer_par.png \\\"SEC fingerprint speedup\\\")\\n\",\n    \"\\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    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0913a9f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Performance heatmaps\\n\",\n    \"\\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\",\n    \"If you what to test the performance of your system, you can run the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"0b353728\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from rdkit import Chem\\n\",\n    \"\\n\",\n    \"from scikit_mol.fingerprints import (\\n\",\n    \"    AtomPairFingerprintTransformer,\\n\",\n    \"    AvalonFingerprintTransformer,\\n\",\n    \"    MHFingerprintTransformer,\\n\",\n    \"    MorganFingerprintTransformer,\\n\",\n    \"    RDKitFingerprintTransformer,\\n\",\n    \"    TopologicalTorsionFingerprintTransformer,\\n\",\n    \")\\n\",\n    \"from scikit_mol.plotting import ParallelTester, plot_heatmap\\n\",\n    \"from scikit_mol.standardizer import Standardizer\\n\",\n    \"\\n\",\n    \"mols = [Chem.MolFromSmiles(\\\"CCCCCCCCBr\\\")] * 100000\\n\",\n    \"transformers = [\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MorganFingerprintTransformer(),\\n\",\n    \"    MolecularDescriptorTransformer(),\\n\",\n    \"    MHFingerprintTransformer(),\\n\",\n    \"    AtomPairFingerprintTransformer(),\\n\",\n    \"    AvalonFingerprintTransformer(),\\n\",\n    \"    RDKitFingerprintTransformer(),\\n\",\n    \"    TopologicalTorsionFingerprintTransformer(),\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"47991a4f\",\n   \"metadata\": {},\n   \"source\": [\n    \"`ParallelTester` accept the following parameters:\\n\",\n    \"- `transformer` - the transformer to test\\n\",\n    \"- `mols` - the dataset to test\\n\",\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    \"- `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\",\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)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"fa00f0eb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <th>100</th>\\n\",\n       \"      <th>100</th>\\n\",\n       \"      <th>1000</th>\\n\",\n       \"      <th>10000</th>\\n\",\n       \"      <th>100000</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.002394</td>\\n\",\n       \"      <td>0.003997</td>\\n\",\n       \"      <td>0.003997</td>\\n\",\n       \"      <td>0.031515</td>\\n\",\n       \"      <td>0.479826</td>\\n\",\n       \"      <td>3.228737</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.012707</td>\\n\",\n       \"      <td>1.278691</td>\\n\",\n       \"      <td>1.278691</td>\\n\",\n       \"      <td>1.246285</td>\\n\",\n       \"      <td>1.507089</td>\\n\",\n       \"      <td>3.614131</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1.238397</td>\\n\",\n       \"      <td>1.330802</td>\\n\",\n       \"      <td>1.330802</td>\\n\",\n       \"      <td>1.20337</td>\\n\",\n       \"      <td>1.389253</td>\\n\",\n       \"      <td>2.891467</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1.657188</td>\\n\",\n       \"      <td>1.669887</td>\\n\",\n       \"      <td>1.669887</td>\\n\",\n       \"      <td>1.601371</td>\\n\",\n       \"      <td>1.635777</td>\\n\",\n       \"      <td>2.541485</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     10        100       100       1000      10000     100000\\n\",\n       \"1  0.002394  0.003997  0.003997  0.031515  0.479826  3.228737\\n\",\n       \"2  0.012707  1.278691  1.278691  1.246285  1.507089  3.614131\\n\",\n       \"4  1.238397  1.330802  1.330802   1.20337  1.389253  2.891467\\n\",\n       \"8  1.657188  1.669887  1.669887  1.601371  1.635777  2.541485\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"transformer = MorganFingerprintTransformer()\\n\",\n    \"df = ParallelTester(transformer, mols).test()\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"71725395\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"6e352da5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Axes: title={'center': 'Morgan Fingerprint\\\\nMax single-threaded speed 31731 mols/s\\\\nCPU: AMD Ryzen 9 4900HS with Radeon Graphics'}, xlabel='Number of mols', ylabel='Number of jobs'>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_heatmap(df, \\\"Morgan Fingerprint\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0972c254\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\",\n    \"If you want to see the absolute time, you can set `normalize=False`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"c99487eb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Axes: title={'center': 'Morgan Fingerprint\\\\nMax single-threaded speed 31731 mols/s\\\\nCPU: AMD Ryzen 9 4900HS with Radeon Graphics'}, xlabel='Number of mols', ylabel='Number of jobs'>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_heatmap(df, \\\"Morgan Fingerprint\\\", normalize=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a98d12af\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below is example heatmaps for the native `scikit-mol` transformers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"6111704c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAo0AAAJNCAYAAABURU/5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC2F0lEQVR4nOzdZ1hUxx4G8HdpS5PeRQEBRUBFsStgR8WusTeMxsSS2BVj7JHYNdYktthii7332Av2XlDsIF0p0nbuBy8b111YMCAI7+8+e+POzpkzZ1iW2f+UIxFCCBARERERZUOjoCtARERERIUfO41EREREpBY7jURERESkFjuNRERERKQWO41EREREpBY7jURERESkFjuNRERERKQWO41EREREpBY7jURERESkFjuNRFRoODo6onfv3p/lXL1794ajo6NCmkQiwcSJEz/L+YmIvjTsNBIVAzdu3ECHDh3g4OAAXV1dlCxZEo0bN8aCBQvkeaZNm4bt27cXXCWJiKhQ0yroChBR/jpz5gzq16+P0qVLo1+/frCxscGzZ89w7tw5zJ8/H4MHDwbwvtPYoUMHtGnTpmArXICSk5OhpcWPRSIiVfjpSFTE/fzzzzA2NsbFixdhYmKi8Nrr168LplKfwbt376CjowMNjZwPqOjq6uZjjZQlJibCwMDgs56TiOhTcXiaqIgLDQ2Fh4eHUocRAKysrAC8n8uXmJiIP//8ExKJBBKJRD638MmTJxgwYADKlSsHPT09mJub46uvvkJYWJhCWatWrYJEIsHp06cxbNgwWFpawsDAAG3btkVkZKRCXiEEpk6dCnt7e+jr66N+/fq4deuWUv1iYmIwYsQIVKhQAYaGhjAyMkKzZs1w7do1hXzHjx+HRCLBhg0bMG7cOJQsWRL6+vp48+YNAGD79u3w9PSErq4uPD09sW3bNpVt9eGcxrCwMHlbqHp86Pz582jatCmMjY2hr68PPz8/nD59WiHPxIkTIZFIcPv2bXTt2hWmpqaoW7euynoQERVGjDQSFXEODg44e/Ysbt68CU9PT5V51qxZg759+6J69er45ptvAADOzs4AgIsXL+LMmTPo3Lkz7O3tERYWhiVLlqBevXq4ffs29PX1FcoaPHgwTE1NMWHCBISFhWHevHkYNGgQNm7cKM8zfvx4TJ06Fc2bN0fz5s1x+fJlNGnSBKmpqQplPXr0CNu3b8dXX30FJycnRERE4LfffoOfnx9u374NOzs7hfxTpkyBjo4ORowYgZSUFOjo6ODgwYNo37493N3dERwcjOjoaAQGBsLe3j7bdrO0tMSaNWsU0tLS0jB06FDo6OjI044ePYpmzZrB29sbEyZMgIaGBlauXIkGDRrg5MmTqF69ukIZX331FVxdXTFt2jQIIbKtAxFRoSKIqEg7ePCg0NTUFJqamqJWrVpi1KhR4sCBAyI1NVUhn4GBgejVq5fS8UlJSUppZ8+eFQDE6tWr5WkrV64UAESjRo2ETCaTpw8dOlRoamqKuLg4IYQQr1+/Fjo6OiIgIEAh39ixYwUAhTq8e/dOZGRkKJz78ePHQiqVismTJ8vTjh07JgCIMmXKKNXXy8tL2Nrays+f2SYAhIODg0JeAGLChAlK15tpwIABQlNTUxw9elQIIYRMJhOurq7C399f4VqSkpKEk5OTaNy4sTxtwoQJAoDo0qVLluUTERVmHJ4mKuIaN26Ms2fPolWrVrh27RpmzJgBf39/lCxZEjt37lR7vJ6envzfaWlpiI6OhouLC0xMTHD58mWl/N98843C8K2Pjw8yMjLw5MkTAMDhw4eRmpqKwYMHK+QbMmSIUllSqVQ+JzEjIwPR0dEwNDREuXLlVJ67V69eCvV99eoVrl69il69esHY2FihTdzd3dVe+4dWr16NxYsXY8aMGahfvz4A4OrVq3jw4AG6du2K6OhoREVFISoqComJiWjYsCFOnDgBmUymUM63336bq/MSERUW7DQSFQPVqlXD1q1bERsbiwsXLiAoKAhv375Fhw4dcPv27WyPTU5Oxvjx41GqVClIpVJYWFjA0tIScXFxiI+PV8pfunRpheempqYAgNjYWACQdx5dXV0V8llaWsrzZpLJZJg7dy5cXV0Vzn39+nWV53ZyclJ4ntW5AKBcuXLZXveHrl69im+//RZdunTBsGHD5OkPHjwA8L6zamlpqfBYtmwZUlJSlOr5cR2JiL4UnNNIVIzo6OigWrVqqFatGsqWLYvAwEBs3rwZEyZMyPKYwYMHY+XKlRgyZAhq1aoFY2NjSCQSdO7cWSmKBgCampoqyxGfMH9v2rRp+Omnn9CnTx9MmTIFZmZm0NDQwJAhQ1Se+8MoY16JjY1F+/btUbZsWSxbtkzhtcw6zJw5E15eXiqPNzQ0zPc6EhF9Duw0EhVTVatWBfB+CBeA0orgTFu2bEGvXr0we/Zsedq7d+8QFxf3Sed1cHAA8D5KV6ZMGXl6ZGSkPBr54bnr16+P5cuXK6THxcXBwsIiV+f62L1799QeL5PJ0K1bN8TFxeHw4cNKi34yFwsZGRmhUaNGassjIvqScXiaqIg7duyYyijf3r17Afw7TGtgYKCyI6ipqal0/IIFC5CRkfFJ9WnUqBG0tbWxYMEChXLnzZuXo3Nv3rwZL168yNG5bG1t4eXlhT///FNhmPjQoUNqh+UBYNKkSThw4AD++usvlcPK3t7ecHZ2xqxZs5CQkKD0+sdbDRERfckYaSQq4gYPHoykpCS0bdsWbm5uSE1NxZkzZ7Bx40Y4OjoiMDAQwPsO0OHDhzFnzhzY2dnByckJNWrUQIsWLbBmzRoYGxvD3d0dZ8+exeHDh2Fubv5J9bG0tMSIESMQHByMFi1aoHnz5rhy5Qr27dunFD1s0aIFJk+ejMDAQNSuXRs3btzAunXrFCKU6gQHByMgIAB169ZFnz59EBMTgwULFsDDw0NlRy/TjRs3MGXKFPj6+uL169dYu3atwuvdu3eHhoYGli1bhmbNmsHDwwOBgYEoWbIkXrx4gWPHjsHIyAi7du3KXQMRERVWBbp2m4jy3b59+0SfPn2Em5ubMDQ0FDo6OsLFxUUMHjxYREREyPPdvXtX+Pr6Cj09PYWtb2JjY0VgYKCwsLAQhoaGwt/fX9y9e1c4ODgobI+TueXOxYsXFc6fuR3OsWPH5GkZGRli0qRJwtbWVujp6Yl69eqJmzdvKpX57t07MXz4cHm+OnXqiLNnzwo/Pz/h5+endI7NmzerbIO///5blC9fXkilUuHu7i62bt0qevXqle2WO5llZvX40JUrV0S7du2Eubm5kEqlwsHBQXTs2FEcOXJEnidzy53IyMgsflJERIWbRAjuLktERERE2eOcRiIiIiJSi51GIiIiIlKLnUYiIiIiUoudRiIiIiJSi51GIiIiIlKLnUYiIiIiUoudRiqWJBIJJk6cmK/nWLVqFSQSCcLCwvL1PABw/PhxSCQSbNmyJd/PlRccHR3Ru3fvPCsvLCwMEokEq1atyrMyC5PevXvD0dGxoKtRqE2cODHLW2ESUd5gp5FyLbMzJJFIcOrUKaXXhRAoVaoUJBIJWrRoUQA1LLrWr1+v8nZ7RJlkMhmWLFkCLy8v6OnpwdzcHA0aNMC1a9eU8oaGhqJr166wsrKCnp4eXF1d8eOPPyrkuXDhAgYMGABvb29oa2sX2Y5Z+/bt0bx584KuBlGhxtsI0ifT1dXF+vXrUbduXYX0f/75B8+fP4dUKi2gmqmXnJwMLa0v7+2/fv163Lx5E0OGDCnoqlAh1adPH6xbtw49e/bEoEGDkJiYiCtXruD169cK+a5evYp69eqhZMmSGD58OMzNzfH06VM8e/ZMId/evXuxbNkyVKxYEWXKlMH9+/c/5+V8FmlpaTh06BCCg4MLuipEhdqX91eTCo3mzZtj8+bN+PXXXxU6YOvXr4e3tzeioqIKsHbZ09XVLegqFCpJSUnQ19cv6GrQf7Rp0yb8+eef2Lp1K9q2bZtlPplMhh49esDNzQ3Hjh2Dnp5elnm/++47jB49Gnp6ehg0aFCR7DSePHkSb9++RUBAQEFXhahQ4/A0fbIuXbogOjoahw4dkqelpqZiy5Yt6Nq1q8pjZs2ahdq1a8Pc3Bx6enrw9vZWmoe3cuVKSCQSrFixQiF92rRpkEgk2Lt3b7b1CgkJgb+/PywsLKCnpwcnJyf06dNHIc/Hcxoz50M9fPgQvXv3homJCYyNjREYGIikpCSFY5OTk/H999/DwsICJUqUQKtWrfDixYscz5Pct28ffHx8YGBggBIlSiAgIAC3bt1Se1y9evWwZ88ePHnyRD494ON5bjKZDD///DPs7e2hq6uLhg0b4uHDh0rleHp64tKlS/D19YW+vj7Gjh0LAEhJScGECRPg4uICqVSKUqVKYdSoUUhJSVEoY+XKlWjQoAGsrKwglUrh7u6OJUuWKNVZCIGpU6fC3t4e+vr6qF+/fpbXGhcXhyFDhqBUqVKQSqVwcXHB9OnTIZPJlPL17t0bxsbGMDExQa9evRAXF6e2/YD3EaVJkybB1dUVurq6MDc3R926dRXew71794ahoSEePXoEf39/GBgYwM7ODpMnT8bHd12VyWSYN28ePDw8oKurC2tra/Tv3x+xsbFK587pz3379u3w9PSErq4uPD09sW3bthxdGwDMmTMH1atXR9u2bSGTyZCYmKgy38GDB3Hz5k1MmDABenp6SEpKQkZGhsq81tbW2XYq1ZFIJBg0aBA2b94Md3d36OnpoVatWrhx4wYA4LfffoOLiwt0dXVRr149lXOAN2/eDG9vb+jp6cHCwgLdu3fHixcv1J770KFDqFu3LkxMTGBoaIhy5crJ3+sf2rNnD9zd3eW/T+Hh4QgMDIS9vT2kUilsbW3RunXrzzI/mahQK9A7X9MXaeXKlQKAuHjxoqhdu7bo0aOH/LXt27cLDQ0N8eLFC+Hg4CACAgIUjrW3txcDBgwQCxcuFHPmzBHVq1cXAMTu3bsV8rVo0UIYGxuLp0+fCiGEuH79utDR0RFff/11tnWLiIgQpqamomzZsmLmzJnijz/+ED/++KMoX768Qj4AYsKECfLnEyZMEABE5cqVRbt27cTixYtF3759BQAxatQohWM7duwoAIgePXqIRYsWiY4dO4pKlSoplZnZTo8fP5anrV69WkgkEtG0aVOxYMECMX36dOHo6ChMTEwU8qly8OBB4eXlJSwsLMSaNWvEmjVrxLZt24QQQhw7dkxef29vbzF37lwxceJEoa+vL6pXr65Qjp+fn7CxsRGWlpZi8ODB4rfffhPbt28XGRkZokmTJkJfX18MGTJE/Pbbb2LQoEFCS0tLtG7dWqGMatWqid69e4u5c+eKBQsWiCZNmggAYuHChQr5xo0bJwCI5s2bi4ULF4o+ffoIOzs7YWFhIXr16iXPl5iYKCpWrCjMzc3F2LFjxdKlS0XPnj2FRCIRP/zwgzyfTCYTvr6+QkNDQwwYMEAsWLBANGjQQFSsWFEAECtXrsy2DceOHSskEono16+f+OOPP8Ts2bNFly5dxC+//CLP06tXL6GrqytcXV1Fjx49xMKFC0WLFi0EAPHTTz8plNe3b1+hpaUl+vXrJ5YuXSpGjx4tDAwMRLVq1URqaqo8X05/7gcOHBAaGhrC09NTzJkzR/z444/C2NhYeHh4CAcHh2yvLT4+XkgkEjFw4EARFBQkDA0NBQDh5OQkNm7cqJB3+PDhAoA4cuSI8Pb2FgCEjo6O6NSpk4iOjs7yHAMHDhS5/bMBQFSsWFGUKlVK/PLLL+KXX34RxsbGonTp0mLhwoXC3d1dzJ49W4wbN07o6OiI+vXrKxyf+XtUrVo1MXfuXDFmzBihp6cnHB0dRWxsrDxf5u9wpps3bwodHR1RtWpVMX/+fLF06VIxYsQI4evrq1RHNzc3MWLECPnz2rVrC2NjYzFu3DixbNkyMW3aNFG/fn3xzz//5OraiYoadhop1z7sNC5cuFCUKFFCJCUlCSGE+Oqrr+Qf+qo6jZn5MqWmpgpPT0/RoEEDhfRXr14JMzMz0bhxY5GSkiIqV64sSpcuLeLj47Ot27Zt2+R1y05WncY+ffoo5Gvbtq0wNzeXP7906ZIAIIYMGaKQr3fv3mo7jW/fvhUmJiaiX79+CseGh4cLY2NjpXRVAgICVHYeMjuN5cuXFykpKfL0+fPnCwDixo0b8jQ/Pz8BQCxdulShjDVr1ggNDQ1x8uRJhfSlS5cKAOL06dPytI9/jkII4e/vL8qUKSN//vr1a6GjoyMCAgKETCaTp48dO1YAUOg0TpkyRRgYGIj79+8rlDlmzBihqakp//Kwfft2AUDMmDFDnic9PV34+PjkqNNYqVIlpffkx3r16iUAiMGDB8vTZDKZCAgIEDo6OiIyMlIIIcTJkycFALFu3TqF4/fv36+Qnpufu5eXl7C1tRVxcXHytIMHDwoAajuNly9fFgCEubm5sLa2FosXLxbr1q0T1atXFxKJROzbt0+et1WrVvK83bp1E1u2bBE//fST0NLSErVr11b4eX3oUzuNUqlUoXP822+/CQDCxsZGvHnzRp4eFBSk8DuTmpoqrKyshKenp0hOTpbn2717twAgxo8fL0/7uNM4d+5cAUD+88rKo0ePBABx7NgxIYQQsbGxAoCYOXNmrq6TqDjg8DT9Jx07dkRycjJ2796Nt2/fYvfu3VkOTQNQGOaKjY1FfHw8fHx8cPnyZYV8NjY2WLRoEQ4dOgQfHx9cvXoVK1asgJGRUbb1MTExAQDs3r0baWlpub6eb7/9VuG5j48PoqOj8ebNGwDA/v37AQADBgxQyDd48GC1ZR86dAhxcXHo0qULoqKi5A9NTU3UqFEDx44dy3V9PxYYGAgdHR2F+gPAo0ePFPJJpVIEBgYqpG3evBnly5eHm5ubQv0aNGgAAAr1+/DnGB8fj6ioKPj5+eHRo0eIj48HABw+fBipqakYPHiwwopbVYt4Nm/eDB8fH5iamiqcu1GjRsjIyMCJEycAvF+UoaWlhe+++05+rKamZo7aH3j//rh16xYePHigNu+gQYPk/84cYk1NTcXhw4fldTY2Nkbjxo0V6uzt7Q1DQ0N5e+X05/7q1StcvXoVvXr1grGxsfzcjRs3hru7u9r6JiQkAACio6OxY8cOfPfdd+jatSuOHDkCc3NzTJ06VSlvtWrVsHbtWrRv3x6TJ0/GlClTcObMGRw5ckTt+XKjYcOGClMpatSoAeD9iuUSJUoopWe+X0NCQvD69WsMGDBAYR5yQEAA3NzcsGfPnizPmflZsGPHDqUpDh/as2cPjI2N5Qv69PT0oKOjg+PHj6ucZkBUnLHTSP+JpaUlGjVqhPXr12Pr1q3IyMhAhw4dssy/e/du1KxZE7q6ujAzM4OlpSWWLFki72h8qHPnzggICMCFCxfQr18/NGzYUG19/Pz80L59e0yaNAkWFhZo3bo1Vq5cqTQnLyulS5dWeG5qagoA8j8eT548gYaGBpycnBTyubi4qC07s6PSoEEDWFpaKjwOHjwoX92anJyM8PBwhUdOqat/ppIlSyp0LjPrd+vWLaW6lS1bFgAUVt+ePn0ajRo1goGBAUxMTGBpaSmfK5b5s3zy5AkAwNXVVeE8lpaW8np9eO79+/crnbtRo0YK537y5AlsbW1haGiocHy5cuVy0jyYPHky4uLiULZsWVSoUAEjR47E9evXlfJpaGigTJkyCmmZ7ZA5r+3BgweIj4+HlZWVUr0TEhLkdc7pzz2r9srp9WV25J2cnOSdLwAwNDREy5YtceHCBaSnpyvk7dKli0IZmV/4zpw5o/Z8ufHx+zKzU1yqVCmV6R/+vgGqr9/NzU3+uiqdOnVCnTp10LdvX1hbW6Nz587YtGmTUgdyz549aNKkiXwxn1QqxfTp07Fv3z5YW1vD19cXM2bMyNXvIVFRxdXT9J917doV/fr1Q3h4OJo1ayb/hv+xkydPolWrVvD19cXixYtha2sLbW1trFy5EuvXr1fKHx0djZCQEADA7du3IZPJoKGR/feczA2uz507h127duHAgQPo06cPZs+ejXPnzil1Nj6mqampMl18tADiU2T+sVqzZg1sbGyUXs/8o7Vx40alKGBOz5/T+qta2CCTyVChQgXMmTNHZRmZf+BDQ0PRsGFDuLm5Yc6cOShVqhR0dHSwd+9ezJ07N9uoTlZkMhkaN26MUaNGqXw9s8P2X/n6+iI0NBQ7duzAwYMHsWzZMsydOxdLly5F3759c1WWTCaDlZUV1q1bp/J1S0tLeT5A/c/9v7KzswPwfuHKx6ysrJCWlobExEQYGxtnmdfKygqA8peM/yqr92V+/r7p6enhxIkTOHbsGPbs2YP9+/dj48aNaNCgAQ4ePAhNTU0kJSXh+PHjSou4hgwZgpYtW2L79u04cOAAfvrpJwQHB+Po0aOoXLnyf64b0ZeKnUb6z9q2bYv+/fvj3Llz2LhxY5b5/v77b+jq6uLAgQMKeziuXLlSZf6BAwfi7du3CA4ORlBQEObNm4dhw4blqE41a9ZEzZo18fPPP2P9+vXo1q0bNmzYkOuOwcccHBwgk8nw+PFjhYjQxyuUVXF2dgbw/g9zZgRNFX9/f4XVvB/Kz42VnZ2dce3aNTRs2DDb8+zatQspKSnYuXOnQgTp4+F1BwcHAO8jbR9G7SIjI5U6Jc7OzkhISMi2XTLLPHLkCBISEhS+ANy7d0/9Bf6fmZkZAgMDERgYiISEBPj6+mLixIkK7w2ZTIZHjx4pdFYzt5rJHGZ1dnbG4cOHUadOnWxXF+f05/5he30sJ9dnZ2cHGxsblauKX758CV1dXflQsLe3N/744w+lvC9fvgTwb4e3oGW2yb179+TTJDLdu3dP/npWNDQ00LBhQzRs2BBz5szBtGnT8OOPP+LYsWNo1KgRjh49ipSUFDRr1kzpWGdnZwwfPhzDhw/HgwcP4OXlhdmzZ2Pt2rV5d4FEXxgOT9N/ZmhoiCVLlmDixIlo2bJllvk0NTUhkUgUtvYICwvD9u3blfJu2bIFGzduxC+//IIxY8agc+fOGDdunNo94mJjY5WiFF5eXgCQ4yHq7Pj7+wMAFi9erJC+YMGCHB1rZGSEadOmqZxvGRkZCQCwtbVFo0aNFB6ZDAwMVA7l54WOHTvixYsX+OOPP5ReS05Olm/fkhkd+rCd4+PjlTr/jRo1gra2NhYsWKCQV9UdbTp27IizZ8/iwIEDSq/FxcXJh1WbN2+O9PR0hchQRkZGjtofeB+9/pChoSFcXFxUvjcWLlwo/7cQAgsXLoS2trZ8mkTHjh2RkZGBKVOmKB2bnp4u3wYoNz93Ly8v/Pnnnwo/40OHDuH27ds5ur5OnTrh2bNnCl86oqKisGPHDjRo0EAeqW/dujWkUilWrlypEBletmwZgPfzKAuDqlWrwsrKCkuXLlX4Ge3btw937tzJdl/FmJgYpbSPPwv27t2LqlWrKkRck5KS8O7dO4XjnJ2dUaJEiTz5DCH6kjHSSHmiV69eavMEBARgzpw5aNq0Kbp27YrXr19j0aJFcHFxUZhX9vr1a3z33XeoX7++fDHCwoULcezYMfTu3RunTp3Kcpj6zz//xOLFi9G2bVs4Ozvj7du3+OOPP2BkZJQntwjz9vZG+/btMW/ePERHR6NmzZr4559/5J3Z7CJ0RkZGWLJkCXr06IEqVaqgc+fOsLS0xNOnT7Fnzx7UqVNHoaOS1fk3btyIYcOGoVq1avL5anmhR48e2LRpE7799lscO3YMderUQUZGBu7evYtNmzbhwIEDqFq1Kpo0aQIdHR20bNkS/fv3R0JCAv744w9YWVnh1atX8vIsLS0xYsQIBAcHo0WLFmjevDmuXLmCffv2wcLCQuHcI0eOxM6dO9GiRQv07t0b3t7eSExMxI0bN7BlyxaEhYXBwsICLVu2RJ06dTBmzBiEhYXB3d0dW7duzXFH2t3dHfXq1YO3tzfMzMwQEhKCLVu2KCx6Ad5v/r5//3706tULNWrUwL59+7Bnzx6MHTtWHoXz8/ND//79ERwcjKtXr6JJkybQ1tbGgwcPsHnzZsyfPx8dOnTI1c89ODgYAQEBqFu3Lvr06YOYmBgsWLAAHh4e8sUr2QkKCsKmTZvQvn17DBs2DMbGxli6dCnS0tIwbdo0eT4bGxv8+OOPGD9+PJo2bYo2bdrg2rVr+OOPP9ClSxdUq1ZNnvfJkydYs2YNAMini2QuqnFwcECPHj1y1PafQltbG9OnT0dgYCD8/PzQpUsXREREYP78+XB0dMTQoUOzPHby5Mk4ceIEAgIC4ODggNevX2Px4sWwt7eXL3rZu3ev0lSQ+/fvo2HDhujYsSPc3d2hpaWFbdu2ISIiAp07d863ayX6IhTcwm36Un245U52VG25s3z5cuHq6iqkUqlwc3MTK1euVNoqo127dqJEiRIiLCxM4dgdO3YIAGL69OlZnvPy5cuiS5cuonTp0kIqlQorKyvRokULERISopAPWWy58/H2HKr2WkxMTBQDBw4UZmZmwtDQULRp00bcu3dPAFDY70/VsUK83x7H399fGBsbC11dXeHs7Cx69+6tVEdVEhISRNeuXYWJiYnCNiyZW+5s3rxZIf/jx4+VtqLx8/MTHh4eKstPTU0V06dPFx4eHkIqlQpTU1Ph7e0tJk2apLDd0c6dO0XFihWFrq6ucHR0FNOnTxcrVqxQut6MjAwxadIkYWtrK/T09ES9evXEzZs3hYODg8KWO0K835omKChIuLi4CB0dHWFhYSFq164tZs2apbDnYXR0tOjRo4cwMjISxsbGokePHuLKlSs52nJn6tSponr16sLExETo6ekJNzc38fPPPyuU36tXL2FgYCBCQ0Pl+1ZaW1uLCRMmiIyMDKUyf//9d+Ht7S309PREiRIlRIUKFcSoUaPEy5cvFfLl9Of+999/i/LlywupVCrc3d3F1q1bRa9evdRuuZMpNDRUtG3bVhgZGQk9PT3RoEEDceHCBaV8MplMLFiwQJQtW1Zoa2uLUqVKiXHjxim0RWa9Aah8+Pn5qa0PADFw4ECFtMz35cfb2mT1Pt64caOoXLmykEqlwszMTHTr1k08f/5cIc/HnyNHjhwRrVu3FnZ2dkJHR0fY2dmJLl26yLd1unnzpgCg1DZRUVFi4MCBws3NTRgYGAhjY2NRo0YNsWnTJrXXSlTUSYTIgxnHRMXc1atXUblyZaxduxbdunUr6OrQf9C7d29s2bIlR5E9+nLNmDEDc+bMwatXr/J1rjBRUcI5jUS5lJycrJQ2b948aGhowNfXtwBqRES55ejoiLlz57LDSJQLnNNIlEszZszApUuXUL9+fWhpaWHfvn3Yt28fvvnmG6V954iocOrYsWNBV4Hoi8NOI1Eu1a5dG4cOHcKUKVOQkJCA0qVLY+LEifjxxx8LumpERET5hnMaiYiIiEgtzmkkIiIiIrXYaSQiIiIitdhpJCLKI71795bfZjAnedXdC72wmDhxIlcZ/0eZbRgVFaU2r6OjI3r37p3/lSLKJXYaC4nQ0FD0798fZcqUga6uLoyMjFCnTh3Mnz9fYYsXR0dHSCQS+cPKygo+Pj7Ytm2bQnmOjo5o0aKFynOFhIRAIpFg1apV/7neo0aNgkQiQadOnVS+HhYWJq9r5l0kPtatWzdIJBKlP6D16tWTH6uhoQEjIyOUK1cOPXr0yPLezKr07t1boc2kUinKli2L8ePHK90u7Ev08OFDdOjQAaamptDX10fdunWV7gOdU/369YNEIlH53klISMCQIUNgb28PqVSK8uXLK9zO70NxcXH45ptvYGlpCQMDA9SvXx+XL19WmXfnzp2oUqUKdHV1Ubp0aUyYMEF+28BM6v7gqnq/JyQkYMKECfD09ISBgQHMzc3h5eWFH374QX6P5fyWlJSEiRMn4vjx43le9oe/HxKJBHp6eqhYsSLmzZuncGvA4mbXrl1o2bIlrK2toaOjAzMzM/j6+mL27Nl48+ZNQVeP6IvG1dOFwJ49e/DVV19BKpWiZ8+e8PT0RGpqKk6dOoWRI0fi1q1b+P333+X5vby8MHz4cADAy5cv8dtvv6Fdu3ZYsmQJvv32289WbyEE/vrrLzg6OmLXrl14+/YtSpQooTKvrq4u/vrrL4wbN04hPTExETt27ICurq7K4+zt7REcHCzP+/DhQ2zduhVr165Fx44dsXbtWmhra6utq1Qqld9XNz4+Hjt27MCUKVMQGhqKdevW5eayC5Vnz56hVq1a0NTUxMiRI2FgYICVK1eiSZMmOHLkSK72jQwJCcGqVatU/iwyMjLg7++PkJAQDBw4EK6urjhw4AAGDBiA2NhYjB07Vp5XJpMhICAA165dw8iRI2FhYYHFixejXr16uHTpElxdXeV59+3bhzZt2qBevXpYsGABbty4galTp+L169dZdkhzIi0tDb6+vrh79y569eqFwYMHIyEhAbdu3cL69evRtm1b2NnZfXL5Wfnjjz8UOmxJSUmYNGkSgPedvLz24e9HVFQU1q9fj6FDhyIyMhI///xznp+vMJPJZPj666+xatUqVKhQAQMGDECpUqXw9u1bnD17FuPGjcPevXtx5MiRgq6qWvfu3cvyVqlEBapA70dD4tGjR8LQ0FC4ubkp3XZMCCEePHgg5s2bJ3+u6tZ8r169EgYGBqJs2bLZ5st08eLFHN1yTZ2jR48KAOLo0aNCW1tbrFq1SilP5u3C2rVrJwCIq1evKry+bt06oa2tLVq2bCkMDAwUXsvqdnfp6eliwIABAoAYNWqU2npm3hbuQzKZTNSsWVNIJBIRHh6ek8stlAYMGCC0tLTE3bt35WmJiYmiVKlSokqVKjkuRyaTiVq1aok+ffqofO9s2rRJABDLly9XSG/fvr3Q1dUVERER8rSNGzcq3Qru9evXwsTERHTp0kXheHd3d1GpUiWRlpYmT/vxxx+FRCIRd+7ckadldZvHTB/XObO+69atU8qbnJyscEvE/BQZGal0y8pMqt6XuaHq9yM5OVk4ODiIEiVKiPT09E8u+2Mf36KvMAoODhYAxNChQ4VMJlN6/eXLlwq3+VQlIyNDJCcn50v91L2Hib4E/CpTwGbMmIGEhAQsX74ctra2Sq+7uLjghx9+yLYMGxsblC9fHo8fP/7keqSlpeHu3bt49epVjo9Zt24d3N3dUb9+fTRq1CjbiF2tWrXg5OSE9evXK5XRtGlTmJmZ5fi8mpqa+PXXX+Hu7o6FCxciPj4+x8dmkkgkqFu3LoQQePToEQDAz88PlSpVUpm/XLly8Pf3B6A8LPjh48Mh/7i4OAwZMgSlSpWCVCqFi4sLpk+frhCJyhy+nzVrFn7//Xc4OztDKpWiWrVquHjxotrrOHnyJCpXroxy5crJ0/T19dGqVStcvnwZDx48yFF7rFmzBjdv3swyOnXy5EkAQOfOnRXSO3fujHfv3mHHjh3ytC1btsDa2hrt2rWTp1laWqJjx47YsWMHUlJSAAC3b9/G7du38c0330BL699BjwEDBkAIgS1btuSo7qqEhoYCAOrUqaP0Wub0j6zExcXJ32OZoqKioKGhAXNzc4gPdin77rvvYGNjI3/+4ZzGsLAwWFpaAgAmTZokf49MnDhR4XwvXrxAmzZtYGhoCEtLS4wYMQIZGRm5vubMa6tWrRrevn2L169fy9OvX7+O3r17y6e/2NjYoE+fPoiOjlYq49SpU6hWrRp0dXXh7OyM3377LcvzrV27Ft7e3tDT04OZmRk6d+6MZ8+eKeXbvHmzPJ+FhQW6d++OFy9eKOTJnOP5Ke2RlJSE6dOnw8PDAzNnzlQ5/9LW1hajR49WSJNIJBg0aBDWrVsHDw8PSKVS7N+/HwAwa9Ys1K5dG+bm5tDT04O3t7fK9+SHZZQrVw66urrw9vbGiRMnVNY1Li4OvXv3homJCYyNjREYGIikpCSFPKrmNMbFxWHo0KFwdHSEVCqFvb09evbsqTBlY8GCBfDw8IC+vj5MTU1RtWpVpc9cov+CncYCtmvXLpQpUwa1a9f+5DLS0tLw7NkzmJubf3IZL168QPny5REUFJSj/CkpKfj777/RpUsXAECXLl1w9OhRhIeHZ3lMly5dsGHDBvkf3aioKBw8eBBdu3bNdX01NTXRpUsXJCUl4dSpU7k+Hnj/Rx0ATE1NAQA9evTA9evXcfPmTYV8Fy9exP3799G9e3cAwI8//og1a9YoPDI7lFZWVgDe/xHz8/PD2rVr0bNnT/z666+oU6cOgoKCMGzYMKW6rF+/HjNnzkT//v0xdepUhIWFoV27dkhLS8v2GlJSUqCnp6eUrq+vDwC4dOmS2nZ4+/YtRo8ejbFjxyp0gD4+j6amJnR0dNSe58qVK6hSpYrS8Fr16tWRlJSE+/fvy/MBQNWqVRXy2dnZwd7eXv76h2JiYhAVFaX0+HgOn4ODAwBg9erVCp28nDAxMYGnp6fCH/1Tp05BIpEgJiYGt2/flqefPHkSPj4+KsuxtLSUD7G3bdtW/l75sDOdOexvbm6OWbNmwc/PD7Nnz1aYjpJbmV9ETExM5GmHDh3Co0ePEBgYiAULFqBz587YsGEDmjdvrtA+N27cQJMmTfD69WtMnDgRgYGBmDBhgtKcaQD4+eef0bNnT7i6umLOnDkYMmSIfEpEXFycPN+qVavQsWNHaGpqIjg4GP369cPWrVtRt25dhXz/pT1OnTqFuLg4dOnSBZqamrlqr6NHj2Lo0KHo1KkT5s+fL+/0z58/H5UrV8bkyZMxbdo0aGlp4auvvsKePXuUyvjnn38wZMgQdO/eHZMnT0Z0dDSaNm2q9FkCvL8Tzdu3bxEcHIyOHTti1apV8ikMWUlISICPjw8WLFiAJk2aYP78+fj2229x9+5dPH/+HMD7qRHff/893N3dMW/ePEyaNAleXl44f/58rtqDKFsFGeYs7uLj4wUA0bp16xwf4+DgIJo0aSIiIyNFZGSkuHbtmujcubMAIAYPHqyQLzfD05nDyL169cpRPbZs2SIAiAcPHgghhHjz5o3Q1dUVc+fOVciXWe7MmTPFzZs3BQBx8uRJIYQQixYtEoaGhiIxMVHlUF1Ww9OZtm3bJgCI+fPnZ1vXzLIz2+zhw4di1qxZQiKRCE9PT/lQVlxcnNDV1RWjR49WOP77778XBgYGIiEhQWX5p0+fFtra2qJPnz7ytClTpggDAwNx//59hbxjxowRmpqa4unTpwrtY25uLmJiYuT5duzYIQCIXbt2ZXttLVu2FCYmJuLNmzcK6bVq1RIAxKxZs7I9XgghRowYIZycnMS7d++EEKrfO7Nnz1b42X14PQBEixYt5GkGBgYKbZFpz549AoDYv3+/EEKImTNnCgDytvhQtWrVRM2aNeXPM4f2snt8WOekpCRRrlw5AUA4ODiI3r17i+XLlysMo2dn4MCBwtraWv582LBhwtfXV1hZWYklS5YIIYSIjo4WEolE4f3Xq1cv4eDgIH+ubngagJg8ebJCeuXKlYW3t7faOvr5+Qk3Nzf5+/ru3bti5MiRSm0hxPv2+Nhff/0lAIgTJ07I09q0aSN0dXXFkydP5Gm3b98WmpqaCsPTYWFhQlNTU/z8888KZd64cUNoaWnJ01NTU4WVlZXw9PRUGPbdvXu3ACDGjx+fJ+0xf/58AUBs375dIT09PV3ePpmPD4euAQgNDQ1x69YtpTI/brPU1FTh6ekpGjRooJCe+f4LCQmRpz158kTo6uqKtm3bytMy38Mf/260bdtWmJubK6Q5ODgofBaPHz9eABBbt25Vqmfm9bRu3Trbz0uivMBIYwHKXMmX1eKRrBw8eBCWlpawtLREpUqVsHnzZvTo0QPTp0//5Lo4OjpCCJHjFdXr1q1D1apV4eLiAuD9NQQEBGQ7RO3h4YGKFSvir7/+AvA+uta6dWt5tCq3Mldbv337Vm3exMREeZu5uLhgxIgRqFOnDnbs2CEfyjI2Nkbr1q3x119/yaMvGRkZ2LhxI9q0aQMDAwOlcsPDw9GhQwd4eXlh8eLF8vTNmzfDx8cHpqamChGxRo0aISMjQ2noqlOnTvKIJwB59Cpz6Dwr3333HeLi4tCpUydcuXIF9+/fx5AhQxASEgIACivvVbl//z7mz5+PmTNnQiqVZpmva9euMDY2Rp8+fXDo0CGEhYXh999/l1/zh+dJTk5WWVbmApvMvJn/zSqvqrr//fffOHTokNLD2tpaIZ+enh7Onz+PkSNHAngf7fr6669ha2uLwYMHy4fIs+Lj44OIiAjcu3cPwPuIoq+vL3x8fORD9adOnYIQIstIY059vHjNx8dH7c890927d+Xvazc3N8ycOROtWrVS+j3+MBr97t07REVFoWbNmgAgX9WekZGBAwcOoE2bNihdurQ8f/ny5eWR9Exbt26FTCZDx44dFd7fNjY2cHV1la/eDwkJwevXrzFgwACFBVYBAQFwc3NTGbX7lPbI/Cz9eAeGGzduyNsn8/HxkLyfnx/c3d2VyvywzWJjYxEfHw8fHx+VuwDUqlUL3t7e8uelS5dG69atceDAAaWhdVXXFx0dne3K7r///huVKlVC27ZtlV7L/PwyMTHB8+fPczSthehTsdNYgDLnVeWk0/OhGjVq4NChQzh8+DDOnDmDqKgorF69WuUwZXY+dd+1uLg47N27F35+fnj48KH8UadOHYSEhMiHH1Xp2rUrNm/ejIcPH+LMmTOfNDSdKSEhAUDOOt26urryDsbKlStRvnx5vH79WqnNevbsiadPn8o7BocPH0ZERAR69OihVGZ6ejo6duyIjIwMbN26VaHz8+DBA+zfv1/pD1ajRo0AQGG+GQCFP9LAv0PmsbGx2V5Xs2bNsGDBApw4cQJVqlRBuXLlsGfPHvncRHX7AP7www+oXbs22rdvn20+Gxsb7Ny5EykpKWjSpAmcnJwwcuRILFiwQOk8enp6KjtlmdsbZbZ55n+zyqvq/ezr64tGjRopPVSt+DY2NsaMGTMQFhaGsLAwLF++HOXKlcPChQsxZcqUbK83syN48uRJJCYm4sqVK/Dx8YGvr6/8vXHy5EkYGRllOQ82J3R1deXzHjOZmpqq/blncnR0xKFDh3DgwAEsXrwYJUuWRGRkpFJ7xMTE4IcffoC1tTX09PRgaWkJJycnAJDPCY6MjERycrLC6vZMH86ZBd6/v4UQcHV1VXqP37lzR/7+fvLkicrjAcDNzU3++n9tj8zPgMzPhEwuLi7y33tVv8MA5O3wsd27d6NmzZrQ1dWFmZmZfLqBqjnUqtqsbNmySEpKQmRkpEL6p/yuh4aGwtPTM8vXAWD06NEwNDRE9erV4erqioEDB+L06dPZHkOUW9xypwAZGRnBzs5O5byX7FhYWMg7H1nJKlIDQD7pOqttbtTZvHkzUlJSMHv2bMyePVvp9XXr1mU5R6dLly4ICgpCv379YG5ujiZNmnxSHQDI2y0z2pkdTU1NhTbz9/eHm5sb+vfvj507dyqkW1tbY+3atfD19cXatWthY2Ojsr1HjhyJs2fP4vDhw7C3t1d4TSaToXHjxhg1apTK+pQtW1apfqqIHMzHGzRoEAIDA3H9+nXo6OjAy8sLy5cvV3meDx09ehT79+/H1q1b5fM7gfed4eTkZISFhcHMzEz+5cbX1xePHj3CjRs3kJiYiEqVKsn3O/zwPLa2tioXVGWmZW51k7nw69WrVyhVqpRS3urVq6u99pxycHBAnz590LZtW5QpUwbr1q3Lct/QzDo6OTnhxIkT8ih8rVq1YGlpiR9++AFPnjzByZMnUbt27f+0NUpu5999zMDAQOG9WadOHVSpUgVjx45VWMjTsWNHnDlzBiNHjoSXlxcMDQ0hk8nQtGnTT9rTUSaTQSKRYN++fSqv4VM3Lf/U9nBzcwPw/jOhdevWCvXIbJ+s5j6r+nJy8uRJtGrVCr6+vli8eDFsbW2hra2NlStX/ueFJf/ldz075cuXx71797B7927s378ff//9NxYvXozx48ernTNJlFPsNBawFi1a4Pfff8fZs2dRq1atPCvXwcFBYcL+hzKH3DIXC+TWunXr4OnpiQkTJii99ttvv2H9+vVZfkiVLl0aderUwfHjx/Hdd98prJrNjYyMDKxfv16+mXVu2draYujQoZg0aRLOnTsnH6rT1NRE165dsWrVKkyfPh3bt29Hv379lD7oN2zYgHnz5mHevHnw8/NTKt/Z2RkJCQlqO/d5xcDAQOH9c/jwYejp6alcPZzp6dOnAKCwMCPTixcv4OTkhLlz52LIkCHydE1NTXh5eSmcB4DCdXp5eeHkyZOQyWQKHarz589DX19f3sHMLCckJEShg/jy5Us8f/4c33zzTQ6uPHdMTU3h7Oycoy9qPj4+OHHiBJycnODl5YUSJUqgUqVKMDY2xv79+3H58mW1f4w/911UKlasiO7du+O3337DiBEjULp0acTGxuLIkSOYNGkSxo8fL8/78cp6S0tL6OnpqVxxn/mZkcnZ2RlCCDg5OWX7xSTzM+bevXto0KCBUpmf+hn0MR8fHxgbG2PDhg0ICgr6z3sc/v3339DV1cWBAwcURhBWrlypMr+qNrt//z709fWVIqefIqfvWQMDA3Tq1AmdOnVCamoq2rVrh59//hlBQUGfHCQg+hCHpwvYqFGjYGBggL59+yIiIkLp9dDQUMyfPz/X5TZv3hzPnz/H9u3bFdJTUlKwbNkyWFlZoUqVKvL0nG658+zZM5w4cQIdO3ZEhw4dlB6BgYF4+PBhtiv2pk6digkTJmDw4MG5vi7gfYfx+++/x507d/D9999nu31KdgYPHgx9fX388ssvCuk9evRAbGws+vfvj4SEBPmq6Uw3b95E37590b179yy3Q+rYsSPOnj2LAwcOKL0WFxendMeTvHTmzBls3boVX3/9NYyNjbPM16BBA2zbtk3pYWlpiapVq2Lbtm1o2bJllsdHRkZi+vTpqFixokKnsUOHDoiIiMDWrVvlaVFRUdi8eTNatmwp/yPs4eEBNzc3/P777wrzvpYsWQKJRIIOHTp8chtcu3ZN5d1jnjx5gtu3b6scLv2Yj48PwsLCsHHjRvlwtYaGBmrXro05c+YgLS1N7XzGzPm6H68Szk+jRo1CWloa5syZA+DfyNbHkax58+YpPNfU1IS/vz+2b98u/0IBAHfu3FF6H7dr1w6ampqYNGmSUrlCCPm8wapVq8LKygpLly5VmIawb98+3LlzBwEBAf/tYv9PX18fo0aNws2bNzFmzBiVUbvcRPI0NTUhkUgU3pdhYWFKn6eZzp49qzDX8dmzZ9ixYweaNGnyn6PJANC+fXtcu3ZN5Sr2zOv6eK6mjo4O3N3dIYRQuwsDUU4x0ljAnJ2dsX79enTq1Anly5dXuCPMmTNnsHnz5k+6B+k333yDFStW4KuvvkKfPn1QuXJlREdHY+PGjbh58yZWr16tsH1K5pY7vXr1ynYxzPr16yGEQKtWrVS+3rx5c2hpaWHdunWoUaOGyjx+fn4qo3OqxMfHY+3atQDeD6tn3hEmNDQUnTt3Vjs3LTvm5uYIDAzE4sWLcefOHZQvXx4AULlyZXh6emLz5s0oX768QucaAAIDAwFAPnz9odq1a6NMmTIYOXIkdu7ciRYtWqB3797w9vZGYmIibty4gS1btiAsLAwWFhafXPdMT548QceOHdGqVSvY2Njg1q1bWLp0KSpWrIhp06Zle2zp0qWV5lcBwJAhQ2BtbY02bdoopPv5+aFWrVpwcXFBeHg4fv/9dyQkJGD37t0KkZ0OHTqgZs2aCAwMxO3bt+V3hMnIyFCKzGUu3GjSpAk6d+6MmzdvYuHChejbt6/85/EpDh06hAkTJqBVq1aoWbMmDA0N8ejRI6xYsQIpKSlKeyWqktkhvHfvnkJb+vr6Yt++ffL9NLOjp6cHd3d3bNy4EWXLloWZmRk8PT3Vzk/7L9zd3dG8eXMsW7YMP/30E8zNzeHr64sZM2YgLS0NJUuWxMGDB1Xu6zpp0iTs378fPj4+GDBgANLT0+V7/12/fl2ez9nZGVOnTkVQUBDCwsLQpk0blChRAo8fP8a2bdvwzTffYMSIEdDW1sb06dMRGBgIPz8/dOnSBREREfKtbYYOHZpn1z1mzBjcuXMHM2fOxMGDB9G+fXvY29sjNjYWly9fxubNm2FlZZWjiFtAQADmzJmDpk2bomvXrnj9+jUWLVoEFxcXhXbI5OnpCX9/f3z//feQSqXyBWJ5NSw8cuRIbNmyRf557u3tjZiYGOzcuRNLly5FpUqV0KRJE9jY2KBOnTqwtrbGnTt3sHDhQgQEBOR6sSVRlj7/gm1S5f79+6Jfv37C0dFR6OjoiBIlSog6deqIBQsWyLdCESL7rXQ+FhsbK4YOHSqcnJyEtra2MDIyEvXr1xf79u1TypvTLXcqVKggSpcunW2eevXqCSsrK5GWlqaw5U52stpyBx9sqWJoaChcXV1F9+7dxcGDB7MtT13ZmUJDQ4WmpqbSdc+YMUMAENOmTVM6xsHBIcttXz7cxujt27ciKChIuLi4CB0dHWFhYSFq164tZs2aJVJTU4UQItv2QRZbtXwoJiZGtG7dWtjY2AgdHR3h5OQkRo8erbQFT25k9R4bOnSoKFOmjJBKpcLS0lJ07dpVhIaGZlmvr7/+Wpibmwt9fX3h5+cnLl68qDLvtm3bhJeXl5BKpcLe3l6MGzdO3j6ZcntHmEePHonx48eLmjVrCisrK6GlpSUsLS1FQECAOHr0aE6bQlhZWQkAClv1nDp1SgAQPj4+Svk/3nJHCCHOnDkjvL29hY6OjsLPNKv3ZU7vvpLdllTHjx9XONfz589F27ZthYmJiTA2NhZfffWVePnypcr32D///COvb5kyZcTSpUuzrNPff/8t6tatKwwMDISBgYFwc3MTAwcOFPfu3VPIt3HjRlG5cmUhlUqFmZmZ6Natm3j+/LlCnv/aHpm2bdsmmjdvLiwtLYWWlpYwMTERdevWFTNnzhRxcXEKeQGIgQMHqixn+fLlwtXVVUilUuHm5iZWrlypsi6ZZaxdu1aev3LlyuLYsWMqr+Pj9/DKlSsFAPH48WN52sdb7gjxfounQYMGiZIlSwodHR1hb28vevXqJaKiooQQQvz222/C19dXmJubC6lUKpydncXIkSM/292PqHiQCPEfZ98SFUHz58/H0KFDERYWpjIaR0QEvJ+3OnDgQCxcuLCgq0KU7zinkegjQggsX74cfn5+7DASERH9H+c0Ev1fYmIidu7ciWPHjuHGjRsK91MmIiIq7hhpJPq/yMhI+ebjY8eOzXKxDxERUX4JDg5GtWrVUKJECVhZWaFNmzZK216psnnzZri5uUFXVxcVKlTA3r17FV4XQmD8+PGwtbWFnp4eGjVqpHK7qOyw00j0f5mbOMfGxsrvqEJElB0hBOczUp76559/MHDgQJw7dw6HDh1CWloamjRpgsTExCyPOXPmDLp06YKvv/4aV65cQZs2bdCmTRuF/T1nzJiBX3/9FUuXLsX58+dhYGAAf39/+d26coILYYiIiIgKqcjISFhZWeGff/6Br6+vyjydOnVCYmIidu/eLU+rWbMmvLy8sHTpUgghYGdnh+HDh2PEiBEA3m9pZ21tjVWrVqFz5845qgsjjURERESFVOb9zs3MzLLMc/bsWaU7kPn7++Ps2bMAgMePHyM8PFwhj7GxMWrUqCHPkxNcCENERESUj1JSUhTuigQAUqlU4TaVqshkMgwZMgR16tTJ9qYA4eHhsLa2VkiztrZGeHi4/PXMtKzy5ESR7DTmZnyeiIiICoeCvEf2XI32+VZ2/PgKSncImjBhgtq7Uw0cOBA3b97EqVOn8q1uuVEkO41EREREhUVQUBCGDRumkKYuyjho0CDs3r0bJ06cgL29fbZ5bWxsEBERoZAWEREBGxsb+euZaba2tgp5vLy8cnoZnNNIREREpCHRyLeHVCqFkZGRwiOrTqMQAoMGDcK2bdtw9OhRODk5qa17rVq1cOTIEYW0Q4cOoVatWgAAJycn2NjYKOR58+YNzp8/L8+TE4w0EhERUbGnIZEUdBUAvB+SXr9+PXbs2IESJUrI5xwaGxtDT08PANCzZ0+ULFkSwcHBAIAffvgBfn5+mD17NgICArBhwwaEhITg999/B/D+dpdDhgzB1KlT4erqCicnJ/z000+ws7NDmzZtclw3dhqJiIiICoklS5YAAOrVq6eQvnLlSvTu3RsA8PTpU2ho/DtYXLt2baxfvx7jxo3D2LFj4erqiu3btyssnhk1ahQSExPxzTffIC4uDnXr1sX+/ftzNY+0SO7TyIUwREREX56CXAizSDtnexV+ioFpG/Kt7M+JcxqJiIiISC0OTxMREVGxpylhHE0dthARERERqcVIIxERERV7hWX1dGHGSCMRERERqcVIIxERERV7GpzTqBY7jURERFTssdOoHluIiIiIiNRipJGIiIiKPS6EUY+RRiIiIiJSi5FGIiIiKvY4p1E9thARERERqcVIIxERERV7vI2gemwhIiIiIlKLkUYiIiIq9jinUT12GomIiKjY45Y76rFbTURERERqMdJIRERExR6Hp9VjCxERERGRWow0EhERUbHHSKN6bCEiIiIiUouRRiIiIir2uHpaPUYaiYiIiEgtRhqJiIio2ONtBNVjp5GIiIiKPS6EUY8tRERERERqMdJIRERExR4XwqjHSCMRERERqcVIIxERERV7nNOoHluIiIiIiNRipJGIiIiKPUYa1WMLEREREZFajDQSERFRscfV0+qx00hERETFHoen1WMLEREREZFajDQSERFRscd7T6vHFiIiIiIitRhpJCIiomKPC2HUY6SRiIiIiNRipJGIiIiKPa6eVo8tRERERERqMdJIRERExZ5Ek3E0ddhpJCIiomKPnUb12EJEREREpBYjjURERFTsSbQYR1OHLUREREREarHTmAcuXbqEwYMHo1GjRqhUqRKOHj2q9piLFy+iU6dOqFq1Klq0aIEdO3Yo5dmwYQOaNWuGatWqoVu3brhx40Z+VL/QYDvmHbZl3mFb5g22Y95hW+YPiZZGvj2KiqJzJQUoOTkZ5cqVQ1BQUI7yP3/+HIMGDUK1atWwadMmdOvWDZMmTcLp06flefbv349Zs2ahf//+2LBhA8qVK4fvvvsO0dHR+XUZBY7tmHfYlnmHbZk32I55h21Z9J04cQItW7aEnZ0dJBIJtm/fnm3+3r17QyKRKD08PDzkeSZOnKj0upubW67qxTmNeaBu3bqoW7dujvNv3rwZJUuWxIgRIwAAZcqUwZUrV7B27VrUqVMHALBmzRq0a9cObdq0AQCMGzcOJ06cwPbt2/H111/n+TUUBmzHvMO2zDtsy7zBdsw7bMv8UZhWTycmJqJSpUro06cP2rVrpzb//Pnz8csvv8ifp6eno1KlSvjqq68U8nl4eODw4cPy51pauesGFp4WKkauX7+OmjVrKqTVrl0b169fBwCkpaXhzp07Cnk0NDRQs2ZNeR5iO+YltmXeYVvmDbZj3mFbfnmaNWuGqVOnom3btjnKb2xsDBsbG/kjJCQEsbGxCAwMVMinpaWlkM/CwiJX9SrUncZnz56hT58+2eZJSUnBmzdvFB4pKSmfqYafJioqCubm5gpp5ubmSEhIwLt37xAbG4uMjAyVeaKioj5nVQs1tmPeYVvmHbZl3mA75h22Zc7k55zGz91XWb58ORo1agQHBweF9AcPHsDOzg5lypRBt27d8PTp01yVW6g7jTExMfjzzz+zzRMcHAxjY2OFx8yZMz9TDYmIiKgokGhK8u2hqq8SHBycL9fx8uVL7Nu3D3379lVIr1GjBlatWoX9+/djyZIlePz4MXx8fPD27dscl12gcxp37tyZ7euPHj1SW0ZQUBCGDRumkCaE+E/1ym8WFhZKk4ujo6NhaGgIXV1daGpqQlNTU2We3IaSizK2Y95hW+YdtmXeYDvmHbZlwVPVV5FKpflyrj///BMmJiby+amZmjVrJv93xYoVUaNGDTg4OGDTpk05nrdaoJ3GNm3aQCKRZNvJk0gk2ZYhlUqVGv7du3d5Ur/8UrFiRZw6dUoh7dy5c6hYsSIAQFtbG+XLl8f58+fRoEEDAIBMJsP58+fRuXPnz17fwortmHfYlnmHbZk32I55h22ZM/m5NY6qvkp+EEJgxYoV6NGjB3R0dLLNa2JigrJly+Lhw4c5Lr9Ah6dtbW2xdetWyGQylY/Lly8XZPVyLCkpCXfv3sXdu3cBAC9evMDdu3fx6tUrAO9XNf3444/y/F999RWeP3+OuXPn4vHjx9i4cSMOHjyI7t27y/P06NEDW7duxc6dO/Ho0SNMnToVycnJSt8cihK2Y95hW+YdtmXeYDvmHbYlZeWff/7Bw4cPcxQ5TEhIQGhoKGxtbXNcfoFGGr29vXHp0iW0bt1a5evqopCFxa1btxTmDsyaNQsA0KpVK0yZMgVRUVEIDw+Xv25vb4+FCxdi5syZWLduHaytrTFhwgT51gcA0LRpU8TGxmLx4sWIiopCuXLlsHjxYqWJykUJ2zHvsC3zDtsyb7Ad8w7bMn8Upk24ExISFCKAjx8/xtWrV2FmZobSpUsjKCgIL168wOrVqxWOW758OWrUqAFPT0+lMkeMGIGWLVvCwcEBL1++xIQJE6CpqYkuXbrkuF4SUYC9spMnTyIxMRFNmzZV+XpiYiJCQkLg5+eXq3IL+/A0ERERKdPV1S2wc1+uMyffyq5yepj6TB84fvw46tevr5Teq1cvrFq1Cr1790ZYWBiOHz8ufy0+Ph62traYP38++vXrp3Rs586dceLECURHR8PS0hJ169bFzz//DGdn5xzXq0A7jfmFnUYiIqIvT0F2Gq/4zsu3siufGJJvZX9OhScWS0RERESFFm8jSERERMVeYZrTWFix00hERETFXmG693RhxRYiIiIiIrUYaSQiIqJij8PT6rGFiIiIiEgtRhqJiIio2GOkUT22EBERERGpxUgjERERFXtcPa0eW4iIiIiI1GKkkYiIiIo9zmlUjy1ERERERGox0khERETFHiON6rHTSERERMUeF8KoxxYiIiIiIrUYaSQiIqJij8PT6rGFiIiIiEgtRhqJiIio2JNoSgq6CoUeI41EREREpBYjjURERFTscU6jemwhIiIiIlKLkUYiIiIq9hhpVI+dRiIiIir2uLm3emwhIiIiIlKLkUYiIiIq9jg8rR5biIiIiIjUYqSRiIiIij3OaVSPLUREREREajHSSERERMUe5zSqxxYiIiIiIrUYaSQiIqJiT6LBOJo67DQSERERaUoKugaFHrvVRERERKQWI41ERERU7HF4Wj22EBERERGpxUgjERERFXvc3Fs9thARERERqcVIIxERERV7nNOoHluIiIiIiNRipJGIiIiI+zSqxU4jERERFXscnlaPLUREREREajHSSERERMUet9xRjy1ERERERGox0khERETFHuc0qscWIiIiIiK1GGkkIiIi4pY7ajHSSERERFSInDhxAi1btoSdnR0kEgm2b9+ebf7jx49DIpEoPcLDwxXyLVq0CI6OjtDV1UWNGjVw4cKFXNWLnUYiIiIq9iQaGvn2yK3ExERUqlQJixYtytVx9+7dw6tXr+QPKysr+WsbN27EsGHDMGHCBFy+fBmVKlWCv78/Xr9+nePyOTxNRERExV5h2nKnWbNmaNasWa6Ps7KygomJicrX5syZg379+iEwMBAAsHTpUuzZswcrVqzAmDFjclR+4WkhIiIioiIoJSUFb968UXikpKTk+Xm8vLxga2uLxo0b4/Tp0/L01NRUXLp0CY0aNZKnaWhooFGjRjh79myOyy+SkcYl+t0KugpESvqfnFvQVSgSHpe/XtBVKDKS0+MLugpFxquEBwVdhSKhZZmJBXZuiUb+LYQJDg7GpEmTFNImTJiAiRMn5kn5tra2WLp0KapWrYqUlBQsW7YM9erVw/nz51GlShVERUUhIyMD1tbWCsdZW1vj7t27OT5Pkew0EhERERUWQUFBGDZsmEKaVCrNs/LLlSuHcuXKyZ/Xrl0boaGhmDt3LtasWZNn52GnkYiIiCgf5zRKpdI87STmRPXq1XHq1CkAgIWFBTQ1NREREaGQJyIiAjY2Njkuk3MaiYiIiIqYq1evwtbWFgCgo6MDb29vHDlyRP66TCbDkSNHUKtWrRyXyUgjERERFXuF6TaCCQkJePjwofz548ePcfXqVZiZmaF06dIICgrCixcvsHr1agDAvHnz4OTkBA8PD7x79w7Lli3D0aNHcfDgQXkZw4YNQ69evVC1alVUr14d8+bNQ2Jionw1dU6w00hERERUiISEhKB+/fry55nzIXv16oVVq1bh1atXePr0qfz11NRUDB8+HC9evIC+vj4qVqyIw4cPK5TRqVMnREZGYvz48QgPD4eXlxf279+vtDgmOxIhhMiD6ytU5mq0L+gqECnh6um8wdXTeYerp/MOV0/njYJcPR256mK+lW3Zu1q+lf05MdJIRERExV5+brlTVBSeAXwiIiIiKrQYaSQiIiIqRAthCiu2EBERERGpxUgjERERFXuSfNzcu6hgCxERERGRWow0EhERUbFXmDb3LqzYQkRERESkFiONREREVOxJNLlPozrsNBIRERFxeFotthARERERqcVIIxERERV73HJHPbYQEREREanFSCMREREVexINLoRRh5FGIiIiIlKLkUYiIiIq9jinUT22EBERERGpxUgjEREREec0qsVOIxERERV7Egk7jepweJqIiIiI1GKkkYiIiIjD02ox0khEREREajHSSERERMUeN/dWj5FGIiIiIlKLkUYiIiIirp5Wi5FGIiIiIlKLkUYiIiIq9jinUT1GGomIiIhILUYaiYiIiBhGU4udRiIiIir2eBtB9divJiIiIiK1GGkkIiIi4kIYtRhpJCIiIiK1GGkkIiIiYqRRLUYaiYiIiEgtRhqJiIio2OPqafUYaSQiIiIitRhpJCIiImIYTS12GomIiKjY472n1WO/moiIiIjUYqSRiIiIiAth1GKkkYiIiIjUYqSRiIiIij3OaVSPkUYiIiIiUouRRiIiIiJGGtVip/ETGNiZweeX7nBsVgXa+jqIexiOg30WIeJSqFLehku+QcX+/jg+dAWuzN+TZZkSDQ3UnNgR5bv5wsDGBAkvY3H7z2M4P3WLPI++lTHq/tIDDk0qQWpigBcnbuPY98sR9/BVvlzn55aTNlBFU0cLNca/P07fxgSJr2Jxfspm3Fp5FADg2bcR3Hv4wdyzNADg9aVHOPXjOkRcfJjv11RQLt27jtX7N+N22H1ExcdgzqCJqF+lTpb5r9y/iflb/kDYq2d4l5oCW3NrtK8XgO5N2svzbDq2C1uO7cLLqAgAQJmSDvimZXfUrVg936+noO3bcgrb1x1HXMxbOLrYoe+wtnD1KK0y708DFuPWFeXPgiq1y2Pc7L5IT8/A+t/24fKZO4h4GQN9Q11UrOqKHgMCYGZpnN+XUuAObr2IPX+dQXxMAko7W6PXkGZwdi+ZZf7Et++w6Y+jCPnnLhLeJsPC2hg9vveHVy1XAIAsQ4a/V/6D0wdvIC46AaYWJeDbrBLa9PIp0nf4OL3rPo5vuYu3scmwLWOKtt95o3Q5c5V5Lx56hI1zziukaWlr4JedneTPU5LTsGflNdw68xyJb1NhZm2Auq3LonaAa75eB31Z2GnMJamJATqd+hnPj93EtuZTkRz5BiautngXm6CU17lNddjUKIuEF9Fqy606ug0qfeuPA70XIPrWM1hXdUaTFYOQEp+Eqwv2AgBabhsNWVoGdrb5BalvklFlWEu0PzQBf3r8gPSklDy/1s8tJ22gSsDG4dC3NsGhvosR9/AVDGxNFeam2Pt54O6GU3h15h7S36Wh2qg2aHdgPFZ7DkHiy5jPcWmfXXLKO5QtVQat6/pj+KJJavPrSXXRqUFrlC1VBnpSXVx5cBNT/5wPPR1dtK8XAACwNrXA4A5fo7R1SUAAu04fxNAFE7Bh4hI4l3TM5ysqOKcOX8HKX3ei/6gOKOtRGrs3nsTkob9jwYbRMDEroZR/VHBvpKeny5+/jU/CsJ6zUbtBRQBAyrtUPLr3HF8FNoajqx0S3iZjxdztCB61AjNXDv1s11UQzh65hXULD6LP8AA4u5fE/s3n8cvwdZi1fiCMTQ2U8qenZeCXYWthZKKP76d0gJmlEaLC46BfQleeZ9e60zi8PQTfjm0NeycrPLr7Er8H74SeoRRNO9T4nJf32Vz95wl2/n4F7QdXQ+ly5ji5/R7+GHcMo/5ogRImuiqP0dXXxqg/AuTPP+5P7/z9Ch5ei0CXUbVgZm2A+5fCsXVRCIzN9eBR0z4/L6fQKMpfMvIK5zTmUrXRbZHwLAoHv16EiIsP8SbsNZ4euob4RxEK+QzszFD/177Y330+MtIy1JZrV6scQndexOO9l/HmSSQe/H0OTw5eg001FwCAiast7GqVw9EBvyMiJBSx91/iyHe/Q0tPB25d6ubLtX5u6tpAFQd/L5T088C2gJ/x9Mh1vHkSiVfn7uPlmXvyPPt7zMf1JQcQeS0Msfde4FC/JZBoSFC6YYXPcVkFom7F6hjYLhANvHP23nBzcEGzmg3gXNIRdhY2CKjVCLU9vXHlwQ15Hj+vWvCpWAMO1vZwsLHHoPZ9oK+rh+uhd/LrMgqFXX+dQONWNdGwRXWUcrJB/1HtIZVq4+juCyrzlzDWh6m5kfxx7cJ9SKXaqN2gEgDAwFAPE3/9FnUaeaGkgxXKeTqg7/C2CL37HJHhsZ/z0j67fRvPon7LKvAL8IK9kyX6jAiAVFcb/+y5ojL/8T1XkPAmGUODO6FcxdKwtDVB+cqOcHCxkee5f/M5vOuWQ+XaZWFpa4Ia9d1RoXoZPLr98nNd1mf3z7Z7qNHMGdWblIGNgzHaD64GbakWLh58lPVBEsDITE/+KGGqp/By2J0oVG3kBJeK1jCzNkTN5i6wLWOCp/eK5hdrlTTy8ZFLJ06cQMuWLWFnZweJRILt27dnm3/r1q1o3LgxLC0tYWRkhFq1auHAgQMKeSZOnAiJRKLwcHNzy1W92GnMpTItqyLiUigCNg5H//AV6HZpJjz7NlLMJJGg6ervcWnWDkTffpajcl+evYdSDSrAxNUWAGBR0QF2dd0Qtv/9h6mmVBsAkP4u9d+DhEBGShrs6pT/7xdWCKhrA1WcW1XD65BQVBvVBv2e/Y7edxfAZ2ZPaOrqZHmMlr4ONLU18S5GOTpM79198hDXHt5GlXIVVb6eIcvA/vPHkJzyDhWd3T9z7T6ftLR0hN57jorV/h2i09DQQMVqZXHv5pMclXFk13nUbVwZunrSLPMkJbyDRCKBQQm9LPN86dLTMvD4/it4ejvJ0zQ0JPCs6oQHt56rPOby6ftw9bDHqjn78F2r2Rjdcwl2rD4JWYZMnqespz1uXXqMV0/fj+g8eRiOe9efoVLNrL9sfsnS0zLw4kEMynr923HW0JDA1csaT+5EZXlcanI6pvbagSk9dmDlpBMIfxKv8LpjeQvcOvcC8VFJEELg4bUIRL14i7JVbLIokfJTYmIiKlWqhEWLFuUo/4kTJ9C4cWPs3bsXly5dQv369dGyZUtcuaL499PDwwOvXr2SP06dOpWrenF4OpeMy1ij4rf+uDx3Fy4Eb4VNNRfUn98HstR03F59HABQbXQbiPQMXPk16zmMH7v4yzZIjfTR+86vkGXIoKGpgdPj1uPu+pMAgNi7L/DmSSTqTuuOw98uRVpiCqoMbYESpSxgYGuaH5f62alrA1WMnaxhV9cN6e9SsbPdDOhZGKHBon7QMyuBg1+r/mXzmd4DCS9j8fTw9fy6lC+W//AuiH0bj4yMDPRv3QPtfJsrvP7g+WP0+vl7pKalQk+qh9mDJsC5pEMB1Tb/vY1LhCxDpjQMbWJmiBdPXqs9/sGtp3j6KBwDx3bKMk9qShrWLN6Duo29oG+gemixKHgbnwRZhoCxmeIwtJGpAV4+Ud3Zef0yFrfDH6N24woYNbMLwp/HYtWcvUjPkKF9oB8AoGX3ukhOSsHI7ougoaEBmUyGr/o1QJ0mRXMkIfFNCmQyAUNTxfdKCVNdvH7+VuUxlvZG6Di0BmydTPAuMQ3H/76DhcMOYcTS5jCx1AcAtP3OG5t/vYApPXZAQ/N9FOqrH6rDuYJVvl9TYVGYhqebNWuGZs2a5Tj/vHnzFJ5PmzYNO3bswK5du1C5cmV5upaWFmxsPv2LQIF3GpOTk3Hp0iWYmZnB3V0xYvHu3Tts2rQJPXv2zPL4lJQUpKQozudLFxnQkmjmS30lGhJEhITi9I/rAQCRVx/D3LMUKvRvgturj8OqShlU/j4A67xH5qrcsh1rw62rD/Z2m4foW89g5eUEv7mBSHwZi9urj0OWnoFd7Weg8bIBGBCzGrL0DDw9fB2P914GCs/7/D9R1waqSDQkgBDY130+Ut8kAQBODF+FFptH4MjAP5DxYWQW76cXlOtUB5vrT0BGSlp+X9IXZ8WYOUhKeYcboXfw65ZlKGVlh2Y1G8hfd7Sxx4aJS5GQnIjDIScxftlMLBs9u0h3HP+Lw7vOw8HZNstFM+npGZg1bjWEEOg/qsNnrl3hJ2QCRiYG6DuyBTQ0NeBUzg6xkW+w56+z8k7j+aO3cPrQTQwc3w4lnSzx5EEE1i44IF8QQ++jiI7lLf597m6BGd/swbl9D9G05/vRhFM77+Pp3WgETvCFqbU+Ht2IxLbFITAy10PZyow2/leq+ipSqRRSadYjEP+FTCbD27dvYWZmppD+4MED2NnZQVdXF7Vq1UJwcDBKl1b9+aRKgQ5P379/H+XLl4evry8qVKgAPz8/vHr170rg+Ph4BAYGZltGcHAwjI2NFR6HcS/bY/6LxFdxiL6jOJQSc+cFjEq//4Us6VMe+lbG6PvkN/yQugk/pG6CsaMVfGf1Qp9HS7Is13dGT1ycvg33N55G9M2nuLP2H1yetwvVxrST53l9+RHWVRmBRSY98LtdX2xrPhW65oaIfxyRZblfkpy0wccSX8Ui4UWMvMMIADF3nkOioYES9oorCb2Ht0LV0W2x1X8Kom7kbGixuClpaQtXeye082uObk3a47cdaxRe19bSRmnrknB3LIvvO3yNsqXK4K/D2wqotvmvhIkBNDQ1EBejGMGJi0mAibnyIpgPvUtOwenDV9GwperV5enpGZj142pEhsdi4q/9i3SUEXg/11NDU4L4mESF9DexiTA2N1R5jIm5IWxKmUND898/VXaOFoiLSUD6/+eKr19yGC271UGtRp4o7WwNn6YV0bRjDexcm7thty+FgZEUGhoSJMS+U0h/G/sORqY5ew9pammgpLMpol6+f1+npaRj35/X0fKbyvCoWRJ2Tqao26osKvmWxj9/F+05ywo0JPn2UNVXCQ4OzrdLmTVrFhISEtCxY0d5Wo0aNbBq1Srs378fS5YswePHj+Hj44O3b1VHqFUp0E7j6NGj4enpidevX+PevXsoUaIE6tSpg6dPn+a4jKCgIMTHxys8GqFcvtX55em7MCtrp5BmWtYWb55EAgDurPkHayoNw9rKw+WPhBfRuDRrJ7Y1nZJluVr6UgiZUEgTGTKVO9SnvklCctQbmLjYwrqqM0J3XMyDKyt4uWmDTC/P3IOBnRm0P/iDa1LWDrKMDLx9/u+q9aojW6PGuA7Y1myKyq2RSJlMyJCann00VgiB1PTUbPN8ybS1teBczh7XQx7I02QyGa6HPEA5z+yjq2eOXkNaWjr8mnorvZbZYXz1PAoTf/0WJYyVVw4XNVramnAqa4tblx7L02QygZuXHsPVQ/Xq3LIVSiHiRQxkH3wuhD+LgYm5IbS0348mpb5Lg8ZHw4oaGhpKnyVFhZa2Jkq6muHB1XB5mkwm8PBqBBw+iCZmR5Yhw6uwOBiZvZ9Dm5EukJEuUxqe1dCQQMhUlUC5paqvEhQUlC/nWr9+PSZNmoRNmzbByurf6QXNmjXDV199hYoVK8Lf3x979+5FXFwcNm3alOOyC3R4+syZMzh8+DAsLCxgYWGBXbt2YcCAAfDx8cGxY8dgYKD+g1RVeDe/hqYB4PK8Xeh0ehqqBbXD/U1nYFPdBRX6Ncbh/ksBAO9iEpQWWGSkZSAxPBax9/9dzdf+0AQ83H4B1xbtAwA82hWC6mPb4+3TSETfegbLyk6oMrSlfK9BAHDtUAvJkW/w9mkUzCuURr15fRC6/SKeHrqWb9f7OeWkDepM6wZDOzMc6L0AAHB3/UnUGNcBTVYMxNmJG6FnYQTfGT1xa+VR+dB01VFtUGtSZ+zrNg9vwiKhb20CAEhLeIe0xHdK9SgKkt4l49nrF/LnL6LCce/pQxgZGMHW3Aq/blmO17FRmNpvNABg45EdsDG3gqNtKQDA5Xs3sGb/FnRp1EZexq9blqNOhWqwNbdC4rtk7Dt3FCH3rmHxsPz7tlwYtOziiwVTNsDFrRRcPUpj14YTSHmXigYt3kcQ509aD3NLY3QfEKBw3JFdF1Dd11OpQ5ienoGZY//Eo3vPMXZWX8hkMsRGvwEAGBrpQ1u7wGcN5ZtmnWrht2nb4eRmB+fydti/+TxSktPg19wLALBk6naYWpRA528bAgAatamKg1svYs38/WjSvjrCn0djx5pT8O/wb/S2cu2y2L7mJMytjWDvZIWwB+HYt/Ec/AK8CuAKPw+/tuWwYfY52LuaybfcSU1JR7XG7xcZ/TXrLIzN9dA80AsAcHDdTTi4mcPCrgSSE1NxfMsdxL5OQnV/ZwCAroE2ylSwwu7lV6Et1YSplQEe3XiNkCNhaNWvclbVKHLy8zaC+TkU/aENGzagb9++2Lx5Mxo1apRtXhMTE5QtWxYPH+Z8z+IC/XRKTk6Glta/VZBIJFiyZAkGDRoEPz8/rF+/vgBrp1pESCh2tZuButO6oeZPXyH+8WscH7oy28Uaqhg720DP4t/hrWPfL0PtKV3QYNE30LcyQsLLWNz4/RDOTd4sz2Ngawq/2b2hb22MxFdxuL3mOM5PyX7j6y9JjtrAxhQlSv/7bTot8R3+bjIZ9X/9Gl0vzsC76Le4v/kMTo/7S56n4rf+0JJqo+UWxXmmZydtxLlJOf+G9SW5HXYf/WaMkD+fveH9l5qWdRpj8tejEBUfjfCYfxdyyITAgr9X4EVkOLQ0NWBvaYfvv+qLDn7/doRi3sThp2UzEBUfA0M9A7jaO2HxsGDU9FCOpBUldRtVxpvYRPy17ADiot/AybUkfprbT744JioiDhof/bF58eQ17lx7jPHzv1EqLyYyHhdP3gIADO85W+G1yYu+g2eVornqFwBqNfTA27hEbFl+HPExCXBwscboWV1hbPZ+eDo6Il4h2mVubYwxs7thzYKDCApcClMLIzTtUB0tu/27UX2voU2xZdlxrJyzD29iE2FqUQINWldBu95+n/36PhcvPwckxKfgwNobeBvzDnbOpug7pZ58G53Y10kK7ZickIrNv17A25h30C+hg5IuZhg8uxFsHP7dTL77mNrYu+oa1s84i6S3qTC10kezXhVRK6Dovh+Lmr/++gt9+vTBhg0bEBAQoDZ/QkICQkND0aNHjxyfQyKEKLAYfvXq1TF48GCVFR40aBDWrVuHN2/eICND/T6HH5qr0V59JqLPrP/JuQVdhSLhcXmues8ryenx6jNRjrxKeKA+E6nVsszEAjt3+n31N+L4VFplVd+tJysJCQnyCGDlypUxZ84c1K9fH2ZmZihdujSCgoLw4sULrF69GsD7IelevXph/vz5aNfu33UAenp6MDZ+/+VgxIgRaNmyJRwcHPDy5UtMmDABV69exe3bt2FpaZmjehXonMa2bdvir7/+UvnawoUL0aVLFxRgn5aIiIiKCYmGJN8euRUSEoLKlSvLt8sZNmwYKleujPHjxwMAXr16pbD+4/fff0d6ejoGDhwIW1tb+eOHH36Q53n+/Dm6dOmCcuXKoWPHjjA3N8e5c+dy3GEEPiHSuH//fhgaGqJu3fd3mli0aBH++OMPuLu7Y9GiRTA1Lfg9AxlppMKIkca8wUhj3mGkMe8w0pg3CjLSmPEw/+5+o+lipj7TFyDXkcaRI0fizZv3k7Zv3LiB4cOHo3nz5nj8+DGGDRuW5xUkIiIiyneF6DaChVWuF8I8fvxYvgn333//jRYtWmDatGm4fPkymjdvruZoIiIiIvoS5br/q6Ojg6Sk9xspHz58GE2aNAEAmJmZySOQRERERF8SiUSSb4+iIteRxrp162LYsGGoU6cOLly4gI0bNwJ4f3cXe3vVG7QSERER0Zct15HGhQsXQktLC1u2bMGSJUtQsmRJAMC+ffvQtGnTPK8gERERUb7Lx9sIFhW5jjSWLl0au3fvVkqfO5crQ4mIiIiKqk+6I0xGRga2bduGO3fe38i8fPnyaNOmjcLdXYiIiIi+GEUnIJhvct3Lu3XrFlq2bImIiAiUK1cOADB9+nRYWlpi165d8PT0zPNKEhEREeWrIrRgJb/kek5j37594enpiefPn+Py5cu4fPkynj17hooVK+Kbb5Tvs0pEREREX75cRxqvXr2KkJAQhTu/mJqa4ueff0a1atXytHJEREREn4NgoFGtXEcay5Yti4iICKX0169fw8XFJU8qRURERESFS44ijR9u2h0cHIzvv/8eEydORM2aNQEA586dw+TJkzF9+vT8qSURERFRfmKkUa0cdRpNTEwUdjQXQqBjx47yNCEEAKBly5bIyMjIh2oSERERUUHKUafx2LFj+V0PIiIiooLD1dNq5ajT6Ofnl9/1ICIiIqJC7JN2446Li8Py5cvlm3t7eHigT58+MDY2ztPKEREREVHhkOvV0yEhIXB2dsbcuXMRExODmJgYzJkzB87Ozrh8+XJ+1JGIiIgoXwlJ/j2KilxHGocOHYpWrVrhjz/+kN82MD09HX379sWQIUNw4sSJPK8kERERERWsXHcaQ0JCFDqMAKClpYVRo0ahatWqeVo5IiIios+CC2HUyvXwtJGREZ4+faqU/uzZM5QoUSJPKkVEREREhUuuO42dOnXC119/jY0bN+LZs2d49uwZNmzYgL59+6JLly75UUciIiKi/CXJx0cRkevh6VmzZkEikaBnz55IT08HAGhra+O7777DL7/8kucVJCIiIqKCl+tOo46ODubPn4/g4GCEhoYCAJydnaGvr5/nlSMiIiL6LIpQRDC/fNI+jQCgr6+PChUq5GVdiIiIiKiQylGnsV27dli1ahWMjIzQrl27bPMaGhrCw8MD3377LTf7JiIioi9CUdpPMb/kqNNobGwMyf+XoqvrCKakpGDp0qU4ffo0du7c+d9rSERERJTfuOWOWjnqNK5cuVLlv7Ny+/ZtVKtW7dNrRURERESFyifPacxOuXLlcObMmfwomoiIiCjvMdCoVq73acwJTU1NVKpUKT+KJiIiIqICkC+RRiIiIqIvCiONauVLpJGIiIiIipYcdRqrVKmC2NhYAMDkyZORlJSUr5UiIiIi+pyERJJvj6IiR53GO3fuIDExEQAwadIkJCQk5GuliIiIiKhwydGcRi8vLwQGBqJu3boQQmDWrFkwNDRUmXf8+PF5WkEiIiIiKng56jSuWrUKEyZMwO7duyGRSLBv3z5oaSkfKpFI2GkkIiKiL0/RGUXONznqNJYrVw4bNmwAAGhoaODIkSOwsrLK14oRERERUeGR6y13ZDJZftSDiIiIqOAUoQUr+eWT9mkMDQ3FvHnzcOfOHQCAu7s7fvjhBzg7O+dp5YiIiIiocMj1Po0HDhyAu7s7Lly4gIoVK6JixYo4f/48PDw8cOjQofyoIxEREVH+kuTjo4jIdaRxzJgxGDp0KH755Rel9NGjR6Nx48Z5VjkiIiIiKhxyHWm8c+cOvv76a6X0Pn364Pbt23lSKSIiIqLPSUjy71FU5LrTaGlpiatXryqlX716lSuqiYiIiIqoXA9P9+vXD9988w0ePXqE2rVrAwBOnz6N6dOnY9iwYXleQSIiIqJ8x9XTauW60/jTTz+hRIkSmD17NoKCggAAdnZ2mDhxIr7//vs8ryARERERFbxcdxolEgmGDh2KoUOH4u3btwCAEiVK5HnFiIiIiD4bBhrV+qR9GjOxs0hERERFAjuNauV6IQwRERER5Z8TJ06gZcuWsLOzg0Qiwfbt29Uec/z4cVSpUgVSqRQuLi5YtWqVUp5FixbB0dERurq6qFGjBi5cuJCrerHTSERERMWekEjy7ZFbiYmJqFSpEhYtWpSj/I8fP0ZAQADq16+Pq1evYsiQIejbty8OHDggz7Nx40YMGzYMEyZMwOXLl1GpUiX4+/vj9evXOa6XRAghcn01hdxcjfYFXQUiJf1Pzi3oKhQJj8tfL+gqFBnJ6fEFXYUi41XCg4KuQpHQsszEAjv3u+R3+Va2rp7uJx8rkUiwbds2tGnTJss8o0ePxp49e3Dz5k15WufOnREXF4f9+/cDAGrUqIFq1aph4cKFAACZTIZSpUph8ODBGDNmTI7qkqtIY1paGho2bIgHD/jLQUREREXIF3wbwbNnz6JRo0YKaf7+/jh79iwAIDU1FZcuXVLIo6GhgUaNGsnz5ESuFsJoa2vj+nV+yyciIiLKqZSUFKSkpCikSaVSSKXSPCk/PDwc1tbWCmnW1tZ48+YNkpOTERsbi4yMDJV57t69m+Pz5Hr1dPfu3bF8+XKle08TUfa03M0LugpFwoWnuwu6CkVGTGLO5zJR9qLfRBV0FYqEghyeBvJvtl5w8C+YNGmSQtqECRMwceLEfDtnfsh1pzE9PR0rVqzA4cOH4e3tDQMDA4XX58yZk2eVIyIiIvrSBQUFKd01L6+ijABgY2ODiIgIhbSIiAgYGRlBT08Pmpqa0NTUVJnHxsYmx+fJdafx5s2bqFKlCgDg/v37Cq9JeAseIiIi+gKJfIw06ubhULQqtWrVwt69exXSDh06hFq1agEAdHR04O3tjSNHjsgX1MhkMhw5cgSDBg3K8Xly3Wk8duxYbg8hIiIiKtQEZAVdBbmEhAQ8fPhQ/vzx48e4evUqzMzMULp0aQQFBeHFixdYvXo1AODbb7/FwoULMWrUKPTp0wdHjx7Fpk2bsGfPHnkZw4YNQ69evVC1alVUr14d8+bNQ2JiIgIDA3Ncr0++I8zDhw8RGhoKX19f6OnpQQjBSCMRERHRfxQSEoL69evLn2cObffq1QurVq3Cq1ev8PTpU/nrTk5O2LNnD4YOHYr58+fD3t4ey5Ytg7+/vzxPp06dEBkZifHjxyM8PBxeXl7Yv3+/0uKY7OR6n8bo6Gh07NgRx44dg0QiwYMHD1CmTBn06dMHpqammD17dm6Kyxfcp5EKo4HRqwu6CkXCuqfDC7oKRQYXwuQdLoTJG9Oaniiwcye9e5tvZevrFo3bLuf6jjBDhw6FtrY2nj59Cn19fXl6p06d5BtIEhEREVHRkuvh6YMHD+LAgQOwt7dXSHd1dcWTJ0/yrGJEREREn0thmtNYWOU60piYmKgQYcwUExOTryuDiIiIiKjg5LrT6OPjI1+tA7zfZkcmk2HGjBkKkzaJiIiIvhQiH/9XVOR6eHrGjBlo2LAhQkJCkJqailGjRuHWrVuIiYnB6dOn86OORERERFTAch1p9PT0xP3791G3bl20bt0aiYmJaNeuHa5cuQJnZ+f8qCMRERFR/hKy/HsUEZ+0T6OxsTF+/PHHvK4LERERUYEoSsPI+eWTOo2xsbFYvnw57ty5AwBwd3dHYGAgzMzM8rRyRERERFQ45Hp4+sSJE3B0dMSvv/6K2NhYxMbG4tdff4WTkxNOnCi4TTmJiIiIPpWALN8eRUWuI40DBw5Ep06dsGTJEmhqagIAMjIyMGDAAAwcOBA3btzI80oSERERUcHKdaTx4cOHGD58uLzDCACampoYNmyYws21iYiIiL4csnx8FA257jRWqVJFPpfxQ3fu3EGlSpXypFJEREREVLjkaHj6+vXr8n9///33+OGHH/Dw4UPUrFkTAHDu3DksWrQIv/zyS/7UkoiIiCgfcfW0ejnqNHp5eUEikUCIfxt01KhRSvm6du2KTp065V3tiIiIiKhQyFGn8fHjx/ldDyIiIqICU5RWOeeXHHUaHRwc8rseRERERAWIw9PqfNLm3i9fvsSpU6fw+vVryGSKPfPvv/8+TypGRERERIVHrjuNq1atQv/+/aGjowNzc3NIJBL5axKJhJ1GIiIi+uJweFq9XHcaf/rpJ4wfPx5BQUHQ0Mj1jj1ERERE9AXKdacxKSkJnTt3ZoeRiIiIigxuuaNernt+X3/9NTZv3pwfdSEiIiKiQirXkcbg4GC0aNEC+/fvR4UKFaCtra3w+pw5c/KsckRERESfB+c0qvNJncYDBw6gXLlyAKC0EIaIiIiIip5cdxpnz56NFStWoHfv3vlQHSIiIqLPj3Ma1ct1p1EqlaJOnTr5URciIiKiAsEtd9TL9UKYH374AQsWLMiPuhARERFRIZXrSOOFCxdw9OhR7N69Gx4eHkoLYbZu3ZpnlSMiIiL6PDg8rU6uO40mJiZo165dftSFiIiIiAqpXHcaV65cmR/1ICIiIiowQnBOozq8rQsRERERqZXrSKOTk1O2+zE+evToP1WIiIiI6HPjljvq5brTOGTIEIXnaWlpuHLlCvbv34+RI0fmVb2IiIiIqBDJdafxhx9+UJm+aNEihISE/OcKEREREX1+nNOoTp7NaWzWrBn+/vvvvCqOiIiI6LMR+fi/oiLPOo1btmyBmZlZXhVHRERERIVIroenK1eurLAQRgiB8PBwREZGYvHixXlaOSIiIqLPgbcRVC/XncY2bdooPNfQ0IClpSXq1asHNze3vKoXERERERUiue40TpgwIT/qQURERFSAis7cw/zCzb2JiIiISK0cRxo1NDSy3dQbACQSCdLT0/9zpYiIiIg+J85pVC/HncZt27Zl+drZs2fx66+/QiZjgxMREREVRTnuNLZu3Vop7d69exgzZgx27dqFbt26YfLkyXlaOSIiIqLPQSYyCroKhd4nzWl8+fIl+vXrhwoVKiA9PR1Xr17Fn3/+CQcHh7yuHxEREVG+k4mMfHsUFbnqNMbHx2P06NFwcXHBrVu3cOTIEezatQuenp75VT8iIiIiKgRyPDw9Y8YMTJ8+HTY2Nvjrr79UDlcTERERfYkEik5EML/kuNM4ZswY6OnpwcXFBX/++Sf+/PNPlfm2bt2aZ5UjIiIiosIhx53Gnj17qt1yp7gwsDODzy/d4disCrT1dRD3MBwH+yxCxKVQAECTFYPg0bu+wjFh+69gW/OpWZbZ59ESGDtaKaVfXbwPxwYtAwA0XNofpRtWhKGdKVIT3uHVmXs4OWYtYu+9yMOrKzgSDQ3UnNgR5bv5wsDGBAkvY3H7z2M4P3VLlseoamsAiL71DKsrDAEAVPzWHxW/9YeRo6X8tfNTNiNs/5V8uY7CIOTKJaxauxq3791BZFQU5k2fjYZ+yu30oYuXQjBz/hw8fBwKG2trfNO7L9q0aCV/PSMjA4uX/YY9+/ciKiYalhaWaB3QEv0D+xb5z4ZL+5/j/K5nSIxLhZWDARr3KQs7FyOVea8ff4W9i+8qpGlqa2DkOj/583vnI3Hl0AuEP3qLdwnpCJxRFdaOJfL1GgqLW0eicW1fNJLj02FWWhd1utnAqoy+2uMeno/H0aXP4VC5BPy/Ly1PP77sBe6fjlPIa+9piObDi/Yc+5ql28LHqTMMdcwQ/jYUu+7Mx/P4Oyrzelj7wq9Md5jrl4SmRAtRSc9xKmwjrr48KM+jo6kH/7L94W5dF/raxohNfoUzT7bgwrOdn+uSClxRmnuYX3LcaVy1alU+VuPLITUxQKdTP+P5sZvY1nwqkiPfwMTVFu9iExTyPd53GQf7LJI/z0hJy7bcv6qPhkTz3ymmFp6l0f7QBDzYfFae9vrSI9xddxJvn0ZC18wQNSd0QrsDP2FFmQEQRWC7o6qj26DSt/440HsBom89g3VVZzRZMQgp8Um4umCvymOOD1mBU0Fr5c81tDTQ/eoc3N9yRp6W8Dwap4LWIu7BK0ACuPeqj1bbR2NdlZGIvv0s36+rICQnv0NZ17Jo27I1howZoTb/85cvMHD49/iqbQf8MmkqzoVcwMTgKbC0sECdmrUBACvWrMKmrVvw8/hJcHZyxq27t/HT1IkoYWCIbp265PclFZg7ZyJwdPVD+PcrBztXI1zc8wwbf76Gb+bVgIGxjspjpHqa6De/hvy5BIqd6rSUDNi7GaN8LSvs++1evta/MAk9H4+zGyLg09MWVmX0cONQDPbOfoJOwa7QM8r6z9HbqFSc3xgOm7KqO5elKhjC72s7+XNNraJ934oKNg3Q3G0gtt+ajedxt1Hb8SsEVp2FOSe7ITE1Til/UtobHA9dg8jEp8iQpcHNqjbae45BYmosHkRdBAA0dxsIZ7Mq2HR9KmKTw+FqXg2t3Ifizbto3I08/ZmvkABg0aJFmDlzJsLDw1GpUiUsWLAA1atXV5m3Xr16+Oeff5TSmzdvjj179gAAevfurTRK7O/vj/379+e4Trm+jWB+E0IU6qhFtdFtkfAsCge//rdD+CbstVK+jJR0JEXE5bjc5Kg3Cs+dxrRF3MNXeP7PLXnajT8O/XvOJ5E489Nf6HFtDowcLRH/KCIXV1E42dUqh9CdF/F472UA76+xXGcf2FRzyfKY1DdJSH2TJH/u3Lo6dE0NcGvlMXnao90hCsecGbcelb5tApuaZYtsp9Gndh341K6T4/ybtm5BSbuSGPnDMABAGacyuHLtKtZsWCfvNF69cQ31ff3gW8cHAFDSzg77Du7Hjds38/4CCpELu5+hUkM7VKxvCwBo2q8cQi9H4/qxV6jVJotolkQCQxNplmV6+toAAOJeJ+d5fQuz6wej4eZrinI+pgAAn562eHrtLe6djIVXgKXKY2QygaO/PYd3GyuE309CSpJyNEhDSwJ9Y+18rXthUtexIy4+243LL/YBAHbcmo1ylrXgXTIAJx6vU8r/OOaqwvMzT7agsl1TOJhUlHcaHUw8cfnlfnnei893oXqpVihlUr7YdBoLU6Rx48aNGDZsGJYuXYoaNWpg3rx58Pf3x71792BlpTwquXXrVqSmpsqfR0dHo1KlSvjqq68U8jVt2hQrV66UP5dKs/6cUqXQfR2TSqW4c0d1iL0wKNOyKiIuhSJg43D0D1+BbpdmwrNvI6V89vU80D98BXrd+RUNFn8DXTPDHJ9DQ1sL5bv54ubKo1nm0dKXwiOwPuIfReDts+hPupbC5uXZeyjVoAJMXN//cbao6AC7um65Gkb27NMQTw9fx9unkSpfl2hooGynOtAy0MWrs8UnwqPOtZvXUbOa4jfY2jVr4dqNG/LnXhUq4fzFCwh7+gQAcO/BfVy+dhV1a+W8c/qlyUiXIfxRAhwrmMrTJBoSOFYww4v7b7I8LvVdBhYPOINF353Blhk3EPks8XNUt1DLSJchKiwZ9h4G8jSJhgQl3Q0Q8TDrzvPlHZHQM9KCm69plnle3U3E6u/vYmPQA5xc/RLvEorunck0JVqwMyqLh9H/fhkWEAiNvoTSJh45KsPZrAosDUohLPaaPO1J3E2Ut6oDI6kFAKCMWWVYGJSSdyrp85ozZw769euHwMBAuLu7Y+nSpdDX18eKFStU5jczM4ONjY38cejQIejr6yt1GqVSqUI+U9Osf69UKbBI47Bhw1SmZ2Rk4JdffoG5uTmA9w2XnZSUFKSkpCikpYsMaEk086aiHzEuY42K3/rj8txduBC8FTbVXFB/fh/IUtNxe/VxAEDYgSt4uO0c4h+/homzDer83BVt947DhtpjczSM7NKmOqQmBri96pjSaxW/84fP9B7QMdRDzN0X+LvJJMjSisYH5MVftkFqpI/ed36FLEMGDU0NnB63HnfXn8zR8Qa2pnBsVhn7us1Tes3cszQ6n5kGLV0dpCa8w652MxBz53keX8GXKzo6GuZm5gpp5mbmSEhMwLt376Crq4uvewYiITERrTq1g6aGJjJkGfj+24Fo0bR5AdU6/yW9SYOQCRiYKA5DG5hoI/ql6o6guZ0+mn/nBisHA6QkpeP8zmdYO+4Svp5THUbmup+j2oXSu7cZEDIoDUPrGWshLjxJ5THh9xNx72Qs2k9yzrJc+wqGcPQuASMLHbyJTMWFv19j35ynaD3OCRoahXfU6lPp6xhDU0MLCamxCukJKTGwNCidxVGAVMsAY+r9DS0NHchEBnbenqvQ8dx1ez7aeo7EmPpbkSFLh4AM227OVOhYFnUykX/TvFT1VaRSqcpIX2pqKi5duoSgoCB5moaGBho1aoSzZ88q5Vdl+fLl6Ny5MwwMDBTSjx8/DisrK5iamqJBgwaYOnWqvL+VEwXWaZw3bx4qVaoEExMThXQhBO7cuQMDA4McDVMHBwdj0qRJCmlN4IamcM/L6spJNCSICAnF6R/XAwAirz6GuWcpVOjfRN5pvL/x31B+9M2niLr+BH1CF8O+ngeeHb2hqlgFHn0aImzfFSS+ilV67e66k3h66DoMbE3hPbwVAjYOx8a6P6qdM/klKNuxNty6+mBvt3mIvvUMVl5O8JsbiMSXsfK2zY57r3pIiUvEw+0XlF6LvfcSayuPgNRYH64dasF/1SBsrjeeHcdcOHDkEPYc2Ifpk6fB2akM7j24h+lzZ8sXxNB7Jcsao2RZY4Xnfwy9gKuHXsK3c5kCrNmXJTU5A8f+eAGf3nbQLZH1nyqXGv+2tVkpXZjZ62LD6Ad4dTcRJd1zPsJT1KWmJ2HBma8h1dSDs7k3mrsNREzyS/lwdC2H9ihl4o7Vl8YgLjkcjmZe7+c0pkQhNPpSwVb+M5Hl45Y7qvoqEyZMwMSJE5XyRkVFISMjA9bW1grp1tbWuHv3rlL+j124cAE3b97E8uXLFdKbNm2Kdu3awcnJCaGhoRg7diyaNWuGs2fPQlMzZ4G2Aus0Tps2Db///jtmz56NBg0ayNO1tbWxatUquLvnrNMXFBSkFLX8zbhnntb1Q4mv4hD9UUcj5s4LuLarmeUx8Y8jkBQZDxMXG7WdxhKlLVG6UQXsaj9T5euZc/jiHr7Cq3P3MSDmT7i0rYF7G07l/mIKGd8ZPXFx+jZ5pzv65lOUcLBAtTHtctRp9AhsiDtr/1EZeZWlpSM+NBwA8PryI9hUdUHlHwJw5Nvf8vQavlTm5uaIjlGc5hAdEw1DA0Po6r6Pjs1eMA9f9+yNZo39AQBlXVzx8lU4lq1eWWQ7jfpG2pBoSJAYl6qQnhiXBoNs5ix+SFNLA9ZOhogNL17zFz+mW0ITEg0g+Y3i72dyfDr0VSyCeROZirdRaTgw/6k8TYj3//3j61voFOwKIyvlhUhGVjrQNdREfEQqSuZP7KBAJaXGI0OWDkMdxWFFQ6kZ3qbEZHmcgEBM0vudNl69fQhLAwf4lemOxzFXoaWhgyZl+2HdlR9xL/IcACA84RFsS7jAx7Fzsek05idVfZXczifMqeXLl6NChQpKi2Y6d+4s/3eFChVQsWJFODs74/jx42jYsGGOyi6wTuOYMWPQsGFDdO/eHS1btkRwcDC0tXM/kVlVeDe/hqYB4OXpuzAra6eQZlrWFm+eqJ5DBwCGJc2gZ15CZeTwYx6B9ZH8+g0e71H/SyqRvP8/TWnRmACupS+FkAmFNJEhgyQHQ0z2fh4wdbXFruVHcnYyDQk0dYpGu+WFSp4VcfKs4mT3sxfOo1KFCvLn7969g4ZEcRq0pqZGkVi5nxVNLQ3YlDFE2M1YlK3+fqGGkAk8uRmLKk1L5qgMmUwg8mkinCub5WdVCz1NLQ1YOOrhxe1EOFZ5v12RkAm8vJMIj4bKbWNiK0WHKYrD0he3vkbaOxlqd7WBgZnqP18JMWl4l5gBfZNCt84zT2SIdLx8cx8u5t648/p9sEACCZzNq+Dsk205LkcikUBL4/1noKZEC1oa2hDio89fyCCRFLqlD/lG5ONCmKyGolWxsLCApqYmIiIUF7hGRETAxsYm22MTExOxYcMGTJ48We15ypQpAwsLCzx8+DDHncYCfTdUq1YNly5dQmRkJKpWrYqbN28W6pXTAHB53i7Y1CyLakHtYOxsg3Jd6qJCv8a4tvj9knVtA134zOgJmxquMHKwRKkGFdBq+xjEPQzHkwNX5eW0PzQBlQY2UyxcIoFH7wa4vfo4RIbiH2JjJ2tUG9MWVlXKoEQpC9jWKoeATSOQnpyKx3uLxrfAR7tCUH1sezg1rwIjB0s4t6mOKkNb4uH28/I8daZ1g/+qwUrHevZpiFfn7iP6lvJq6DrTuqGkjzuMHCxh7lkadaZ1Q6l6Hri7/kS+Xk9BSkpKwt3793D3/vvFPi9evsDd+/fwKvwVAGDe4gUYO+knef6O7TrgxYvnmLNgHh6FPcaGLZtw8Mgh9OjcTZ7Hr64vfl+1HCdOn8SLly9x5PhRrP5rLRqo2f/xS1e9RSlcO/IKN46/QtTzRBxYdh+pKRmoWO/9gq1dC2/j+PpQef5TWx7j8bUYxEUkI/zRW+z69TbeRL5DpYb/ftlMTkhDRNhbRD9/P5cv5mUSIsLeIiFOcc5TUVOxiTnu/hOL+6fiEPsyBSdXv0Jaigxl676Pmh374zkubH7/h1JLWwNm9roKD6m+JrR136dramkg7V0Gzm0MR0RoEt5GpeLF7QQc/PUpjK10UMqz6A5NnwrbhKr2LVDZriksDRzQ2mM4dDT1cPnF+63JOlQYiyZlv5Hn9yvTDS7mVWGqZwtLAwfUdeyEynb+8n0aUzKS8CjmCpqV+w5OZl4w1bNFlZJNUdnOH7cjiu7nZGGlo6MDb29vHDnybxBEJpPhyJEjqFWrVrbHbt68GSkpKejevbva8zx//hzR0dGwtbXNcd0K/KuYoaEh/vzzT2zYsAGNGjVCRkbhWfKuSkRIKHa1m4G607qh5k9fIf7xaxwfulK+WEOWIYNFBQe496wHqYk+El7G4umhazjz01/ISP13WMbY2QZ6Foqb+ZZuVBFGDpa4uUI5Wpb+LhUl67qj8g8toGtqgKSIeDw/cRsb64xFcmTWqzi/JMe+X4baU7qgwaJvoG9lhISXsbjx+yGcm7xZnsfAxhQlSlsoHKdjpA+X9jVxfIjqVWX6Vsbw/3MwDGxNkRqfhKjrT7C16RQ8PXw9X6+nIN26cxt9Bv77R2Pm/PcLylo1b4mfx09CZFQUXoWHy1+3tyuJRbN/xYz5s7F201+wtrLGxKCf5NvtAMDY4aOw8PfFmDozGDGxsbC0sESHNu3x3df/nqcoKl/bGklv0nBy0+P3m3s7GqLT2IryxTFvolIUvuy+S0jHvt/uIjEuFboG2rApY4juU6vAwv7fCekPQqIUNgDfMe82AKBOB0f4dHT6TFf2+TnXMEby23SEbH+NpPh0mJfWRfNhDtA3fv+nKCE6LVeBA4mGBDHP3uH+6TikJsmgb6IFe09DVG1rBU3tohshuxF+FAY6Jmjk2gclpGZ49eYhVoaMkC+OMdGzhsC/UUMdTT20ch8GY11LpGWkIDLxKTZdn4ob4f/u0LHh6iT4l/0GHSv+BH1tI8Qlh+Pggz9w/tmOz359BaUwbbkzbNgw9OrVC1WrVkX16tUxb948JCYmIjAwEMD7G66ULFkSwcHBCsctX74cbdq0UVrckpCQgEmTJqF9+/awsbFBaGgoRo0aBRcXF/j7++e4XhLxcTy6AD1//hyXLl1Co0aNlFb85MZcjfZ5WCuivDEwenVBV6FIWPd0eEFXociISVTeY5Y+TfSbqIKuQpEwrWnBRTbvxO7Lt7LLmzZTn+kjCxculG/u7eXlhV9//RU1ary/aUC9evXg6OiocOOVe/fuwc3NDQcPHkTjxo0VykpOTkabNm1w5coVxMXFwc7ODk2aNMGUKVOUFtxkp1B1GvMKO41UGLHTmDfYacw77DTmHXYa80ZBdhpvxezOt7I9zFrkW9mfU9GN3xMRERFRninwOY1EREREBa0wzWksrNhpJCIiomIvPzf3Lio4PE1EREREajHSSERERMVefm7uXVQw0khEREREajHSSERERMUeF8Kox0gjEREREanFSCMREREVe4w0qsdIIxERERGpxUgjERERFXsyISvoKhR6jDQSERERkVqMNBIREVGxxzvCqMdOIxERERV73NxbPQ5PExEREZFajDQSERFRscctd9RjpJGIiIiI1GKkkYiIiIo9RhrVY6SRiIiIiNRipJGIiIiKPW65o97/2rvzuCir/Q/gnxmWGXZQlE1FVFxABJdE3G+SuFzTtDSvP/erWVoppklXxa1AMzWN1BbT7k00M83K8BZuaQiKOwohLpgwbLLvzJzfH17HJgYfKDaZz/v1el4yz3OeM+ccHuHL9zzPGWYaiYiIiEgSM41ERERk8HhPozQGjURERGTwuLi3NE5PExEREZEkZhqJiIjI4HF6WhozjUREREQkiZlGIiIiMnjMNEpjppGIiIiIJDHTSERERAZPIzQN3YRGj5lGIiIiIpLETCMREREZPN7TKI1BIxERERk8tYZBoxROTxMRERGRJGYaiYiIyODxQRhpzDQSERERkSRmGomIiMjgaXhPoyRmGomIiIhIEjONREREZPC45I40ZhqJiIiISBIzjURERGTw1Mw0SmLQSERERAZPo+GSO1I4PU1EREREkphpJCIiIoPHB2GkMdNIRERERJKYaSQiIiKDx8W9pTHTSERERESSmGkkIiIig6cRfHpaCjONRERERI1MWFgY2rZtC6VSCV9fX8TExFRZdufOnZDJZDqbUqnUKSOEwPLly+Hk5AQzMzP4+/sjMTGxRm1i0EhEREQGT61R19lWU3v37kVgYCCCg4Nx/vx5eHt7IyAgAOnp6VWeY21tjdTUVO12584dnePr1q3D5s2bsW3bNkRHR8PCwgIBAQEoKSmpdrsYNBIREZHB0wh1nW01tWHDBsyaNQvTp0+Hh4cHtm3bBnNzc+zYsaPKc2QyGRwdHbWbg4OD9pgQAps2bcLSpUsxevRodOvWDZ9//jlSUlJw8ODBareLQSMRERFRHSotLUVeXp7OVlpaqrdsWVkZYmNj4e/vr90nl8vh7++PqKioKt+joKAArq6uaN26NUaPHo24uDjtsVu3bkGlUunUaWNjA19f38fW+Ud8EIaonpRE3WvoJjQJ6VYcx9qSdj+zoZvQZGTlFDR0E+gvqssld0JCQrBy5UqdfcHBwVixYkWlspmZmVCr1TqZQgBwcHBAfHy83vo7deqEHTt2oFu3bsjNzcX69evRt29fxMXFoVWrVlCpVNo6/ljnw2PVwaCRiIiIqA4FBQUhMDBQZ59Coai1+v38/ODn56d93bdvX3Tp0gXbt2/H6tWra+19GDQSERGRwavLJXcUCkW1g0R7e3sYGRkhLS1NZ39aWhocHR2rVYeJiQm6d++OGzduAID2vLS0NDg5OenU6ePjU606Ad7TSERERNRomJqaomfPnoiMjNTu02g0iIyM1MkmPo5arcaVK1e0AaKbmxscHR116szLy0N0dHS16wSYaSQiIiKC+k885VxXAgMDMXXqVPTq1Qu9e/fGpk2bUFhYiOnTpwMApkyZAhcXF4SEhAAAVq1ahT59+qBDhw7IycnBu+++izt37uCf//wngAdPVs+fPx9r1qyBu7s73NzcsGzZMjg7O2PMmDHVbheDRiIiIqJGZMKECcjIyMDy5cuhUqng4+ODiIgI7YMsycnJkMsfTRZnZ2dj1qxZUKlUsLOzQ8+ePfHLL7/Aw8NDW2bx4sUoLCzE7NmzkZOTg/79+yMiIqLSIuCPIxNCiNrrZuOwUT6uoZtAVMnM70IauglNwlarhQ3dhCaDT0/XHj49XTt2Tb3SYO/9Qcw/6qzueb1311nd9YmZRiIiIjJ4/OxpaXwQhoiIiIgkMdNIREREBq8uF/duKphpJCIiIiJJzDQSERGRwWtMS+40Vsw0EhEREZEkZhqJiIjI4Gk0fHpaCjONRERERCSJmUYiIiIyeBre0yiJQSMREREZPC65I43T00REREQkiZlGIiIiMnhcckcaM41EREREJImZRiIiIjJ4XHJHGjONRERERCSJmUYiIiIyeFxyRxozjUREREQkiZlGIiIiMngajWjoJjR6DBqJiIjI4GkEH4SRwulpIiIiIpLETCMREREZPE5PS2OmkYiIiIgkMdNIREREBo+ZRmnMNBIRERGRJGYaiYiIyOBpBDONUphpJCIiIiJJzDQSERGRwdNouE6jFGYaiYiIiEgSM41ERERk8Pj0tDQGjURERGTw+CCMNE5PExEREZEkZhqJiIjI4HF6WhozjUREREQkiZlGIiIiMngawSV3pDDTSERERESSmGkkIiIig8d7GqUx00hEREREkphpJCIiIoPHTKM0Bo1ERERk8AQX95bE6WkiIiIiksRMIxERERk8jYZL7khhppGIiIiIJDHTSERERAaPD8JIY6aRiIiIiCQx00hEREQGT8OnpyUxaKwhmVyOPivGo8ukgbBwtEVBSjau7TqG6DVfacss0OzXe+7JxZ8jdv03eo/1CR4Pv+AJOvvux9/DLo/XtK9t2jlg4LtT4dy/M4wUJrgTcRHHXvsERem5tdCz+ucywAO93hiNlj3bwdK5GQ49txZJ38TolPFb+SK8/ukPha05Uk4nIPKVj5BzI7XKOqvz/ekTPB6dJvSHVevmUJdVID32Jk4v3Q1VTGKd9bW+nU+6in8f/RrxvyUhM+8+3p3xFgZ7+VXr3Es3r+GlsCC0c3TF7kWbdY59eep7/Ofo18jKz4a7sxsWjX0Jnq4d66ILjcq1ozm4euQ+inPVsGutgN/EFmjRzkzyvJsxeTj+kQptfCzgP89Fb5nT/05Dwolc+E5oAc9n7Gq76Y1OP7dx+FuHSbBSNENK3g0cuLwByTnX9Jb1choE/45TYW/RCnKZMTIL7+L4jXDE/hahLfNi96Xo3WakznnxaWfw0ZkFddqPxmBIpxcxvOs02JjZ4+79BPwnJgQ3M6/qLTvIfRz6tR+FVrbuAIDbWdfw1YX3dcrvmnpF77l7zr2HH+J21nr76cnDoLGGer05Bt5zAnBk2hZkxd2FQ6/2GLpjHkpzi3Bxy2EAwHanmTrntB3eHUM/eQU39p95bN2ZV5Ox/5mV2teaCrX2a2NzBcYeWY6MS7fx1ZAVAIC+qyZi9KEghPsFAU/gX0gmFgpkXL6Nq59F4tmv36x0vNfiMfB5dQSOTNuCvFvp6LvqRYyNWIZdnq9DXVqut87qfH+yf03BsVc/Qe7NNBibmaL7gr9j7JFl+Mx9Hooz8+q0z/WluKwEHV3c8KzvM1j82TvVPi+/uADBuzfiKXdvZOXn6Bz774WfsengJ1jywlx0de2I8BOH8Or25fgqaBuaWdnWbgcakZsx+Yj5MgN9/68lWrRTIu6nHBzZdA/j1rSFmXXVP0LzM8sRsy8TDu5VB5e3z+cj42YJzG2N6qLpjY6P8xCM9nwN+y6vQ3J2HAa2m4DZfhsRGvkiCsqyK5UvKsvDT7/uQlr+bag1FfBw7IcXu/8LBaXZSMiI1pa7nhaFPRfWaF9XaPT/fGhKercNwMSnFmHXmdVIyriMAI/JeMN/O948OAr5Jfcrle/s+BTO3PoBN9JDUK4uw0ivGXjjme341zfPIbsoHQDw2t7BOud0azUAM/quxLk7P9VHlxoc72mUxnsaa8jZrxOSDp3FrcPnkXcnA4n7z+DOfy/B8akO2jJFaTk6W/tne+PusavIvZX22Lo1FWqd80qy8h+9b7/OsG7bAv+d/gGyriYj62oyjkzbAode7dHmaa86629duh1xAb8sC0fSwRi9x3u8/nfEvP0Vbh46i8wrdxAxdQssnO3QfkzvKuuszvcnIfwUkiMvI/dWGrKu3cXJwJ1Q2FjAvptrrfexofTr0gsvj5iMv3WrXnbxoZB9HyKgxyB4te1c6dju4wcxxi8Az/r6o51jGwS98AqUpgociv6xtprdKF39MRudBlijY38b2Dkr0O//WsLYVIZfT1X9B4ZGI3Di41T0eLY5rFqY6C1TmF2OM+EZGPRPR8iNZHXV/EZlUIeJOHPnEM4mf4+0/Nv46tI6lKtL0dv173rLJ2VdwJXUE0gvuIOsonv4+eaXSM1Lgltzb51yFZoy5Jfe127F5fl662tKhnlMwYnE/fj5xkGk5N7EzqhVKFMXY2CH5/SW3/7zEhxN2Ivk7ASk5t3Cp78EQw45PBx9tWVyS7J0tu6t/4brqhhkFPxWX91qUBqNps62PyMsLAxt27aFUqmEr68vYmL0/64EgI8//hgDBgyAnZ0d7Ozs4O/vX6n8tGnTIJPJdLZhw4bVqE0MGmsoJSoBrZ/2gq27EwDAvpsrnPt3xu2IC3rLm7e0gdvIHri6I1Kybjt3J8z67WPMuPEhhv37dVi1ttceM1aYAAI6GTZ1SRmERsC5f+Vf8E86GzcHWDjZIfmny9p9ZXlFUEUnwtmvU5Xn1fT7IzcxhtfsZ1CSU4iMS7drtQ9PmkPRP+FelgqzAiZWOlZeUY74326gd8dHv6zlcjl6u/vgyp2E+mxmvVJXCGTdKYGzh4V2n0wug3MXC2TcLK7yvIvfZkFpbYSOA2z0HhcagZOfquAVYAc7F0Wtt7sxMpIZo5VNJ/yacVa7T0Dg14yzaGvXtVp1uNv3QgvLNriZpfv/uYN9D6wc9j2WDNmDcd0WwdzEulbb3tgYyY3RtrkH4lIezV4JCMSlnEGHFt6POfMRhZESRnJjFJTpv73JWtkc3q0G4GTigVppM9XM3r17ERgYiODgYJw/fx7e3t4ICAhAenq63vLHjx/HxIkTcezYMURFRaF169YYOnQo7t27p1Nu2LBhSE1N1W7h4eE1ahenp2vobOgBKKzNMe36ZmjUGsiN5Di9dDfid/+st7zH1MEozy/Gja+j9R5/SBWdiCPTP0B2QgosnOzQZ/kLGH9yDT73mo/yghKknvkV5YUl6L92Mk6/9QUgk6F/6P9BbmwEC6emdx+UuaMtgAdZ298rSsuFuYNtledV9/vjNrInRoQvgIm5AoWp2fh66EqdzK6hSc5IQdh3u/DRq6EwNqo8VZpTmAe1RoNmVrrXWjMrW9xOb7pZiNICNYQGMLPWHRMzayPkqMr0nqNKLMavp/IwZnnVmevLEdmQyWXwGGJbm81t1CwUtjCSGyO/VHfqNL/0PlpaVT1WSmMLBAccgrHcFBqhxv7L63UCz/j0M7iSehz3C1PR3MIFIzzmYLbfRrx/chYEmuZizVYKOxjJjZFbkqWzP7ckC042btWqY3zPBcgpzsC1FP23TfVv/yxKyosQayBT00DjehBmw4YNmDVrFqZPnw4A2LZtG77//nvs2LEDS5YsqVT+iy++0Hn9ySefYP/+/YiMjMSUKVO0+xUKBRwdHf90uxo8aPzggw8QExODESNG4MUXX8S///1vhISEQKPRYOzYsVi1ahWMjatuZmlpKUpLS3X2VQg1jGV1c49Qx/F90fkfA3B40iZkxd1FSx83DNo4HYUp2bj2+fFK5T2nD8H13T9XeQ/eQ7/PhGVeuQNV9K+YeXsbOo7vh7gdkSjOzMN349/DkA9no/urIyA0Agnhp5AWmwTB+zC0qvv9uXvsKv7T/Q2Y2VvBa9YzGLl3IcL7LEFxRtO4p7Em1Bo1lv77Xcwe9g+4ttT/sAZVT3mJBic/TUW/KQ5QWun/GZR5uwTXfsrG6OWukMkMY1r6ryitKMJ7x6fC1MgM7i16YXTX15BVeA9J/8s2Xrz3KKhJzU9CSt4NLH1mPzrY90Bi5rmGanajNrLrTPi6DUfokRko1+j/42eA+3OIuvl9lcepZvTFKgqFAgpF5ZmGsrIyxMbGIigoSLtPLpfD398fUVFR1Xq/oqIilJeXo1mzZjr7jx8/jpYtW8LOzg5PP/001qxZg+bNm1e7Hw0aNK5Zswbr1q3D0KFDsWDBAty5cwfvvvsuFixYALlcjo0bN8LExAQrV66sso6QkJBKx4eiM4bBo07aPHDdFJxdewC/7j0NAMi6mgwrV3s8tWRspaDRpX8XNOvsgu9ffK/G71OaW4TsX1Nh2+HRXwTJP17CZ+5zoWxuBVGhRmluEWanfILcvY+/V/JJVKTKAQCYO9ii8H9fP3ht89hp5Op+fyqKSpGbpEJukgqq6ERMS/gAXWcOwdlQw5uKKSotxvW7N/DrvZt49+ttAB78xS2EQJ+Fo7Flzir4uHnASC7H/XzdhxXu5+eguXXTy3Q/pLA0gkwOFOepdfYX56lhblM5KMxLL0NBZgV+2vJoSuhh8uKz2b9i3Jq2SEssRnG+GnsX33xURgPEfJmBuJ+yMX5tu7rpTAMrLM2BWlMBK4XuLzErRTPk/yFj9nsCApmFD7LZKXmJcLBqiyEdpyApSv8tJ/eLUlBQmg17i1ZNNmjML82GWlMBG6XuL3sbZXPkFlc9lgAw3HMqRnrNwLr/zsLd7F/1lunYsgecbdzw4Yk3aq3NT4K6fBBGX6wSHByMFStWVCqbmZkJtVoNBwcHnf0ODg6Ij4+v1vu9+eabcHZ2hr+/v3bfsGHDMHbsWLi5uSEpKQlvvfUWhg8fjqioKBjpmWHSp0GDxp07d2Lnzp0YO3YsLl26hJ49e2LXrl2YNGkSAKBz585YvHjxY4PGoKAgBAYG6uzbbjOlitJ/nbG5olJmT6g1kMkrZww8ZwxB2rkbyLx8p8bvY2KhhG17B1z/T+UnCh9Oo7b+W1eYt7TBzUNnK5V50uXeSkNhajZaD/HSBommVmZw9HXHpW1HqjyvJt+f35PJZTBS6H9goamzUJgjfPEHOvu+Ov09ziVeRui0ILg0c4CJsQk6t+qAs79e1i7do9FocDbxEl7oP1JftU2CkbEMzV2VSLleBNfulgAe3I+YEl+ELn+zrVTexskUz63UnWqNPZCJ8hIN+kxsCYtmJmjvZw1nD3OdMkc2/ob2fR48bNNUqUUFfstNgHuLXriqOgkAkEEG9xa9cOrWVxJnPyKDHMZy0yqP2yhbwNzUBnmlmX+5zY2VWlOB21nX4OHki/N3jwJ4MJYeTn3wU3zV96iN8JyOUd1mYf2Pc3A7S/8yRwAw0H0sbmXGVRlUUs3pi1X0ZRlrQ2hoKPbs2YPjx49DqVRq97/44ovar728vNCtWze0b98ex48fx5AhQ6pVd4MGjSkpKejVqxcAwNvbG3K5HD4+PtrjPXr0QEpKymPr0JferaupaQC4+e059H5rHPKTM5AVdxcturuhx4JRiPvsqE45UyszdHzBDyff2KW3nnE/BuPGwRhcCvsBADDg3Sm4+e055N/JgIVzM/itmACNWoOE8FPaczym/Q33r/+G4ow8OPl1wuBNM3B+03fI/vXxY9RYmVgodTKp1m4t0cK7LUruFyD/bibOv/8dfP/1PHISU5F7Kx19V01EYUq2ztPWfxxHqe+PsbkCvv8ah6RDZ1GYmgMzeyt4zx0GS5dmSNxXvbT/k6CotBh3Mx+tZ5mSlYaEezdhY24JR7uW+OC7XcjIzcLKSYGQy+Xo4KQb6DSztIWpsanO/n8MHoOVuzeiS+sO8HTtiPAT36C4rASjfP3RlHV9xg4/71DB3lWBFm4PltypKNWgY78HD1uc+DQVFrbG6DWuBYxN5JUebDE1f/Dz6OF+I0sjKC11f0bJjWQwtzGGjWPVwVBTcOJGOCb2WIa7OfFIzo7DoPYvwtRIiZjk7wAAE3ssR15xBr6/vhUAMMR9Cu7mXEdm4T0Yy03QxaEverUehq8urQMAmBqZIaDTTFxOPYa8kizYW7TC3z3nIrPwN8SnP/4+8iddxLXPMav/27iVFYebmVcQ0GUyFMZm+PnGQQDA7P5vI7soHfvOvw8AGNF1Bsb6zMW2k28is+CeNktZUlGE0opHD3UpTSzQ2/UZhJ9bX+99amh1mWmsaipaH3t7exgZGSEtTXcWMS0tTfJ+xPXr1yM0NBQ//fQTunXr9tiy7dq1g729PW7cuPFkBI2Ojo64du0a2rRpg8TERKjValy7dg2enp4AgLi4OLRs2bIhm1jJsdc+Qd/VE/F02GyYt7RGQUo2rnz0I86s2qdTrtOL/QGZDPG/C/p+z6a9I8zsrbSvrVyaY8TuBVA2t0JxRh5STl3HHr8gnXUDm3VyQf93JkHZzBJ5tzMQ885+nN/4bd10tB449GqPF46t0r4evOHBDb9xO4/hvzM+wLl1B2FioYT/9jlQ2Fog5VQ8vh6+Wuf+0D+Oo9T3R6g1sOvkglFfDYbS3holWflIO3sDXw5ciqxrd+up53Xv+t0bmBP2lvb1xm8+BQCMfOpprPjHAmTm3YcqO6NGdQ7tPgA5BbnYHvEFsvKy0dGlHTa/tBLNrZru9DQAtOtthZKCCpz/JgvFeWo0a63A0PkuMLN58OOzMKuC9yZW08WUSFgq7DCs8z9hrWiOe3mJ+OjMAhSUPphRsTNzgBCPHl4xNVJiXLdFsDVriXJ1KdIK7uCL2BW4mPJgNQohNHCyaY9ebYbDzMQKeSWZSEiPxg/xH0HdxNdqjLl9BNbKZhjrMxc2ZvZIvh+P9T/NQd7/pvqbWTjpPNjxdKfxMDEyxat/26hTz4GLH+Lgpa3a133aDgdkMpy59UP9dIQqMTU1Rc+ePREZGYkxY8YAeDCzExkZiXnz5lV53rp16/D222/jyJEj2oTc4/z222/IysqCk5NTtdsmE6LhHhdatmwZtm/fjtGjRyMyMhITJkzA7t27ERQUBJlMhrfffhvPP/88NmzYUKN6N8rH1VGLif68md+FNHQTmoStVgsbuglNRtr9pjuFW9+ycgoauglNQlWfSlMfXthed59ute+lmk317927F1OnTsX27dvRu3dvbNq0CV9++SXi4+Ph4OCAKVOmwMXFBSEhD36vrF27FsuXL8fu3bvRr18/bT2WlpawtLREQUEBVq5ciXHjxsHR0RFJSUlYvHgx8vPzceXKlWpnQRs007hy5UqYmZkhKioKs2bNwpIlS+Dt7Y3FixejqKgIo0aNwurVqxuyiURERGQAGtMnwkyYMAEZGRlYvnw5VCoVfHx8EBERoX04Jjk5GXL5o6W2t27dirKyMjz//PM69Tx82MbIyAiXL1/Grl27kJOTA2dnZwwdOhSrV6+u0b2VDZpprCvMNFJjxExj7WCmsfYw01h7mGmsHQ2ZaRy31b3O6t7/cmKd1V2fGnydRiIiIqKG1pgyjY0VP0aQiIiIiCQx00hEREQGrzF9jGBjxUwjEREREUlippGIiIgMHu9plMZMIxERERFJYqaRiIiIDN7vPoyIqsCgkYiIiAwep6elcXqaiIiIiCQx00hEREQGjyvuSGOmkYiIiIgkMdNIREREBk/DB2EkMdNIRERERJKYaSQiIiKDJ/j0tCRmGomIiIhIEjONREREZPB4T6M0Bo1ERERk8PiJMNI4PU1EREREkphpJCIiIoOn4erekphpJCIiIiJJzDQSERGRweM9jdKYaSQiIiIiScw0EhERkcHjkjvSmGkkIiIiIknMNBIREZHB48cISmPQSERERAaP09PSOD1NRERERJKYaSQiIiKDx7W9pTHTSERERESSmGkkIiIig6fhgzCSmGkkIiIiIknMNBIREZHB48cISmOmkYiIiIgkMdNIREREBo/rNEpj0EhEREQGj9PT0jg9TURERESSmGkkIiIig6fh6t6SmGkkIiIiIknMNBIREZHB4z2N0phpJCIiIiJJzDQSERGRweOSO9KYaSQiIiIiScw0EhERkcETGj49LYWZRiIiIiKSxEwjERERGTze0yiNQSMREREZPK7tLY3T00RERESNTFhYGNq2bQulUglfX1/ExMQ8tvy+ffvQuXNnKJVKeHl54fDhwzrHhRBYvnw5nJycYGZmBn9/fyQmJtaoTQwaiYiIyOBpNKLOtprau3cvAgMDERwcjPPnz8Pb2xsBAQFIT0/XW/6XX37BxIkTMXPmTFy4cAFjxozBmDFjcPXqVW2ZdevWYfPmzdi2bRuio6NhYWGBgIAAlJSUVLtdDBqJiIiIGpENGzZg1qxZmD59Ojw8PLBt2zaYm5tjx44desu///77GDZsGBYtWoQuXbpg9erV6NGjBz744AMAD7KMmzZtwtKlSzF69Gh069YNn3/+OVJSUnDw4MFqt4tBIxERERk8oam7rSbKysoQGxsLf39/7T65XA5/f39ERUXpPScqKkqnPAAEBARoy9+6dQsqlUqnjI2NDXx9fausUx8+CENERERUh0pLS1FaWqqzT6FQQKFQVCqbmZkJtVoNBwcHnf0ODg6Ij4/XW79KpdJbXqVSaY8/3FdVmepokkHjAs3+hm6CpNLSUoSEhCAoKEjvRUPVw3GsPU/KWL6Jbxu6CZKelLFs7DiOtYdjKS3+45Q6q3vFihVYuXKlzr7g4GCsWLGizt6zLnB6uoGUlpZi5cqVlf7yoJrhONYejmXt4VjWDo5j7eFYNqygoCDk5ubqbEFBQXrL2tvbw8jICGlpaTr709LS4OjoqPccR0fHx5Z/+G9N6tSHQSMRERFRHVIoFLC2ttbZqsr4mpqaomfPnoiMjNTu02g0iIyMhJ+fn95z/Pz8dMoDwI8//qgt7+bmBkdHR50yeXl5iI6OrrJOfZrk9DQRERHRkyowMBBTp05Fr1690Lt3b2zatAmFhYWYPn06AGDKlClwcXFBSEgIAOD111/HoEGD8N5772HkyJHYs2cPzp07h48++ggAIJPJMH/+fKxZswbu7u5wc3PDsmXL4OzsjDFjxlS7XQwaiYiIiBqRCRMmICMjA8uXL4dKpYKPjw8iIiK0D7IkJydDLn80Wdy3b1/s3r0bS5cuxVtvvQV3d3ccPHgQXbt21ZZZvHgxCgsLMXv2bOTk5KB///6IiIiAUqmsdrtkQvCDcxoCb0quHRzH2sOxrD0cy9rBcaw9HEuqDQwaiYiIiEgSH4QhIiIiIkkMGomIiIhIEoNGIiIiIpLEoJGIiIiIJDForEMnT57EqFGj4OzsDJlMhoMHD+ocF0Jg+fLlcHJygpmZGfz9/ZGYmNgwjW1kamPs7t+/j0mTJsHa2hq2traYOXMmCgoK6rEX9a++xu3y5csYMGAAlEolWrdujXXr1tV11+rVihUrIJPJdLbOnTtrj5eUlGDu3Llo3rw5LC0tMW7cuEqftJCcnIyRI0fC3NwcLVu2xKJFi1BRUaFT5vjx4+jRowcUCgU6dOiAnTt31kf36lRjugb37duHzp07Q6lUwsvLC4cPH671/taVkJAQPPXUU7CyskLLli0xZswYJCQk6JSpz+swLCwMbdu2hVKphK+vL2JiYmq9z/QEEFRnDh8+LP71r3+Jr7/+WgAQBw4c0DkeGhoqbGxsxMGDB8WlS5fEs88+K9zc3ERxcXHDNLgRqY2xGzZsmPD29hZnzpwRP//8s+jQoYOYOHFiPfekftXHuOXm5goHBwcxadIkcfXqVREeHi7MzMzE9u3b66ubdS44OFh4enqK1NRU7ZaRkaE9PmfOHNG6dWsRGRkpzp07J/r06SP69u2rPV5RUSG6du0q/P39xYULF8Thw4eFvb29CAoK0pa5efOmMDc3F4GBgeLatWtiy5YtwsjISERERNRrX2tbY7kGT58+LYyMjMS6devEtWvXxNKlS4WJiYm4cuVKnY9BbQgICBCfffaZuHr1qrh48aIYMWKEaNOmjSgoKNCWqa/rcM+ePcLU1FTs2LFDxMXFiVmzZglbW1uRlpZWP4NBjQaDxnryxx+eGo1GODo6infffVe7LycnRygUChEeHt4ALWy8/szYXbt2TQAQZ8+e1Zb54YcfhEwmE/fu3au3tjekuhq3Dz/8UNjZ2YnS0lJtmTfffFN06tSpjntUf4KDg4W3t7feYzk5OcLExETs27dPu+/69esCgIiKihJCPAic5HK5UKlU2jJbt24V1tbW2nFbvHix8PT01Kl7woQJIiAgoJZ703Aa8hocP368GDlypE57fH19xUsvvVSrfawv6enpAoA4ceKEEKJ+r8PevXuLuXPnal+r1Wrh7OwsQkJCar+j1KhxerqB3Lp1CyqVCv7+/tp9NjY28PX1RVRUVAO2rPGrzthFRUXB1tYWvXr10pbx9/eHXC5HdHR0vbe5MaitcYuKisLAgQNhamqqLRMQEICEhARkZ2fXU2/qXmJiIpydndGuXTtMmjQJycnJAIDY2FiUl5frjGPnzp3Rpk0bnXH08vLSfnoD8GCM8vLyEBcXpy3z+zoelmnK///r8xpsauObm5sLAGjWrBmA+rsOy8rKEBsbq1NGLpfD39//iR1L+vMYNDYQlUoFADr/mR++fniM9KvO2KlUKrRs2VLnuLGxMZo1a2aw41tb46ZSqfTW8fv3eNL5+vpi586diIiIwNatW3Hr1i0MGDAA+fn5UKlUMDU1ha2trc45fxxHqTGqqkxeXh6Ki4vrqGcNqz6vwarKPInXqEajwfz589GvXz/tx8LV13WYmZkJtVrdZMaS/hp+9jQR0R8MHz5c+3W3bt3g6+sLV1dXfPnllzAzM2vAlpEhmjt3Lq5evYpTp041dFPIwDHT2EAcHR0BoNKTbmlpadpjpF91xs7R0RHp6ek6xysqKnD//n2DHd/aGjdHR0e9dfz+PZoaW1tbdOzYETdu3ICjoyPKysqQk5OjU+aP4yg1RlWVsba2brKBaX1eg1WVedKu0Xnz5uG7777DsWPH0KpVK+3++roO7e3tYWRk1CTGkv46Bo0NxM3NDY6OjoiMjNTuy8vLQ3R0NPz8/BqwZY1fdcbOz88POTk5iI2N1ZY5evQoNBoNfH19673NjUFtjZufnx9OnjyJ8vJybZkff/wRnTp1gp2dXT31pn4VFBQgKSkJTk5O6NmzJ0xMTHTGMSEhAcnJyTrjeOXKFZ3g58cff4S1tTU8PDy0ZX5fx8MyTfn/f31eg0/6+AohMG/ePBw4cABHjx6Fm5ubzvH6ug5NTU3Rs2dPnTIajQaRkZFPzFhSLWroJ3Gasvz8fHHhwgVx4cIFAUBs2LBBXLhwQdy5c0cI8WDpCVtbW/HNN9+Iy5cvi9GjR3PJnf+pjbEbNmyY6N69u4iOjhanTp0S7u7uTX7JnfoYt5ycHOHg4CAmT54srl69Kvbs2SPMzc2b1JI7CxcuFMePHxe3bt0Sp0+fFv7+/sLe3l6kp6cLIR4sddKmTRtx9OhRce7cOeHn5yf8/Py05z9c6mTo0KHi4sWLIiIiQrRo0ULvUieLFi0S169fF2FhYU1iyZ3Gcg2ePn1aGBsbi/Xr14vr16+L4ODgJ2rJnZdfflnY2NiI48eP6yz9VFRUpC1TX9fhnj17hEKhEDt37hTXrl0Ts2fPFra2tjpPZZNhYNBYh44dOyYAVNqmTp0qhHiw/MSyZcuEg4ODUCgUYsiQISIhIaFhG91I1MbYZWVliYkTJwpLS0thbW0tpk+fLvLz8xugN/Wnvsbt0qVLon///kKhUAgXFxcRGhpaX12sFxMmTBBOTk7C1NRUuLi4iAkTJogbN25ojxcXF4tXXnlF2NnZCXNzc/Hcc8+J1NRUnTpu374thg8fLszMzIS9vb1YuHChKC8v1ylz7Ngx4ePjI0xNTUW7du3EZ599Vh/dq1ON6Rr88ssvRceOHYWpqanw9PQU33//fZ31u7bpG0MAOtdIfV6HW7ZsEW3atBGmpqaid+/e4syZM3XRbWrkZEIIUR8ZTSIiIiJ6cvGeRiIiIiKSxKCRiIiIiCQxaCQiIiIiSQwaiYiIiEgSg0YiIiIiksSgkYiIiIgkMWgkIiIiIkkMGomoTty+fRsymQwXL15s6KZoxcfHo0+fPlAqlfDx8anX9x48eDDmz59fr+9JRFSbGDQSNVHTpk2DTCZDaGiozv6DBw9CJpM1UKsaVnBwMCwsLJCQkFDp83aJiOjxGDQSNWFKpRJr165FdnZ2Qzel1pSVlf3pc5OSktC/f3+4urqiefPmtdgqIqKmj0EjURPm7+8PR0dHhISEVFlmxYoVlaZqN23ahLZt22pfT5s2DWPGjME777wDBwcH2NraYtWqVaioqMCiRYvQrFkztGrVCp999lml+uPj49G3b18olUp07doVJ06c0Dl+9epVDB8+HJaWlnBwcMDkyZORmZmpPT548GDMmzcP8+fPh729PQICAvT2Q6PRYNWqVWjVqhUUCgV8fHwQERGhPS6TyRAbG4tVq1ZBJpNhxYoVeusZPHgwXn31VcyfPx92dnZwcHDAxx9/jMLCQkyfPh1WVlbo0KEDfvjhB53zTpw4gd69e0OhUMDJyQlLlixBRUWF3vcAgA8//BDu7u5QKpVwcHDA888/X2VZIqLGgEEjURNmZGSEd955B1u2bMFvv/32l+o6evQoUlJScPLkSWzYsAHBwcH4+9//Djs7O0RHR2POnDl46aWXKr3PokWLsHDhQly4cAF+fn4YNWoUsrKyAAA5OTl4+umn0b17d5w7dw4RERFIS0vD+PHjderYtWsXTE1Ncfr0aWzbtk1v+95//3289957WL9+PS5fvoyAgAA8++yzSExMBACkpqbC09MTCxcuRGpqKt54440q+7pr1y7Y29sjJiYGr776Kl5++WW88MIL6Nu3L86fP4+hQ4di8uTJKCoqAgDcu3cPI0aMwFNPPYVLly5h69at+PTTT7FmzRq99Z87dw6vvfYaVq1ahYSEBERERGDgwIHV+0YQETUUQURN0tSpU8Xo0aOFEEL06dNHzJgxQwghxIEDB8Tv/+sHBwcLb29vnXM3btwoXF1ddepydXUVarVau69Tp05iwIAB2tcVFRXCwsJChIeHCyGEuHXrlgAgQkNDtWXKy8tFq1atxNq1a4UQQqxevVoMHTpU573v3r0rAIiEhAQhhBCDBg0S3bt3l+yvs7OzePvtt3X2PfXUU+KVV17Rvvb29hbBwcGPrWfQoEGif//+lfo1efJk7b7U1FQBQERFRQkhhHjrrbdEp06dhEaj0ZYJCwsTlpaW2jEbNGiQeP3114UQQuzfv19YW1uLvLw8yX4RETUWzDQSGYC1a9di165duH79+p+uw9PTE3L5ox8ZDg4O8PLy0r42MjJC8+bNkZ6ernOen5+f9mtjY2P06tVL245Lly7h2LFjsLS01G6dO3cG8OD+w4d69uz52Lbl5eUhJSUF/fr109nfr1+/P9Xnbt26VerX7/vq4OAAANq+Xr9+HX5+fjoPGPXr1w8FBQV6M7zPPPMMXF1d0a5dO0yePBlffPGFNmtJRNRYMWgkMgADBw5EQEAAgoKCKh2Ty+UQQujsKy8vr1TOxMRE57VMJtO7T6PRVLtdBQUFGDVqFC5evKizJSYm6kzXWlhYVLvO2iDV14fBYU36+ntWVlY4f/48wsPD4eTkhOXLl8Pb2xs5OTl/us1ERHWNQSORgQgNDcW3336LqKgonf0tWrSASqXSCRxrc23FM2fOaL+uqKhAbGwsunTpAgDo0aMH4uLi0LZtW3To0EFnq0mgaG1tDWdnZ5w+fVpn/+nTp+Hh4VE7HXmMLl26ICoqSmcMT58+DSsrK7Rq1UrvOcbGxvD398e6detw+fJl3L59G0ePHq3zthIR/VkMGokMhJeXFyZNmoTNmzfr7B88eDAyMjKwbt06JCUlISwsrNKTwX9FWFgYDhw4gPj4eMydOxfZ2dmYMWMGAGDu3Lm4f/8+Jk6ciLNnzyIpKQlHjhzB9OnToVara/Q+ixYtwtq1a7F3714kJCRgyZIluHjxIl5//fVa60tVXnnlFdy9exevvvoq4uPj8c033yA4OBiBgYE6U/oPfffdd9i8eTMuXryIO3fu4PPPP4dGo0GnTp3qvK1ERH8Wg0YiA7Jq1apKU6pdunTBhx9+iLCwMHh7eyMmJuaxTxbXVGhoKEJDQ+Ht7Y1Tp07h0KFDsLe3BwBtdlCtVmPo0KHw8vLC/PnzYWtrqzfYepzXXnsNgYGBWLhwIby8vBAREYFDhw7B3d291vpSFRcXFxw+fBgxMTHw9vbGnDlzMHPmTCxdulRveVtbW3z99dd4+umn0aVLF2zbtg3h4eHw9PSs87YSEf1ZMvHHm5mIiIiIiP6AmUYiIiIiksSgkYiIiIgkMWgkIiIiIkkMGomIiIhIEoNGIiIiIpLEoJGIiIiIJDFoJCIiIiJJDBqJiIiISBKDRiIiIiKSxKCRiIiIiCQxaCQiIiIiSQwaiYiIiEjS/wNLF1IjlmxNAQAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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bE8uXLERcXh7p16+LixYtYu3YtfH195ZPE8lNe3itEVDiw0UhFRlBQEHR1dZXW+MukoaGBFi1aICgoCK9fv0bt2rVRqlQpPHv2TKmXEQD8/PwQHh6OZcuW4fDhw3BxccGGDRuwbds2tQtBu7q64syZM/D390dgYCBkMhlq1KiBDRs2FMp3YXt6euLQoUMYPXo0fv31V5QqVQq//fYb7t69q/Re6/Hjx6Ns2bKYO3cuJk+eDOD9pIjGjRtn2WBXpUyZMliwYAHGjBmD+/fvw8HBAVu2bMlyFm9esra2xtKlSxEYGIg+ffogIyMDJ0+e/KxG4+fcL6qUKlUKXbp0wfHjx7F+/XpoaWmhfPny2Lp1K9q3b6/2+BUrVsDR0RFr1qzBrl27YG1tDX9//y/6ZqS8uleIqHCQiLwcHU5ERYqvry9u376Nhw8f5mm59vb2cHNzw/79+/O0XCIiyj8c00hEAN6PCf3Qw4cPcfDgQdSrV69gAiIiokKFj6eJCADg6OgIPz8/ODo64smTJ1iyZAl0dHTULh5ORETfBjYaiQgA0LRpU2zatAnh4eGQSqWoVasWpk2bluXizERE9G3hmEYiIiIiUotjGomIiIhILTYaiYiIiEgtNhqpyJNIJJg0aVK+nmPNmjWQSCQKb4DJL6dOnYJEIsH27dvz/Vx5wd7eHn5+fnlWXlhYGCQSCdasWZNnZRYmfn5++f4axW9FUb9XiL40NhopW5mNIYlEgrNnzyrtF0KgVKlSkEgkaNmyZQFEWHRt3LhR6TVwRJn8/Pzk35sfbuXLl1fKK5PJMGPGDDg4OEBXVxeVKlXCpk2bVJYrk8mwZMkSeHh4QE9PD+bm5mjQoAH++++/LGMJCgqCRCKBoaFhnl1fYbBv3z5oaGggPDy8oEMhKhQ4e5pyRFdXV/7e5A/9/fffeP78OaRSaQFFpl5ycjK0tL6+W33jxo24desWhg8fXtChUCEllUqxYsUKhTQTExOlfD///DP++OMP9OvXD9WqVcOePXvQtWtXSCQSpVcS9u7dG0FBQejRowcGDx6MxMREXLt2DZGRkSpjSEhIwNixY+Xv8y5KDhw4AE9Pz3x/FzrR1+Lr+01KBaJ58+bYtm0b/vzzT4UG2MaNG+Hp6Yno6OgCjC57urq6BR1CoZKUlAR9ff2CDoPygJaWFrp165ZtnhcvXmD27NkYNGgQFi5cCADo27cv6tatizFjxuD777+HpqYmAGDr1q1Yu3at/B3aOTFlyhQYGRmhfv362L1792ddT2Fz8OBB9O7du6DDICo0+HiacqRLly54/fo1jh49Kk9LTU3F9u3b0bVrV5XHzJo1C7Vr14a5uTn09PTg6empNA5v9erVkEgkWLVqlUL6tGnTIJFIcPDgwWzjunz5Mpo0aYLixYtDT08PDg4OSj/kPx7TOGnSJEgkEoSEhMDPzw+mpqYwMTFBr169kJSUpHBscnIyhg4diuLFi8PIyAitW7fGixcvcjxO8tChQ/D29oaBgQGMjIzQokUL3L59W+1x9erVw4EDB/DkyRP5Y8ePx7nJZDJMnToVtra20NXVRcOGDRESEqJUjpubG65cuQIfHx/o6+tjwoQJAICUlBQEBATA2dkZUqkUpUqVwtixY5GSkqJQxurVq9GgQQNYWlpCKpXCxcUFS5YsUYpZCIEpU6bA1tYW+vr6qF+/fpbXGhcXh+HDh6NUqVKQSqVwdnbG9OnTIZPJlPL5+fnBxMQEpqam6NmzJ+Li4tTWHwCkpaVh8uTJKFOmDHR1dWFubo46deoo3MN+fn4wNDTEo0eP0KRJExgYGKBEiRL47bff8PFqZDKZDPPmzYOrqyt0dXVhZWWFAQMGIDY2VuncOf267969G25ubtDV1YWbmxt27dqVo2v7UEZGBt68eZPl/j179iAtLQ0DBw6Up0kkEvz00094/vw5zp8/L0+fM2cOqlevjrZt20ImkyExMTHbcz98+BBz587FnDlzctWbn1nvT58+RcuWLWFoaIiSJUti0aJFAICbN2+iQYMGMDAwgJ2dHTZu3KhUxqNHj/D999/DzMwM+vr6qFmzJg4cOKD23OHh4ejVqxdsbW0hlUphY2ODNm3aKI1HvnnzJp49e4YWLVrI0xYsWABXV1fo6+ujWLFiqFq1qsrYiIosQZSN1atXCwDi0qVLonbt2qJ79+7yfbt37xYaGhrixYsXws7OTrRo0ULhWFtbWzFw4ECxcOFCMWfOHFG9enUBQOzfv18hX8uWLYWJiYl4+vSpEEKIGzduCB0dHdGnT59sY4uIiBDFihUTZcuWFTNnzhR//fWX+Pnnn0WFChUU8gEQAQEB8s8BAQECgKhcubJo166dWLx4sejbt68AIMaOHatwbMeOHQUA0b17d7Fo0SLRsWNH4e7urlRmZj09fvxYnrZu3TohkUhE06ZNxYIFC8T06dOFvb29MDU1VcinypEjR4SHh4coXry4WL9+vVi/fr3YtWuXEEKIkydPyuP39PQUc+fOFZMmTRL6+vqievXqCuXUrVtXWFtbCwsLCzFkyBCxbNkysXv3bpGRkSEaN24s9PX1xfDhw8WyZcvE4MGDhZaWlmjTpo1CGdWqVRN+fn5i7ty5YsGCBaJx48YCgFi4cKFCvl9++UUAEM2bNxcLFy4UvXv3FiVKlBDFixcXPXv2lOdLTEwUlSpVEubm5mLChAli6dKlokePHkIikYhhw4bJ88lkMuHj4yM0NDTEwIEDxYIFC0SDBg1EpUqVBACxevXqbOtwwoQJQiKRiH79+om//vpLzJ49W3Tp0kX88ccf8jw9e/YUurq6okyZMqJ79+5i4cKFomXLlgKA+PXXXxXK69u3r9DS0hL9+vUTS5cuFePGjRMGBgaiWrVqIjU1VZ4vp1/3w4cPCw0NDeHm5ibmzJkjfv75Z2FiYiJcXV2FnZ1dtteWGbtEIhH6+voCgChWrJgYOHCgePv2rVLcBgYGQiaTKaSHhIQIAOLPP/8UQggRHx8vJBKJGDRokPD39xeGhoYCgHBwcBBbtmxRGUPz5s1FkyZN5PEYGBiojTszr66urnBxcRE//vijWLRokahdu7b861qiRAkxZswYsWDBAuHq6io0NTXFo0eP5MeHh4cLKysrYWRkJH7++WcxZ84c4e7uLjQ0NMTOnTvl+R4/fqx0r9SuXVuYmJiIX375RaxYsUJMmzZN1K9fX/z9998KMf7xxx/C0tJSXm/Lly8XAESHDh3EsmXLxPz580WfPn3E0KFDc3TNREUBG42UrQ8bjQsXLhRGRkYiKSlJCCHE999/L+rXry+EECobjZn5MqWmpgo3NzfRoEEDhfRXr14JMzMz8d1334mUlBRRuXJlUbp0aREfH59tbLt27ZLHlp2sGo29e/dWyNe2bVthbm4u/3zlyhUBQAwfPlwhn5+fn9pG49u3b4Wpqano16+fwrHh4eHCxMREKV2VFi1aqGw8ZDYaK1SoIFJSUuTp8+fPFwDEzZs35Wl169YVAMTSpUsVyli/fr3Q0NAQZ86cUUhfunSpACD++ecfedrHX0chhGjSpIlwdHSUf46MjBQ6OjqiRYsWCo2TCRMmCAAKjcbff/9dGBgYiAcPHiiUOX78eKGpqSn/42H37t0CgJgxY4Y8T3p6uvD29s5Ro9Hd3V3pnvxYz549BQAxZMgQeZpMJhMtWrQQOjo6IioqSgghxJkzZwQAERQUpHB8cHCwQnpuvu4eHh7CxsZGxMXFydOOHDkiAOSo0Th+/Hgxbtw4sWXLFrFp0yb5tXh5eYm0tDR5vhYtWih8rTIlJiYKAGL8+PFCCCGuXr0qAAhzc3NhZWUlFi9eLIKCgkT16tWFRCIRhw4dUjh+//79QktLS9y+fVtel7lpNAIQ06ZNk6fFxsYKPT09IZFIxObNm+Xp9+7dU/p+Gz58uACgcP++fftWODg4CHt7e5GRkSGEUG40xsbGCgBi5syZamP09vZWuG/btGkjXF1dc3R9REUVH09TjnXs2BHJycnYv38/3r59i/3792f5aBoA9PT05P+OjY1FfHw8vL29cfXqVYV81tbWWLRoEY4ePQpvb29cv34dq1atgrGxcbbxmJqaAgD279+PtLS0XF/Pjz/+qPDZ29sbr1+/lj/qCw4OBgCFx3oAMGTIELVlHz16FHFxcejSpQuio6Plm6amJmrUqIGTJ0/mOt6P9erVCzo6OgrxA+8f231IKpWiV69eCmnbtm1DhQoVUL58eYX4GjRoAAAK8X34dYyPj0d0dDTq1q2LR48eIT4+HgBw7NgxpKamYsiQIZBIJPL8qibxbNu2Dd7e3ihWrJjCuRs1aoSMjAycPn0awPvxZFpaWvjpp5/kx2pqauao/oH398ft27fx8OFDtXkHDx4s/7dEIsHgwYORmpqKY8eOyWM2MTHBd999pxCzp6cnDA0N5fWV06/7q1evcP36dfTs2VNh4sp3330HFxeXHF1fYGAg/vjjD3Ts2BGdO3fGmjVrMHXqVPzzzz8Kw0CSk5NVTlTLHOubnJwM4P2EFgB4/fo19uzZg59++gldu3bF8ePHYW5ujilTpsiPTU1NxYgRI/Djjz/mOF5V+vbtK/+3qakpypUrBwMDA3Ts2FGeXq5cOZiamirc1wcPHkT16tUVJuYZGhqif//+CAsLw507d1SeT09PDzo6Ojh16pTKYQWZ4uLicP78eYVH06ampnj+/DkuXbr0SddKVBRwIgzlmIWFBRo1aoSNGzciKSkJGRkZ6NChQ5b59+/fjylTpuD69esK4+Q+bFRk6ty5MzZs2IADBw6gf//+aNiwodp46tati/bt22Py5MmYO3cu6tWrB19fX3Tt2jVHs7lLly6t8LlYsWIA3jdwjY2N8eTJE2hoaMDBwUEhn7Ozs9qyMxsqmY2wj2U2iJOTk+UNr0w5namZXfwfKlmypELjMjO+u3fvwsLCQmXZH86U/eeffxAQEIDz588rjfmMj4+HiYkJnjx5AgBK76m2sLCQx/XhuW/cuKH23E+ePIGNjY3SMi7lypVTedzHfvvtN7Rp0wZly5aFm5sbmjZtiu7du6NSpUoK+TQ0NODo6KiQVrZsWQCQj3N7+PAh4uPjYWlpmW3MOf26Z1VfwPvr+/gPq5waMWIEfv31Vxw7dkw+K1pPT09pnCoAvHv3Tr7/w/86ODigRo0a8nyGhoZo1aoVNmzYgPT0dGhpaWHu3LmIjo7G5MmTPylO4H2j9eN7wMTEBLa2tko/I0xMTBTu6ydPnijEmKlChQry/W5ubkr7pVIppk+fjlGjRsHKygo1a9ZEy5Yt0aNHD4Xvu8OHDwMAGjduLE8bN24cjh07hurVq8PZ2RmNGzdG165d4eXl9QlXT/R1YqORcqVr167o168fwsPD0axZM3lv38fOnDmD1q1bw8fHB4sXL4aNjQ20tbWxevVqlQPHX79+jcuXLwMA7ty5A5lMBg2N7DvCMxe4/vfff7Fv3z4cPnwYvXv3xuzZs/Hvv/+qXTMuc8box0QevI49c0LH+vXrVTYCMycNbNmyRakXMKfnz2n8H/YUfhhfxYoVMWfOHJVllCpVCgAQGhqKhg0bonz58pgzZw5KlSoFHR0dHDx4EHPnzlWauJITMpkM3333HcaOHatyf2aD7XP5+PggNDQUe/bswZEjR7BixQrMnTsXS5cuVejhygmZTAZLS0sEBQWp3J/Z+Mnp1z2/ZK6rGBMTI0+zsbHByZMnIYRQaIy9evUKAFCiRAmF/1pZWSmVa2lpibS0NPnEmClTpmDgwIF48+aNvGc+ISEBQgiEhYVBX18/ywZ2pqzu3/z8vgTe9363atUKu3fvxuHDh/Hrr78iMDAQJ06cQOXKlQG878n08vJS6AWuUKEC7t+/j/379yM4OBg7duzA4sWLMXHixM9qPBN9TdhopFxp27YtBgwYgH///RdbtmzJMt+OHTugq6uLw4cPK/T6rV69WmX+QYMG4e3btwgMDIS/vz/mzZuHkSNH5iimmjVrombNmpg6dSo2btyIH374AZs3b851w+BjdnZ2kMlkePz4sUKP0MczlFVxcnIC8P6XbaNGjbLM16RJE4XZvB9S1SObV5ycnPDff/+hYcOG2Z5n3759SElJwd69exV6Nj9+vG5nZwfgfU/bh712UVFRSj2fTk5OSEhIyLZeMss8fvw4EhISFP4AuH//vvoL/H9mZmbo1asXevXqhYSEBPj4+GDSpEkK94ZMJsOjR48UGqsPHjwAAPmMdScnJxw7dgxeXl4qG+EfXhug/uv+YX19LDfX97G3b98iOjpaoQfPw8MDK1aswN27dxUeJV+4cEG+H3jfaLS2tsaLFy+Uyn358iV0dXVhZGSEp0+fIiEhATNmzMCMGTOU8jo4OKBNmzb5uvyOnZ2dynq6d++efH92nJycMGrUKIwaNQoPHz6Eh4cHZs+ejQ0bNkAIgeDgYIwePVrpOAMDA3Tq1AmdOnVCamoq2rVrh6lTp8Lf359Le9E3gWMaKVcMDQ2xZMkSTJo0Ca1atcoyn6amJiQSCTIyMuRpYWFhKn+RbN++HVu2bMEff/yB8ePHo3Pnzvjll1/kv7izEhsbq9T7kPkLUNXjuNxq0qQJAGDx4sUK6QsWLMjRscbGxpg2bZrK8ZZRUVEA3vcCNWrUSGHLZGBgoPToOq907NgRL168wF9//aW0Lzk5Wd6jlNnr82E9x8fHKzX+GzVqBG1tbSxYsEAhr6o32nTs2BHnz5+XPwL8UFxcHNLT0wG8Xxs0PT1dYXmfjIyMHNU/8L73+kOGhoZwdnZWeW9krl8IvL/WhQsXQltbWz5MomPHjsjIyMDvv/+udGx6erp8GaDcfN09PDywdu1aha/x0aNHsxyP96F3797h7du3Sum///47hBBo2rSpPK1NmzbQ1tZWuI+FEFi6dClKliyJ2rVry9M7deqEZ8+eKfwhEx0djT179qBBgwbQ0NCApaUldu3apbTVr18furq62LVrF/z9/dVew+do3rw5Ll68qLBcUGJiIpYvXw57e/ssx1kmJSXJH8tncnJygpGRkfy+uHTpEiIjIxXGMwLK95OOjg5cXFwghPikMdVEXyP2NFKu9ezZU22eFi1aYM6cOWjatCm6du2KyMhILFq0CM7Ozrhx44Y8X2RkJH766SfUr19fPhlh4cKFOHnyJPz8/HD27NksH1OvXbsWixcvRtu2beHk5IS3b9/ir7/+grGxMZo3b/7Z1+np6Yn27dtj3rx5eP36NWrWrIm///5b3pjNrofO2NgYS5YsQffu3VGlShV07twZFhYWePr0KQ4cOAAvLy+FhkpW59+yZQtGjhyJatWqyceW5YXu3btj69at+PHHH3Hy5El4eXkhIyMD9+7dw9atW3H48GFUrVoVjRs3ho6ODlq1aoUBAwYgISEBf/31FywtLeWPN4H3j2dHjx6NwMBAtGzZEs2bN8e1a9dw6NAhFC9eXOHcY8aMwd69e9GyZUv4+fnB09MTiYmJuHnzJrZv346wsDAUL14crVq1gpeXF8aPH4+wsDC4uLhg586dOW5Iu7i4oF69evD09ISZmRkuX76M7du3K0x6Ad6PrQsODkbPnj1Ro0YNHDp0CAcOHMCECRPkPXZ169bFgAEDEBgYiOvXr6Nx48bQ1tbGw4cPsW3bNsyfPx8dOnTI1dc9MDAQLVq0QJ06ddC7d2/ExMTI1wHMnJSSlfDwcFSuXBldunSRvzbw8OHDOHjwIJo2bYo2bdrI89ra2mL48OGYOXMm0tLSUK1aNezevRtnzpxBUFCQwuNgf39/bN26Fe3bt8fIkSNhYmKCpUuXIi0tDdOmTQMA6Ovrw9fXVymm3bt34+LFiyr35bXx48dj06ZNaNasGYYOHQozMzOsXbsWjx8/xo4dO7L8mfHgwQM0bNgQHTt2hIuLC7S0tLBr1y5ERETIx4AeOHBAZcOzcePGsLa2hpeXF6ysrHD37l0sXLgQLVq0gJGRUb5fM1GhUBBTtunr8eGSO9lRteTOypUrRZkyZYRUKhXly5cXq1evli93k6ldu3bCyMhIhIWFKRy7Z88eAUBMnz49y3NevXpVdOnSRZQuXVpIpVJhaWkpWrZsKS5fvqyQD1ksuZO5nMrH1/rhWnqJiYli0KBBwszMTBgaGgpfX19x//59AUBhvT9VxwrxfnmcJk2aCBMTE6GrqyucnJyEn5+fUoyqJCQkiK5duwpTU1OFZVgyl9zZtm2bQn5Va9LVrVs3y2VCUlNTxfTp04Wrq6uQSqWiWLFiwtPTU0yePFlhuaO9e/eKSpUqCV1dXWFvby+mT58uVq1apXS9GRkZYvLkycLGxkbo6emJevXqiVu3bgk7OzuFpUuEeL88ir+/v3B2dhY6OjqiePHionbt2mLWrFkKax6+fv1adO/eXRgbGwsTExPRvXt3ce3atRwtuTNlyhRRvXp1YWpqKvT09ET58uXF1KlTFcrPXCYmNDRUvm6llZWVCAgIkC/b8qHly5cLT09PoaenJ4yMjETFihXF2LFjxcuXLxXy5fTrvmPHDlGhQgUhlUqFi4uL2Llzp+jZs6faJXdiY2NFt27dhLOzs9DX1xdSqVS4urqKadOmKVxfpoyMDDFt2jRhZ2cndHR0hKurq9iwYYPKskNDQ0Xbtm2FsbGx0NPTEw0aNBAXL17MNh4hcr/kjqq8Wd2vqn6+hIaGig4dOghTU1Ohq6srqlevrrQG7MffE9HR0WLQoEGifPnywsDAQJiYmIgaNWqIrVu3yo+pWrWqGDhwoFIMy5YtEz4+PsLc3FxIpVLh5OQkxowZo3ZpMKKiRCJEHo0uJvpGXL9+HZUrV8aGDRvwww8/FHQ49Bn8/Pywfft2tT179G2IiIiAjY0N9u/fnydPK4iKGo5pJMpG5hp2H5o3bx40NDTg4+NTABERUX6Jj4/HxIkTUb9+/YIOhahQ4phGomzMmDEDV65cQf369aGlpYVDhw7h0KFD6N+/v3xZGiIqGsqWLZujd8oTfavYaCTKRu3atXH06FH8/vvvSEhIQOnSpTFp0iT8/PPPBR0aERHRF8UxjURERESkFsc0EhEREZFabDQSERERkVpsNBIR5RE/Pz/5qwdzklfd+9ELi0mTJuXray2/BZl1GB0drTavvb09/Pz88j8oolxio7GQCA0NxYABA+Do6AhdXV0YGxvDy8sL8+fPV1j2xd7eHhKJRL5ZWlrC29sbu3btUijP3t4eLVu2VHmuy5cvQyKRYM2aNZ8d99ixYyGRSNCpUyeV+8PCwuSxTpkyRWWeH374ARKJROkXaL169eTHamhowNjYGOXKlUP37t2zfF+zKn5+fgp1JpVKUbZsWUycOFHplWJfo5CQEHTo0AHFihWDvr4+6tSpo/Ru6Jzq168fJBKJynsnISEBw4cPh62tLaRSKSpUqKDwir8PxcXFoX///rCwsICBgQHq16+Pq1evqsy7d+9eVKlSBbq6uihdujQCAgLkrxLMpO4Xrqr7PSEhAQEBAXBzc4OBgQHMzc3h4eGBYcOG4eXLlzmpjs+WlJSESZMm4dSpU3le9offHxKJBHp6eqhUqRLmzZsHmUyW5+f7Wuzbtw+tWrWClZUVdHR0YGZmBh8fH8yePRtv3rwp6PCIvmqcPV0IHDhwAN9//z2kUil69OgBNzc3pKam4uzZsxgzZgxu376N5cuXy/N7eHhg1KhRAICXL19i2bJlaNeuHZYsWYIff/zxi8UthMCmTZtgb2+Pffv24e3bt1m+TktXVxebNm3CL7/8opCemJiIPXv2QFdXV+Vxtra2CAwMlOcNCQnBzp07sWHDBnTs2BEbNmyAtra22lilUilWrFgB4P1abHv27MHvv/+O0NBQBAUF5eayC5Vnz56hVq1a0NTUxJgxY2BgYIDVq1ejcePGOH78eK7Wkrx8+TLWrFmj8muRkZGBJk2a4PLlyxg0aBDKlCmDw4cPY+DAgYiNjcWECRPkeWUyGVq0aIH//vsPY8aMQfHixbF48WLUq1cPV65cQZkyZeR5Dx06BF9fX9SrVw8LFizAzZs3MWXKFERGRmbZIM2JtLQ0+Pj44N69e+jZsyeGDBmChIQE3L59Gxs3bkTbtm1RokSJTy4/K3/99ZdCgy0pKQmTJ08G8L6Rl9c+/P6Ijo7Gxo0bMWLECERFRWHq1Kl5fr7CTCaToU+fPlizZg0qVqyIgQMHolSpUnj79i3Onz+PX375BQcPHsTx48cLOlS17t+/n+WrEIkKVIG+j4bEo0ePhKGhoShfvrzSq8iEEOLhw4di3rx58s+qXqf16tUrYWBgIMqWLZttvkyXLl3K0WvY1Dlx4oQAIE6cOCG0tbXFmjVrlPJkvsarXbt2AoC4fv26wv6goCChra0tWrVqpfRasaxeKZaeni4GDhwoAIixY8eqjVPVK8tkMpmoWbOmkEgkIjw8PCeXWygNHDhQaGlpiXv37snTEhMTRalSpUSVKlVyXI5MJhO1atUSvXv3VnnvbN26VQAQK1euVEhv37690NXVFREREfK0LVu2KL3mMDIyUpiamoouXbooHO/i4iLc3d1FWlqaPO3nn38WEolE3L17V56W1asfM30cc2a8QUFBSnmTk5O/2KvfoqKilF5jmSk3r91TRdX3R3JysrCzsxNGRkYiPT39k8v+2Mev/yyMAgMDBQAxYsQIIZPJlPa/fPlS4dWfqmRkZIjk5OR8iU/dPUz0NeCfMgVsxowZSEhIwMqVK2FjY6O039nZGcOGDcu2DGtra1SoUAGPHz/+5DjS0tJw7949vHr1KsfHBAUFwcXFBfXr10ejRo2y7bGrVasWHBwcsHHjRqUymjZtCjMzsxyfV1NTE3/++SdcXFywcOFCxMfH5/jYTBKJBHXq1IEQAo8ePQIA1K1bF+7u7irzlytXDk2aNAGg/Fjww+3DR/5xcXEYPnw4SpUqBalUCmdnZ0yfPl2hJyrz8f2sWbOwfPlyODk5QSqVolq1arh06ZLa6zhz5gwqV66McuXKydP09fXRunVrXL16FQ8fPsxRfaxfvx63bt3KsnfqzJkzAIDOnTsrpHfu3Bnv3r3Dnj175Gnbt2+HlZUV2rVrJ0+zsLBAx44dsWfPHqSkpAAA7ty5gzt37qB///7Q0vrfQ4+BAwdCCIHt27fnKHZVQkNDAQBeXl5K+zKHf2QlLi5Ofo9lio6OhoaGBszNzSE+WKXsp59+grW1tfzzh2Maw8LCYGFhAQCYPHmy/B75ePHoFy9ewNfXF4aGhrCwsMDo0aORkZGR62vOvLZq1arh7du3iIyMlKffuHEDfn5+8uEv1tbW6N27N16/fq1UxtmzZ1GtWjXo6urCyckJy5Yty/J8GzZsgKenJ/T09GBmZobOnTvj2bNnSvm2bdsmz1e8eHF069YNL168UMiTOcbzU+ojKSkJ06dPh6urK2bOnKly/KWNjQ3GjRunkCaRSDB48GAEBQXB1dUVUqkUwcHBAIBZs2ahdu3aMDc3h56eHjw9PVXekx+WUa5cOejq6sLT0xOnT59WGWtcXBz8/PxgamoKExMT9OrVC0lJSQp5VI1pjIuLw4gRI2Bvbw+pVApbW1v06NFDYcjGggUL4OrqCn19fRQrVgxVq1ZV+plL9DnYaCxg+/btg6OjI2rXrv3JZaSlpeHZs2cwNzf/5DJevHiBChUqwN/fP0f5U1JSsGPHDnTp0gUA0KVLF5w4cQLh4eFZHtOlSxds3rxZ/ks3OjoaR44cQdeuXXMdr6amJrp06YKkpCScPXs218cD73+pA0CxYsUAAN27d8eNGzdw69YthXyXLl3CgwcP0K1bNwDAzz//jPXr1ytsmQ1KS0tLAO9/idWtWxcbNmxAjx498Oeff8LLywv+/v4YOXKkUiwbN27EzJkzMWDAAEyZMgVhYWFo164d0tLSsr2GlJQU6OnpKaXr6+sDAK5cuaK2Ht6+fYtx48ZhwoQJCg2gj8+jqakJHR0dtee5du0aqlSpovR4rXr16khKSsKDBw/k+QCgatWqCvlKlCgBW1tb+f4PxcTEIDo6Wmn7eAyfnZ0dAGDdunUKjbycMDU1hZubm8Iv/bNnz0IikSAmJgZ37tyRp585cwbe3t4qy7GwsJA/Ym/btq38XvmwMZ352N/c3ByzZs1C3bp1MXv2bIXhKLmV+YeIqampPO3o0aN49OgRevXqhQULFqBz587YvHkzmjdvrlA/N2/eROPGjREZGYlJkyahV69eCAgIUBozDQBTp05Fjx49UKZMGcyZMwfDhw+XD4mIi4uT51uzZg06duwITU1NBAYGol+/fti5cyfq1KmjkO9z6uPs2bOIi4tDly5doKmpmav6OnHiBEaMGIFOnTph/vz58kb//PnzUblyZfz222+YNm0atLS08P333+PAgQNKZfz9998YPnw4unXrht9++w2vX79G06ZNlX6WAEDHjh3x9u1bBAYGomPHjlizZo18CENWEhIS4O3tjQULFqBx48aYP38+fvzxR9y7dw/Pnz8H8H5oxNChQ+Hi4oJ58+Zh8uTJ8PDwwIULF3JVH0TZKshuzm9dfHy8ACDatGmT42Ps7OxE48aNRVRUlIiKihL//fef6Ny5swAghgwZopAvN4+nMx8j9+zZM0dxbN++XQAQDx8+FEII8ebNG6Grqyvmzp2rkC+z3JkzZ4pbt24JAOLMmTNCCCEWLVokDA0NRWJiospHdVk9ns60a9cuAUDMnz8/21gzy86ss5CQEDFr1iwhkUiEm5ub/FFWXFyc0NXVFePGjVM4fujQocLAwEAkJCSoLP+ff/4R2traonfv3vK033//XRgYGIgHDx4o5B0/frzQ1NQUT58+Vagfc3NzERMTI8+3Z88eAUDs27cv22tr1aqVMDU1FW/evFFIr1WrlgAgZs2ale3xQggxevRo4eDgIN69eyeEUH3vzJ49W+Fr9+H1ABAtW7aUpxkYGCjURaYDBw4IACI4OFgIIcTMmTMFAHldfKhatWqiZs2a8s+Zj/ay2z6MOSkpSZQrV04AEHZ2dsLPz0+sXLlS4TF6dgYNGiSsrKzkn0eOHCl8fHyEpaWlWLJkiRBCiNevXwuJRKJw//Xs2VPY2dnJP6t7PA1A/PbbbwrplStXFp6enmpjrFu3rihfvrz8vr53754YM2aMUl0I8b4+PrZp0yYBQJw+fVqe5uvrK3R1dcWTJ0/kaXfu3BGampoKj6fDwsKEpqammDp1qkKZN2/eFFpaWvL01NRUYWlpKdzc3BQe++7fv18AEBMnTsyT+pg/f74AIHbv3q2Qnp6eLq+fzO3DR9cAhIaGhrh9+7ZSmR/XWWpqqnBzcxMNGjRQSM+8/y5fvixPe/LkidDV1RVt27aVp2Xewx9/b7Rt21aYm5srpNnZ2Sn8LJ44caIAIHbu3KkUZ+b1tGnTJtufl0R5gT2NBShzJl9Wk0eycuTIEVhYWMDCwgLu7u7Ytm0bunfvjunTp39yLPb29hBC5HhGdVBQEKpWrQpnZ2cA76+hRYsW2T6idnV1RaVKlbBp0yYA73vX2rRpI++tyq3M2dZv375VmzcxMVFeZ87Ozhg9ejS8vLywZ88e+aMsExMTtGnTBps2bZL3vmRkZGDLli3w9fWFgYGBUrnh4eHo0KEDPDw8sHjxYnn6tm3b4O3tjWLFiin0iDVq1AgZGRlKj646deok7/EEIO+9ynx0npWffvoJcXFx6NSpE65du4YHDx5g+PDhuHz5MgAozLxX5cGDB5g/fz5mzpwJqVSaZb6uXbvCxMQEvXv3xtGjRxEWFobly5fLr/nD8yQnJ6ssK3OCTWbezP9mlVdV7Dt27MDRo0eVNisrK4V8enp6uHDhAsaMGQPgfW9Xnz59YGNjgyFDhsgfkWfF29sbERERuH//PoD3PYo+Pj7w9vaWP6o/e/YshBBZ9jTm1MeT17y9vdV+3TPdu3dPfl+XL18eM2fOROvWrZW+jz/sjX737h2io6NRs2ZNAJDPas/IyMDhw4fh6+uL0qVLy/NXqFBB3pOeaefOnZDJZOjYsaPC/W1tbY0yZcrIZ+9fvnwZkZGRGDhwoMIEqxYtWqB8+fIqe+0+pT4yf5Z+vALDzZs35fWTuX38SL5u3bpwcXFRKvPDOouNjUV8fDy8vb1VrgJQq1YteHp6yj+XLl0abdq0weHDh5Uerau6vtevX2c7s3vHjh1wd3dH27ZtlfZl/vwyNTXF8+fPczSshehTsdFYgDLHVeWk0fOhGjVq4OjRozh27BjOnTuH6OhorFu3TuVjyux86rprcXFxOHjwIOrWrYuQkBD55uXlhcuXL8sfP6rStWtXbNu2DSEhITh37twnPZrOlJCQACBnjW5dXV15A2P16tWoUKECIiMjleqsR48eePr0qbxhcOzYMURERKB79+5KZaanp6Njx47IyMjAzp07FRo/Dx8+RHBwsNIvrEaNGgGAwngzAAq/pIH/PTKPjY3N9rqaNWuGBQsW4PTp06hSpQrKlSuHAwcOyMcmqlsHcNiwYahduzbat2+fbT5ra2vs3bsXKSkpaNy4MRwcHDBmzBgsWLBA6Tx6enoqG2WZyxtl1nnmf7PKq+p+9vHxQaNGjZQ2VTO+TUxMMGPGDISFhSEsLAwrV65EuXLlsHDhQvz+++/ZXm9mQ/DMmTNITEzEtWvX4O3tDR8fH/m9cebMGRgbG2c5DjYndHV15eMeMxUrVkzt1z2Tvb09jh49isOHD2Px4sUoWbIkoqKilOojJiYGw4YNg5WVFfT09GBhYQEHBwcAkI8JjoqKQnJyssLs9kwfjpkF3t/fQgiUKVNG6R6/e/eu/P5+8uSJyuMBoHz58vL9n1sfmT8DMn8mZHJ2dpZ/36v6HgYgr4eP7d+/HzVr1oSuri7MzMzkww1UjaFWVWdly5ZFUlISoqKiFNI/5Xs9NDQUbm5uWe4HgHHjxsHQ0BDVq1dHmTJlMGjQIPzzzz/ZHkOUW1xypwAZGxujRIkSKse9ZKd48eLyxkdWsuqpASAfdJ3VMjfqbNu2DSkpKZg9ezZmz56ttD8oKCjLMTpdunSBv78/+vXrB3NzczRu3PiTYgAgr7fM3s7saGpqKtRZkyZNUL58eQwYMAB79+5VSLeyssKGDRvg4+ODDRs2wNraWmV9jxkzBufPn8exY8dga2ursE8mk+G7777D2LFjVcZTtmxZpfhUETkYjzd48GD06tULN27cgI6ODjw8PLBy5UqV5/nQiRMnEBwcjJ07d8rHdwLvG8PJyckICwuDmZmZ/I8bHx8fPHr0CDdv3kRiYiLc3d3l6x1+eB4bGxuVE6oy0zKXusmc+PXq1SuUKlVKKW/16tXVXntO2dnZoXfv3mjbti0cHR0RFBSU5bqhmTE6ODjg9OnT8l74WrVqwcLCAsOGDcOTJ09w5swZ1K5d+7OWRsnt+LuPGRgYKNybXl5eqFKlCiZMmKAwkadjx444d+4cxowZAw8PDxgaGkImk6Fp06aftKajTCaDRCLBoUOHVF7Dpy5a/qn1Ub58eQDvfya0adNGIY7M+slq7LOqP07OnDmD1q1bw8fHB4sXL4aNjQ20tbWxevXqz55Y8jnf69mpUKEC7t+/j/379yM4OBg7duzA4sWLMXHiRLVjJolyio3GAtayZUssX74c58+fR61atfKsXDs7O4UB+x/KfOSWOVkgt4KCguDm5oaAgAClfcuWLcPGjRuz/CFVunRpeHl54dSpU/jpp58UZs3mRkZGBjZu3ChfzDq3bGxsMGLECEyePBn//vuv/FGdpqYmunbtijVr1mD69OnYvXs3+vXrp/SDfvPmzZg3bx7mzZuHunXrKpXv5OSEhIQEtY37vGJgYKBw/xw7dgx6enoqZw9nevr0KQAoTMzI9OLFCzg4OGDu3LkYPny4PF1TUxMeHh4K5wGgcJ0eHh44c+YMZDKZQoPqwoUL0NfXlzcwM8u5fPmyQgPx5cuXeP78Ofr375+DK8+dYsWKwcnJKUd/qHl7e+P06dNwcHCAh4cHjIyM4O7uDhMTEwQHB+Pq1atqfxl/6beoVKpUCd26dcOyZcswevRolC5dGrGxsTh+/DgmT56MiRMnyvN+PLPewsICenp6KmfcZ/7MyOTk5AQhBBwcHLL9wyTzZ8z9+/fRoEEDpTI/9WfQx7y9vWFiYoLNmzfD39//s9c43LFjB3R1dXH48GGFJwirV69WmV9VnT148AD6+vpKPaefIqf3rIGBATp16oROnTohNTUV7dq1w9SpU+Hv7//JnQREH+Lj6QI2duxYGBgYoG/fvoiIiFDaHxoaivnz5+e63ObNm+P58+fYvXu3QnpKSgpWrFgBS0tLVKlSRZ6e0yV3nj17htOnT6Njx47o0KGD0tarVy+EhIRkO2NvypQpCAgIwJAhQ3J9XcD7BuPQoUNx9+5dDB06NNvlU7IzZMgQ6Ovr448//lBI7969O2JjYzFgwAAkJCTIZ01nunXrFvr27Ytu3bpluRxSx44dcf78eRw+fFhpX1xcnNIbT/LSuXPnsHPnTvTp0wcmJiZZ5mvQoAF27dqltFlYWKBq1arYtWsXWrVqleXxUVFRmD59OipVqqTQaOzQoQMiIiKwc+dOeVp0dDS2bduGVq1ayX8Ju7q6onz58li+fLnCuK8lS5ZAIpGgQ4cOn1wH//33n8q3xzx58gR37txR+bj0Y97e3ggLC8OWLVvkj6s1NDRQu3ZtzJkzB2lpaWrHM2aO1/14lnB+Gjt2LNLS0jBnzhwA/+vZ+rgna968eQqfNTU10aRJE+zevVv+BwUA3L17V+k+bteuHTQ1NTF58mSlcoUQ8nGDVatWhaWlJZYuXaowDOHQoUO4e/cuWrRo8XkX+//09fUxduxY3Lp1C+PHj1fZa5ebnjxNTU1IJBKF+zIsLEzp52mm8+fPK4x1fPbsGfbs2YPGjRt/dm8yALRv3x7//fefylnsmdf18VhNHR0duLi4QAihdhUGopxiT2MBc3JywsaNG9GpUydUqFBB4Y0w586dw7Zt2z7pHaT9+/fHqlWr8P3336N3796oXLkyXr9+jS1btuDWrVtYt26dwvIpmUvu9OzZM9vJMBs3boQQAq1bt1a5v3nz5tDS0kJQUBBq1KihMk/dunVV9s6pEh8fjw0bNgB4/1g9840woaGh6Ny5s9qxadkxNzdHr169sHjxYty9excVKlQAAFSuXBlubm7Ytm0bKlSooNC4BoBevXoBgPzx9Ydq164NR0dHjBkzBnv37kXLli3h5+cHT09PJCYm4ubNm9i+fTvCwsJQvHjxT44905MnT9CxY0e0bt0a1tbWuH37NpYuXYpKlSph2rRp2R5bunRppfFVADB8+HBYWVnB19dXIb1u3bqoVasWnJ2dER4ejuXLlyMhIQH79+9X6Nnp0KEDatasiV69euHOnTvyN8JkZGQo9cxlTtxo3LgxOnfujFu3bmHhwoXo27ev/OvxKY4ePYqAgAC0bt0aNWvWhKGhIR49eoRVq1YhJSVFaa1EVTIbhPfv31eoSx8fHxw6dEi+nmZ29PT04OLigi1btqBs2bIwMzODm5ub2vFpn8PFxQXNmzfHihUr8Ouvv8Lc3Bw+Pj6YMWMG0tLSULJkSRw5ckTluq6TJ09GcHAwvL29MXDgQKSnp8vX/rtx44Y8n5OTE6ZMmQJ/f3+EhYXB19cXRkZGePz4MXbt2oX+/ftj9OjR0NbWxvTp09GrVy/UrVsXXbp0QUREhHxpmxEjRuTZdY8fPx53797FzJkzceTIEbRv3x62traIjY3F1atXsW3bNlhaWuaox61FixaYM2cOmjZtiq5duyIyMhKLFi2Cs7OzQj1kcnNzQ5MmTTB06FBIpVL5BLG8eiw8ZswYbN++Xf7z3NPTEzExMdi7dy+WLl0Kd3d3NG7cGNbW1vDy8oKVlRXu3r2LhQsXokWLFrmebEmUpS8/YZtUefDggejXr5+wt7cXOjo6wsjISHh5eYkFCxbIl0IRIvuldD4WGxsrRowYIRwcHIS2trYwNjYW9evXF4cOHVLKm9MldypWrChKly6dbZ569eoJS0tLkZaWprDkTnayWnIHHyypYmhoKMqUKSO6desmjhw5km156srOFBoaKjQ1NZWue8aMGQKAmDZtmtIxdnZ2WS778uEyRm/fvhX+/v7C2dlZ6OjoiOLFi4vatWuLWbNmidTUVCGEyLZ+kMVSLR+KiYkRbdq0EdbW1kJHR0c4ODiIcePGKS3BkxtZ3WMjRowQjo6OQiqVCgsLC9G1a1cRGhqaZVx9+vQR5ubmQl9fX9StW1dcunRJZd5du3YJDw8PIZVKha2trfjll1/k9ZMpt2+EefTokZg4caKoWbOmsLS0FFpaWsLCwkK0aNFCnDhxIqdVISwtLQUAhaV6zp49KwAIb29vpfwfL7kjhBDnzp0Tnp6eQkdHR+FrmtV9mdO3r2S3JNWpU6cUzvX8+XPRtm1bYWpqKkxMTMT3338vXr58qfIe+/vvv+XxOjo6iqVLl2YZ044dO0SdOnWEgYGBMDAwEOXLlxeDBg0S9+/fV8i3ZcsWUblyZSGVSoWZmZn44YcfxPPnzxXyfG59ZNq1a5do3ry5sLCwEFpaWsLU1FTUqVNHzJw5U8TFxSnkBSAGDRqkspyVK1eKMmXKCKlUKsqXLy9Wr16tMpbMMjZs2CDPX7lyZXHy5EmV1/HxPbx69WoBQDx+/Fie9vGSO0K8X+Jp8ODBomTJkkJHR0fY2tqKnj17iujoaCGEEMuWLRM+Pj7C3NxcSKVS4eTkJMaMGfPF3n5E3waJEJ85+paoCJo/fz5GjBiBsLAwlb1xRETA+3GrgwYNwsKFCws6FKJ8xzGNRB8RQmDlypWoW7cuG4xERET/j2Maif5fYmIi9u7di5MnT+LmzZsK71MmIiL61rGnkej/RUVFyRcfnzBhQpaTfYiIiPJLYGAgqlWrBiMjI1haWsLX11dp2StVtm3bhvLly0NXVxcVK1bEwYMHFfYLITBx4kTY2NhAT08PjRo1UrlcVHbYaCT6f5mLOMfGxsrfqEJElB0hBMczUp76+++/MWjQIPz77784evQo0tLS0LhxYyQmJmZ5zLlz59ClSxf06dMH165dg6+vL3x9fRXW95wxYwb+/PNPLF26FBcuXICBgQGaNGkif1tXTnAiDBEREVEhFRUVBUtLS/z999/w8fFRmadTp05ITEzE/v375Wk1a9aEh4cHli5dCiEESpQogVGjRmH06NEA3i9pZ2VlhTVr1qBz5845ioU9jURERESFVOb7zs3MzLLMc/78eaU3kDVp0gTnz58HADx+/Bjh4eEKeUxMTFCjRg15npzgRBgiIiKifJSSkqLwViQAkEqlCq+pVEUmk2H48OHw8vLK9qUA4eHhsLKyUkizsrJCeHi4fH9mWlZ5cqJINhpz83yeiIiICoeCfEf2XI32+VZ2/MSKSm8ICggIUPt2qkGDBuHWrVs4e/ZsvsWWG0Wy0UhERERUWPj7+2PkyJEKaep6GQcPHoz9+/fj9OnTsLW1zTavtbU1IiIiFNIiIiJgbW0t35+ZZmNjo5DHw8Mjp5fBMY1EREREGhKNfNukUimMjY0VtqwajUIIDB48GLt27cKJEyfg4OCgNvZatWrh+PHjCmlHjx5FrVq1AAAODg6wtrZWyPPmzRtcuHBBnicn2NNIRERE3zwNiaSgQwDw/pH0xo0bsWfPHhgZGcnHHJqYmEBPTw8A0KNHD5QsWRKBgYEAgGHDhqFu3bqYPXs2WrRogc2bN+Py5ctYvnw5gPevuxw+fDimTJmCMmXKwMHBAb/++itKlCgBX1/fHMfGRiMRERFRIbFkyRIAQL169RTSV69eDT8/PwDA06dPoaHxv4fFtWvXxsaNG/HLL79gwoQJKFOmDHbv3q0weWbs2LFITExE//79ERcXhzp16iA4ODhX40iL5DqNnAhDRET09SnIiTCLtHO2VuGnGJS2Od/K/pI4ppGIiIiI1OLjaSIiIvrmaUrYj6YOa4iIiIiI1GJPIxEREX3zCsvs6cKMPY1EREREpBZ7GomIiOibp8ExjWqx0UhERETfPDYa1WMNEREREZFa7GkkIiKibx4nwqjHnkYiIiIiUos9jURERPTN45hG9VhDRERERKQWexqJiIjom8fXCKrHGiIiIiIitdjTSERERN88jmlUj41GIiIi+uZxyR312KwmIiIiIrXY00hERETfPD6eVo81RERERERqsaeRiIiIvnnsaVSPNUREREREarGnkYiIiL55nD2tHnsaiYiIiEgt9jQSERHRN4+vEVSPjUYiIiL65nEijHqsISIiIiJSiz2NRERE9M3jRBj12NNIRERERGqxp5GIiIi+eRzTqB5riIiIiIjUYk8jERERffPY06gea4iIiIiI1GJPIxEREX3zOHtaPTYaiYiI6JvHx9PqsYaIiIiISC32NBIREdE3j++eVo81RERERERqsaeRiIiIvnmcCKMeexqJiIiISC32NBIREdE3j7On1WMNEREREZFa7GkkIiKib55Ek/1o6rDRSERERN88NhrVYw0RERERkVrsaSQiIqJvnkSL/WjqsIaIiIiISC02GvPAlStXMGTIEDRq1Aju7u44ceKE2mMuXbqETp06oWrVqmjZsiX27NmjlGfz5s1o1qwZqlWrhh9++AE3b97Mj/ALDdZj3mFd5h3WZd5gPeYd1mX+kGhp5NtWVBSdKylAycnJKFeuHPz9/XOU//nz5xg8eDCqVauGrVu34ocffsDkyZPxzz//yPMEBwdj1qxZGDBgADZv3oxy5crhp59+wuvXr/PrMgoc6zHvsC7zDusyb7Ae8w7rsug7ffo0WrVqhRIlSkAikWD37t3Z5vfz84NEIlHaXF1d5XkmTZqktL98+fK5iotjGvNAnTp1UKdOnRzn37ZtG0qWLInRo0cDABwdHXHt2jVs2LABXl5eAID169ejXbt28PX1BQD88ssvOH36NHbv3o0+ffrk+TUUBqzHvMO6zDusy7zBesw7rMv8UZhmTycmJsLd3R29e/dGu3bt1OafP38+/vjjD/nn9PR0uLu74/vvv1fI5+rqimPHjsk/a2nlrhlYeGroG3Ljxg3UrFlTIa127dq4ceMGACAtLQ13795VyKOhoYGaNWvK8xDrMS+xLvMO6zJvsB7zDuvy69OsWTNMmTIFbdu2zVF+ExMTWFtby7fLly8jNjYWvXr1UsinpaWlkK948eK5iqtQNxqfPXuG3r17Z5snJSUFb968UdhSUlK+UISfJjo6Gubm5gpp5ubmSEhIwLt37xAbG4uMjAyVeaKjo79kqIUa6zHvsC7zDusyb7Ae8w7rMmfyc0zjl26rrFy5Eo0aNYKdnZ1C+sOHD1GiRAk4Ojrihx9+wNOnT3NVbqFuNMbExGDt2rXZ5gkMDISJiYnCNnPmzC8UIRERERUFEk1Jvm2q2iqBgYH5ch0vX77EoUOH0LdvX4X0GjVqYM2aNQgODsaSJUvw+PFjeHt74+3btzkuu0DHNO7duzfb/Y8ePVJbhr+/P0aOHKmQJoT4rLjyW/HixZUGF79+/RqGhobQ1dWFpqYmNDU1VebJbVdyUcZ6zDusy7zDuswbrMe8w7oseKraKlKpNF/OtXbtWpiamsrHp2Zq1qyZ/N+VKlVCjRo1YGdnh61bt+Z43GqBNhp9fX0hkUiybeRJJJJsy5BKpUoV/+7duzyJL79UqlQJZ8+eVUj7999/UalSJQCAtrY2KlSogAsXLqBBgwYAAJlMhgsXLqBz585fPN7CivWYd1iXeYd1mTdYj3mHdZkz+bk0jqq2Sn4QQmDVqlXo3r07dHR0ss1ramqKsmXLIiQkJMflF+jjaRsbG+zcuRMymUzldvXq1YIML8eSkpJw79493Lt3DwDw4sUL3Lt3D69evQLwflbTzz//LM///fff4/nz55g7dy4eP36MLVu24MiRI+jWrZs8T/fu3bFz507s3bsXjx49wpQpU5CcnKz0l0NRwnrMO6zLvMO6zBusx7zDuqSs/P333wgJCclRz2FCQgJCQ0NhY2OT4/ILtKfR09MTV65cQZs2bVTuV9cLWVjcvn1bYezArFmzAACtW7fG77//jujoaISHh8v329raYuHChZg5cyaCgoJgZWWFgIAA+dIHANC0aVPExsZi8eLFiI6ORrly5bB48WKlgcpFCesx77Au8w7rMm+wHvMO6zJ/FKZFuBMSEhR6AB8/fozr16/DzMwMpUuXhr+/P168eIF169YpHLdy5UrUqFEDbm5uSmWOHj0arVq1gp2dHV6+fImAgABoamqiS5cuOY5LIgqwVXbmzBkkJiaiadOmKvcnJibi8uXLqFu3bq7KLeyPp4mIiEiZrq5ugZ37qtecfCu7yj8j1Wf6wKlTp1C/fn2l9J49e2LNmjXw8/NDWFgYTp06Jd8XHx8PGxsbzJ8/H/369VM6tnPnzjh9+jRev34NCwsL1KlTB1OnToWTk1OO4yrQRmN+YaORiIjo61OQjcZrPvPyrezKp4fnW9lfUuHpiyUiIiKiQouvESQiIqJvXmEa01hYsdFIRERE37zC9O7pwoo1RERERERqsaeRiIiIvnl8PK0ea4iIiIiI1GJPIxEREX3z2NOoHmuIiIiIiNRiTyMRERF98zh7Wj3WEBERERGpxZ5GIiIi+uZxTKN6rCEiIiIiUos9jURERPTNY0+jemw0EhER0TePE2HUYw0RERERkVrsaSQiIqJvHh9Pq8caIiIiIiK12NNIRERE3zyJpqSgQyj02NNIRERERGqxp5GIiIi+eRzTqB5riIiIiIjUYk8jERERffPY06geG41ERET0zePi3uqxhoiIiIhILfY0EhER0TePj6fVYw0RERERkVrsaSQiIqJvHsc0qscaIiIiIiK12NNIRERE3zyOaVSPNUREREREarGnkYiIiL55Eg32o6nDRiMRERGRpqSgIyj02KwmIiIiIrXY00hERETfPD6eVo81RERERERqsaeRiIiIvnlc3Fs91hARERERqcWeRiIiIvrmcUyjeqwhIiIiIlKLPY1EREREXKdRLTYaiYiI6JvHx9PqsYaIiIiISC32NBIREdE3j0vuqMcaIiIiIiK12NNIRERE3zyOaVSPNUREREREarGnkYiIiIhL7qjFnkYiIiKiQuT06dNo1aoVSpQoAYlEgt27d2eb/9SpU5BIJEpbeHi4Qr5FixbB3t4eurq6qFGjBi5evJiruNhoJCIiom+eREMj37bcSkxMhLu7OxYtWpSr4+7fv49Xr17JN0tLS/m+LVu2YOTIkQgICMDVq1fh7u6OJk2aIDIyMsfl8/E0ERERffMK05I7zZo1Q7NmzXJ9nKWlJUxNTVXumzNnDvr164devXoBAJYuXYoDBw5g1apVGD9+fI7KLzw1RERERFQEpaSk4M2bNwpbSkpKnp/Hw8MDNjY2+O677/DPP//I01NTU3HlyhU0atRInqahoYFGjRrh/PnzOS6/SPY0LtH/oaBDIKJ84l25RkGHUGS4HxlY0CEUGbF6zwo6hCJBFxUK7NwSjfybCBMYGIjJkycrpAUEBGDSpEl5Ur6NjQ2WLl2KqlWrIiUlBStWrEC9evVw4cIFVKlSBdHR0cjIyICVlZXCcVZWVrh3716Oz1MkG41EREREhYW/vz9GjhypkCaVSvOs/HLlyqFcuXLyz7Vr10ZoaCjmzp2L9evX59l52GgkIiIiyscxjVKpNE8biTlRvXp1nD17FgBQvHhxaGpqIiIiQiFPREQErK2tc1wmxzQSERERFTHXr1+HjY0NAEBHRweenp44fvy4fL9MJsPx48dRq1atHJfJnkYiIiL65hWm1wgmJCQgJCRE/vnx48e4fv06zMzMULp0afj7++PFixdYt24dAGDevHlwcHCAq6sr3r17hxUrVuDEiRM4cuSIvIyRI0eiZ8+eqFq1KqpXr4558+YhMTFRPps6J9hoJCIiIipELl++jPr168s/Z46H7NmzJ9asWYNXr17h6dOn8v2pqakYNWoUXrx4AX19fVSqVAnHjh1TKKNTp06IiorCxIkTER4eDg8PDwQHBytNjsmORAgh8uD6CpW5Gu0LOgQiyiecPZ13OHs673D2dN6w1C+42dNRay7lW9kWftXyrewviT2NRERE9M3LzyV3iorC8wCfiIiIiAot9jQSERERFaKJMIUVa4iIiIiI1GJPIxEREX3zJPm4uHdRwRoiIiIiIrXY00hERETfvMK0uHdhxRoiIiIiIrXY00hERETfPIkm12lUh41GIiIiIj6eVos1RERERERqsaeRiIiIvnlcckc91hARERERqcWeRiIiIvrmSTQ4EUYd9jQSERERkVrsaSQiIqJvHsc0qscaIiIiIiK12NNIRERExDGNarHRSERERN88iYSNRnX4eJqIiIiI1GJPIxEREREfT6vFnkYiIiIiUos9jURERPTN4+Le6rGnkYiIiIjUYk8jEREREWdPq8WeRiIiIiJSiz2NRERE9M3jmEb12NNIRERERGqxp5GIiIiI3WhqsdFIRERE3zy+RlA9tquJiIiISC32NBIRERFxIoxa7GkkIiIiIrXY00hERETEnka12NNIRERERGqxp5GIiIi+eZw9rR57GomIiIhILfY0EhEREbEbTS02GomIiOibx3dPq8d2NRERERGpxZ5GIiIiIk6EUYs9jURERESkFnsaiYiI6JvHMY3qsaeRiIiIiNRiTyMRERERexrVYqMxG9XGt4Vz25owK18S6cmpeHnuPs6OX4/YBy/leRouHYDSDSvBsEQxpCa8w6tz93Fm/AbE3n8hz2NUqjgaLO6PUvXdkJbwDnfWncJZ/w0QGbIsz21Z2QF1/ugOq2rOEBkyhOz8F3+PXIO0xHcAgOKV7FBtXDuUrFMeesWNEB8WhZvLjuDanwfyr0LymEEJM3j/0Q32zapAW18HcSHhONJ7ESKuhMrz1JrcGRX7NoLUVB8v/7mP4wOXIy7kVZZllvR2QdXRbWDp6QjDEmbY23Y6QvdcVMijb2mCOn90h11jd0hNDfDi9B2cHLoy23K/Nr0fLYGJvaVS+vXFh3By8AqldHOXUqg1uTMsPR1hYm+JUyNW4dp8xXtJ21AXtX/vAmffGtC3NEbktcc4NXwVIi6HKpVXlBhWtoV1j+rQr2ANHQtDhIzaibhTIVnmt5/UDMVbVVRKTw6Nxu2OqwAAJfp7ocQAL8X9Ya9xu/3KvA2+ELl87SpWb1yHO/fvIio6GvMDZ6Fh3frZHnPx6mXM/HMOQh4/grWlFQb49YFvi9by/YmJiVjw1xIc//skYmJjUb5sOYwfPhoVXVzz+3IK3M4tB7Fp7S7EvI6DU1l7DB/XDy5uZbPMvzVoL3ZvC0ZEeDRMTY1Qt1FtDBjSHVKpjlLeDat2YNmC9fi+a0sMHdM3Py+DvjJsNGbD1scV/y0ORsSlEEi0NOA19Qe0OzwRa12HIT0pBQAQeeUR7gWdwdunUdA1M0TNgE5od/hXrHIcCCGTQaKhAd/9E5AYHoctXhNgYFMMTdYOgSwtHf/8vFHleQ1siqH90QDc33oOJ4esgI6xHurN7Y0mqwdjf8dZAAArTyckR8XjUPf5SHj2Gja1y6HRsh8hy5Dhv0WHvlgdfSqpqQE6nZ2K5ydvYVfzKUiOegPTMjZ4F5sgz1N1rC88hjTHYb8FePM4ErV/64x2wb9ireswZKSkqSxX20CKqBthuLX6OFrvHKcyT6td4yBLy8Be3z+Q+iYZVUa2QvujAQpf16/dpurjINH83+iT4m6l0f5oAB5uO68yv5a+DuIfR+DB9nOoN6eXyjzf/TUQxd1KI7jHn0h4GYMK3Xz+v96GI/FlTL5cR2GgoaeNpAeRiN57E86z2qrN/2zWcTxfcFr+WaIpgeumXog9dl8hX3JIFO4P3Pq/hGz+iCwKkt8lo5xzWbRt2RrD/ceozf/85QsMGj0MHX3b449JU3Hh8kUE/DEFFubF4VWzNgBg4h+/I+RRKAIn/g5LCwvsCz6IfsN+wp6N22FlofxHU1Fx/PBZLJy9CqN+/gkubmWxbeNejBo4GRt3L0IxM1Ol/EcP/Y1lf67H+EmD4eZeHs+evMS0iX9CAgmGjO6tkPfu7YfYu+MwnMrYf5mLKUT4GkH1OKYxG7uaT8GdtSfx+s4zRN94giO9FsLYzgJWnk7yPDf/OooXZ+7gzZMoRF57jHO/boJxaQsY21sAAOwau8PMxRbB3ecj6r8whAVfw/mJm+E+sCk0tFW32R1bVkVGWgZODPoLsQ9eIuJyKI79tAxlOtSCiZM1AOD26hM4NXwVXpy+g/jHEbgXdBq315yAc9sa+V8xeaDauLZIeBaNI30WIeJSCN6EReLp0f8Q/yhCnqfKsJa4OHU7Hu29hOibTxDccwEMShSDk2/1LMsNC76Gc79uQujuiyr3m5axQYla5XBi4HJEXA5F7IOXOP7Tcmjp6aB8lzp5fp0FJTn6DZIi4uSbQ0tPxIW8wvO/b6vMH3E5FGfGrsODLf8gXUWDXFNXB2Xa18SZcevw4swdxIeG49/JWxEXEg73n5rk9+UUqDfnHuPlkrOIO/kwR/kzElKR/jpRvhm4WEPTWBfRe28q5BMZMoV86XHJ+RF+oeFdywtDBwxEo7oNcpR/664dKGlTEmOGjoSTvQO6duiE7+o1xLot7//YfpfyDsdOncDIgUNRtXIVlLYthUF9B6C0bSls2bk9Py+lwG3ZsAet2jVGizYN4eBUCqN//gm6ulIc2H1cZf5b/92Hm0d5fNesLmxKWKF6rcpo1NQbd28r3tNJScn4bcJcjP11EIyMDb7EpRQuGvm45dLp06fRqlUrlChRAhKJBLt37842/86dO/Hdd9/BwsICxsbGqFWrFg4fPqyQZ9KkSZBIJApb+fLlcxUXG425oGOiDwB4F/NW5X4tfSlce9VH/KMIvH32GgBgU6scom8+RVJkvDxf2OHrkJoYwNy1lMpyNKVakKWmA0LI09KTUwEAJetUyDI+qbE+UmISstxfmDi2qoqIK6FosWUUBoSvwg9XZsKtbyP5fhMHKxjYFMPTYzfkaalvkhB+4SFK1Cr3yefVlGoDANLfpf4vUQhkpKShhFfWdfs109DWQoUffHBr9YlPL0NLAxpamkh/p9igTE9ORQmv3P3Q+dYUb1MJby6GITX8jUK6tHQxVAoeiIp7+sNhSkvoWBsVUISF03+3bqBmNcU/EL1q1MJ/t97/TMhIz0BGRgakUqlCHqlUiqs3rn+pML+4tLQ0PLgbCs8aleRpGhoaqFrDHbdv3Fd5jJt7OTy4E4o7tx4AAF4+D8e//1xFzTpVFPLNDVyOWt6eqFrTPf8ugHIkMTER7u7uWLRoUY7ynz59Gt999x0OHjyIK1euoH79+mjVqhWuXbumkM/V1RWvXr2Sb2fPns1VXHw8nVMSCerN7YUXZ+/i9e1nCrsq/dQE3tO7Q8dQDzH3XmBH48mQpaUDAAysTZEUEa+QPykiTr4vSsWpnp24BZ/ZfvAc3QbX5h+AtoEU3oHd3h9jY6oyPJta5VC2kxd2t5z2WZf5pZg4WqHSj01wde4+XAzcCetqzqg/vzdkqem4s+4U9K1NAfyvrjIlRcRD38r0k88be+8F3jyJQp1p3XDsx6VIS0xBlREtYVSqOAxsin36BRVizr7VITU1wJ01Jz+5jLSEd3h57h5q/NIBMXefIykiHuW61IFNrbKICwnPw2iLFu3ihjCp7YhHv+xTSE+49RLJkw7hXVgMtC0MUKKfF8qt6IrbHVdDlpSaRWnfluiY1zA3M1NIMzczQ0JiIt6lvIOBgQHc3Sph6eoVcLRzgLmZGQ4ePYz/bt1EaVvVf5AXBfGxb5GRIYPZR4+hi5mb4EnYc5XHfNesLuJj32JQrwkQEMhIz0CbDk3Ro8/38jzHgs/gwb1QLN8wKz/DL9QK0+PpZs2aoVmzZjnOP2/ePIXP06ZNw549e7Bv3z5UrlxZnq6lpQVra+tPjqvAexqTk5Nx9uxZ3LlzR2nfu3fvsG7dumyPT0lJwZs3bxS2dJGR53E2WNQP5m6lcbDLHKV994LOIKjKGGyt+ytiH7xEiy2j5D1an+L1nWc47LcAniNbYUjiRvR/tRLxYZFIDI+FkAml/OaupdB69zj8+9tWPD363yef90uSaEgQefUR/vl5I6KuP8bNv47i5opjqDigcb6eV5aegX3tZ8C0rA0GxqzDkMSNKFXPDY8PXoWQFc0xZa69GyLs0DUkvor9rHKCe/wJiUSC/i9WYOi7zag8pDnubzqr8p6k98xbuSI94Z3So+035x4j9th9JIdE4c35MDwcuh2aRrow++7Te9G/RYETfwOEQIM2TVGlXi0EbduMZo2aFKpf/oXBtcs3sX7Vdoz0H4CVG2dj6uzxOH/2MtYs3wIAiAiPwp8zV+DXqSNVToyhz6eqrZKSkn9j6GUyGd6+fQuzj/7wevjwIUqUKAFHR0f88MMPePr0aa7KLdCexgcPHqBx48Z4+vQpJBIJ6tSpg82bN8PGxgYAEB8fj169eqFHjx5ZlhEYGIjJkycrpDVGeTSFS57FWX9BXzi28MTWur8i4YXygP/UN0lIfZOEuJBXePXvAwyMWQvntjVwf/NZJIbHwaqas0L+zJ6yxPC4LM95f9NZ3N90FvqWJkhLTIEQAlVGtFQY8wcAZhVs0f7YJNz86xguTt3x2df6pSS+isPru4p/FcfcfYEy7WoCAJL+v270rUwV6knfygRR/4V91rkjrz5CUJXR0DHWh6aOFpKj36Dz+UCFWdtFhVFpC5RuVBH72s/87LLiH0VgW/2J0NKXQmqsh8TwODTfNFLpnqT/Kd66EmIO3IFIz/4PkoyEFKQ8iYG0VNHs7f4Uxc3M8TpG8eft65gYGBoYQFeqCwAobVsKaxb/haTkZCQmJsCiuAVG/ToetiVKFkTIX4RJMSNoamogJiZOIT32dTzMzVXfPysWb0TjFvXQqt13AACnMvZITn6HmVMWo0ff73H/bihiY+LRt+tI+TEZGTL8d/UOdm45iOMXtkFTUzPfrqnQyMcld1S1VQICAjBp0qR8Od+sWbOQkJCAjh07ytNq1KiBNWvWoFy5cnj16hUmT54Mb29v3Lp1C0ZGORseU6A9jePGjYObmxsiIyNx//59GBkZwcvLK1ctX39/f8THxytsjZB3f63XX9AXzr7Vsb3hJLwJi1SbXyJ5/3+ZPY2vzt9H8YqloWdhLM9j9507UuITEXPnWRal/E9SZDzSEt+hXCcvZLxLU+hJNHcphQ4nJuPuulM494vqmdiF1ct/7sGsbAmFtGJlbfDmyfsH9vGPI5D4KhalGv5v6RIdIz1Y1yiDl+dVj9vJrdQ3SUiOfgNTZxtYVXVC6J5LeVJuYeLaqz6SI9/g8YEreVZmelIKEsPjIDU1gF0TDzzaW/TqLS8YeZaCbuliiNpzQ21eDT1tSG1NkRb9dYxJ/hLc3SrhwmXFCW3nL/0Ld7dKSnn19fRgUdwC8W/e4NyF82jgXe8LRfnlaWtro2wFJ1y58L/7SiaT4crFG3CtpPp337t3KdD4qEGkqfH+178QAlWru2PttvlYtXmufCvv4ozvmvtg1ea530aDMZ+paqv4+/vny7k2btyIyZMnY+vWrbC0/N8qAs2aNcP333+PSpUqoUmTJjh48CDi4uKwdevWbEpTVKA9jefOncOxY8dQvHhxFC9eHPv27cPAgQPh7e2NkydPwsBA/ewtqVSqNBBaS5I3N3iDRf1Qrov3+6VZ3ibLewhT4pOQ8S4VJg5WKNupNp4c+Q/JUW9gaGuOauPaIj05FY8Pvv8l/eTIf4i58xxN1w3DmXHrYGBdDLV/74L/FgcjI/X9uEeras5ounYotjeaJF+6xH1QM7w6dw+pCe9g9507vGf0wFn/DUiJTwLw/pF0h+OT8eTwdVyZs08em8iQITlaccB9YXR13j50+mcaqvm3w4Ot52Bd3RkV+32HYwOW/i/P/P2o8XMHxD18hfjHkaj9WxckvoxVmBnd/mgAQnZflC8zpG2gC1Pn/43XMHawhIW7Pd7FJODts2gAQJkOtZAc9QZvn0bDvGJp1JvXG6G7L301j/ZzTCKBq18D3Fl3SmlN0CZrhiDhZQz+mRAE4P1kGXMXWwCApo4WDEuaw8LdHqkJ7xAf+n7Mol1jD0ACxN5/CVNna3jP6IHYey9w+zMm2HwNNPS0FXoApSVMoVfWEhlvkpEa/hYlB/tA28IQYQEHFY4r3qYSEm6+xLvQaKUybYfXQ9zpUKS+ioe2hSFKDqgDIROICb6b79dTUJKSkvD0+f/+UH7x6iXuPbgPE2Nj2FjbYO6SBYiMinr/yBlAx7btsWnHFsxeNB9tW7TGxSuXcPjEMSyeOU9exj//noMAYF/aDk+fP8PsRfPhYGcP35atvvDVfVmdurXBtInzUd7FGRXcymDbxn1ITn6H5m0aAgCm/DIPxS3N8ePQ7gAAL59q2LJhL8qUc4RLxbJ48ewVVizZCC+fatDU1IS+gR4cne0UzqGrJ4WJiZFSelGWn68RVNVWyQ+bN29G3759sW3bNjRq1CjbvKampihbtixCQrJed/ZjBdpoTE5OhpbW/0KQSCRYsmQJBg8ejLp162LjxoLtPXP/qSkAoOOp3xXSD/daiDtrTyL9XSpK1nFB5WEtoVvMAEkR8Xh++g62eE1ActT7hpuQybC7VSAaLu6PzucCkZb4fnHvcxM3y8vT1pfCrHxJaGr/r7FrXc0ZtSZ1grahLmLvvcDxH5fh7oa/5fvLdKgFfUsTVOheFxW615Wnx4dFYpXjT/lSH3kp4nIo9rWbgTrTfkDNX79H/ONInBqxGvc2npHnuTxjN7QNdNFo2Y+Qmhrg5dl72Nnsd4U1Gk2crKFX/H/d6lZVnfD9yd/knzPXHLy95iSO9F4I4P06mHVn+0HfygSJr+JwZ/0pXPi96C3RUbpRJRjbWeDWKuVlOIxKF1cYi2hYohi6XZst/1x1dBtUHd0Gz07dwvYGAQAAqYk+vKb9AENbc6TEJODhzn/xz88bIUvP+zHEhYmBizXKLe8i/1xq1PslY6L33UTYpEPQLm4AqbWxwjGahjowbVgWz2apXgJFx9IIjtNaQctEF+mxyUi4/hz3/DYU6WV3bt27g96DB8g/z/jz/fjwNs1bYuovkxH9OhqvIv43qcq2REksmjUfM+bPwYatm2BlYYnJ43+Rr9EIAG8TEzBvyUJEREXCxNgY39VriKEDBkJb69PHlH8NGjapg7jYeKxcsgkxr2PhXM4BsxYFwMzcFMD7MYofNoB69O0IiUSCFYuDEBUZA9NixvDyqYZ+g38ooCug/LBp0yb07t0bmzdvRosWLdTmT0hIQGhoKLp3757jc0iEEAU2ir169eoYMmSIyoAHDx6MoKAgvHnzBhkZufulNFejfV6FSESFjHflr2Mt0q+B+5GBBR1CkRGrp364EalnqV9wS5+lP3idb2VrlTXPVf6EhAR5D2DlypUxZ84c1K9fH2ZmZihdujT8/f3x4sUL+WThjRs3omfPnpg/fz7atWsnL0dPTw8mJiYAgNGjR6NVq1aws7PDy5cvERAQgOvXr+POnTuwsLDIUVwFOqaxbdu22LRpk8p9CxcuRJcuXVCAbVoiIiL6Rkg0JPm25dbly5dRuXJl+XI5I0eOROXKlTFx4kQAwKtXrxTmfyxfvhzp6ekYNGgQbGxs5NuwYcPkeZ4/f44uXbqgXLly6NixI8zNzfHvv//muMEIfEJPY3BwMAwNDVGnzvu3ZyxatAh//fUXXFxcsGjRIhQrVvCz/9jTSFR0sacx77CnMe+wpzFvFGRPY0ZI/r0OVdPZTH2mr0CuexrHjBmDN2/ej9e7efMmRo0ahebNm+Px48cYOXKkmqOJiIiICqFC9BrBwirXE2EeP34MF5f3ayDu2LEDLVu2xLRp03D16lU0b948zwMkIiIiooKX6/avjo4OkpLeL/ty7NgxNG78/g0eZmZm8h5IIiIioq+JRCLJt62oyHVPY506dTBy5Eh4eXnh4sWL2LLl/WuIHjx4AFtb2zwPkIiIiIgKXq57GhcuXAgtLS1s374dS5YsQcmS71/XdOjQITRt2jTPAyQiIiLKdxqS/NuKiFz3NJYuXRr79+9XSp87d26eBEREREREhc8nvREmIyMDu3btwt277195VaFCBfj6+iq83YWIiIjoq1F0OgTzTa5bebdv30arVq0QERGBcuXevxx9+vTpsLCwwL59++Dm5pbnQRIRERHlqyI0YSW/5HpMY9++feHm5obnz5/j6tWruHr1Kp49e4ZKlSqhf//++REjERERERWwXPc0Xr9+HZcvX1Z480uxYsUwdepUVKtWLU+DIyIiIvoSBDsa1cp1T2PZsmURERGhlB4ZGQlnZ+c8CYqIiIiICpcc9TR+uGh3YGAghg4dikmTJqFmzZoAgH///Re//fYbpk+fnj9REhEREeUn9jSqlaNGo6mpqcKK5kIIdOzYUZ4mhAAAtGrVChkZGfkQJhEREREVpBw1Gk+ePJnfcRAREREVHM6eVitHjca6devmdxxEREREVIh90mrccXFxWLlypXxxb1dXV/Tu3RsmJiZ5GhwRERERFQ65nj19+fJlODk5Ye7cuYiJiUFMTAzmzJkDJycnXL16NT9iJCIiIspXQpJ/W1GR657GESNGoHXr1vjrr7/krw1MT09H3759MXz4cJw+fTrPgyQiIiKigpXrRuPly5cVGowAoKWlhbFjx6Jq1ap5GhwRERHRF8GJMGrl+vG0sbExnj59qpT+7NkzGBkZ5UlQRERERFS45LrR2KlTJ/Tp0wdbtmzBs2fP8OzZM2zevBl9+/ZFly5d8iNGIiIiovwlycetiMj14+lZs2ZBIpGgR48eSE9PBwBoa2vjp59+wh9//JHnARIRERFRwct1o1FHRwfz589HYGAgQkNDAQBOTk7Q19fP8+CIiIiIvogi1COYXz5pnUYA0NfXR8WKFfMyFiIiIiIqpHLUaGzXrh3WrFkDY2NjtGvXLtu8hoaGcHV1xY8//sjFvomIiOirUJTWU8wvOWo0mpiYQPL/U9HVNQRTUlKwdOlS/PPPP9i7d+/nR0hERESU37jkjlo5ajSuXr1a5b+zcufOHVSrVu3ToyIiIiKiQuWTxzRmp1y5cjh37lx+FE1ERESU99jRqFau12nMCU1NTbi7u+dH0URERERUAPKlp5GIiIjoq8KeRrXypaeRiIiIiIqWHDUaq1SpgtjYWADAb7/9hqSkpHwNioiIiOhLEhJJvm1FRY4ajXfv3kViYiIAYPLkyUhISMjXoIiIiIiocMnRmEYPDw/06tULderUgRACs2bNgqGhocq8EydOzNMAiYiIiKjg5ajRuGbNGgQEBGD//v2QSCQ4dOgQtLSUD5VIJGw0EhER0den6DxFzjc5ajSWK1cOmzdvBgBoaGjg+PHjsLS0zNfAiIiIiKjwyPWSOzKZLD/iICIiIio4RWjCSn75pHUaQ0NDMW/ePNy9excA4OLigmHDhsHJySlPgyMiIiKiwiHX6zQePnwYLi4uuHjxIipVqoRKlSrhwoULcHV1xdGjR/MjRiIiIqL8JcnHrYjIdU/j+PHjMWLECPzxxx9K6ePGjcN3332XZ8ERERERUeGQ657Gu3fvok+fPkrpvXv3xp07d/IkKCIiIqIvSUjybysqct1otLCwwPXr15XSr1+/zhnVREREREVUrh9P9+vXD/3798ejR49Qu3ZtAMA///yD6dOnY+TIkXkeIBEREVG+4+xptXLdaPz1119hZGSE2bNnw9/fHwBQokQJTJo0CUOHDs3zAImIiIio4OW60SiRSDBixAiMGDECb9++BQAYGRnleWBEREREXww7GtX6pHUaM7GxSEREREUCG41q5XoiDBERERHln9OnT6NVq1YoUaIEJBIJdu/erfaYU6dOoUqVKpBKpXB2dsaaNWuU8ixatAj29vbQ1dVFjRo1cPHixVzFxUYjERERffOERJJvW24lJibC3d0dixYtylH+x48fo0WLFqhfvz6uX7+O4cOHo2/fvjh8+LA8z5YtWzBy5EgEBATg6tWrcHd3R5MmTRAZGZnjuCRCCJHrqynk5mq0L+gQiCifeFeuUdAhFBnuRwYWdAhFRqzes4IOoUiw1K9QYOd+l/wu38rW1dP95GMlEgl27doFX1/fLPOMGzcOBw4cwK1bt+RpnTt3RlxcHIKDgwEANWrUQLVq1bBw4UIAgEwmQ6lSpTBkyBCMHz8+R7HkqqcxLS0NDRs2xMOHD3NzGBEREVHh9hW/RvD8+fNo1KiRQlqTJk1w/vx5AEBqaiquXLmikEdDQwONGjWS58mJXE2E0dbWxo0bN3JzCBEREdE3LSUlBSkpKQppUqkUUqk0T8oPDw+HlZWVQpqVlRXevHmD5ORkxMbGIiMjQ2Wee/fu5fg8uZ493a1bN6xcuVLp3dNERF9CiR7VCzqEIuOdfor6TJQjD+POFHQIRUJBPp4G8m+0XmDgH5g8ebJCWkBAACZNmpRv58wPuW40pqenY9WqVTh27Bg8PT1hYGCgsH/OnDl5FhwRERHR187f31/prXl51csIANbW1oiIiFBIi4iIgLGxMfT09KCpqQlNTU2VeaytrXN8nlw3Gm/duoUqVaoAAB48eKCwT8JX8BAREdFXSORjT6NuHj6KVqVWrVo4ePCgQtrRo0dRq1YtAICOjg48PT1x/Phx+YQamUyG48ePY/DgwTk+T64bjSdPnsztIURERESFmoCsoEOQS0hIQEhIiPzz48ePcf36dZiZmaF06dLw9/fHixcvsG7dOgDAjz/+iIULF2Ls2LHo3bs3Tpw4ga1bt+LAgQPyMkaOHImePXuiatWqqF69OubNm4fExET06tUrx3F98hthQkJCEBoaCh8fH+jp6UEIwZ5GIiIios90+fJl1K9fX/4589F2z549sWbNGrx69QpPnz6V73dwcMCBAwcwYsQIzJ8/H7a2tlixYgWaNGkiz9OpUydERUVh4sSJCA8Ph4eHB4KDg5Umx2Qn1+s0vn79Gh07dsTJkychkUjw8OFDODo6onfv3ihWrBhmz56dm+LyBddpJCq6Os0dUtAhFBlG/SsWdAhFxo24HQUdQpHgZdO/wM6d9O5tvpWtr1s0Xruc6zfCjBgxAtra2nj69Cn09fXl6Z06dZIvIElERERERUuuH08fOXIEhw8fhq2trUJ6mTJl8OTJkzwLjIiIiOhLKUxjGgurXPc0JiYmKvQwZoqJicnXmUFEREREVHBy3Wj09vaWz9YB3i+zI5PJMGPGDIVBm0RERERfC5GP/ysqcv14esaMGWjYsCEuX76M1NRUjB07Frdv30ZMTAz++eef/IiRiIiIiApYrnsa3dzc8ODBA9SpUwdt2rRBYmIi2rVrh2vXrsHJySk/YiQiIiLKX0KWf1sR8UnrNJqYmODnn3/O61iIiIiICkRReoycXz6p0RgbG4uVK1fi7t27AAAXFxf06tULZmZmeRocERERERUOuX48ffr0adjb2+PPP/9EbGwsYmNj8eeff8LBwQGnT5/OjxiJiIiI8pWALN+2oiLXPY2DBg1Cp06dsGTJEmhqagIAMjIyMHDgQAwaNAg3b97M8yCJiIiIqGDluqcxJCQEo0aNkjcYAUBTUxMjR45UeLk2ERER0ddDlo9b0ZDrRmOVKlXkYxk/dPfuXbi7u+dJUERERERUuOTo8fSNGzfk/x46dCiGDRuGkJAQ1KxZEwDw77//YtGiRfjjjz/yJ0oiIiKifMTZ0+rlqNHo4eEBiUQCIf5XoWPHjlXK17VrV3Tq1CnvoiMiIiKiQiFHjcbHjx/ndxxEREREBaYozXLOLzlqNNrZ2eV3HEREREQFiI+n1fmkxb1fvnyJs2fPIjIyEjKZYst86NCheRIYERERERUeuW40rlmzBgMGDICOjg7Mzc0hkUjk+yQSCRuNRERE9NXh42n1ct1o/PXXXzFx4kT4+/tDQyPXK/YQERER0Vco143GpKQkdO7cmQ1GIiIiKjK45I56uW759enTB9u2bcuPWIiIiIiokMp1T2NgYCBatmyJ4OBgVKxYEdra2gr758yZk2fBEREREX0ZHNOozic1Gg8fPoxy5coBgNJEGCIiIiIqenLdaJw9ezZWrVoFPz+/fAiHiIiI6MvjmEb1ct1olEql8PLyyo9YiIiIiAoEl9xRL9cTYYYNG4YFCxbkRyxEREREVEjluqfx4sWLOHHiBPbv3w9XV1eliTA7d+7Ms+CIiIiIvgw+nlYn141GU1NTtGvXLj9iISIiIqJCKteNxtWrV+dHHEREREQFRgiOaVSHr3UhIiIiIrVy3dPo4OCQ7XqMjx49+qyAiIiIiL40LrmjXq4bjcOHD1f4nJaWhmvXriE4OBhjxozJq7iIiIiIqBDJdaNx2LBhKtMXLVqEy5cvf3ZARERERF8exzSqk2djGps1a4YdO3bkVXFEREREX4zIx/8VFXnWaNy+fTvMzMzyqjgiIiIiKkRy/Xi6cuXKChNhhBAIDw9HVFQUFi9enKfBEREREX0JfI2gerluNPr6+ip81tDQgIWFBerVq4fy5cvnVVxEREREVIjkutEYEBCQH3EQERERFaCiM/Ywv3BxbyIiIiJSK8c9jRoaGtku6g0AEokE6enpnx0UERER0ZfEMY3q5bjRuGvXriz3nT9/Hn/++SdkMlY4ERERUVGU40ZjmzZtlNLu37+P8ePHY9++ffjhhx/w22+/5WlwRERERF+CTGQUdAiF3ieNaXz58iX69euHihUrIj09HdevX8fatWthZ2eX1/ERERER5TuZyMi3rajIVaMxPj4e48aNg7OzM27fvo3jx49j3759cHNzy6/4iIiIiKgQyPHj6RkzZmD69OmwtrbGpk2bVD6uJiIiIvoaCRSdHsH8kuNG4/jx46GnpwdnZ2esXbsWa9euVZlv586deRYcERERERUOOW409ujRQ+2SO0VRtfFt4dy2JszKl0R6cipenruPs+PXI/bBS3mehksHoHTDSjAsUQypCe/w6tx9nBm/AbH3XwAAileyQ7Vx7VCyTnnoFTdCfFgUbi47gmt/HshRDJo6Wuj87x+w9HDAhsqjEPVfmFIeEydrdLs6C7IMGZaY9ciTa/8SDEqYwfuPbrBvVgXa+jqICwnHkd6LEHElVClvwyX9UWlAE5wasQrX5mdfd+4Dm8JzdBsYWJsi6r8wnBy6EhGXQv5XlpqvWVGQm7oFANu6rvj+pPJktmU2fZAUEaeUXm1cW9QJ7Iar8/fj7xGr8zr8QkOnhAkMPUtB29IImoZSxOy7hXePorPOX9IUxTt4KKWH/3UOsqRUAIBh1dLQdS4OrWL6EOkypL56gzdnQ5ERl5xfl1FobN28A+vXBuH16xiUKeuMMeNGwq2ii8q86WnpWL1qHfbvO4ioyGjY2ZfGkGEDUdurpsr8a1atw8I/l6JL144YNXZ4Pl5FwTu+6xqCN19GfEwiSjlb4IehDeBYwSbL/Elv32HHyrO4ejoEiW/fwdzKGF0G10Olmo4AgN2rz2Hv2vMKx1iXKoZp63vn63UUJkVp7GF+yXGjcc2aNfkYRuFl6+OK/xYHI+JSCCRaGvCa+gPaHZ6Ita7DkJ6UAgCIvPII94LO4O3TKOiaGaJmQCe0O/wrVjkOhJDJYOXphOSoeBzqPh8Jz17DpnY5NFr2I2QZMvy36JDaGLxn9EDiy1jAw0Hlfg0tTTTfOAIvztyFTe1yeXr9+UlqaoBOZ6fi+clb2NV8CpKj3sC0jA3exSYo5XXyrQ7rGmWR8OK12nLLdqwNn9l+OP7TMoRfeIgqw1uiXfCvWFN+CJKj3gBQ/zX72uWmbj+2utxgpL75X+MlKTJeKY9VVSdU7P+dyj9gihqJtibSohORdCccZi1zPn47Yu0FiNT//RLKbDAC7xuWif+9RFrEG0BDAuPajjBv646o9Rch0r/++y8rRw4fw9zZf8L/5zFwq+iKTUFbMGTgCOzYswlmZmZK+RcvWoZDBw7j54njYe9gh3/PXcCYkeOxcu0ylC+v+LPu9q072Ll9D8qUdf5Sl1NgLp64hy2L/0b3kY3gWMEGR7dfwZwxOzBtfW8YF9NXyp+eloFZo7fDuJg+Bk5uhWLFDfE64g30DKUK+Uram2P07O/lnzU0v72OosJk0aJFmDlzJsLDw+Hu7o4FCxagevXqKvPWq1cPf//9t1J68+bNceDA+04WPz8/pafETZo0QXBwcI5jKnRvhBGicL3GZ1fzKbiz9iRe33mG6BtPcKTXQhjbWcDK00me5+ZfR/HizB28eRKFyGuPce7XTTAubQFjewsAwO3VJ3Bq+Cq8OH0H8Y8jcC/oNG6vOQHntjXUnt++aWWU/s4dp8eoHg4AALWndEHsvRd4sO3c51/wF1RtXFskPIvGkT6LEHEpBG/CIvH06H+IfxShkM+ghBnq/9kXwd3mIyNN/V+CVUa0wq0Vx3BnzUnE3H2OYz8uQ3pSCtx6N5TnUfc1+9rltG5VSY6MR1JEnHzDR9+T2ga6aLZhOI71X5qjRujXLuVJDN6ef4x3oVn3LqoiS0qDLClVvn0oZs8NJN8NR3pMEtKjExF39B60jHWhbWmUl6EXOkHrN8O3XWu09m0JRycH+P8yFrq6UuzdvV9l/oMHDqNXn56o410btrYl0aFjO9SuUxtB6zYp5EtKSsKvEybj54njYWRUtOsQAA5vuwKfFhXh3cwNJe3N0WPkd9DR1caZgzdV5j9z8BYS377D4CltUKZiSRS3MUE5j1Io7WypkE9DUwMm5gbyzchUuQFalBWm2dNbtmzByJEjERAQgKtXr8Ld3R1NmjRBZGSkyvw7d+7Eq1ev5NutW7egqamJ77//XiFf06ZNFfJt2rRJZXlZKXSNRqlUirt37xZ0GFnSMXn/TfQu5q3K/Vr6Urj2qo/4RxF4+yzrXjGpsT5SYrL/hatvaYJGy3/C4R5/yns1P1aqvhvKdqiNE4P/yuEVFB6Oraoi4kooWmwZhQHhq/DDlZlw69tIMZNEgqbrhuLKrD14feeZ2jI1tLVg5emEp8du/C9RCDw9dgM2NcuqPCanX7OvSY7qNgs/XJuN/i9WoN3hiSihoue6wcK+eHzwCp4ev6HiaMpk8UNVWPWtBfO2laBjY5xtXonO+4c+spSi+0attLQ03Lt7HzVqVJWnaWhooHqNarhx45bqY1JToSPVUUjTlerg+jXFe2/6tNnw8q6NGjWr5X3ghUx6Wgae3I+Ai2dpeZqGhgQunqUReueVymOunwuFk0sJbJh3HMPbLsGvfmuwf8MFyDIUe7UjXsRiRPulGNtlBZZPOYDXEW/y9Vooa3PmzEG/fv3Qq1cvuLi4YOnSpdDX18eqVatU5jczM4O1tbV8O3r0KPT19ZUajVKpVCFfsWLFchVXjh9P57WRI0eqTM/IyMAff/wBc3NzAO8rLjspKSlISVFsUKWLDGhJNPMm0A9JJKg3txdenL2L17cVGzCVfmoC7+ndoWOoh5h7L7Cj8WTI0lT/ArCpVQ5lO3lhd8tp2Z6u8erBuLHsMCKuhMLYTrkHTNfMEI1XD0Zw9z+R+vbrGwtl4miFSj82wdW5+3AxcCesqzmj/vzekKWm4866UwCAauN8IdIzcjz+U6+4ETS0NJXG4CVFxqNY+ZIKabn5mn1tclK3H0t8FYtjPy5FxOVQaEq14da3ITqc/A2ba45H5LXHAICynbxgWcURG6uP+4JX83XJSExB3PH7SIt8C2hqwMDVBubtPRC95SrSolT/oWhS1xkpL+OR/jrxC0f75cTFxiEjIwNm5oqPoc3MzRAW9kTlMTVr1cDG9ZtRpYoHbEuVxMULl3HixN8KjZ3DwUdx7959rAtama/xFxZv45MhkwkYmxkopBsX08erpzEqj4l6GYe74W9Q87sKGP5HO0S+iMX6eceRkZ6BNn61AQCOLjboM74prEuZIf51IvasPYc/hm7Gb6v9oKevo7LcokYm8m9oiKq2ilQqhVQqVcqbmpqKK1euwN/fX56moaGBRo0a4fz580r5VVm5ciU6d+4MAwPF++TUqVOwtLREsWLF0KBBA0yZMkXe3sqJAms0zps3D+7u7jA1NVVIF0Lg7t27MDAwyNHEm8DAQEyePFkhrTHKoylUD6z+HA0W9YO5W2ls9f5Zad+9oDN4evQGDGyKwXNUa7TYMgpb6vyMjJQ0hXzmrqXQevc4/PvbVjw9+l+W5/IY0hw6Rnq4FJj16xsbLf8J9zedxYszdz79ogqQREOCiMuh+OfnjQCAqOuPYe5WChUHNMaddadgWcURlYe2QJDnmHw5f06/Zl8jdXWrSuyDlwoTvF6dvw9TR2tUGd4KwT3/hKGtOerN642djX8rEnWUXzLikpH0wYSWuFdvoGmiB4PKtog7ck8pv0n9MtAyN0D0tmtfMsyvwuixwzHltz/QoW0XSCQSlLQtidatW2DvnvePs8PDIzB7xjwsWjpf5S9fek+I941Kv1HfQUNTA/blrBAbnYDgzZfljcZKNf43Zr6UkwUcK1hjTOe/cOnkffi0qFhQoX9RsnxcckdVWyUgIACTJk1SyhsdHY2MjAxYWVkppFtZWeHePeWfIR+7ePEibt26hZUrFf+Qatq0Kdq1awcHBweEhoZiwoQJaNasGc6fPw9NzZx1tBVYo3HatGlYvnw5Zs+ejQYNGsjTtbW1sWbNGri45KzR5+/vr9Rrucwk72cP11/QF44tPLG17q9IeKH811zqmySkvklCXMgrvPr3AQbGrIVz2xq4v/msPI9ZBVu0PzYJN/86hotTd2R7vlL1K8KmVlkMfbdZIb3rpRm4F3Qah3stRKkGFeHUuho8R7V+v1MCaGhqYljqVhwbsBS3V5/4/AvPR4mv4vD67nOFtJi7L1Cm3fuZkSW9K0Df0gR9nyyT79fQ0oTPrJ6oPKwlVjn+pFRmcvRbyNIzoG9lqpCub2mCpPA4hbScfM2+VurqNqfCL4WghFd5AICVpxMMrEzxw5WZ8v0aWpqw9XGBx6Bm+FO3c5GYRJQf0iLeQKeEiVK6Sb0y0HUwR/T265AlqB6CUlSYFjOFpqYmYl4r/vyMeR0D8+LKk2AAoJhZMcyeNx0pKSmIj3sDC8viWDB/MUqWfP/U4N6de4iJiUW3Lr3kx2RkZODa1evYumUHzl08leNfhl8LIxM9aGhI8CZGsVf6TWwSTD7qfcxkYm4ATU0NaGj+b0SajZ054mMSkZ6WAS1t5TrSN9KFlW0xRL6Iy9P4v1Wq2ir59YfOypUrUbFiRaVJM507d5b/u2LFiqhUqRKcnJxw6tQpNGzY8ONiVCqwRuP48ePRsGFDdOvWDa1atUJgYCC0tbVzXY6q7t28fjRdf0FfOPtWx7b6AXgTpnoQ6ockkvf/pyn93/WYu5RC++OTcHfdKZz7ZaPaMk4N+7/27jwuqqr/A/hnQBj2TXQGUBEFWRRBURD3lEQrk9QyMzUsbdHKKC17VHApcclMJbXS1H6lZqVtihlKi5IoghtLau6ssoPsc35/8DQ2MnKxBxhkPu/Xa14595575pzTRb5+7zlnNuPogtvlzB3tMO7AQvz45GpkHvsTALCr/zzI/vGXQNcx/ugzNwS7BrytNbBtadKPpMKum6PGMdtuDii6kgMASPnsF825iQDGRi9Ayv/9eteAWFVVjayEi+g43BsXv42vPSiToePwnvWuVNf2/+x+JjW2DdXOpzNKM/IBAFdjTmO792yN8yO2zEJ+6g0cX7GHAWM9jNpZoKZUczGM9VA3mHS1x82vk1BTVK6jljUfIyMjeHi6Iz4+AUOHDQEAqFQqHI8/gSeeHFfvtXK5HO0V7VBdVY1DMbF48MHaX3B9A/pg51efaZRdvPAdOLs4Y2ro060uYASANkaGcHZXIOXkVfQe5AYAUKkEUhKuYthjvlqvcevhiD9+ToVKJWBgUPsEL+taPqzbmmsNGAGg/FYlctILYT1CeyDaGokm3HLnbo+itbG3t4ehoSGysjQXLmZlZUGpVNZ7bWlpKXbu3InFi+tun3anLl26wN7eHhcuXGj5QSMA9O3bFwkJCZg5cyb69OmDzz//vMXtBTksajrcJw7CdyGRqCwuU2ewKgpvoaa8EtYuCnSb0B9XfjqFspwiWHRoi75vPobqskpc2pcAoPaR9PiYRbhyIAkJq79X1yFqVCi7WTvRWNHXFSO3vYKvgiJQmp6H4muaKzWrSmp/qRRezFQHhHmpmnsKKvq4QqhEnfmWLdXJNd9jwpF30XfeWPz55VEo/V3hPf1B/Pz8RgBAeV4Jyu9YLFRTVYPSzHyNx6jjDobjwt54dVB48v3vEbz1ZWSfuIjM+PPoNfsRGJnL1YFmQ/6f3e+kxhYABrw7CRaOdjjwzDoAQK9XH0bhpWzknruGNiZG6PFcEDoO64FvgpcAqL0H77y3qkrLUZZXfN/cc/+GzMgQhtam6veG1iZoY28BUVGFmuIKWPZ3gaGFXP3o2dy3A2qKylCVewuyNgYw6+4A4w62yN17ezqK9QNuMHVXIO/7MxCVNTD475wxVUU1UNN6g+9Jk59ExIKl8PLyQPceXvji810oKyvH6DGPAAAWzl+M9u3bYdYrtU8Rzp45h+zsHHRzd0NOdg4+2rgZQiUw5ZlJAABzc3O4unbV+AwTU1PYWFvXOd6aBD/uh0+WRaOzuxIunkoc/OokKsqrMHBU7ZZQH7+7H7b2Fhg/YxAA4IExPojZk4Qd6w5h+NheyLpegB8/P4bhY3up69z1YSx8+3dFW4UVCnJLsPfTo5AZyBAw3EMnfdRnxsbG8PPzQ0xMDEJCQgDU/gMrJiYGs2bNqvfa3bt3o6KiAk8//bTk51y/fh25ublwcLj7/p530mnQCAAWFhbYtm0bdu7ciaCgINTUtKzNNX1eHAkAeCJ2icbxA6HrkbztMKrLK+E00Au9Xn0EJrbmuJVViOu/JmPXgLfVewK6jQ+EWXtreE4eAs/JQ9R1FF7OVj9iNTKTw87DCYZ3+Vdfa5R14iK+H7sCA9+dhH4LHkfhpWzEvvYpUr/47Z7qse6qhKn97W02/vzyKEzbWSNw0ZMwU9ogJ+kS9oxaqt5vsCH/z+53DRlbc6UtLDvZq98bGrfBkFVTYeFkh6pblbh5+gq+fnAxrsdqX9mqL4zaW2ps1m09uHYfwFvJmSg4mApDczkMLU3U52WGMlgNcoWhhTFElQpVN0uQu+cUKq8XqMuY96x9vGo//vYvbQDI/ykVZSmZTdcZHRsRHIT8/AJs3PAxcm/moZu7G9Z9uBpt/7s4JjMjCway209PKioqsSHqI9y4ng5TM1MMGBiIxUsXwtKq9W+rUx//YR4oLijD3k+PoDDvFjq6tsNrK8apH0/nZRXB4B8JGLv2VghbOQ4718di4bTtsG1ngaBxvfHQxNurzfNzSrBxyY8oLSqHpbUp3LydMP/Dp2ClR9vutKTNvcPCwjB16lT06dMH/v7+WLNmDUpLSxEaWjsVY8qUKXBycsKyZcs0rtu8eTNCQkLqLG4pKSnBokWLMG7cOCiVSly8eBFz586Fq6srgoODG9wumWhBGyNev34dCQkJCAoKqrPi5168b1D/ow4iun9NeP9lXTeh1bCcoR8LHJrD6YL656lTwwxwmKGzz07Jl/6yjX/L03bUPV+zfv169ebevr6+WLt2LQICavd3Hjp0KDp37qzxxStpaWnw8PDATz/9hAcffFCjrrKyMoSEhCAxMREFBQVwdHTEiBEjsGTJkjoLburTooLGxsKgkaj1YtDYeBg0Nh4GjY1Dl0HjuTztm8w3hu52jzRZ3c2pxW3uTUREREQtj87nNBIRERHpWkua09hSMWgkIiIivdeUm3u3Fnw8TURERESSmGkkIiIivdeUm3u3Fsw0EhEREZEkZhqJiIhI73EhjDRmGomIiIhIEjONREREpPeYaZTGTCMRERERSWKmkYiIiPSeSqh03YQWj5lGIiIiIpLETCMRERHpPX4jjDQGjURERKT3uLm3ND6eJiIiIiJJzDQSERGR3uOWO9KYaSQiIiIiScw0EhERkd5jplEaM41EREREJImZRiIiItJ73HJHGjONRERERCSJmUYiIiLSe5zTKI1BIxEREek9bu4tjY+niYiIiEgSM41ERESk9/h4WhozjUREREQkiZlGIiIi0nvMNEpjppGIiIiIJDHTSERERHpPJVS6bkKLx0wjEREREUlippGIiIj0Huc0SmPQSERERHqvRsWgUQofTxMRERGRJGYaiYiISO9xIYw0ZhqJiIiISBIzjURERKT3VJzTKImZRiIiIiKSxEwjERER6T1uuSONmUYiIiIiksRMIxEREem9GmYaJTFoJCIiIr2nUnHLHSl8PE1EREREkphpJCIiIr3HhTDSmGkkIiIiIknMNBIREZHe4+be0phpJCIiIiJJzDQSERGR3lMJrp6WwkwjERERUQsTFRWFzp07w8TEBAEBAYiPj79r2a1bt0Imk2m8TExMNMoIIbBw4UI4ODjA1NQUQUFBOH/+/D21iUEjERER6b0aVU2Tve7Vrl27EBYWhvDwcJw8eRI+Pj4IDg5Gdnb2Xa+xsrJCRkaG+nXlyhWN8ytWrMDatWuxceNGHDt2DObm5ggODkZ5eXmD28WgkYiIiPSeStQ02eterV69GtOnT0doaCi8vLywceNGmJmZYcuWLXe9RiaTQalUql8KhUJ9TgiBNWvWYP78+RgzZgx69uyJ7du3Iz09HXv37m1wuxg0EhERETWhiooKFBUVabwqKiq0lq2srERCQgKCgoLUxwwMDBAUFIS4uLi7fkZJSQmcnZ3RsWNHjBkzBufOnVOfu3TpEjIzMzXqtLa2RkBAQL113okLYYjovnL2nR913YRWY+jUvrpuQqtRWJGl6ybQ/6gpt9xZtmwZFi1apHEsPDwcERERdcrevHkTNTU1GplCAFAoFEhNTdVav7u7O7Zs2YKePXuisLAQq1atQv/+/XHu3Dl06NABmZmZ6jrurPPvcw3BoJGIiIioCc2bNw9hYWEax+RyeaPVHxgYiMDAQPX7/v37w9PTE5s2bcKSJUsa7XMYNBIREZHea8otd+RyeYODRHt7exgaGiIrSzN7nZWVBaVS2aA6jIyM0KtXL1y4cAEA1NdlZWXBwcFBo05fX98G1QlwTiMRERFRi2FsbAw/Pz/ExMSoj6lUKsTExGhkE+tTU1ODM2fOqANEFxcXKJVKjTqLiopw7NixBtcJMNNIREREhJp/scq5qYSFhWHq1Kno06cP/P39sWbNGpSWliI0NBQAMGXKFDg5OWHZsmUAgMWLF6Nfv35wdXVFQUEBVq5ciStXruC5554DULuyevbs2Vi6dCnc3Nzg4uKCBQsWwNHRESEhIQ1uF4NGIiIiohZkwoQJyMnJwcKFC5GZmQlfX19ER0erF7JcvXoVBga3Hxbn5+dj+vTpyMzMhK2tLfz8/HD06FF4eXmpy8ydOxelpaWYMWMGCgoKMHDgQERHR9fZBLw+MiGEaLxutgzvG4zTdROIqIl0t++i6ya0GkP/jNB1E1qNnwtW67oJrcJDnRfo7LPXxz/VZHXP8v+iyepuTsw0EhERkd7jd09L40IYIiIiIpLETCMRERHpvabc3Lu1YKaRiIiIiCQx00hERER6ryVtudNSMdNIRERERJKYaSQiIiK9p1Jx9bQUZhqJiIiISBIzjURERKT3VJzTKIlBIxEREek9brkjjY+niYiIiEgSM41ERESk97jljjRmGomIiIhIEjONREREpPe45Y40ZhqJiIiISBIzjURERKT3uOWONGYaiYiIiEgSM41ERESk91QqoesmtHgMGomIiEjvqQQXwkjh42kiIiIiksRMIxEREek9Pp6WxkwjEREREUlippGIiIj0HjON0phpJCIiIiJJzDQSERGR3lMJZhqlMNNIRERERJKYaSQiIiK9p1Jxn0YpzDQSERERkSRmGomIiEjvcfW0NAaNREREpPe4EEYaH08TERERkSRmGomIiEjv8fG0NGYaiYiIiEgSM41ERESk91SCW+5IYaaRiIiIiCQx00hERER6j3MapTHTSERERESSmGkkIiIivcdMozQGjURERKT3BDf3lsTH00REREQkiZlGIiIi0nsqFbfckcJMIxERERFJYqaRiIiI9B4XwkhjppGIiIiIJDHTSERERHpPxdXTkhg01qPvW4/B9bF+sPNwQnVZJdKPpuH3tz5D/p/p6jLWXRQYvHIqHAd6wFBuhCvRSTj8yie4lV2oLvPo3rfQzrczzNpboyK/FFd/Po3f3voMpRn5Wj/Xyrkdnr20Ueu5H55YhfNfxcG+pzP6vjkWTgM9YGpvicLLOTiz6Sckrv2xcQehCU37awOsO7evczzpw/04POsTAIBDv27ov/QpOAS4QVWjQk7SZXwzcglqyisl6+/75mMYuOxpnPzgB/zy2qcAALmtBQIXTYDzgz6w6mSPWzlFuPhtPI4u2InKoluN20EdasjY/pNBG0P0nTcWXlOGwsLJDvlp6fjtrc9w5UCSuozMwAD9Ip6A56TBMFfaoCQ9H8nbDuPY0q+asis6Z9vPBZ1nDoWljxNMlNZInLoVOfvP1XuNclwvuMwaCjMXe1QXl+NmTCr+XPQjqvLr3mPKEB/0/OhpZO8/i6Sp25qqGzp3IjEBW/9vO5JTU5Bz8ybWrHgPw4c8UO81xxNOYOUHq3Hhr4tQKhSYEfocQh55VKNMVnY23o/6AL8fPYryinJ07NARSxdEoLunV1N2R+d+/y4Nh75KRnFeGRy72GLsS33h7GGvtWz8Txex4704jWNtjAyw8oen1O+L88vw/eZEpCVkoKy0El17tMfYmX3RzsmqSftB9xcGjfXoMLg7Tn0YjazjFyBrY4AB70zC2AMLsa37q6i+VYE2ZnKMPbAQOacu46vhEQCA/osnYsx387AjcB7w33+1XIs9i/hlX6M0owAWTnYYvHIKHtn9BnYN/I/Wzy2+lotNDs9qHPOe8SD6vDEGl/cnAgAUfl1RllOI/ZM/QMm1XDj0d0fQphegqlHhVNT+phuURrTD/03IDG/PkLDv0QnjDobj/O7av9wc+nXDY/vn43jkHsS+shmq6hq08+kMNGCFm6JPV3jPeBA5py5rHLdwtIWFgx1+m7MducnXYOXcDsM3PA8LBzv88MSqxuyeTkmN7Z36L50Iz0mDcXDGRuSn3oBzsC8e/WYudg74D3KSLgEA+rwZAp8XgnHgmXXIPXcNij5dMWLLLFQU3kLSun3N0i9dMDQzRvG5dNzYcRy+W6dKlrfx7wzv9U8ibcF3yPkpGXIHa3itGAev1eNxKnS7RlmTjrboFvEI8uP+aqrmtxhlZeXo5tYNj40eg9lvviFZ/nr6DcwMewWPjx2PyEVL8ceJeES8uwTt7O0xoF9/AEBhURGmzAhF3959sGHNOtja2uLq1auwsrRs6u7oVGLsZez9KAGPvxwAZ4+2+GVPKjb95xDmbX4UljYmWq8xMTPCvM23A26Z7PY5IQQ2L/oFhoYGeDZiCEzMjBD7TQo2vBWDNz8eDbmJfoQKnNMoTT/uhH9pz0NLNd7/FLoeL2R/CoVfV9z4LRmOAzxg1bkdPu/9BiqLywAAB55Zh5fytqHTMG9cjTkNAEhc84O6juKrOTi+fA8e3fMmDNoYQlVdU+dzhUqFW1kFGsdcQ/zx5+6jqCotBwCc+/SQxvnCS1lwCOwG18cC7pugsexmkcZ7l7ceQ8GFDFz/pTaLM2R1KBLX7cPx5XvUZf6Z5b0bI3MTjPq/2fh5xkb4/2ecxrncc9fww+Mr1e8L/8rCkflfYORnr0JmaABR0zq2XJAa2zt5Pj0E8e9+jcv7TwIATm88gE7De8IvbDSip6wFADgGuuPid8dxaV9tmaIrOXB/chCUfV2bsCe6d/NQGm4eSmtwees+zii7lo+rnxwBAJRdzce17X/A5eWhmgUNZPDe8BQurvgJNv26wMha+y/71mJQ/wEY1H9Ag8t/+c1XcHJ0wpxXwwAAXVy6IDEpCZ/t+FwdNG75bCuU7RVYunCR+roOjk6N2/AWKPabFASOdEVAcFcAwOOvBCAl/gaOHbiAoAk9tF8kA6zsTLWeyrlRjCspNzF30yNw6GwDABj/cgDCn/wKiYcvod8ot6boRovT0rbciYqKwsqVK5GZmQkfHx+sW7cO/v7+Wst+/PHH2L59O86ePQsA8PPzw7vvvqtR/plnnsG2bZpPM4KDgxEdHd3gNnEhzD0wtjYDAJTnFQMA2siNAAHUVFSpy9SUV0KoBBwHemitQ25rAY+nBiP9aJrWgFGb9r27oH2vLji7OabecnIrM1TklTSozpbGwKgNPCcNxtn/BsOm7azg0K8byrILMeH3dzAjYzMeP7wYjgO0j+s/DVv/HC7tS1AH7VLk1maoLLrVagLGO905ttoYyo1QXV6lcay6rAKOAz3V79Pj0tBxmDds3BwAAPY9neE40AOXoxObpuH3qcITV2DiaA374bX3qnE7CyhGe+Pmz6ka5bq+8SAqb5bgxhfHddHMFu/UmdPo11fzF2T/foE4deaM+n3sr7/Ay9MLYfPmYsjI4Xh88kR8tfeb5m5qs6quqsH183no1ttBfczAQAa3Xg64knzzrtdVllVj8eQ9WDTpG2wOj0XG5QKNOgHAyNhQo842Rob461xO43eCJO3atQthYWEIDw/HyZMn4ePjg+DgYGRnZ2stHxsbi4kTJ+Lw4cOIi4tDx44dMWLECNy4cUOj3MiRI5GRkaF+7dix457axaCxoWQyDH0/FDd+T0HuuWsAgIw//kRVaTkGLp+MNqbGaGMmx6BVU2HQxhDmDrYalw+MfBqzij/HS7nbYNnJHt+FRDb4o3s8Oxy5ydeQEXf3bIdDoDu6TRiA0x8f/Hf90zHXEH/IbcyRvPUwgNq5ogDQL3wCznzyM/aMWorsxL8w7ucI2Lg63LWebhMGoH3vLvh93ucN+lyTtpYImP84znz88//eiRbqzrHV5sqBJPi9Nrp2bGUydArqCdex/TTu4+ORe/DnriN4JmUtXqnYhadPrkLiBz8g9YvfmqMb942C+Ms48+IO9Px4EoJuRGLouXBUF5Uj5a3bGXObgM5weqovksN267ClLVtubi7a2rXVONbWri1KSktQXl77xOV6+g18+c1XcO7YERs/iMITY8cjcvVKfPvj97pocrMoLaqASiXqPIa2tDVBUX6Z1mvad7DCk2GBeDZiCCa9OQAqIbD2tQMoyCkFACg6WsO2vTl+2JKIW8UVqK6qQcyucyi4eQtFedrrbI1UQjTZ616tXr0a06dPR2hoKLy8vLBx40aYmZlhy5YtWst//vnneOmll+Dr6wsPDw988sknUKlUiInRTDbJ5XIolUr1y9bWVmt9d6PzoHH9+vWYMmUKdu7cCQD47LPP4OXlBQ8PD7z99tuorq6u9/qKigoUFRVpvKpFwzJ492JY1HS07dEJ+yauVh8ru1mEH554D10e6YNZxZ9jZsFnMLE2R1bCRYg75kacWPkt/q/3G/h6xCKIGhWCt73SoM81NDGG+8RBOLvl7lnGtt074tG9b+KPxV/i6sFT/66DOtZ92nBc3p+oXhwkM6i9Nc989BOStx5GTtIl/BK2Fflp6eg+bZjWOiw6tMXQNdOw/+kPNLK/d2NsaYqQH95GbvI1/BGxq/E608LcObbaxM7egvzzGZia8gFerdiFB9Y9h3NbD2nMH+32RH94PDUI+yatwed+c3DgmfXwe30MvKYMbYZe3D/Mu7WH+zuP4q/3fsYfD36AhAkfw7STHTxX1k6VMDSXw3v9RCSHfYWqvNaz+EoXVCoVPN098OpLL8PT3QOPPzYO48Y8hi+/ad2Ls+5VZ6926PtgFzh1tYNrTwWmLRwCC2s5ju47DwAwbGOA0IWDkXOjGP8ZvxtvProTF05lwrOvI2T/nPxI/5q2WKWiokJr2crKSiQkJCAoKEh9zMDAAEFBQYiL0z4v/U63bt1CVVUV7OzsNI7Hxsaiffv2cHd3x4svvojc3Nx76odO5zQuXboUK1aswIgRI/Daa6/hypUrWLlyJV577TUYGBjg/fffh5GRERYtWnTXOpYtW1bn/Ah4YCQab+XcA+ueQ5eH/fDlkAUouZGnce7qwVP41G0mTNpaQlTXoKLwFmakf4LCXVka5cpzi1GeW4yC8xnIS7mO6dc+hkO/bsj44896P7vb+EAYmRkjZfsvWs/beXbAuJ8jcObjnxH/ztf/W0d1xLJTO3QK8sb3427PNfw7wMlNvq5RNi/lOiw7al8hqPDrCnOFDSYl3K7HoI0hOgz2gu/MUVhr8iTEf4MgIwsTPLZ/PqqKy/H92BUNnipwv9E2ttqU3SzC92OXw1BuBJO2lihNz8PAyKdR+NftRyGDV0zB8eW12UYAyD17FZbO9uj71lgkb49tym7cV1xeHYaC+Mu4HFX7M1uSnIGUW9/A//uZuLAsGvL2ljB1toPv/4Wqr5EZ1P5iDkqPxJH+K1F2+d7+Im+N2rZti9w8zXHIzcuFhbkFTExqs2zt7O3R1aWLRpkunV3w8+H6p/Lcz8yt5DAwkKG4oFzjeHF+Oaxstc9ZvJNhGwM4udrhZnqx+lhHt7aYs+FhlJVWoqZKBQsbE7z/yn507Na2nppal6ZcCKMtVgkPD0dERESdsjdv3kRNTQ0UCoXGcYVCgdTU1DrltXnzzTfh6OioEXiOHDkSY8eOhYuLCy5evIi3334bo0aNQlxcHAwNDeup7TadBo1bt27F1q1bMXbsWJw6dQp+fn7Ytm0bJk2aBADw8PDA3Llz6w0a582bh7CwMI1jm6ynNFobH1j3HFxD/LH7gXAUXdY+lwCoDQoBoOMDPWDW3hp/fXf3eUp/Z9EM5UaSn9992jD89d2JOgsbAKCtV0eMi4lAyvZYHJ3/hWRdLVX30AdQll2ESz8mqI8VXc5GyY1c2Lo7apS17eZw1zl0V2NOY7v3bI1jI7bMQn7qDRxfsUcdMBpbmuKx6AWoqajCt2OWNSgreb/SNrb1qamoQml6HgzaGMJtbD/8ufuo+lwbM3mdDLqoUakDHqplaGoMccc/QkRN7bjJZDKUns/G0cGaK/Vd542EobkcafO/RfmNguZqaovm490Tvx09onEsLv4YfLy91e99e/ri8pXLGmUuX70CB+Xdp7Dc79oYGaKDmx3+TMyEd/+OAGqDnfNJmRj4aLcG1aGqUSHjUgE8/R3rnDM1NwYA5NwowrXzeRg11afxGq/HtMUqcrm8ST4rMjISO3fuRGxsrPofWADw5JNPqv/s7e2Nnj17omvXroiNjcXw4cMbVLdOg8b09HT06dMHAODj4wMDAwP4+vqqz/fu3Rvp6fWvlpXL5XUGvo2sYRGzlGFR0+E+cRC+C4lEZXEZzBQ2AICKwlvqfQK9nnkAeSnXUZZTBIdAdwxdMw0n1/ygXuWr9HeDoq8r0n9PQXl+KWy6KtB/8UQUXMhQz1E0d7TD+J8jED11LbKOX1B/vnVXJToM9sKeh9+p07a23TtifMwiXDmQhITV36vbJmpUWgPMFksmQ/dnhiF5e2ydhSgnVn2LwIgJuHnqMrKTLsNr6lDYeTjhh8dv/8IddzAcF/bG41TUflSVlKvnm/6tqrQcZXnF6uPGlqYYe2Ah2pjJET35AxhbmcHYqnaBU1lOkTqwbBXqGdvgrS+jJD0PR96unfup9HeDhZMdcpIuw8LJDv3Cn4DMwAAnVuxVX/PX9yfg//Y4FF/NQe65a2jXywW9XxtdZyV/a2Nobgwzl9vZbdNOdrDs4Yiq/Fsov1EA1/+MgomDNc7Oqp1ik3MgGV6rx6PDM4HIPZwGucIK7kseRWHCVVRk1f5slqRqPomoKizXerw1uXXrFq5ev/3zeSP9BlL/TIO1lRUclA5YE7UO2TnZeDdiCQDgibHjsXP3LqxetwYho8cg/sRx/BRzEFGrP1DXMWXiJEx+LhQfb92M4OEP4kzyOXy99xssnDe/2fvXnIaO9cQXq46iYzc7OLvb45c9Kagsr0bAiNrV1J+vOAJrezM8Mq0XAODA/52Gs6c97B0tUVZSicNfJSM/uxT9Rt7e+SDp1yuwsJbDpr05Mi4VYM/GE/AO7AAPv7qBZWvVlJlGbbHK3djb28PQ0BBZWZp/H2RlZUGpVNZ77apVqxAZGYmff/4ZPXv2rLdsly5dYG9vjwsXLtwfQaNSqURycjI6deqE8+fPo6amBsnJyejevTsA4Ny5c2jfvu4Gxc3F58WRAIAnYpdoHD8Quh7J22oXFdi5O2Hgu5NgYmeBoss5iH/3a5x8//Yk7KpbFXB9LACBERNgZC5HaUY+Lh9IwrEJX6Gmsna+pqGRIew8nGBkpnlD9Zg2DMXXc3Hlp7rzFN3GB8KsvTU8Jw+B5+Qh6uOFl7OxpcuLjTMAzaBTUE9YObfTOmcz8YMf0cbEGENWh8LEzgI5py7j6xGLUfjX7R8k665KmNo3fE+29r27wKFf7b/Gp134UOPcZpcXUHSl9awUrG9sLTvZa2QNDU2M0H/JRFh3UaCqpByX9p1E9JS1qCi8Pefu8CufoP+SiRgWNQNm7a1Qkp6PMx8dxB+LW/diDiufDui79/bPlMeS2r3ubuw8gXOv7IJcYQUTJxv1+fRdJ2BoIUenaf3hHvEIqorKkPf7RZxffP9svN8UzqUkY9pLM9TvV66pnR/+6MOj8c7CRcjJvYmMrEz1+Q6OTohavRYr1ryH/9u1A4r2CkS8vUC93Q4A9PDqjjUrVmHNh+uxcfPHcHJ0xNzX3sAjIx9qvo7pQK+hnVFSWIHo7adRlF8Gpy62eP6dYbD87+Pp/JxSjScAt0oq8eWaYyjKL4OZhTE6uNnhlfeDoXS2UZcpyivDt5sSUFxQDis7U/QJcsGIp7zv/GhqBsbGxvDz80NMTAxCQkIAQL2oZdasWXe9bsWKFXjnnXdw4MABdUKuPtevX0dubi4cHBqemZcJobvvzVmwYAE2bdqEMWPGICYmBhMmTMAXX3yBefPmQSaT4Z133sH48eOxevVq6cr+4X2DcdKFiOi+1N2+i3QhapChf0bougmtxs8F9/Z7irR7qPMCnX3245sa9nj/39j9fP3rF+60a9cuTJ06FZs2bYK/vz/WrFmDL7/8EqmpqVAoFJgyZQqcnJywbNkyAMDy5cuxcOFCfPHFFxgw4PZ+qBYWFrCwsEBJSQkWLVqEcePGQalU4uLFi5g7dy6Ki4tx5syZBmdBdZppXLRoEUxNTREXF4fp06fjrbfego+PD+bOnYtbt25h9OjRWLJkiXRFRERERP+DlvSNMBMmTEBOTg4WLlyIzMxM+Pr6Ijo6Wr045urVqzAwuL0BzoYNG1BZWYnx48dr1PP3YhtDQ0OcPn0a27ZtQ0FBARwdHTFixAgsWbLknuZW6jTT2FSYaSRqvZhpbDzMNDYeZhobhy4zjeM2NN0333z94vkmq7s58WsEiYiISO+1pExjS6Xzzb2JiIiIqOVjppGIiIj03r/5uj99w0wjEREREUlippGIiIj0Huc0SmOmkYiIiIgkMdNIREREek+0om+RbSoMGomIiEjv8fG0ND6eJiIiIiJJzDQSERGR3uOOO9KYaSQiIiIiScw0EhERkd5TcSGMJGYaiYiIiEgSM41ERESk9wRXT0tippGIiIiIJDHTSERERHqPcxqlMWgkIiIivcdvhJHGx9NEREREJImZRiIiItJ7Ku7uLYmZRiIiIiKSxEwjERER6T3OaZTGTCMRERERSWKmkYiIiPQet9yRxkwjEREREUlippGIiIj0Hr9GUBqDRiIiItJ7fDwtjY+niYiIiEgSM41ERESk97i3tzRmGomIiIhIEjONREREpPdUXAgjiZlGIiIiIpLETCMRERHpPX6NoDRmGomIiIhIEjONREREpPe4T6M0Bo1ERESk9/h4WhofTxMRERGRJGYaiYiISO+puLu3JGYaiYiIiEgSM41ERESk9zinURozjUREREQkiZlGIiIi0nvcckcaM41EREREJImZRiIiItJ7QsXV01KYaSQiIiIiScw0EhERkd7jnEZpDBqJiIhI73Fvb2l8PE1ERETUwkRFRaFz584wMTFBQEAA4uPj6y2/e/dueHh4wMTEBN7e3ti3b5/GeSEEFi5cCAcHB5iamiIoKAjnz5+/pzYxaCQiIiK9p1KJJnvdq127diEsLAzh4eE4efIkfHx8EBwcjOzsbK3ljx49iokTJ+LZZ59FYmIiQkJCEBISgrNnz6rLrFixAmvXrsXGjRtx7NgxmJubIzg4GOXl5Q1uF4NGIiIiohZk9erVmD59OkJDQ+Hl5YWNGzfCzMwMW7Zs0Vr+gw8+wMiRIzFnzhx4enpiyZIl6N27N9avXw+gNsu4Zs0azJ8/H2PGjEHPnj2xfft2pKenY+/evQ1uF4NGIiIi0ntC1XSve1FZWYmEhAQEBQWpjxkYGCAoKAhxcXFar4mLi9MoDwDBwcHq8pcuXUJmZqZGGWtrawQEBNy1Tm24EIaIiIioCVVUVKCiokLjmFwuh1wur1P25s2bqKmpgUKh0DiuUCiQmpqqtf7MzEyt5TMzM9Xn/z52tzIN0SqDxtdUX+u6CZIqKiqwbNkyzJs3T+tNQw3DcWw8HMvGw7FsHPfTOD5ks0DXTajX/TSWupL6cXqT1R0REYFFixZpHAsPD0dERESTfWZT4ONpHamoqMCiRYvq/MuD7g3HsfFwLBsPx7JxcBwbD8dSt+bNm4fCwkKN17x587SWtbe3h6GhIbKysjSOZ2VlQalUar1GqVTWW/7v/95LndowaCQiIiJqQnK5HFZWVhqvu2V8jY2N4efnh5iYGPUxlUqFmJgYBAYGar0mMDBQozwAHDx4UF3excUFSqVSo0xRURGOHTt21zq1aZWPp4mIiIjuV2FhYZg6dSr69OkDf39/rFmzBqWlpQgNDQUATJkyBU5OTli2bBkA4NVXX8WQIUPw3nvv4eGHH8bOnTtx4sQJfPTRRwAAmUyG2bNnY+nSpXBzc4OLiwsWLFgAR0dHhISENLhdDBqJiIiIWpAJEyYgJycHCxcuRGZmJnx9fREdHa1eyHL16lUYGNx+WNy/f3988cUXmD9/Pt5++224ublh79696NGjh7rM3LlzUVpaihkzZqCgoAADBw5EdHQ0TExMGtwumRD84hxd4KTkxsFxbDwcy8bDsWwcHMfGw7GkxsCgkYiIiIgkcSEMEREREUli0EhEREREkhg0EhEREZEkBo1EREREJIlBYxP69ddfMXr0aDg6OkImk2Hv3r0a54UQWLhwIRwcHGBqaoqgoCCcP39eN41tYRpj7PLy8jBp0iRYWVnBxsYGzz77LEpKSpqxF82vucbt9OnTGDRoEExMTNCxY0esWLGiqbvWrCIiIiCTyTReHh4e6vPl5eWYOXMm2rZtCwsLC4wbN67ONy1cvXoVDz/8MMzMzNC+fXvMmTMH1dXVGmViY2PRu3dvyOVyuLq6YuvWrc3RvSbVku7B3bt3w8PDAyYmJvD29sa+ffsavb9NZdmyZejbty8sLS3Rvn17hISEIC0tTaNMc96HUVFR6Ny5M0xMTBAQEID4+PhG7zPdBwQ1mX379on//Oc/4ptvvhEAxJ49ezTOR0ZGCmtra7F3715x6tQp8eijjwoXFxdRVlammwa3II0xdiNHjhQ+Pj7ijz/+EL/99ptwdXUVEydObOaeNK/mGLfCwkKhUCjEpEmTxNmzZ8WOHTuEqamp2LRpU3N1s8mFh4eL7t27i4yMDPUrJydHff6FF14QHTt2FDExMeLEiROiX79+on///urz1dXVokePHiIoKEgkJiaKffv2CXt7ezFv3jx1mb/++kuYmZmJsLAwkZycLNatWycMDQ1FdHR0s/a1sbWUe/DIkSPC0NBQrFixQiQnJ4v58+cLIyMjcebMmSYfg8YQHBwsPv30U3H27FmRlJQkHnroIdGpUydRUlKiLtNc9+HOnTuFsbGx2LJlizh37pyYPn26sLGxEVlZWc0zGNRiMGhsJnf+5alSqYRSqRQrV65UHysoKBByuVzs2LFDBy1suf7N2CUnJwsA4vjx4+oy+/fvFzKZTNy4caPZ2q5LTTVuH374obC1tRUVFRXqMm+++aZwd3dv4h41n/DwcOHj46P1XEFBgTAyMhK7d+9WH0tJSREARFxcnBCiNnAyMDAQmZmZ6jIbNmwQVlZW6nGbO3eu6N69u0bdEyZMEMHBwY3cG93R5T34xBNPiIcfflijPQEBAeL5559v1D42l+zsbAFA/PLLL0KI5r0P/f39xcyZM9Xva2pqhKOjo1i2bFnjd5RaND6e1pFLly4hMzMTQUFB6mPW1tYICAhAXFycDlvW8jVk7OLi4mBjY4M+ffqoywQFBcHAwADHjh1r9ja3BI01bnFxcRg8eDCMjY3VZYKDg5GWlob8/Pxm6k3TO3/+PBwdHdGlSxdMmjQJV69eBQAkJCSgqqpKYxw9PDzQqVMnjXH09vZWf3sDUDtGRUVFOHfunLrMP+v4u0xr/vlvznuwtY1vYWEhAMDOzg5A892HlZWVSEhI0ChjYGCAoKCg+3Ys6d9j0KgjmZmZAKDxw/z3+7/PkXYNGbvMzEy0b99e43ybNm1gZ2ent+PbWOOWmZmptY5/fsb9LiAgAFu3bkV0dDQ2bNiAS5cuYdCgQSguLkZmZiaMjY1hY2Ojcc2d4yg1RncrU1RUhLKysibqmW415z14tzL34z2qUqkwe/ZsDBgwQP21cM11H968eRM1NTWtZizpf8PvniYiusOoUaPUf+7ZsycCAgLg7OyML7/8EqampjpsGemjmTNn4uzZs/j999913RTSc8w06ohSqQSAOivdsrKy1OdIu4aMnVKpRHZ2tsb56upq5OXl6e34Nta4KZVKrXX88zNaGxsbG3Tr1g0XLlyAUqlEZWUlCgoKNMrcOY5SY3S3MlZWVq02MG3Oe/BuZe63e3TWrFn44YcfcPjwYXTo0EF9vLnuQ3t7exgaGraKsaT/HYNGHXFxcYFSqURMTIz6WFFREY4dO4bAwEAdtqzla8jYBQYGoqCgAAkJCeoyhw4dgkqlQkBAQLO3uSVorHELDAzEr7/+iqqqKnWZgwcPwt3dHba2ts3Um+ZVUlKCixcvwsHBAX5+fjAyMtIYx7S0NFy9elVjHM+cOaMR/Bw8eBBWVlbw8vJSl/lnHX+Xac0//815D97v4yuEwKxZs7Bnzx4cOnQILi4uGueb6z40NjaGn5+fRhmVSoWYmJj7ZiypEel6JU5rVlxcLBITE0ViYqIAIFavXi0SExPFlStXhBC1W0/Y2NiIb7/9Vpw+fVqMGTOGW+78V2OM3ciRI0WvXr3EsWPHxO+//y7c3Nxa/ZY7zTFuBQUFQqFQiMmTJ4uzZ8+KnTt3CjMzs1a15c7rr78uYmNjxaVLl8SRI0dEUFCQsLe3F9nZ2UKI2q1OOnXqJA4dOiROnDghAgMDRWBgoPr6v7c6GTFihEhKShLR0dGiXbt2Wrc6mTNnjkhJSRFRUVGtYsudlnIPHjlyRLRp00asWrVKpKSkiPDw8Ptqy50XX3xRWFtbi9jYWI2tn27duqUu01z34c6dO4VcLhdbt24VycnJYsaMGcLGxkZjVTbpBwaNTejw4cMCQJ3X1KlThRC1208sWLBAKBQKIZfLxfDhw0VaWppuG91CNMbY5ebmiokTJwoLCwthZWUlQkNDRXFxsQ5603yaa9xOnTolBg4cKORyuXBychKRkZHN1cVmMWHCBOHg4CCMjY2Fk5OTmDBhgrhw4YL6fFlZmXjppZeEra2tMDMzE4899pjIyMjQqOPy5cti1KhRwtTUVNjb24vXX39dVFVVaZQ5fPiw8PX1FcbGxqJLly7i008/bY7uNamWdA9++eWXolu3bsLY2Fh0795d/Pjjj03W78ambQwBaNwjzXkfrlu3TnTq1EkYGxsLf39/8ccffzRFt6mFkwkhRHNkNImIiIjo/sU5jUREREQkiUEjEREREUli0EhEREREkhg0EhEREZEkBo1EREREJIlBIxERERFJYtBIRERERJIYNBJRk7h8+TJkMhmSkpJ03RS11NRU9OvXDyYmJvD19W3Wzx46dChmz57drJ9JRNSYGDQStVLPPPMMZDIZIiMjNY7v3bsXMplMR63SrfDwcJibmyMtLa3O9+0SEVH9GDQStWImJiZYvnw58vPzdd2URlNZWfmvr7148SIGDhwIZ2dntG3bthFbRUTU+jFoJGrFgoKCoFQqsWzZsruWiYiIqPOods2aNejcubP6/TPPPIOQkBC8++67UCgUsLGxweLFi1FdXY05c+bAzs4OHTp0wKefflqn/tTUVPTv3x8mJibo0aMHfvnlF43zZ8+exahRo2BhYQGFQoHJkyfj5s2b6vNDhw7FrFmzMHv2bNjb2yM4OFhrP1QqFRYvXowOHTpALpfD19cX0dHR6vMymQwJCQlYvHgxZDIZIiIitNYzdOhQvPzyy5g9ezZsbW2hUCjw8ccfo7S0FKGhobC0tISrqyv279+vcd0vv/wCf39/yOVyODg44K233kJ1dbXWzwCADz/8EG5ubjAxMYFCocD48ePvWpaIqCVg0EjUihkaGuLdd9/FunXrcP369f+prkOHDiE9PR2//vorVq9ejfDwcDzyyCOwtbXFsWPH8MILL+D555+v8zlz5szB66+/jsTERAQGBmL06NHIzc0FABQUFGDYsGHo1asXTpw4gejoaGRlZeGJJ57QqGPbtm0wNjbGkSNHsHHjRq3t++CDD/Dee+9h1apVOH36NIKDg/Hoo4/i/PnzAICMjAx0794dr7/+OjIyMvDGG2/cta/btm2Dvb094uPj8fLLL+PFF1/E448/jv79++PkyZMYMWIEJk+ejFu3bgEAbty4gYceegh9+/bFqVOnsGHDBmzevBlLly7VWv+JEyfwyiuvYPHixUhLS0N0dDQGDx7csP8RRES6IoioVZo6daoYM2aMEEKIfv36iWnTpgkhhNizZ4/4549+eHi48PHx0bj2/fffF87Ozhp1OTs7i5qaGvUxd3d3MWjQIPX76upqYW5uLnbs2CGEEOLSpUsCgIiMjFSXqaqqEh06dBDLly8XQgixZMkSMWLECI3PvnbtmgAg0tLShBBCDBkyRPTq1Uuyv46OjuKdd97RONa3b1/x0ksvqd/7+PiI8PDweusZMmSIGDhwYJ1+TZ48WX0sIyNDABBxcXFCCCHefvtt4e7uLlQqlbpMVFSUsLCwUI/ZkCFDxKuvviqEEOLrr78WVlZWoqioSLJfREQtBTONRHpg+fLl2LZtG1JSUv51Hd27d4eBwe2/MhQKBby9vdXvDQ0N0bZtW2RnZ2tcFxgYqP5zmzZt0KdPH3U7Tp06hcOHD8PCwkL98vDwAFA7//Bvfn5+9batqKgI6enpGDBggMbxAQMG/Ks+9+zZs06//tlXhUIBAOq+pqSkIDAwUGOB0YABA1BSUqI1w/vggw/C2dkZXbp0weTJk/H555+rs5ZERC0Vg0YiPTB48GAEBwdj3rx5dc4ZGBhACKFxrKqqqk45IyMjjfcymUzrMZVK1eB2lZSUYPTo0UhKStJ4nT9/XuNxrbm5eYPrbAxSff07OLyXvv6TpaUlTp48iR07dsDBwQELFy6Ej48PCgoK/nWbiYiaGoNGIj0RGRmJ77//HnFxcRrH27Vrh8zMTI3AsTH3Vvzjjz/Uf66urkZCQgI8PT0BAL1798a5c+fQuXNnuLq6arzuJVC0srKCo6Mjjhw5onH8yJEj8PLyapyO1MPT0xNxcXEaY3jkyBFYWlqiQ4cOWq9p06YNgoKCsGLFCpw+fRqXL1/GoUOHmrytRET/FoNGIj3h7e2NSZMmYe3atRrHhw4dipycHKxYsQIXL15EVFRUnZXB/4uoqCjs2bMHqampmDlzJvLz8zFt2jQAwMyZM5GXl4eJEyfi+PHjuHjxIg4cOIDQ0FDU1NTc0+fMmTMHy5cvx65du5CWloa33noLSUlJePXVVxutL3fz0ksv4dq1a3j55ZeRmpqKb7/9FuHh4QgLC9N4pP+3H374AWvXrkVSUhKuXLmC7du3Q6VSwd3dvcnbSkT0bzFoJNIjixcvrvNI1dPTEx9++CGioqLg4+OD+Pj4elcW36vIyEhERkbCx8cHv//+O7777jvY29sDgDo7WFNTgxEjRsDb2xuzZ8+GjY2N1mCrPq+88grCwsLw+uuvw9vbG9HR0fjuu+/g5ubWaH25GycnJ+zbtw/x8fHw8fHBCy+8gGeffRbz58/XWt7GxgbffPMNhg0bBk9PT2zcuBE7duxA9+7dm7ytRET/lkzcOZmJiIiIiOgOzDQSERERkSQGjUREREQkiUEjEREREUli0EhEREREkhg0EhEREZEkBo1EREREJIlBIxERERFJYtBIRERERJIYNBIRERGRJAaNRERERCSJQSMRERERSWLQSERERESS/h+ZmIUnmzrb1gAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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AgyxGW/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==\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for transformer in transformers:\\n\",\n    \"    tester = ParallelTester(\\n\",\n    \"        transformer,\\n\",\n    \"        mols,\\n\",\n    \"        n_mols=[10, 100, 1000, 5000, 10000, 20000],\\n\",\n    \"        n_jobs=[1, 2, 4, 8],\\n\",\n    \"    )\\n\",\n    \"    results = tester.test()\\n\",\n    \"    plot_heatmap(results, transformer.__class__.__name__)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"4fc52005\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Axes: title={'center': 'SMILES to Mol\\\\nMax single-threaded speed 26505 mols/s\\\\nCPU: AMD Ryzen 9 4900HS with Radeon Graphics'}, xlabel='Number of mols', ylabel='Number of jobs'>\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 800x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"smiles = [\\\"CCCCCCCCBr\\\"] * 100000\\n\",\n    \"transformer = SmilesToMolTransformer()\\n\",\n    \"df = ParallelTester(\\n\",\n    \"    transformer, smiles, n_mols=[10, 100, 1000, 5000, 10000, 20000], n_jobs=[1, 2, 4, 8]\\n\",\n    \").test()\\n\",\n    \"plot_heatmap(df, \\\"SMILES to Mol\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"467c1132\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Modifing multiprocessing backend\\n\",\n    \"\\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    \"\\n\",\n    \"```python\\n\",\n    \"from joblib import parallel_backend\\n\",\n    \"from sckit_mol.transformers import MorganFingerprintTransformer\\n\",\n    \"transformer = MorganFingerprintTransformer(radius=2, nBits=2048)\\n\",\n    \"with parallel_backend('multiprocessing', n_jobs=2):\\n\",\n    \"    transformer.transform(mols)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"If you have ray or dask installed, you can also use these backends. For example, to use ray:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"from joblib import parallel_backend\\n\",\n    \"from ray.util.joblib import register_ray\\n\",\n    \"register_ray()\\n\",\n    \"with parallel_backend('ray', n_jobs=2):\\n\",\n    \"    transformer.transform(mols)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"And for dask:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"from joblib import parallel_backend\\n\",\n    \"from dask.distributed import Client\\n\",\n    \"client = Client()\\n\",\n    \"with parallel_backend('dask', n_jobs=2):\\n\",\n    \"    transformer.transform(mols)\\n\",\n    \"```\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.9.4 ('rdkit')\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.21\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/08_external_library_skopt.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"c0f4155f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:44.212745Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:44.212299Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:47.165987Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:47.162995Z\"\n    },\n    \"title\": \"Needs scikit-optimize\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: scikit-optimize in /home/esben/python_envs/vscode/lib/python3.10/site-packages (0.10.2)\\r\\n\",\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\",\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\",\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\",\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\",\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\",\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\",\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\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"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\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install scikit-optimize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"49f80040\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:47.174431Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:47.173616Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:47.507299Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:47.506634Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"f1268213\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:47.510217Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:47.509924Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.065273Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.064596Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"\\n\",\n    \"from sklearn.pipeline import make_pipeline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"7239cf27\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.068114Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.067809Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.129879Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.129159Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from skopt.space import Real, Integer, Categorical\\n\",\n    \"from skopt.utils import use_named_args\\n\",\n    \"\\n\",\n    \"from skopt import gp_minimize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"aabbba9d\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.132744Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.132424Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.173318Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.172690Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"full_set = False\\n\",\n    \"\\n\",\n    \"if full_set:\\n\",\n    \"    csv_file = \\\"SLC6A4_active_excape_export.csv\\\"\\n\",\n    \"    if not os.path.exists(csv_file):\\n\",\n    \"        import urllib.request\\n\",\n    \"\\n\",\n    \"        url = \\\"https://ndownloader.figshare.com/files/25747817\\\"\\n\",\n    \"        urllib.request.urlretrieve(url, csv_file)\\n\",\n    \"else:\\n\",\n    \"    csv_file = \\\"../tests/data/SLC6A4_active_excapedb_subset.csv\\\"\\n\",\n    \"\\n\",\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"trf = SmilesToMolTransformer()\\n\",\n    \"data[\\\"ROMol\\\"] = trf.transform(data.SMILES.values).flatten()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"488a3e82\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.176242Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.175854Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.188154Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.187463Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-1 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: black;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: block;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-1 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-1 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-1 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 1ex;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-1\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>Pipeline(steps=[(&#x27;morganfingerprinttransformer&#x27;,\\n\",\n       \"                 MorganFingerprintTransformer()),\\n\",\n       \"                (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-1\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-1\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow \\\">&nbsp;&nbsp;Pipeline<a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\\\">?<span>Documentation for Pipeline</span></a><span class=\\\"sk-estimator-doc-link \\\">i<span>Not fitted</span></span></label><div class=\\\"sk-toggleable__content \\\"><pre>Pipeline(steps=[(&#x27;morganfingerprinttransformer&#x27;,\\n\",\n       \"                 MorganFingerprintTransformer()),\\n\",\n       \"                (&#x27;ridge&#x27;, Ridge())])</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-2\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-2\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow \\\">MorganFingerprintTransformer</label><div class=\\\"sk-toggleable__content \\\"><pre>MorganFingerprintTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-3\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-3\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow \\\">&nbsp;Ridge<a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\\\">?<span>Documentation for Ridge</span></a></label><div class=\\\"sk-toggleable__content \\\"><pre>Ridge()</pre></div> </div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('morganfingerprinttransformer',\\n\",\n       \"                 MorganFingerprintTransformer()),\\n\",\n       \"                ('ridge', Ridge())])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe = make_pipeline(MorganFingerprintTransformer(), Ridge())\\n\",\n    \"pipe\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"44811b8e\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.190932Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.190654Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.195099Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.194411Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'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\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(pipe.get_params())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"49eb7dbe\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.197795Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.197479Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.205564Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.205022Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_bits = 4096\\n\",\n    \"\\n\",\n    \"morgan_space = [\\n\",\n    \"    Categorical([True, False], name=\\\"morganfingerprinttransformer__useCounts\\\"),\\n\",\n    \"    Categorical([True, False], name=\\\"morganfingerprinttransformer__useFeatures\\\"),\\n\",\n    \"    Integer(512, max_bits, name=\\\"morganfingerprinttransformer__fpSize\\\"),\\n\",\n    \"    Integer(1, 3, name=\\\"morganfingerprinttransformer__radius\\\"),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"regressor_space = [Real(1e-2, 1e3, \\\"log-uniform\\\", name=\\\"ridge__alpha\\\")]\\n\",\n    \"\\n\",\n    \"search_space = morgan_space + regressor_space\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"3818beb2\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.207944Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.207727Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:48.211453Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:48.210957Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@use_named_args(search_space)\\n\",\n    \"def objective(**params):\\n\",\n    \"    for key, value in params.items():\\n\",\n    \"        print(f\\\"{key}:{value} - {type(value)}\\\")\\n\",\n    \"    pipe.set_params(**params)\\n\",\n    \"\\n\",\n    \"    return -np.mean(\\n\",\n    \"        cross_val_score(\\n\",\n    \"            pipe,\\n\",\n    \"            data.ROMol,\\n\",\n    \"            data.pXC50,\\n\",\n    \"            cv=2,\\n\",\n    \"            n_jobs=-1,\\n\",\n    \"            scoring=\\\"neg_mean_absolute_error\\\",\\n\",\n    \"        )\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"aa6b3af8\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:48.213752Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:48.213531Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:50.948132Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:50.947483Z\"\n    },\n    \"lines_to_next_cell\": 0,\n    \"title\": \"THIS takes forever on my machine with a GradientBoostingRegressor\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morganfingerprinttransformer__useCounts:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:3587 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:3 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:13.116515715358098 - <class 'float'>\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morganfingerprinttransformer__useCounts:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:715 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:2 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:2.445263057083992 - <class 'float'>\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morganfingerprinttransformer__useCounts:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:1920 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:3 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:0.48638570461894715 - <class 'float'>\\n\",\n      \"morganfingerprinttransformer__useCounts:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:3942 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:1 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:224.09712855921126 - <class 'float'>\\n\",\n      \"morganfingerprinttransformer__useCounts:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:2377 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:2 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:40.10174523739503 - <class 'float'>\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morganfingerprinttransformer__useCounts:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:3231 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:1 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:2.333469328026273 - <class 'float'>\\n\",\n      \"morganfingerprinttransformer__useCounts:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:1288 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:1 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:0.41754668393896904 - <class 'float'>\\n\",\n      \"morganfingerprinttransformer__useCounts:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:1897 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:3 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:1.777255838269662 - <class 'float'>\\n\",\n      \"morganfingerprinttransformer__useCounts:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:False - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:868 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:3 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:18.43742127649598 - <class 'float'>\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morganfingerprinttransformer__useCounts:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__useFeatures:True - <class 'bool'>\\n\",\n      \"morganfingerprinttransformer__fpSize:3202 - <class 'numpy.int64'>\\n\",\n      \"morganfingerprinttransformer__radius:2 - <class 'numpy.int64'>\\n\",\n      \"ridge__alpha:0.4219258607446576 - <class 'float'>\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Best score=0.5968'\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0)\\n\",\n    \"\\\"Best score=%.4f\\\" % pipe_gp.fun\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"42d8fc82\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:50.951138Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:50.950922Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:50.956593Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:50.956046Z\"\n    },\n    \"lines_to_next_cell\": 0\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best parameters:\\n\",\n      \"{'morganfingerprinttransformer__useCounts': False, 'morganfingerprinttransformer__useFeatures': False, 'morganfingerprinttransformer__fpSize': 3231, 'morganfingerprinttransformer__radius': 1, 'ridge__alpha': 2.333469328026273}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"\\\"\\\"Best parameters:\\\"\\\"\\\")\\n\",\n    \"print({param.name: value for param, value in zip(pipe_gp.space, pipe_gp.x)})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c5e9fda9\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:27:50.959607Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:27:50.959088Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:27:51.588690Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:27:51.587982Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Axes: title={'center': 'Convergence plot'}, xlabel='Number of calls $n$', ylabel='$\\\\\\\\min f(x)$ after $n$ calls'>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from skopt.plots import plot_convergence\\n\",\n    \"\\n\",\n    \"plot_convergence(pipe_gp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"aebdc43d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0e70fca3\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Example: Using Multiple Different Fingerprint Transformer\\n\",\n    \"\\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    \"\\n\",\n    \"Following steps will happen:\\n\",\n    \"* Data Parsing\\n\",\n    \"* Pipeline Building\\n\",\n    \"* Training Phase\\n\",\n    \"* Analysis\\n\",\n    \"\\n\",\n    \"Authors: @VincentAlexanderScholz, @RiesBen\\n\",\n    \"\\n\",\n    \"## Imports:\\n\",\n    \"First we will import all the stuff that we will need for our work.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"b705b5c9\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:50.685069Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:50.684787Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:52.211375Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:52.210158Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from time import time\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"\\n\",\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"from sklearn.pipeline import Pipeline, make_pipeline\\n\",\n    \"from sklearn.linear_model import Ridge\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"from scikit_mol import fingerprints\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2a4eb825\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Get Data:\\n\",\n    \"In this step we will check if the SLC6A4 data set is already present or needs to be downloaded.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**WARNING:** The Dataset is a simple and very well selected\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"34b2618a\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:52.215818Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:52.214649Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:52.271743Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:52.270511Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 out of 200 SMILES failed in conversion\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"full_set = False\\n\",\n    \"\\n\",\n    \"# if not present download example data\\n\",\n    \"if full_set:\\n\",\n    \"    csv_file = \\\"SLC6A4_active_excape_export.csv\\\"\\n\",\n    \"    if not os.path.exists(csv_file):\\n\",\n    \"        import urllib.request\\n\",\n    \"\\n\",\n    \"        url = \\\"https://ndownloader.figshare.com/files/25747817\\\"\\n\",\n    \"        urllib.request.urlretrieve(url, csv_file)\\n\",\n    \"else:\\n\",\n    \"    csv_file = \\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\"\\n\",\n    \"\\n\",\n    \"# Parse Database\\n\",\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"\\n\",\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8dba759\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Build Pipeline:\\n\",\n    \"In this stage we will build the Pipeline consisting of the featurization part (fingerprint transformers) and the model part (Ridge Regression).\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"e06042cc\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:52.275673Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:52.274777Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:22:52.289345Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:22:52.288093Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'fp_transformer': [MorganFingerprintTransformer(),\\n\",\n       \"   AvalonFingerprintTransformer()],\\n\",\n       \"  'fp_transformer__fpSize': [256, 512, 1024, 2048, 4096],\\n\",\n       \"  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])},\\n\",\n       \" {'fp_transformer': [RDKitFingerprintTransformer(),\\n\",\n       \"   AtomPairFingerprintTransformer(),\\n\",\n       \"   MACCSKeysFingerprintTransformer()],\\n\",\n       \"  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])}]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"regressor = Ridge()\\n\",\n    \"optimization_pipe = Pipeline(\\n\",\n    \"    [\\n\",\n    \"        (\\n\",\n    \"            \\\"fp_transformer\\\",\\n\",\n    \"            \\\"fp_transformer\\\",\\n\",\n    \"        ),  # this is a placeholder for different transformers\\n\",\n    \"        (\\\"regressor\\\", regressor),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"param_grid = [  # Here pass different Options and Approaches\\n\",\n    \"    {\\n\",\n    \"        \\\"fp_transformer\\\": [\\n\",\n    \"            fingerprints.MorganFingerprintTransformer(),\\n\",\n    \"            fingerprints.AvalonFingerprintTransformer(),\\n\",\n    \"        ],\\n\",\n    \"        \\\"fp_transformer__fpSize\\\": [2**x for x in range(8, 13)],\\n\",\n    \"    },\\n\",\n    \"    {\\n\",\n    \"        \\\"fp_transformer\\\": [\\n\",\n    \"            fingerprints.RDKitFingerprintTransformer(),\\n\",\n    \"            fingerprints.AtomPairFingerprintTransformer(),\\n\",\n    \"            fingerprints.MACCSKeysFingerprintTransformer(),\\n\",\n    \"        ],\\n\",\n    \"    },\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"global_options = {\\n\",\n    \"    \\\"regressor__alpha\\\": np.linspace(0.1, 1, 5),\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"[params.update(global_options) for params in param_grid]\\n\",\n    \"\\n\",\n    \"param_grid\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"521aa24a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Train Model\\n\",\n    \"In this section, the combinatorial approaches are trained.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"f1cf66df\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:22:52.293059Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:22:52.292608Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:23:24.403914Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:23:24.402481Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Runtime: 32.10\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Split Data\\n\",\n    \"mol_list_train, mol_list_test, y_train, y_test = train_test_split(\\n\",\n    \"    data.ROMol, data.pXC50, random_state=0\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Define Search Process\\n\",\n    \"grid = GridSearchCV(optimization_pipe, n_jobs=1, param_grid=param_grid)\\n\",\n    \"\\n\",\n    \"# Train\\n\",\n    \"t0 = time()\\n\",\n    \"grid.fit(mol_list_train, y_train.values)\\n\",\n    \"t1 = time()\\n\",\n    \"\\n\",\n    \"print(f\\\"Runtime: {t1-t0:0.2F}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"55aa1549\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analysis\\n\",\n    \"\\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?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"f80006f8\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:23:24.408236Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:23:24.407494Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:23:24.478389Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:23:24.477178Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>mean_fit_time</th>\\n\",\n       \"      <th>std_fit_time</th>\\n\",\n       \"      <th>mean_score_time</th>\\n\",\n       \"      <th>std_score_time</th>\\n\",\n       \"      <th>param_fp_transformer</th>\\n\",\n       \"      <th>param_fp_transformer__fpSize</th>\\n\",\n       \"      <th>param_regressor__alpha</th>\\n\",\n       \"      <th>params</th>\\n\",\n       \"      <th>split0_test_score</th>\\n\",\n       \"      <th>split1_test_score</th>\\n\",\n       \"      <th>split2_test_score</th>\\n\",\n       \"      <th>split3_test_score</th>\\n\",\n       \"      <th>split4_test_score</th>\\n\",\n       \"      <th>mean_test_score</th>\\n\",\n       \"      <th>std_test_score</th>\\n\",\n       \"      <th>rank_test_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.011185</td>\\n\",\n       \"      <td>0.003272</td>\\n\",\n       \"      <td>0.003625</td>\\n\",\n       \"      <td>0.000579</td>\\n\",\n       \"      <td>MorganFingerprintTransformer()</td>\\n\",\n       \"      <td>256.0</td>\\n\",\n       \"      <td>0.100</td>\\n\",\n       \"      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\\n\",\n       \"      <td>0.017975</td>\\n\",\n       \"      <td>0.394682</td>\\n\",\n       \"      <td>0.524598</td>\\n\",\n       \"      <td>0.542116</td>\\n\",\n       \"      <td>0.310238</td>\\n\",\n       \"      <td>0.357922</td>\\n\",\n       \"      <td>0.190209</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.009765</td>\\n\",\n       \"      <td>0.000383</td>\\n\",\n       \"      <td>0.003377</td>\\n\",\n       \"      <td>0.000101</td>\\n\",\n       \"      <td>MorganFingerprintTransformer()</td>\\n\",\n       \"      <td>256.0</td>\\n\",\n       \"      <td>0.325</td>\\n\",\n       \"      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\\n\",\n       \"      <td>0.078758</td>\\n\",\n       \"      <td>0.449548</td>\\n\",\n       \"      <td>0.554241</td>\\n\",\n       \"      <td>0.572363</td>\\n\",\n       \"      <td>0.330543</td>\\n\",\n       \"      <td>0.397090</td>\\n\",\n       \"      <td>0.181071</td>\\n\",\n       \"      <td>24</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.009901</td>\\n\",\n       \"      <td>0.000303</td>\\n\",\n       \"      <td>0.003631</td>\\n\",\n       \"      <td>0.000097</td>\\n\",\n       \"      <td>MorganFingerprintTransformer()</td>\\n\",\n       \"      <td>256.0</td>\\n\",\n       \"      <td>0.550</td>\\n\",\n       \"      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\\n\",\n       \"      <td>0.128221</td>\\n\",\n       \"      <td>0.490253</td>\\n\",\n       \"      <td>0.575230</td>\\n\",\n       \"      <td>0.593237</td>\\n\",\n       \"      <td>0.344076</td>\\n\",\n       \"      <td>0.426203</td>\\n\",\n       \"      <td>0.173061</td>\\n\",\n       \"      <td>23</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.010876</td>\\n\",\n       \"      <td>0.000710</td>\\n\",\n       \"      <td>0.003938</td>\\n\",\n       \"      <td>0.000378</td>\\n\",\n       \"      <td>MorganFingerprintTransformer()</td>\\n\",\n       \"      <td>256.0</td>\\n\",\n       \"      <td>0.775</td>\\n\",\n       \"      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\\n\",\n       \"      <td>0.169585</td>\\n\",\n       \"      <td>0.521723</td>\\n\",\n       \"      <td>0.590890</td>\\n\",\n       \"      <td>0.608380</td>\\n\",\n       \"      <td>0.353866</td>\\n\",\n       \"      <td>0.448889</td>\\n\",\n       \"      <td>0.166100</td>\\n\",\n       \"      <td>22</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0.010237</td>\\n\",\n       \"      <td>0.000522</td>\\n\",\n       \"      <td>0.003698</td>\\n\",\n       \"      <td>0.000063</td>\\n\",\n       \"      <td>MorganFingerprintTransformer()</td>\\n\",\n       \"      <td>256.0</td>\\n\",\n       \"      <td>1.000</td>\\n\",\n       \"      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\\n\",\n       \"      <td>0.204831</td>\\n\",\n       \"      <td>0.546774</td>\\n\",\n       \"      <td>0.603010</td>\\n\",\n       \"      <td>0.619752</td>\\n\",\n       \"      <td>0.361324</td>\\n\",\n       \"      <td>0.467138</td>\\n\",\n       \"      <td>0.160060</td>\\n\",\n       \"      <td>21</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>60</th>\\n\",\n       \"      <td>0.101976</td>\\n\",\n       \"      <td>0.002168</td>\\n\",\n       \"      <td>0.026431</td>\\n\",\n       \"      <td>0.002031</td>\\n\",\n       \"      <td>MACCSKeysFingerprintTransformer()</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.100</td>\\n\",\n       \"      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\\n\",\n       \"      <td>-1.649022</td>\\n\",\n       \"      <td>-1.943461</td>\\n\",\n       \"      <td>-0.602509</td>\\n\",\n       \"      <td>-0.418328</td>\\n\",\n       \"      <td>-0.752525</td>\\n\",\n       \"      <td>-1.073169</td>\\n\",\n       \"      <td>0.606987</td>\\n\",\n       \"      <td>65</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>61</th>\\n\",\n       \"      <td>0.101812</td>\\n\",\n       \"      <td>0.003023</td>\\n\",\n       \"      <td>0.026544</td>\\n\",\n       \"      <td>0.001390</td>\\n\",\n       \"      <td>MACCSKeysFingerprintTransformer()</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.325</td>\\n\",\n       \"      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\\n\",\n       \"      <td>-0.969593</td>\\n\",\n       \"      <td>-0.813087</td>\\n\",\n       \"      <td>-0.188690</td>\\n\",\n       \"      <td>0.003831</td>\\n\",\n       \"      <td>-0.314764</td>\\n\",\n       \"      <td>-0.456461</td>\\n\",\n       \"      <td>0.372595</td>\\n\",\n       \"      <td>64</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>62</th>\\n\",\n       \"      <td>0.102681</td>\\n\",\n       \"      <td>0.001273</td>\\n\",\n       \"      <td>0.026926</td>\\n\",\n       \"      <td>0.001608</td>\\n\",\n       \"      <td>MACCSKeysFingerprintTransformer()</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.550</td>\\n\",\n       \"      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\\n\",\n       \"      <td>-0.657588</td>\\n\",\n       \"      <td>-0.505782</td>\\n\",\n       \"      <td>-0.045940</td>\\n\",\n       \"      <td>0.124510</td>\\n\",\n       \"      <td>-0.171340</td>\\n\",\n       \"      <td>-0.251228</td>\\n\",\n       \"      <td>0.289700</td>\\n\",\n       \"      <td>62</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>63</th>\\n\",\n       \"      <td>0.100703</td>\\n\",\n       \"      <td>0.004015</td>\\n\",\n       \"      <td>0.026171</td>\\n\",\n       \"      <td>0.001515</td>\\n\",\n       \"      <td>MACCSKeysFingerprintTransformer()</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.775</td>\\n\",\n       \"      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\\n\",\n       \"      <td>-0.468371</td>\\n\",\n       \"      <td>-0.356825</td>\\n\",\n       \"      <td>0.036642</td>\\n\",\n       \"      <td>0.182939</td>\\n\",\n       \"      <td>-0.087318</td>\\n\",\n       \"      <td>-0.138587</td>\\n\",\n       \"      <td>0.242115</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64</th>\\n\",\n       \"      <td>0.101780</td>\\n\",\n       \"      <td>0.001939</td>\\n\",\n       \"      <td>0.027233</td>\\n\",\n       \"      <td>0.001860</td>\\n\",\n       \"      <td>MACCSKeysFingerprintTransformer()</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1.000</td>\\n\",\n       \"      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\\n\",\n       \"      <td>-0.339715</td>\\n\",\n       \"      <td>-0.266652</td>\\n\",\n       \"      <td>0.092180</td>\\n\",\n       \"      <td>0.218357</td>\\n\",\n       \"      <td>-0.028878</td>\\n\",\n       \"      <td>-0.064942</td>\\n\",\n       \"      <td>0.210919</td>\\n\",\n       \"      <td>57</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>65 rows × 16 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\\\\n\",\n       \"0        0.011185      0.003272         0.003625        0.000579   \\n\",\n       \"1        0.009765      0.000383         0.003377        0.000101   \\n\",\n       \"2        0.009901      0.000303         0.003631        0.000097   \\n\",\n       \"3        0.010876      0.000710         0.003938        0.000378   \\n\",\n       \"4        0.010237      0.000522         0.003698        0.000063   \\n\",\n       \"..            ...           ...              ...             ...   \\n\",\n       \"60       0.101976      0.002168         0.026431        0.002031   \\n\",\n       \"61       0.101812      0.003023         0.026544        0.001390   \\n\",\n       \"62       0.102681      0.001273         0.026926        0.001608   \\n\",\n       \"63       0.100703      0.004015         0.026171        0.001515   \\n\",\n       \"64       0.101780      0.001939         0.027233        0.001860   \\n\",\n       \"\\n\",\n       \"                 param_fp_transformer  param_fp_transformer__fpSize  \\\\\\n\",\n       \"0      MorganFingerprintTransformer()                         256.0   \\n\",\n       \"1      MorganFingerprintTransformer()                         256.0   \\n\",\n       \"2      MorganFingerprintTransformer()                         256.0   \\n\",\n       \"3      MorganFingerprintTransformer()                         256.0   \\n\",\n       \"4      MorganFingerprintTransformer()                         256.0   \\n\",\n       \"..                                ...                           ...   \\n\",\n       \"60  MACCSKeysFingerprintTransformer()                           NaN   \\n\",\n       \"61  MACCSKeysFingerprintTransformer()                           NaN   \\n\",\n       \"62  MACCSKeysFingerprintTransformer()                           NaN   \\n\",\n       \"63  MACCSKeysFingerprintTransformer()                           NaN   \\n\",\n       \"64  MACCSKeysFingerprintTransformer()                           NaN   \\n\",\n       \"\\n\",\n       \"    param_regressor__alpha                                             params  \\\\\\n\",\n       \"0                    0.100  {'fp_transformer': MorganFingerprintTransforme...   \\n\",\n       \"1                    0.325  {'fp_transformer': MorganFingerprintTransforme...   \\n\",\n       \"2                    0.550  {'fp_transformer': MorganFingerprintTransforme...   \\n\",\n       \"3                    0.775  {'fp_transformer': MorganFingerprintTransforme...   \\n\",\n       \"4                    1.000  {'fp_transformer': MorganFingerprintTransforme...   \\n\",\n       \"..                     ...                                                ...   \\n\",\n       \"60                   0.100  {'fp_transformer': MACCSKeysFingerprintTransfo...   \\n\",\n       \"61                   0.325  {'fp_transformer': MACCSKeysFingerprintTransfo...   \\n\",\n       \"62                   0.550  {'fp_transformer': MACCSKeysFingerprintTransfo...   \\n\",\n       \"63                   0.775  {'fp_transformer': MACCSKeysFingerprintTransfo...   \\n\",\n       \"64                   1.000  {'fp_transformer': MACCSKeysFingerprintTransfo...   \\n\",\n       \"\\n\",\n       \"    split0_test_score  split1_test_score  split2_test_score  \\\\\\n\",\n       \"0            0.017975           0.394682           0.524598   \\n\",\n       \"1            0.078758           0.449548           0.554241   \\n\",\n       \"2            0.128221           0.490253           0.575230   \\n\",\n       \"3            0.169585           0.521723           0.590890   \\n\",\n       \"4            0.204831           0.546774           0.603010   \\n\",\n       \"..                ...                ...                ...   \\n\",\n       \"60          -1.649022          -1.943461          -0.602509   \\n\",\n       \"61          -0.969593          -0.813087          -0.188690   \\n\",\n       \"62          -0.657588          -0.505782          -0.045940   \\n\",\n       \"63          -0.468371          -0.356825           0.036642   \\n\",\n       \"64          -0.339715          -0.266652           0.092180   \\n\",\n       \"\\n\",\n       \"    split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\\\\n\",\n       \"0            0.542116           0.310238         0.357922        0.190209   \\n\",\n       \"1            0.572363           0.330543         0.397090        0.181071   \\n\",\n       \"2            0.593237           0.344076         0.426203        0.173061   \\n\",\n       \"3            0.608380           0.353866         0.448889        0.166100   \\n\",\n       \"4            0.619752           0.361324         0.467138        0.160060   \\n\",\n       \"..                ...                ...              ...             ...   \\n\",\n       \"60          -0.418328          -0.752525        -1.073169        0.606987   \\n\",\n       \"61           0.003831          -0.314764        -0.456461        0.372595   \\n\",\n       \"62           0.124510          -0.171340        -0.251228        0.289700   \\n\",\n       \"63           0.182939          -0.087318        -0.138587        0.242115   \\n\",\n       \"64           0.218357          -0.028878        -0.064942        0.210919   \\n\",\n       \"\\n\",\n       \"    rank_test_score  \\n\",\n       \"0                25  \\n\",\n       \"1                24  \\n\",\n       \"2                23  \\n\",\n       \"3                22  \\n\",\n       \"4                21  \\n\",\n       \"..              ...  \\n\",\n       \"60               65  \\n\",\n       \"61               64  \\n\",\n       \"62               62  \\n\",\n       \"63               59  \\n\",\n       \"64               57  \\n\",\n       \"\\n\",\n       \"[65 rows x 16 columns]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_training_stats = pd.DataFrame(grid.cv_results_)\\n\",\n    \"df_training_stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"a6041579\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:23:24.481751Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:23:24.481189Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:23:24.710744Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:23:24.709653Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1400x500 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Best Fingerprint Method / Performance\\n\",\n    \"res_dict = {}\\n\",\n    \"for i, row in df_training_stats.iterrows():\\n\",\n    \"    fp_name = row[\\\"param_fp_transformer\\\"]\\n\",\n    \"    if (\\n\",\n    \"        fp_name in res_dict\\n\",\n    \"        and row[\\\"mean_test_score\\\"] > res_dict[fp_name][\\\"mean_test_score\\\"]\\n\",\n    \"    ):\\n\",\n    \"        res_dict[fp_name] = row.to_dict()\\n\",\n    \"    elif not fp_name in res_dict:\\n\",\n    \"        res_dict[fp_name] = row.to_dict()\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(list(res_dict.values()))\\n\",\n    \"df = df.sort_values(by=\\\"mean_test_score\\\")\\n\",\n    \"\\n\",\n    \"# plot test score vs. approach\\n\",\n    \"plt.figure(figsize=[14, 5])\\n\",\n    \"plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\\n\",\n    \"plt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14)\\n\",\n    \"plt.ylabel(\\\"mean score\\\", fontsize=14)\\n\",\n    \"plt.title(\\\"Best Model of Fingerprint Transformer Type\\\", fontsize=18)\\n\",\n    \"pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"3ee14366\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:23:24.713891Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:23:24.713590Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:23:25.088793Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:23:25.087682Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1400x500 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Best Fingerprint Method / Performance\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"res_dict = defaultdict(list)\\n\",\n    \"for i, row in df_training_stats.iterrows():\\n\",\n    \"    fp_name = row[\\\"param_fp_transformer\\\"]\\n\",\n    \"    if \\\"Morgan\\\" in str(fp_name):\\n\",\n    \"        res_dict[fp_name].append(row)\\n\",\n    \"\\n\",\n    \"for fp_type, rows in res_dict.items():\\n\",\n    \"    df = pd.DataFrame(rows)\\n\",\n    \"    df = df.sort_values(by=\\\"mean_test_score\\\")\\n\",\n    \"\\n\",\n    \"    # plot test score vs. approach\\n\",\n    \"    xlabels = map(\\n\",\n    \"        lambda x: \\\"_\\\".join(x),\\n\",\n    \"        zip(\\n\",\n    \"            df.param_fp_transformer__fpSize.astype(str),\\n\",\n    \"            df.param_regressor__alpha.astype(str),\\n\",\n    \"        ),\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    plt.figure(figsize=[14, 5])\\n\",\n    \"    plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\\n\",\n    \"    plt.xticks(range(len(df)), xlabels, rotation=90, fontsize=14)\\n\",\n    \"    plt.ylabel(\\\"mean score\\\", fontsize=14)\\n\",\n    \"    plt.xlabel(\\\"bitsize_alpha\\\", fontsize=14)\\n\",\n    \"\\n\",\n    \"    plt.title(\\n\",\n    \"        \\\"Fingerprint Transformer \\\" + str(fp_type).split(\\\"(\\\")[0] + \\\" per Bitsize\\\",\\n\",\n    \"        fontsize=18,\\n\",\n    \"    )\\n\",\n    \"    pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"20f7e139\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-08T16:23:25.091970Z\",\n     \"iopub.status.busy\": \"2025-05-08T16:23:25.091624Z\",\n     \"iopub.status.idle\": \"2025-05-08T16:23:25.443209Z\",\n     \"shell.execute_reply\": \"2025-05-08T16:23:25.441976Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1600x900 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot ALL test score vs. approach\\n\",\n    \"df = df_training_stats.sort_values(by=\\\"mean_test_score\\\")\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=[16, 9])\\n\",\n    \"plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\\n\",\n    \"plt.ylabel(\\\"mean score\\\", fontsize=14)\\n\",\n    \"plt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14)\\n\",\n    \"plt.title(\\\"test score vs. approach\\\", fontsize=18)\\n\",\n    \"pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f407671c\",\n   \"metadata\": {},\n   \"source\": [\n    \"This file have the following licence:\\n\",\n    \"\\n\",\n    \"                                 Apache License\\n\",\n    \"                           Version 2.0, January 2004\\n\",\n    \" 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However, in accepting such obligations, You may act only\\n\",\n    \"      on Your own behalf and on Your sole responsibility, not on behalf\\n\",\n    \"      of any other Contributor, and only if You agree to indemnify,\\n\",\n    \"      defend, and hold each Contributor harmless for any liability\\n\",\n    \"      incurred by, or claims asserted against, such Contributor by reason\\n\",\n    \"      of your accepting any such warranty or additional liability.\\n\",\n    \"\\n\",\n    \"   END OF TERMS AND CONDITIONS\\n\",\n    \"\\n\",\n    \"   APPENDIX: How to apply the Apache License to your work.\\n\",\n    \"\\n\",\n    \"      To apply the Apache License to your work, attach the following\\n\",\n    \"      boilerplate notice, with the fields enclosed by brackets \\\"[]\\\"\\n\",\n    \"      replaced with your own identifying information. (Don't include\\n\",\n    \"      the brackets!)  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We also recommend that a\\n\",\n    \"      file or class name and description of purpose be included on the\\n\",\n    \"      same \\\"printed page\\\" as the copyright notice for easier\\n\",\n    \"      identification within third-party archives.\\n\",\n    \"\\n\",\n    \"   Copyright 2023 Esben Jannik Bjerrum\\n\",\n    \"\\n\",\n    \"   Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\n    \"   you may not use this file except in compliance with the License.\\n\",\n    \"   You may obtain a copy of the License at\\n\",\n    \"\\n\",\n    \"       http://www.apache.org/licenses/LICENSE-2.0\\n\",\n    \"\\n\",\n    \"   Unless required by applicable law or agreed to in writing, software\\n\",\n    \"   distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\n    \"   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\n    \"   See the License for the specific language governing permissions and\\n\",\n    \"   limitations under the License.\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/10_pipeline_pandas_output.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"454d87b5\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Preserving feature information in DataFrames\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"cb457069\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:31.171627Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:31.171255Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:32.152283Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:32.151641Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from pathlib import Path\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.ensemble import RandomForestRegressor\\n\",\n    \"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\\n\",\n    \"from sklearn.preprocessing import FunctionTransformer\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"from sklearn.compose import make_column_selector, make_column_transformer\\n\",\n    \"from scikit_mol.standardizer import Standardizer\\n\",\n    \"from scikit_mol.descriptors import MolecularDescriptorTransformer\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"bc72ca09\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:32.155106Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:32.154793Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:32.161683Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:32.161035Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"csv_file = Path(\\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\")\\n\",\n    \"assert csv_file.is_file()\\n\",\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"data.drop_duplicates(subset=\\\"Ambit_InchiKey\\\", inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f482cac3\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"6019d09f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:32.164164Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:32.163946Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:32.168382Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:32.167874Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"fe9efa0e\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:32.171037Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:32.170722Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:32.174941Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:32.174393Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"column_smiles = \\\"SMILES\\\"\\n\",\n    \"column_target = \\\"pXC50\\\"\\n\",\n    \"\\n\",\n    \"smis_train = data_train.loc[:, column_smiles]\\n\",\n    \"target_train = data_train.loc[:, column_target]\\n\",\n    \"smis_test = data_test.loc[:, column_smiles]\\n\",\n    \"target_test = data_test.loc[:, column_target]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b4cca39\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Descriptors pipeline that returns DataFrames\\n\",\n    \"\\n\",\n    \"Define a pipeline that:\\n\",\n    \"\\n\",\n    \"- converts SMILES strings to Mol objects\\n\",\n    \"- standardizes the molecules\\n\",\n    \"- computes molecular descriptors\\n\",\n    \"\\n\",\n    \"Then, we will configure the pipeline to return output in Pandas DataFrames.\\n\",\n    \"The column names will correspond to the descriptor names.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"33ce774b\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:32.177459Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:32.177241Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:32.194656Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:32.194079Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-5 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-5 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-5 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-5 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-5 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-5 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-5 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-5\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;qed&#x27;, &#x27;SPS&#x27;,\\n\",\n       \"                                                           &#x27;MolWt&#x27;,\\n\",\n       \"                                                           &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                           &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                           &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                                           &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                                           &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan3&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGLO&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-16\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-16\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>Pipeline</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\\\">?<span>Documentation for Pipeline</span></a><span class=\\\"sk-estimator-doc-link fitted\\\">i<span>Fitted</span></span></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;qed&#x27;, &#x27;SPS&#x27;,\\n\",\n       \"                                                           &#x27;MolWt&#x27;,\\n\",\n       \"                                                           &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                           &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                           &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                                           &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                                           &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan3&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGLO&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...]))])</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-17\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-17\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>SmilesToMolTransformer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\\\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-18\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-18\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>Standardizer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\\\">?<span>Documentation for Standardizer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>Standardizer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-19\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-19\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\\\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\\n\",\n       \"                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                          &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                          &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                          &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan3&#x27;, &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MWLOW&#x27;, &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\\n\",\n       \"                ('standardizer', Standardizer()),\\n\",\n       \"                ('moleculardescriptortransformer',\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\\n\",\n       \"                                                           'MaxEStateIndex',\\n\",\n       \"                                                           'MinAbsEStateIndex',\\n\",\n       \"                                                           'MinEStateIndex',\\n\",\n       \"                                                           'qed', 'SPS',\\n\",\n       \"                                                           'MolWt',\\n\",\n       \"                                                           'HeavyAtomMolWt',\\n\",\n       \"                                                           'ExactMolWt',\\n\",\n       \"                                                           'NumValenceElectrons',\\n\",\n       \"                                                           'NumRadicalElectrons',\\n\",\n       \"                                                           'MaxPartialCharge',\\n\",\n       \"                                                           'MinPartialCharge',\\n\",\n       \"                                                           'MaxAbsPartialCharge',\\n\",\n       \"                                                           'MinAbsPartialCharge',\\n\",\n       \"                                                           'FpDensityMorgan1',\\n\",\n       \"                                                           'FpDensityMorgan2',\\n\",\n       \"                                                           'FpDensityMorgan3',\\n\",\n       \"                                                           'BCUT2D_MWHI',\\n\",\n       \"                                                           'BCUT2D_MWLOW',\\n\",\n       \"                                                           'BCUT2D_CHGHI',\\n\",\n       \"                                                           'BCUT2D_CHGLO',\\n\",\n       \"                                                           'BCUT2D_LOGPHI',\\n\",\n       \"                                                           'BCUT2D_LOGPLOW',\\n\",\n       \"                                                           'BCUT2D_MRHI',\\n\",\n       \"                                                           'BCUT2D_MRLOW',\\n\",\n       \"                                                           'AvgIpc', 'BalabanJ',\\n\",\n       \"                                                           'BertzCT', 'Chi0', ...]))])\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"descriptors_pipeline = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(),\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MolecularDescriptorTransformer(),\\n\",\n    \")\\n\",\n    \"descriptors_pipeline.set_output(transform=\\\"pandas\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"2cb55603\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:32.196995Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:32.196792Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:34.209289Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:34.208617Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>MaxAbsEStateIndex</th>\\n\",\n       \"      <th>MaxEStateIndex</th>\\n\",\n       \"      <th>MinAbsEStateIndex</th>\\n\",\n       \"      <th>MinEStateIndex</th>\\n\",\n       \"      <th>qed</th>\\n\",\n       \"      <th>SPS</th>\\n\",\n       \"      <th>MolWt</th>\\n\",\n       \"      <th>HeavyAtomMolWt</th>\\n\",\n       \"      <th>ExactMolWt</th>\\n\",\n       \"      <th>NumValenceElectrons</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>fr_sulfide</th>\\n\",\n       \"      <th>fr_sulfonamd</th>\\n\",\n       \"      <th>fr_sulfone</th>\\n\",\n       \"      <th>fr_term_acetylene</th>\\n\",\n       \"      <th>fr_tetrazole</th>\\n\",\n       \"      <th>fr_thiazole</th>\\n\",\n       \"      <th>fr_thiocyan</th>\\n\",\n       \"      <th>fr_thiophene</th>\\n\",\n       \"      <th>fr_unbrch_alkane</th>\\n\",\n       \"      <th>fr_urea</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>13.448610</td>\\n\",\n       \"      <td>13.448610</td>\\n\",\n       \"      <td>0.056985</td>\\n\",\n       \"      <td>-0.432587</td>\\n\",\n       \"      <td>0.353101</td>\\n\",\n       \"      <td>14.289474</td>\\n\",\n       \"      <td>522.591980</td>\\n\",\n       \"      <td>490.335999</td>\\n\",\n       \"      <td>522.233032</td>\\n\",\n       \"      <td>200.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>12.863074</td>\\n\",\n       \"      <td>12.863074</td>\\n\",\n       \"      <td>0.026212</td>\\n\",\n       \"      <td>-0.050849</td>\\n\",\n       \"      <td>0.682187</td>\\n\",\n       \"      <td>16.033333</td>\\n\",\n       \"      <td>425.558014</td>\\n\",\n       \"      <td>398.342010</td>\\n\",\n       \"      <td>425.188538</td>\\n\",\n       \"      <td>158.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>13.424788</td>\\n\",\n       \"      <td>13.424788</td>\\n\",\n       \"      <td>0.266700</td>\\n\",\n       \"      <td>-0.413763</td>\\n\",\n       \"      <td>0.443905</td>\\n\",\n       \"      <td>15.852942</td>\\n\",\n       \"      <td>465.588013</td>\\n\",\n       \"      <td>432.324005</td>\\n\",\n       \"      <td>465.259155</td>\\n\",\n       \"      <td>180.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>12.725823</td>\\n\",\n       \"      <td>12.725823</td>\\n\",\n       \"      <td>0.052996</td>\\n\",\n       \"      <td>-0.052996</td>\\n\",\n       \"      <td>0.577709</td>\\n\",\n       \"      <td>17.812500</td>\\n\",\n       \"      <td>478.467987</td>\\n\",\n       \"      <td>445.204010</td>\\n\",\n       \"      <td>477.206207</td>\\n\",\n       \"      <td>174.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>6.356910</td>\\n\",\n       \"      <td>6.356910</td>\\n\",\n       \"      <td>0.898244</td>\\n\",\n       \"      <td>0.898244</td>\\n\",\n       \"      <td>0.658108</td>\\n\",\n       \"      <td>13.052631</td>\\n\",\n       \"      <td>246.313004</td>\\n\",\n       \"      <td>232.201004</td>\\n\",\n       \"      <td>246.115692</td>\\n\",\n       \"      <td>92.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>154</th>\\n\",\n       \"      <td>6.217065</td>\\n\",\n       \"      <td>6.217065</td>\\n\",\n       \"      <td>0.175664</td>\\n\",\n       \"      <td>0.175664</td>\\n\",\n       \"      <td>0.916154</td>\\n\",\n       \"      <td>35.700001</td>\\n\",\n       \"      <td>312.239990</td>\\n\",\n       \"      <td>293.088013</td>\\n\",\n       \"      <td>311.084381</td>\\n\",\n       \"      <td>108.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>155</th>\\n\",\n       \"      <td>9.458345</td>\\n\",\n       \"      <td>9.458345</td>\\n\",\n       \"      <td>0.420312</td>\\n\",\n       \"      <td>0.420312</td>\\n\",\n       \"      <td>0.378112</td>\\n\",\n       \"      <td>21.714285</td>\\n\",\n       \"      <td>465.644989</td>\\n\",\n       \"      <td>430.364990</td>\\n\",\n       \"      <td>465.289246</td>\\n\",\n       \"      <td>180.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>156</th>\\n\",\n       \"      <td>13.267371</td>\\n\",\n       \"      <td>13.267371</td>\\n\",\n       \"      <td>0.300870</td>\\n\",\n       \"      <td>-4.299737</td>\\n\",\n       \"      <td>0.919340</td>\\n\",\n       \"      <td>23.565218</td>\\n\",\n       \"      <td>328.377991</td>\\n\",\n       \"      <td>305.194000</td>\\n\",\n       \"      <td>328.176239</td>\\n\",\n       \"      <td>128.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>157</th>\\n\",\n       \"      <td>6.238476</td>\\n\",\n       \"      <td>6.238476</td>\\n\",\n       \"      <td>0.127623</td>\\n\",\n       \"      <td>-0.127623</td>\\n\",\n       \"      <td>0.918995</td>\\n\",\n       \"      <td>19.428572</td>\\n\",\n       \"      <td>323.222992</td>\\n\",\n       \"      <td>307.095001</td>\\n\",\n       \"      <td>322.063965</td>\\n\",\n       \"      <td>110.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>158</th>\\n\",\n       \"      <td>6.371723</td>\\n\",\n       \"      <td>6.371723</td>\\n\",\n       \"      <td>0.086367</td>\\n\",\n       \"      <td>0.086367</td>\\n\",\n       \"      <td>0.911854</td>\\n\",\n       \"      <td>17.136364</td>\\n\",\n       \"      <td>296.414001</td>\\n\",\n       \"      <td>272.221985</td>\\n\",\n       \"      <td>296.188873</td>\\n\",\n       \"      <td>116.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>159 rows × 217 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  MinEStateIndex  \\\\\\n\",\n       \"0            13.448610       13.448610           0.056985       -0.432587   \\n\",\n       \"1            12.863074       12.863074           0.026212       -0.050849   \\n\",\n       \"2            13.424788       13.424788           0.266700       -0.413763   \\n\",\n       \"3            12.725823       12.725823           0.052996       -0.052996   \\n\",\n       \"4             6.356910        6.356910           0.898244        0.898244   \\n\",\n       \"..                 ...             ...                ...             ...   \\n\",\n       \"154           6.217065        6.217065           0.175664        0.175664   \\n\",\n       \"155           9.458345        9.458345           0.420312        0.420312   \\n\",\n       \"156          13.267371       13.267371           0.300870       -4.299737   \\n\",\n       \"157           6.238476        6.238476           0.127623       -0.127623   \\n\",\n       \"158           6.371723        6.371723           0.086367        0.086367   \\n\",\n       \"\\n\",\n       \"          qed        SPS       MolWt  HeavyAtomMolWt  ExactMolWt  \\\\\\n\",\n       \"0    0.353101  14.289474  522.591980      490.335999  522.233032   \\n\",\n       \"1    0.682187  16.033333  425.558014      398.342010  425.188538   \\n\",\n       \"2    0.443905  15.852942  465.588013      432.324005  465.259155   \\n\",\n       \"3    0.577709  17.812500  478.467987      445.204010  477.206207   \\n\",\n       \"4    0.658108  13.052631  246.313004      232.201004  246.115692   \\n\",\n       \"..        ...        ...         ...             ...         ...   \\n\",\n       \"154  0.916154  35.700001  312.239990      293.088013  311.084381   \\n\",\n       \"155  0.378112  21.714285  465.644989      430.364990  465.289246   \\n\",\n       \"156  0.919340  23.565218  328.377991      305.194000  328.176239   \\n\",\n       \"157  0.918995  19.428572  323.222992      307.095001  322.063965   \\n\",\n       \"158  0.911854  17.136364  296.414001      272.221985  296.188873   \\n\",\n       \"\\n\",\n       \"     NumValenceElectrons  ...  fr_sulfide  fr_sulfonamd  fr_sulfone  \\\\\\n\",\n       \"0                  200.0  ...         0.0           0.0         0.0   \\n\",\n       \"1                  158.0  ...         0.0           0.0         0.0   \\n\",\n       \"2                  180.0  ...         0.0           0.0         0.0   \\n\",\n       \"3                  174.0  ...         0.0           0.0         0.0   \\n\",\n       \"4                   92.0  ...         0.0           0.0         0.0   \\n\",\n       \"..                   ...  ...         ...           ...         ...   \\n\",\n       \"154                108.0  ...         0.0           0.0         0.0   \\n\",\n       \"155                180.0  ...         0.0           0.0         0.0   \\n\",\n       \"156                128.0  ...         0.0           0.0         0.0   \\n\",\n       \"157                110.0  ...         0.0           0.0         0.0   \\n\",\n       \"158                116.0  ...         0.0           0.0         0.0   \\n\",\n       \"\\n\",\n       \"     fr_term_acetylene  fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  \\\\\\n\",\n       \"0                  0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"1                  0.0           0.0          0.0          0.0           1.0   \\n\",\n       \"2                  0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"3                  0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"4                  0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"..                 ...           ...          ...          ...           ...   \\n\",\n       \"154                0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"155                0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"156                0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"157                0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"158                0.0           0.0          0.0          0.0           0.0   \\n\",\n       \"\\n\",\n       \"     fr_unbrch_alkane  fr_urea  \\n\",\n       \"0                 0.0      0.0  \\n\",\n       \"1                 0.0      0.0  \\n\",\n       \"2                 0.0      0.0  \\n\",\n       \"3                 0.0      0.0  \\n\",\n       \"4                 0.0      0.0  \\n\",\n       \"..                ...      ...  \\n\",\n       \"154               0.0      0.0  \\n\",\n       \"155               0.0      0.0  \\n\",\n       \"156               0.0      0.0  \\n\",\n       \"157               0.0      0.0  \\n\",\n       \"158               0.0      0.0  \\n\",\n       \"\\n\",\n       \"[159 rows x 217 columns]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_descriptors = descriptors_pipeline.transform(smis_train)\\n\",\n    \"df_descriptors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40d6024a\",\n   \"metadata\": {},\n   \"source\": [\n    \"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).\\n\",\n    \"\\n\",\n    \"Let's define a pipeline that returns Morgan fingerprints in a DataFrame.\\n\",\n    \"Columns will be named with the pattern `fp_morgan_1`, `fp_morgan_2`, ...,`fp_morgan_N`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"f56c539c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:34.211959Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:34.211717Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:34.220439Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:34.219746Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-6 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-6 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-6 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-6 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-6 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-6 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-6 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-6\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;morganfingerprinttransformer&#x27;,\\n\",\n       \"                 MorganFingerprintTransformer())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-20\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-20\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>Pipeline</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\\\">?<span>Documentation for Pipeline</span></a><span class=\\\"sk-estimator-doc-link fitted\\\">i<span>Fitted</span></span></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;morganfingerprinttransformer&#x27;,\\n\",\n       \"                 MorganFingerprintTransformer())])</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-21\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-21\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>SmilesToMolTransformer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\\\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-22\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-22\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>Standardizer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\\\">?<span>Documentation for Standardizer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>Standardizer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-23\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-23\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\\\">?<span>Documentation for MorganFingerprintTransformer</span></a></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>MorganFingerprintTransformer()</pre></div> </div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\\n\",\n       \"                ('standardizer', Standardizer()),\\n\",\n       \"                ('morganfingerprinttransformer',\\n\",\n       \"                 MorganFingerprintTransformer())])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fingerprints_pipeline = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(),\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MorganFingerprintTransformer(),\\n\",\n    \")\\n\",\n    \"fingerprints_pipeline.set_output(transform=\\\"pandas\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"781d1bc8\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:34.222936Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:34.222716Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:34.618391Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:34.617722Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fp_morgan_1</th>\\n\",\n       \"      <th>fp_morgan_2</th>\\n\",\n       \"      <th>fp_morgan_3</th>\\n\",\n       \"      <th>fp_morgan_4</th>\\n\",\n       \"      <th>fp_morgan_5</th>\\n\",\n       \"      <th>fp_morgan_6</th>\\n\",\n       \"      <th>fp_morgan_7</th>\\n\",\n       \"      <th>fp_morgan_8</th>\\n\",\n       \"      <th>fp_morgan_9</th>\\n\",\n       \"      <th>fp_morgan_10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>fp_morgan_2039</th>\\n\",\n       \"      <th>fp_morgan_2040</th>\\n\",\n       \"      <th>fp_morgan_2041</th>\\n\",\n       \"      <th>fp_morgan_2042</th>\\n\",\n       \"      <th>fp_morgan_2043</th>\\n\",\n       \"      <th>fp_morgan_2044</th>\\n\",\n       \"      <th>fp_morgan_2045</th>\\n\",\n       \"      <th>fp_morgan_2046</th>\\n\",\n       \"      <th>fp_morgan_2047</th>\\n\",\n       \"      <th>fp_morgan_2048</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>154</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>155</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>156</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>157</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>158</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>159 rows × 2048 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     fp_morgan_1  fp_morgan_2  fp_morgan_3  fp_morgan_4  fp_morgan_5  \\\\\\n\",\n       \"0              0            1            0            0            0   \\n\",\n       \"1              0            1            0            0            0   \\n\",\n       \"2              0            1            0            0            0   \\n\",\n       \"3              0            0            0            0            0   \\n\",\n       \"4              0            0            0            0            0   \\n\",\n       \"..           ...          ...          ...          ...          ...   \\n\",\n       \"154            0            0            0            0            0   \\n\",\n       \"155            0            1            0            0            0   \\n\",\n       \"156            0            0            0            0            0   \\n\",\n       \"157            0            1            0            0            0   \\n\",\n       \"158            0            1            0            0            0   \\n\",\n       \"\\n\",\n       \"     fp_morgan_6  fp_morgan_7  fp_morgan_8  fp_morgan_9  fp_morgan_10  ...  \\\\\\n\",\n       \"0              0            0            0            0             0  ...   \\n\",\n       \"1              0            0            0            0             0  ...   \\n\",\n       \"2              0            0            0            0             0  ...   \\n\",\n       \"3              0            0            0            1             0  ...   \\n\",\n       \"4              0            0            0            0             0  ...   \\n\",\n       \"..           ...          ...          ...          ...           ...  ...   \\n\",\n       \"154            0            0            0            0             0  ...   \\n\",\n       \"155            0            0            0            0             0  ...   \\n\",\n       \"156            0            0            0            0             0  ...   \\n\",\n       \"157            0            0            0            0             0  ...   \\n\",\n       \"158            0            0            0            0             0  ...   \\n\",\n       \"\\n\",\n       \"     fp_morgan_2039  fp_morgan_2040  fp_morgan_2041  fp_morgan_2042  \\\\\\n\",\n       \"0                 0               0               0               0   \\n\",\n       \"1                 0               0               0               0   \\n\",\n       \"2                 0               0               0               0   \\n\",\n       \"3                 0               0               0               0   \\n\",\n       \"4                 0               0               0               0   \\n\",\n       \"..              ...             ...             ...             ...   \\n\",\n       \"154               0               0               0               0   \\n\",\n       \"155               0               0               0               0   \\n\",\n       \"156               0               0               0               0   \\n\",\n       \"157               0               0               0               0   \\n\",\n       \"158               0               0               0               0   \\n\",\n       \"\\n\",\n       \"     fp_morgan_2043  fp_morgan_2044  fp_morgan_2045  fp_morgan_2046  \\\\\\n\",\n       \"0                 0               0               0               0   \\n\",\n       \"1                 0               0               0               0   \\n\",\n       \"2                 0               0               0               0   \\n\",\n       \"3                 0               0               0               0   \\n\",\n       \"4                 0               0               0               0   \\n\",\n       \"..              ...             ...             ...             ...   \\n\",\n       \"154               0               0               0               0   \\n\",\n       \"155               0               0               0               0   \\n\",\n       \"156               0               0               0               0   \\n\",\n       \"157               0               0               0               0   \\n\",\n       \"158               0               0               0               0   \\n\",\n       \"\\n\",\n       \"     fp_morgan_2047  fp_morgan_2048  \\n\",\n       \"0                 0               0  \\n\",\n       \"1                 0               0  \\n\",\n       \"2                 0               0  \\n\",\n       \"3                 1               0  \\n\",\n       \"4                 0               0  \\n\",\n       \"..              ...             ...  \\n\",\n       \"154               0               0  \\n\",\n       \"155               0               0  \\n\",\n       \"156               0               0  \\n\",\n       \"157               0               0  \\n\",\n       \"158               0               0  \\n\",\n       \"\\n\",\n       \"[159 rows x 2048 columns]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_fingerprints = fingerprints_pipeline.transform(smis_train)\\n\",\n    \"df_fingerprints\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"19a13ca2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analyze feature importance of regression pipeline\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"4872ecab\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:34.621103Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:34.620854Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:34.640701Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:34.640027Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-7 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-7 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-7 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-7 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-7 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-7 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-7 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-7\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;qed&#x27;, &#x27;SPS&#x27;,\\n\",\n       \"                                                           &#x27;MolWt&#x27;,\\n\",\n       \"                                                           &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                           &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                           &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                                           &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                                           &#x27;MaxPa...\\n\",\n       \"                                                           &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan3&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGLO&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])),\\n\",\n       \"                (&#x27;standardscaler&#x27;, StandardScaler()),\\n\",\n       \"                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-24\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-24\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>Pipeline</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\\\">?<span>Documentation for Pipeline</span></a><span class=\\\"sk-estimator-doc-link \\\">i<span>Not fitted</span></span></div></label><div class=\\\"sk-toggleable__content \\\"><pre>Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;, SmilesToMolTransformer()),\\n\",\n       \"                (&#x27;standardizer&#x27;, Standardizer()),\\n\",\n       \"                (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                           &#x27;qed&#x27;, &#x27;SPS&#x27;,\\n\",\n       \"                                                           &#x27;MolWt&#x27;,\\n\",\n       \"                                                           &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                           &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                           &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                                           &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                                           &#x27;MaxPa...\\n\",\n       \"                                                           &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                                           &#x27;FpDensityMorgan3&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MWLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_CHGLO&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                           &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                           &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                           &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])),\\n\",\n       \"                (&#x27;standardscaler&#x27;, StandardScaler()),\\n\",\n       \"                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-25\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-25\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>SmilesToMolTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\\\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-26\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-26\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>Standardizer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\\\">?<span>Documentation for Standardizer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>Standardizer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-27\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-27\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\\\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\\n\",\n       \"                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                          &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                          &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                          &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan3&#x27;, &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MWLOW&#x27;, &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-28\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-28\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>StandardScaler</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\\\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>StandardScaler()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-29\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-29\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>RandomForestRegressor</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\\\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('smilestomoltransformer', SmilesToMolTransformer()),\\n\",\n       \"                ('standardizer', Standardizer()),\\n\",\n       \"                ('moleculardescriptortransformer',\\n\",\n       \"                 MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\\n\",\n       \"                                                           'MaxEStateIndex',\\n\",\n       \"                                                           'MinAbsEStateIndex',\\n\",\n       \"                                                           'MinEStateIndex',\\n\",\n       \"                                                           'qed', 'SPS',\\n\",\n       \"                                                           'MolWt',\\n\",\n       \"                                                           'HeavyAtomMolWt',\\n\",\n       \"                                                           'ExactMolWt',\\n\",\n       \"                                                           'NumValenceElectrons',\\n\",\n       \"                                                           'NumRadicalElectrons',\\n\",\n       \"                                                           'MaxPa...\\n\",\n       \"                                                           'MaxAbsPartialCharge',\\n\",\n       \"                                                           'MinAbsPartialCharge',\\n\",\n       \"                                                           'FpDensityMorgan1',\\n\",\n       \"                                                           'FpDensityMorgan2',\\n\",\n       \"                                                           'FpDensityMorgan3',\\n\",\n       \"                                                           'BCUT2D_MWHI',\\n\",\n       \"                                                           'BCUT2D_MWLOW',\\n\",\n       \"                                                           'BCUT2D_CHGHI',\\n\",\n       \"                                                           'BCUT2D_CHGLO',\\n\",\n       \"                                                           'BCUT2D_LOGPHI',\\n\",\n       \"                                                           'BCUT2D_LOGPLOW',\\n\",\n       \"                                                           'BCUT2D_MRHI',\\n\",\n       \"                                                           'BCUT2D_MRLOW',\\n\",\n       \"                                                           'AvgIpc', 'BalabanJ',\\n\",\n       \"                                                           'BertzCT', 'Chi0', ...])),\\n\",\n       \"                ('standardscaler', StandardScaler()),\\n\",\n       \"                ('randomforestregressor', RandomForestRegressor(max_depth=5))])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"params_random_forest = {\\n\",\n    \"    \\\"max_depth\\\": 5,  # Setting a low maximum depth to avoid overfitting\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"regression_pipeline = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(),\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MolecularDescriptorTransformer(),\\n\",\n    \"    StandardScaler(),  # Scale the descriptors\\n\",\n    \"    RandomForestRegressor(**params_random_forest),\\n\",\n    \")\\n\",\n    \"regression_pipeline.set_output(transform=\\\"pandas\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"f0b2f44f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:34.643396Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:34.643148Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.656343Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.655703Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\",\n      \"/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\\n\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"regression_pipeline.fit(smis_train, target_train)\\n\",\n    \"pred_test = regression_pipeline.predict(smis_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3aa6802d\",\n   \"metadata\": {\n    \"lines_to_next_cell\": 2\n   },\n   \"source\": [\n    \"Let's define a simple function to compute regression metrics, and use it to evaluate the test set performance of the pipeline.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"8b59851a\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.658965Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.658741Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.666594Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.665979Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'RMSE': 0.8539077762549014,\\n\",\n       \" 'MAE': 0.7222501985900492,\\n\",\n       \" 'R2': 0.16145333000722317}\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def compute_metrics(y_true, y_pred):\\n\",\n    \"    result = {\\n\",\n    \"        \\\"RMSE\\\": mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5,\\n\",\n    \"        \\\"MAE\\\": mean_absolute_error(y_true=y_true, y_pred=y_pred),\\n\",\n    \"        \\\"R2\\\": r2_score(y_true=y_true, y_pred=y_pred),\\n\",\n    \"    }\\n\",\n    \"    return result\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"performance = compute_metrics(y_true=target_test, y_pred=pred_test)\\n\",\n    \"performance\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"68528957\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.668899Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.668697Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.673651Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.672957Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-8 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-8 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-8 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-8 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-8 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-8 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-8 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-8\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>RandomForestRegressor(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator fitted sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-30\\\" type=\\\"checkbox\\\" checked><label for=\\\"sk-estimator-id-30\\\" class=\\\"sk-toggleable__label fitted sk-toggleable__label-arrow\\\"><div><div>RandomForestRegressor</div></div><div><a class=\\\"sk-estimator-doc-link fitted\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\\\">?<span>Documentation for RandomForestRegressor</span></a><span class=\\\"sk-estimator-doc-link fitted\\\">i<span>Fitted</span></span></div></label><div class=\\\"sk-toggleable__content fitted\\\"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"RandomForestRegressor(max_depth=5)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"regressor = regression_pipeline[-1]\\n\",\n    \"regressor\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8f32688\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"24011e90\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.677173Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.676324Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.690728Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.690184Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>feature</th>\\n\",\n       \"      <th>importance</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>MaxAbsEStateIndex</td>\\n\",\n       \"      <td>0.003240</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>MaxEStateIndex</td>\\n\",\n       \"      <td>0.001821</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>MinAbsEStateIndex</td>\\n\",\n       \"      <td>0.002461</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>MinEStateIndex</td>\\n\",\n       \"      <td>0.004588</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>qed</td>\\n\",\n       \"      <td>0.009382</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>212</th>\\n\",\n       \"      <td>fr_thiazole</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>213</th>\\n\",\n       \"      <td>fr_thiocyan</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>214</th>\\n\",\n       \"      <td>fr_thiophene</td>\\n\",\n       \"      <td>0.000835</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>215</th>\\n\",\n       \"      <td>fr_unbrch_alkane</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>216</th>\\n\",\n       \"      <td>fr_urea</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>217 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               feature  importance\\n\",\n       \"0    MaxAbsEStateIndex    0.003240\\n\",\n       \"1       MaxEStateIndex    0.001821\\n\",\n       \"2    MinAbsEStateIndex    0.002461\\n\",\n       \"3       MinEStateIndex    0.004588\\n\",\n       \"4                  qed    0.009382\\n\",\n       \"..                 ...         ...\\n\",\n       \"212        fr_thiazole    0.000000\\n\",\n       \"213        fr_thiocyan    0.000000\\n\",\n       \"214       fr_thiophene    0.000835\\n\",\n       \"215   fr_unbrch_alkane    0.000000\\n\",\n       \"216            fr_urea    0.000000\\n\",\n       \"\\n\",\n       \"[217 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_importance = pd.DataFrame(\\n\",\n    \"    {\\n\",\n    \"        \\\"feature\\\": regressor.feature_names_in_,\\n\",\n    \"        \\\"importance\\\": regressor.feature_importances_,\\n\",\n    \"    }\\n\",\n    \")\\n\",\n    \"df_importance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"64ac369d\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sort the features by most to least important:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"713d24f1\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.693255Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.693046Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.700756Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.700214Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>feature</th>\\n\",\n       \"      <th>importance</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>PEOE_VSA6</td>\\n\",\n       \"      <td>0.138689</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>VSA_EState5</td>\\n\",\n       \"      <td>0.062832</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>MaxAbsPartialCharge</td>\\n\",\n       \"      <td>0.052241</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>VSA_EState6</td>\\n\",\n       \"      <td>0.048135</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>SlogP_VSA6</td>\\n\",\n       \"      <td>0.027086</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>212</th>\\n\",\n       \"      <td>fr_term_acetylene</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>213</th>\\n\",\n       \"      <td>fr_thiazole</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>214</th>\\n\",\n       \"      <td>fr_thiocyan</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>215</th>\\n\",\n       \"      <td>fr_unbrch_alkane</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>216</th>\\n\",\n       \"      <td>fr_urea</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>217 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                 feature  importance\\n\",\n       \"0              PEOE_VSA6    0.138689\\n\",\n       \"1            VSA_EState5    0.062832\\n\",\n       \"2    MaxAbsPartialCharge    0.052241\\n\",\n       \"3            VSA_EState6    0.048135\\n\",\n       \"4             SlogP_VSA6    0.027086\\n\",\n       \"..                   ...         ...\\n\",\n       \"212    fr_term_acetylene    0.000000\\n\",\n       \"213          fr_thiazole    0.000000\\n\",\n       \"214          fr_thiocyan    0.000000\\n\",\n       \"215     fr_unbrch_alkane    0.000000\\n\",\n       \"216              fr_urea    0.000000\\n\",\n       \"\\n\",\n       \"[217 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_importance.sort_values(\\n\",\n    \"    by=\\\"importance\\\", ascending=False, inplace=True, ignore_index=True\\n\",\n    \")\\n\",\n    \"df_importance\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"4b97778f\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.703238Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.703004Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.707129Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.706557Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The 5 most important features are:\\n\",\n      \"PEOE_VSA6\\n\",\n      \"VSA_EState5\\n\",\n      \"MaxAbsPartialCharge\\n\",\n      \"VSA_EState6\\n\",\n      \"SlogP_VSA6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n_top_features = 5\\n\",\n    \"top_features = df_importance.head(n_top_features).loc[:, \\\"feature\\\"].tolist()\\n\",\n    \"print(f\\\"The {n_top_features} most important features are:\\\")\\n\",\n    \"for feature in top_features:\\n\",\n    \"    print(feature)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f79c93a0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Including external features\\n\",\n    \"\\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    \"\\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    \"\\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    \"\\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    \"\\n\",\n    \"We have precomputed these features and stored them in a file:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"bf8ddaf9\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.709630Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.709389Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.755699Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.755108Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Ambit_InchiKey</th>\\n\",\n       \"      <th>cddd_1</th>\\n\",\n       \"      <th>cddd_2</th>\\n\",\n       \"      <th>cddd_3</th>\\n\",\n       \"      <th>cddd_4</th>\\n\",\n       \"      <th>cddd_5</th>\\n\",\n       \"      <th>cddd_6</th>\\n\",\n       \"      <th>cddd_7</th>\\n\",\n       \"      <th>cddd_8</th>\\n\",\n       \"      <th>cddd_9</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>cddd_503</th>\\n\",\n       \"      <th>cddd_504</th>\\n\",\n       \"      <th>cddd_505</th>\\n\",\n       \"      <th>cddd_506</th>\\n\",\n       \"      <th>cddd_507</th>\\n\",\n       \"      <th>cddd_508</th>\\n\",\n       \"      <th>cddd_509</th>\\n\",\n       \"      <th>cddd_510</th>\\n\",\n       \"      <th>cddd_511</th>\\n\",\n       \"      <th>cddd_512</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>RBCQCVSMIQCOMN-PCQZLOAONA-N</td>\\n\",\n       \"      <td>0.034484</td>\\n\",\n       \"      <td>0.288955</td>\\n\",\n       \"      <td>-0.038534</td>\\n\",\n       \"      <td>0.485996</td>\\n\",\n       \"      <td>0.398606</td>\\n\",\n       \"      <td>-0.077340</td>\\n\",\n       \"      <td>0.417946</td>\\n\",\n       \"      <td>-0.114399</td>\\n\",\n       \"      <td>0.340925</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-0.442085</td>\\n\",\n       \"      <td>-0.122121</td>\\n\",\n       \"      <td>-0.755012</td>\\n\",\n       \"      <td>0.580011</td>\\n\",\n       \"      <td>-0.999539</td>\\n\",\n       \"      <td>-0.466382</td>\\n\",\n       \"      <td>0.378511</td>\\n\",\n       \"      <td>0.579342</td>\\n\",\n       \"      <td>0.753847</td>\\n\",\n       \"      <td>0.812943</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>ALZTYVXVRZIERJ-UHFFFAOYNA-N</td>\\n\",\n       \"      <td>-0.784729</td>\\n\",\n       \"      <td>0.146379</td>\\n\",\n       \"      <td>0.442466</td>\\n\",\n       \"      <td>0.187816</td>\\n\",\n       \"      <td>0.042911</td>\\n\",\n       \"      <td>-0.007460</td>\\n\",\n       \"      <td>0.012899</td>\\n\",\n       \"      <td>0.170997</td>\\n\",\n       \"      <td>-0.322217</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-0.434862</td>\\n\",\n       \"      <td>0.216556</td>\\n\",\n       \"      <td>-0.687221</td>\\n\",\n       \"      <td>-0.103207</td>\\n\",\n       \"      <td>-0.999198</td>\\n\",\n       \"      <td>-0.335400</td>\\n\",\n       \"      <td>0.136468</td>\\n\",\n       \"      <td>0.550440</td>\\n\",\n       \"      <td>-0.019943</td>\\n\",\n       \"      <td>-0.173729</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>MOEMPBAHOJKXBG-MRXNPFEDNA-N</td>\\n\",\n       \"      <td>-0.751528</td>\\n\",\n       \"      <td>-0.506115</td>\\n\",\n       \"      <td>0.412968</td>\\n\",\n       \"      <td>0.341948</td>\\n\",\n       \"      <td>0.822811</td>\\n\",\n       \"      <td>-0.713795</td>\\n\",\n       \"      <td>0.159594</td>\\n\",\n       \"      <td>-0.453231</td>\\n\",\n       \"      <td>0.053278</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.530237</td>\\n\",\n       \"      <td>-0.131153</td>\\n\",\n       \"      <td>-0.007292</td>\\n\",\n       \"      <td>-0.065849</td>\\n\",\n       \"      <td>-0.978371</td>\\n\",\n       \"      <td>-0.653190</td>\\n\",\n       \"      <td>0.404358</td>\\n\",\n       \"      <td>-0.079914</td>\\n\",\n       \"      <td>0.711537</td>\\n\",\n       \"      <td>0.445195</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>HEKGBDCRHYILPL-QWOVJGMINA-N</td>\\n\",\n       \"      <td>-0.757406</td>\\n\",\n       \"      <td>0.000328</td>\\n\",\n       \"      <td>0.670389</td>\\n\",\n       \"      <td>0.856043</td>\\n\",\n       \"      <td>0.002886</td>\\n\",\n       \"      <td>0.064478</td>\\n\",\n       \"      <td>0.181017</td>\\n\",\n       \"      <td>-0.229966</td>\\n\",\n       \"      <td>0.598979</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.670772</td>\\n\",\n       \"      <td>-0.372262</td>\\n\",\n       \"      <td>-0.571060</td>\\n\",\n       \"      <td>-0.443543</td>\\n\",\n       \"      <td>-0.986363</td>\\n\",\n       \"      <td>0.118407</td>\\n\",\n       \"      <td>-0.077974</td>\\n\",\n       \"      <td>-0.097596</td>\\n\",\n       \"      <td>0.283461</td>\\n\",\n       \"      <td>-0.099510</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>SNNRWIBSGBMYRF-UKRRQHHQNA-N</td>\\n\",\n       \"      <td>-0.477663</td>\\n\",\n       \"      <td>-0.129261</td>\\n\",\n       \"      <td>-0.332024</td>\\n\",\n       \"      <td>-0.591108</td>\\n\",\n       \"      <td>0.786510</td>\\n\",\n       \"      <td>-0.007520</td>\\n\",\n       \"      <td>-0.171381</td>\\n\",\n       \"      <td>-0.048844</td>\\n\",\n       \"      <td>0.565125</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.342538</td>\\n\",\n       \"      <td>0.680307</td>\\n\",\n       \"      <td>0.662410</td>\\n\",\n       \"      <td>-0.493203</td>\\n\",\n       \"      <td>-0.987440</td>\\n\",\n       \"      <td>-0.731436</td>\\n\",\n       \"      <td>0.016999</td>\\n\",\n       \"      <td>-0.503085</td>\\n\",\n       \"      <td>-0.066302</td>\\n\",\n       \"      <td>-0.377198</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>189</th>\\n\",\n       \"      <td>PIKWEFAACQLYMF-UHFFFAOYNA-N</td>\\n\",\n       \"      <td>-0.354424</td>\\n\",\n       \"      <td>0.037133</td>\\n\",\n       \"      <td>0.261493</td>\\n\",\n       \"      <td>0.191034</td>\\n\",\n       \"      <td>0.203483</td>\\n\",\n       \"      <td>-0.718652</td>\\n\",\n       \"      <td>0.481088</td>\\n\",\n       \"      <td>-0.077800</td>\\n\",\n       \"      <td>-0.359651</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.465482</td>\\n\",\n       \"      <td>0.181667</td>\\n\",\n       \"      <td>0.008707</td>\\n\",\n       \"      <td>0.374962</td>\\n\",\n       \"      <td>-0.998080</td>\\n\",\n       \"      <td>-0.015004</td>\\n\",\n       \"      <td>-0.071801</td>\\n\",\n       \"      <td>-0.205790</td>\\n\",\n       \"      <td>-0.394928</td>\\n\",\n       \"      <td>0.386006</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>190</th>\\n\",\n       \"      <td>AUZWJAMWJZUPHQ-UHFFFAOYNA-N</td>\\n\",\n       \"      <td>-0.647606</td>\\n\",\n       \"      <td>-0.604185</td>\\n\",\n       \"      <td>-0.070398</td>\\n\",\n       \"      <td>0.109305</td>\\n\",\n       \"      <td>0.667468</td>\\n\",\n       \"      <td>-0.239701</td>\\n\",\n       \"      <td>-0.332139</td>\\n\",\n       \"      <td>-0.490862</td>\\n\",\n       \"      <td>0.302364</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.268934</td>\\n\",\n       \"      <td>0.103272</td>\\n\",\n       \"      <td>-0.120228</td>\\n\",\n       \"      <td>-0.133202</td>\\n\",\n       \"      <td>-0.981479</td>\\n\",\n       \"      <td>-0.683975</td>\\n\",\n       \"      <td>0.748666</td>\\n\",\n       \"      <td>-0.171097</td>\\n\",\n       \"      <td>0.053143</td>\\n\",\n       \"      <td>0.144776</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>191</th>\\n\",\n       \"      <td>JCEWQICHOLLRDL-WUFINQPMNA-N</td>\\n\",\n       \"      <td>-0.681951</td>\\n\",\n       \"      <td>-0.346629</td>\\n\",\n       \"      <td>0.387501</td>\\n\",\n       \"      <td>-0.760321</td>\\n\",\n       \"      <td>0.003585</td>\\n\",\n       \"      <td>0.173832</td>\\n\",\n       \"      <td>0.584196</td>\\n\",\n       \"      <td>0.314204</td>\\n\",\n       \"      <td>-0.375850</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-0.401828</td>\\n\",\n       \"      <td>0.187382</td>\\n\",\n       \"      <td>0.632996</td>\\n\",\n       \"      <td>0.507790</td>\\n\",\n       \"      <td>-0.999535</td>\\n\",\n       \"      <td>0.041612</td>\\n\",\n       \"      <td>0.090283</td>\\n\",\n       \"      <td>-0.432323</td>\\n\",\n       \"      <td>-0.191279</td>\\n\",\n       \"      <td>0.136006</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>192</th>\\n\",\n       \"      <td>NGRIUVQYFBDXMT-JYAVWHMHNA-N</td>\\n\",\n       \"      <td>-0.622850</td>\\n\",\n       \"      <td>-0.760069</td>\\n\",\n       \"      <td>-0.175192</td>\\n\",\n       \"      <td>0.306767</td>\\n\",\n       \"      <td>0.828635</td>\\n\",\n       \"      <td>-0.251226</td>\\n\",\n       \"      <td>0.095201</td>\\n\",\n       \"      <td>0.029581</td>\\n\",\n       \"      <td>-0.098511</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-0.428120</td>\\n\",\n       \"      <td>0.510929</td>\\n\",\n       \"      <td>-0.112762</td>\\n\",\n       \"      <td>0.072157</td>\\n\",\n       \"      <td>-0.974629</td>\\n\",\n       \"      <td>-0.724549</td>\\n\",\n       \"      <td>0.754821</td>\\n\",\n       \"      <td>0.580699</td>\\n\",\n       \"      <td>0.437276</td>\\n\",\n       \"      <td>0.079424</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>193</th>\\n\",\n       \"      <td>ZWLWOTHDIGRTNE-UHFFFAOYNA-N</td>\\n\",\n       \"      <td>-0.740121</td>\\n\",\n       \"      <td>-0.462145</td>\\n\",\n       \"      <td>0.351089</td>\\n\",\n       \"      <td>0.104331</td>\\n\",\n       \"      <td>0.579055</td>\\n\",\n       \"      <td>-0.524488</td>\\n\",\n       \"      <td>-0.763961</td>\\n\",\n       \"      <td>-0.339895</td>\\n\",\n       \"      <td>0.693571</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>-0.461685</td>\\n\",\n       \"      <td>-0.582436</td>\\n\",\n       \"      <td>0.441268</td>\\n\",\n       \"      <td>0.113779</td>\\n\",\n       \"      <td>-0.919631</td>\\n\",\n       \"      <td>-0.569133</td>\\n\",\n       \"      <td>0.647876</td>\\n\",\n       \"      <td>-0.348418</td>\\n\",\n       \"      <td>0.395181</td>\\n\",\n       \"      <td>0.272382</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>194 rows × 513 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                  Ambit_InchiKey    cddd_1    cddd_2    cddd_3    cddd_4  \\\\\\n\",\n       \"0    RBCQCVSMIQCOMN-PCQZLOAONA-N  0.034484  0.288955 -0.038534  0.485996   \\n\",\n       \"1    ALZTYVXVRZIERJ-UHFFFAOYNA-N -0.784729  0.146379  0.442466  0.187816   \\n\",\n       \"2    MOEMPBAHOJKXBG-MRXNPFEDNA-N -0.751528 -0.506115  0.412968  0.341948   \\n\",\n       \"3    HEKGBDCRHYILPL-QWOVJGMINA-N -0.757406  0.000328  0.670389  0.856043   \\n\",\n       \"4    SNNRWIBSGBMYRF-UKRRQHHQNA-N -0.477663 -0.129261 -0.332024 -0.591108   \\n\",\n       \"..                           ...       ...       ...       ...       ...   \\n\",\n       \"189  PIKWEFAACQLYMF-UHFFFAOYNA-N -0.354424  0.037133  0.261493  0.191034   \\n\",\n       \"190  AUZWJAMWJZUPHQ-UHFFFAOYNA-N -0.647606 -0.604185 -0.070398  0.109305   \\n\",\n       \"191  JCEWQICHOLLRDL-WUFINQPMNA-N -0.681951 -0.346629  0.387501 -0.760321   \\n\",\n       \"192  NGRIUVQYFBDXMT-JYAVWHMHNA-N -0.622850 -0.760069 -0.175192  0.306767   \\n\",\n       \"193  ZWLWOTHDIGRTNE-UHFFFAOYNA-N -0.740121 -0.462145  0.351089  0.104331   \\n\",\n       \"\\n\",\n       \"       cddd_5    cddd_6    cddd_7    cddd_8    cddd_9  ...  cddd_503  \\\\\\n\",\n       \"0    0.398606 -0.077340  0.417946 -0.114399  0.340925  ... -0.442085   \\n\",\n       \"1    0.042911 -0.007460  0.012899  0.170997 -0.322217  ... -0.434862   \\n\",\n       \"2    0.822811 -0.713795  0.159594 -0.453231  0.053278  ...  0.530237   \\n\",\n       \"3    0.002886  0.064478  0.181017 -0.229966  0.598979  ...  0.670772   \\n\",\n       \"4    0.786510 -0.007520 -0.171381 -0.048844  0.565125  ...  0.342538   \\n\",\n       \"..        ...       ...       ...       ...       ...  ...       ...   \\n\",\n       \"189  0.203483 -0.718652  0.481088 -0.077800 -0.359651  ...  0.465482   \\n\",\n       \"190  0.667468 -0.239701 -0.332139 -0.490862  0.302364  ...  0.268934   \\n\",\n       \"191  0.003585  0.173832  0.584196  0.314204 -0.375850  ... -0.401828   \\n\",\n       \"192  0.828635 -0.251226  0.095201  0.029581 -0.098511  ... -0.428120   \\n\",\n       \"193  0.579055 -0.524488 -0.763961 -0.339895  0.693571  ... -0.461685   \\n\",\n       \"\\n\",\n       \"     cddd_504  cddd_505  cddd_506  cddd_507  cddd_508  cddd_509  cddd_510  \\\\\\n\",\n       \"0   -0.122121 -0.755012  0.580011 -0.999539 -0.466382  0.378511  0.579342   \\n\",\n       \"1    0.216556 -0.687221 -0.103207 -0.999198 -0.335400  0.136468  0.550440   \\n\",\n       \"2   -0.131153 -0.007292 -0.065849 -0.978371 -0.653190  0.404358 -0.079914   \\n\",\n       \"3   -0.372262 -0.571060 -0.443543 -0.986363  0.118407 -0.077974 -0.097596   \\n\",\n       \"4    0.680307  0.662410 -0.493203 -0.987440 -0.731436  0.016999 -0.503085   \\n\",\n       \"..        ...       ...       ...       ...       ...       ...       ...   \\n\",\n       \"189  0.181667  0.008707  0.374962 -0.998080 -0.015004 -0.071801 -0.205790   \\n\",\n       \"190  0.103272 -0.120228 -0.133202 -0.981479 -0.683975  0.748666 -0.171097   \\n\",\n       \"191  0.187382  0.632996  0.507790 -0.999535  0.041612  0.090283 -0.432323   \\n\",\n       \"192  0.510929 -0.112762  0.072157 -0.974629 -0.724549  0.754821  0.580699   \\n\",\n       \"193 -0.582436  0.441268  0.113779 -0.919631 -0.569133  0.647876 -0.348418   \\n\",\n       \"\\n\",\n       \"     cddd_511  cddd_512  \\n\",\n       \"0    0.753847  0.812943  \\n\",\n       \"1   -0.019943 -0.173729  \\n\",\n       \"2    0.711537  0.445195  \\n\",\n       \"3    0.283461 -0.099510  \\n\",\n       \"4   -0.066302 -0.377198  \\n\",\n       \"..        ...       ...  \\n\",\n       \"189 -0.394928  0.386006  \\n\",\n       \"190  0.053143  0.144776  \\n\",\n       \"191 -0.191279  0.136006  \\n\",\n       \"192  0.437276  0.079424  \\n\",\n       \"193  0.395181  0.272382  \\n\",\n       \"\\n\",\n       \"[194 rows x 513 columns]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"file_cddd_features = Path(\\\"../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\\\")\\n\",\n    \"assert file_cddd_features.is_file()\\n\",\n    \"df_cddd = pd.read_csv(file_cddd_features)\\n\",\n    \"df_cddd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"92dc6bf5\",\n   \"metadata\": {\n    \"lines_to_next_cell\": 2\n   },\n   \"source\": [\n    \"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:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"db83be01\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.758200Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.757994Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.769048Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.768408Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def combine_datasets(data, cddd):\\n\",\n    \"    data_combined = pd.merge(\\n\",\n    \"        left=data,\\n\",\n    \"        right=cddd,\\n\",\n    \"        on=\\\"Ambit_InchiKey\\\",\\n\",\n    \"        how=\\\"inner\\\",\\n\",\n    \"        validate=\\\"one_to_one\\\",\\n\",\n    \"    )\\n\",\n    \"    return data_combined\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_combined_train = combine_datasets(data_train, df_cddd)\\n\",\n    \"data_combined_test = combine_datasets(data_test, df_cddd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"dae995b7\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.771772Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.771516Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.775128Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.774567Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The CDDD descriptors couldn't be computed for few molecules and can be removed as commented out below. The Datafile is now prefiltered\\n\",\n    \"# target_train = data_train.loc[data_train[\\\"Ambit_InchiKey\\\"].isin(data_combined_train[\\\"Ambit_InchiKey\\\"]), column_target]\\n\",\n    \"# target_test = data_test.loc[data_test[\\\"Ambit_InchiKey\\\"].isin(data_combined_test[\\\"Ambit_InchiKey\\\"]), column_target]\\n\",\n    \"\\n\",\n    \"target_train = data_combined_train.loc[:, column_target]\\n\",\n    \"target_test = data_combined_test.loc[:, column_target]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2ec82fc8\",\n   \"metadata\": {},\n   \"source\": [\n    \"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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"dc6de049\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.777567Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.777354Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.808615Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.808024Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-9 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-9 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-9 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-9 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-9 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-9 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-9 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-9\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\\n\",\n       \"                                                  SmilesToMolTransformer()),\\n\",\n       \"                                                 (&#x27;standardizer&#x27;,\\n\",\n       \"                                                  Standardizer()),\\n\",\n       \"                                                 (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                                                  MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;qed&#x27;,\\n\",\n       \"                                                                                            &#x27;SPS&#x27;,\\n\",\n       \"                                                                                            &#x27;MolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;Num...\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;AvgIpc&#x27;,\\n\",\n       \"                                                                                            &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                                                            &#x27;BertzCT&#x27;,\\n\",\n       \"                                                                                            &#x27;Chi0&#x27;, ...]))]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\\n\",\n       \"                                (&#x27;pipeline-2&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\\n\",\n       \"                                                  FunctionTransformer())]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-31\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-31\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>ColumnTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\\\">?<span>Documentation for ColumnTransformer</span></a><span class=\\\"sk-estimator-doc-link \\\">i<span>Not fitted</span></span></div></label><div class=\\\"sk-toggleable__content \\\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\\n\",\n       \"                                                  SmilesToMolTransformer()),\\n\",\n       \"                                                 (&#x27;standardizer&#x27;,\\n\",\n       \"                                                  Standardizer()),\\n\",\n       \"                                                 (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                                                  MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;qed&#x27;,\\n\",\n       \"                                                                                            &#x27;SPS&#x27;,\\n\",\n       \"                                                                                            &#x27;MolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;Num...\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;AvgIpc&#x27;,\\n\",\n       \"                                                                                            &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                                                            &#x27;BertzCT&#x27;,\\n\",\n       \"                                                                                            &#x27;Chi0&#x27;, ...]))]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\\n\",\n       \"                                (&#x27;pipeline-2&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\\n\",\n       \"                                                  FunctionTransformer())]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</pre></div> </div></div><div class=\\\"sk-parallel\\\"><div class=\\\"sk-parallel-item\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-32\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-32\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>pipeline-1</div></div></label><div class=\\\"sk-toggleable__content \\\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-33\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-33\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>SmilesToMolTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\\\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-34\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-34\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>Standardizer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\\\">?<span>Documentation for Standardizer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>Standardizer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-35\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-35\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\\\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\\n\",\n       \"                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                          &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                          &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                          &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan3&#x27;, &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MWLOW&#x27;, &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div></div></div></div></div></div><div class=\\\"sk-parallel-item\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-36\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-36\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>pipeline-2</div></div></label><div class=\\\"sk-toggleable__content \\\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-37\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-37\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>NoneType(...)</div><div class=\\\"caption\\\">FunctionTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.FunctionTransformer.html\\\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"ColumnTransformer(transformers=[('pipeline-1',\\n\",\n       \"                                 Pipeline(steps=[('smilestomoltransformer',\\n\",\n       \"                                                  SmilesToMolTransformer()),\\n\",\n       \"                                                 ('standardizer',\\n\",\n       \"                                                  Standardizer()),\\n\",\n       \"                                                 ('moleculardescriptortransformer',\\n\",\n       \"                                                  MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\\n\",\n       \"                                                                                            'MaxEStateIndex',\\n\",\n       \"                                                                                            'MinAbsEStateIndex',\\n\",\n       \"                                                                                            'MinEStateIndex',\\n\",\n       \"                                                                                            'qed',\\n\",\n       \"                                                                                            'SPS',\\n\",\n       \"                                                                                            'MolWt',\\n\",\n       \"                                                                                            'HeavyAtomMolWt',\\n\",\n       \"                                                                                            'ExactMolWt',\\n\",\n       \"                                                                                            'Num...\\n\",\n       \"                                                                                            'BCUT2D_LOGPHI',\\n\",\n       \"                                                                                            'BCUT2D_LOGPLOW',\\n\",\n       \"                                                                                            'BCUT2D_MRHI',\\n\",\n       \"                                                                                            'BCUT2D_MRLOW',\\n\",\n       \"                                                                                            'AvgIpc',\\n\",\n       \"                                                                                            'BalabanJ',\\n\",\n       \"                                                                                            'BertzCT',\\n\",\n       \"                                                                                            'Chi0', ...]))]),\\n\",\n       \"                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\\n\",\n       \"                                ('pipeline-2',\\n\",\n       \"                                 Pipeline(steps=[('functiontransformer',\\n\",\n       \"                                                  FunctionTransformer())]),\\n\",\n       \"                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# A pipeline to compute scikit-mol descriptors\\n\",\n    \"descriptors_pipeline = make_pipeline(\\n\",\n    \"    SmilesToMolTransformer(),\\n\",\n    \"    Standardizer(),\\n\",\n    \"    MolecularDescriptorTransformer(),\\n\",\n    \")\\n\",\n    \"# A pipeline that just passes the input data.\\n\",\n    \"# We will use it to preserve the CDDD features and pass them to downstream steps.\\n\",\n    \"identity_pipeline = make_pipeline(\\n\",\n    \"    FunctionTransformer(),\\n\",\n    \")\\n\",\n    \"combined_transformer = make_column_transformer(\\n\",\n    \"    (descriptors_pipeline, make_column_selector(pattern=\\\"SMILES\\\")),\\n\",\n    \"    (identity_pipeline, make_column_selector(pattern=r\\\"^cddd_\\\\d+$\\\")),\\n\",\n    \"    remainder=\\\"drop\\\",\\n\",\n    \")\\n\",\n    \"combined_transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"6ee85c3c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.811016Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.810811Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:37.883600Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:37.882894Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-10 {\\n\",\n       \"  /* Definition of color scheme common for light and dark mode */\\n\",\n       \"  --sklearn-color-text: #000;\\n\",\n       \"  --sklearn-color-text-muted: #666;\\n\",\n       \"  --sklearn-color-line: gray;\\n\",\n       \"  /* Definition of color scheme for unfitted estimators */\\n\",\n       \"  --sklearn-color-unfitted-level-0: #fff5e6;\\n\",\n       \"  --sklearn-color-unfitted-level-1: #f6e4d2;\\n\",\n       \"  --sklearn-color-unfitted-level-2: #ffe0b3;\\n\",\n       \"  --sklearn-color-unfitted-level-3: chocolate;\\n\",\n       \"  /* Definition of color scheme for fitted estimators */\\n\",\n       \"  --sklearn-color-fitted-level-0: #f0f8ff;\\n\",\n       \"  --sklearn-color-fitted-level-1: #d4ebff;\\n\",\n       \"  --sklearn-color-fitted-level-2: #b3dbfd;\\n\",\n       \"  --sklearn-color-fitted-level-3: cornflowerblue;\\n\",\n       \"\\n\",\n       \"  /* Specific color for light theme */\\n\",\n       \"  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\\n\",\n       \"  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\\n\",\n       \"  --sklearn-color-icon: #696969;\\n\",\n       \"\\n\",\n       \"  @media (prefers-color-scheme: dark) {\\n\",\n       \"    /* Redefinition of color scheme for dark theme */\\n\",\n       \"    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\\n\",\n       \"    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\\n\",\n       \"    --sklearn-color-icon: #878787;\\n\",\n       \"  }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 pre {\\n\",\n       \"  padding: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 input.sk-hidden--visually {\\n\",\n       \"  border: 0;\\n\",\n       \"  clip: rect(1px 1px 1px 1px);\\n\",\n       \"  clip: rect(1px, 1px, 1px, 1px);\\n\",\n       \"  height: 1px;\\n\",\n       \"  margin: -1px;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  padding: 0;\\n\",\n       \"  position: absolute;\\n\",\n       \"  width: 1px;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-dashed-wrapped {\\n\",\n       \"  border: 1px dashed var(--sklearn-color-line);\\n\",\n       \"  margin: 0 0.4em 0.5em 0.4em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  padding-bottom: 0.4em;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-container {\\n\",\n       \"  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\\n\",\n       \"     but bootstrap.min.css set `[hidden] { display: none !important; }`\\n\",\n       \"     so we also need the `!important` here to be able to override the\\n\",\n       \"     default hidden behavior on the sphinx rendered scikit-learn.org.\\n\",\n       \"     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\\n\",\n       \"  display: inline-block !important;\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-text-repr-fallback {\\n\",\n       \"  display: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-parallel-item,\\n\",\n       \"div.sk-serial,\\n\",\n       \"div.sk-item {\\n\",\n       \"  /* draw centered vertical line to link estimators */\\n\",\n       \"  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\\n\",\n       \"  background-size: 2px 100%;\\n\",\n       \"  background-repeat: no-repeat;\\n\",\n       \"  background-position: center center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Parallel-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel-item::after {\\n\",\n       \"  content: \\\"\\\";\\n\",\n       \"  width: 100%;\\n\",\n       \"  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\\n\",\n       \"  flex-grow: 1;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel {\\n\",\n       \"  display: flex;\\n\",\n       \"  align-items: stretch;\\n\",\n       \"  justify-content: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  position: relative;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel-item {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel-item:first-child::after {\\n\",\n       \"  align-self: flex-end;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel-item:last-child::after {\\n\",\n       \"  align-self: flex-start;\\n\",\n       \"  width: 50%;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-parallel-item:only-child::after {\\n\",\n       \"  width: 0;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Serial-specific style estimator block */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-serial {\\n\",\n       \"  display: flex;\\n\",\n       \"  flex-direction: column;\\n\",\n       \"  align-items: center;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  padding-right: 1em;\\n\",\n       \"  padding-left: 1em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\\n\",\n       \"clickable and can be expanded/collapsed.\\n\",\n       \"- Pipeline and ColumnTransformer use this feature and define the default style\\n\",\n       \"- Estimators will overwrite some part of the style using the `sk-estimator` class\\n\",\n       \"*/\\n\",\n       \"\\n\",\n       \"/* Pipeline and ColumnTransformer style (default) */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-toggleable {\\n\",\n       \"  /* Default theme specific background. It is overwritten whether we have a\\n\",\n       \"  specific estimator or a Pipeline/ColumnTransformer */\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable label */\\n\",\n       \"#sk-container-id-10 label.sk-toggleable__label {\\n\",\n       \"  cursor: pointer;\\n\",\n       \"  display: flex;\\n\",\n       \"  width: 100%;\\n\",\n       \"  margin-bottom: 0;\\n\",\n       \"  padding: 0.5em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  text-align: center;\\n\",\n       \"  align-items: start;\\n\",\n       \"  justify-content: space-between;\\n\",\n       \"  gap: 0.5em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 label.sk-toggleable__label .caption {\\n\",\n       \"  font-size: 0.6rem;\\n\",\n       \"  font-weight: lighter;\\n\",\n       \"  color: var(--sklearn-color-text-muted);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 label.sk-toggleable__label-arrow:before {\\n\",\n       \"  /* Arrow on the left of the label */\\n\",\n       \"  content: \\\"▸\\\";\\n\",\n       \"  float: left;\\n\",\n       \"  margin-right: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-icon);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Toggleable content - dropdown */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-toggleable__content {\\n\",\n       \"  max-height: 0;\\n\",\n       \"  max-width: 0;\\n\",\n       \"  overflow: hidden;\\n\",\n       \"  text-align: left;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-toggleable__content.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-toggleable__content pre {\\n\",\n       \"  margin: 0.2em;\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-toggleable__content.fitted pre {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\\n\",\n       \"  /* Expand drop-down */\\n\",\n       \"  max-height: 200px;\\n\",\n       \"  max-width: 100%;\\n\",\n       \"  overflow: auto;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\\n\",\n       \"  content: \\\"▾\\\";\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Pipeline/ColumnTransformer-specific style */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific style */\\n\",\n       \"\\n\",\n       \"/* Colorize estimator box */\\n\",\n       \"#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-label label.sk-toggleable__label,\\n\",\n       \"#sk-container-id-10 div.sk-label label {\\n\",\n       \"  /* The background is the default theme color */\\n\",\n       \"  color: var(--sklearn-color-text-on-default-background);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover, darken the color of the background */\\n\",\n       \"#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Label box, darken color on hover, fitted */\\n\",\n       \"#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator label */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-label label {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  font-weight: bold;\\n\",\n       \"  display: inline-block;\\n\",\n       \"  line-height: 1.2em;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-label-container {\\n\",\n       \"  text-align: center;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Estimator-specific */\\n\",\n       \"#sk-container-id-10 div.sk-estimator {\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  border: 1px dotted var(--sklearn-color-border-box);\\n\",\n       \"  border-radius: 0.25em;\\n\",\n       \"  box-sizing: border-box;\\n\",\n       \"  margin-bottom: 0.5em;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-estimator.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-0);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* on hover */\\n\",\n       \"#sk-container-id-10 div.sk-estimator:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 div.sk-estimator.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-2);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Specification for estimator info (e.g. \\\"i\\\" and \\\"?\\\") */\\n\",\n       \"\\n\",\n       \"/* Common style for \\\"i\\\" and \\\"?\\\" */\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link,\\n\",\n       \"a:link.sk-estimator-doc-link,\\n\",\n       \"a:visited.sk-estimator-doc-link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: smaller;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1em;\\n\",\n       \"  height: 1em;\\n\",\n       \"  width: 1em;\\n\",\n       \"  text-decoration: none !important;\\n\",\n       \"  margin-left: 0.5em;\\n\",\n       \"  text-align: center;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted,\\n\",\n       \"a:link.sk-estimator-doc-link.fitted,\\n\",\n       \"a:visited.sk-estimator-doc-link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"div.sk-estimator:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link:hover,\\n\",\n       \".sk-estimator-doc-link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover,\\n\",\n       \"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\\n\",\n       \".sk-estimator-doc-link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* Span, style for the box shown on hovering the info icon */\\n\",\n       \".sk-estimator-doc-link span {\\n\",\n       \"  display: none;\\n\",\n       \"  z-index: 9999;\\n\",\n       \"  position: relative;\\n\",\n       \"  font-weight: normal;\\n\",\n       \"  right: .2ex;\\n\",\n       \"  padding: .5ex;\\n\",\n       \"  margin: .5ex;\\n\",\n       \"  width: min-content;\\n\",\n       \"  min-width: 20ex;\\n\",\n       \"  max-width: 50ex;\\n\",\n       \"  color: var(--sklearn-color-text);\\n\",\n       \"  box-shadow: 2pt 2pt 4pt #999;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background: var(--sklearn-color-unfitted-level-0);\\n\",\n       \"  border: .5pt solid var(--sklearn-color-unfitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link.fitted span {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background: var(--sklearn-color-fitted-level-0);\\n\",\n       \"  border: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \".sk-estimator-doc-link:hover span {\\n\",\n       \"  display: block;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* \\\"?\\\"-specific style due to the `<a>` HTML tag */\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 a.estimator_doc_link {\\n\",\n       \"  float: right;\\n\",\n       \"  font-size: 1rem;\\n\",\n       \"  line-height: 1em;\\n\",\n       \"  font-family: monospace;\\n\",\n       \"  background-color: var(--sklearn-color-background);\\n\",\n       \"  border-radius: 1rem;\\n\",\n       \"  height: 1rem;\\n\",\n       \"  width: 1rem;\\n\",\n       \"  text-decoration: none;\\n\",\n       \"  /* unfitted */\\n\",\n       \"  color: var(--sklearn-color-unfitted-level-1);\\n\",\n       \"  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 a.estimator_doc_link.fitted {\\n\",\n       \"  /* fitted */\\n\",\n       \"  border: var(--sklearn-color-fitted-level-1) 1pt solid;\\n\",\n       \"  color: var(--sklearn-color-fitted-level-1);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"/* On hover */\\n\",\n       \"#sk-container-id-10 a.estimator_doc_link:hover {\\n\",\n       \"  /* unfitted */\\n\",\n       \"  background-color: var(--sklearn-color-unfitted-level-3);\\n\",\n       \"  color: var(--sklearn-color-background);\\n\",\n       \"  text-decoration: none;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"#sk-container-id-10 a.estimator_doc_link.fitted:hover {\\n\",\n       \"  /* fitted */\\n\",\n       \"  background-color: var(--sklearn-color-fitted-level-3);\\n\",\n       \"}\\n\",\n       \"</style><div id=\\\"sk-container-id-10\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\\n\",\n       \"                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\\n\",\n       \"                                                  Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\\n\",\n       \"                                                                   SmilesToMolTransformer()),\\n\",\n       \"                                                                  (&#x27;standardizer&#x27;,\\n\",\n       \"                                                                   Standardizer()),\\n\",\n       \"                                                                  (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                                                                   MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;qed&#x27;,\\n\",\n       \"                                                                                                             &#x27;SPS&#x27;,\\n\",\n       \"                                                                                                             &#x27;MolW...\\n\",\n       \"                                                                                                             &#x27;Chi0&#x27;, ...]))]),\\n\",\n       \"                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\\n\",\n       \"                                                 (&#x27;pipeline-2&#x27;,\\n\",\n       \"                                                  Pipeline(steps=[(&#x27;functiontransformer&#x27;,\\n\",\n       \"                                                                   FunctionTransformer())]),\\n\",\n       \"                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])),\\n\",\n       \"                (&#x27;standardscaler&#x27;, StandardScaler()),\\n\",\n       \"                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-38\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-38\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>Pipeline</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\\\">?<span>Documentation for Pipeline</span></a><span class=\\\"sk-estimator-doc-link \\\">i<span>Not fitted</span></span></div></label><div class=\\\"sk-toggleable__content \\\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\\n\",\n       \"                 ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\\n\",\n       \"                                                  Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\\n\",\n       \"                                                                   SmilesToMolTransformer()),\\n\",\n       \"                                                                  (&#x27;standardizer&#x27;,\\n\",\n       \"                                                                   Standardizer()),\\n\",\n       \"                                                                  (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                                                                   MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                                                                             &#x27;qed&#x27;,\\n\",\n       \"                                                                                                             &#x27;SPS&#x27;,\\n\",\n       \"                                                                                                             &#x27;MolW...\\n\",\n       \"                                                                                                             &#x27;Chi0&#x27;, ...]))]),\\n\",\n       \"                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\\n\",\n       \"                                                 (&#x27;pipeline-2&#x27;,\\n\",\n       \"                                                  Pipeline(steps=[(&#x27;functiontransformer&#x27;,\\n\",\n       \"                                                                   FunctionTransformer())]),\\n\",\n       \"                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])),\\n\",\n       \"                (&#x27;standardscaler&#x27;, StandardScaler()),\\n\",\n       \"                (&#x27;randomforestregressor&#x27;, RandomForestRegressor(max_depth=5))])</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item sk-dashed-wrapped\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-39\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-39\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\\\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;smilestomoltransformer&#x27;,\\n\",\n       \"                                                  SmilesToMolTransformer()),\\n\",\n       \"                                                 (&#x27;standardizer&#x27;,\\n\",\n       \"                                                  Standardizer()),\\n\",\n       \"                                                 (&#x27;moleculardescriptortransformer&#x27;,\\n\",\n       \"                                                  MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinAbsEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                                                                            &#x27;qed&#x27;,\\n\",\n       \"                                                                                            &#x27;SPS&#x27;,\\n\",\n       \"                                                                                            &#x27;MolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;HeavyAtomMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                                                                            &#x27;Num...\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_LOGPLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                                                                            &#x27;BCUT2D_MRLOW&#x27;,\\n\",\n       \"                                                                                            &#x27;AvgIpc&#x27;,\\n\",\n       \"                                                                                            &#x27;BalabanJ&#x27;,\\n\",\n       \"                                                                                            &#x27;BertzCT&#x27;,\\n\",\n       \"                                                                                            &#x27;Chi0&#x27;, ...]))]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;),\\n\",\n       \"                                (&#x27;pipeline-2&#x27;,\\n\",\n       \"                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\\n\",\n       \"                                                  FunctionTransformer())]),\\n\",\n       \"                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;)])</pre></div> </div></div><div class=\\\"sk-parallel\\\"><div class=\\\"sk-parallel-item\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-40\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-40\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>pipeline-1</div></div></label><div class=\\\"sk-toggleable__content \\\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50&gt;</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-41\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-41\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>SmilesToMolTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.conversions/#scikit_mol.conversions.SmilesToMolTransformer\\\">?<span>Documentation for SmilesToMolTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>SmilesToMolTransformer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-42\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-42\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>Standardizer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.standardizer/#scikit_mol.standardizer.Standardizer\\\">?<span>Documentation for Standardizer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>Standardizer()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-43\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-43\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>MolecularDescriptorTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.descriptors/#scikit_mol.descriptors.MolecularDescriptorTransformer\\\">?<span>Documentation for MolecularDescriptorTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>MolecularDescriptorTransformer(desc_list=[&#x27;MaxAbsEStateIndex&#x27;, &#x27;MaxEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;MinAbsEStateIndex&#x27;, &#x27;MinEStateIndex&#x27;,\\n\",\n       \"                                          &#x27;qed&#x27;, &#x27;SPS&#x27;, &#x27;MolWt&#x27;,\\n\",\n       \"                                          &#x27;HeavyAtomMolWt&#x27;, &#x27;ExactMolWt&#x27;,\\n\",\n       \"                                          &#x27;NumValenceElectrons&#x27;,\\n\",\n       \"                                          &#x27;NumRadicalElectrons&#x27;,\\n\",\n       \"                                          &#x27;MaxPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MaxAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;MinAbsPartialCharge&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan1&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan2&#x27;,\\n\",\n       \"                                          &#x27;FpDensityMorgan3&#x27;, &#x27;BCUT2D_MWHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MWLOW&#x27;, &#x27;BCUT2D_CHGHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_CHGLO&#x27;, &#x27;BCUT2D_LOGPHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_LOGPLOW&#x27;, &#x27;BCUT2D_MRHI&#x27;,\\n\",\n       \"                                          &#x27;BCUT2D_MRLOW&#x27;, &#x27;AvgIpc&#x27;, &#x27;BalabanJ&#x27;,\\n\",\n       \"                                          &#x27;BertzCT&#x27;, &#x27;Chi0&#x27;, ...])</pre></div> </div></div></div></div></div></div></div><div class=\\\"sk-parallel-item\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-label-container\\\"><div class=\\\"sk-label  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-44\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-44\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>pipeline-2</div></div></label><div class=\\\"sk-toggleable__content \\\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0&gt;</pre></div> </div></div><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-serial\\\"><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-45\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-45\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>NoneType(...)</div><div class=\\\"caption\\\">FunctionTransformer</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.FunctionTransformer.html\\\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>FunctionTransformer()</pre></div> </div></div></div></div></div></div></div></div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-46\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-46\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>StandardScaler</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\\\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>StandardScaler()</pre></div> </div></div><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator  sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-47\\\" type=\\\"checkbox\\\" ><label for=\\\"sk-estimator-id-47\\\" class=\\\"sk-toggleable__label  sk-toggleable__label-arrow\\\"><div><div>RandomForestRegressor</div></div><div><a class=\\\"sk-estimator-doc-link \\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\" href=\\\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\\\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\\\"sk-toggleable__content \\\"><pre>RandomForestRegressor(max_depth=5)</pre></div> </div></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"Pipeline(steps=[('columntransformer',\\n\",\n       \"                 ColumnTransformer(transformers=[('pipeline-1',\\n\",\n       \"                                                  Pipeline(steps=[('smilestomoltransformer',\\n\",\n       \"                                                                   SmilesToMolTransformer()),\\n\",\n       \"                                                                  ('standardizer',\\n\",\n       \"                                                                   Standardizer()),\\n\",\n       \"                                                                  ('moleculardescriptortransformer',\\n\",\n       \"                                                                   MolecularDescriptorTransformer(desc_list=['MaxAbsEStateIndex',\\n\",\n       \"                                                                                                             'MaxEStateIndex',\\n\",\n       \"                                                                                                             'MinAbsEStateIndex',\\n\",\n       \"                                                                                                             'MinEStateIndex',\\n\",\n       \"                                                                                                             'qed',\\n\",\n       \"                                                                                                             'SPS',\\n\",\n       \"                                                                                                             'MolW...\\n\",\n       \"                                                                                                             'Chi0', ...]))]),\\n\",\n       \"                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3aff50>),\\n\",\n       \"                                                 ('pipeline-2',\\n\",\n       \"                                                  Pipeline(steps=[('functiontransformer',\\n\",\n       \"                                                                   FunctionTransformer())]),\\n\",\n       \"                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7fb59f3ad2b0>)])),\\n\",\n       \"                ('standardscaler', StandardScaler()),\\n\",\n       \"                ('randomforestregressor', RandomForestRegressor(max_depth=5))])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline_combined = make_pipeline(\\n\",\n    \"    combined_transformer,\\n\",\n    \"    StandardScaler(),\\n\",\n    \"    RandomForestRegressor(**params_random_forest),\\n\",\n    \")\\n\",\n    \"pipeline_combined.set_output(transform=\\\"pandas\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"03960958\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:37.886041Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:37.885822Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:42.859220Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:42.858489Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\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\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\",\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\",\n      \"  return bound(*args, **kwds)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'RMSE': 0.8008666941742995,\\n\",\n       \" 'MAE': 0.6900085446099777,\\n\",\n       \" 'R2': 0.26735673218396616}\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline_combined.fit(data_combined_train, target_train)\\n\",\n    \"pred_combined_test = pipeline_combined.predict(data_combined_test)\\n\",\n    \"performance_combined = compute_metrics(y_true=target_test, y_pred=pred_combined_test)\\n\",\n    \"performance_combined\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c49ecd90\",\n   \"metadata\": {},\n   \"source\": [\n    \"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:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"6ce2fe53\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:42.861769Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:42.861505Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:42.869169Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:42.868553Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>RMSE</th>\\n\",\n       \"      <th>MAE</th>\\n\",\n       \"      <th>R2</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>descriptors</th>\\n\",\n       \"      <td>0.853908</td>\\n\",\n       \"      <td>0.722250</td>\\n\",\n       \"      <td>0.161453</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>combined</th>\\n\",\n       \"      <td>0.800867</td>\\n\",\n       \"      <td>0.690009</td>\\n\",\n       \"      <td>0.267357</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                 RMSE       MAE        R2\\n\",\n       \"descriptors  0.853908  0.722250  0.161453\\n\",\n       \"combined     0.800867  0.690009  0.267357\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_performance = pd.DataFrame(\\n\",\n    \"    [performance, performance_combined], index=[\\\"descriptors\\\", \\\"combined\\\"]\\n\",\n    \")\\n\",\n    \"df_performance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"83b7fd13\",\n   \"metadata\": {},\n   \"source\": [\n    \"All performance metrics were improved by the inclusion of the CDDD features.\\n\",\n    \"Let's analyze the feature importances of the model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"9c98ac71\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:42.872116Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:42.871685Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:42.886733Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:42.886003Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>feature</th>\\n\",\n       \"      <th>importance</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>pipeline-1__PEOE_VSA6</td>\\n\",\n       \"      <td>0.077408</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>pipeline-2__cddd_102</td>\\n\",\n       \"      <td>0.069598</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>pipeline-2__cddd_369</td>\\n\",\n       \"      <td>0.051396</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>pipeline-2__cddd_378</td>\\n\",\n       \"      <td>0.044292</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>pipeline-2__cddd_372</td>\\n\",\n       \"      <td>0.030435</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>724</th>\\n\",\n       \"      <td>pipeline-1__PEOE_VSA3</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>725</th>\\n\",\n       \"      <td>pipeline-1__Ipc</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>726</th>\\n\",\n       \"      <td>pipeline-1__SMR_VSA8</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>727</th>\\n\",\n       \"      <td>pipeline-2__cddd_461</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>728</th>\\n\",\n       \"      <td>pipeline-1__NumRadicalElectrons</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>729 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                             feature  importance\\n\",\n       \"0              pipeline-1__PEOE_VSA6    0.077408\\n\",\n       \"1               pipeline-2__cddd_102    0.069598\\n\",\n       \"2               pipeline-2__cddd_369    0.051396\\n\",\n       \"3               pipeline-2__cddd_378    0.044292\\n\",\n       \"4               pipeline-2__cddd_372    0.030435\\n\",\n       \"..                               ...         ...\\n\",\n       \"724            pipeline-1__PEOE_VSA3    0.000000\\n\",\n       \"725                  pipeline-1__Ipc    0.000000\\n\",\n       \"726             pipeline-1__SMR_VSA8    0.000000\\n\",\n       \"727             pipeline-2__cddd_461    0.000000\\n\",\n       \"728  pipeline-1__NumRadicalElectrons    0.000000\\n\",\n       \"\\n\",\n       \"[729 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"regressor = pipeline_combined[-1]\\n\",\n    \"df_importance = pd.DataFrame(\\n\",\n    \"    {\\n\",\n    \"        \\\"feature\\\": regressor.feature_names_in_,\\n\",\n    \"        \\\"importance\\\": regressor.feature_importances_,\\n\",\n    \"    }\\n\",\n    \").sort_values(by=\\\"importance\\\", ascending=False, ignore_index=True)\\n\",\n    \"df_importance\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"9dbd2a9e\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:42.889250Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:42.889020Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:42.893106Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:42.892486Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The 5 most important features are:\\n\",\n      \"pipeline-1__PEOE_VSA6\\n\",\n      \"pipeline-2__cddd_102\\n\",\n      \"pipeline-2__cddd_369\\n\",\n      \"pipeline-2__cddd_378\\n\",\n      \"pipeline-2__cddd_372\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"top_features = df_importance.head(n_top_features).loc[:, \\\"feature\\\"].tolist()\\n\",\n    \"print(f\\\"The {n_top_features} most important features are:\\\")\\n\",\n    \"for feature in top_features:\\n\",\n    \"    print(feature)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b394662\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we can see, some CDDD features are among the most important features for the regression model.\\n\",\n    \"\\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).\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/11_safe_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f34dacf0\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Safe inference mode\\n\",\n    \"\\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    \"\\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    \"\\n\",\n    \"First some imports and test SMILES and molecules.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"ac780f4c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:44.417205Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:44.417002Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.205864Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.205244Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>],\\n\",\n       \"       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>],\\n\",\n       \"       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>],\\n\",\n       \"       [<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>],\\n\",\n       \"       [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid Molecule: Explicit valence for atom # 0 N, 4, is greater than permitted')],\\n\",\n       \"       [InvalidMol('SmilesToMolTransformer(safe_inference_mode=True)', error='Invalid SMILES: I'm not a SMILES')]],\\n\",\n       \"      dtype=object)\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from rdkit import Chem\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"\\n\",\n    \"# We have some deprecation warnings, we are adressing them, but they just distract from this demonstration\\n\",\n    \"import warnings\\n\",\n    \"\\n\",\n    \"warnings.filterwarnings(\\\"ignore\\\", category=DeprecationWarning)\\n\",\n    \"\\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\",\n    \"smiles_with_invalid = smiles + [\\\"N(C)(C)(C)C\\\", \\\"I'm not a SMILES\\\"]\\n\",\n    \"\\n\",\n    \"smi2mol = SmilesToMolTransformer(safe_inference_mode=True)\\n\",\n    \"\\n\",\n    \"mols_with_invalid = smi2mol.transform(smiles_with_invalid)\\n\",\n    \"mols_with_invalid\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bdd18682\",\n   \"metadata\": {},\n   \"source\": [\n    \"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:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"44a6019c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.208884Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.208436Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.213259Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.212730Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>], dtype=object),\\n\",\n       \" array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>], dtype=object),\\n\",\n       \" array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>], dtype=object),\\n\",\n       \" array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>], dtype=object)]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[mol for mol in mols_with_invalid if mol]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"176a44de\",\n   \"metadata\": {},\n   \"source\": [\n    \"or\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"8286fd44\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.215847Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.215431Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.219372Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.218875Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([<rdkit.Chem.rdchem.Mol object at 0x7efcedcd6d50>,\\n\",\n       \"       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6dc0>,\\n\",\n       \"       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6e30>,\\n\",\n       \"       <rdkit.Chem.rdchem.Mol object at 0x7efcedcd6ea0>], dtype=object)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mask = mols_with_invalid.astype(bool)\\n\",\n    \"mols_with_invalid[mask]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c7be8909\",\n   \"metadata\": {},\n   \"source\": [\n    \"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)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"9a705642\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.221712Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.221465Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.246566Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.245960Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"masked_array(\\n\",\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\",\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\",\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\",\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\",\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\",\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\",\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\",\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\",\n       \"        [--, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --,\\n\",\n       \"         --, --, --, --, --, --, --, --, --]],\\n\",\n       \"  mask=[[False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False],\\n\",\n       \"        [False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False],\\n\",\n       \"        [False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False],\\n\",\n       \"        [False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False, False, False,\\n\",\n       \"         False, False, False, False, False, False, False],\\n\",\n       \"        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\\n\",\n       \"          True,  True,  True,  True,  True,  True,  True,  True,  True,\\n\",\n       \"          True,  True,  True,  True,  True,  True,  True],\\n\",\n       \"        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\\n\",\n       \"          True,  True,  True,  True,  True,  True,  True,  True,  True,\\n\",\n       \"          True,  True,  True,  True,  True,  True,  True]],\\n\",\n       \"  fill_value=1e+20)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"\\n\",\n    \"mfp = MorganFingerprintTransformer(radius=2, fpSize=25, safe_inference_mode=True)\\n\",\n    \"fps = mfp.transform(mols_with_invalid)\\n\",\n    \"fps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a5e2b301\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"37987dc9\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.249048Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.248844Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.318911Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.318291Z\"\n    },\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/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\",\n      \"  warnings.warn(\\n\",\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\",\n      \"  warnings.warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.,  1.,  0.,  1., nan, nan])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.safeinference import SafeInferenceWrapper\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"regressor = LogisticRegression()\\n\",\n    \"wrapper = SafeInferenceWrapper(regressor, safe_inference_mode=True)\\n\",\n    \"wrapper.fit(fps, [0, 1, 0, 1, 0, 1])\\n\",\n    \"wrapper.predict(fps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7aa1223f\",\n   \"metadata\": {},\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f08d26d5\",\n   \"metadata\": {},\n   \"source\": [\n    \"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\",\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    \"\\n\",\n    \"## Setting safe_inference_mode post-training\\n\",\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:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"51436aa8\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.321557Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.321253Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.333442Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.332830Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Without safe inference mode:\\n\",\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      \"\\n\",\n      \"With safe inference mode:\\n\",\n      \"[ 1.  0.  1.  0. nan nan]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.safeinference import set_safe_inference_mode\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"pipe = Pipeline(\\n\",\n    \"    [\\n\",\n    \"        (\\\"smi2mol\\\", SmilesToMolTransformer()),\\n\",\n    \"        (\\\"mfp\\\", MorganFingerprintTransformer(radius=2, fpSize=25)),\\n\",\n    \"        (\\\"safe_regressor\\\", SafeInferenceWrapper(LogisticRegression())),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"pipe.fit(smiles, [1, 0, 1, 0])\\n\",\n    \"\\n\",\n    \"print(\\\"Without safe inference mode:\\\")\\n\",\n    \"try:\\n\",\n    \"    pipe.predict(smiles_with_invalid)\\n\",\n    \"except Exception as e:\\n\",\n    \"    print(\\\"Prediction failed with exception: \\\", e)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_safe_inference_mode(pipe, True)\\n\",\n    \"\\n\",\n    \"print(\\\"With safe inference mode:\\\")\\n\",\n    \"print(pipe.predict(smiles_with_invalid))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cf53d58f\",\n   \"metadata\": {},\n   \"source\": [\n    \"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 :-)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"685e22fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Combining safe_inference_mode with pandas output\\n\",\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"b8dbd88c\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.336071Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.335859Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.351873Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.351251Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fp_morgan_1</th>\\n\",\n       \"      <th>fp_morgan_2</th>\\n\",\n       \"      <th>fp_morgan_3</th>\\n\",\n       \"      <th>fp_morgan_4</th>\\n\",\n       \"      <th>fp_morgan_5</th>\\n\",\n       \"      <th>fp_morgan_6</th>\\n\",\n       \"      <th>fp_morgan_7</th>\\n\",\n       \"      <th>fp_morgan_8</th>\\n\",\n       \"      <th>fp_morgan_9</th>\\n\",\n       \"      <th>fp_morgan_10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>fp_morgan_16</th>\\n\",\n       \"      <th>fp_morgan_17</th>\\n\",\n       \"      <th>fp_morgan_18</th>\\n\",\n       \"      <th>fp_morgan_19</th>\\n\",\n       \"      <th>fp_morgan_20</th>\\n\",\n       \"      <th>fp_morgan_21</th>\\n\",\n       \"      <th>fp_morgan_22</th>\\n\",\n       \"      <th>fp_morgan_23</th>\\n\",\n       \"      <th>fp_morgan_24</th>\\n\",\n       \"      <th>fp_morgan_25</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>4 rows × 25 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   fp_morgan_1  fp_morgan_2  fp_morgan_3  fp_morgan_4  fp_morgan_5  \\\\\\n\",\n       \"0            0            0            0            0            0   \\n\",\n       \"1            0            0            0            0            0   \\n\",\n       \"2            0            0            0            0            0   \\n\",\n       \"3            1            0            0            0            0   \\n\",\n       \"\\n\",\n       \"   fp_morgan_6  fp_morgan_7  fp_morgan_8  fp_morgan_9  fp_morgan_10  ...  \\\\\\n\",\n       \"0            0            0            0            1             1  ...   \\n\",\n       \"1            0            0            1            1             1  ...   \\n\",\n       \"2            0            0            0            1             1  ...   \\n\",\n       \"3            0            0            0            1             1  ...   \\n\",\n       \"\\n\",\n       \"   fp_morgan_16  fp_morgan_17  fp_morgan_18  fp_morgan_19  fp_morgan_20  \\\\\\n\",\n       \"0             0             1             0             1             1   \\n\",\n       \"1             0             1             0             0             1   \\n\",\n       \"2             0             1             0             1             1   \\n\",\n       \"3             0             1             0             0             1   \\n\",\n       \"\\n\",\n       \"   fp_morgan_21  fp_morgan_22  fp_morgan_23  fp_morgan_24  fp_morgan_25  \\n\",\n       \"0             1             0             1             1             0  \\n\",\n       \"1             1             0             0             1             0  \\n\",\n       \"2             1             0             0             0             0  \\n\",\n       \"3             1             0             1             0             1  \\n\",\n       \"\\n\",\n       \"[4 rows x 25 columns]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mfp.set_output(transform=\\\"pandas\\\")\\n\",\n    \"\\n\",\n    \"mols = smi2mol.transform(smiles)\\n\",\n    \"\\n\",\n    \"fps = mfp.transform(mols)\\n\",\n    \"fps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"092ca859\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then lets see if we transform a batch with an invalid molecule:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"710ceeb0\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.354427Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.354176Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.377892Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.377253Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fp_morgan_1</th>\\n\",\n       \"      <th>fp_morgan_2</th>\\n\",\n       \"      <th>fp_morgan_3</th>\\n\",\n       \"      <th>fp_morgan_4</th>\\n\",\n       \"      <th>fp_morgan_5</th>\\n\",\n       \"      <th>fp_morgan_6</th>\\n\",\n       \"      <th>fp_morgan_7</th>\\n\",\n       \"      <th>fp_morgan_8</th>\\n\",\n       \"      <th>fp_morgan_9</th>\\n\",\n       \"      <th>fp_morgan_10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>fp_morgan_16</th>\\n\",\n       \"      <th>fp_morgan_17</th>\\n\",\n       \"      <th>fp_morgan_18</th>\\n\",\n       \"      <th>fp_morgan_19</th>\\n\",\n       \"      <th>fp_morgan_20</th>\\n\",\n       \"      <th>fp_morgan_21</th>\\n\",\n       \"      <th>fp_morgan_22</th>\\n\",\n       \"      <th>fp_morgan_23</th>\\n\",\n       \"      <th>fp_morgan_24</th>\\n\",\n       \"      <th>fp_morgan_25</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>6 rows × 25 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   fp_morgan_1  fp_morgan_2  fp_morgan_3  fp_morgan_4  fp_morgan_5  \\\\\\n\",\n       \"0          0.0          0.0          0.0          0.0          0.0   \\n\",\n       \"1          0.0          0.0          0.0          0.0          0.0   \\n\",\n       \"2          0.0          0.0          0.0          0.0          0.0   \\n\",\n       \"3          1.0          0.0          0.0          0.0          0.0   \\n\",\n       \"4          NaN          NaN          NaN          NaN          NaN   \\n\",\n       \"5          NaN          NaN          NaN          NaN          NaN   \\n\",\n       \"\\n\",\n       \"   fp_morgan_6  fp_morgan_7  fp_morgan_8  fp_morgan_9  fp_morgan_10  ...  \\\\\\n\",\n       \"0          0.0          0.0          0.0          1.0           1.0  ...   \\n\",\n       \"1          0.0          0.0          1.0          1.0           1.0  ...   \\n\",\n       \"2          0.0          0.0          0.0          1.0           1.0  ...   \\n\",\n       \"3          0.0          0.0          0.0          1.0           1.0  ...   \\n\",\n       \"4          NaN          NaN          NaN          NaN           NaN  ...   \\n\",\n       \"5          NaN          NaN          NaN          NaN           NaN  ...   \\n\",\n       \"\\n\",\n       \"   fp_morgan_16  fp_morgan_17  fp_morgan_18  fp_morgan_19  fp_morgan_20  \\\\\\n\",\n       \"0           0.0           1.0           0.0           1.0           1.0   \\n\",\n       \"1           0.0           1.0           0.0           0.0           1.0   \\n\",\n       \"2           0.0           1.0           0.0           1.0           1.0   \\n\",\n       \"3           0.0           1.0           0.0           0.0           1.0   \\n\",\n       \"4           NaN           NaN           NaN           NaN           NaN   \\n\",\n       \"5           NaN           NaN           NaN           NaN           NaN   \\n\",\n       \"\\n\",\n       \"   fp_morgan_21  fp_morgan_22  fp_morgan_23  fp_morgan_24  fp_morgan_25  \\n\",\n       \"0           1.0           0.0           1.0           1.0           0.0  \\n\",\n       \"1           1.0           0.0           0.0           1.0           0.0  \\n\",\n       \"2           1.0           0.0           0.0           0.0           0.0  \\n\",\n       \"3           1.0           0.0           1.0           0.0           1.0  \\n\",\n       \"4           NaN           NaN           NaN           NaN           NaN  \\n\",\n       \"5           NaN           NaN           NaN           NaN           NaN  \\n\",\n       \"\\n\",\n       \"[6 rows x 25 columns]\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fps = mfp.transform(mols_with_invalid)\\n\",\n    \"fps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b87b46b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"bbfe1ec0\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2024-11-24T09:28:45.380434Z\",\n     \"iopub.status.busy\": \"2024-11-24T09:28:45.380233Z\",\n     \"iopub.status.idle\": \"2024-11-24T09:28:45.393639Z\",\n     \"shell.execute_reply\": \"2024-11-24T09:28:45.393095Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fp_morgan_1</th>\\n\",\n       \"      <th>fp_morgan_2</th>\\n\",\n       \"      <th>fp_morgan_3</th>\\n\",\n       \"      <th>fp_morgan_4</th>\\n\",\n       \"      <th>fp_morgan_5</th>\\n\",\n       \"      <th>fp_morgan_6</th>\\n\",\n       \"      <th>fp_morgan_7</th>\\n\",\n       \"      <th>fp_morgan_8</th>\\n\",\n       \"      <th>fp_morgan_9</th>\\n\",\n       \"      <th>fp_morgan_10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>fp_morgan_16</th>\\n\",\n       \"      <th>fp_morgan_17</th>\\n\",\n       \"      <th>fp_morgan_18</th>\\n\",\n       \"      <th>fp_morgan_19</th>\\n\",\n       \"      <th>fp_morgan_20</th>\\n\",\n       \"      <th>fp_morgan_21</th>\\n\",\n       \"      <th>fp_morgan_22</th>\\n\",\n       \"      <th>fp_morgan_23</th>\\n\",\n       \"      <th>fp_morgan_24</th>\\n\",\n       \"      <th>fp_morgan_25</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>4 rows × 25 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   fp_morgan_1  fp_morgan_2  fp_morgan_3  fp_morgan_4  fp_morgan_5  \\\\\\n\",\n       \"0            0            0            0            0            0   \\n\",\n       \"1            0            0            0            0            0   \\n\",\n       \"2            0            0            0            0            0   \\n\",\n       \"3            1            0            0            0            0   \\n\",\n       \"\\n\",\n       \"   fp_morgan_6  fp_morgan_7  fp_morgan_8  fp_morgan_9  fp_morgan_10  ...  \\\\\\n\",\n       \"0            0            0            0            1             1  ...   \\n\",\n       \"1            0            0            1            1             1  ...   \\n\",\n       \"2            0            0            0            1             1  ...   \\n\",\n       \"3            0            0            0            1             1  ...   \\n\",\n       \"\\n\",\n       \"   fp_morgan_16  fp_morgan_17  fp_morgan_18  fp_morgan_19  fp_morgan_20  \\\\\\n\",\n       \"0             0             1             0             1             1   \\n\",\n       \"1             0             1             0             0             1   \\n\",\n       \"2             0             1             0             1             1   \\n\",\n       \"3             0             1             0             0             1   \\n\",\n       \"\\n\",\n       \"   fp_morgan_21  fp_morgan_22  fp_morgan_23  fp_morgan_24  fp_morgan_25  \\n\",\n       \"0             1             0             1             1             0  \\n\",\n       \"1             1             0             0             1             0  \\n\",\n       \"2             1             0             0             0             0  \\n\",\n       \"3             1             0             1             0             1  \\n\",\n       \"\\n\",\n       \"[4 rows x 25 columns]\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mfp_float = MorganFingerprintTransformer(\\n\",\n    \"    radius=2, fpSize=25, safe_inference_mode=True, dtype=np.float32\\n\",\n    \")\\n\",\n    \"mfp_float.set_output(transform=\\\"pandas\\\")\\n\",\n    \"fps = mfp_float.transform(mols)\\n\",\n    \"fps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c7b382c\",\n   \"metadata\": {},\n   \"source\": [\n    \"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.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/12_custom_fingerprint_transformer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"de742d6d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating custom fingerprint transformers\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0e0a8a2b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Basics\\n\",\n    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"1ed1c7f0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\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\",\n      \"  pid = os.fork()\\n\",\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\",\n      \"  pid = os.fork()\\n\",\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\",\n      \"  pid = os.fork()\\n\",\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\",\n      \"  pid = os.fork()\\n\",\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\",\n      \"  pid = os.fork()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[2., 2., 2., ..., 2., 2., 2.],\\n\",\n       \"       [2., 2., 2., ..., 2., 2., 2.],\\n\",\n       \"       [2., 2., 2., ..., 2., 2., 2.],\\n\",\n       \"       ...,\\n\",\n       \"       [2., 2., 2., ..., 2., 2., 2.],\\n\",\n       \"       [2., 2., 2., ..., 2., 2., 2.],\\n\",\n       \"       [2., 2., 2., ..., 2., 2., 2.]], shape=(100, 64))\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scikit_mol.fingerprints.baseclasses import BaseFpsTransformer\\n\",\n    \"import numpy as np\\n\",\n    \"from rdkit import Chem\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DummyFingerprintTransformer(BaseFpsTransformer):\\n\",\n    \"    def __init__(self, fpSize=64, n_jobs=1, safe_inference_mode=False):\\n\",\n    \"        self.fpSize = fpSize\\n\",\n    \"        super().__init__(\\n\",\n    \"            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, name=\\\"dummy\\\"\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def _transform_mol(self, mol):\\n\",\n    \"        return mol.GetNumAtoms() * np.ones(self.fpSize)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trans = DummyFingerprintTransformer(n_jobs=4)\\n\",\n    \"mols = [Chem.MolFromSmiles(\\\"CC\\\")] * 100\\n\",\n    \"trans.transform(mols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"619ebaf3\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Non-pickable objects\\n\",\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    \"\\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    \"\\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.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"4e944033\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       ...,\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n       \"       [0, 0, 0, ..., 0, 0, 0]], shape=(100, 512), dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from rdkit.Chem import rdFingerprintGenerator\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class UnpickableFingerprintTransformer(BaseFpsTransformer):\\n\",\n    \"    def __init__(self, fpSize=1024, n_jobs=1, safe_inference_mode=False, **kwargs):\\n\",\n    \"        self.fpSize = fpSize\\n\",\n    \"        super().__init__(\\n\",\n    \"            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, **kwargs\\n\",\n    \"        )\\n\",\n    \"        self.fp_gen = rdFingerprintGenerator.GetRDKitFPGenerator(\\n\",\n    \"            maxPath=2, fpSize=self.fpSize\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def _transform_mol(self, mol):\\n\",\n    \"        return self.fp_gen.GetFingerprintAsNumPy(mol)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512)\\n\",\n    \"trans.transform(mols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b3f2c4c4\",\n   \"metadata\": {},\n   \"source\": [\n    \"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`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"e569b656\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"n_jobs=1 is fine\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\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\",\n      \"  pid = os.fork()\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"n_jobs=2 is not fine, because the generator passed as an argument is not picklable\\n\",\n      \"Error msg: Could not pickle the task to send it to the workers.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\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\",\n      \"  pid = os.fork()\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class BadTransformer(BaseFpsTransformer):\\n\",\n    \"    def __init__(self, generator, n_jobs=1):\\n\",\n    \"        self.generator = generator\\n\",\n    \"        super().__init__(n_jobs=n_jobs)\\n\",\n    \"\\n\",\n    \"    def _transform_mol(self, mol):\\n\",\n    \"        return self.generator.GetFingerprint(mol)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fp_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=10)\\n\",\n    \"BadTransformer(fp_gen, n_jobs=1).transform(mols)\\n\",\n    \"print(\\\"n_jobs=1 is fine\\\")\\n\",\n    \"try:\\n\",\n    \"    BadTransformer(fp_gen, n_jobs=2).transform(mols)\\n\",\n    \"except Exception as e:\\n\",\n    \"    print(\\n\",\n    \"        \\\"n_jobs=2 is not fine, because the generator passed as an argument is not picklable\\\"\\n\",\n    \"    )\\n\",\n    \"    print(f\\\"Error msg: {e}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"25517562\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Fingerprint name\\n\",\n    \"\\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    \"\\n\",\n    \"And the name resolution works as follows (in order of priority):\\n\",\n    \"- if the fingerprint has a name set during the initialization of the base class, it is used\\n\",\n    \"- if the name of the class follows the pattern `XFingerprintTransformer`, the name (`fp_X`) is extracted from it\\n\",\n    \"- as a last resort, the name is set to name of the class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"07dbac7a\",\n   \"metadata\": {\n    \"lines_to_next_cell\": 2\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['namedtansformer1_1' 'namedtansformer1_2' 'namedtansformer1_3' ...\\n\",\n      \" 'namedtansformer1_1022' 'namedtansformer1_1023' 'namedtansformer1_1024']\\n\",\n      \"['fp_fancy_1' 'fp_fancy_2' 'fp_fancy_3' ... 'fp_fancy_1022'\\n\",\n      \" 'fp_fancy_1023' 'fp_fancy_1024']\\n\",\n      \"['fp_fancy_1' 'fp_fancy_2' 'fp_fancy_3' ... 'fp_fancy_1022'\\n\",\n      \" 'fp_fancy_1023' 'fp_fancy_1024']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class NamedTansformer1(UnpickableFingerprintTransformer):\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class NamedTansformer2(UnpickableFingerprintTransformer):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super().__init__(name=\\\"fp_fancy\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class FancyFingerprintTransformer(UnpickableFingerprintTransformer):\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(NamedTansformer1().get_feature_names_out())\\n\",\n    \"print(NamedTansformer2().get_feature_names_out())\\n\",\n    \"print(FancyFingerprintTransformer().get_feature_names_out())\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/13_applicability_domain.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ee549099\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Applicability Domain Estimation\\n\",\n    \"\\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    \"\\n\",\n    \"We'll explore two different approaches:\\n\",\n    \"1. Using Morgan binary fingerprints with a k-Nearest Neighbors based applicability domain\\n\",\n    \"2. Using count-based Morgan fingerprints with dimensionality reduction and a leverage-based applicability domain\\n\",\n    \"\\n\",\n    \"First, let's import the necessary libraries and load our dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"40500fae\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:14.229954Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:14.229779Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:17.380877Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:17.380373Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from rdkit import Chem\\n\",\n    \"from rdkit.Chem import PandasTools\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.ensemble import RandomForestRegressor\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.decomposition import PCA\\n\",\n    \"\\n\",\n    \"from scikit_mol.conversions import SmilesToMolTransformer\\n\",\n    \"from scikit_mol.fingerprints import MorganFingerprintTransformer\\n\",\n    \"from scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e5d1277e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load and Prepare Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"79d3b853\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:17.383214Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:17.382749Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:17.445075Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:17.444550Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 out of 200 SMILES failed in conversion\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the dataset\\n\",\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\",\n    \"# The subset has been filtered to only include nicely predicted compounds, and is thus artificial.\\n\",\n    \"\\n\",\n    \"full_set = False\\n\",\n    \"\\n\",\n    \"if full_set:\\n\",\n    \"    csv_file = \\\"SLC6A4_active_excape_export.csv\\\"\\n\",\n    \"    if not os.path.exists(csv_file):\\n\",\n    \"        import urllib.request\\n\",\n    \"\\n\",\n    \"        url = \\\"https://ndownloader.figshare.com/files/25747817\\\"\\n\",\n    \"        urllib.request.urlretrieve(url, csv_file)\\n\",\n    \"        percentile = 95\\n\",\n    \"else:\\n\",\n    \"    csv_file = \\\"../../tests/data/SLC6A4_active_excapedb_subset.csv\\\"\\n\",\n    \"    percentile = 90\\n\",\n    \"data = pd.read_csv(csv_file)\\n\",\n    \"\\n\",\n    \"# Add RDKit mol objects\\n\",\n    \"PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\\\"SMILES\\\")\\n\",\n    \"print(f\\\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\\\")\\n\",\n    \"\\n\",\n    \"# Split into train/val/test\\n\",\n    \"X = data.ROMol\\n\",\n    \"y = data.pXC50\\n\",\n    \"\\n\",\n    \"X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n\",\n    \"X_train, X_val, y_train, y_val = train_test_split(\\n\",\n    \"    X_temp, y_temp, test_size=0.25, random_state=42\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e2896ad5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example 1: k-NN Applicability Domain with Binary Morgan Fingerprints\\n\",\n    \"\\n\",\n    \"In this example, we'll use binary Morgan fingerprints and a k-NN based applicability domain with Tanimoto distance.\\n\",\n    \"This is particularly suitable for binary fingerprints as the Tanimoto coefficient is a natural similarity measure for them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"9c89148b\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:17.446860Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:17.446670Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:17.835548Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:17.835023Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create pipeline for binary fingerprints\\n\",\n    \"binary_fp_pipe = Pipeline(\\n\",\n    \"    [\\n\",\n    \"        (\\\"fp\\\", MorganFingerprintTransformer(fpSize=2048, radius=2)),\\n\",\n    \"        (\\\"rf\\\", RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Train the model\\n\",\n    \"binary_fp_pipe.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"# Get predictions and errors\\n\",\n    \"y_pred_test = binary_fp_pipe.predict(X_test)\\n\",\n    \"abs_errors = np.abs(y_test - y_pred_test)\\n\",\n    \"\\n\",\n    \"# Create and fit k-NN AD estimator\\n\",\n    \"knn_ad = KNNApplicabilityDomain(\\n\",\n    \"    n_neighbors=3, distance_metric=\\\"tanimoto\\\", percentile=percentile\\n\",\n    \")\\n\",\n    \"knn_ad.fit(binary_fp_pipe.named_steps[\\\"fp\\\"].transform(X_train))\\n\",\n    \"\\n\",\n    \"# Fit threshold using validation set\\n\",\n    \"knn_ad.fit_threshold(binary_fp_pipe.named_steps[\\\"fp\\\"].transform(X_val))\\n\",\n    \"\\n\",\n    \"# Get AD scores for test set\\n\",\n    \"knn_scores = knn_ad.transform(binary_fp_pipe.named_steps[\\\"fp\\\"].transform(X_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a7f33a3f\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's visualize the relationship between prediction errors and AD scores:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"ee7b2f64\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:17.837494Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:17.837306Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:18.097264Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:18.096667Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"95th percentile of errors inside domain: 1.31\\n\",\n      \"95th percentile of errors outside domain: 1.17\\n\",\n      \"Fraction of samples outside domain: 0.15\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 6))\\n\",\n    \"plt.scatter(knn_scores, abs_errors, alpha=0.5)\\n\",\n    \"plt.axvline(x=knn_ad.threshold_, color=\\\"r\\\", linestyle=\\\"--\\\", label=\\\"AD Threshold\\\")\\n\",\n    \"plt.xlabel(\\\"k-NN AD Score\\\")\\n\",\n    \"plt.ylabel(\\\"Absolute Prediction Error\\\")\\n\",\n    \"plt.title(\\\"Prediction Errors vs k-NN AD Scores\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"# Calculate error statistics\\n\",\n    \"in_domain = knn_ad.predict(binary_fp_pipe.named_steps[\\\"fp\\\"].transform(X_test))\\n\",\n    \"errors_in = abs_errors[in_domain == 1]\\n\",\n    \"errors_out = abs_errors[in_domain == -1]\\n\",\n    \"\\n\",\n    \"print(f\\\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\\\")\\n\",\n    \"print(f\\\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\\\")\\n\",\n    \"print(f\\\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"09bdc3b2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example 2: Leverage-based AD with Count-based Morgan Fingerprints\\n\",\n    \"\\n\",\n    \"In this example, we'll use count-based Morgan fingerprints, reduce their dimensionality with PCA,\\n\",\n    \"and apply a leverage-based applicability domain estimator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"fe4a6819\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:18.099081Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:18.098853Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:18.569295Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:18.568707Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create pipeline for count-based fingerprints with PCA\\n\",\n    \"count_fp_pipe = Pipeline(\\n\",\n    \"    [\\n\",\n    \"        (\\\"fp\\\", MorganFingerprintTransformer(fpSize=2048, radius=2, useCounts=True)),\\n\",\n    \"        (\\\"pca\\\", PCA(n_components=0.9)),  # Keep 90% of variance\\n\",\n    \"        (\\\"scaler\\\", StandardScaler()),\\n\",\n    \"        (\\\"rf\\\", RandomForestRegressor(n_estimators=100, random_state=42)),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Train the model\\n\",\n    \"count_fp_pipe.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"# Get predictions and errors\\n\",\n    \"y_pred_test = count_fp_pipe.predict(X_test)\\n\",\n    \"abs_errors = np.abs(y_test - y_pred_test)\\n\",\n    \"\\n\",\n    \"# Create and fit leverage AD estimator\\n\",\n    \"leverage_ad = LeverageApplicabilityDomain(percentile=percentile)\\n\",\n    \"X_train_transformed = count_fp_pipe.named_steps[\\\"scaler\\\"].transform(\\n\",\n    \"    count_fp_pipe.named_steps[\\\"pca\\\"].transform(\\n\",\n    \"        count_fp_pipe.named_steps[\\\"fp\\\"].transform(X_train)\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"leverage_ad.fit(X_train_transformed)\\n\",\n    \"\\n\",\n    \"# Fit threshold using validation set\\n\",\n    \"X_val_transformed = count_fp_pipe.named_steps[\\\"scaler\\\"].transform(\\n\",\n    \"    count_fp_pipe.named_steps[\\\"pca\\\"].transform(\\n\",\n    \"        count_fp_pipe.named_steps[\\\"fp\\\"].transform(X_val)\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"leverage_ad.fit_threshold(X_val_transformed)\\n\",\n    \"\\n\",\n    \"# Get AD scores for test set\\n\",\n    \"X_test_transformed = count_fp_pipe.named_steps[\\\"scaler\\\"].transform(\\n\",\n    \"    count_fp_pipe.named_steps[\\\"pca\\\"].transform(\\n\",\n    \"        count_fp_pipe.named_steps[\\\"fp\\\"].transform(X_test)\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"leverage_scores = leverage_ad.transform(X_test_transformed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d7723e37\",\n   \"metadata\": {},\n   \"source\": [\n    \"Visualize the relationship between prediction errors and leverage scores:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"57d73a11\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:18.571392Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:18.571206Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:18.710911Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:18.710436Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"95th percentile of errors inside domain: 1.39\\n\",\n      \"95th percentile of errors outside domain: 1.22\\n\",\n      \"Fraction of samples outside domain: 0.07\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 6))\\n\",\n    \"plt.scatter(leverage_scores, abs_errors, alpha=0.5)\\n\",\n    \"plt.axvline(x=leverage_ad.threshold_, color=\\\"r\\\", linestyle=\\\"--\\\", label=\\\"AD Threshold\\\")\\n\",\n    \"plt.xlabel(\\\"Leverage AD Score\\\")\\n\",\n    \"plt.ylabel(\\\"Absolute Prediction Error\\\")\\n\",\n    \"plt.title(\\\"Prediction Errors vs Leverage Scores\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"# Calculate error statistics\\n\",\n    \"in_domain = leverage_ad.predict(X_test_transformed)\\n\",\n    \"errors_in = abs_errors[in_domain == 1]\\n\",\n    \"errors_out = abs_errors[in_domain == -1]\\n\",\n    \"\\n\",\n    \"print(f\\\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\\\")\\n\",\n    \"print(f\\\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\\\")\\n\",\n    \"print(f\\\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e22b19f0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Testing Famous Drugs\\n\",\n    \"\\n\",\n    \"Let's test some well-known drugs to see if they fall within our model's applicability domain:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"1d33100d\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:18.712701Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:18.712517Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:18.747175Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:18.746652Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>k-NN Score</th>\\n\",\n       \"      <th>k-NN Status</th>\\n\",\n       \"      <th>Leverage Score</th>\\n\",\n       \"      <th>Leverage Status</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Drug</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Aspirin</th>\\n\",\n       \"      <td>0.813836</td>\\n\",\n       \"      <td>Outside</td>\\n\",\n       \"      <td>0.154726</td>\\n\",\n       \"      <td>Inside</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Viagra</th>\\n\",\n       \"      <td>0.829481</td>\\n\",\n       \"      <td>Outside</td>\\n\",\n       \"      <td>0.374886</td>\\n\",\n       \"      <td>Inside</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Heroin</th>\\n\",\n       \"      <td>0.852061</td>\\n\",\n       \"      <td>Outside</td>\\n\",\n       \"      <td>0.107878</td>\\n\",\n       \"      <td>Inside</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         k-NN Score k-NN Status  Leverage Score Leverage Status\\n\",\n       \"Drug                                                           \\n\",\n       \"Aspirin    0.813836     Outside        0.154726          Inside\\n\",\n       \"Viagra     0.829481     Outside        0.374886          Inside\\n\",\n       \"Heroin     0.852061     Outside        0.107878          Inside\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Define famous drugs\\n\",\n    \"famous_drugs = {\\n\",\n    \"    \\\"Aspirin\\\": \\\"CC(=O)OC1=CC=CC=C1C(=O)O\\\",\\n\",\n    \"    \\\"Viagra\\\": \\\"CCc1nn(C)c2c(=O)[nH]c(nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4\\\",\\n\",\n    \"    \\\"Heroin\\\": \\\"CN1CC[C@]23[C@H]4Oc5c(O)ccc(CC1[C@H]2C=C[C@@H]4O3)c5\\\",\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Function to process a drug through both AD pipelines\\n\",\n    \"def check_drug_applicability(smiles, name):\\n\",\n    \"    mol = Chem.MolFromSmiles(smiles)\\n\",\n    \"\\n\",\n    \"    # k-NN AD\\n\",\n    \"    fp_binary = binary_fp_pipe.named_steps[\\\"fp\\\"].transform([mol])\\n\",\n    \"    knn_score = knn_ad.transform(fp_binary)[0][0]\\n\",\n    \"    knn_status = \\\"Inside\\\" if knn_ad.predict(fp_binary)[0] == 1 else \\\"Outside\\\"\\n\",\n    \"\\n\",\n    \"    # Leverage AD\\n\",\n    \"    fp_count = count_fp_pipe.named_steps[\\\"fp\\\"].transform([mol])\\n\",\n    \"    fp_pca = count_fp_pipe.named_steps[\\\"pca\\\"].transform(fp_count)\\n\",\n    \"    fp_scaled = count_fp_pipe.named_steps[\\\"scaler\\\"].transform(fp_pca)\\n\",\n    \"    leverage_score = leverage_ad.transform(fp_scaled)[0][0]\\n\",\n    \"    leverage_status = \\\"Inside\\\" if leverage_ad.predict(fp_scaled)[0] == 1 else \\\"Outside\\\"\\n\",\n    \"\\n\",\n    \"    return {\\n\",\n    \"        \\\"knn_score\\\": knn_score,\\n\",\n    \"        \\\"knn_status\\\": knn_status,\\n\",\n    \"        \\\"leverage_score\\\": leverage_score,\\n\",\n    \"        \\\"leverage_status\\\": leverage_status,\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Process each drug\\n\",\n    \"results = []\\n\",\n    \"for name, smiles in famous_drugs.items():\\n\",\n    \"    result = check_drug_applicability(smiles, name)\\n\",\n    \"    results.append(\\n\",\n    \"        {\\n\",\n    \"            \\\"Drug\\\": name,\\n\",\n    \"            \\\"k-NN Score\\\": result[\\\"knn_score\\\"],\\n\",\n    \"            \\\"k-NN Status\\\": result[\\\"knn_status\\\"],\\n\",\n    \"            \\\"Leverage Score\\\": result[\\\"leverage_score\\\"],\\n\",\n    \"            \\\"Leverage Status\\\": result[\\\"leverage_status\\\"],\\n\",\n    \"        }\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"# Display results\\n\",\n    \"pd.DataFrame(results).set_index(\\\"Drug\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a5241345\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's visualize where these drugs fall in our AD plots:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"3aaf4485\",\n   \"metadata\": {\n    \"execution\": {\n     \"iopub.execute_input\": \"2025-05-11T09:39:18.748976Z\",\n     \"iopub.status.busy\": \"2025-05-11T09:39:18.748794Z\",\n     \"iopub.status.idle\": \"2025-05-11T09:39:19.086510Z\",\n     \"shell.execute_reply\": \"2025-05-11T09:39:19.086029Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x500 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot for k-NN AD\\n\",\n    \"plt.figure(figsize=(12, 5))\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.scatter(knn_scores, abs_errors, alpha=0.2, label=\\\"Test compounds\\\")\\n\",\n    \"plt.axvline(x=knn_ad.threshold_, color=\\\"r\\\", linestyle=\\\"--\\\", label=\\\"AD Threshold\\\")\\n\",\n    \"\\n\",\n    \"for result in results:\\n\",\n    \"    plt.axvline(x=result[\\\"k-NN Score\\\"], color=\\\"g\\\", alpha=0.5, label=f\\\"{result['Drug']}\\\")\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"k-NN AD Score\\\")\\n\",\n    \"plt.ylabel(\\\"Absolute Prediction Error\\\")\\n\",\n    \"plt.title(\\\"k-NN AD Scores\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1.05, 1), loc=\\\"upper left\\\")\\n\",\n    \"\\n\",\n    \"# Plot for Leverage AD\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.scatter(leverage_scores, abs_errors, alpha=0.2, label=\\\"Test compounds\\\")\\n\",\n    \"plt.axvline(x=leverage_ad.threshold_, color=\\\"r\\\", linestyle=\\\"--\\\", label=\\\"AD Threshold\\\")\\n\",\n    \"\\n\",\n    \"for result in results:\\n\",\n    \"    plt.axvline(\\n\",\n    \"        x=result[\\\"Leverage Score\\\"], color=\\\"g\\\", alpha=0.5, label=f\\\"{result['Drug']}\\\"\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"Leverage AD Score\\\")\\n\",\n    \"plt.ylabel(\\\"Absolute Prediction Error\\\")\\n\",\n    \"plt.title(\\\"Leverage AD Scores\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1.05, 1), loc=\\\"upper left\\\")\\n\",\n    \"\\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c018e68\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Conclusions\\n\",\n    \"\\n\",\n    \"This notebook demonstrated two different approaches to applicability domain estimation:\\n\",\n    \"\\n\",\n    \"1. The k-NN based approach with binary fingerprints and Tanimoto distance provides a chemical similarity-based assessment\\n\",\n    \"of whether new compounds are similar enough to the training set.\\n\",\n    \"\\n\",\n    \"2. The leverage-based approach with count-based fingerprints and dimensionality reduction focuses on the statistical\\n\",\n    \"novelty of compounds in the reduced feature space.\\n\",\n    \"\\n\",\n    \"The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given\\n\",\n    \"that our training set is focused on SLC6A4 actives, while these drugs have different primary targets.\\n\",\n    \"\\n\",\n    \"The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors (when using the full set),\\n\",\n    \"validating the usefulness of these approaches for identifying potentially unreliable predictions.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"jupytext\": {\n   \"formats\": \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"vscode\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.12.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "docs/notebooks/README.md",
    "content": "# Scikit-Mol notebooks with examples\n\n## Documentation\n\nThis is a collection of notebooks in the notebooks directory which demonstrates some different aspects and use cases\n\n- [Basic Usage and fingerprint transformers](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/01_basic_usage.ipynb)\n- [Descriptor transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/02_descriptor_transformer.ipynb)\n- [Pipelining with Scikit-Learn classes](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/03_example_pipeline.ipynb)\n- [Molecular standardization](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/04_standardizer.ipynb)\n- [Sanitizing SMILES input](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/05_smiles_sanitization.ipynb)\n- [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/06_hyperparameter_tuning.ipynb)\n- [Using parallel execution to speed up descriptor and fingerprint calculations](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/07_parallel_transforms.ipynb)\n- [Using skopt for hyperparameter tuning](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/08_external_library_skopt.ipynb)\n- [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)\n- [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)\n- [Working with pipelines and estimators in safe inference mode](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/11_safe_inference.ipynb)\n"
  },
  {
    "path": "docs/notebooks/pair_notebook.sh",
    "content": "jupytext --set-formats ipynb,py:percent $1"
  },
  {
    "path": "docs/notebooks/run_notebooks.sh",
    "content": "# Sync the .py and .ipynb\nsource ./sync_notebooks.sh\n# Execute the notebooks, gives a .nbconvert.ipynb extension\njupyter nbconvert --to notebook --execute *ipynb\n# move the .nbconvert.ipynb to the original .ipynb\nfor file in *.nbconvert.ipynb; do \n    fname=${file/.nbconvert.ipynb/};\n    rm $fname.ipynb\n    mv $file $fname.ipynb\ndone\n\n"
  },
  {
    "path": "docs/notebooks/scripts/01_basic_usage.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: Python 3.9.4 ('rdkit')\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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#\n\n# %% [markdown]\n# 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.\n\n# %%\nfrom IPython.core.display import HTML\n\n# %%\nfrom rdkit import Chem\n\nsmiles_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\nmols = [Chem.MolFromSmiles(smiles) for smiles in smiles_strings]\n\n# %% [markdown]\n# Next we import the Morgan fingerprint transformer\n\n# %%\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\ntransformer = MorganFingerprintTransformer(radius=3)\nprint(transformer)\n\n# %% [markdown]\n# 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.\n\n# %%\ntransformer\n\n# %% [markdown]\n# If we want to get all the settings explicitly, we can use the .get_params() method.\n\n# %%\nparameters = transformer.get_params()\nparameters\n\n# %% [markdown]\n# 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.\n\n# %%\nparameters[\"radius\"] = 2\nparameters[\"fpSize\"] = 256\ntransformer.set_params(**parameters)\nprint(transformer)\n\n# %% [markdown]\n# 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.\n\n# %%\nfps = transformer.transform(mols)\nprint(f\"fps is a {type(fps)} with shape {fps.shape} and data type {fps.dtype}\")\n\n# %% [markdown]\n# 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\n\n# %%\ny = range(len(mols))\ntransformer.transform(mols, y)\n\n# %% [markdown]\n# 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.\n\n# %%\nfrom scikit_mol.conversions import SmilesToMolTransformer\n\nsmi2mol = SmilesToMolTransformer()\nprint(smi2mol)\n\n# %%\nprint(smi2mol.transform(smiles_strings))\n"
  },
  {
    "path": "docs/notebooks/scripts/02_descriptor_transformer.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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.\n\n# %%\nfrom rdkit import Chem\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\n# %% [markdown]\n# After instantiation of the descriptor transformer, we can query which descriptors it found available in the RDKit framework.\n\n# %%\ndescriptor = MolecularDescriptorTransformer()\navailable_descriptors = descriptor.available_descriptors\nprint(f\"There are {len(available_descriptors)} available descriptors\")\nprint(f\"The first five descriptor names: {available_descriptors[:5]}\")\n\n# %% [markdown]\n# We can transform molecules to their descriptor profiles\n\n# %%\nsmiles_list = [\"CCCC\", \"c1ccccc1\"]\nmols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\n\nfeatures = descriptor.transform(mols)\n_ = plt.plot(np.array(features).T)\n\n# %% [markdown]\n# If we only want some of them, this can be specified at object instantiation.\n\n# %%\nsome_descriptors = MolecularDescriptorTransformer(\n    desc_list=[\"HeavyAtomCount\", \"FractionCSP3\", \"RingCount\", \"MolLogP\", \"MolWt\"]\n)\nprint(f\"Selected descriptors are {some_descriptors.selected_descriptors}\")\nfeatures = some_descriptors.transform(mols)\n\n# %% [markdown]\n# If we want to update the selected descriptors on an already existing object, this can be done via the .set_params() method\n\n# %%\nprint(\n    some_descriptors.set_params(\n        desc_list=[\"HeavyAtomCount\", \"FractionCSP3\", \"RingCount\"]\n    )\n)\n\n# %%\n"
  },
  {
    "path": "docs/notebooks/scripts/03_example_pipeline.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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\n\n# %%\nimport os\nimport rdkit\nfrom rdkit import Chem\nfrom rdkit.Chem import PandasTools\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom time import time\nimport numpy as np\n\n# %%\ncsv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\ndata = pd.read_csv(csv_file)\n# %% [markdown]\n# 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.\n\n# %%\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n# %% [markdown]\n# Then, let's import some tools from scikit-learn and two transformers from scikit-mol\n\n# %%\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import train_test_split\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.conversions import SmilesToMolTransformer\n\n# %%\nmol_list_train, mol_list_test, y_train, y_test = train_test_split(\n    data.ROMol, data.pXC50, random_state=0\n)\n\n# %% [markdown]\n# After a split into train and test, we'll build the first pipeline\n\n# %%\npipe = Pipeline(\n    [(\"mol_transformer\", MorganFingerprintTransformer()), (\"Regressor\", Ridge())]\n)\nprint(pipe)\n\n# %% [markdown]\n# We can do the fit by simply providing the list of RDKit molecule objects\n\n# %%\npipe.fit(mol_list_train, y_train)\nprint(f\"Train score is :{pipe.score(mol_list_train,y_train):0.2F}\")\nprint(f\"Test score is  :{pipe.score(mol_list_test, y_test):0.2F}\")\n# %% [markdown]\n# Nevermind the performance, or the exact value of the prediction, this is for demonstration purpures. We can easily predict on lists of molecules\n\n# %%\npipe.predict([Chem.MolFromSmiles(\"c1ccccc1C(=O)[OH]\")])\n\n# %% [markdown]\n# 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!\n\n# %%\nsmiles_pipe = Pipeline(\n    [(\"smiles_transformer\", SmilesToMolTransformer()), (\"pipe\", pipe)]\n)\nprint(smiles_pipe)\n\n# %%\nsmiles_pipe.predict([\"c1ccccc1C(=O)[OH]\"])\n\n# %% [markdown]\n# 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().\n\n# %%\nsmiles_pipe.get_params()\n"
  },
  {
    "path": "docs/notebooks/scripts/04_standardizer.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: Python 3.9.4 ('rdkit')\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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\n\n# %%\nfrom rdkit import Chem\nfrom scikit_mol.standardizer import Standardizer\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.linear_model import Ridge\n\n# %% [markdown]\n# For demonstration let's create some molecules with different protonation states. The two first molecules are Benzoic acid and Sodium benzoate.\n\n# %%\nsmiles_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\nsmi2mol = SmilesToMolTransformer()\n\nmols = smi2mol.transform(smiles_strings)\nfor mol in mols[0:2]:\n    display(mol)\n\n# %% [markdown]\n# We can simply use the transformer directly and get a list of standardized molecules\n\n# %%\n# You can just run straight up like this. Note that neutralising is optional\nstandardizer = Standardizer()\nstandard_mols = standardizer.transform(mols)\nstandard_smiles = smi2mol.inverse_transform(standard_mols)\nstandard_smiles\n\n# %% [markdown]\n# Some of the molecules were desalted and neutralized.\n#\n# A typical use case would be to add the standardizer to a pipeline for prediction\n\n# %%\n# Typical use case is to use it in an sklearn pipeline, like below\npredictor = Ridge()\n\nstd_pipe = make_pipeline(\n    SmilesToMolTransformer(),\n    Standardizer(),\n    MorganFingerprintTransformer(useCounts=True),\n    predictor,\n)\nnonstd_pipe = make_pipeline(\n    SmilesToMolTransformer(), MorganFingerprintTransformer(useCounts=True), predictor\n)\n\nfake_y = range(len(smiles_strings))\n\nstd_pipe.fit(smiles_strings, fake_y)\n\n\nprint(f\"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}\")\nprint(f\"Predictions with standardization:    {std_pipe.predict(smiles_strings)}\")\n\n\n# %% [markdown]\n# 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.\n#\n# 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.\n#\n# 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.\n\n# %%\nnonstd_pipe.fit(smiles_strings, fake_y)\nprint(f\"Predictions with no standardization: {nonstd_pipe.predict(smiles_strings)}\")\nprint(f\"Predictions with standardization:    {std_pipe.predict(smiles_strings)}\")\n"
  },
  {
    "path": "docs/notebooks/scripts/05_smiles_sanitization.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: Python 3.9.4 ('rdkit')\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # SMILES sanitation\n# 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.\n\n# %%\nimport pandas as pd\nfrom rdkit.Chem import PandasTools\n\ncsv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"  # Hmm, maybe better to download directly\ndata = pd.read_csv(csv_file)\n\n\n# %% [markdown]\n# Now, this example dataset contain all sanitizable SMILES, so for demonstration purposes, we will corrupt one of them\n\n# %%\ndata.loc[1, \"SMILES\"] = \"CN(C)(C)(C)\"\n\n# %%\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"Dataset contains {data.ROMol.isna().sum()} unparsable mols\")\n\n\n# %% [markdown]\n# 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.\n\n# %%\nfrom scikit_mol.utilities import CheckSmilesSanitization\n\nsmileschecker = CheckSmilesSanitization()\n\nsmiles_list_valid, y_valid, smiles_errors, y_errors = smileschecker.sanitize(\n    list(data.SMILES), list(data.pXC50)\n)\n\n# %% [markdown]\n# 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\n\n# %%\nsmileschecker.errors\n\n# %% [markdown]\n# The checker can also be used only on X\n\n# %%\nsmiles_list_valid, X_errors = smileschecker.sanitize(list(data.SMILES))\nsmileschecker.errors\n"
  },
  {
    "path": "docs/notebooks/scripts/06_hyperparameter_tuning.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: Python 3.9.4 ('rdkit')\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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\n\n# %%\nimport os\nimport rdkit\nfrom rdkit import Chem\nfrom rdkit.Chem import PandasTools\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom time import time\nimport numpy as np\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import train_test_split\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.conversions import SmilesToMolTransformer\n\n# %% [markdown]\n# 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.\n\n# %%\nfull_set = False\n\nif 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)\nelse:\n    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n\n# %% [markdown]\n# The CSV data is loaded into a Pandas dataframe and the PandasTools utility from RDKit is used to add a column with RDKit molecules\n\n# %%\ndata = pd.read_csv(csv_file)\n\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n\n# %% [markdown]\n# We use the train_test_split to, well, split the dataframe's molecule columns and pXC50 column into lists for train and testing\n\n# %%\nmol_list_train, mol_list_test, y_train, y_test = train_test_split(\n    data.ROMol, data.pXC50, random_state=42\n)\n\n\n# %% [markdown]\n# 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.\n\n# %%\n# 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\nfrom scikit_mol.standardizer import Standardizer\n\nstandardizer = Standardizer()\nmol_list_std_train = standardizer.transform(mol_list_train)\n\n# %% [markdown]\n# A simple pipeline with a MorganTransformer and a Ridge() regression for demonstration.\n\n# %%\nmoltransformer = MorganFingerprintTransformer()\nregressor = Ridge()\n\noptimization_pipe = make_pipeline(moltransformer, regressor)\n\n\n# %% [markdown]\n# 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.\n\n# %% Now hyperparameter tuning\nfrom sklearn.model_selection import RandomizedSearchCV\n\n# from sklearn.utils.fixes import loguniform\nfrom scipy.stats import loguniform\n\n# %% [markdown]\n# 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.\n\n# %% Which keys do we have?\noptimization_pipe.get_params().keys()\n\n# %% [markdown]\n# 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.\n\n# %%\nparam_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}\n\n# %% [markdown]\n# 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.\n\n\n# %% From https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py\n# Utility function to report best scores\ndef 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(\"\")\n\n\n# %% [markdown]\n# 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\n# %%\nn_iter_search = 25\nrandom_search = RandomizedSearchCV(\n    optimization_pipe, param_distributions=param_dist, n_iter=n_iter_search, cv=3\n)\nt0 = time()\nrandom_search.fit(mol_list_std_train, y_train.values)\nt1 = time()\n\nprint(f\"Runtime: {t1-t0:0.2F} for {n_iter_search} iterations)\")\n\n# %%\nreport(random_search.cv_results_)\n# %% [markdown]\n# 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\n\n# %% [markdown]\n# 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\n\n# %%\ninference_pipe = make_pipeline(standardizer, random_search.best_estimator_)\n\nprint(\n    f\"No Standardization {random_search.best_estimator_.score(mol_list_test, y_test):0.4F}\"\n)\nprint(f\"With Standardization {inference_pipe.score(mol_list_test, y_test):0.4F}\")\n\n# %% Building an inference pipeline, it appears our test-data was pretty standard [markdown]\n# 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.\n# %%\n# Intergrating the Standardizer and challenge it with some different forms and salts of benzoic acid\nsmiles_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]\nmols_list = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]\n\nprint(\n    f\"Predictions with no standardization: {random_search.best_estimator_.predict(mols_list)}\"\n)\nprint(f\"Predictions with standardization:    {inference_pipe.predict(mols_list)}\")\n\n# %% [markdown]\n# 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#\n\n# %% [markdown]\n#\n"
  },
  {
    "path": "docs/notebooks/scripts/07_parallel_transforms.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: Python 3.9.4 ('rdkit')\n#     language: python\n#     name: python3\n# ---\n\n# %%\n\n# %% [markdown]\n# # 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.\n\n# %%\nimport pathlib\nimport time\n\nimport pandas as pd\nfrom rdkit.Chem import PandasTools\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\n# %% [markdown]\n# ## 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.\n\n# %%\nfull_set = False\n\nif 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)\nelse:\n    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n\n# %%\ndata = pd.read_csv(csv_file)\n\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n\n# %% [markdown]\n# ## Evaluating the Impact of Parallelism on Transformations\n#\n# Let's start by creating a baseline for our calculations without using parallelism.\n\n# %% A demonstration of the speedup that can be had for the descriptor transformer\ntransformer = MolecularDescriptorTransformer(n_jobs=1)\n\n\n# %%\ndef 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\ntest_transformer(transformer)\n\n# %% [markdown]\n#\n# Let's see if parallelism can help us speed up our transformations.\n\n# %%\ntransformer = MolecularDescriptorTransformer(n_jobs=2)\ntest_transformer(transformer)\n\n# %% [markdown]\n# 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.\n\n# %% Some of the benchmarking plots\ntransformer = MorganFingerprintTransformer(n_jobs=1)\ntest_transformer(transformer)\ntransformer.n_jobs = 2\ntest_transformer(transformer)\n\n\n# %% [markdown]\n# 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#\n\n# %% [markdown]\n# ## 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.\n\n# %%\nfrom rdkit import Chem\n\nfrom scikit_mol.fingerprints import (\n    AtomPairFingerprintTransformer,\n    AvalonFingerprintTransformer,\n    MHFingerprintTransformer,\n    MorganFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n)\nfrom scikit_mol.plotting import ParallelTester, plot_heatmap\nfrom scikit_mol.standardizer import Standardizer\n\nmols = [Chem.MolFromSmiles(\"CCCCCCCCBr\")] * 100000\ntransformers = [\n    Standardizer(),\n    MorganFingerprintTransformer(),\n    MolecularDescriptorTransformer(),\n    MHFingerprintTransformer(),\n    AtomPairFingerprintTransformer(),\n    AvalonFingerprintTransformer(),\n    RDKitFingerprintTransformer(),\n    TopologicalTorsionFingerprintTransformer(),\n]\n\n# %% [markdown]\n# `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)\n\n# %%\ntransformer = MorganFingerprintTransformer()\ndf = ParallelTester(transformer, mols).test()\ndf\n\n# %% [markdown]\n# 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.\n\n# %%\nplot_heatmap(df, \"Morgan Fingerprint\")\n\n# %% [markdown]\n# 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`.\n\n# %%\nplot_heatmap(df, \"Morgan Fingerprint\", normalize=False)\n\n# %% [markdown]\n# Below is example heatmaps for the native `scikit-mol` transformers.\n\n# %%\nfor 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__)\n\n# %%\nsmiles = [\"CCCCCCCCBr\"] * 100000\ntransformer = SmilesToMolTransformer()\ndf = ParallelTester(\n    transformer, smiles, n_mols=[10, 100, 1000, 5000, 10000, 20000], n_jobs=[1, 2, 4, 8]\n).test()\nplot_heatmap(df, \"SMILES to Mol\")\n\n# %% [markdown]\n# ## 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# ```\n"
  },
  {
    "path": "docs/notebooks/scripts/08_external_library_skopt.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% Needs scikit-optimize\n# !pip install scikit-optimize\n\n# %%\nimport os\nimport numpy as np\nimport pandas as pd\n\n# %%\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import cross_val_score\n\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.conversions import SmilesToMolTransformer\n\nfrom sklearn.pipeline import make_pipeline\n\n# %%\nfrom skopt.space import Real, Integer, Categorical\nfrom skopt.utils import use_named_args\n\nfrom skopt import gp_minimize\n\n# %%\nfull_set = False\n\nif 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)\nelse:\n    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n\ndata = pd.read_csv(csv_file)\ntrf = SmilesToMolTransformer()\ndata[\"ROMol\"] = trf.transform(data.SMILES.values).flatten()\n\n# %%\npipe = make_pipeline(MorganFingerprintTransformer(), Ridge())\npipe\n# %%\nprint(pipe.get_params())\n\n# %%\nmax_bits = 4096\n\nmorgan_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\nregressor_space = [Real(1e-2, 1e3, \"log-uniform\", name=\"ridge__alpha\")]\n\nsearch_space = morgan_space + regressor_space\n\n\n# %%\n@use_named_args(search_space)\ndef 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    )\n\n\n# %% THIS takes forever on my machine with a GradientBoostingRegressor\npipe_gp = gp_minimize(objective, search_space, n_calls=10, random_state=0)\n\"Best score=%.4f\" % pipe_gp.fun\n# %%\nprint(\"\"\"Best parameters:\"\"\")\nprint({param.name: value for param, value in zip(pipe_gp.space, pipe_gp.x)})\n# %%\nfrom skopt.plots import plot_convergence\n\nplot_convergence(pipe_gp)\n\n# %%\n"
  },
  {
    "path": "docs/notebooks/scripts/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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#\n\n# %%\nimport os\nimport numpy as np\nimport pandas as pd\nfrom time import time\nfrom matplotlib import pyplot as plt\n\nfrom rdkit.Chem import PandasTools\n\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import train_test_split\n\nfrom scikit_mol import fingerprints\n\n\n# %% [markdown]\n# ## 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\n\n# %%\nfull_set = False\n\n# if not present download example data\nif 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)\nelse:\n    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n\n# Parse Database\ndata = pd.read_csv(csv_file)\n\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n\n# %% [markdown]\n# ## 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.\n\n# %%\nregressor = Ridge()\noptimization_pipe = Pipeline(\n    [\n        (\n            \"fp_transformer\",\n            \"fp_transformer\",\n        ),  # this is a placeholder for different transformers\n        (\"regressor\", regressor),\n    ]\n)\n\nparam_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\nglobal_options = {\n    \"regressor__alpha\": np.linspace(0.1, 1, 5),\n}\n\n[params.update(global_options) for params in param_grid]\n\nparam_grid\n\n# %% [markdown]\n# ## Train Model\n# In this section, the combinatorial approaches are trained.\n\n# %%\n# Split Data\nmol_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\ngrid = GridSearchCV(optimization_pipe, n_jobs=1, param_grid=param_grid)\n\n# Train\nt0 = time()\ngrid.fit(mol_list_train, y_train.values)\nt1 = time()\n\nprint(f\"Runtime: {t1-t0:0.2F}\")\n\n# %% [markdown]\n# ## 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?\n\n# %%\ndf_training_stats = pd.DataFrame(grid.cv_results_)\ndf_training_stats\n\n# %%\n# Best Fingerprint Method / Performance\nres_dict = {}\nfor 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\ndf = pd.DataFrame(list(res_dict.values()))\ndf = df.sort_values(by=\"mean_test_score\")\n\n# plot test score vs. approach\nplt.figure(figsize=[14, 5])\nplt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\nplt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14)\nplt.ylabel(\"mean score\", fontsize=14)\nplt.title(\"Best Model of Fingerprint Transformer Type\", fontsize=18)\npass\n\n\n# %%\n# Best Fingerprint Method / Performance\nfrom collections import defaultdict\n\nres_dict = defaultdict(list)\nfor 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\nfor 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\n\n\n# %%\n# plot ALL test score vs. approach\ndf = df_training_stats.sort_values(by=\"mean_test_score\")\n\nplt.figure(figsize=[16, 9])\nplt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\nplt.ylabel(\"mean score\", fontsize=14)\nplt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14)\nplt.title(\"test score vs. approach\", fontsize=18)\npass\n\n# %% [markdown]\n# 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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For the purposes of this definition,\n#       \"control\" means (i) the power, direct or indirect, to cause the\n#       direction or management of such entity, whether by contract or\n#       otherwise, or (ii) ownership of fifty percent (50%) or more of the\n#       outstanding shares, or (iii) beneficial ownership of such entity.\n#\n#       \"You\" (or \"Your\") shall mean an individual or Legal Entity\n#       exercising permissions granted by this License.\n#\n#       \"Source\" form shall mean the preferred form for making modifications,\n#       including but not limited to software source code, documentation\n#       source, and configuration files.\n#\n#       \"Object\" form shall mean any form resulting from mechanical\n#       transformation or translation of a Source form, including but\n#       not limited to compiled object code, generated documentation,\n#       and conversions to other media types.\n#\n#       \"Work\" shall mean the work of authorship, whether in Source or\n#       Object form, made available under the License, as indicated by a\n#       copyright notice that is included in or attached to the work\n#       (an example is provided in the Appendix below).\n#\n#       \"Derivative Works\" shall mean any work, whether in Source or Object\n#       form, that is based on (or derived from) the Work and for which the\n#       editorial revisions, annotations, elaborations, or other modifications\n#       represent, as a whole, an original work of authorship. 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  },
  {
    "path": "docs/notebooks/scripts/10_pipeline_pandas_output.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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.\n\n# %%\nfrom pathlib import Path\nimport pandas as pd\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nfrom sklearn.preprocessing import FunctionTransformer\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom sklearn.compose import make_column_selector, make_column_transformer\nfrom scikit_mol.standardizer import Standardizer\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\n# %%\ncsv_file = Path(\"../../tests/data/SLC6A4_active_excapedb_subset.csv\")\nassert csv_file.is_file()\ndata = pd.read_csv(csv_file)\ndata.drop_duplicates(subset=\"Ambit_InchiKey\", inplace=True)\n\n# %% [markdown]\n# 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.\n\n# %%\ndata_train, data_test = train_test_split(data, test_size=0.2, random_state=42)\n\n# %%\ncolumn_smiles = \"SMILES\"\ncolumn_target = \"pXC50\"\n\nsmis_train = data_train.loc[:, column_smiles]\ntarget_train = data_train.loc[:, column_target]\nsmis_test = data_test.loc[:, column_smiles]\ntarget_test = data_test.loc[:, column_target]\n\n# %% [markdown]\n# ## 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.\n\n# %%\ndescriptors_pipeline = make_pipeline(\n    SmilesToMolTransformer(),\n    Standardizer(),\n    MolecularDescriptorTransformer(),\n)\ndescriptors_pipeline.set_output(transform=\"pandas\")\n\n# %%\ndf_descriptors = descriptors_pipeline.transform(smis_train)\ndf_descriptors\n\n# %% [markdown]\n# 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).\n#\n# Let's define a pipeline that returns Morgan fingerprints in a DataFrame.\n# Columns will be named with the pattern `fp_morgan_1`, `fp_morgan_2`, ...,`fp_morgan_N`.\n\n# %%\nfingerprints_pipeline = make_pipeline(\n    SmilesToMolTransformer(),\n    Standardizer(),\n    MorganFingerprintTransformer(),\n)\nfingerprints_pipeline.set_output(transform=\"pandas\")\n\n# %%\ndf_fingerprints = fingerprints_pipeline.transform(smis_train)\ndf_fingerprints\n\n# %% [markdown]\n# ## 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.\n\n# %%\nparams_random_forest = {\n    \"max_depth\": 5,  # Setting a low maximum depth to avoid overfitting\n}\n\nregression_pipeline = make_pipeline(\n    SmilesToMolTransformer(),\n    Standardizer(),\n    MolecularDescriptorTransformer(),\n    StandardScaler(),  # Scale the descriptors\n    RandomForestRegressor(**params_random_forest),\n)\nregression_pipeline.set_output(transform=\"pandas\")\n\n# %%\nregression_pipeline.fit(smis_train, target_train)\npred_test = regression_pipeline.predict(smis_test)\n\n\n# %% [markdown]\n# Let's define a simple function to compute regression metrics, and use it to evaluate the test set performance of the pipeline.\n\n\n# %%\ndef 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\nperformance = compute_metrics(y_true=target_test, y_pred=pred_test)\nperformance\n\n# %%\nregressor = regression_pipeline[-1]\nregressor\n\n# %% [markdown]\n# 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.\n\n# %%\ndf_importance = pd.DataFrame(\n    {\n        \"feature\": regressor.feature_names_in_,\n        \"importance\": regressor.feature_importances_,\n    }\n)\ndf_importance\n\n# %% [markdown]\n# Sort the features by most to least important:\n\n# %%\ndf_importance.sort_values(\n    by=\"importance\", ascending=False, inplace=True, ignore_index=True\n)\ndf_importance\n\n# %%\nn_top_features = 5\ntop_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\nprint(f\"The {n_top_features} most important features are:\")\nfor feature in top_features:\n    print(feature)\n\n# %% [markdown]\n# ## 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:\n\n# %%\nfile_cddd_features = Path(\"../../tests/data/CDDD_SLC6A4_active_excapedb_subset.csv.gz\")\nassert file_cddd_features.is_file()\ndf_cddd = pd.read_csv(file_cddd_features)\ndf_cddd\n\n\n# %% [markdown]\n# 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:\n\n\n# %%\ndef 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\ndata_combined_train = combine_datasets(data_train, df_cddd)\ndata_combined_test = combine_datasets(data_test, df_cddd)\n\n# %%\n# 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\ntarget_train = data_combined_train.loc[:, column_target]\ntarget_test = data_combined_test.loc[:, column_target]\n\n# %% [markdown]\n# 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.\n\n# %%\n# A pipeline to compute scikit-mol descriptors\ndescriptors_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.\nidentity_pipeline = make_pipeline(\n    FunctionTransformer(),\n)\ncombined_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)\ncombined_transformer\n\n# %%\npipeline_combined = make_pipeline(\n    combined_transformer,\n    StandardScaler(),\n    RandomForestRegressor(**params_random_forest),\n)\npipeline_combined.set_output(transform=\"pandas\")\n\n# %%\npipeline_combined.fit(data_combined_train, target_train)\npred_combined_test = pipeline_combined.predict(data_combined_test)\nperformance_combined = compute_metrics(y_true=target_test, y_pred=pred_combined_test)\nperformance_combined\n\n# %% [markdown]\n# 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:\n\n# %%\ndf_performance = pd.DataFrame(\n    [performance, performance_combined], index=[\"descriptors\", \"combined\"]\n)\ndf_performance\n\n# %% [markdown]\n# All performance metrics were improved by the inclusion of the CDDD features.\n# Let's analyze the feature importances of the model:\n\n# %%\nregressor = pipeline_combined[-1]\ndf_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)\ndf_importance\n\n# %%\ntop_features = df_importance.head(n_top_features).loc[:, \"feature\"].tolist()\nprint(f\"The {n_top_features} most important features are:\")\nfor feature in top_features:\n    print(feature)\n\n# %% [markdown]\n# 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).\n"
  },
  {
    "path": "docs/notebooks/scripts/11_safe_inference.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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.\n\n# %%\nfrom rdkit import Chem\nfrom scikit_mol.conversions import SmilesToMolTransformer\n\n# We have some deprecation warnings, we are adressing them, but they just distract from this demonstration\nimport warnings\n\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\nsmiles = [\"C1=CC=C(C=C1)F\", \"C1=CC=C(C=C1)O\", \"C1=CC=C(C=C1)N\", \"C1=CC=C(C=C1)Cl\"]\nsmiles_with_invalid = smiles + [\"N(C)(C)(C)C\", \"I'm not a SMILES\"]\n\nsmi2mol = SmilesToMolTransformer(safe_inference_mode=True)\n\nmols_with_invalid = smi2mol.transform(smiles_with_invalid)\nmols_with_invalid\n\n# %% [markdown]\n# 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:\n\n# %%\n[mol for mol in mols_with_invalid if mol]\n\n# %% [markdown]\n# or\n\n# %%\nmask = mols_with_invalid.astype(bool)\nmols_with_invalid[mask]\n\n# %% [markdown]\n# 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)\n\n# %%\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\nmfp = MorganFingerprintTransformer(radius=2, fpSize=25, safe_inference_mode=True)\nfps = mfp.transform(mols_with_invalid)\nfps\n\n\n# %% [markdown]\n# 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.\n\n# %%\nfrom scikit_mol.safeinference import SafeInferenceWrapper\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\n\nregressor = LogisticRegression()\nwrapper = SafeInferenceWrapper(regressor, safe_inference_mode=True)\nwrapper.fit(fps, [0, 1, 0, 1, 0, 1])\nwrapper.predict(fps)\n\n\n# %% [markdown]\n#\n\n# %% [markdown]\n# 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:\n\n# %%\nfrom scikit_mol.safeinference import set_safe_inference_mode\nfrom sklearn.pipeline import Pipeline\n\npipe = Pipeline(\n    [\n        (\"smi2mol\", SmilesToMolTransformer()),\n        (\"mfp\", MorganFingerprintTransformer(radius=2, fpSize=25)),\n        (\"safe_regressor\", SafeInferenceWrapper(LogisticRegression())),\n    ]\n)\n\npipe.fit(smiles, [1, 0, 1, 0])\n\nprint(\"Without safe inference mode:\")\ntry:\n    pipe.predict(smiles_with_invalid)\nexcept Exception as e:\n    print(\"Prediction failed with exception: \", e)\nprint()\n\nset_safe_inference_mode(pipe, True)\n\nprint(\"With safe inference mode:\")\nprint(pipe.predict(smiles_with_invalid))\n\n# %% [markdown]\n# 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 :-)\n\n# %% [markdown]\n# ## 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.\n\n# %%\nmfp.set_output(transform=\"pandas\")\n\nmols = smi2mol.transform(smiles)\n\nfps = mfp.transform(mols)\nfps\n\n# %% [markdown]\n# Then lets see if we transform a batch with an invalid molecule:\n\n# %%\nfps = mfp.transform(mols_with_invalid)\nfps\n\n# %% [markdown]\n# 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.\n\n# %%\nmfp_float = MorganFingerprintTransformer(\n    radius=2, fpSize=25, safe_inference_mode=True, dtype=np.float32\n)\nmfp_float.set_output(transform=\"pandas\")\nfps = mfp_float.transform(mols)\nfps\n\n# %% [markdown]\n# 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.\n"
  },
  {
    "path": "docs/notebooks/scripts/12_custom_fingerprint_transformer.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: .venv\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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.\n\n# %% [markdown]\n# ## 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.\n\n# %%\nfrom scikit_mol.fingerprints.baseclasses import BaseFpsTransformer\nimport numpy as np\nfrom rdkit import Chem\n\n\nclass 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\ntrans = DummyFingerprintTransformer(n_jobs=4)\nmols = [Chem.MolFromSmiles(\"CC\")] * 100\ntrans.transform(mols)\n\n# %% [markdown]\n# ## 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.\n\n# %%\nfrom rdkit.Chem import rdFingerprintGenerator\n\n\nclass 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\ntrans = UnpickableFingerprintTransformer(n_jobs=4, fpSize=512)\ntrans.transform(mols)\n\n\n# %% [markdown]\n# 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`).\n\n# %%\nclass 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\nfp_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=10)\nBadTransformer(fp_gen, n_jobs=1).transform(mols)\nprint(\"n_jobs=1 is fine\")\ntry:\n    BadTransformer(fp_gen, n_jobs=2).transform(mols)\nexcept 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}\")\n\n\n# %% [markdown]\n# ## 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\n\n# %%\nclass NamedTansformer1(UnpickableFingerprintTransformer):\n    pass\n\n\nclass NamedTansformer2(UnpickableFingerprintTransformer):\n    def __init__(self):\n        super().__init__(name=\"fp_fancy\")\n\n\nclass FancyFingerprintTransformer(UnpickableFingerprintTransformer):\n    pass\n\n\nprint(NamedTansformer1().get_feature_names_out())\nprint(NamedTansformer2().get_feature_names_out())\nprint(FancyFingerprintTransformer().get_feature_names_out())\n\n"
  },
  {
    "path": "docs/notebooks/scripts/13_applicability_domain.py",
    "content": "# ---\n# jupyter:\n#   jupytext:\n#     formats: docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.16.6\n#   kernelspec:\n#     display_name: vscode\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # 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:\n\n# %%\nimport os\nimport numpy as np\nimport pandas as pd\nfrom rdkit import Chem\nfrom rdkit.Chem import PandasTools\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.applicability import KNNApplicabilityDomain, LeverageApplicabilityDomain\n\n# %% [markdown]\n# ## Load and Prepare Data\n\n# %%\n# 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\nfull_set = False\n\nif 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\nelse:\n    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n    percentile = 90\ndata = pd.read_csv(csv_file)\n\n# Add RDKit mol objects\nPandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\nprint(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")\n\n# Split into train/val/test\nX = data.ROMol\ny = data.pXC50\n\nX_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\nX_train, X_val, y_train, y_val = train_test_split(\n    X_temp, y_temp, test_size=0.25, random_state=42\n)\n\n# %% [markdown]\n# ## 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.\n\n# %%\n# Create pipeline for binary fingerprints\nbinary_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\nbinary_fp_pipe.fit(X_train, y_train)\n\n# Get predictions and errors\ny_pred_test = binary_fp_pipe.predict(X_test)\nabs_errors = np.abs(y_test - y_pred_test)\n\n# Create and fit k-NN AD estimator\nknn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric=\"tanimoto\", percentile=percentile)\nknn_ad.fit(binary_fp_pipe.named_steps[\"fp\"].transform(X_train))\n\n# Fit threshold using validation set\nknn_ad.fit_threshold(binary_fp_pipe.named_steps[\"fp\"].transform(X_val))\n\n# Get AD scores for test set\nknn_scores = knn_ad.transform(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))\n\n# %% [markdown]\n# Let's visualize the relationship between prediction errors and AD scores:\n\n# %%\nplt.figure(figsize=(10, 6))\nplt.scatter(knn_scores, abs_errors, alpha=0.5)\nplt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\nplt.xlabel(\"k-NN AD Score\")\nplt.ylabel(\"Absolute Prediction Error\")\nplt.title(\"Prediction Errors vs k-NN AD Scores\")\nplt.legend()\nplt.show()\n\n# Calculate error statistics\nin_domain = knn_ad.predict(binary_fp_pipe.named_steps[\"fp\"].transform(X_test))\nerrors_in = abs_errors[in_domain == 1]\nerrors_out = abs_errors[in_domain == -1]\n\nprint(f\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\")\nprint(f\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\")\nprint(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")\n\n# %% [markdown]\n# ## 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.\n\n# %%\n# Create pipeline for count-based fingerprints with PCA\ncount_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\ncount_fp_pipe.fit(X_train, y_train)\n\n# Get predictions and errors\ny_pred_test = count_fp_pipe.predict(X_test)\nabs_errors = np.abs(y_test - y_pred_test)\n\n# Create and fit leverage AD estimator\nleverage_ad = LeverageApplicabilityDomain(percentile=percentile)\nX_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)\nleverage_ad.fit(X_train_transformed)\n\n# Fit threshold using validation set\nX_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)\nleverage_ad.fit_threshold(X_val_transformed)\n\n# Get AD scores for test set\nX_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)\nleverage_scores = leverage_ad.transform(X_test_transformed)\n\n# %% [markdown]\n# Visualize the relationship between prediction errors and leverage scores:\n\n# %%\nplt.figure(figsize=(10, 6))\nplt.scatter(leverage_scores, abs_errors, alpha=0.5)\nplt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\nplt.xlabel(\"Leverage AD Score\")\nplt.ylabel(\"Absolute Prediction Error\")\nplt.title(\"Prediction Errors vs Leverage Scores\")\nplt.legend()\nplt.show()\n\n# Calculate error statistics\nin_domain = leverage_ad.predict(X_test_transformed)\nerrors_in = abs_errors[in_domain == 1]\nerrors_out = abs_errors[in_domain == -1]\n\nprint(f\"95th percentile of errors inside domain: {np.percentile(errors_in, 95):.2f}\")\nprint(f\"95th percentile of errors outside domain: {np.percentile(errors_out, 95):.2f}\")\nprint(f\"Fraction of samples outside domain: {(in_domain == -1).mean():.2f}\")\n\n# %% [markdown]\n# ## Testing Famous Drugs\n#\n# Let's test some well-known drugs to see if they fall within our model's applicability domain:\n\n# %%\n# Define famous drugs\nfamous_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\ndef 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\nresults = []\nfor 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\npd.DataFrame(results).set_index(\"Drug\")\n\n# %% [markdown]\n# Let's visualize where these drugs fall in our AD plots:\n\n# %%\n# Plot for k-NN AD\nplt.figure(figsize=(12, 5))\nplt.subplot(1, 2, 1)\nplt.scatter(knn_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\nplt.axvline(x=knn_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n\nfor result in results:\n    plt.axvline(x=result[\"k-NN Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\")\n\nplt.xlabel(\"k-NN AD Score\")\nplt.ylabel(\"Absolute Prediction Error\")\nplt.title(\"k-NN AD Scores\")\nplt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n\n# Plot for Leverage AD\nplt.subplot(1, 2, 2)\nplt.scatter(leverage_scores, abs_errors, alpha=0.2, label=\"Test compounds\")\nplt.axvline(x=leverage_ad.threshold_, color=\"r\", linestyle=\"--\", label=\"AD Threshold\")\n\nfor result in results:\n    plt.axvline(\n        x=result[\"Leverage Score\"], color=\"g\", alpha=0.5, label=f\"{result['Drug']}\"\n    )\n\nplt.xlabel(\"Leverage AD Score\")\nplt.ylabel(\"Absolute Prediction Error\")\nplt.title(\"Leverage AD Scores\")\nplt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n\nplt.tight_layout()\nplt.show()\n\n# %% [markdown]\n# ## 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.\n"
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    "path": "docs/notebooks/sync_notebooks.sh",
    "content": "jupytext --sync [0-9]*.py"
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    "path": "docs/overrides/main.html",
    "content": "{% extends \"base.html\" %}\n{% block outdated %}\n  You're not viewing the latest version.\n  <a href=\"{{ '../' ~ base_url }}\"> \n    <strong>Click here to go to latest.</strong>\n  </a>\n{% endblock %}\n\n{% block site_meta %}\n{{ super() }}\n<meta name=\"readthedocs-addons-api-version\" content=\"1\" />\n{% endblock %}"
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    "content": "site_name: \"scikit-mol\"\nsite_description: \"scikit-learn classes for molecular vectorization using RDKit\"\nrepo_url: \"https://github.com/EBjerrum/scikit-mol\"\nrepo_name: \"EBjerrum/scikit-mol\"\ncopyright: Copyright 2022 - 2025\n\nuse_directory_urls: true\ndocs_dir: \"docs\"\n\ntheme: \n  name: material\n  features:\n    - navigation.tabs\n    - navigation.expand\n\nextra_javascript:\n    - assets/js/readthedocs.js\n\nextra_css:\n  - assets/css/tweak-width.css\n\nwatch:\n  - \"scikit_mol\"\n\nplugins:\n  - search\n  - autorefs\n  - mkdocstrings:\n      handlers:\n        python:\n          options:\n            docstring_style: numpy\n          inventories:\n            - https://scikit-learn.org/stable/objects.inv\n            - https://docs.python.org/objects.inv\n            - https://www.rdkit.org/docs/objects.inv\n  - mkdocs-jupyter:\n      execute: false\n      include: [\"*.ipynb\"]\n\nnav:\n  - Overview: index.md\n  - API:\n    - scikit-mol.applicability: api/scikit_mol.applicability.md\n    - scikit-mol.core: api/scikit_mol.core.md\n    - scikit-mol.conversion: api/scikit_mol.conversions.md\n    - scikit-mol.descriptors: api/scikit_mol.descriptors.md\n    - scikit-mol.fingerprints: api/scikit_mol.fingerprints.md\n    - scikit_mol.fingerprints.baseclasses: api/fingerprints.base.md\n    - scikit-mol.parallel: api/scikit_mol.parallel.md\n    - scikit-mol.plotting: api/scikit_mol.plotting.md\n    - scikit-mol.safeinference: api/scikit_mol.safeinference.md\n    - scikit-mol.standardizer: api/scikit_mol.standardizer.md\n    \n  - Notebooks:\n    - Basic Usage and fingerprint transformers: notebooks/01_basic_usage.ipynb\n    - Descriptor transformer: notebooks/02_descriptor_transformer.ipynb\n    - Pipelining with Scikit-Learn classes: notebooks/03_example_pipeline.ipynb\n    - Molecular standardization: notebooks/04_standardizer.ipynb\n    - Sanitizing SMILES input: notebooks/05_smiles_sanitization.ipynb\n    - Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer: notebooks/06_hyperparameter_tuning.ipynb\n    - Using parallel execution to speed up descriptor and fingerprint calculations: notebooks/07_parallel_transforms.ipynb\n    - Using skopt for hyperparameter tuning: notebooks/08_external_library_skopt.ipynb\n    - Testing different fingerprints as part of the hyperparameter optimization: notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb\n    - Using pandas output for easy feature importance analysis and combine pre-existing values with new computations: notebooks/10_pipeline_pandas_output.ipynb\n    - Working with pipelines and estimators in safe inference mode: notebooks/11_safe_inference.ipynb\n    - Creating custom fingerptint transformers: notebooks/12_custom_fingerprint_transformer.ipynb\n    - Estimating applicability domain using feature based estimators: notebooks/13_applicability_domain.ipynb\n\n  - Contributing: contributing.md"
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    "path": "pyproject.toml",
    "content": "[build-system]\nrequires = [\"hatchling\", \"hatch-vcs\"]\nbuild-backend = \"hatchling.build\"\n\n[project]\nname = \"scikit-mol\"\ndynamic = [\"version\"]\ndescription = \"scikit-learn classes for molecule transformation\"\nreadme = \"README.md\"\nlicense = \"LGPL-3.0\"\nrequires-python = \">=3.9,<3.14\"\nauthors = [\n    { name = \"Esben Jannik Bjerrum\", email = \"esben@cheminformania.com\" },\n]\nclassifiers = [\n    \"Development Status :: 3 - Alpha\",\n    \"Intended Audience :: Science/Research\",\n    \"License :: OSI Approved :: Apache Software License\",\n    \"Operating System :: MacOS\",\n    \"Operating System :: Microsoft :: Windows\",\n    \"Operating System :: POSIX\",\n    \"Operating System :: Unix\",\n    \"Programming Language :: Python\",\n    \"Programming Language :: Python :: 3 :: Only\",\n    \"Programming Language :: Python :: 3.9\",\n    \"Programming Language :: Python :: 3.10\",\n    \"Programming Language :: Python :: 3.11\",\n    \"Programming Language :: Python :: 3.12\",\n    \"Topic :: Scientific/Engineering\",\n    \"Topic :: Utilities\",\n]\ndependencies = [\n    \"numpy\",\n    \"packaging\",\n    \"pandas\",\n    \"rdkit\",\n    \"scikit-learn\",\n]\n\n\n[project.urls]\nDownload = \"https://github.com/EBjerrum/scikit-mol\"\nHomepage = \"https://github.com/EBjerrum/scikit-mol\"\n\n[tool.hatch]\nbuild.hooks.vcs.version-file = \"scikit_mol/_version.py\"\nversion.source = \"vcs\"  # Get the version from git, eg: 0.0.6.dev0+g1234567\n# Drop the local version part (eg: +g1234567) or pypi will reject package\nversion.raw-options.local_scheme = \"no-local-version\"\n# A manually triggered GitHub release workflow may generate a new tag\n# with .devN suffix. We need to tell setuptools_scm to ignore past tags with\n# this suffix when calculating the version number else it refuses to\n# bump the version number.\nversion.raw-options.git_describe_command = [\n    \"git\", \"describe\", \"--dirty\", \"--tags\", \"--long\",\n    \"--match\", \"v*.[0-9]\",\n    \"--match\", \"v*.[0-9][0-9]\",\n    \"--match\", \"v*.[0-9][0-9][0-9]\",\n]\nversion.raw-options.version_scheme = \"no-guess-dev\"\n\n[tool.hatch.build.targets.sdist]\ninclude = [\n    \"/scikit_mol\",\n]\n\n[tool.hatch.build.targets.wheel]\ninclude = [\n    \"/scikit_mol\",\n]\n\n\n[tool.pytest.ini_options]\naddopts = [\n  \"-v\",\n]\n\n[tool.coverage.run]\nsource = [\"scikit_mol\"]\n\n[tool.coverage.report]\nexclude_lines = [\n  \"pragma: no cover\",\n  \"raise NotImplementedError\",\n  \"if TYPE_CHECKING:\",\n  \"if MYPY:\",\n  \"^\\\\s+[.][.][.]$\",\n]\n\n[dependency-groups]\ndev = [\n    \"ipykernel>=6.29.5\",\n    \"jupytext==1.16.6\",\n    \"pre-commit>=3.5.0\",\n    \"pytest>6\",\n    \"pytest-cov>=5.0.0\",\n    \"ruff==0.8.6\",\n    \"seaborn>=0.13.2\",\n]\ndocs = [\n    \"mkdocs>=1.6.1\",\n    \"mkdocs-jupyter>=0.25.1\",\n    \"mkdocs-material>=9.6.2\",\n    \"mkdocstrings[python]>=0.28.0\",\n]\n\n\n[tool.jupytext]\nformats = \"docs//notebooks//ipynb,docs//notebooks//scripts//py:percent\"\n"
  },
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  },
  {
    "path": "ruff.toml",
    "content": "required-version = \"0.8.6\"\n# Exclude a variety of commonly ignored directories.\nexclude = [\n    \".bzr\",\n    \".direnv\",\n    \".eggs\",\n    \".git\",\n    \".git-rewrite\",\n    \".hg\",\n    \".ipynb_checkpoints\",\n    \".mypy_cache\",\n    \".nox\",\n    \".pants.d\",\n    \".pyenv\",\n    \".pytest_cache\",\n    \".pytype\",\n    \".ruff_cache\",\n    \".svn\",\n    \".tox\",\n    \".venv\",\n    \".vscode\",\n    \"__pypackages__\",\n    \"_build\",\n    \"buck-out\",\n    \"build\",\n    \"dist\",\n    \"node_modules\",\n    \"site-packages\",\n    \"venv\",\n    \"notebooks\",\n    \"docs/notebooks/scripts\",\n]\n\n# Same as Black.\nline-length = 88\nindent-width = 4\n\n# Assume Python 3.9\ntarget-version = \"py39\"\n\n[lint]\n# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.\n# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or\n# McCabe complexity (`C901`) by default.\nselect = [\"B\", \"I\", \"E4\", \"E7\", \"E9\", \"F\", \"S\", \"PTH\", \"RUF\"]\nignore = [\"B005\", \"B028\", \"S101\"]\n\n# Allow fix for all enabled rules (when `--fix`) is provided.\nfixable = [\"ALL\"]\nunfixable = []\n\n# Allow unused variables when underscore-prefixed.\ndummy-variable-rgx = \"^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$\"\n\n[lint.per-file-ignores]\n# Ignore all directories named `tests`.\n\"tests/**\" = [\"B\", \"E4\", \"E7\", \"E9\", \"F\", \"S\", \"PTH\", \"RUF\"]\n\n[format]\n# Like Black, use double quotes for strings.\nquote-style = \"double\"\n\n# Like Black, indent with spaces, rather than tabs.\nindent-style = \"space\"\n\n# Like Black, respect magic trailing commas.\nskip-magic-trailing-comma = false\n\n# Like Black, automatically detect the appropriate line ending.\nline-ending = \"auto\"\n\n# Enable auto-formatting of code examples in docstrings. Markdown,\n# reStructuredText code/literal blocks and doctests are all supported.\n#\n# This is currently disabled by default, but it is planned for this\n# to be opt-out in the future.\ndocstring-code-format = false\n\n# Set the line length limit used when formatting code snippets in\n# docstrings.\n#\n# This only has an effect when the `docstring-code-format` setting is\n# enabled.\ndocstring-code-line-length = \"dynamic\"\n\n"
  },
  {
    "path": "scikit_mol/__init__.py",
    "content": "try:\n    from scikit_mol._version import __version__\nexcept ImportError:  # pragma: no cover\n    __version__ = \"not-installed\"\n"
  },
  {
    "path": "scikit_mol/_constants.py",
    "content": "DOCS_VERSION = \"latest\"\nDOCS_BASE_URL = f\"https://scikit-mol.readthedocs.org/en/{DOCS_VERSION}/api/\"\n"
  },
  {
    "path": "scikit_mol/applicability/LICENSE.MIT",
    "content": "Copyright (c) 2023 Olivier J. M. Béquignon\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  },
  {
    "path": "scikit_mol/applicability/README.md",
    "content": "# Applicability Domain Estimators\n\nThis module contains applicability domain estimators for chemical modeling.\n\n## License Information\n\nFiles 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).\n\n- Files containing the following header are adapted from MLChemAD (originally MIT licensed):\n\n  ```python\n  \"\"\"\n  This module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\n  Original work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\n  Modifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\n  See LICENSE.MIT in this directory for the original MIT license.\n  \"\"\"\n  ```\n\n- All other files are original implementations under scikit-mol's GPL/LGPL license.\n\nThe original MLChemAD MIT license is preserved in LICENSE.MIT for reference.\n"
  },
  {
    "path": "scikit_mol/applicability/__init__.py",
    "content": "from .base import BaseApplicabilityDomain\nfrom .bounding_box import BoundingBoxApplicabilityDomain\nfrom .convex_hull import ConvexHullApplicabilityDomain\nfrom .hotelling import HotellingT2ApplicabilityDomain\nfrom .isolation_forest import IsolationForestApplicabilityDomain\nfrom .kernel_density import KernelDensityApplicabilityDomain\nfrom .knn import KNNApplicabilityDomain\nfrom .leverage import LeverageApplicabilityDomain\nfrom .local_outlier import LocalOutlierFactorApplicabilityDomain\nfrom .mahalanobis import MahalanobisApplicabilityDomain\nfrom .standardization import StandardizationApplicabilityDomain\nfrom .topkat import TopkatApplicabilityDomain\n\n__all__ = [\n    \"BaseApplicabilityDomain\",\n    \"BoundingBoxApplicabilityDomain\",\n    \"ConvexHullApplicabilityDomain\",\n    \"HotellingT2ApplicabilityDomain\",\n    \"IsolationForestApplicabilityDomain\",\n    \"KNNApplicabilityDomain\",\n    \"KernelDensityApplicabilityDomain\",\n    \"LeverageApplicabilityDomain\",\n    \"LocalOutlierFactorApplicabilityDomain\",\n    \"MahalanobisApplicabilityDomain\",\n    \"StandardizationApplicabilityDomain\",\n    \"TopkatApplicabilityDomain\",\n]\n"
  },
  {
    "path": "scikit_mol/applicability/base.py",
    "content": "\"\"\"Base class for applicability domain estimators.\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Any, ClassVar, Optional, Union\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils import check_array\nfrom sklearn.utils._set_output import _SetOutputMixin, _wrap_method_output\nfrom sklearn.utils.validation import check_is_fitted\n\n\nclass _ADOutputMixin(_SetOutputMixin):\n    \"\"\"Extends sklearn's _SetOutputMixin to handle predict and score_transform methods.\"\"\"\n\n    def __init_subclass__(cls, **kwargs):\n        # First handle transform/fit_transform via parent\n        super().__init_subclass__(auto_wrap_output_keys=(\"transform\",), **kwargs)\n\n        # Add our additional methods\n        for method in [\"predict\", \"score_transform\"]:\n            if method not in cls.__dict__:\n                continue\n            wrapped_method = _wrap_method_output(getattr(cls, method), \"transform\")\n            setattr(cls, method, wrapped_method)\n\n\ndef _safe_flatten(X: Union[ArrayLike, pd.DataFrame]) -> NDArray[np.float64]:\n    \"\"\"Safely flatten numpy arrays or pandas DataFrames to 1D array.\n\n    Parameters\n    ----------\n    X : array-like or DataFrame of shape (n_samples, n_features)\n        Input data to flatten\n\n    Returns\n    -------\n    flattened : ndarray of shape (n_samples,)\n        Flattened 1D array\n    \"\"\"\n    if hasattr(X, \"to_numpy\"):  # pandas DataFrame\n        return X.to_numpy().ravel()\n    return np.asarray(X).ravel()\n\n\nclass BaseApplicabilityDomain(BaseEstimator, TransformerMixin, _ADOutputMixin, ABC):\n    \"\"\"Base class for applicability domain estimators.\n\n    Parameters\n    ----------\n    percentile : float or None, default=None\n        Percentile of samples to consider within domain (0-100).\n        If None:\n        - For methods with statistical thresholds: use statistical method\n        - For percentile-only methods: use 99.0 (include 99% of training samples)\n    feature_name : str, default=\"AD_estimator\"\n        Name for the output feature column.\n\n    Notes\n    -----\n    Subclasses must define `_scoring_convention` as either:\n    - 'high_outside': Higher scores indicate samples outside domain (e.g., distances)\n    - 'high_inside': Higher scores indicate samples inside domain (e.g., likelihoods)\n\n    The raw scores from `.transform()` should maintain their natural interpretation,\n    while `.predict()` will handle the conversion to ensure consistent output\n    (1 = inside domain, -1 = outside domain).\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    threshold_ : float\n        Current threshold for domain membership.\n    \"\"\"\n\n    _supports_threshold_fitting: ClassVar[bool] = True\n    _scoring_convention: ClassVar[str]  # Must be set by subclasses\n\n    def __init__(\n        self, percentile: Optional[float] = None, feature_name: str = \"AD_estimator\"\n    ) -> None:\n        if not hasattr(self, \"_scoring_convention\"):\n            raise TypeError(\n                f\"Class {self.__class__.__name__} must define _scoring_convention \"\n                \"as either 'high_outside' or 'high_inside'\"\n            )\n        if self._scoring_convention not in [\"high_outside\", \"high_inside\"]:\n            raise ValueError(\n                f\"Invalid _scoring_convention '{self._scoring_convention}'. \"\n                \"Must be either 'high_outside' or 'high_inside'\"\n            )\n        if percentile is not None and not 0 <= percentile <= 100:\n            raise ValueError(\"percentile must be between 0 and 100\")\n        self.percentile = percentile\n        self.feature_name = feature_name\n        self._check_params = {\n            \"estimator\": self,\n            \"accept_sparse\": False,\n            \"dtype\": None,\n            \"ensure_all_finite\": True,\n            \"ensure_2d\": True,\n        }\n\n    @abstractmethod\n    def fit(self, X: ArrayLike, y: Optional[Any] = None) -> \"BaseApplicabilityDomain\":\n        \"\"\"Fit the applicability domain estimator.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : BaseApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        raise NotImplementedError(\"Subclasses should implement fit\")\n\n    def fit_threshold(\n        self,\n        X: Union[ArrayLike, pd.DataFrame],\n        target_percentile: Optional[float] = None,\n    ) -> \"BaseApplicabilityDomain\":\n        \"\"\"Update threshold estimation using new data.\"\"\"\n        check_is_fitted(self)\n        X = check_array(X, **self._check_params)\n\n        if target_percentile is not None:\n            if not 0 <= target_percentile <= 100:\n                raise ValueError(\"target_percentile must be between 0 and 100\")\n            self.percentile = target_percentile\n\n        # Use statistical threshold if available and percentile is None\n        if self.percentile is None and hasattr(self, \"_set_statistical_threshold\"):\n            self._set_statistical_threshold(X)\n            return self\n\n        # Otherwise use percentile-based threshold\n        scores = _safe_flatten(self.transform(X))\n\n        if self.percentile is None:\n            # Default percentile for methods without statistical thresholds\n            if self._scoring_convention == \"high_outside\":\n                self.threshold_ = np.percentile(scores, 99.0)\n            else:  # high_inside\n                self.threshold_ = np.percentile(scores, 1.0)\n        else:\n            if self._scoring_convention == \"high_outside\":\n                self.threshold_ = np.percentile(scores, self.percentile)\n            else:  # high_inside\n                self.threshold_ = np.percentile(scores, 100 - self.percentile)\n\n        return self\n\n    def transform(\n        self, X: Union[ArrayLike, pd.DataFrame], y: Optional[Any] = None\n    ) -> Union[NDArray[np.float64], pd.DataFrame]:\n        \"\"\"Calculate applicability domain scores.\n\n        Parameters\n        ----------\n        X : array-like or pandas DataFrame\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray or pandas DataFrame\n            Method-specific scores. Interpretation depends on `_scoring_convention`:\n            - 'high_outside': Higher scores indicate samples further from training data\n            - 'high_inside': Higher scores indicate samples closer to training data\n            Shape (n_samples, 1).\n        \"\"\"\n        check_is_fitted(self)\n        X = check_array(X, **self._check_params)\n\n        # Calculate scores\n        scores = self._transform(X)\n\n        return scores\n\n    @abstractmethod\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Implementation of the transform method.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            Validated input data.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            Method-specific scores.\n        \"\"\"\n        raise NotImplementedError(\"Subclasses should implement _transform\")\n\n    def predict(\n        self, X: Union[ArrayLike, pd.DataFrame]\n    ) -> Union[NDArray[np.int_], pd.DataFrame]:\n        \"\"\"Predict whether samples are within the applicability domain.\n\n        Returns\n        -------\n        predictions : ndarray of shape (n_samples,)\n            Returns 1 for inside and -1 for outside.\n        \"\"\"\n        check_is_fitted(self)\n        X = check_array(X, **self._check_params)\n\n        # Calculate predictions\n        scores = _safe_flatten(self.transform(X))\n        if self._scoring_convention == \"high_outside\":\n            predictions = np.where(scores <= self.threshold_, 1, -1)\n        else:  # high_inside\n            predictions = np.where(scores >= self.threshold_, 1, -1)\n\n        return predictions.ravel()\n\n    def score_transform(\n        self, X: Union[ArrayLike, pd.DataFrame]\n    ) -> Union[NDArray[np.float64], pd.DataFrame]:\n        \"\"\"Transform raw scores to [0,1] range using sigmoid.\n\n        Parameters\n        ----------\n        X : array-like or DataFrame of shape (n_samples, n_features)\n            The samples to transform.\n\n        Returns\n        -------\n        scores : ndarray or DataFrame of shape (n_samples, 1)\n            Transformed scores in [0,1] range. Higher values indicate\n            samples more likely to be within domain, regardless of\n            the method's raw score convention.\n        \"\"\"\n        check_is_fitted(self)\n        scores = _safe_flatten(self.transform(X))\n\n        # TODO: the sharpness ought to somehow be fitted to the range of the raw_scores\n        if self._scoring_convention == \"high_outside\":\n            # Flip sign for sigmoid so higher output = more likely inside\n            return (1 / (1 + np.exp(scores - self.threshold_))).reshape(-1, 1)\n        else:  # high_inside\n            # No sign flip needed\n            return (1 / (1 + np.exp(self.threshold_ - scores))).reshape(-1, 1)\n\n    def get_feature_names_out(self, input_features=None) -> NDArray[np.str_]:\n        \"\"\"Get feature name for output column.\"\"\"\n        # TODO: what is the mechanism around input_features?\n        return np.array([f\"{self.feature_name}\"])\n"
  },
  {
    "path": "scikit_mol/applicability/bounding_box.py",
    "content": "\"\"\"\nBounding box applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional, Tuple, Union\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass BoundingBoxApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain defined by feature value ranges.\n\n    Samples falling outside the allowed range for any feature are considered\n    outside the domain. The range for each feature is defined by percentiles\n    of the training set distribution.\n\n    Parameters\n    ----------\n    percentile : float or tuple of float, default=(0.1, 99.9)\n        Percentile(s) of the training set distribution used to define\n        the bounding box. If float, uses (percentile, 100-percentile).\n    feature_name : str, default=\"BoundingBox\"\n        Prefix for feature names in output.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    min_ : ndarray of shape (n_features,)\n        Minimum allowed value for each feature.\n    max_ : ndarray of shape (n_features,)\n        Maximum allowed value for each feature.\n    threshold_ : float\n        Current threshold for domain membership (always 0.5).\n\n    Notes\n    -----\n    The bounding box method is simple but effective, especially for chemical\n    descriptors with clear physical interpretations. For high-dimensional or\n    correlated features, other methods may be more appropriate.\n\n    Examples\n    --------\n    >>> from sklearn.pipeline import make_pipeline\n    >>> from sklearn.preprocessing import StandardScaler\n    >>> from scikit_mol.applicability import BoundingBoxApplicabilityDomain\n    >>>\n    >>> # Basic usage\n    >>> ad = BoundingBoxApplicabilityDomain(percentile=1)\n    >>> ad.fit(X_train)\n    >>> predictions = ad.predict(X_test)\n    >>>\n    >>> # With preprocessing\n    >>> pipe = make_pipeline(\n    ...     StandardScaler(),\n    ...     BoundingBoxApplicabilityDomain(percentile=1)\n    ... )\n    >>> pipe.fit(X_train)\n    >>> predictions = pipe.predict(X_test)\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n    _supports_threshold_fitting = False\n\n    def __init__(\n        self,\n        percentile: Union[float, Tuple[float, float]] = (0.1, 99.9),\n        feature_name: str = \"BoundingBox\",\n    ) -> None:\n        super().__init__(percentile=None, feature_name=feature_name)\n\n        if isinstance(percentile, (int, float)):\n            if not 0 <= percentile <= 100:\n                raise ValueError(\"percentile must be between 0 and 100\")\n            self.box_percentile = (percentile, 100 - percentile)\n        else:\n            if not all(0 <= p <= 100 for p in percentile):\n                raise ValueError(\"percentiles must be between 0 and 100\")\n            if len(percentile) != 2:\n                raise ValueError(\"percentile must be a float or tuple of 2 floats\")\n            if percentile[0] >= percentile[1]:\n                raise ValueError(\"first percentile must be less than second\")\n            self.box_percentile = percentile\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"BoundingBoxApplicabilityDomain\":\n        \"\"\"Fit the bounding box applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : BoundingBoxApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = self._validate_data(X)\n        self.n_features_in_ = X.shape[1]\n\n        # Calculate bounds\n        self.min_ = np.percentile(X, self.box_percentile[0], axis=0)\n        self.max_ = np.percentile(X, self.box_percentile[1], axis=0)\n\n        # Fixed threshold since we count violations\n        self.threshold_ = 0.5\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate the number of features outside their bounds.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        violations : ndarray of shape (n_samples, 1)\n            Number of features outside their bounds for each sample.\n            Zero indicates all features within bounds.\n        \"\"\"\n        violations = np.sum((X < self.min_) | (X > self.max_), axis=1)\n        return violations.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/convex_hull.py",
    "content": "\"\"\"\nConvex hull applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom scipy import optimize\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass ConvexHullApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain defined as the convex hull of the training data.\n\n    The convex hull approach determines if a point belongs to the convex hull of the\n    training set by checking if it can be represented as a convex combination of\n    training points.\n\n    Parameters\n    ----------\n    percentile : float or None, default=None\n        Not used, present for API consistency.\n    feature_name : str, default=\"ConvexHull\"\n        Prefix for feature names in output.\n\n    Notes\n    -----\n    The method is based on the `highs` solver from `scipy.optimize`. Note that this\n    method can be computationally expensive for high-dimensional data or large\n    training sets, as it requires solving a linear programming problem for each\n    test point.\n\n    For high-dimensional data (e.g., fingerprints), consider using dimensionality\n    reduction before applying this method.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    points_ : ndarray of shape (n_features + 1, n_samples)\n        Transformed training points used for convex hull calculations.\n    threshold_ : float\n        Fixed at 0.5 since output is binary (inside/outside hull).\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n    _supports_threshold_fitting = False\n\n    def __init__(\n        self, percentile: Optional[float] = None, feature_name: str = \"ConvexHull\"\n    ) -> None:\n        super().__init__(percentile=None, feature_name=feature_name)\n        self.threshold_ = 0.5  # Fixed threshold since output is binary\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"ConvexHullApplicabilityDomain\":\n        \"\"\"Fit the convex hull applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : ConvexHullApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = self._validate_data(X)\n        self.n_features_in_ = X.shape[1]\n\n        # Add ones column and transpose for convex hull calculations\n        self.points_ = np.r_[X.T, np.ones((1, X.shape[0]))].astype(np.float32)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate distance from convex hull for each sample.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        distances : ndarray of shape (n_samples, 1)\n            Distance from convex hull. Zero for points inside the hull,\n            positive for points outside.\n        \"\"\"\n        distances = []\n        for sample in X:\n            # Append 1 to sample vector\n            sample_ext = np.r_[sample, 1].astype(np.float32)\n\n            # Try to solve the linear programming problem\n            result = optimize.linprog(\n                np.ones(self.points_.shape[1], dtype=np.float32),\n                A_eq=self.points_,\n                b_eq=sample_ext,\n                method=\"highs\",\n            )\n            # Distance is positive if no solution found, 0 if solution exists\n            distances.append(0.0 if result.success else 1.0)\n\n        return np.array(distances).reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/hotelling.py",
    "content": "\"\"\"\nHotelling T² applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom scipy.stats import f as f_dist\nfrom sklearn.utils import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass HotellingT2ApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on Hotelling's T² statistic.\n\n    Uses Hotelling's T² statistic to define an elliptical confidence region\n    around the training data. The threshold can be set using either the\n    F-distribution (statistical approach) or adjusted using a validation set.\n\n    Parameters\n    ----------\n    significance : float, default=0.05\n        Significance level for F-distribution threshold.\n    percentile : float or None, default=None\n        If not None, overrides significance-based threshold.\n        Must be between 0 and 100.\n    feature_name : str, default=\"HotellingT2\"\n        Prefix for feature names in output.\n\n    Notes\n    -----\n    Lower volume protrusion scores indicate samples closer to the training\n    data center. By default, the threshold is set using the F-distribution\n    with a significance level of 0.05 (95% confidence).\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    t2_ : ndarray of shape (n_features,)\n        Hotelling T² ellipse parameters.\n    threshold_ : float\n        Current threshold for volume protrusions.\n\n    References\n    ----------\n    .. [1] Hotelling, H. (1931). The generalization of Student's ratio.\n           The Annals of Mathematical Statistics, 2(3), 360-378.\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n    _supports_threshold_fitting = True\n\n    def __init__(\n        self,\n        significance: float = 0.05,\n        percentile: Optional[float] = None,\n        feature_name: str = \"HotellingT2\",\n    ) -> None:\n        if not 0 < significance < 1:\n            raise ValueError(\"significance must be between 0 and 1\")\n        super().__init__(percentile=percentile, feature_name=feature_name)\n        self.significance = significance\n\n    def _set_statistical_threshold(self, X: NDArray) -> None:\n        \"\"\"Set threshold using F-distribution.\"\"\"\n        n_samples = X.shape[0]\n        f_stat = (\n            (n_samples - 1)\n            / n_samples\n            * self.n_features_in_\n            * (n_samples**2 - 1)\n            / (n_samples * (n_samples - self.n_features_in_))\n        )\n        f_stat *= f_dist.ppf(\n            1 - self.significance, self.n_features_in_, n_samples - self.n_features_in_\n        )\n        self.threshold_ = f_stat\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"HotellingT2ApplicabilityDomain\":\n        \"\"\"Fit the Hotelling T² applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : HotellingT2ApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        # Determine the Hotelling T² ellipse\n        self.t2_ = np.sqrt((1 / X.shape[0]) * (X**2).sum(axis=0))\n\n        # Set initial threshold\n        if self.percentile is not None:\n            self.fit_threshold(X)\n        else:\n            self._set_statistical_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate volume protrusion scores for samples.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            The volume protrusion scores. Higher values indicate samples\n            further from the training data center.\n        \"\"\"\n        protrusions = (X**2 / self.t2_**2).sum(axis=1)\n        return protrusions.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/isolation_forest.py",
    "content": "\"\"\"\nIsolation Forest applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass IsolationForestApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on Isolation Forest.\n\n    Uses Isolation Forest to identify outliers based on the isolation depth\n    of samples in random decision trees.\n\n    Parameters\n    ----------\n    n_estimators : int, default=100\n        Number of trees in the forest.\n    contamination : float, default=0.01\n        Expected proportion of outliers in the training data.\n    random_state : Optional[int], default=None\n        Controls the randomness of the forest.\n    percentile : float or None, default=None\n        Percentile of training set scores to use as threshold (0-100).\n        If None, uses contamination-based threshold from IsolationForest.\n    feature_name : str, default=\"IsolationForest\"\n        Name for feature names in output.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    iforest_ : IsolationForest\n        Fitted isolation forest model.\n    threshold_ : float\n        Current threshold for domain membership.\n\n    Notes\n    -----\n    The scoring convention is 'high_inside' because higher scores from\n    IsolationForest indicate samples more similar to the training data.\n\n    References\n    ----------\n    .. [1] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest.\n           In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422).\n    \"\"\"\n\n    _scoring_convention = \"high_inside\"\n    _supports_threshold_fitting = True\n\n    def __init__(\n        self,\n        n_estimators: int = 100,\n        contamination: float = 0.01,\n        random_state: Optional[int] = None,\n        percentile: Optional[float] = None,\n        feature_name: str = \"IsolationForest\",\n    ) -> None:\n        if not 0 < contamination < 1:\n            raise ValueError(\"contamination must be between 0 and 1\")\n        super().__init__(percentile=percentile, feature_name=feature_name)\n        self.n_estimators = n_estimators\n        self.contamination = contamination\n        self.random_state = random_state\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"IsolationForestApplicabilityDomain\":\n        \"\"\"Fit the isolation forest applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : IsolationForestApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        self.iforest_ = IsolationForest(\n            n_estimators=self.n_estimators,\n            contamination=self.contamination,\n            random_state=self.random_state,\n        )\n        self.iforest_.fit(X)\n\n        # Set initial threshold\n        if self.percentile is not None:\n            self.fit_threshold(X)\n        else:\n            # Use IsolationForest's default threshold\n            self.threshold_ = self.iforest_.offset_\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate anomaly scores for samples.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            The anomaly scores of the samples.\n            Higher scores indicate samples more similar to training data.\n        \"\"\"\n        scores = self.iforest_.score_samples(X)\n        return scores.reshape(-1, 1)\n\n    # def fit_threshold(self, X, target_percentile=95):\n    #     \"\"\"Update the threshold using new data without refitting the model.\n\n    #     Parameters\n    #     ----------\n    #     X : array-like of shape (n_samples, n_features)\n    #         Data to compute threshold from.\n    #     target_percentile : float, default=95\n    #         Target percentile of samples to include within domain.\n\n    #     Returns\n    #     -------\n    #     self : object\n    #         Returns the instance itself.\n    #     \"\"\"\n    #     check_is_fitted(self)\n    #     X = check_array(X)\n\n    #     if not 0 <= target_percentile <= 100:\n    #         raise ValueError(\"target_percentile must be between 0 and 100\")\n\n    #     # Get decision function scores\n    #     scores = self.iforest_.score_samples(X)\n\n    #     # Set threshold to achieve desired percentile\n    #     self.threshold_ = np.percentile(scores, 100 - target_percentile)\n\n    #     return self\n\n    # def predict(self, X):\n    #     \"\"\"Predict whether samples are within the applicability domain.\n\n    #     Parameters\n    #     ----------\n    #     X : array-like of shape (n_samples, n_features)\n    #         The samples to predict.\n\n    #     Returns\n    #     -------\n    #     y_pred : ndarray of shape (n_samples,)\n    #         Returns 1 for samples inside the domain and -1 for samples outside\n    #         (following scikit-learn's convention for outlier detection).\n    #     \"\"\"\n    #     scores = self._transform(X).ravel()\n    #     if hasattr(self, \"threshold_\"):\n    #         return np.where(scores > self.threshold_, 1, -1)\n    #     return self.iforest_.predict(X)\n"
  },
  {
    "path": "scikit_mol/applicability/kernel_density.py",
    "content": "\"\"\"\nKernel Density applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)Chem\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.neighbors import KernelDensity\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass KernelDensityApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on kernel density estimation.\n\n    Uses kernel density estimation to model the distribution of the training data.\n    Samples with density below a threshold (determined by percentile of training\n    data densities) are considered outside the domain.\n\n    Parameters\n    ----------\n    bandwidth : float, default=1.0\n        The bandwidth of the kernel.\n    kernel : str, default='gaussian'\n        The kernel to use. Options: ['gaussian', 'tophat', 'epanechnikov',\n        'exponential', 'linear', 'cosine'].\n    percentile : float or None, default=None\n        The percentile of training set densities to use as threshold (0-100).\n        If None, uses 99.0 (exclude bottom 1% of training samples).\n    feature_name : str, default=\"KernelDensity\"\n        Name for the output feature column.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    kde_ : KernelDensity\n        Fitted kernel density estimator.\n    threshold_ : float\n        Density threshold for domain membership.\n\n    Notes\n    -----\n    The scoring convention is 'high_inside' because higher density scores\n    indicate samples more similar to the training data.\n\n    Examples\n    --------\n    >>> from scikit_mol.applicability import KernelDensityApplicabilityDomain\n    >>> ad = KernelDensityApplicabilityDomain(bandwidth=1.0)\n    >>> ad.fit(X_train)\n    >>> predictions = ad.predict(X_test)\n    \"\"\"\n\n    _scoring_convention = \"high_inside\"\n\n    def __init__(\n        self,\n        bandwidth: float = 1.0,\n        kernel: str = \"gaussian\",\n        percentile: Optional[float] = None,\n        feature_name: str = \"KernelDensity\",\n    ) -> None:\n        super().__init__(percentile=percentile or 99.0, feature_name=feature_name)\n        self.bandwidth = bandwidth\n        self.kernel = kernel\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"KernelDensityApplicabilityDomain\":\n        \"\"\"Fit the kernel density applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : KernelDensityApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        # Fit KDE\n        self.kde_ = KernelDensity(bandwidth=self.bandwidth, kernel=self.kernel)\n        self.kde_.fit(X)\n\n        # Set initial threshold based on training data\n        self.fit_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate log density scores for samples.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            The log density scores of the samples. Higher scores indicate samples\n            more similar to the training data.\n        \"\"\"\n        scores = self.kde_.score_samples(X)\n        return scores.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/knn.py",
    "content": "\"\"\"\nK-Nearest Neighbors applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Callable, ClassVar, Literal, Optional, Union\n\nimport numpy as np\nfrom numpy.typing import ArrayLike\nfrom sklearn.neighbors import NearestNeighbors\nfrom sklearn.utils import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass KNNApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain defined using K-nearest neighbors.\n\n    Determines domain membership based on the mean distance to k nearest neighbors\n    in the training set. Higher distances indicate samples further from the\n    training distribution.\n\n    Parameters\n    ----------\n    n_neighbors : int, default=5\n        Number of neighbors to use for distance calculation.\n    percentile : float or None, default=None\n        Percentile of training set distances to use as threshold (0-100).\n        If None, uses 99.0 (include 99% of training samples).\n    distance_metric : str or callable, default='euclidean'\n        Distance metric to use. As examples:\n        - 'euclidean': Euclidean distance (default)\n        - 'manhattan': Manhattan distance\n        - 'cosine': Cosine distance\n        - 'tanimoto': Tanimoto distance for binary fingerprints (same as 'jaccard')\n        - 'jaccard': Jaccard distance for binary fingerprints\n        - callable: Custom distance metric function(X, Y) -> array-like\n        Any distance metric supported by sklearn.neighbors.NearestNeighbors can also be used.\n        Note: Only distance metrics are supported (higher values = more distant) currently.\n    n_jobs : int, default=None\n        Number of parallel jobs to run for neighbors search.\n        None means 1 unless in a joblib.parallel_backend context.\n        -1 means using all processors.\n    feature_name : str, default='KNN'\n        Prefix for feature names in output.\n\n    Notes\n    -----\n    For binary fingerprints, the Tanimoto distance is equivalent to the Jaccard distance.\n    Both 'tanimoto' and 'jaccard' options use scipy's implementation of the Jaccard\n    distance metric.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    threshold_ : float\n        Distance threshold for domain membership.\n    nn_ : NearestNeighbors\n        Fitted nearest neighbors model.\n\n    Examples\n    --------\n    >>> import numpy as np\n    >>> from scikit_mol.applicability import KNNApplicabilityDomain\n    >>>\n    >>> # Generate example data\n    >>> rng = np.random.RandomState(0)\n    >>> X_train = rng.normal(0, 1, (100, 5))\n    >>> X_test = rng.normal(0, 2, (20, 5))  # More spread out than training\n    >>>\n    >>> # Fit AD model\n    >>> ad = KNNApplicabilityDomain(n_neighbors=5, percentile=95)\n    >>> ad.fit(X_train)\n    >>>\n    >>> # Get raw distance scores (higher = more distant)\n    >>> distances = ad.transform(X_test)\n    >>>\n    >>> # Get domain membership predictions\n    >>> predictions = ad.predict(X_test)  # 1 = inside, -1 = outside\n    >>>\n    >>> # Get probability-like scores\n    >>> scores = ad.score_transform(X_test)  # Higher = more likely inside\n    \"\"\"\n\n    _scoring_convention: ClassVar[str] = (\n        \"high_outside\"  # Higher distance = outside domain\n    )\n\n    def __init__(\n        self,\n        n_neighbors: int = 5,\n        percentile: Optional[float] = None,\n        distance_metric: Union[\n            Literal[\"euclidean\", \"manhattan\", \"cosine\", \"tanimoto\", \"jaccard\"], Callable\n        ] = \"euclidean\",\n        n_jobs: Optional[int] = None,\n        feature_name: str = \"KNN\",\n    ) -> None:\n        super().__init__(percentile=percentile, feature_name=feature_name)\n        self.n_neighbors = n_neighbors\n        self.distance_metric = distance_metric\n        self.n_jobs = n_jobs\n\n    @property\n    def distance_metric(self) -> Union[Callable, str]:\n        return self._distance_metric\n\n    @distance_metric.setter\n    def distance_metric(self, value: Union[str, Callable]) -> None:\n        if not isinstance(value, (str, Callable)):\n            raise ValueError(\"distance_metric must be a string or callable\")\n        if value == \"tanimoto\":\n            self._distance_metric = \"jaccard\"  # Use scipy's jaccard metric\n        else:\n            self._distance_metric = value\n\n    def fit(self, X: ArrayLike, y=None) -> \"KNNApplicabilityDomain\":\n        \"\"\"Fit the KNN applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : KNNApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        if not isinstance(self.n_neighbors, int) or self.n_neighbors < 1:\n            raise ValueError(\"n_neighbors must be a positive integer\")\n\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        # Fit nearest neighbors model\n        self.nn_ = NearestNeighbors(\n            n_neighbors=self.n_neighbors + 1,  # +1 because point is its own neighbor\n            metric=self.distance_metric,\n            n_jobs=self.n_jobs,\n        )\n\n        if self.distance_metric == \"jaccard\":\n            X = X.astype(bool)\n\n        self.nn_.fit(X)\n\n        # Set initial threshold based on training data\n        self.fit_threshold(X)\n\n        return self\n\n    def _transform(self, X: np.ndarray) -> np.ndarray:\n        \"\"\"Calculate mean distance to k nearest neighbors in training set.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            Validated input data.\n\n        Returns\n        -------\n        distances : ndarray of shape (n_samples, 1)\n            Mean distance to k nearest neighbors. Higher values indicate samples\n            further from the training set.\n        \"\"\"\n\n        if self.distance_metric == \"jaccard\":\n            X = X.astype(bool)\n\n        distances, _ = self.nn_.kneighbors(X)\n        mean_distances = distances[:, 1:].mean(axis=1)  # Skip first (self) neighbor\n        return mean_distances.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/leverage.py",
    "content": "\"\"\"\nLeverage-based applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass LeverageApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain defined using the leverage approach.\n\n    The leverage approach measures how far a sample is from the center of the\n    feature space using the diagonal elements of the hat matrix H = X(X'X)^(-1)X'.\n    Higher leverage values indicate samples further from the center of the training data.\n\n    Parameters\n    ----------\n    threshold_factor : float, default=3\n        Factor used in calculating the leverage threshold h* = threshold_factor * (p+1)/n\n        where p is the number of features and n is the number of samples.\n    percentile : float or None, default=None\n        If not None, overrides the statistical threshold with a percentile-based one.\n        See BaseApplicabilityDomain for details.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    threshold_ : float\n        Calculated leverage threshold.\n    var_covar_ : ndarray of shape (n_features, n_features)\n        Variance-covariance matrix of the training data.\n\n    Notes\n    -----\n    The statistical threshold h* = 3 * (p+1)/n is a commonly used rule of thumb\n    in regression diagnostics, where p is the number of features and n is the\n    number of training samples.\n\n    Input data should be scaled (e.g., using StandardScaler) to ensure all features\n    contribute equally. For high-dimensional data like fingerprints, dimensionality\n    reduction (e.g., PCA) is strongly recommended to avoid computational issues with\n    the variance-covariance matrix inversion.\n\n    Examples\n    --------\n    >>> from sklearn.pipeline import Pipeline\n    >>> from sklearn.preprocessing import StandardScaler\n    >>> from sklearn.decomposition import PCA\n    >>> from scikit_mol.applicability import LeverageApplicabilityDomain\n    >>>\n    >>> # Create pipeline with scaling and dimensionality reduction\n    >>> pipe = Pipeline([\n    ...     ('scaler', StandardScaler()),\n    ...     ('pca', PCA(n_components=0.95)),  # Keep 95% of variance\n    ...     ('ad', LeverageApplicabilityDomain())\n    ... ])\n    >>>\n    >>> # Fit pipeline\n    >>> X_train = [[0, 1, 2], [1, 2, 3], [2, 3, 4]]  # Example data\n    >>> pipe.fit(X_train)\n    >>>\n    >>> # Predict domain membership for new samples\n    >>> X_test = [[0, 1, 2], [10, 20, 30]]\n    >>> pipe.predict(X_test)  # Returns [1, -1] (in/out of domain)\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n    _supports_threshold_fitting = True\n\n    def __init__(\n        self,\n        threshold_factor: float = 3,\n        percentile: Optional[float] = None,\n        feature_name: str = \"Leverage\",\n    ) -> None:\n        super().__init__(percentile=percentile, feature_name=feature_name)\n        self.threshold_factor = threshold_factor\n\n    def _set_statistical_threshold(self, X: NDArray) -> None:\n        \"\"\"Set the statistical threshold h* = threshold_factor * (p+1)/n.\"\"\"\n        n_samples = X.shape[0]\n        self.threshold_ = self.threshold_factor * (self.n_features_in_ + 1) / n_samples\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"LeverageApplicabilityDomain\":\n        \"\"\"Fit the leverage applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Ignored\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : LeverageApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        # Calculate variance-covariance matrix\n        self.var_covar_ = np.linalg.inv(X.T.dot(X))\n\n        # Set initial threshold\n        self._set_statistical_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate leverage values.\n\n        Higher values indicate samples further from the center of the training data.\n        \"\"\"\n        h = np.sum(X.dot(self.var_covar_) * X, axis=1)\n        return h.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/local_outlier.py",
    "content": "\"\"\"\nLocal Outlier Factor applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass LocalOutlierFactorApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on Local Outlier Factor (LOF).\n\n    LOF measures the local deviation of density of a sample with respect to its\n    neighbors, identifying samples that have substantially lower density than\n    their neighbors.\n\n    Parameters\n    ----------\n    n_neighbors : int, default=20\n        Number of neighbors to use for LOF calculation.\n    contamination : float, default=0.1\n        Expected proportion of outliers in the data set.\n    metric : str, default='euclidean'\n        Metric to use for distance computation.\n    percentile : float or None, default=None\n        Percentile of training set scores to use as threshold (0-100).\n        If None, uses contamination-based threshold from LOF.\n    feature_name : str, default=\"LOF\"\n        Name for the output feature column.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    lof_ : LocalOutlierFactor\n        Fitted LOF estimator.\n    threshold_ : float\n        Current threshold for domain membership.\n\n    Notes\n    -----\n    The scoring convention is 'high_outside' because higher LOF scores\n    indicate samples that are more likely to be outliers.\n\n    References\n    ----------\n    .. [1] Breunig et al. (2000). LOF: Identifying Density-Based Local Outliers.\n           In: Proc. 2000 ACM SIGMOD Int. Conf. Manag. Data, ACM, pp. 93-104.\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n\n    def __init__(\n        self,\n        n_neighbors: int = 20,\n        contamination: float = 0.1,\n        metric: str = \"euclidean\",\n        percentile: Optional[float] = None,\n        feature_name: str = \"LOF\",\n    ) -> None:\n        super().__init__(percentile=percentile, feature_name=feature_name)\n        self.n_neighbors = n_neighbors\n        self.contamination = contamination\n        self.metric = metric\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"LocalOutlierFactorApplicabilityDomain\":\n        \"\"\"Fit the LOF applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : LocalOutlierFactorApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        self.lof_ = LocalOutlierFactor(\n            n_neighbors=self.n_neighbors,\n            metric=self.metric,\n            contamination=self.contamination,\n            novelty=True,\n        )\n        self.lof_.fit(X)\n\n        # Set initial threshold based on training data\n        self.fit_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate LOF scores for samples.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            The LOF scores of the samples. Higher scores indicate samples\n            that are more likely to be outliers.\n        \"\"\"\n        # Get negative LOF scores (higher means more likely to be outlier)\n        scores = -self.lof_.score_samples(X)\n        return scores.reshape(-1, 1)\n\n    def _set_statistical_threshold(self, X):\n        \"\"\"Set the statistical threshold for the LOF scores.\"\"\"\n        self.threshold_ = -self.lof_.offset_\n\n    # def predict(self, X):\n    #     \"\"\"Predict whether samples are within the applicability domain.\n\n    #     Parameters\n    #     ----------\n    #     X : array-like of shape (n_samples, n_features)\n    #         The samples to predict.\n\n    #     Returns\n    #     -------\n    #     y_pred : ndarray of shape (n_samples,)\n    #         Returns 1 for samples inside the domain and -1 for samples outside\n    #         (following scikit-learn's convention for outlier detection).\n    #     \"\"\"\n    #     return self.lof_.predict(X)\n"
  },
  {
    "path": "scikit_mol/applicability/mahalanobis.py",
    "content": "\"\"\"\nMahalanobis distance applicability domain.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom scipy import linalg, stats\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass MahalanobisApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on Mahalanobis distance.\n\n    Uses Mahalanobis distance to measure how many standard deviations a sample\n    is from the training set mean, taking into account the covariance structure\n    of the data. For multivariate normal data, the squared Mahalanobis distances\n    follow a chi-square distribution.\n\n    Parameters\n    ----------\n    percentile : float or None, default=None\n        Percentile of training set scores to use as threshold (0-100).\n        If None, uses 95.0 (exclude top 5% of training samples).\n    feature_name : str, default=\"Mahalanobis\"\n        Name for the output feature column.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    mean_ : ndarray of shape (n_features,)\n        Mean of training data.\n    covariance_ : ndarray of shape (n_features, n_features)\n        Covariance matrix of training data.\n    threshold_ : float\n        Current threshold for domain membership.\n\n    Notes\n    -----\n    The scoring convention is 'high_outside' because higher Mahalanobis\n    distances indicate samples further from the training data mean.\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n\n    def __init__(\n        self,\n        percentile: Optional[float] = None,\n        feature_name: str = \"Mahalanobis\",\n    ) -> None:\n        super().__init__(percentile=percentile or 95.0, feature_name=feature_name)\n\n    def _set_statistical_threshold(self, X: NDArray) -> None:\n        \"\"\"Set threshold based on chi-square distribution.\n\n        For multivariate normal data, squared Mahalanobis distances follow\n        a chi-square distribution with degrees of freedom equal to the\n        number of features.\n        \"\"\"\n        df = self.n_features_in_\n        self.threshold_ = np.sqrt(stats.chi2.ppf(0.95, df))\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"MahalanobisApplicabilityDomain\":\n        \"\"\"Fit the Mahalanobis distance applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : MahalanobisApplicabilityDomain\n            Returns the instance itself.\n\n        Raises\n        ------\n        ValueError\n            If X has fewer samples than features, making covariance estimation unstable.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        n_samples, n_features = X.shape\n        self.n_features_in_ = n_features\n\n        if n_samples <= n_features:\n            raise ValueError(\n                f\"n_samples ({n_samples}) must be greater than n_features ({n_features}) \"\n                \"for stable covariance estimation.\"\n            )\n\n        # Calculate mean and covariance\n        self.mean_ = np.mean(X, axis=0)\n        self.covariance_ = np.cov(X, rowvar=False, ddof=1)\n\n        # Add small regularization to ensure positive definiteness\n        min_eig = np.min(linalg.eigvalsh(self.covariance_))\n        if min_eig < 1e-6:\n            self.covariance_ += (abs(min_eig) + 1e-6) * np.eye(n_features)\n\n        # Set initial threshold based on training data\n        self.fit_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate Mahalanobis distances.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        distances : ndarray of shape (n_samples, 1)\n            The Mahalanobis distances of the samples. Higher distances indicate\n            samples further from the training data mean.\n        \"\"\"\n        # Calculate Mahalanobis distances using stable computation\n        diff = X - self.mean_\n        try:\n            # Try Cholesky decomposition first (more stable)\n            L = linalg.cholesky(self.covariance_, lower=True)\n            mahal_dist = np.sqrt(\n                np.sum(linalg.solve_triangular(L, diff.T, lower=True) ** 2, axis=0)\n            )\n        except linalg.LinAlgError:\n            # Fallback to standard computation if Cholesky fails\n            inv_covariance = linalg.pinv(\n                self.covariance_\n            )  # Use pseudo-inverse for stability\n            mahal_dist = np.sqrt(np.sum(diff @ inv_covariance * diff, axis=1))\n\n        return mahal_dist.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/standardization.py",
    "content": "\"\"\"\nStandardization approach applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom scipy import stats\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass StandardizationApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain based on standardized feature values.\n\n    Samples are considered within the domain if their standardized features\n    fall within a certain number of standard deviations from the mean.\n    The maximum absolute standardized value across all features is used\n    as the score.\n\n    Parameters\n    ----------\n    percentile : float or None, default=None\n        Percentile of training set scores to use as threshold (0-100).\n        If None, uses 95.0 (exclude top 5% of training samples).\n    feature_name : str, default=\"Standardization\"\n        Name for the output feature column.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    scaler_ : StandardScaler\n        Fitted standard scaler.\n    threshold_ : float\n        Current threshold for domain membership.\n\n    Notes\n    -----\n    The scoring convention is 'high_outside' because higher standardized\n    values indicate samples further from the training data mean.\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n\n    def __init__(\n        self,\n        percentile: Optional[float] = None,\n        feature_name: str = \"Standardization\",\n    ) -> None:\n        super().__init__(percentile=percentile or 95.0, feature_name=feature_name)\n\n    def _set_statistical_threshold(self, X: NDArray) -> None:\n        \"\"\"Set threshold based on normal distribution.\n\n        For normally distributed data, ~95% of values fall within\n        2 standard deviations of the mean.\n        \"\"\"\n        self.threshold_ = stats.norm.ppf(0.975)  # 2 standard deviations\n\n    def fit(\n        self, X: ArrayLike, y: Optional[Any] = None\n    ) -> \"StandardizationApplicabilityDomain\":\n        \"\"\"Fit the standardization applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : StandardizationApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n\n        # Fit standard scaler\n        self.scaler_ = StandardScaler()\n        self.scaler_.fit(X)\n\n        # Set initial threshold based on training data\n        self.fit_threshold(X)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate maximum absolute standardized values.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        scores : ndarray of shape (n_samples, 1)\n            The maximum absolute standardized values. Higher values indicate\n            samples further from the training data mean.\n        \"\"\"\n        # Calculate standardized values and take max absolute value per sample\n        X_std = self.scaler_.transform(X)\n        scores = np.max(np.abs(X_std), axis=1)\n        return scores.reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/applicability/topkat.py",
    "content": "\"\"\"\nTOPKAT's Optimal Prediction Space (OPS) applicability domain.\n\nThis module was adapted from [MLChemAD](https://github.com/OlivierBeq/MLChemAD)\nOriginal work Copyright (c) 2023 Olivier J. M. Béquignon (MIT License)\nModifications Copyright (c) 2025 scikit-mol contributors (LGPL License)\nSee LICENSE.MIT in this directory for the original MIT license.\n\n\"\"\"\n\nfrom typing import Any, Optional\n\nimport numpy as np\nfrom numpy.typing import ArrayLike, NDArray\nfrom sklearn.utils.validation import check_array\n\nfrom .base import BaseApplicabilityDomain\n\n\nclass TopkatApplicabilityDomain(BaseApplicabilityDomain):\n    \"\"\"Applicability domain defined using TOPKAT's Optimal Prediction Space (OPS).\n\n    The method transforms the input space (P-space) to a normalized space (S-space),\n    then projects it to the Optimal Prediction Space using eigendecomposition.\n\n    Parameters\n    ----------\n    percentile : float or None, default=None\n        Not used, present for API consistency.\n    feature_name : str, default=\"TOPKAT\"\n        Name for the output feature column.\n\n    Attributes\n    ----------\n    n_features_in_ : int\n        Number of features seen during fit.\n    X_min_ : ndarray of shape (n_features,)\n        Minimum values of training features.\n    X_max_ : ndarray of shape (n_features,)\n        Maximum values of training features.\n    eigen_val_ : ndarray of shape (n_features + 1,)\n        Eigenvalues of the S-space transformation.\n    eigen_vec_ : ndarray of shape (n_features + 1, n_features + 1)\n        Eigenvectors of the S-space transformation.\n    threshold_ : float\n        Fixed threshold based on dimensionality.\n\n    Notes\n    -----\n    The scoring convention is 'high_outside' because higher OPS distances\n    indicate samples further from the training data.\n\n    References\n    ----------\n    .. [1] Gombar, Vijay K. (1996). Method and apparatus for validation of model-based\n           predictions (US Patent No. 6-036-349) USPTO.\n    \"\"\"\n\n    _scoring_convention = \"high_outside\"\n    _supports_threshold_fitting = False\n\n    def __init__(\n        self,\n        percentile: Optional[float] = None,\n        feature_name: str = \"TOPKAT\",\n    ) -> None:\n        super().__init__(percentile=None, feature_name=feature_name)\n\n    def fit(self, X: ArrayLike, y: Optional[Any] = None) -> \"TopkatApplicabilityDomain\":\n        \"\"\"Fit the TOPKAT applicability domain.\n\n        Parameters\n        ----------\n        X : array-like of shape (n_samples, n_features)\n            Training data.\n        y : Any, optional (default=None)\n            Not used, present for API consistency.\n\n        Returns\n        -------\n        self : TopkatApplicabilityDomain\n            Returns the instance itself.\n        \"\"\"\n        X = check_array(X, **self._check_params)\n        self.n_features_in_ = X.shape[1]\n        n_samples = X.shape[0]\n\n        # Store scaling factors\n        self.X_min_ = X.min(axis=0)\n        self.X_max_ = X.max(axis=0)\n\n        # Transform P-space to S-space\n        denom = np.where(\n            (self.X_max_ - self.X_min_) != 0, (self.X_max_ - self.X_min_), 1\n        )\n        S = (2 * X - self.X_max_ - self.X_min_) / denom\n\n        # Add column of ones\n        S = np.c_[np.ones(n_samples), S]\n\n        # Calculate eigendecomposition\n        self.eigen_val_, self.eigen_vec_ = np.linalg.eigh(S.T.dot(S))\n\n        # Ensure real values (numerical stability)\n        self.eigen_val_ = np.real(self.eigen_val_)\n        self.eigen_vec_ = np.real(self.eigen_vec_)\n\n        # Set fixed threshold based on dimensionality\n        self.threshold_ = 5 * (self.n_features_in_ + 1) / (2 * self.n_features_in_)\n\n        return self\n\n    def _transform(self, X: NDArray) -> NDArray[np.float64]:\n        \"\"\"Calculate OPS distance scores for samples.\n\n        Parameters\n        ----------\n        X : ndarray of shape (n_samples, n_features)\n            The data to transform.\n\n        Returns\n        -------\n        distances : ndarray of shape (n_samples, 1)\n            OPS distance scores. Higher values indicate samples further\n            from the training data.\n        \"\"\"\n        # Transform to S-space\n        denom = np.where(\n            (self.X_max_ - self.X_min_) != 0, (self.X_max_ - self.X_min_), 1\n        )\n        S = (2 * X - self.X_max_ - self.X_min_) / denom\n\n        # Add column of ones\n        if X.ndim == 1:\n            S = np.r_[1, S].reshape(1, -1)\n        else:\n            S = np.c_[np.ones(X.shape[0]), S]\n\n        # Project to OPS\n        OPS = S.dot(self.eigen_vec_)\n\n        # Calculate OPS distances - matching MLChemAD's approach\n        denom = np.divide(\n            np.ones_like(self.eigen_val_, dtype=float),\n            self.eigen_val_,\n            out=np.zeros_like(self.eigen_val_),\n            where=self.eigen_val_ != 0,\n        )\n        distances = (OPS * OPS).dot(denom)\n\n        return distances.reshape(-1, 1)\n\n    # def predict(self, X):\n    #     \"\"\"Predict whether samples are within the applicability domain.\n\n    #     Parameters\n    #     ----------\n    #     X : array-like of shape (n_samples, n_features)\n    #         The samples to predict.\n\n    #     Returns\n    #     -------\n    #     y_pred : ndarray of shape (n_samples,)\n    #         Returns 1 for samples inside the domain and -1 for samples outside\n    #         (following scikit-learn's convention for outlier detection).\n    #     \"\"\"\n    #     scores = self._transform(X).ravel()\n    #     threshold = self.threshold_\n    #     return np.where(scores < threshold, 1, -1)\n"
  },
  {
    "path": "scikit_mol/conversions.py",
    "content": "from collections.abc import Sequence\nfrom typing import Optional, Union\n\nimport numpy as np\nfrom numpy.typing import NDArray\nfrom rdkit import Chem\nfrom rdkit.rdBase import BlockLogs\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom scikit_mol._constants import DOCS_BASE_URL\nfrom scikit_mol.core import (\n    InvalidMol,\n    NoFitNeededMixin,\n    check_transform_input,\n    feature_names_default_mol,\n)\nfrom scikit_mol.parallel import parallelized_with_batches\n\n# from scikit_mol._invalid import InvalidMol\n\n\nclass SmilesToMolTransformer(TransformerMixin, NoFitNeededMixin, BaseEstimator):\n    \"\"\"\n    Transformer for converting SMILES strings to RDKit mol objects.\n\n    This transformer can be included in pipelines during development and training,\n    but the safe inference mode should only be enabled when deploying models for\n    inference in production environments.\n    \"\"\"\n\n    _doc_link_module = \"scikit_mol\"\n    _doc_link_template = (\n        DOCS_BASE_URL + \"{estimator_module}/#{estimator_module}.{estimator_name}\"\n    )\n\n    def __init__(\n        self, n_jobs: Optional[None] = None, safe_inference_mode: bool = False\n    ):\n        \"\"\"\n        Parameters\n        -----------\n        n_jobs : int, optional default=None\n            The maximum number of concurrently running jobs.\n            `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`.\n        safe_inference_mode : bool, default=False\n            If `True`, enables safeguards for handling invalid data during inference.\n            This should only be set to `True` when deploying models to production.\n        \"\"\"\n        self.n_jobs = n_jobs\n        self.safe_inference_mode = safe_inference_mode\n\n    @feature_names_default_mol\n    def get_feature_names_out(self, input_features=None):\n        return input_features\n\n    def fit(self, X=None, y=None):\n        \"\"\"Included for scikit-learn compatibility, does nothing\"\"\"\n        return self\n\n    def transform(\n        self, X_smiles_list: Sequence[str], y=None\n    ) -> NDArray[Union[Chem.Mol, InvalidMol]]:\n        \"\"\"Converts SMILES into RDKit mols\n\n        Parameters\n        ----------\n        X_smiles_list : Sequence[str]\n            sequence of SMILES strings to transform\n\n        Returns\n        -------\n        NDArray[Union[Chem.Mol, InvalidMol]]\n            Array of RDKit mol objects or InvalidMol objects if a SMILES string is invalid and `safe_inference_mode=True`\n        Raises\n        ------\n        ValueError\n            Raises ValueError if a SMILES string is unparsable by RDKit and `safe_inference_mode=False`\n        \"\"\"\n\n        arrays = parallelized_with_batches(self._transform, X_smiles_list, self.n_jobs)\n        arr = np.concatenate(arrays)\n        return arr\n\n    @check_transform_input\n    def _transform(self, X):\n        X_out = []\n        with BlockLogs():\n            for smiles in X:\n                mol = Chem.MolFromSmiles(smiles, sanitize=False)\n                if mol:\n                    errors = Chem.DetectChemistryProblems(mol)\n                    if errors:\n                        error_message = \"\\n\".join(error.Message() for error in errors)\n                        message = f\"Invalid Molecule: {error_message}\"\n                        X_out.append(InvalidMol(str(self), message))\n                    else:\n                        Chem.SanitizeMol(mol)\n                        X_out.append(mol)\n                else:\n                    message = f\"Invalid SMILES: {smiles}\"\n                    X_out.append(InvalidMol(str(self), message))\n        if not self.safe_inference_mode and not all(X_out):\n            fails = [x for x in X_out if not x]\n            raise ValueError(\n                f\"Invalid input found: {fails}.\"\n            )  # TODO with this approach we get all errors, but we do process ALL the smiles first which could be slow\n        return np.array(X_out).reshape(-1, 1)\n\n    @check_transform_input\n    def inverse_transform(self, X_mols_list, y=None):\n        X_out = []\n\n        for mol in X_mols_list:\n            if isinstance(mol, Chem.Mol):\n                try:\n                    smiles = Chem.MolToSmiles(mol)\n                    X_out.append(smiles)\n                except Exception as e:\n                    X_out.append(\n                        InvalidMol(str(self), f\"Error converting Mol to SMILES: {e}\")\n                    )\n            else:\n                X_out.append(InvalidMol(str(self), f\"Not a Mol: {mol}\"))\n\n        if not self.safe_inference_mode and not all(isinstance(x, str) for x in X_out):\n            fails = [x for x in X_out if not isinstance(x, str)]\n            raise ValueError(f\"Invalid Mols found: {fails}.\")\n\n        return np.array(X_out).reshape(-1, 1)\n"
  },
  {
    "path": "scikit_mol/core.py",
    "content": "\"\"\"\nCore functionality for scikit-mol.\n\nUsers of scikit-mol should not need to use this module directly.\nUsers who want to create their own transformers should use this module.\n\"\"\"\n\nimport functools\nfrom dataclasses import dataclass\n\nimport numpy as np\nfrom packaging.version import Version\n\nSKLEARN_VERSION_PANDAS_OUT = Version(\"1.2\")\n\nDEFAULT_MOL_COLUMN_NAME = \"ROMol\"\n\n\nclass NoFitNeededMixin:\n    \"\"\"\n    Mixin class to add a `__sklearn_is_fitted__` method to a transformer, which does not need to be fitted.\n    \"\"\"\n\n    def __sklearn_is_fitted__(self):\n        return True\n\n\n@dataclass\nclass InvalidMol:\n    \"\"\"Represents molecules which raised an error during a pipeline step.\n    Evaluates to `False` in boolean contexts.\n\n    Parameters\n    -----------\n    pipeline_step : str\n        The name of the pipeline step where the error occurred.\n    error : str\n        The error message.\n    \"\"\"\n\n    pipeline_step: str\n    error: str\n\n    def __bool__(self):\n        return False\n\n    def __repr__(self):\n        return f\"InvalidMol('{self.pipeline_step}', error='{self.error}')\"\n\n\ndef _validate_transform_input(X):\n    \"\"\"Validate and adapt the input of the _transform method\"\"\"\n    try:\n        shape = X.shape\n    except AttributeError:\n        # If X is not array-like or dataframe-like,\n        # we just return it as is, so users can use simple lists and sequences.\n        return X\n    # If X is an array-like or dataframe-like, we make sure it is compatible with\n    # the scikit-learn API, and that it contains a single column:\n    # scikit-mol transformers need a single column with smiles or mols.\n    if len(shape) == 1:\n        return X  # Flatt Arrays and list-like data are also supported #TODO, add a warning about non-2D data if logging is implemented\n    if shape[1] != 1:\n        raise ValueError(\n            \"Only one column supported. You may want to use a ColumnTransformer https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html \"\n        )\n    return np.array(X).flatten()\n\n\ndef check_transform_input(method):\n    \"\"\"\n    Decorator to check the input of the _transform method\n    and make it compatible with the scikit-learn API and with downstream methods.\n    \"\"\"\n\n    @functools.wraps(method)\n    def wrapper(obj, X):\n        X = _validate_transform_input(X)\n        result = method(obj, X)\n        # If the output of the _transform method\n        # must be changed depending on the initial type of X, do it here.\n        return result\n\n    return wrapper\n\n\ndef feature_names_default_mol(method):\n    \"\"\"\n    Decorator that returns the default feature names for the mol object\n    \"\"\"\n\n    @functools.wraps(method)\n    def wrapper(obj, input_features=None):\n        prefix = DEFAULT_MOL_COLUMN_NAME\n        if input_features is not None:\n            return np.array([f\"{prefix}_{name}\" for name in input_features])\n        else:\n            return np.array([prefix])\n\n    return wrapper\n"
  },
  {
    "path": "scikit_mol/descriptors.py",
    "content": "import functools\nfrom typing import List, Optional, Union\n\nimport numpy as np\nfrom rdkit.Chem import Descriptors\nfrom rdkit.Chem.rdchem import Mol\nfrom rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom scikit_mol._constants import DOCS_BASE_URL\nfrom scikit_mol.core import NoFitNeededMixin, check_transform_input\nfrom scikit_mol.parallel import parallelized_with_batches\n\n\nclass MolecularDescriptorTransformer(TransformerMixin, NoFitNeededMixin, BaseEstimator):\n    \"\"\"Descriptor calculation transformer\n\n    Parameters\n    ----------\n    desc_list : (List of descriptor names)\n        A list of RDKit descriptors to include in the calculation\n    n_jobs : int optional default: None\n        The maximum number of concurrently running jobs.\n        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.\n    safe_inference_mode : bool\n        If True, enables safeguards for handling invalid data during inference.\n        This should only be set to True when deploying models to production.\n\n    Returns\n    -------\n    np.array\n        Descriptor values, shape (samples, len(descriptor list))\n\n\n    \"\"\"\n\n    _doc_link_module = \"scikit_mol\"\n    _doc_link_template = (\n        DOCS_BASE_URL + \"{estimator_module}/#{estimator_module}.{estimator_name}\"\n    )\n\n    def __init__(\n        self,\n        desc_list: Optional[str] = None,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.float32,\n    ):\n        self.desc_list = desc_list\n        self.n_jobs = n_jobs\n        self.safe_inference_mode = safe_inference_mode\n        self.dtype = dtype\n\n    def _get_desc_calculator(self) -> MolecularDescriptorCalculator:\n        if self.desc_list:\n            unknown_descriptors = [\n                desc_name\n                for desc_name in self.desc_list\n                if desc_name not in self.available_descriptors\n            ]\n            assert not unknown_descriptors, f\"Unknown descriptor names {unknown_descriptors} specified, please check available_descriptors property\\nPlease check available list {self.available_descriptors}\"\n        else:\n            self.desc_list = self.available_descriptors\n        return MolecularDescriptorCalculator(self.desc_list)\n\n    @property\n    def desc_list(self):\n        \"\"\"Descriptor names of currently selected descriptors\"\"\"\n        return self._desc_list\n\n    def get_feature_names_out(self, input_features=None):\n        return np.array(self.selected_descriptors)\n\n    @desc_list.setter\n    def desc_list(self, desc_list):\n        self._desc_list = desc_list\n        self.calculators = self._get_desc_calculator()\n\n    @property\n    def available_descriptors(self) -> List[str]:\n        \"\"\"List of names of all available descriptor\"\"\"\n        return [descriptor[0] for descriptor in Descriptors._descList]\n\n    @property\n    def selected_descriptors(self) -> List[str]:\n        \"\"\"List of the names of the descriptors in the descriptor calculator\"\"\"\n        return list(self.calculators.GetDescriptorNames())\n\n    @property\n    def start_method(self):\n        return self._start_method\n\n    @start_method.setter\n    def start_method(self, start_method):\n        \"\"\"Allowed methods are spawn, fork and forkserver on macOS and Linux, only spawn is possible on Windows.\n        None will choose the default for the OS and version of Python.\"\"\"\n        allowed_start_methods = [\"spawn\", \"fork\", \"forkserver\", None]\n        assert (\n            start_method in allowed_start_methods\n        ), f\"start_method not in allowed methods {allowed_start_methods}\"\n        self._start_method = start_method\n\n    def _transform_mol(self, mol: Mol) -> Union[np.ndarray, np.ma.MaskedArray]:\n        if not mol:\n            if self.safe_inference_mode:\n                return np.ma.masked_all(len(self.desc_list))\n            else:\n                raise ValueError(f\"Invalid molecule provided: {mol}\")\n        try:\n            return np.array(list(self.calculators.CalcDescriptors(mol)))\n        except Exception as e:\n            if self.safe_inference_mode:\n                return np.ma.masked_all(len(self.desc_list))\n            else:\n                raise e\n\n    def fit(self, x, y=None):\n        \"\"\"Included for scikit-learn compatibility, does nothing\"\"\"\n        return self\n\n    @check_transform_input\n    def _transform(self, x: List[Mol]) -> Union[np.ndarray, np.ma.MaskedArray]:\n        if self.safe_inference_mode:\n            arrays = [self._transform_mol(mol) for mol in x]\n            return np.ma.array(arrays, dtype=self.dtype)\n        else:\n            arr = np.zeros((len(x), len(self.desc_list)), dtype=self.dtype)\n            for i, mol in enumerate(x):\n                arr[i, :] = self._transform_mol(mol)\n            return arr\n\n    def transform(self, x: List[Mol], y=None) -> Union[np.ndarray, np.ma.MaskedArray]:\n        \"\"\"Transform a list of molecules into an array of descriptor values\n        Parameters\n        ----------\n        x : (List, np.array, pd.Series)\n            A list of RDKit molecules\n        y : NoneType, optional\n            Target values for scikit-learn compatibility, not used, by default None\n\n        Returns\n        -------\n        Union[np.ndarray, np.ma.MaskedArray]\n            Descriptors, shape (samples, length of .selected_descriptors)\n\n        \"\"\"\n        fn = functools.partial(parallel_helper, self.get_params())\n        arrays = parallelized_with_batches(fn, x, self.n_jobs)\n        if self.safe_inference_mode:\n            arrays = np.ma.concatenate(arrays)\n        else:\n            arrays = np.concatenate(arrays)\n        return arrays\n\n\n# May be safer to instantiate the transformer object in the child process, and only transfer the parameters\n# There were issues with freezing when using RDKit 2022.3\ndef parallel_helper(params, mols):\n    \"\"\"Will get a tuple with Desc2DTransformer parameters and mols to transform.\n    Will then instantiate the transformer and transform the molecules\"\"\"\n    from scikit_mol.descriptors import MolecularDescriptorTransformer\n\n    transformer = MolecularDescriptorTransformer(**params)\n    y = transformer._transform(mols)\n    return y\n"
  },
  {
    "path": "scikit_mol/fingerprints/__init__.py",
    "content": "from scikit_mol._constants import DOCS_BASE_URL\n\nfrom .atompair import AtomPairFingerprintTransformer\nfrom .avalon import AvalonFingerprintTransformer\n\n# TODO, these baseclasses needed for backwards compatibility with tests, needs to be removed when tests updated\nfrom .baseclasses import (\n    FpsGeneratorTransformer,\n    FpsTransformer,\n)\nfrom .maccs import MACCSKeysFingerprintTransformer\nfrom .minhash import MHFingerprintTransformer, SECFingerprintTransformer\nfrom .morgan import MorganFingerprintTransformer\nfrom .rdkitfp import RDKitFingerprintTransformer\nfrom .topologicaltorsion import (\n    TopologicalTorsionFingerprintTransformer,\n)\n\n__all__ = [\n    \"AtomPairFingerprintTransformer\",\n    \"AvalonFingerprintTransformer\",\n    \"FpsGeneratorTransformer\",\n    \"FpsTransformer\",\n    \"MACCSKeysFingerprintTransformer\",\n    \"MHFingerprintTransformer\",\n    \"MorganFingerprintTransformer\",\n    \"RDKitFingerprintTransformer\",\n    \"SECFingerprintTransformer\",\n    \"TopologicalTorsionFingerprintTransformer\",\n]\n\nfor name in __all__:\n    if name.startswith(\"Fps\") or name.startswith(\"_\"):\n        continue\n    cls = locals()[name]\n    cls._doc_link_module = \"scikit_mol\"\n    cls._doc_link_template = (\n        DOCS_BASE_URL\n        + \"scikit_mol.fingerprints/#scikit_mol.fingerprints.{estimator_name}\"\n    )\n"
  },
  {
    "path": "scikit_mol/fingerprints/atompair.py",
    "content": "from typing import Optional, Sequence\n\nimport numpy as np\nfrom rdkit.Chem.rdFingerprintGenerator import GetAtomPairGenerator\n\nfrom .baseclasses import FpsGeneratorTransformer\n\n\nclass AtomPairFingerprintTransformer(FpsGeneratorTransformer):\n    \"\"\"\n    AtomPair fingerprints encode pairs of atoms at various topological or 3D distances in a molecule.\n    They are useful for capturing structural relationships and connectivity patterns.\n\n    \"\"\"\n\n    _regenerate_on_properties = (\n        \"fpSize\",\n        \"includeChirality\",\n        \"use2D\",\n        \"minLength\",\n        \"maxLength\",\n    )\n\n    def __init__(\n        self,\n        minLength: int = 1,\n        maxLength: int = 30,\n        fromAtoms: Optional[Sequence] = None,\n        ignoreAtoms: Optional[Sequence] = None,\n        atomInvariants: Optional[Sequence] = None,\n        includeChirality: bool = False,\n        use2D: bool = True,\n        confId: int = -1,\n        fpSize: int = 2048,\n        useCounts: bool = False,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n    ):\n        \"\"\"Transform RDKit mols into Count or bit-based hashed AtomPair Fingerprints\n\n        Parameters\n        ----------\n        minLength : int, optional\n            Minimum distance between atom pairs, by default 1\n        maxLength : int, optional\n            Maximum distance between atom pairs, by default 30\n        fromAtoms : Sequence, optional\n            Atom indices to use as starting points, by default None\n        ignoreAtoms : array-like, optional\n            Atom indices to exclude, by default None\n        atomInvariants : array-like, optional\n            Atom invariants to use, by default None\n        includeChirality : bool, optional\n            Include chirality in calculation of the fingerprint keys, by default False\n        use2D : bool, optional\n            Use 2D distances (topological) instead of 3D, by default True\n        confId : int, optional\n            Which conformer to use for 3D distance calculations, by default -1\n        fpSize : int, optional\n            Size of the hashed fingerprint, by default 2048\n        useCounts : bool, optional\n            If toggled will create the count and not bit-based fingerprint, by default False\n        n_jobs : int, optional\n            The maximum number of concurrently running jobs.\n            `None` is a marker for 'unset' that will be interpreted as `n_jobs=1` unless the call is performed under a\n            `joblib.parallel_config()` context manager that sets another value for `n_jobs`.\n        safe_inference_mode : bool, optional\n            If `True`, will return masked arrays for invalid mols, by default `False`\n        \"\"\"\n        self._initializing = True\n        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode)\n        self.fpSize = fpSize\n        self.use2D = use2D\n        self.includeChirality = includeChirality\n        self.minLength = minLength\n        self.maxLength = maxLength\n\n        self.useCounts = useCounts\n        self.confId = confId\n        self.fromAtoms = fromAtoms\n        self.ignoreAtoms = ignoreAtoms\n        self.atomInvariants = atomInvariants\n        self._generate_fp_generator()\n        delattr(self, \"_initializing\")\n\n    def _generate_fp_generator(self):\n        self._fpgen = GetAtomPairGenerator(\n            minDistance=int(self.minLength),\n            maxDistance=int(self.maxLength),\n            includeChirality=bool(self.includeChirality),\n            use2D=bool(self.use2D),\n            fpSize=int(self.fpSize),\n        )\n\n    def _transform_mol(self, mol) -> np.array:\n        if self.useCounts:\n            return self._fpgen.GetCountFingerprintAsNumPy(\n                mol,\n                fromAtoms=self.fromAtoms,\n                ignoreAtoms=self.ignoreAtoms,\n                customAtomInvariants=self.atomInvariants,\n            )\n        else:\n            return self._fpgen.GetFingerprintAsNumPy(\n                mol,\n                fromAtoms=self.fromAtoms,\n                ignoreAtoms=self.ignoreAtoms,\n                customAtomInvariants=self.atomInvariants,\n            )\n"
  },
  {
    "path": "scikit_mol/fingerprints/avalon.py",
    "content": "from typing import Optional\n\nimport numpy as np\nfrom rdkit.Avalon import pyAvalonTools\n\nfrom .baseclasses import FpsTransformer\n\n\nclass AvalonFingerprintTransformer(FpsTransformer):\n    \"Fingerprint from the Avalon toolkit, https://doi.org/10.1021/ci050413p\"\n\n    def __init__(\n        self,\n        fpSize: int = 512,\n        isQuery: bool = False,\n        resetVect: bool = False,\n        bitFlags: int = 15761407,\n        useCounts: bool = False,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.int8,\n    ):\n        \"\"\"Transform RDKit mols into Count or bit-based Avalon Fingerprints\n\n        Parameters\n        ----------\n        fpSize : int, optional\n            Size of the fingerprint, by default 512\n        isQuery : bool, optional\n            Use the fingerprint for a query structure, by default False\n        resetVect : bool, optional\n            Reset vector, by default False. NB: only used in GetAvalonFP (not for GetAvalonCountFP)\n        bitFlags : int, optional\n            Substructure fingerprint (32767) or similarity fingerprint (15761407), by default 15761407\n        useCounts : bool, optional\n            If toggled will create the count and not bit-based fingerprint, by default False\n        n_jobs : int, optional\n            The number of jobs to run in parallel, by default None\n        safe_inference_mode : bool, optional\n            If True, enables safe inference mode, by default False\n        dtype : numpy.dtype, optional\n            Data type of the fingerprint array, by default numpy.int8\n        \"\"\"\n        super().__init__(\n            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype\n        )\n        self.fpSize = fpSize\n        self.isQuery = isQuery\n        self.resetVect = resetVect\n        self.bitFlags = bitFlags\n        self.useCounts = useCounts\n\n    def _mol2fp(self, mol):\n        if self.useCounts:\n            return pyAvalonTools.GetAvalonCountFP(\n                mol,\n                nBits=int(self.fpSize),\n                isQuery=bool(self.isQuery),\n                bitFlags=int(self.bitFlags),\n            )\n        else:\n            return pyAvalonTools.GetAvalonFP(\n                mol,\n                nBits=int(self.fpSize),\n                isQuery=bool(self.isQuery),\n                resetVect=bool(self.resetVect),\n                bitFlags=int(self.bitFlags),\n            )\n"
  },
  {
    "path": "scikit_mol/fingerprints/baseclasses.py",
    "content": "import functools\nimport inspect\nimport re\nfrom abc import ABC, abstractmethod\nfrom typing import Optional, Type\nfrom warnings import simplefilter, warn\n\n# from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect\nimport numpy as np\nfrom rdkit import DataStructs\nfrom scipy.sparse import lil_matrix\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom scikit_mol.core import NoFitNeededMixin, check_transform_input\nfrom scikit_mol.parallel import parallelized_with_batches\n\nsimplefilter(\"always\", DeprecationWarning)\n\n_PATTERN_FINGERPRINT_TRANSFORMER = re.compile(\n    r\"^(?P<fingerprint_name>\\w+)FingerprintTransformer$\"\n)\n\n\nclass BaseFpsTransformer(TransformerMixin, NoFitNeededMixin, ABC, BaseEstimator):\n    \"\"\"Base class for fingerprint transformers\n\n    Parameters\n    ----------\n    name : Optional[str], optional\n        name of the fingerprint, used for column prefix in when the output mode is set to `pandas`, by default None\n    n_jobs : Optional[str], optional\n        The maximum number of concurrently running jobs.\n        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`.\n    safe_inference_mode : bool\n        If `True`, enables safeguards for handling invalid data during inference.\n        This should only be set to `True` when deploying models to production.\n    \"\"\"\n\n    def __init__(\n        self,\n        name: Optional[str] = None,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n    ):\n        self.n_jobs = n_jobs\n        self.safe_inference_mode = safe_inference_mode\n        self._fp_name = name\n\n    # TODO, remove when finally deprecating nBits and dtype\n    @property\n    def nBits(self):\n        warn(\n            \"nBits will be replaced by fpSize, due to changes harmonization!\",\n            DeprecationWarning,\n            stacklevel=2,\n        )\n        return self.fpSize\n\n    # TODO, remove when finally deprecating nBits and dtype\n    @nBits.setter\n    def nBits(self, nBits):\n        if nBits is not None:\n            warn(\n                \"nBits will be replaced by fpSize, due to changes harmonization!\",\n                DeprecationWarning,\n                stacklevel=3,\n            )\n            self.fpSize = nBits\n\n    def _get_column_prefix(self) -> str:\n        if hasattr(self, \"_fp_name\") and self._fp_name:\n            return str(self._fp_name).lower()\n        cls_name = type(self).__name__\n        matched = _PATTERN_FINGERPRINT_TRANSFORMER.match(cls_name)\n        if matched:\n            fingerprint_name = matched.group(\"fingerprint_name\")\n            return f\"fp_{fingerprint_name.lower()}\"\n        else:\n            return cls_name.lower()\n\n    def _get_n_digits_column_suffix(self) -> int:\n        return len(str(self.fpSize))\n\n    def get_display_feature_names_out(self, input_features=None):\n        \"\"\"Get feature names for display purposes\n\n        All feature names will have the same length,\n        since the different elements will be prefixed with zeros\n        depending on the number of bits.\n        \"\"\"\n        prefix = self._get_column_prefix()\n        n_digits = self._get_n_digits_column_suffix()\n        return np.array(\n            [f\"{prefix}_{str(i).zfill(n_digits)}\" for i in range(1, self.fpSize + 1)]\n        )\n\n    def get_feature_names_out(self, input_features=None):\n        \"\"\"Get feature names for fingerprint transformers\n\n        This method is used by the scikit-learn set_output API\n        to get the column names of the transformed dataframe.\n        \"\"\"\n        prefix = self._get_column_prefix()\n        return np.array([f\"{prefix}_{i}\" for i in range(1, self.fpSize + 1)])\n\n    def _safe_transform_mol(self, mol):\n        \"\"\"Handle safe inference mode with masked arrays\"\"\"\n        if not mol and self.safe_inference_mode:\n            return np.ma.masked_all(self.fpSize)\n\n        try:\n            result = self._transform_mol(mol)\n            return result\n        except Exception as e:\n            if self.safe_inference_mode:\n                return np.ma.masked_all(self.fpSize)\n            else:\n                raise e\n\n    @abstractmethod\n    def _transform_mol(self, mol):\n        \"\"\"Transform a single molecule to numpy array\"\"\"\n        raise NotImplementedError\n\n    def fit(self, X, y=None):\n        \"\"\"Included for scikit-learn compatibility\n\n        Also sets the column prefix for use by the transform method with dataframe output.\n        \"\"\"\n        return self\n\n    @check_transform_input\n    def _transform(self, X):\n        if self.safe_inference_mode:\n            arrays = [self._safe_transform_mol(mol) for mol in X]\n            return np.ma.stack(arrays)\n        else:\n            arrays = [self._transform_mol(mol) for mol in X]\n            return np.stack(arrays)\n\n    def _transform_sparse(self, X):\n        arr = np.zeros((len(X), self.fpSize), dtype=self.dtype)\n        for i, mol in enumerate(X):\n            arr[i, :] = self._transform_mol(mol)\n\n        return lil_matrix(arr)\n\n    def transform(self, X, y=None):\n        \"\"\"Transform a list of RDKit molecule objects into a fingerprint array\n\n        Parameters\n        ----------\n        X : (List, np.array, pd.Series)\n            A list of RDKit molecules\n        y : NoneType, optional\n            Target values for scikit-learn compatibility, not used, by default None\n\n        Returns\n        -------\n        np.array\n            Fingerprints, shape (samples, fingerprint size)\n        \"\"\"\n        func = functools.partial(\n            parallel_helper,\n            cls=self.__class__,\n            parameters=self.get_params(),\n        )\n        arrays = parallelized_with_batches(func, X, self.n_jobs)\n        if self.safe_inference_mode:\n            arr = np.ma.concatenate(arrays)\n            return arr\n        else:\n            return np.concatenate(arrays)\n\n\nclass FpsTransformer(BaseFpsTransformer):\n    \"\"\"Classic fingerprint transformer using mol2fp pattern\"\"\"\n\n    def __init__(\n        self,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.int8,\n    ):\n        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode)\n        self.dtype = dtype\n\n    def _transform_mol(self, mol):\n        \"\"\"Implements the mol -> rdkit fingerprint data structure -> numpy array pattern\"\"\"\n        fp = self._mol2fp(mol)\n        return self._fp2array(fp)\n\n    @abstractmethod\n    def _mol2fp(self, mol):\n        \"\"\"Generate fingerprint from mol\n\n        MUST BE OVERWRITTEN\n        \"\"\"\n        raise NotImplementedError(\"_mol2fp not implemented\")\n\n    def _fp2array(self, fp):\n        \"\"\"Convert RDKit fingerprint data structure to numpy array\"\"\"\n        if fp:\n            arr = np.zeros((self.fpSize,), dtype=self.dtype)\n            DataStructs.ConvertToNumpyArray(fp, arr)\n            return arr\n        else:\n            return np.ma.masked_all((self.fpSize,), dtype=self.dtype)\n\n    @check_transform_input\n    def _transform(self, X):\n        if self.safe_inference_mode:\n            arrays = [self._safe_transform_mol(mol) for mol in X]\n            return np.ma.stack(arrays)\n        else:\n            arr = np.zeros((len(X), self.fpSize), dtype=self.dtype)\n            for i, mol in enumerate(X):\n                arr[i, :] = self._transform_mol(mol)\n            return arr\n\n    # TODO, remove when finally deprecating nBits\n    def _get_param_names(self):\n        \"\"\"Get parameter names excluding deprecated parameters\"\"\"\n        params = super()._get_param_names()\n        # Remove deprecated parameters before they're accessed\n        return [p for p in params if p not in (\"nBits\")]\n\n\nclass FpsGeneratorTransformer(BaseFpsTransformer):\n    \"\"\"Abstract base class for fingerprint transformers based on (unpicklable)fingerprint generators\"\"\"\n\n    _regenerate_on_properties = ()\n\n    def __getstate__(self):\n        # Get the state of the parent class\n        state = super().__getstate__()\n        state.update(self.get_params())\n        # Remove the potentiallyunpicklable property from the state\n        state.pop(\"_fpgen\", None)  # fpgen is not picklable\n        return state\n\n    def __setstate__(self, state):\n        # Restore the state of the parent class\n        super().__setstate__(state)\n        # Re-create the unpicklable property\n        # Do we need this part of code? params variable is not used and tests pass without it\n        generatort_keys = inspect.signature(\n            self._generate_fp_generator\n        ).parameters.keys()\n        params = [  # noqa: F841\n            setattr(self, k, state[\"_\" + k])\n            if \"_\" + k in state\n            else setattr(self, k, state[k])\n            for k in generatort_keys\n        ]\n        self._generate_fp_generator()\n\n    # TODO: overload set_params in order to not make multiple calls to _generate_fp_generator\n\n    def __setattr__(self, name: str, value):\n        super().__setattr__(name, value)\n        if (\n            not hasattr(self, \"_initializing\")\n            and name in self._regenerate_on_properties\n        ):\n            self._generate_fp_generator()\n\n    @abstractmethod\n    def _generate_fp_generator(self):\n        raise NotImplementedError(\"_generate_fp_generator not implemented\")\n\n    @abstractmethod\n    def _transform_mol(self, mol) -> np.array:\n        \"\"\"Generate numpy array descriptor from RDKit molecule\n\n        MUST BE OVERWRITTEN\n        \"\"\"\n        raise NotImplementedError(\"_transform_mol not implemented\")\n\n    # TODO, remove when finally deprecating nBits and dtype\n    @property\n    def dtype(self):\n        warn(\n            \"dtype is no longer supported, due to move to generator based fingerprints\",\n            DeprecationWarning,\n            stacklevel=2,\n        )\n        return None\n\n    # TODO, remove when finally deprecating nBits and dtype\n    @dtype.setter\n    def dtype(self, dtype):\n        if dtype is not None:\n            warn(\n                \"dtype is no longer supported, due to move to generator based fingerprints\",\n                DeprecationWarning,\n                stacklevel=3,\n            )\n        pass\n\n    # TODO, remove when finally deprecating nBits and dtype\n    def _get_param_names(self):\n        \"\"\"Get parameter names excluding deprecated parameters\"\"\"\n        params = super()._get_param_names()\n        # Remove deprecated parameters before they're accessed\n        return [p for p in params if p not in (\"dtype\", \"nBits\")]\n\n\ndef parallel_helper(X_mols, cls: Type[BaseFpsTransformer], parameters: dict):\n    \"\"\"Parallel_helper takes a tuple with class, the objects parameters and the mols to process.\n    Then instantiates the class with the parameters and processes the mol.\n    Intention is to be able to do this in child processes as some classes can't be pickled.\n    This is a workaround for the fact that joblib doesn't support initialiers like multiprocessing does.\n    \"\"\"\n    transformer = cls(**parameters)\n    return transformer._transform(X_mols)\n"
  },
  {
    "path": "scikit_mol/fingerprints/maccs.py",
    "content": "from typing import Optional\n\nimport numpy as np\nfrom rdkit.Chem import rdMolDescriptors\n\nfrom .baseclasses import FpsTransformer\n\n\nclass MACCSKeysFingerprintTransformer(FpsTransformer):\n    \"\"\"MACCS keys fingerprinter calculates the 167 fixed MACCS keys\"\"\"\n\n    def __init__(\n        self,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.int8,\n        fpSize=167,\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        n_jobs : int, optional default=None\n            The maximum number of concurrently running jobs.\n            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`.\n        safe_inference_mode : bool, optional\n            If `True`, will return masked arrays for invalid mols, by default `False`\n        dtype : np.dtype, optional\n            Data type of the fingerprint array, by default np.int8\n        fpSize : int, optional\n            Size of the fingerprint, by default 167\n\n        Raises\n        ------\n        ValueError\n            _description_\n        \"\"\"\n\n        super().__init__(\n            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype\n        )\n        if fpSize != 167:\n            raise ValueError(\n                \"fpSize can only be 167, matching the number of defined MACCS keys!\"\n            )\n        self._fpSize = fpSize\n\n    @property\n    def fpSize(self):\n        return self._fpSize\n\n    @fpSize.setter\n    def fpSize(self, fpSize):\n        if fpSize != 167:\n            raise ValueError(\n                \"fpSize can only be 167, matching the number of defined MACCS keys!\"\n            )\n        self._fpSize = fpSize\n\n    def _mol2fp(self, mol):\n        return rdMolDescriptors.GetMACCSKeysFingerprint(mol)\n"
  },
  {
    "path": "scikit_mol/fingerprints/minhash.py",
    "content": "from typing import Optional\nfrom warnings import warn\n\nimport numpy as np\nfrom rdkit.Chem import rdMHFPFingerprint\n\nfrom .baseclasses import FpsTransformer\n\n\n# TODO move to use FpsGeneratorTransformer\nclass MHFingerprintTransformer(FpsTransformer):\n    \"Transforms the RDKit mol into the [MinHash fingerprint (MHFP)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0321-8).\"\n\n    def __init__(\n        self,\n        radius: int = 3,\n        rings: bool = True,\n        isomeric: bool = False,\n        kekulize: bool = False,\n        min_radius: int = 1,\n        fpSize: int = 2048,\n        seed: int = 42,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.int32,\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        radius : int, optional\n            The MHFP radius.\n        rings : bool, optional\n            Whether to include rings in the shingling.\n        isomeric : bool, optional\n            Whether the isomeric SMILES to be considered\n        kekulize : bool, optional\n            Whether to kekulize the extracted SMILES\n        min_radius : int, optional\n            The minimum radius that is used to extract n-gram.\n        fpSize : int, optional\n            The number of permutations used for hashing. Defaults to `2048`, this is effectively the length of the FP.\n        seed : int, optional\n            The value used to seed numpy.random.\n        n_jobs : int or None, optional\n            The maximum number of concurrently running jobs.\n            `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`.\n        safe_inference_mode : bool, optional\n            Whether to use safe inference mode.\n        dtype : numpy.dtype, optional\n            The data type of the fingerprint. Defaults to `numpy.int32`.\n        \"\"\"\n        super().__init__(\n            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype\n        )\n        self.radius = radius\n        self.rings = rings\n        self.isomeric = isomeric\n        self.kekulize = kekulize\n        self.min_radius = min_radius\n        # Set the .n_permutations and .seed without creating the encoder twice\n        self.fpSize = fpSize\n        self._seed = seed\n        # create the encoder instance\n        self._recreate_encoder()\n\n    def __getstate__(self):\n        # Get the state of the parent class\n        state = super().__getstate__()\n        # Remove the unpicklable property from the state\n        state.pop(\"mhfp_encoder\", None)  # mhfp_encoder is not picklable\n        return state\n\n    def __setstate__(self, state):\n        # Restore the state of the parent class\n        super().__setstate__(state)\n        # Re-create the unpicklable property\n        self._recreate_encoder()\n\n    def _mol2fp(self, mol):\n        fp = self.mhfp_encoder.EncodeMol(\n            mol, self.radius, self.rings, self.isomeric, self.kekulize, self.min_radius\n        )\n        return fp\n\n    def _fp2array(self, fp):\n        return np.array(fp)\n\n    def _recreate_encoder(self):\n        self.mhfp_encoder = rdMHFPFingerprint.MHFPEncoder(\n            int(self.fpSize), int(self._seed)\n        )\n\n    @property\n    def seed(self):\n        return self._seed\n\n    @seed.setter\n    def seed(self, seed):\n        self._seed = seed\n        # each time the seed parameter is modified refresh an instance of the encoder\n        self._recreate_encoder()\n\n    @property\n    def n_permutations(self):\n        warn(\n            \"n_permutations will be replace by fpSize, due to changes harmonization!\",\n            DeprecationWarning,\n        )\n        return self.fpSize\n\n    @n_permutations.setter\n    def n_permutations(self, n_permutations):\n        warn(\n            \"n_permutations will be replace by fpSize, due to changes harmonization!\",\n            DeprecationWarning,\n        )\n        self.fpSize = n_permutations\n        # each time the n_permutations parameter is modified refresh an instance of the encoder\n        self._recreate_encoder()\n\n\n# TODO use FpsGeneratorTransformer instead\nclass SECFingerprintTransformer(FpsTransformer):\n    # https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0321-8\n    def __init__(\n        self,\n        radius: int = 3,\n        rings: bool = True,\n        isomeric: bool = False,\n        kekulize: bool = False,\n        min_radius: int = 1,\n        fpSize: int = 2048,\n        n_permutations: int = 0,\n        seed: int = 0,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = np.int8,\n    ):\n        \"\"\"Transforms the RDKit mol into the SMILES extended connectivity fingerprint (SECFP)\n\n        Args:\n            radius (int, optional): The MHFP radius. Defaults to 3.\n            rings (bool, optional): Whether to include rings in the shingling. Defaults to True.\n            isomeric (bool, optional): Whether the isomeric SMILES to be considered. Defaults to False.\n            kekulize (bool, optional): Whether to kekulize the extracted SMILES. Defaults to False.\n            min_radius (int, optional): The minimum radius that is used to extract n-gram. Defaults to 1.\n            fpSize (int, optional): The length of the folded fingerprint. Defaults to 2048.\n            n_permutations (int, optional): The number of permutations used for hashing. Defaults to 0.\n            seed (int, optional): The value used to seed numpy.random. Defaults to 0.\n        \"\"\"\n        super().__init__(\n            n_jobs=n_jobs, safe_inference_mode=safe_inference_mode, dtype=dtype\n        )\n        self.radius = radius\n        self.rings = rings\n        self.isomeric = isomeric\n        self.kekulize = kekulize\n        self.min_radius = min_radius\n        self.fpSize = fpSize\n        # Set the .n_permutations and seed without creating the encoder twice\n        self._n_permutations = n_permutations\n        self._seed = seed\n        # create the encoder instance\n        self._recreate_encoder()\n\n    def __getstate__(self):\n        # Get the state of the parent class\n        state = super().__getstate__()\n        # Remove the unpicklable property from the state\n        state.pop(\"mhfp_encoder\", None)  # mhfp_encoder is not picklable\n        return state\n\n    def __setstate__(self, state):\n        # Restore the state of the parent class\n        super().__setstate__(state)\n        # Re-create the unpicklable property\n        self._recreate_encoder()\n\n    def _mol2fp(self, mol):\n        return self.mhfp_encoder.EncodeSECFPMol(\n            mol,\n            int(self.radius),\n            bool(self.rings),\n            bool(self.isomeric),\n            bool(self.kekulize),\n            int(self.min_radius),\n            int(self.fpSize),\n        )\n\n    def _recreate_encoder(self):\n        self.mhfp_encoder = rdMHFPFingerprint.MHFPEncoder(\n            self._n_permutations, self._seed\n        )\n\n    @property\n    def seed(self):\n        return self._seed\n\n    @seed.setter\n    def seed(self, seed):\n        self._seed = seed\n        # each time the seed parameter is modified refresh an instance of the encoder\n        self._recreate_encoder()\n\n    @property\n    def n_permutations(self):\n        return self._n_permutations\n\n    @n_permutations.setter\n    def n_permutations(self, n_permutations):\n        self._n_permutations = n_permutations\n        # each time the n_permutations parameter is modified refresh an instance of the encoder\n        self._recreate_encoder()\n\n    @property\n    def length(self):\n        warn(\n            \"length will be replace by fpSize, due to changes harmonization!\",\n            DeprecationWarning,\n        )\n        return self.fpSize\n"
  },
  {
    "path": "scikit_mol/fingerprints/morgan.py",
    "content": "from typing import Optional\n\nimport numpy as np\nfrom rdkit.Chem.rdFingerprintGenerator import (\n    GetMorganFeatureAtomInvGen,\n    GetMorganGenerator,\n)\n\nfrom .baseclasses import FpsGeneratorTransformer\n\n\nclass MorganFingerprintTransformer(FpsGeneratorTransformer):\n    \"\"\"RDKit-based Morgan fingerprints transformer for molecular feature generation.\n\n    Generates fingerprints equivalent to Extended Connectivity Fingerprints (ECFP) from Pipeline Pilot.\n    Features are created by hashing atomic environments within specified radius.\n    Supports optional inclusion of chirality and bond types for enhanced structural representation.\n    \"\"\"\n\n    _regenerate_on_properties = (\n        \"radius\",\n        \"fpSize\",\n        \"useChirality\",\n        \"useFeatures\",\n        \"useBondTypes\",\n    )\n\n    def __init__(\n        self,\n        fpSize=2048,\n        radius: int = 2,\n        useChirality: bool = False,\n        useBondTypes: bool = True,\n        useFeatures: bool = False,\n        useCounts: bool = False,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n        dtype: np.dtype = None,\n        nBits: Optional[int] = None,\n    ):\n        \"\"\"Transform RDKit mols into Count or bit-based hashed MorganFingerprints\n\n        Parameters\n        ----------\n        fpSize : int, optional\n            Size of the hashed fingerprint, by default 2048\n        radius : int, optional\n            Radius of the fingerprint, by default 2\n        useChirality : bool, optional\n            Include chirality in calculation of the fingerprint keys, by default `False`\n        useBondTypes : bool, optional\n            Include bondtypes in calculation of the fingerprint keys, by default `True`\n        useFeatures : bool, optional\n            use chemical features, rather than atom-type in calculation of the fingerprint keys, by default `False`\n        useCounts : bool, optional\n            If toggled will create the count and not bit-based fingerprint, by default `False`\n        n_jobs : int, optional default=None\n            The maximum number of concurrently running jobs.\n            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`.\n        safe_inference_mode : bool, optional\n            If `True`, will return masked arrays for invalid mols, by default `False`\n        \"\"\"\n\n        self._initializing = True\n        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode)\n        self.fpSize = fpSize\n        self.radius = radius\n        self.useChirality = useChirality\n        self.useFeatures = useFeatures\n        self.useCounts = useCounts\n        self.useBondTypes = useBondTypes\n        self.dtype = dtype\n        self.nBits = nBits\n\n        self._generate_fp_generator()\n        delattr(self, \"_initializing\")\n\n    def _generate_fp_generator(self):\n        if self.useFeatures:\n            atomInvariantsGenerator = GetMorganFeatureAtomInvGen()\n        else:\n            atomInvariantsGenerator = None\n\n        self._fpgen = GetMorganGenerator(\n            radius=int(self.radius),\n            fpSize=int(self.fpSize),\n            includeChirality=bool(self.useChirality),\n            useBondTypes=bool(self.useBondTypes),\n            atomInvariantsGenerator=atomInvariantsGenerator,\n        )\n\n    def _transform_mol(self, mol) -> np.array:\n        if self.useCounts:\n            return self._fpgen.GetCountFingerprintAsNumPy(mol)\n        else:\n            return self._fpgen.GetFingerprintAsNumPy(mol)\n"
  },
  {
    "path": "scikit_mol/fingerprints/rdkitfp.py",
    "content": "from typing import Optional\n\nimport numpy as np\nfrom rdkit.Chem.rdFingerprintGenerator import GetRDKitFPGenerator\n\nfrom .baseclasses import FpsGeneratorTransformer\n\n\nclass RDKitFingerprintTransformer(FpsGeneratorTransformer):\n    _regenerate_on_properties = (\n        \"minPath\",\n        \"maxPath\",\n        \"useHs\",\n        \"branchedPaths\",\n        \"useBondOrder\",\n        \"countSimulation\",\n        \"fpSize\",\n        \"countBounds\",\n        \"numBitsPerFeature\",\n    )\n\n    def __init__(\n        self,\n        minPath: int = 1,\n        maxPath: int = 7,\n        useHs: bool = True,\n        branchedPaths: bool = True,\n        useBondOrder: bool = True,\n        countSimulation: bool = False,\n        countBounds=None,\n        fpSize: int = 2048,\n        numBitsPerFeature: int = 2,\n        useCounts: bool = False,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n    ):\n        \"\"\"Calculates the RDKit fingerprints\n\n        Parameters\n        ----------\n        minPath : int, optional\n            the minimum path length (in bonds) to be included, by default 1\n        maxPath : int, optional\n            the maximum path length (in bonds) to be included, by default 7\n        useHs : bool, optional\n            toggles inclusion of Hs in paths (if the molecule has explicit Hs), by default True\n        branchedPaths : bool, optional\n            toggles generation of branched subgraphs, not just linear paths, by default True\n        useBondOrder : bool, optional\n            toggles inclusion of bond orders in the path hashes, by default True\n        countSimulation : bool, optional\n            if set, use count simulation while generating the fingerprint, by default False\n        countBounds : _type_, optional\n            boundaries for count simulation, corresponding bit will be set if the count is higher than the number provided for that spot, by default None\n        fpSize : int, optional\n            size of the generated fingerprint, does not affect the sparse versions, by default 2048\n        numBitsPerFeature : int, optional\n            the number of bits set per path/subgraph found, by default 2\n        n_jobs : int, optional default=None\n            The maximum number of concurrently running jobs.\n            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`.\n        safe_inference_mode : bool, optional\n            If `True`, will return masked arrays for invalid mols, by default `False`\n\n        \"\"\"\n        self._initializing = True\n        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode)\n        self.minPath = minPath\n        self.maxPath = maxPath\n        self.useHs = useHs\n        self.branchedPaths = branchedPaths\n        self.useBondOrder = useBondOrder\n        self.countSimulation = countSimulation\n        self.fpSize = fpSize\n        self.numBitsPerFeature = numBitsPerFeature\n        self.countBounds = countBounds\n\n        self.useCounts = useCounts\n\n        self._generate_fp_generator()\n        delattr(self, \"_initializing\")\n\n    def _transform_mol(self, mol) -> np.array:\n        if self.useCounts:\n            return self._fpgen.GetCountFingerprintAsNumPy(mol)\n        else:\n            return self._fpgen.GetFingerprintAsNumPy(mol)\n\n    def _generate_fp_generator(self):\n        self._fpgen = GetRDKitFPGenerator(\n            minPath=int(self.minPath),\n            maxPath=int(self.maxPath),\n            useHs=bool(self.useHs),\n            branchedPaths=bool(self.branchedPaths),\n            useBondOrder=bool(self.useBondOrder),\n            countSimulation=bool(self.countSimulation),\n            fpSize=int(self.fpSize),\n            countBounds=bool(self.countBounds),\n            numBitsPerFeature=int(self.numBitsPerFeature),\n        )\n"
  },
  {
    "path": "scikit_mol/fingerprints/topologicaltorsion.py",
    "content": "from typing import Optional, Sequence\n\nimport numpy as np\nfrom rdkit.Chem.rdFingerprintGenerator import GetTopologicalTorsionGenerator\n\nfrom .baseclasses import FpsGeneratorTransformer\n\n\nclass TopologicalTorsionFingerprintTransformer(FpsGeneratorTransformer):\n    \"\"\"\n    Transformer for generating topological torsion fingerprints.\n    \"\"\"\n\n    _regenerate_on_properties = (\"fpSize\", \"includeChirality\", \"targetSize\")\n\n    def __init__(\n        self,\n        targetSize: int = 4,\n        fromAtoms: Optional[Sequence] = None,\n        ignoreAtoms: Optional[Sequence] = None,\n        atomInvariants: Optional[Sequence] = None,\n        confId: int = -1,\n        includeChirality: bool = False,\n        fpSize: int = 2048,\n        useCounts: bool = False,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        targetSize : int, optional\n            The number of atoms to include in the torsion, by default 4.\n        fromAtoms : list, optional\n            Atom indices to include in the fingerprint generation, by default None.\n        ignoreAtoms : list, optional\n            Atom indices to exclude in the fingerprint generation, by default None.\n        atomInvariants : list, optional\n            Custom atom invariants to be used, by default None.\n        confId : int, optional\n            Conformation ID to use, by default -1.\n        includeChirality : bool, optional\n            Whether to include chirality in the fingerprint, by default False.\n        fpSize : int, optional\n            Size of the fingerprint, by default 2048.\n        useCounts : bool, optional\n            Whether to use counts in the fingerprint, by default False.\n        n_jobs : int, optional default=None\n            The maximum number of concurrently running jobs.\n            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`.\n        safe_inference_mode : bool, optional\n            If `True`, will return masked arrays for invalid mols, by default `False`\n        \"\"\"\n        self._initializing = True\n        super().__init__(n_jobs=n_jobs, safe_inference_mode=safe_inference_mode)\n        self.fpSize = fpSize\n        self.includeChirality = includeChirality\n        self.targetSize = targetSize\n\n        self.fromAtoms = fromAtoms\n        self.ignoreAtoms = ignoreAtoms\n        self.atomInvariants = atomInvariants\n        self.confId = confId\n        self.useCounts = useCounts\n\n        self._generate_fp_generator()\n        delattr(self, \"_initializing\")\n\n    def _generate_fp_generator(self):\n        \"\"\"\n        Generate the fingerprint generator.\n        \"\"\"\n        self._fpgen = GetTopologicalTorsionGenerator(\n            torsionAtomCount=int(self.targetSize),\n            includeChirality=bool(self.includeChirality),\n            fpSize=int(self.fpSize),\n        )\n\n    def _transform_mol(self, mol) -> np.array:\n        \"\"\"\n        Transform a molecule into its fingerprint representation.\n\n        Parameters\n        ----------\n        mol : RDKit Mol\n            The molecule to transform.\n\n        Returns\n        -------\n        np.array\n            The fingerprint of the molecule as a NumPy array.\n        \"\"\"\n        if self.useCounts:\n            return self._fpgen.GetCountFingerprintAsNumPy(\n                mol,\n                fromAtoms=self.fromAtoms,\n                ignoreAtoms=self.ignoreAtoms,\n                customAtomInvariants=self.atomInvariants,\n            )\n        else:\n            return self._fpgen.GetFingerprintAsNumPy(\n                mol,\n                fromAtoms=self.fromAtoms,\n                ignoreAtoms=self.ignoreAtoms,\n                customAtomInvariants=self.atomInvariants,\n            )\n"
  },
  {
    "path": "scikit_mol/parallel.py",
    "content": "from collections.abc import Sequence\nfrom typing import Any, Callable, Optional, Union\n\nimport numpy as np\nimport pandas as pd\nfrom joblib import Parallel, delayed, effective_n_jobs\n\n\ndef parallelized_with_batches(\n    fn: Callable[[Sequence[Any]], Any],\n    inputs_list: Union[np.ndarray, pd.DataFrame],\n    n_jobs: Optional[int] = None,\n    **job_kwargs: Any,\n) -> Sequence[Optional[Any]]:\n    \"\"\"\n    Execute a function in parallel with batches of inputs.\n    Parameters\n    ----------\n    fn : Callable[[Sequence[Any]], Any]\n        The function to be executed in parallel. It should accept a sequence of inputs.\n    inputs_list : np.ndarray\n        The list of inputs to be processed in parallel.\n    n_jobs : Optional[int], default=None\n        The number of jobs to run in parallel. If `None`, it will be determined automatically.\n    **job_kwargs : Any\n        Additional keyword arguments to pass to the `joblib.Parallel` object.\n    Returns\n    -------\n    Sequence[Optional[Any]]\n        A sequence of results from the function executed in parallel.\n    \"\"\"\n    n_jobs = effective_n_jobs(n_jobs)\n    if n_jobs > len(inputs_list):\n        n_jobs = len(inputs_list)\n    pool = Parallel(n_jobs=n_jobs, **job_kwargs)\n\n    if isinstance(inputs_list, (pd.DataFrame, pd.Series)):\n        indexes = np.array_split(range(len(inputs_list)), n_jobs)\n        input_chunks = [inputs_list.iloc[idx] for idx in indexes]\n    else:\n        input_chunks = np.array_split(inputs_list, n_jobs)\n\n    results = pool(delayed(fn)(chunk) for chunk in input_chunks)\n    return results\n"
  },
  {
    "path": "scikit_mol/plotting.py",
    "content": "import os\nimport platform\nimport re\nimport subprocess\nimport time\nfrom typing import TYPE_CHECKING, Optional, Sequence\n\nimport joblib\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\nif TYPE_CHECKING:\n    from rdkit import Chem\n\n\nclass ParallelTester:\n    \"\"\"\n    A class to test the performance of a transformer on a set of molecules using parallel processing.\\\n    \"\"\"\n\n    def __init__(\n        self,\n        transformer: object,\n        mols: list[\"Chem.Mol\"],\n        n_mols: Sequence[int] = (10, 100, 100, 1000, 10000, 100000),\n        n_jobs: Sequence[int] = (1, 2, 4, 8),\n        backend: str = \"loky\",\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        transformer : object\n            The transformer object that has a `transform` method to apply to the molecules\n        mols : Sequence[Chem.Mol]\n            A list of molecules to be transformed\n        n_mols : Sequence[int], optional\n            A tuple of integers specifying the number of molecules to test with\n        n_jobs : Sequence[int], optional\n            A tuple of integers specifying the number of parallel jobs to test with\n        backend : str, optional\n            The parallel backend to use\n        \"\"\"\n        self.mols = mols\n        n_mols = sorted(n_mols)\n        if max(n_mols) > len(mols):\n            raise ValueError(\n                f\"Maximum number of molecules {max(n_mols)} is greater than the number of molecules {len(mols)}\"\n            )\n        self.n_mols = n_mols\n        n_jobs = sorted(n_jobs)\n        if max(n_jobs) > os.cpu_count():\n            raise ValueError(\n                f\"Maximum number of jobs {max(n_jobs)} is greater than the number of CPUs {os.cpu_count()}\"\n            )\n        self.n_jobs = n_jobs\n        self.transformer = transformer\n        self.backend = backend\n\n    def _test_single(self, mols, n_jobs):\n        start = time.perf_counter()\n        with joblib.parallel_backend(self.backend, n_jobs=n_jobs):\n            self.transformer.transform(mols)\n        return time.perf_counter() - start\n\n    def test(self) -> pd.DataFrame:\n        \"\"\"Tests the transformer on various subsets of molecules with different numbers of parallel jobs and returns the results as a DataFrame.\n\n        Returns\n        -------\n        pandas.DataFrame\n            A DataFrame containing the time taken to transform the molecules with different numbers of molecules and parallel jobs.\n        \"\"\"\n        results = pd.DataFrame(columns=self.n_mols, index=self.n_jobs)\n        for n_mol in self.n_mols:\n            for n_job in self.n_jobs:\n                results.at[n_job, n_mol] = self._test_single(self.mols[:n_mol], n_job)\n        return results\n\n\ndef get_processor_name() -> str:\n    \"\"\"\n    Retrieves the name of the processor on the current system.\n\n    Returns\n    -------\n    str\n        The name of the processor. Returns an empty string if the processor name cannot be determined.\n\n    Notes\n    -----\n    - On Windows, it uses `platform.processor()`.\n    - On macOS (Darwin), it uses the `sysctl` command to get the CPU brand string.\n    - On Linux, it reads `/proc/cpuinfo` to find the model name.\n    \"\"\"\n    if platform.system() == \"Windows\":\n        return platform.processor()\n    elif platform.system() == \"Darwin\":\n        os.environ[\"PATH\"] = os.environ[\"PATH\"] + os.pathsep + \"/usr/sbin\"\n        command = \"sysctl -n machdep.cpu.brand_string\"\n        return subprocess.check_output(command).strip()  # noqa: S603\n    elif platform.system() == \"Linux\":\n        command = \"cat /proc/cpuinfo\"\n        all_info = subprocess.check_output(command, shell=True).decode().strip()  # noqa: S602\n        for line in all_info.split(\"\\n\"):\n            if \"model name\" in line:\n                return re.sub(\n                    pattern=\".*model name.*:\", repl=\"\", string=line, count=1\n                ).strip()\n    return \"\"\n\n\ndef plot_heatmap(\n    df: pd.DataFrame, name: Optional[str] = None, normalize: bool = True\n) -> plt.Axes:\n    \"\"\"\n    Plots a heatmap of the given DataFrame from [ParallelTester][scikit_mol.plotting.ParallelTester].\n\n    Parameters\n    ----------\n    df : pandas.DataFrame\n        The DataFrame containing the data to be plotted\n    name : str, optional\n        The name to be used in the title of the plot\n    normalize : bool, optional\n        If True, normalize the DataFrame by the first row\n    Returns\n    -------\n    matplotlib.axes.Axes\n        The Axes object of the plot\n\n\n    Notes\n    -----\n    The function normalizes the DataFrame by dividing by the first row if `normalize` is True.\n    The colormap used is \"PiYG_r\". The title of the plot includes the maximum single-threaded speed\n    and the CPU name.\n    \"\"\"\n    df = df.astype(float)\n    max_speed = (df.columns / df.loc[1]).max()\n    v_min, v_max = None, None\n    if normalize:\n        df = df / df.loc[1]\n        cmap = sns.color_palette(\"PiYG_r\", as_cmap=True)\n        v_min = 0.0\n        v_max = 2.0\n    else:\n        cmap = sns.color_palette(\"PiYG_r\", as_cmap=True)\n    plt.figure(figsize=(8, 6))\n    g = sns.heatmap(df, annot=True, fmt=\".2f\", cmap=cmap, vmin=v_min, vmax=v_max)\n    title = \"\"\n    if name:\n        title = name + \"\\n\"\n    title += f\"Max single-threaded speed {max_speed:.0f} mols/s\"\n    title += \"\\nCPU: \" + get_processor_name()\n    plt.title(title)\n    plt.xlabel(\"Number of mols\")\n    plt.ylabel(\"Number of jobs\")\n    return g\n"
  },
  {
    "path": "scikit_mol/safeinference.py",
    "content": "\"\"\"Wrapper for sklearn estimators and pipelines to handle errors.\"\"\"\n\nimport warnings\nfrom functools import wraps\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils import check_array\nfrom sklearn.utils.metaestimators import available_if\nfrom sklearn.utils.validation import NotFittedError, check_is_fitted\n\nfrom scikit_mol._constants import DOCS_BASE_URL\n\nfrom .utilities import set_safe_inference_mode\n\n__all__ = [\"MaskedArrayError\", \"SafeInferenceWrapper\", \"set_safe_inference_mode\"]\n\n\nclass MaskedArrayError(ValueError):\n    \"\"\"Raised when a masked array is passed but safe_inference_mode is False.\"\"\"\n\n    pass\n\n\ndef filter_invalid_rows(warn_on_invalid=False, replace_value=np.nan):\n    def decorator(func):\n        @wraps(func)\n        def wrapper(obj, X, y=None, *args, **kwargs):\n            if not getattr(obj, \"safe_inference_mode\", True):\n                if isinstance(X, np.ma.MaskedArray) and X.mask.any():\n                    raise MaskedArrayError(\n                        f\"Masked array detected with safe_inference_mode=False and {X.mask.any(axis=1).sum()} filtered rows. \"\n                        \"Set safe_inference_mode=True to process masked arrays for inference of production models.\"\n                    )\n                return func(obj, X, y, *args, **kwargs)\n            if not hasattr(obj, \"replace_value\"):\n                raise ValueError(\n                    \"replace_value must be set in the SafeInferenceWrapper\"\n                )\n            else:\n                replace_value = obj.replace_value\n\n            # Initialize valid_mask as all True\n            valid_mask = np.ones(X.shape[0], dtype=bool)\n\n            # Handle masked arrays\n            if isinstance(X, np.ma.MaskedArray):\n                try:\n                    valid_mask &= ~X.mask.any(axis=1)\n                # workaround for situation where mask is single boolean (all masked/ all unmasked) and no axis present\n                except np.exceptions.AxisError:\n                    valid_mask &= ~X.mask\n\n            # Handle non-finite values if required\n            if getattr(obj, \"mask_nonfinite\", True):\n                if isinstance(X, np.ma.MaskedArray):\n                    valid_mask &= np.isfinite(X.data).all(axis=1)\n                else:\n                    valid_mask &= np.isfinite(X).all(axis=1)\n\n            if warn_on_invalid and not np.all(valid_mask):\n                warnings.warn(\n                    f\"SafeInferenceWrapper is in safe_inference_mode during use of {func.__name__} and invalid data detected. \"\n                    \"This mode is intended for safe inference in production, not for training and evaluation.\",\n                    UserWarning,\n                )\n\n            valid_indices = np.where(valid_mask)[0]\n            reduced_X = X[valid_mask]\n\n            if y is not None:\n                # TODO, how can we check y in the same way as the estimator?\n                y = check_array(\n                    y,\n                    ensure_all_finite=False,  # accept_sparse=\"csr\",\n                    ensure_2d=False,\n                    dtype=None,\n                    input_name=\"y\",\n                    estimator=obj,\n                )\n                reduced_y = y[valid_mask]\n            else:\n                reduced_y = None\n\n            # handle case where all rows are masked e.g. single invalid input is passed\n            if len(valid_indices) == 0:\n                result = np.array([])\n            else:\n                result = func(obj, reduced_X, reduced_y, *args, **kwargs)\n\n            if result is None:\n                return None\n            if isinstance(result, np.ndarray):\n                if result.ndim == 1:\n                    output = np.full(X.shape[0], replace_value)\n                else:\n                    output = np.full((X.shape[0], result.shape[1]), replace_value)\n                output[valid_indices] = result\n                return output\n            elif isinstance(result, pd.DataFrame):\n                output = pd.DataFrame(index=range(X.shape[0]), columns=result.columns)\n                output.iloc[valid_indices] = result\n                return output\n            elif isinstance(result, pd.Series):\n                output = pd.Series(index=range(X.shape[0]), dtype=result.dtype)\n                output.iloc[valid_indices] = result\n                return output\n            else:\n                return result\n\n        return wrapper\n\n    return decorator\n\n\nclass SafeInferenceWrapper(TransformerMixin, BaseEstimator):\n    \"\"\"Wrapper for sklearn estimators to ensure safe inference in production environments.\n\n    This wrapper is designed to be applied to trained models for use in production settings.\n    While it can be included during model development and training, the safe inference mode\n    should only be enabled when deploying models for inference in production.\"\"\"\n\n    _doc_link_module = \"scikit_mol\"\n    _doc_link_template = (\n        DOCS_BASE_URL + \"{estimator_module}/#{estimator_module}.{estimator_name}\"\n    )\n\n    def __init__(\n        self,\n        estimator: BaseEstimator,\n        safe_inference_mode: bool = False,\n        replace_value: Union[int, float, str] = np.nan,\n        mask_nonfinite: bool = True,\n    ):\n        \"\"\"\n        Parameters\n        -----------\n        estimator : BaseEstimator\n            The trained sklearn estimator to be wrapped.\n        safe_inference_mode : bool, default=False\n            If True, enables safeguards for handling invalid data during inference.\n            This should only be set to True when deploying models to production.\n        replace_value : any, default=np.nan\n            The value to use for replacing invalid data points.\n        \"\"\"\n\n        self.estimator = estimator\n        self.safe_inference_mode = safe_inference_mode\n        self.replace_value = replace_value\n        self.mask_nonfinite = mask_nonfinite\n\n    @property\n    def n_features_in_(self):\n        return self.estimator.n_features_in_\n\n    @filter_invalid_rows(warn_on_invalid=True)\n    def fit(self, X, y=None, **fit_params):\n        return self.estimator.fit(X, y, **fit_params)\n\n    @available_if(lambda self: hasattr(self.estimator, \"predict\"))\n    @filter_invalid_rows()\n    def predict(self, X, y=None):\n        return self.estimator.predict(X)\n\n    @available_if(lambda self: hasattr(self.estimator, \"predict_proba\"))\n    @filter_invalid_rows()\n    def predict_proba(self, X, y=None):\n        return self.estimator.predict_proba(X)\n\n    @available_if(lambda self: hasattr(self.estimator, \"decision_function\"))\n    @filter_invalid_rows()\n    def decision_function(self, X, y=None):\n        return self.estimator.decision_function(X)\n\n    @available_if(lambda self: hasattr(self.estimator, \"transform\"))\n    @filter_invalid_rows()\n    def transform(self, X, y=None):\n        return self.estimator.transform(X)\n\n    @available_if(lambda self: hasattr(self.estimator, \"fit_transform\"))\n    @filter_invalid_rows(warn_on_invalid=True)\n    def fit_transform(self, X, y=None, **fit_params):\n        return self.estimator.fit_transform(X, y, **fit_params)\n\n    @available_if(lambda self: hasattr(self.estimator, \"score\"))\n    @filter_invalid_rows(warn_on_invalid=True)\n    def score(self, X, y=None):\n        return self.estimator.score(X, y)\n\n    @available_if(lambda self: hasattr(self.estimator, \"get_feature_names_out\"))\n    @filter_invalid_rows(warn_on_invalid=True)\n    def get_feature_names_out(self, *args, **kwargs):\n        return self.estimator.get_feature_names_out(*args, **kwargs)\n\n    def __sklearn_is_fitted__(self):\n        try:\n            check_is_fitted(self.estimator)\n            return True\n        except NotFittedError:\n            return False\n"
  },
  {
    "path": "scikit_mol/standardizer.py",
    "content": "# A scikit-learn compatible molecule standardizer\n# Author: Son Ha\n\n\nimport functools\nfrom typing import Optional\n\nimport numpy as np\nfrom rdkit import Chem\nfrom rdkit.Chem.MolStandardize import rdMolStandardize\nfrom rdkit.rdBase import BlockLogs\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom scikit_mol._constants import DOCS_BASE_URL\nfrom scikit_mol.core import (\n    InvalidMol,\n    NoFitNeededMixin,\n    check_transform_input,\n    feature_names_default_mol,\n)\nfrom scikit_mol.parallel import parallelized_with_batches\n\n\nclass Standardizer(TransformerMixin, NoFitNeededMixin, BaseEstimator):\n    \"\"\"Standardize molecules with RDKit\"\"\"\n\n    _doc_link_module = \"scikit_mol\"\n    _doc_link_template = (\n        DOCS_BASE_URL + \"{estimator_module}/#{estimator_module}.{estimator_name}\"\n    )\n\n    def __init__(\n        self,\n        neutralize: bool = True,\n        n_jobs: Optional[int] = None,\n        safe_inference_mode: bool = False,\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        neutralize : bool, optional\n            If True, neutralizes the molecule\n        n_jobs : Optional[int], optional\n            The maximum number of concurrently running jobs.\n            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\n        safe_inference_mode : bool, optional\n            If True, enables safeguards for handling invalid data during inference.\n            This should only be set to True when deploying models to production\n        \"\"\"\n        self.neutralize = neutralize\n        self.n_jobs = n_jobs\n        self.safe_inference_mode = safe_inference_mode\n\n    def fit(self, X, y=None):\n        return self\n\n    def _standardize_mol(self, mol):\n        if not mol:\n            if self.safe_inference_mode:\n                if isinstance(mol, InvalidMol):\n                    return mol\n                else:\n                    return InvalidMol(str(self), f\"Invalid input molecule: {mol}\")\n            else:\n                raise ValueError(f\"Invalid input molecule: {mol}\")\n\n        try:\n            block = BlockLogs()  # Block all RDkit logging\n            # Normalizing functional groups\n            clean_mol = rdMolStandardize.Cleanup(mol)\n            # Get parents fragments\n            parent_clean_mol = rdMolStandardize.FragmentParent(clean_mol)\n            # Neutralise\n            if self.neutralize:\n                uncharger = rdMolStandardize.Uncharger()\n                uncharged_parent_clean_mol = uncharger.uncharge(parent_clean_mol)\n            else:\n                uncharged_parent_clean_mol = parent_clean_mol\n            del block  # Release logging block to previous state\n            Chem.SanitizeMol(uncharged_parent_clean_mol)\n            return uncharged_parent_clean_mol\n        except Exception as e:\n            if self.safe_inference_mode:\n                return InvalidMol(str(self), f\"Standardization failed: {e}\")\n            else:\n                raise\n\n    def _transform(self, X):\n        return np.array([self._standardize_mol(mol) for mol in X]).reshape(-1, 1)\n\n    @feature_names_default_mol\n    def get_feature_names_out(self, input_features=None):\n        return input_features\n\n    @check_transform_input\n    def transform(self, X, y=None):\n        parameters = self.get_params()\n        func = functools.partial(parallel_helper, self.__class__.__name__, parameters)\n        arrays = parallelized_with_batches(func, X, self.n_jobs)\n        arr = np.concatenate(arrays)\n        return arr\n\n\ndef parallel_helper(classname, parameters, X_mols):\n    \"\"\"Parallel_helper takes a tuple with classname, the objects parameters and the mols to process.\n    Then instantiates the class with the parameters and processes the mol.\n    Intention is to be able to do this in child processes as some classes can't be pickled\"\"\"\n    from scikit_mol import standardizer\n\n    transformer = getattr(standardizer, classname)(**parameters)\n    return transformer._transform(X_mols)\n"
  },
  {
    "path": "scikit_mol/utilities.py",
    "content": "# For a non-scikit-learn check smiles sanitizer class\n\nimport warnings\n\nimport pandas as pd\nfrom rdkit import Chem\nfrom sklearn.base import BaseEstimator\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import FeatureUnion, Pipeline\n\n\nclass CheckSmilesSanitization:\n    def __init__(self, return_mol=False):\n        self.return_mol = return_mol\n        self.errors = pd.DataFrame()\n\n    def sanitize(self, X_smiles_list, y=None):\n        if y:\n            y_out = []\n            X_out = []\n            y_errors = []\n            X_errors = []\n\n            for smiles, y_value in zip(X_smiles_list, y):\n                mol = Chem.MolFromSmiles(smiles)\n                if mol:\n                    if self.return_mol:\n                        X_out.append(mol)\n                    else:\n                        X_out.append(smiles)\n                    y_out.append(y_value)\n                else:\n                    X_errors.append(smiles)\n                    y_errors.append(y_value)\n\n            if X_errors:\n                print(\n                    f\"Error in parsing {len(X_errors)} SMILES. Unparsable SMILES can be found in self.errors\"\n                )\n\n            self.errors = pd.DataFrame({\"SMILES\": X_errors, \"y\": y_errors})\n\n            return X_out, y_out, X_errors, y_errors\n\n        else:\n            X_out = []\n            X_errors = []\n\n            for smiles in X_smiles_list:\n                mol = Chem.MolFromSmiles(smiles)\n                if mol:\n                    if self.return_mol:\n                        X_out.append(mol)\n                    else:\n                        X_out.append(smiles)\n                else:\n                    X_errors.append(smiles)\n\n            if X_errors:\n                print(\n                    f\"Error in parsing {len(X_errors)} SMILES. Unparsable SMILES can be found in self.errors\"\n                )\n\n            self.errors = pd.DataFrame({\"SMILES\": X_errors})\n\n            return X_out, X_errors\n\n    \"\"\"\n    R\n\n    :param estimator: A scikit-learn estimator, pipeline, or custom wrapper\n    :param value: Boolean value to set for safe_inference_mode\n    \"\"\"\n\n\ndef set_safe_inference_mode(estimator: BaseEstimator, value: bool) -> BaseEstimator:\n    \"\"\"Recursively set the safe_inference_mode parameter for all compatible estimators.\n\n    Parameters\n    ----------\n    estimator :\n         A scikit-learn estimator, pipeline, or custom wrapper\n    value :\n        Boolean value to set for safe_inference_mode\n    Returns\n    -------\n    BaseEstimator\n        The estimator with the `safe_inference_mode` parameter set to the specified value\n    \"\"\"\n\n    def _set_safe_inference_mode_recursive(est, val):\n        if hasattr(est, \"safe_inference_mode\"):\n            est.safe_inference_mode = val\n\n        # Handle Pipeline\n        if isinstance(est, Pipeline):\n            for _, step in est.steps:\n                _set_safe_inference_mode_recursive(step, val)\n\n        # Handle FeatureUnion\n        elif isinstance(est, FeatureUnion):\n            for _, transformer in est.transformer_list:\n                _set_safe_inference_mode_recursive(transformer, val)\n\n        # Handle ColumnTransformer\n        elif isinstance(est, ColumnTransformer):\n            for _, transformer, _ in est.transformers:\n                _set_safe_inference_mode_recursive(transformer, val)\n\n        # Handle SafeInferenceWrapper\n        elif hasattr(est, \"estimator\") and isinstance(est.estimator, BaseEstimator):\n            _set_safe_inference_mode_recursive(est.estimator, val)\n\n        # Handle other estimators with get_params\n        elif isinstance(est, BaseEstimator):\n            params = est.get_params(deep=False)\n            for _, param_value in params.items():\n                if isinstance(param_value, BaseEstimator):\n                    _set_safe_inference_mode_recursive(param_value, val)\n\n    # Apply the recursive function\n    _set_safe_inference_mode_recursive(estimator, value)\n\n    # Final check\n    params = estimator.get_params(deep=True)\n    mismatched_params = [\n        key.rstrip(\"__safe_inference_mode\")\n        for key, val in params.items()\n        if key.endswith(\"__safe_inference_mode\") and val != value\n    ]\n\n    if mismatched_params:\n        warnings.warn(\n            f\"The following components have 'safe_inference_mode' set to a different value than requested: {mismatched_params}. \"\n            \"This could be due to nested estimators that were not properly handled.\",\n            UserWarning,\n        )\n\n    return estimator\n"
  },
  {
    "path": "setup.cfg",
    "content": "[metadata]\nname = scikit_mol\nurl = https://github.com/EBjerrum/scikit-mol\ndownload_url = https://github.com/EBjerrum/scikit-mol\nlicense = LGPL-3.0\nlicense_file = LICENSE\ndescription = scikit-learn classes for molecule transformation\nlong_description = file: README.md\nlong_description_content_type = text/markdown\nauthor = Esben Jannik Bjerrum\nauthor_email = esben@cheminformania.com\nclassifiers =\n    Development Status :: 3 - Alpha\n    Intended Audience :: Science/Research\n    License :: OSI Approved :: Apache Software License\n    Programming Language :: Python\n    Programming Language :: Python :: 3 :: Only\n    Programming Language :: Python :: 3.8\n    Programming Language :: Python :: 3.9\n    Programming Language :: Python :: 3.10\n    Topic :: Scientific/Engineering\n    Topic :: Utilities\n    Operating System :: Microsoft :: Windows\n    Operating System :: POSIX\n    Operating System :: Unix\n    Operating System :: MacOS\n\n[options]\npackages = find:\npython_requires = >=3.8\ninstall_requires =\n    rdkit\n    numpy\n    pandas\n    scikit-learn\n    packaging\n\n[options.packages.find]\nexclude =\n    tests/*\n\n[options.extras_require]\ndev =\n    pytest>=6\n    pytest-cov\n    jupytext\n    pre-commit\n    ruff==0.8.6\n"
  },
  {
    "path": "tests/__init__.py",
    "content": ""
  },
  {
    "path": "tests/applicability/__init__.py",
    "content": ""
  },
  {
    "path": "tests/applicability/conftest.py",
    "content": "import numpy as np\nimport pytest\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\nfrom scikit_mol.applicability import (\n    BoundingBoxApplicabilityDomain,\n    ConvexHullApplicabilityDomain,\n    HotellingT2ApplicabilityDomain,\n    IsolationForestApplicabilityDomain,\n    KernelDensityApplicabilityDomain,\n    KNNApplicabilityDomain,\n    LeverageApplicabilityDomain,\n    LocalOutlierFactorApplicabilityDomain,\n    MahalanobisApplicabilityDomain,\n    StandardizationApplicabilityDomain,\n    TopkatApplicabilityDomain,\n)\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\nfrom ..fixtures import mols_list\n\n\n@pytest.fixture(\n    params=[\n        (KNNApplicabilityDomain, dict(n_neighbors=3)),\n        (LeverageApplicabilityDomain, dict(threshold_factor=3)),\n        (BoundingBoxApplicabilityDomain, dict(percentile=(1, 99))),\n        (ConvexHullApplicabilityDomain, dict()),  # No special parameters needed\n        (HotellingT2ApplicabilityDomain, dict(significance=0.05)),\n        (\n            IsolationForestApplicabilityDomain,\n            dict(\n                n_estimators=100,\n                contamination=0.1,\n                random_state=42,  # Add fixed random state\n            ),\n        ),\n        (\n            KernelDensityApplicabilityDomain,\n            dict(bandwidth=1.0, kernel=\"gaussian\"),\n        ),\n        (\n            LocalOutlierFactorApplicabilityDomain,\n            dict(\n                n_neighbors=3, contamination=0.1\n            ),  # Reduced from 20 to 3 for small test datasets\n        ),\n        (MahalanobisApplicabilityDomain, dict()),  # No special parameters needed\n        (StandardizationApplicabilityDomain, dict()),  # No special parameters needed\n        (TopkatApplicabilityDomain, dict()),  # No special parameters needed\n    ]\n)\ndef ad_estimator(request):\n    \"\"\"Fixture providing fresh AD estimator instances.\"\"\"\n    estimator_class, params = request.param\n    return estimator_class(**params)\n\n\n@pytest.fixture\ndef reduced_fingerprints(mols_list):\n    \"\"\"Create dimensionality-reduced fingerprints for AD testing.\"\"\"\n    # Generate larger fingerprints first\n    fps = MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list)\n    # Reduce dimensionality while preserving ~90% variance\n    pca = PCA(n_components=0.9)\n    return StandardScaler().fit_transform(pca.fit_transform(fps))\n\n\n@pytest.fixture\ndef binary_fingerprints(mols_list):\n    \"\"\"Binary fingerprints for testing e.g. Tanimoto distance.\"\"\"\n    return MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list)\n\n\n@pytest.fixture\ndef ad_test_data():\n    \"\"\"Simple 2D data with clear in/out domain regions.\"\"\"\n    rng = np.random.RandomState(42)  # Fixed seed for reproducibility\n    X_train = rng.uniform(0, 1, (20, 2))\n    X_test_in = rng.uniform(0.25, 0.75, (5, 2))\n    X_test_out = rng.uniform(2, 3, (5, 2))\n    X_test = np.vstack([X_test_in, X_test_out])\n    y_test = np.array([1] * 5 + [-1] * 5)\n    return X_train, X_test, y_test\n"
  },
  {
    "path": "tests/applicability/test_base.py",
    "content": "\"\"\"Common tests for all applicability domain estimators.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_almost_equal, assert_array_equal\nfrom sklearn.utils.estimator_checks import check_estimator\n\n\ndef test_basic_functionality(ad_estimator, reduced_fingerprints):\n    \"\"\"Test basic fit/transform on reduced fingerprints.\"\"\"\n    ad_estimator.fit(reduced_fingerprints)\n    scores = ad_estimator.transform(reduced_fingerprints)\n    assert scores.shape == (len(reduced_fingerprints), 1)\n    assert np.isfinite(scores).all()\n\n\ndef test_predict_functionality(ad_estimator, ad_test_data):\n    \"\"\"Test predict method returns expected values.\"\"\"\n    X_train, X_test, expected = ad_test_data\n\n    # Fit and predict\n    ad_estimator.fit(X_train)\n    predictions = ad_estimator.predict(X_test)\n\n    # Check output format\n    assert predictions.shape == (len(X_test),)  # Should be 1D\n    assert set(np.unique(predictions)) <= {-1, 1}  # Only -1 and 1 allowed\n\n    # Check predictions make sense (in/out of domain)\n    accuracy = np.mean(predictions == expected)\n    assert accuracy >= 0.8  # Allow some misclassification\n\n\ndef test_score_transform(ad_estimator, ad_test_data):\n    \"\"\"Test score_transform returns valid probability-like scores.\"\"\"\n    X_train, X_test, expected = ad_test_data\n\n    # Fit and get scores\n    ad_estimator.fit(X_train)\n    scores = ad_estimator.score_transform(X_test)\n\n    # Check output format\n    assert scores.shape == (len(X_test), 1)\n    assert np.all((0 <= scores) & (scores <= 1))  # Scores in [0,1]\n\n    # Check scores correlate with domain membership\n    in_domain = expected == 1\n    mean_in = np.mean(scores[in_domain])\n    mean_out = np.mean(scores[~in_domain])\n    assert mean_in > mean_out  # Inside domain should have higher scores\n\n\n@pytest.mark.threshold_fitting\ndef test_threshold_setting(ad_estimator, reduced_fingerprints):\n    \"\"\"Test threshold setting and percentile behavior.\"\"\"\n    if not ad_estimator._supports_threshold_fitting:\n        pytest.skip(\"Estimator does not support threshold fitting\")\n\n    # Test default threshold\n    ad_estimator.fit(reduced_fingerprints)\n    pred_default = ad_estimator.predict(reduced_fingerprints)\n\n    # Test custom percentile\n    ad_estimator.percentile = 90\n    ad_estimator.fit_threshold(reduced_fingerprints)\n    pred_90 = ad_estimator.predict(reduced_fingerprints)\n\n    # More samples should be outside with stricter threshold\n    n_inside_default = np.sum(pred_default == 1)\n    n_inside_90 = np.sum(pred_90 == 1)\n    assert n_inside_90 <= n_inside_default\n\n\ndef test_feature_names(ad_estimator, reduced_fingerprints):\n    \"\"\"Test feature names are properly handled.\"\"\"\n    ad_estimator.fit(reduced_fingerprints)\n\n    # Check feature names exist and match name\n    feature_names = ad_estimator.get_feature_names_out()\n    assert len(feature_names) == 1\n    assert feature_names[0] == ad_estimator.feature_name\n\n\ndef test_pandas_output(ad_estimator, reduced_fingerprints):\n    \"\"\"Test pandas DataFrame output functionality.\"\"\"\n    ad_estimator.set_output(transform=\"pandas\")\n    ad_estimator.fit(reduced_fingerprints)\n\n    # Test transform output\n    scores_df = ad_estimator.transform(reduced_fingerprints)\n    assert hasattr(scores_df, \"columns\")\n    assert len(scores_df.columns) == 1\n    assert scores_df.columns[0] == ad_estimator.feature_name\n\n    # Test predict output\n    pred_df = ad_estimator.predict(reduced_fingerprints)\n    assert hasattr(pred_df, \"columns\")\n    assert len(pred_df.columns) == 1\n\n\ndef test_input_validation(ad_estimator):\n    \"\"\"Test input validation and error handling.\"\"\"\n    # Test fitting with invalid input\n    with pytest.raises(ValueError):\n        ad_estimator.fit([[]])  # Empty data\n\n    with pytest.raises(ValueError):\n        ad_estimator.fit([[1], [2, 3]])  # Inconsistent dimensions\n\n    # Test invalid percentile only if threshold fitting is supported\n    if ad_estimator._supports_threshold_fitting:\n        with pytest.raises(ValueError):\n            ad_estimator.percentile = 101\n            ad_estimator.fit([[1, 2]])\n\n\ndef test_refit_consistency(ad_estimator, reduced_fingerprints):\n    \"\"\"Test consistency when refitting with same data.\"\"\"\n    ad_estimator.fit(reduced_fingerprints)\n    scores1 = ad_estimator.transform(reduced_fingerprints)\n\n    ad_estimator.fit(reduced_fingerprints)\n    scores2 = ad_estimator.transform(reduced_fingerprints)\n\n    assert_array_almost_equal(scores1, scores2)\n"
  },
  {
    "path": "tests/applicability/test_bounding_box.py",
    "content": "\"\"\"Tests specific to Bounding Box applicability domain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom scikit_mol.applicability import BoundingBoxApplicabilityDomain\n\n\ndef test_bounding_box_bounds(ad_test_data):\n    \"\"\"Test the bounds calculation.\"\"\"\n    X_train, _, _ = ad_test_data\n    ad = BoundingBoxApplicabilityDomain(percentile=(1, 99))\n    ad.fit(X_train)\n\n    # Check bounds match numpy percentile\n    expected_min = np.percentile(X_train, 1, axis=0)\n    expected_max = np.percentile(X_train, 99, axis=0)\n\n    assert np.allclose(ad.min_, expected_min)\n    assert np.allclose(ad.max_, expected_max)\n\n\ndef test_bounding_box_violations():\n    \"\"\"Test violation counting.\"\"\"\n    X_train = np.array([[1, 1], [2, 2], [3, 3]])\n    X_test = np.array(\n        [\n            [2, 2],  # Inside bounds (0 violations)\n            [0, 2],  # One violation\n            [0, 4],  # Two violations\n        ]\n    )\n\n    ad = BoundingBoxApplicabilityDomain(percentile=(0, 100))\n    ad.fit(X_train)\n\n    scores = ad.transform(X_test)\n    assert scores[0, 0] == 0  # Inside bounds\n    assert scores[1, 0] == 1  # One violation\n    assert scores[2, 0] == 2  # Two violations\n\n\ndef test_bounding_box_percentile_validation():\n    \"\"\"Test percentile parameter validation.\"\"\"\n    # Invalid single percentile\n    with pytest.raises(ValueError):\n        BoundingBoxApplicabilityDomain(percentile=101)\n\n    # Invalid tuple length\n    with pytest.raises(ValueError):\n        BoundingBoxApplicabilityDomain(percentile=(1, 2, 3))\n\n    # Invalid order\n    with pytest.raises(ValueError):\n        BoundingBoxApplicabilityDomain(percentile=(99, 1))\n\n\ndef test_bounding_box_pipeline():\n    \"\"\"Test bounding box works in pipeline with scaling.\"\"\"\n    X = np.random.randn(10, 5)\n    pipe = Pipeline(\n        [(\"scaler\", StandardScaler()), (\"ad\", BoundingBoxApplicabilityDomain())]\n    )\n\n    # Should run without errors\n    pipe.fit(X)\n    scores = pipe.transform(X)\n    assert scores.shape == (len(X), 1)\n"
  },
  {
    "path": "tests/applicability/test_convex_hull.py",
    "content": "\"\"\"Tests specific to Convex Hull applicability domain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom sklearn.decomposition import PCA\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom scikit_mol.applicability import ConvexHullApplicabilityDomain\n\n\ndef test_convex_hull_simple():\n    \"\"\"Test with simple 2D data where result is obvious.\"\"\"\n    # Create a triangle of points\n    X_train = np.array([[0, 0], [1, 0], [0, 1]])\n    X_test = np.array(\n        [\n            [0.5, 0.25],  # Inside triangle\n            [2, 2],  # Outside triangle\n        ]\n    )\n\n    ad = ConvexHullApplicabilityDomain()\n    ad.fit(X_train)\n\n    scores = ad.transform(X_test)\n    assert scores[0, 0] == 0.0  # Inside point\n    assert scores[1, 0] == 1.0  # Outside point\n\n\ndef test_convex_hull_pipeline():\n    \"\"\"Test convex hull works in pipeline with dimensionality reduction.\"\"\"\n    pipe = Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"pca\", PCA(n_components=2)),  # Reduce to 2D for speed\n            (\"ad\", ConvexHullApplicabilityDomain()),\n        ]\n    )\n\n    # Generate random high-dimensional data\n    X = np.random.randn(10, 5)\n\n    # Should run without errors\n    pipe.fit(X)\n    scores = pipe.transform(X)\n    assert scores.shape == (len(X), 1)\n    assert np.all((scores == 0) | (scores == 1))  # Binary output\n\n\ndef test_convex_hull_numerical_stability():\n    \"\"\"Test numerical stability with nearly colinear points.\"\"\"\n    X_train = np.array(\n        [\n            [0, 0],\n            [1, 0],\n            [2, 1e-10],  # Nearly colinear\n        ]\n    )\n    X_test = np.array([[0.5, 0]])\n\n    ad = ConvexHullApplicabilityDomain()\n    ad.fit(X_train)\n\n    # Should not raise and give consistent results\n    scores = ad.transform(X_test)\n    assert np.all(np.isfinite(scores))\n\n\ndef test_convex_hull_single_point():\n    \"\"\"Test behavior with single point (degenerate hull).\"\"\"\n    X_train = np.array([[1, 1]])\n    X_test = np.array([[1, 1], [2, 2]])\n\n    ad = ConvexHullApplicabilityDomain()\n    ad.fit(X_train)\n\n    scores = ad.transform(X_test)\n    assert scores[0, 0] == 0.0  # Same point\n    assert scores[1, 0] == 1.0  # Different point\n"
  },
  {
    "path": "tests/applicability/test_hotelling.py",
    "content": "\"\"\"Tests specific to Hotelling T² applicability domain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom scikit_mol.applicability import HotellingT2ApplicabilityDomain\n\n\ndef test_hotelling_threshold():\n    \"\"\"Test F-distribution threshold calculation.\"\"\"\n    X = np.random.randn(100, 3)  # 100 samples, 3 features\n\n    ad = HotellingT2ApplicabilityDomain(significance=0.05)\n    ad.fit(X)\n\n    # Threshold should be positive\n    assert ad.threshold_ > 0\n\n    # More stringent significance should give higher threshold\n    ad_strict = HotellingT2ApplicabilityDomain(significance=0.01)\n    ad_strict.fit(X)\n    assert ad_strict.threshold_ > ad.threshold_\n\n\ndef test_hotelling_scores():\n    \"\"\"Test score calculation with known data.\"\"\"\n    # Create data with known center and spread\n    X_train = np.array([[0, 0], [1, 0], [-1, 0], [0, 1], [0, -1]])\n\n    X_test = np.array(\n        [\n            [0, 0],  # Center point\n            [2, 0],  # Further out\n            [10, 10],  # Far out\n        ]\n    )\n\n    ad = HotellingT2ApplicabilityDomain()\n    ad.fit(X_train)\n\n    scores = ad.transform(X_test)\n\n    # Scores should increase with distance from center\n    assert scores[0, 0] < scores[1, 0] < scores[2, 0]\n\n\ndef test_hotelling_significance_validation():\n    \"\"\"Test significance parameter validation.\"\"\"\n    with pytest.raises(ValueError):\n        HotellingT2ApplicabilityDomain(significance=0)\n\n    with pytest.raises(ValueError):\n        HotellingT2ApplicabilityDomain(significance=1)\n\n    with pytest.raises(ValueError):\n        HotellingT2ApplicabilityDomain(significance=-0.5)\n\n\ndef test_hotelling_pipeline():\n    \"\"\"Test Hotelling works in pipeline with scaling.\"\"\"\n    pipe = Pipeline(\n        [(\"scaler\", StandardScaler()), (\"ad\", HotellingT2ApplicabilityDomain())]\n    )\n\n    X = np.random.randn(10, 5)\n\n    # Should run without errors\n    pipe.fit(X)\n    scores = pipe.transform(X)\n    assert scores.shape == (len(X), 1)\n    assert np.all(scores >= 0)  # Scores should be non-negative\n\n\ndef test_hotelling_threshold_fitting():\n    \"\"\"Test threshold fitting with percentile.\"\"\"\n    X = np.random.randn(100, 3)\n\n    ad = HotellingT2ApplicabilityDomain(percentile=90)\n    ad.fit(X)\n\n    # Get scores and check threshold matches 90th percentile\n    scores = ad.transform(X)\n    expected_threshold = np.percentile(scores, 90)\n    assert np.isclose(ad.threshold_, expected_threshold)\n"
  },
  {
    "path": "tests/applicability/test_isolation_forest.py",
    "content": "\"\"\"Tests specific to Isolation Forest applicability domain.\"\"\"\n\nimport numpy as np\n\nfrom scikit_mol.applicability import IsolationForestApplicabilityDomain\n\n\ndef test_refit_consistency():\n    \"\"\"Test consistency when refitting with same data.\"\"\"\n    X = np.random.RandomState(42).normal(0, 1, (100, 2))\n\n    # Use fixed random state\n    ad = IsolationForestApplicabilityDomain(\n        n_estimators=100, contamination=0.1, random_state=42\n    )\n\n    # First fit\n    ad.fit(X)\n    scores1 = ad.transform(X)\n\n    # Second fit\n    ad.fit(X)\n    scores2 = ad.transform(X)\n\n    assert np.allclose(scores1, scores2)\n"
  },
  {
    "path": "tests/applicability/test_kernel_density.py",
    "content": "\"\"\"Tests for KernelDensityApplicabilityDomain.\"\"\"\n\nimport pytest\n\nfrom scikit_mol.applicability import KernelDensityApplicabilityDomain\n\n\n@pytest.fixture\ndef ad_estimator():\n    \"\"\"Fixture providing a KernelDensityApplicabilityDomain instance.\"\"\"\n    return KernelDensityApplicabilityDomain()\n\n\ndef test_kernel_parameter():\n    \"\"\"Test different kernel parameters.\"\"\"\n    kernels = [\"gaussian\", \"tophat\", \"epanechnikov\", \"exponential\", \"linear\", \"cosine\"]\n    # Create data with clear density gradient\n    X = [[0, 0], [0.1, 0.1], [0.2, 0.2], [2, 2]]\n\n    for kernel in kernels:\n        ad = KernelDensityApplicabilityDomain(kernel=kernel)\n        ad.fit(X)\n        scores = ad.transform(X)\n        assert scores.shape == (4, 1)\n        # First point should have higher density than last point\n        assert scores[0, 0] > scores[-1, 0], f\"Failed for kernel {kernel}\"\n\n\ndef test_bandwidth_effect():\n    \"\"\"Test effect of bandwidth parameter on scores.\"\"\"\n    X = [[0, 0], [1, 1], [2, 2]]\n    test_point = [[10, 10]]  # Far from training data\n\n    # Larger bandwidth should give higher scores to outliers\n    ad_small = KernelDensityApplicabilityDomain(bandwidth=0.1)\n    ad_large = KernelDensityApplicabilityDomain(bandwidth=10.0)\n\n    ad_small.fit(X)\n    ad_large.fit(X)\n\n    score_small = ad_small.transform(test_point)\n    score_large = ad_large.transform(test_point)\n\n    assert score_large[0, 0] > score_small[0, 0]\n"
  },
  {
    "path": "tests/applicability/test_knn.py",
    "content": "\"\"\"Tests specific to KNN applicability domain.\"\"\"\n\nimport numpy as np\nimport pytest\n\nfrom scikit_mol.applicability import KNNApplicabilityDomain\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\n\n\n@pytest.fixture\ndef binary_fingerprints(mols_list):\n    \"\"\"Binary fingerprints for testing Tanimoto distance.\"\"\"\n    return MorganFingerprintTransformer(fpSize=1024).fit_transform(mols_list)\n\n\ndef test_knn_tanimoto(binary_fingerprints):\n    \"\"\"Test KNN with Tanimoto distance on binary fingerprints.\"\"\"\n    ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric=\"tanimoto\")\n    ad.fit(binary_fingerprints)\n    scores = ad.transform(binary_fingerprints)\n    assert scores.shape == (len(binary_fingerprints), 1)\n    assert np.all((0 <= scores) & (scores <= 1))  # Tanimoto distances are [0,1]\n\n\n# ... other KNN-specific tests ...\n"
  },
  {
    "path": "tests/applicability/test_leverage.py",
    "content": "\"\"\"Tests specific to Leverage applicability domain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom sklearn.decomposition import PCA\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom scikit_mol.applicability import LeverageApplicabilityDomain\n\n\ndef test_leverage_statistical_threshold(ad_test_data):\n    \"\"\"Test the statistical threshold calculation.\"\"\"\n    X_train, _, _ = ad_test_data\n    ad = LeverageApplicabilityDomain(threshold_factor=3)\n    ad.fit(X_train)\n\n    # Check threshold matches formula h* = 3 * (p+1)/n\n    n_samples, n_features = X_train.shape\n    expected_threshold = 3 * (n_features + 1) / n_samples\n    assert np.isclose(ad.threshold_, expected_threshold)\n\n\ndef test_leverage_pipeline(reduced_fingerprints):\n    \"\"\"Test leverage works in pipeline with scaling and PCA.\"\"\"\n    pipe = Pipeline(\n        [\n            (\"scaler\", StandardScaler()),\n            (\"pca\", PCA(n_components=0.95)),\n            (\"ad\", LeverageApplicabilityDomain()),\n        ]\n    )\n\n    # Should run without errors\n    pipe.fit(reduced_fingerprints)\n    scores = pipe.transform(reduced_fingerprints)\n    assert scores.shape == (len(reduced_fingerprints), 1)\n\n\ndef test_leverage_threshold_factor():\n    \"\"\"Test different threshold factors.\"\"\"\n    X = np.array([[1, 2], [3, 4], [5, 6]])\n\n    ad1 = LeverageApplicabilityDomain(threshold_factor=3)\n    ad2 = LeverageApplicabilityDomain(threshold_factor=2)\n\n    ad1.fit(X)\n    ad2.fit(X)\n\n    # Higher threshold factor should result in higher threshold\n    assert ad1.threshold_ > ad2.threshold_\n\n\ndef test_leverage_var_covar_matrix(ad_test_data):\n    \"\"\"Test the variance-covariance matrix calculation.\"\"\"\n    X_train, _, _ = ad_test_data\n    ad = LeverageApplicabilityDomain()\n    ad.fit(X_train)\n\n    # Check matrix properties\n    assert ad.var_covar_.shape == (X_train.shape[1], X_train.shape[1])\n    assert np.allclose(ad.var_covar_, ad.var_covar_.T)  # Should be symmetric\n"
  },
  {
    "path": "tests/applicability/test_local_outlier.py",
    "content": "\"\"\"Tests for LocalOutlierFactorApplicabilityDomain.\"\"\"\n\nimport numpy as np\nimport pytest\n\nfrom scikit_mol.applicability import LocalOutlierFactorApplicabilityDomain\n\n\n@pytest.fixture\ndef ad_estimator():\n    \"\"\"Fixture providing a LocalOutlierFactorApplicabilityDomain instance.\"\"\"\n    return LocalOutlierFactorApplicabilityDomain()\n\n\ndef test_n_neighbors_effect():\n    \"\"\"Test effect of n_neighbors parameter on scores.\"\"\"\n    # Create data with clear outlier\n    X = np.vstack([np.random.randn(50, 2), [[10, 10]]])\n    outlier = np.array([[10, 10]])\n\n    # Compare different n_neighbors settings\n    ad_small = LocalOutlierFactorApplicabilityDomain(n_neighbors=2)\n    ad_large = LocalOutlierFactorApplicabilityDomain(n_neighbors=5)\n\n    ad_small.fit(X)\n    ad_large.fit(X)\n\n    score_small = ad_small.transform(outlier)\n    score_large = ad_large.transform(outlier)\n\n    # Scores should be different but both should identify the point as an outlier\n    assert score_small != score_large\n    assert ad_small.predict(outlier) == -1\n    assert ad_large.predict(outlier) == -1\n\n\ndef test_metric_parameter():\n    \"\"\"Test different metric parameters.\"\"\"\n    metrics = [\"euclidean\", \"manhattan\", \"cosine\"]\n    X = np.random.randn(10, 2)\n\n    for metric in metrics:\n        ad = LocalOutlierFactorApplicabilityDomain(metric=metric)\n        ad.fit(X)\n        scores = ad.transform(X)\n        assert scores.shape == (10, 1)\n\n\ndef test_contamination_effect():\n    \"\"\"Test effect of contamination parameter on predictions.\"\"\"\n    X = np.random.randn(100, 2)\n\n    # Compare different contamination levels\n    ad_low = LocalOutlierFactorApplicabilityDomain(contamination=0.05)\n    ad_high = LocalOutlierFactorApplicabilityDomain(contamination=0.25)\n\n    ad_low.fit(X)\n    ad_high.fit(X)\n\n    pred_low = ad_low.predict(X)\n    pred_high = ad_high.predict(X)\n\n    # Higher contamination should result in more outliers\n    assert np.sum(pred_high == -1) > np.sum(pred_low == -1)\n"
  },
  {
    "path": "tests/applicability/test_mahalanobis.py",
    "content": "\"\"\"Tests for MahalanobisApplicabilityDomain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_almost_equal\n\nfrom scikit_mol.applicability import MahalanobisApplicabilityDomain\n\n\n@pytest.fixture\ndef ad_estimator():\n    \"\"\"Fixture providing a MahalanobisApplicabilityDomain instance.\"\"\"\n    return MahalanobisApplicabilityDomain()\n\n\ndef test_statistical_threshold():\n    \"\"\"Test chi-square based statistical threshold.\"\"\"\n    # Create multivariate normal data\n    n_samples = 1000\n    n_features = 3\n    mean = np.zeros(n_features)\n    cov = np.eye(n_features)\n    X = np.random.multivariate_normal(mean, cov, n_samples)\n\n    # Fit with statistical threshold\n    ad = MahalanobisApplicabilityDomain(percentile=None)\n    ad.fit(X)\n\n    # For standard normal data, ~95% should be within threshold\n    predictions = ad.predict(X)\n    inside_ratio = np.mean(predictions == 1)\n    assert 0.93 <= inside_ratio <= 0.97  # Allow some variation\n\n\ndef test_mean_covariance():\n    \"\"\"Test mean and covariance computation.\"\"\"\n    X = np.array([[1, 2], [3, 4], [5, 6]])\n    ad = MahalanobisApplicabilityDomain()\n    ad.fit(X)\n\n    # Check mean computation\n    expected_mean = np.array([3, 4])\n    assert_array_almost_equal(ad.mean_, expected_mean)\n\n    # Check covariance computation\n    expected_cov = np.array([[4, 4], [4, 4]])\n    assert_array_almost_equal(ad.covariance_, expected_cov)\n\n\ndef test_distance_properties():\n    \"\"\"Test properties of Mahalanobis distances.\"\"\"\n    # Create data with clear outlier\n    X = np.vstack([np.random.randn(50, 2), [[10, 10]]])\n    outlier = np.array([[10, 10]])\n\n    ad = MahalanobisApplicabilityDomain()\n    ad.fit(X)\n\n    # Distance to mean should be zero\n    mean_dist = ad.transform(ad.mean_.reshape(1, -1))\n    assert_array_almost_equal(mean_dist, [[0]], decimal=10)\n\n    # Outlier should have large distance and be predicted outside\n    outlier_dist = ad.transform(outlier)\n    assert outlier_dist[0, 0] > ad.threshold_\n    assert ad.predict(outlier) == -1\n"
  },
  {
    "path": "tests/applicability/test_standardization.py",
    "content": "\"\"\"Tests for StandardizationApplicabilityDomain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_almost_equal\n\nfrom scikit_mol.applicability import StandardizationApplicabilityDomain\n\n\n@pytest.fixture\ndef ad_estimator():\n    \"\"\"Fixture providing a StandardizationApplicabilityDomain instance.\"\"\"\n    return StandardizationApplicabilityDomain()\n\n\ndef test_statistical_threshold():\n    \"\"\"Test normal distribution based statistical threshold.\"\"\"\n    # Create standard normal data\n    n_samples = 1000\n    n_features = 3\n    X = np.random.randn(n_samples, n_features)\n\n    # Fit with statistical threshold\n    ad = StandardizationApplicabilityDomain(percentile=None)\n    ad.fit(X)\n\n    # For standard normal data, ~95% should be within threshold\n    predictions = ad.predict(X)\n    inside_ratio = np.mean(predictions == 1)\n    assert 0.93 <= inside_ratio <= 0.97  # Allow some variation\n\n\ndef test_standardization():\n    \"\"\"Test standardization of features.\"\"\"\n    X = np.array([[1, 2], [3, 4], [5, 6]])\n    ad = StandardizationApplicabilityDomain()\n    ad.fit(X)\n\n    # Transform data\n    X_std = ad.scaler_.transform(X)\n\n    # Check standardization properties\n    assert_array_almost_equal(np.mean(X_std, axis=0), [0, 0])\n    assert_array_almost_equal(np.std(X_std, axis=0), [1, 1])\n\n\ndef test_max_absolute_score():\n    \"\"\"Test that scores are maximum absolute standardized values.\"\"\"\n    # Create data with known standardized values\n    X = np.array([[0, 0], [1, 2], [3, -4]])\n    ad = StandardizationApplicabilityDomain()\n    ad.fit(X)\n\n    # Create test point with one extreme standardized value\n    X_test = np.array([[0, 10]])  # Second feature will be very large when standardized\n    scores = ad.transform(X_test)\n\n    # Score should be the maximum absolute standardized value\n    X_std = ad.scaler_.transform(X_test)\n    expected_score = np.max(np.abs(X_std))\n    assert_array_almost_equal(scores, [[expected_score]])\n\n\ndef test_outlier_detection():\n    \"\"\"Test outlier detection on simple dataset.\"\"\"\n    # Create data with clear outlier\n    X = np.vstack([np.random.randn(50, 2), [[10, 10]]])\n    outlier = np.array([[10, 10]])\n\n    ad = StandardizationApplicabilityDomain()\n    ad.fit(X)\n\n    # Outlier should have high score and be predicted outside\n    outlier_score = ad.transform(outlier)\n    assert outlier_score[0, 0] > ad.threshold_\n    assert ad.predict(outlier) == -1\n"
  },
  {
    "path": "tests/applicability/test_topkat.py",
    "content": "\"\"\"Tests for TopkatApplicabilityDomain.\"\"\"\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_almost_equal\n\nfrom scikit_mol.applicability import TopkatApplicabilityDomain\n\n\n@pytest.fixture\ndef ad_estimator():\n    \"\"\"Fixture providing a TopkatApplicabilityDomain instance.\"\"\"\n    return TopkatApplicabilityDomain()\n\n\ndef test_ops_transformation():\n    \"\"\"Test OPS transformation and distance calculation.\"\"\"\n    # Create simple test data\n    X_train = np.array([[0, 0], [1, 1], [2, 2]])\n    X_test = np.array([[0.5, 0.5], [10, 10]])\n\n    # Fit AD model\n    ad = TopkatApplicabilityDomain()\n    ad.fit(X_train)\n\n    # Check distances\n    distances = ad.transform(X_test)\n    assert distances.shape == (2, 1)\n    assert distances[0] < distances[1]  # Interpolated point should have lower distance\n\n\ndef test_fixed_threshold():\n    \"\"\"Test that threshold is based on dimensionality.\"\"\"\n    X = np.random.randn(10, 3)\n    ad = TopkatApplicabilityDomain()\n    ad.fit(X)\n\n    # Check threshold formula\n    expected_threshold = 5 * (3 + 1) / (2 * 3)  # n_features = 3\n    assert_array_almost_equal(ad.threshold_, expected_threshold)\n\n\ndef test_eigendecomposition():\n    \"\"\"Test eigendecomposition properties.\"\"\"\n    X = np.random.randn(10, 2)\n    ad = TopkatApplicabilityDomain()\n    ad.fit(X)\n\n    # Check eigenvalue/vector shapes\n    assert ad.eigen_val_.shape == (3,)  # n_features + 1\n    assert ad.eigen_vec_.shape == (3, 3)  # (n_features + 1, n_features + 1)\n\n    # Check eigenvalues are real and sorted\n    assert np.all(np.isreal(ad.eigen_val_))\n    assert np.all(np.diff(ad.eigen_val_) >= 0)  # Sorted in ascending order\n"
  },
  {
    "path": "tests/conftest.py",
    "content": "import hashlib\nimport shutil\nfrom pathlib import Path, PurePath\nfrom urllib.parse import urlsplit\nfrom urllib.request import urlopen\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport sklearn\n\n\n# Register custom marks\ndef pytest_configure(config):\n    config.addinivalue_line(\n        \"markers\",\n        \"threshold_fitting: mark tests that verify threshold fitting functionality\",\n    )\n\n\nTEST_DATA_URL = \"https://ndownloader.figshare.com/files/25747817\"\nTEST_DATA_MD5 = \"1ec89bde544c3c4bc400d5b75315921e\"\n\n\ndef md5(fn):\n    m = hashlib.md5()\n    with open(fn, \"rb\") as f:\n        for chunk in iter(lambda: f.read(4096), b\"\"):\n            m.update(chunk)\n    return m.hexdigest()\n\n\n@pytest.fixture(scope=\"session\")\ndef data_pth(tmp_path_factory) -> Path:\n    \"\"\"download the smallest aperio test image svs or use local\"\"\"\n    filename = PurePath(urlsplit(TEST_DATA_URL).path).name\n    data_dir = Path(__file__).parent / \"data\"\n    data_dir.mkdir(parents=True, exist_ok=True)\n    data_fn = data_dir / filename\n\n    if not data_fn.is_file():\n        # download svs from openslide test images\n        with urlopen(TEST_DATA_URL) as response, open(data_fn, \"wb\") as out_file:\n            shutil.copyfileobj(response, out_file)\n\n        if md5(data_fn) != TEST_DATA_MD5:  # pragma: no cover\n            shutil.rmtree(data_fn)\n            pytest.fail(\"incorrect md5\")\n\n    yield data_fn.absolute()\n\n\n@pytest.fixture()\ndef data(data_pth) -> pd.DataFrame:\n    yield pd.read_csv(data_pth)\n\n\n@pytest.fixture(scope=\"module\")\ndef pandas_output():\n    \"\"\"Set sklearn to output pandas dataframes\"\"\"\n    sklearn.set_config(transform_output=\"pandas\")\n    yield\n    sklearn.set_config(transform_output=\"default\")\n\n\n# Fixed Numpy random seed in all tests automatically\n@pytest.fixture(autouse=True)\ndef setup_random():\n    \"\"\"Set fixed random seed before each test.\"\"\"\n    np.random.seed(0xDEADFACE)\n"
  },
  {
    "path": "tests/fixtures.py",
    "content": "import os\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport sklearn\nfrom packaging.version import Version\nfrom rdkit import Chem\nfrom rdkit.Chem import rdMolDescriptors\nfrom sklearn.compose import make_column_selector, make_column_transformer\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import FunctionTransformer\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.core import (\n    DEFAULT_MOL_COLUMN_NAME,\n    SKLEARN_VERSION_PANDAS_OUT,\n    InvalidMol,\n)\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\nfrom scikit_mol.fingerprints import (\n    AtomPairFingerprintTransformer,\n    AvalonFingerprintTransformer,\n    MACCSKeysFingerprintTransformer,\n    MHFingerprintTransformer,\n    MorganFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    SECFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n)\nfrom scikit_mol.standardizer import Standardizer\n\n# TODO these should really go into the conftest.py, so that they are automatically imported in the tests\n\n_SMILES_LIST = [\n    \"O=C(O)c1ccccc1\",\n    \"O=C([O-])c1ccccc1\",\n    \"O=C([O-])c1ccccc1.[Na+]\",\n    \"O=C(O[Na])c1ccccc1\",\n    \"C[N+](C)C.O=C([O-])c1ccccc1\",\n]\n_CANONICAL_SMILES_LIST = [\n    Chem.MolToSmiles(Chem.MolFromSmiles(smiles)) for smiles in _SMILES_LIST\n]\n\n\n@pytest.fixture\ndef smiles_list():\n    return _CANONICAL_SMILES_LIST.copy()\n\n\n_CONTAINER_CREATORS = [\n    lambda x: x,\n    lambda x: np.array(x),\n    lambda x: np.array(x).reshape(-1, 1),\n]\n_names_to_test = [\n    \"molecule\",\n    \"mol\",\n    \"smiles\",\n    DEFAULT_MOL_COLUMN_NAME,\n    \"hello\",\n    None,\n]\nfor name in _names_to_test:\n    _CONTAINER_CREATORS.append(lambda x, name=name: pd.Series(x, name=name))\n    _CONTAINER_CREATORS.append(\n        lambda x, name=name: pd.DataFrame({name: x}) if name else pd.DataFrame(x)\n    )\n\n\n@pytest.fixture(\n    params=[container(_CANONICAL_SMILES_LIST) for container in _CONTAINER_CREATORS]\n)\ndef smiles_container(\n    request,\n):\n    return request.param.copy()\n\n\n@pytest.fixture\ndef chiral_smiles_list():  # Need to be a certain size, so the fingerprints reacts to different max_lenǵths and radii\n    return [\n        Chem.MolToSmiles(Chem.MolFromSmiles(smiles))\n        for smiles in [\n            \"N[C@@H](C)C(=O)OCCCCCCCCCCCC\",\n            \"C1C[C@H]2CCCC[C@H]2CC1CCCCCCCCC\",\n            \"N[C@@H](C)C(=O)Oc1ccccc1CCCCCCCCCCCCCCCCCCN[H]\",\n        ]\n    ]\n\n\n@pytest.fixture\ndef smiles_list_with_invalid(smiles_list):\n    data = smiles_list.copy()\n    data.extend([\"invalid\"])\n    return data\n\n\n@pytest.fixture\ndef invalid_smiles_list():\n    return [\"NOT MOL\", \"invalid\", \"C(=O)OX\", \"XC1CCCCC1\"]\n\n\n_MOLS_LIST = [Chem.MolFromSmiles(smiles) for smiles in _SMILES_LIST]\n\n\n@pytest.fixture\ndef mols_list():\n    return _MOLS_LIST.copy()\n\n\n@pytest.fixture(params=[container(_MOLS_LIST) for container in _CONTAINER_CREATORS])\ndef mols_container(request):\n    return request.param.copy()\n\n\n@pytest.fixture\ndef chiral_mols_list(chiral_smiles_list):\n    return [Chem.MolFromSmiles(smiles) for smiles in chiral_smiles_list]\n\n\n@pytest.fixture\ndef mols_with_invalid_container(smiles_list_with_invalid):\n    mols = []\n    for smiles in smiles_list_with_invalid:\n        mol = Chem.MolFromSmiles(smiles)\n        if mol is None:\n            mols.append(InvalidMol(\"TestError\", f\"Invalid SMILES: {smiles}\"))\n        else:\n            mols.append(mol)\n    return mols\n\n\n@pytest.fixture\ndef fingerprint(mols_list):\n    return rdMolDescriptors.GetHashedMorganFingerprint(mols_list[0], 2, nBits=1000)\n\n\n_DIR_DATA = Path(__file__).parent / \"data\"\n_FILE_SLC6A4 = _DIR_DATA / \"SLC6A4_active_excapedb_subset.csv\"\n_FILE_SLC6A4_WITH_CDDD = _DIR_DATA / \"CDDD_SLC6A4_active_excapedb_subset.csv.gz\"\n\n\n@pytest.fixture\ndef SLC6A4_subset():\n    data = pd.read_csv(_FILE_SLC6A4)\n    return data\n\n\n@pytest.fixture\ndef SLC6A4_subset_with_cddd(SLC6A4_subset):\n    data = SLC6A4_subset.copy().drop_duplicates(subset=\"Ambit_InchiKey\")\n    cddd = pd.read_csv(_FILE_SLC6A4_WITH_CDDD, index_col=\"Ambit_InchiKey\")\n    data = data.merge(\n        cddd,\n        left_on=\"Ambit_InchiKey\",\n        right_index=True,\n        how=\"inner\",\n        validate=\"one_to_one\",\n    )\n    return data\n\n\nskip_pandas_output_test = pytest.mark.skipif(\n    Version(sklearn.__version__) < SKLEARN_VERSION_PANDAS_OUT,\n    reason=f\"requires scikit-learn {SKLEARN_VERSION_PANDAS_OUT} or higher\",\n)\n\n_FEATURIZER_CLASSES = [\n    MACCSKeysFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    AtomPairFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n    MorganFingerprintTransformer,\n    SECFingerprintTransformer,\n    MHFingerprintTransformer,\n    AvalonFingerprintTransformer,\n    MolecularDescriptorTransformer,\n]\n\n\n@pytest.fixture(params=_FEATURIZER_CLASSES)\ndef featurizer(request):\n    return request.param()\n\n\n@pytest.fixture\ndef combined_transformer(featurizer):\n    descriptors_pipeline = make_pipeline(\n        SmilesToMolTransformer(),\n        Standardizer(),\n        featurizer,\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    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    return transformer\n\n\n@pytest.fixture\ndef morgan_transformer():\n    return MorganFingerprintTransformer()\n\n\n@pytest.fixture\ndef rdkit_transformer():\n    return RDKitFingerprintTransformer()\n\n\n@pytest.fixture\ndef atompair_transformer():\n    return AtomPairFingerprintTransformer()\n\n\n@pytest.fixture\ndef topologicaltorsion_transformer():\n    return TopologicalTorsionFingerprintTransformer()\n"
  },
  {
    "path": "tests/test_desctransformer.py",
    "content": "import time\n\nimport joblib\nimport numpy as np\nimport numpy.ma as ma\nimport pandas as pd\nimport pytest\nimport sklearn\nfrom packaging.version import Version\nfrom rdkit.Chem import Descriptors\nfrom sklearn import clone\nfrom sklearn.pipeline import Pipeline\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\n\nfrom .fixtures import (\n    mols_container,\n    mols_list,\n    mols_with_invalid_container,\n    skip_pandas_output_test,\n    smiles_container,\n    smiles_list,\n    smiles_list_with_invalid,\n)\n\n\n@pytest.fixture\ndef default_descriptor_transformer():\n    return MolecularDescriptorTransformer()\n\n\n@pytest.fixture\ndef selected_descriptor_transformer():\n    return MolecularDescriptorTransformer(\n        desc_list=[\"HeavyAtomCount\", \"FractionCSP3\", \"RingCount\", \"MolLogP\", \"MolWt\"]\n    )\n\n\ndef test_descriptor_transformer_clonability(default_descriptor_transformer):\n    for t in [default_descriptor_transformer]:\n        params = t.get_params()\n        t2 = clone(t)\n        params_2 = t2.get_params()\n        # Parameters of cloned transformers should be the same\n        assert all([params[key] == params_2[key] for key in params.keys()])\n        # Cloned transformers should not be the same object\n        assert t2 != t\n\n\ndef test_descriptor_transformer_set_params(default_descriptor_transformer):\n    for t in [default_descriptor_transformer]:\n        params = t.get_params()\n        # change extracted dictionary\n        params[\"desc_list\"] = [\"HeavyAtomCount\", \"FractionCSP3\"]\n        # change params in transformer\n        t.set_params(desc_list=[\"HeavyAtomCount\", \"FractionCSP3\"])\n        # get parameters as dictionary and assert that it is the same\n        params_2 = t.get_params()\n        assert all([params[key] == params_2[key] for key in params.keys()])\n        assert len(default_descriptor_transformer.selected_descriptors) == 2\n\n\ndef test_descriptor_transformer_available_descriptors(\n    default_descriptor_transformer, selected_descriptor_transformer\n):\n    # Default have as many as in RDkit and all are selected\n    assert len(default_descriptor_transformer.available_descriptors) == len(\n        Descriptors._descList\n    )\n    assert len(default_descriptor_transformer.selected_descriptors) == len(\n        Descriptors._descList\n    )\n    # Default have as many as in RDkit but only 5 are selected\n    assert len(selected_descriptor_transformer.available_descriptors) == len(\n        Descriptors._descList\n    )\n    assert len(selected_descriptor_transformer.selected_descriptors) == 5\n\n\ndef test_descriptor_transformer_transform(\n    mols_container, default_descriptor_transformer\n):\n    features = default_descriptor_transformer.transform(mols_container)\n    assert len(features) == len(mols_container)\n    assert len(features[0]) == len(Descriptors._descList)\n\n\ndef test_descriptor_transformer_wrong_descriptors():\n    with pytest.raises(AssertionError):\n        MolecularDescriptorTransformer(\n            desc_list=[\n                \"Color\",\n                \"Icecream content\",\n                \"ChokolateDarkness\",\n                \"Content42\",\n                \"MolWt\",\n            ]\n        )\n\n\ndef test_descriptor_transformer_parallel(mols_list, default_descriptor_transformer):\n    default_descriptor_transformer.set_params(n_jobs=2)\n    features = default_descriptor_transformer.transform(mols_list)\n    assert len(features) == len(mols_list)\n    assert len(features[0]) == len(Descriptors._descList)\n    # Now with Rdkit 2022.3 creating a second transformer and running it, froze the process\n    transformer2 = MolecularDescriptorTransformer(\n        **default_descriptor_transformer.get_params()\n    )\n    features2 = transformer2.transform(mols_list)\n    assert len(features2) == len(mols_list)\n    assert len(features2[0]) == len(Descriptors._descList)\n\n\n# This test may fail on windows and mac (due to spawn rather than fork?)\n# def test_descriptor_transformer_parallel_speedup(mols_list, default_descriptor_transformer):\n#     n_phys_cpus = joblib.cpu_count(only_physical_cores=True)\n#     mols_list = mols_list*50\n#     if n_phys_cpus > 1:\n#         t0 = time.time()\n#         features = default_descriptor_transformer.transform(mols_list)\n#         t_single = time.time()-t0\n\n#         default_descriptor_transformer.set_params(parallel=True)\n#         t0 = time.time()\n#         features = default_descriptor_transformer.transform(mols_list)\n#         t_par = time.time()-t0\n\n#         assert(t_par < t_single/(n_phys_cpus/1.5)) # div by 1.5 as we don't assume full speedup\n\n\ndef test_transform_with_safe_inference_mode(mols_with_invalid_container):\n    transformer = MolecularDescriptorTransformer(safe_inference_mode=True)\n    descriptors = transformer.transform(mols_with_invalid_container)\n\n    assert isinstance(descriptors, ma.MaskedArray)\n    assert len(descriptors) == len(mols_with_invalid_container)\n\n    # Check that the last row (corresponding to the InvalidMol) is fully masked\n    assert np.all(descriptors.mask[-1])\n\n    # Check that other rows are not masked\n    assert not np.any(descriptors.mask[:-1])\n\n\ndef test_transform_without_safe_inference_mode(mols_with_invalid_container):\n    transformer = MolecularDescriptorTransformer(safe_inference_mode=False)\n    with pytest.raises(\n        Exception\n    ):  # You might want to be more specific about the exception type\n        transformer.transform(mols_with_invalid_container)\n\n\ndef test_transform_parallel_with_safe_inference_mode(mols_with_invalid_container):\n    transformer = MolecularDescriptorTransformer(safe_inference_mode=True, n_jobs=2)\n    descriptors = transformer.transform(mols_with_invalid_container)\n\n    assert isinstance(descriptors, ma.MaskedArray)\n    assert len(descriptors) == len(mols_with_invalid_container)\n\n    # Check that the last row (corresponding to the InvalidMol) is fully masked\n    assert np.all(descriptors.mask[-1])\n\n    # Check that other rows are not masked\n    assert not np.any(descriptors.mask[:-1])\n\n\ndef test_transform_parallel_without_safe_inference_mode(mols_with_invalid_container):\n    transformer = MolecularDescriptorTransformer(safe_inference_mode=False, n_jobs=2)\n    with pytest.raises(\n        Exception\n    ):  # You might want to be more specific about the exception type\n        transformer.transform(mols_with_invalid_container)\n\n\ndef test_safe_inference_mode_setting():\n    transformer = MolecularDescriptorTransformer()\n    assert not transformer.safe_inference_mode  # Default should be False\n\n    transformer.set_params(safe_inference_mode=True)\n    assert transformer.safe_inference_mode\n\n    transformer.set_params(safe_inference_mode=False)\n    assert not transformer.safe_inference_mode\n\n\n# TODO, if these tests are run before the others, these tests will fail, probably due to pandas output?\n@skip_pandas_output_test\ndef test_descriptor_transformer_pandas_output(\n    mols_container,\n    default_descriptor_transformer,\n    selected_descriptor_transformer,\n    pandas_output,\n):\n    for transformer in [\n        default_descriptor_transformer,\n        selected_descriptor_transformer,\n    ]:\n        features = transformer.transform(mols_container)\n        assert isinstance(features, pd.DataFrame)\n        assert features.shape[0] == len(mols_container)\n        assert features.columns.tolist() == transformer.selected_descriptors\n\n\n@skip_pandas_output_test\ndef test_descriptor_transformer_pandas_output_pipeline(\n    smiles_container, default_descriptor_transformer, pandas_output\n):\n    pipeline = Pipeline(\n        [(\"s2m\", SmilesToMolTransformer()), (\"desc\", default_descriptor_transformer)]\n    )\n    features = pipeline.fit_transform(smiles_container)\n    assert isinstance(features, pd.DataFrame)\n    assert features.shape[0] == len(smiles_container)\n    assert (\n        features.columns.tolist() == default_descriptor_transformer.selected_descriptors\n    )\n"
  },
  {
    "path": "tests/test_fptransformers.py",
    "content": "import pickle\nimport tempfile\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom rdkit import Chem\nfrom sklearn import clone\n\nfrom scikit_mol.fingerprints import (\n    AvalonFingerprintTransformer,\n    MACCSKeysFingerprintTransformer,\n    MHFingerprintTransformer,\n    SECFingerprintTransformer,\n)\n\nfrom .fixtures import (\n    chiral_mols_list,\n    chiral_smiles_list,\n    fingerprint,\n    mols_container,\n    mols_list,\n    mols_with_invalid_container,\n    smiles_container,\n    smiles_list,\n    smiles_list_with_invalid,\n)\n\n\n@pytest.fixture\ndef maccs_transformer():\n    return MACCSKeysFingerprintTransformer()\n\n\n@pytest.fixture\ndef secfp_transformer():\n    return SECFingerprintTransformer()\n\n\n@pytest.fixture\ndef mhfp_transformer():\n    return MHFingerprintTransformer()\n\n\n@pytest.fixture\ndef avalon_transformer():\n    return AvalonFingerprintTransformer()\n\n\ndef test_clonability(\n    maccs_transformer,\n    secfp_transformer,\n    mhfp_transformer,\n    avalon_transformer,\n):\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        mhfp_transformer,\n        avalon_transformer,\n    ]:\n        params = t.get_params()\n        t2 = clone(t)\n        params_2 = t2.get_params()\n        # Parameters of cloned transformers should be the same\n        assert all([params[key] == params_2[key] for key in params.keys()])\n        # Cloned transformers should not be the same object\n        assert t2 != t\n\n\ndef test_set_params(\n    secfp_transformer,\n    mhfp_transformer,\n    avalon_transformer,\n):\n    for t in [avalon_transformer]:\n        params = t.get_params()\n        # change extracted dictionary\n        params[\"fpSize\"] = 4242\n        # change params in transformer\n        t.set_params(fpSize=4242)\n        # get parameters as dictionary and assert that it is the same\n        params_2 = t.get_params()\n        assert all([params[key] == params_2[key] for key in params.keys()])\n\n    for t in [secfp_transformer, mhfp_transformer]:\n        params = t.get_params()\n        params[\"fpSize\"] = 4242\n        t.set_params(fpSize=4242)\n        params_2 = t.get_params()\n        assert all([params[key] == params_2[key] for key in params.keys()])\n\n\ndef test_transform(\n    mols_container,\n    maccs_transformer,\n    secfp_transformer,\n    mhfp_transformer,\n    avalon_transformer,\n):\n    # Test the different transformers\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        mhfp_transformer,\n        avalon_transformer,\n    ]:\n        params = t.get_params()\n        print(type(t), params)\n        fps = t.transform(mols_container)\n        # Assert that the same length of input and output\n        assert len(fps) == len(mols_container)\n\n        # assert that the size of the fingerprint is the expected size\n        fpsize = params[\"fpSize\"]\n\n        assert len(fps[0]) == fpsize\n\n\ndef test_transform_parallel(\n    mols_container,\n    maccs_transformer,\n    secfp_transformer,\n    mhfp_transformer,\n    avalon_transformer,\n):\n    # Test the different transformers\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        mhfp_transformer,\n        avalon_transformer,\n    ]:\n        t.set_params(n_jobs=2)\n        params = t.get_params()\n        fps = t.transform(mols_container)\n        # Assert that the same length of input and output\n        assert len(fps) == len(mols_container)\n\n        # assert that the size of the fingerprint is the expected size\n        fpsize = params[\"fpSize\"]\n\n        assert len(fps[0]) == fpsize\n\n\ndef test_picklable(\n    maccs_transformer,\n    secfp_transformer,\n    avalon_transformer,\n):\n    # Test the different transformers\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        avalon_transformer,\n    ]:\n        with tempfile.NamedTemporaryFile() as f:\n            pickle.dump(t, f)\n            f.seek(0)\n            t2 = pickle.load(f)\n            assert t.get_params() == t2.get_params()\n\n\ndef assert_transformer_set_params(tr_class, new_params, mols_list):\n    default_params = tr_class().get_params()\n    for key in new_params.keys():\n        tr = tr_class()\n        params = tr.get_params()\n        params[key] = new_params[key]\n\n        fps_default = tr.transform(mols_list)\n\n        tr.set_params(**params)\n        new_tr = tr_class(**params)\n\n        fps_reset_params = tr.transform(mols_list)\n        fps_init_new_params = new_tr.transform(mols_list)\n\n        # Now fp_default should not be the same as fp_reset_params\n        assert ~np.all(\n            [\n                np.array_equal(fp_default, fp_reset_params)\n                for fp_default, fp_reset_params in zip(fps_default, fps_reset_params)\n            ]\n        ), f\"Assertation error, FP appears the same, although the {key} should be changed from {default_params[key]} to {params[key]}\"\n        # fp_reset_params and fp_init_new_params should however be the same\n        assert np.all(\n            [\n                np.array_equal(fp_init_new_params, fp_reset_params)\n                for fp_init_new_params, fp_reset_params in zip(\n                    fps_init_new_params, fps_reset_params\n                )\n            ]\n        ), f\"Assertation error, FP appears to be different, although the {key} should be changed back as well as initialized to {params[key]}\"\n\n\ndef test_SECFingerprintTransformer(chiral_mols_list):\n    new_params = {\n        \"isomeric\": True,\n        \"kekulize\": True,\n        \"fpSize\": 1048,\n        \"min_radius\": 2,\n        #'n_permutations': 2, # The SECFp is not using this setting\n        \"radius\": 2,\n        \"rings\": False,\n        #'seed': 1 # The SECFp is not using this setting\n    }\n    assert_transformer_set_params(\n        SECFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_MHFingerprintTransformer(chiral_mols_list):\n    new_params = {\n        \"radius\": 2,\n        \"rings\": False,\n        \"isomeric\": True,\n        \"kekulize\": True,\n        \"min_radius\": 2,\n        \"fpSize\": 4096,\n        \"seed\": 44,\n    }\n    assert_transformer_set_params(\n        MHFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_AvalonFingerprintTransformer(chiral_mols_list):\n    new_params = {\n        \"fpSize\": 1024,\n        \"isQuery\": True,\n        # 'resetVect': True, #TODO: this doesn't change the FP\n        \"bitFlags\": 32767,\n    }\n    assert_transformer_set_params(\n        AvalonFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_transform_with_safe_inference_mode(\n    mols_with_invalid_container,\n    maccs_transformer,\n    secfp_transformer,\n    avalon_transformer,\n):\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        avalon_transformer,\n    ]:\n        t.set_params(safe_inference_mode=True)\n        print(type(t))\n        fps = t.transform(mols_with_invalid_container)\n\n        assert len(fps) == len(mols_with_invalid_container)\n\n        # Check that the last row (corresponding to the InvalidMol) contains NaNs\n        assert np.all(fps.mask[-1])\n\n        # Check that other rows don't contain NaNs\n        assert not np.any(fps.mask[:-1])\n\n\ndef test_transform_without_safe_inference_mode(\n    mols_with_invalid_container,\n    maccs_transformer,\n    secfp_transformer,\n    avalon_transformer,\n    # MHFP seem to accept invalid mols and return 0,0,0,0's\n):\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        avalon_transformer,\n    ]:\n        t.set_params(safe_inference_mode=False)\n        with pytest.raises(\n            Exception\n        ):  # You might want to be more specific about the exception type\n            print(f\"testing {type(t)}\")\n            t.transform(mols_with_invalid_container)\n\n\n# Add this test to check parallel processing with error handling\ndef test_transform_parallel_with_safe_inference_mode(\n    mols_with_invalid_container,\n    maccs_transformer,\n    secfp_transformer,\n    avalon_transformer,\n):\n    for t in [\n        maccs_transformer,\n        secfp_transformer,\n        avalon_transformer,\n    ]:\n        t.set_params(safe_inference_mode=True, n_jobs=2)\n        fps = t.transform(mols_with_invalid_container)\n\n        assert len(fps) == len(mols_with_invalid_container)\n\n        print(fps.mask)\n        # Check that the last row (corresponding to the InvalidMol) is masked\n        assert np.all(\n            fps.mask[-1]\n        )  # Mask should be true for all elements in the last row\n\n        # Check that other rows don't contain any masked values\n        assert not np.any(fps.mask[:-1, :])\n"
  },
  {
    "path": "tests/test_fptransformersgenerator.py",
    "content": "import pickle\nimport tempfile\n\nimport numpy as np\nimport pytest\nfrom sklearn import clone\n\nfrom scikit_mol.fingerprints import (\n    AtomPairFingerprintTransformer,\n    MorganFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n)\n\nfrom .fixtures import (\n    chiral_mols_list,\n    chiral_smiles_list,\n    fingerprint,\n    mols_container,\n    mols_list,\n    smiles_container,\n    smiles_list,\n)\n\ntest_transformers = [\n    AtomPairFingerprintTransformer,\n    MorganFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n]\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_fpstransformer_transform_mol(transformer_class, mols_list):\n    transformer = transformer_class()\n\n    fp = transformer._transform_mol(mols_list[0])\n    # See that fp is the correct type, shape and bit count\n    assert type(fp) == type(np.array([0]))\n    assert fp.shape == (2048,)\n\n    if isinstance(transformer, RDKitFingerprintTransformer):\n        assert fp.sum() == 104\n    elif isinstance(transformer, AtomPairFingerprintTransformer):\n        assert fp.sum() == 32\n    elif isinstance(transformer, TopologicalTorsionFingerprintTransformer):\n        assert fp.sum() == 12\n    elif isinstance(transformer, MorganFingerprintTransformer):\n        assert fp.sum() == 14\n    else:\n        raise NotImplementedError(f\"missing Assert for {transformer_class}\")\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_clonability(transformer_class):\n    transformer = transformer_class()\n\n    params = transformer.get_params()\n    t2 = clone(transformer)\n    params_2 = t2.get_params()\n    # Parameters of cloned transformers should be the same\n    assert all([params[key] == params_2[key] for key in params.keys()])\n    # Cloned transformers should not be the same object\n    assert t2 != transformer\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_set_params(transformer_class):\n    transformer = transformer_class()\n    params = transformer.get_params()\n    # change extracted dictionary\n    params[\"fpSize\"] = 4242\n    # change params in transformer\n    transformer.set_params(fpSize=4242)\n    # get parameters as dictionary and assert that it is the same\n    params_2 = transformer.get_params()\n    assert all([params[key] == params_2[key] for key in params.keys()])\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_transform(mols_container, transformer_class):\n    transformer = transformer_class()\n    # Test the different transformers\n    params = transformer.get_params()\n    fps = transformer.transform(mols_container)\n    # Assert that the same length of input and output\n    assert len(fps) == len(mols_container)\n\n    fpsize = params[\"fpSize\"]\n\n    assert len(fps[0]) == fpsize\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_transform_parallel(mols_container, transformer_class):\n    transformer = transformer_class()\n    # Test the different transformers\n    transformer.set_params(n_jobs=2)\n    params = transformer.get_params()\n    fps = transformer.transform(mols_container)\n    # Assert that the same length of input and output\n    assert len(fps) == len(mols_container)\n\n    fpsize = params[\"fpSize\"]\n    assert len(fps[0]) == fpsize\n\n\n@pytest.mark.parametrize(\"transformer_class\", test_transformers)\ndef test_picklable(transformer_class):\n    # Test the different transformers\n    transformer = transformer_class()\n    p = transformer.get_params()\n\n    with tempfile.NamedTemporaryFile() as f:\n        pickle.dump(transformer, f)\n        f.seek(0)\n        t2 = pickle.load(f)\n        print(p)\n        print(vars(transformer))\n        print(vars(t2))\n        assert transformer.get_params() == t2.get_params()\n\n\n@pytest.mark.parametrize(\"transfomer\", test_transformers)\ndef assert_transformer_set_params(transfomer, new_params, mols_list):\n    default_params = transfomer().get_params()\n\n    for key in new_params.keys():\n        tr = transfomer()\n        params = tr.get_params()\n        params[key] = new_params[key]\n\n        fps_default = tr.transform(mols_list)\n\n        tr.set_params(**params)\n        new_tr = transfomer(**params)\n        fps_reset_params = tr.transform(mols_list)\n        fps_init_new_params = new_tr.transform(mols_list)\n\n        # Now fp_default should not be the same as fp_reset_params\n\n        assert ~np.all(\n            [\n                np.array_equal(fp_default, fp_reset_params)\n                for fp_default, fp_reset_params in zip(fps_default, fps_reset_params)\n            ]\n        ), f\"Assertation error, FP appears the same, although the {key} should be changed from {default_params[key]} to {params[key]}\"\n        # fp_reset_params and fp_init_new_params should however be the same\n        assert np.all(\n            [\n                np.array_equal(fp_init_new_params, fp_reset_params)\n                for fp_init_new_params, fp_reset_params in zip(\n                    fps_init_new_params, fps_reset_params\n                )\n            ]\n        ), f\"Assertation error, FP appears to be different, although the {key} should be changed back as well as initialized to {params[key]}\"\n\n\ndef test_morgan_set_params(chiral_mols_list):\n    new_params = {\n        \"fpSize\": 1024,\n        \"radius\": 1,\n        \"useBondTypes\": False,  # TODO, why doesn't this change the FP?\n        \"useChirality\": True,\n        \"useCounts\": True,\n        \"useFeatures\": True,\n    }\n\n    assert_transformer_set_params(\n        MorganFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_atompairs_set_params(chiral_mols_list):\n    new_params = {\n        #'atomInvariants': 1,\n        #'confId': -1,\n        #'fromAtoms': 1,\n        #'ignoreAtoms': 0,\n        \"includeChirality\": True,\n        \"maxLength\": 3,\n        \"minLength\": 3,\n        \"fpSize\": 1024,\n        #'nBitsPerEntry': 3, #TODO: seem deprecated with the generators?\n        #'use2D': True, #TODO, understand why this can't be set different\n        \"useCounts\": True,\n    }\n\n    assert_transformer_set_params(\n        AtomPairFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_topologicaltorsion_set_params(chiral_mols_list):\n    new_params = {  #'atomInvariants': 0,\n        #'fromAtoms': 0,\n        #'ignoreAtoms': 0,\n        #'includeChirality': True, #TODO, figure out why this setting seems to give same FP whether toggled or not\n        \"fpSize\": 1024,\n        #'nBitsPerEntry': 3, #Todo: not setable with the generators?\n        \"targetSize\": 5,\n        \"useCounts\": True,\n    }\n\n    assert_transformer_set_params(\n        TopologicalTorsionFingerprintTransformer, new_params, chiral_mols_list\n    )\n\n\ndef test_RDKitFPTransformer(chiral_mols_list):\n    new_params = {  #'atomInvariantsGenerator': None,\n        #'branchedPaths': False,\n        #'countBounds': 0, #TODO: What does this do?\n        \"countSimulation\": True,\n        \"fpSize\": 1024,\n        \"maxPath\": 3,\n        \"minPath\": 2,\n        \"numBitsPerFeature\": 3,\n        \"useBondOrder\": False,  # TODO, why doesn't this change the FP?\n        #'useHs': False, #TODO, why doesn't this change the FP?\n    }\n    assert_transformer_set_params(\n        RDKitFingerprintTransformer, new_params, chiral_mols_list\n    )\n"
  },
  {
    "path": "tests/test_parameter_types.py",
    "content": "import numpy as np\nimport pytest\nfrom rdkit import Chem\n\nfrom .fixtures import (\n    atompair_transformer,\n    mols_list,\n    morgan_transformer,\n    rdkit_transformer,\n    smiles_list,\n    topologicaltorsion_transformer,\n)\nfrom .test_fptransformers import (\n    avalon_transformer,\n)\n\n\ndef test_Transformer_exotic_types(\n    mols_list,\n    morgan_transformer,\n    atompair_transformer,\n    topologicaltorsion_transformer,\n    avalon_transformer,\n):\n    for transformer in [\n        morgan_transformer,\n        atompair_transformer,\n        topologicaltorsion_transformer,\n        avalon_transformer,\n    ]:\n        params = transformer.get_params()\n\n        for useCounts in [np.bool_(True), np.bool_(False)]:\n            for key, value in params.items():\n                if isinstance(value, int):\n                    exotic_type_value = np.int64(value)\n                elif isinstance(value, bool):\n                    exotic_type_value = np.bool_(value)\n                else:\n                    print(f\"{key}:{value}:{type(value)}\")\n                    exotic_type_value = value\n\n                exotic_params = {key: exotic_type_value, \"useCounts\": useCounts}\n                print(exotic_params)\n                transformer.set_params(**exotic_params)\n                transformer.transform(mols_list)\n\n\ndef test_RDKFp_exotic_types(mols_list, rdkit_transformer):\n    transformer = rdkit_transformer\n    params = transformer.get_params()\n\n    for key, value in params.items():\n        if isinstance(value, int):\n            exotic_type_value = np.int64(value)\n        elif isinstance(value, bool):\n            exotic_type_value = np.bool_(value)\n        else:\n            print(f\"{key}:{value}:{type(value)}\")\n            exotic_type_value = value\n\n        exotic_params = {key: exotic_type_value}\n        print(exotic_params)\n        transformer.set_params(**exotic_params)\n        transformer.transform(mols_list)\n"
  },
  {
    "path": "tests/test_safeinferencemode.py",
    "content": "import numpy as np\nimport pandas as pd\nimport pytest\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.pipeline import Pipeline\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.fingerprints import MorganFingerprintTransformer\nfrom scikit_mol.safeinference import SafeInferenceWrapper\nfrom scikit_mol.utilities import set_safe_inference_mode\n\nfrom .fixtures import (\n    SLC6A4_subset,\n    invalid_smiles_list,\n    skip_pandas_output_test,\n    smiles_list,\n)\n\n\ndef equal_val(value, expected_value):\n    try:\n        if np.isnan(expected_value):\n            return np.isnan(value)\n        else:\n            return value == expected_value\n    except TypeError:\n        return value == expected_value\n\n\n@pytest.fixture(params=[1, 2])\ndef transformer(request):\n    return MorganFingerprintTransformer(fpSize=5, n_jobs=request.param)\n\n\n@pytest.fixture(params=[np.nan, None, np.inf, 0, -100])\ndef smiles_pipeline(request, transformer):\n    return Pipeline(\n        [\n            (\"s2m\", SmilesToMolTransformer()),\n            (\"FP\", transformer),\n            (\n                \"RF\",\n                SafeInferenceWrapper(\n                    RandomForestRegressor(n_estimators=3, random_state=42),\n                    replace_value=request.param,\n                ),\n            ),\n        ]\n    )\n\n\n@pytest.fixture\ndef smiles_pipeline_trained(smiles_pipeline, SLC6A4_subset):\n    X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50\n    X_smiles = X_smiles.to_frame()\n    # Train the model\n    smiles_pipeline.fit(X_smiles, Y)\n    return smiles_pipeline\n\n\ndef test_safeinference_wrapper_basic(smiles_pipeline, SLC6A4_subset):\n    X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50\n    X_smiles = X_smiles.to_frame()\n\n    # Set safe inference mode\n    set_safe_inference_mode(smiles_pipeline, True)\n\n    # Train the model\n    smiles_pipeline.fit(X_smiles, Y)\n\n    # Test prediction\n    predictions = smiles_pipeline.predict(X_smiles)\n\n    assert len(predictions) == len(X_smiles)\n    assert not np.any(\n        equal_val(predictions, smiles_pipeline.named_steps[\"RF\"].replace_value)\n    )\n\n\ndef test_safeinference_wrapper_with_single_invalid_smiles(smiles_pipeline_trained):\n    set_safe_inference_mode(smiles_pipeline_trained, True)\n    replace_value = smiles_pipeline_trained.named_steps[\"RF\"].replace_value\n    # Test prediction\n    prediction = smiles_pipeline_trained.predict([\"invalid_smiles\"])\n    assert len(prediction) == 1\n    assert equal_val(prediction[0], replace_value)\n\n\ndef test_safeinference_wrapper_with_invalid_smiles(\n    smiles_pipeline, SLC6A4_subset, invalid_smiles_list\n):\n    X_smiles, Y = SLC6A4_subset.SMILES[:100], SLC6A4_subset.pXC50[:100]\n    X_smiles = X_smiles.to_frame()\n\n    # Set safe inference mode\n    set_safe_inference_mode(smiles_pipeline, True)\n\n    # Train the model\n    smiles_pipeline.fit(X_smiles, Y)\n    replace_value = smiles_pipeline.named_steps[\"RF\"].replace_value\n    # Create a test set with invalid SMILES\n    X_test = pd.DataFrame({\"SMILES\": X_smiles[\"SMILES\"].tolist() + invalid_smiles_list})\n    len_invalid = len(invalid_smiles_list)\n    # Test prediction with invalid SMILES\n    predictions = smiles_pipeline.predict(X_test)\n    invalid_predictions = predictions[-len_invalid:]\n    assert len(predictions) == len(X_test)\n    assert np.all(equal_val(invalid_predictions, replace_value))\n\n\ndef test_safeinference_wrapper_without_safe_mode(\n    smiles_pipeline, SLC6A4_subset, invalid_smiles_list\n):\n    X_smiles, Y = SLC6A4_subset.SMILES[:100], SLC6A4_subset.pXC50[:100]\n    X_smiles = X_smiles.to_frame()\n\n    # Ensure safe inference mode is off (default behavior)\n    set_safe_inference_mode(smiles_pipeline, False)\n\n    # Train the model\n    smiles_pipeline.fit(X_smiles, Y)\n\n    # Create a test set with invalid SMILES\n    X_test = pd.DataFrame({\"SMILES\": X_smiles[\"SMILES\"].tolist() + invalid_smiles_list})\n\n    # Test prediction with invalid SMILES\n    with pytest.raises(Exception):\n        smiles_pipeline.predict(X_test)\n\n\n@skip_pandas_output_test\ndef test_safeinference_wrapper_pandas_output(\n    smiles_pipeline, SLC6A4_subset, pandas_output\n):\n    X_smiles = SLC6A4_subset.SMILES[:100].to_frame()\n\n    # Set safe inference mode\n    set_safe_inference_mode(smiles_pipeline, True)\n\n    # Fit and transform (up to the FP step)\n    result = smiles_pipeline[:-1].fit_transform(X_smiles)\n    assert isinstance(result, pd.DataFrame)\n    assert result.shape[0] == len(X_smiles)\n    assert result.shape[1] == smiles_pipeline.named_steps[\"FP\"].fpSize\n\n\n@skip_pandas_output_test\ndef test_safeinference_wrapper_get_feature_names_out(smiles_pipeline):\n    # Get feature names from the FP step\n    feature_names = smiles_pipeline.named_steps[\"FP\"].get_feature_names_out()\n    assert len(feature_names) == smiles_pipeline.named_steps[\"FP\"].fpSize\n    assert all(isinstance(name, str) for name in feature_names)\n"
  },
  {
    "path": "tests/test_sanitizer.py",
    "content": "import numpy as np\nimport pandas as pd\nimport pytest\nfrom rdkit import Chem\n\nfrom scikit_mol.utilities import CheckSmilesSanitization\n\nfrom .fixtures import smiles_list, smiles_list_with_invalid\n\n\n@pytest.fixture\ndef sanitizer():\n    return CheckSmilesSanitization()\n\n\n@pytest.fixture\ndef return_mol_sanitizer():\n    return CheckSmilesSanitization(return_mol=True)\n\n\ndef test_checksmilessanitation(smiles_list, smiles_list_with_invalid, sanitizer):\n    smiles_list_sanitized, errors = sanitizer.sanitize(smiles_list_with_invalid)\n    assert len(smiles_list_with_invalid) > len(smiles_list_sanitized)\n    assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)])\n    assert errors[0] == sanitizer.errors.SMILES[0]\n\n\ndef test_checksmilessanitation_x_and_y(\n    smiles_list, smiles_list_with_invalid, sanitizer\n):\n    smiles_list_sanitized, y_sanitized, errors, y_errors = sanitizer.sanitize(\n        smiles_list_with_invalid, list(range(len(smiles_list_with_invalid)))\n    )\n    assert len(smiles_list_with_invalid) > len(smiles_list_sanitized)\n    assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)])\n    assert errors[0] == sanitizer.errors.SMILES[0]\n    # Test that y is correctly split into y_error and the rest\n    assert all([a == b for a, b in zip(y_sanitized, list(range(len(smiles_list) - 1)))])\n    assert y_errors[0] == len(smiles_list_with_invalid) - 1  # Last smiles is invalid\n\n\ndef test_checksmilessanitation_np(smiles_list, smiles_list_with_invalid, sanitizer):\n    smiles_list_sanitized, errors = sanitizer.sanitize(\n        np.array(smiles_list_with_invalid)\n    )\n    assert len(smiles_list_with_invalid) > len(smiles_list_sanitized)\n    assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)])\n    assert errors[0] == sanitizer.errors.SMILES[0]\n\n\ndef test_checksmilessanitation_numpy(smiles_list, smiles_list_with_invalid, sanitizer):\n    smiles_list_sanitized, errors = sanitizer.sanitize(\n        pd.Series(smiles_list_with_invalid)\n    )\n    assert len(smiles_list_with_invalid) > len(smiles_list_sanitized)\n    assert all([a == b for a, b in zip(smiles_list, smiles_list_sanitized)])\n    assert errors[0] == sanitizer.errors.SMILES[0]\n\n\ndef test_checksmilessanitation_return_mol(\n    smiles_list, smiles_list_with_invalid, return_mol_sanitizer\n):\n    smiles_list_sanitized, errors = return_mol_sanitizer.sanitize(\n        smiles_list_with_invalid\n    )\n    assert len(smiles_list_with_invalid) > len(smiles_list_sanitized)\n    assert all(\n        [\n            a == b\n            for a, b in zip(\n                smiles_list,\n                [Chem.MolToSmiles(smiles) for smiles in smiles_list_sanitized],\n            )\n        ]\n    )\n    assert errors[0] == return_mol_sanitizer.errors.SMILES[0]\n"
  },
  {
    "path": "tests/test_scikit_mol.py",
    "content": "\ndef test_load_data(data):\n    assert len(data) > 0\n\n"
  },
  {
    "path": "tests/test_smilestomol.py",
    "content": "import numpy as np\nimport pandas as pd\nimport pytest\nimport sklearn\nfrom packaging.version import Version\nfrom rdkit import Chem\nfrom sklearn import clone\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.core import (\n    DEFAULT_MOL_COLUMN_NAME,\n    SKLEARN_VERSION_PANDAS_OUT,\n    InvalidMol,\n)\n\nfrom .fixtures import (\n    skip_pandas_output_test,\n    smiles_container,\n    smiles_list,\n    smiles_list_with_invalid,\n)\n\n\n@pytest.fixture\ndef smilestomol_transformer():\n    return SmilesToMolTransformer()\n\n\ndef test_smilestomol(smiles_container, smilestomol_transformer):\n    result_mols = smilestomol_transformer.transform(smiles_container)\n    result_smiles = [Chem.MolToSmiles(mol) for mol in result_mols.flatten()]\n    if isinstance(smiles_container, pd.DataFrame):\n        expected_smiles = smiles_container.iloc[:, 0].tolist()\n    else:\n        expected_smiles = smiles_container\n    assert all([a == b for a, b in zip(expected_smiles, result_smiles)])\n\n\ndef test_smilestomol_transform(smilestomol_transformer, smiles_container):\n    result = smilestomol_transformer.transform(smiles_container)\n    assert len(result) == len(smiles_container)\n    assert all(isinstance(mol, Chem.Mol) for mol in result.flatten())\n\n\ndef test_smilestomol_fit(smilestomol_transformer, smiles_container):\n    result = smilestomol_transformer.fit(smiles_container)\n    assert result == smilestomol_transformer\n\n\ndef test_smilestomol_clone(smilestomol_transformer):\n    t2 = clone(smilestomol_transformer)\n    params = smilestomol_transformer.get_params()\n    params_2 = t2.get_params()\n    assert all([params[key] == params_2[key] for key in params.keys()])\n\n\ndef test_smilestomol_unsanitzable(smiles_list_with_invalid, smilestomol_transformer):\n    with pytest.raises(ValueError):\n        smilestomol_transformer.transform(smiles_list_with_invalid)\n\n\ndef test_descriptor_transformer_parallel(smiles_container, smilestomol_transformer):\n    smilestomol_transformer.set_params(n_jobs=2)\n    mol_list = smilestomol_transformer.transform(smiles_container)\n    if isinstance(smiles_container, pd.DataFrame):\n        expected_smiles = smiles_container.iloc[:, 0].tolist()\n    else:\n        expected_smiles = smiles_container\n    assert all(\n        [\n            a == b\n            for a, b in zip(\n                expected_smiles, [Chem.MolToSmiles(mol) for mol in mol_list.flatten()]\n            )\n        ]\n    )\n\n\ndef test_smilestomol_inverse_transform(smilestomol_transformer, smiles_container):\n    mols = smilestomol_transformer.transform(smiles_container)\n    result = smilestomol_transformer.inverse_transform(mols)\n    assert len(result) == len(smiles_container)\n    assert all(isinstance(smiles, str) for smiles in result.flatten())\n\n\ndef test_smilestomol_inverse_transform_with_invalid(\n    smiles_list_with_invalid, smilestomol_transformer\n):\n    smilestomol_transformer.set_params(safe_inference_mode=True)\n\n    # Forward transform\n    mols = smilestomol_transformer.transform(smiles_list_with_invalid)\n\n    # Inverse transform\n    result = smilestomol_transformer.inverse_transform(mols)\n\n    assert len(result) == len(smiles_list_with_invalid)\n\n    # Check that all but the last element are the same as the original SMILES\n    for original, res in zip(smiles_list_with_invalid[:-1], result[:-1].flatten()):\n        assert isinstance(res, str)\n        assert original == res\n\n    # Check that the last element is an InvalidMol instance\n    assert isinstance(result[-1].item(), InvalidMol)\n    assert \"Invalid SMILES\" in result[-1].item().error\n    assert smiles_list_with_invalid[-1] in result[-1].item().error\n\n\ndef test_smilestomol_get_feature_names_out(smilestomol_transformer):\n    feature_names = smilestomol_transformer.get_feature_names_out()\n    assert feature_names == [DEFAULT_MOL_COLUMN_NAME]\n\n\ndef test_smilestomol_safe_inference(smiles_list_with_invalid, smilestomol_transformer):\n    smilestomol_transformer.set_params(safe_inference_mode=True)\n    result = smilestomol_transformer.transform(smiles_list_with_invalid)\n\n    assert len(result) == len(smiles_list_with_invalid)\n    assert isinstance(result, np.ndarray)\n\n    # Check that all but the last element are valid RDKit Mol objects\n    for mol in result[:-1].flatten():\n        assert isinstance(mol, Chem.Mol)\n        assert mol is not None\n\n    # Check that the last element is an InvalidMol instance\n    last_mol = result[-1].item()\n    assert isinstance(last_mol, InvalidMol)\n\n    # Check if the error message is correctly set for the invalid SMILES\n    assert \"Invalid SMILES\" in last_mol.error\n    assert smiles_list_with_invalid[-1] in last_mol.error\n\n\n@pytest.mark.skipif(\n    not skip_pandas_output_test,\n    reason=\"Pandas output not supported in this sklearn version\",\n)\ndef test_smilestomol_safe_inference_pandas_output(\n    smiles_list_with_invalid, smilestomol_transformer, pandas_output\n):\n    smilestomol_transformer.set_params(safe_inference_mode=True)\n    result = smilestomol_transformer.transform(smiles_list_with_invalid)\n\n    assert len(result) == len(smiles_list_with_invalid)\n    assert isinstance(result, pd.DataFrame)\n    assert result.columns == [DEFAULT_MOL_COLUMN_NAME]\n\n    # Check that all but the last element are valid RDKit Mol objects\n    for mol in result[DEFAULT_MOL_COLUMN_NAME][:-1]:\n        assert isinstance(mol, Chem.Mol)\n        assert mol is not None\n\n    # Check that the last element is an InvalidMol instance\n    last_mol = result[DEFAULT_MOL_COLUMN_NAME].iloc[-1]\n    assert isinstance(last_mol, InvalidMol)\n\n    # Check if the error message is correctly set for the invalid SMILES\n    assert \"Invalid SMILES\" in last_mol.error\n    assert smiles_list_with_invalid[-1] in last_mol.error\n\n\n@skip_pandas_output_test\ndef test_pandas_output(smiles_container, smilestomol_transformer, pandas_output):\n    mols = smilestomol_transformer.transform(smiles_container)\n    assert isinstance(mols, pd.DataFrame)\n    assert mols.shape[0] == len(smiles_container)\n    assert mols.columns.tolist() == [DEFAULT_MOL_COLUMN_NAME]\n"
  },
  {
    "path": "tests/test_transformers.py",
    "content": "# checking that the new transformers can work within a scikitlearn pipeline of the kind\n# Pipeline([(\"s2m\", SmilesToMol()), (\"FP\", FPTransformer()), (\"RF\", RandomForestRegressor())])\n# using some test data stored in ./data/SLC6A4_active_excape_subset.csv\n\n# to run as\n# pytest tests/test_transformers.py --> tests/test_transformers.py::test_transformer PASSED\n\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport sklearn\nfrom packaging.version import Version\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.pipeline import Pipeline\n\nfrom scikit_mol.conversions import SmilesToMolTransformer\nfrom scikit_mol.core import SKLEARN_VERSION_PANDAS_OUT\nfrom scikit_mol.descriptors import MolecularDescriptorTransformer\nfrom scikit_mol.fingerprints import (\n    AtomPairFingerprintTransformer,\n    AvalonFingerprintTransformer,\n    MACCSKeysFingerprintTransformer,\n    MHFingerprintTransformer,\n    MorganFingerprintTransformer,\n    RDKitFingerprintTransformer,\n    SECFingerprintTransformer,\n    TopologicalTorsionFingerprintTransformer,\n)\nfrom scikit_mol.fingerprints.baseclasses import BaseFpsTransformer\n\nfrom .fixtures import (\n    SLC6A4_subset,\n    SLC6A4_subset_with_cddd,\n    combined_transformer,\n    featurizer,\n    mols_container,\n    skip_pandas_output_test,\n)\n\n\ndef test_transformer(SLC6A4_subset):\n    # load some toy data for quick testing on a small number of samples\n    X_smiles, Y = SLC6A4_subset.SMILES, SLC6A4_subset.pXC50\n    X_smiles = X_smiles.to_frame()\n    X_train, X_test = X_smiles[:128], X_smiles[128:]\n    Y_train, Y_test = Y[:128], Y[128:]\n\n    # run FP with default parameters except when useCounts can be given as an argument\n    FP_dict = {\n        \"MACCSTransformer\": [MACCSKeysFingerprintTransformer, None],\n        \"RDKitFPTransformer\": [RDKitFingerprintTransformer, None],\n        \"AtomPairFingerprintTransformer\": [AtomPairFingerprintTransformer, False],\n        \"AtomPairFingerprintTransformer useCounts\": [\n            AtomPairFingerprintTransformer,\n            True,\n        ],\n        \"TopologicalTorsionFingerprintTransformer\": [\n            TopologicalTorsionFingerprintTransformer,\n            False,\n        ],\n        \"TopologicalTorsionFingerprintTransformer useCounts\": [\n            TopologicalTorsionFingerprintTransformer,\n            True,\n        ],\n        \"MorganTransformer\": [MorganFingerprintTransformer, False],\n        \"MorganTransformer useCounts\": [MorganFingerprintTransformer, True],\n        \"SECFingerprintTransformer\": [SECFingerprintTransformer, None],\n        \"MHFingerprintTransformer\": [MHFingerprintTransformer, None],\n        \"AvalonFingerprintTransformer\": [AvalonFingerprintTransformer, None],\n    }\n\n    # fit on toy data and print train/test score if successful or collect the failed FP\n    failed_FP = []\n    for FP_name, (FP, useCounts) in FP_dict.items():\n        try:\n            print(\n                f\"\\nrunning pipeline fitting and scoring for {FP_name} with useCounts={useCounts}\"\n            )\n            if useCounts is None:\n                pipeline = Pipeline(\n                    [\n                        (\"s2m\", SmilesToMolTransformer()),\n                        (\"FP\", FP()),\n                        (\"RF\", RandomForestRegressor()),\n                    ]\n                )\n            else:\n                pipeline = Pipeline(\n                    [\n                        (\"s2m\", SmilesToMolTransformer()),\n                        (\"FP\", FP(useCounts=useCounts)),\n                        (\"RF\", RandomForestRegressor()),\n                    ]\n                )\n            pipeline.fit(X_train, Y_train)\n            train_score = pipeline.score(X_train, Y_train)\n            test_score = pipeline.score(X_test, Y_test)\n            print(\n                f\"\\nfitting and scoring completed train_score={train_score}, test_score={test_score}\"\n            )\n        except:\n            print(\n                f\"\\n!!!! FAILED pipeline fitting and scoring for {FP_name} with useCounts={useCounts}\"\n            )\n            failed_FP.append(FP_name)\n            pass\n\n    # overall result\n    assert len(failed_FP) == 0, f\"the following FP have failed {failed_FP}\"\n\n\n@skip_pandas_output_test\ndef test_transformer_pandas_output(SLC6A4_subset, pandas_output):\n    # load some toy data for quick testing on a small number of samples\n    X_smiles = SLC6A4_subset.SMILES\n    X_smiles = X_smiles.to_frame()\n\n    # run FP with default parameters except when useCounts can be given as an argument\n    FP_dict = {\n        \"MACCSTransformer\": [MACCSKeysFingerprintTransformer, None],\n        \"RDKitFPTransformer\": [RDKitFingerprintTransformer, None],\n        \"AtomPairFingerprintTransformer\": [AtomPairFingerprintTransformer, False],\n        \"AtomPairFingerprintTransformer useCounts\": [\n            AtomPairFingerprintTransformer,\n            True,\n        ],\n        \"TopologicalTorsionFingerprintTransformer\": [\n            TopologicalTorsionFingerprintTransformer,\n            False,\n        ],\n        \"TopologicalTorsionFingerprintTransformer useCounts\": [\n            TopologicalTorsionFingerprintTransformer,\n            True,\n        ],\n        \"MorganTransformer\": [MorganFingerprintTransformer, False],\n        \"MorganTransformer useCounts\": [MorganFingerprintTransformer, True],\n        \"SECFingerprintTransformer\": [SECFingerprintTransformer, None],\n        \"MHFingerprintTransformer\": [MHFingerprintTransformer, None],\n        \"AvalonFingerprintTransformer\": [AvalonFingerprintTransformer, None],\n    }\n\n    # fit on toy data and check that the output is a pandas dataframe\n    failed_FP = []\n    for FP_name, (FP, useCounts) in FP_dict.items():\n        try:\n            print(\n                f\"\\nrunning pipeline fitting and scoring for {FP_name} with useCounts={useCounts}\"\n            )\n            if useCounts is None:\n                pipeline = Pipeline([(\"s2m\", SmilesToMolTransformer()), (\"FP\", FP())])\n            else:\n                pipeline = Pipeline(\n                    [(\"s2m\", SmilesToMolTransformer()), (\"FP\", FP(useCounts=useCounts))]\n                )\n            pipeline.fit(X_smiles)\n            X_transformed = pipeline.transform(X_smiles)\n            assert isinstance(\n                X_transformed, pd.DataFrame\n            ), f\"the output of {FP_name} is not a pandas dataframe\"\n            assert (\n                X_transformed.shape[0] == len(X_smiles)\n            ), f\"the number of rows in the output of {FP_name} is not equal to the number of samples\"\n            assert (\n                len(X_transformed.columns) == pipeline.named_steps[\"FP\"].fpSize\n            ), f\"the number of columns in the output of {FP_name} is not equal to the number of bits\"\n            print(f\"\\nfitting and transforming completed\")\n\n        except Exception as err:\n            print(\n                f\"\\n!!!! FAILED pipeline fitting and transforming for {FP_name} with useCounts={useCounts}\"\n            )\n            print(\"\\n\".join(err.args))\n            failed_FP.append(FP_name)\n            pass\n\n    # overall result\n    assert (\n        len(failed_FP) == 0\n    ), f\"the following FP have failed pandas transformation {failed_FP}\"\n\n\n@skip_pandas_output_test\ndef test_pandas_out_same_values(featurizer, mols_container):\n    featurizer_default = sklearn.base.clone(featurizer)\n    featurizer_default.set_output(transform=\"default\")\n    featurizer_pandas = sklearn.base.clone(featurizer)\n    featurizer_pandas.set_output(transform=\"pandas\")\n    result_default = featurizer_default.fit_transform(mols_container)\n    result_pandas = featurizer_pandas.fit_transform(mols_container)\n    assert isinstance(result_default, np.ndarray)\n    assert isinstance(result_pandas, pd.DataFrame)\n    assert result_default.shape == result_pandas.shape\n    featurizer_class_with_nan = MolecularDescriptorTransformer\n    if isinstance(featurizer, featurizer_class_with_nan):\n        assert (\n            pd.isna(result_default) == pd.isna(result_pandas.values)\n        ).all(), (\n            \"NaN values are not in the same positions in the default and pandas output\"\n        )\n        nan_replacement = 0.0\n        result_default = np.nan_to_num(result_default, nan=nan_replacement)\n        result_pandas = result_pandas.fillna(nan_replacement)\n    else:\n        assert (result_default == result_pandas.values).all()\n\n\n@skip_pandas_output_test\ndef test_combined_transformer_pandas_out(\n    combined_transformer, SLC6A4_subset_with_cddd, pandas_output\n):\n    result = combined_transformer.fit_transform(SLC6A4_subset_with_cddd)\n    assert isinstance(result, pd.DataFrame)\n    assert result.shape[0] == SLC6A4_subset_with_cddd.shape[0]\n    n_cddd_features = SLC6A4_subset_with_cddd.columns.str.match(r\"^cddd_\\d+$\").sum()\n    pipeline_skmol = combined_transformer.named_transformers_[\"pipeline-1\"]\n    featurizer_skmol = pipeline_skmol[-1]\n    if isinstance(featurizer_skmol, BaseFpsTransformer):\n        n_skmol_features = featurizer_skmol.fpSize\n    elif isinstance(featurizer_skmol, MolecularDescriptorTransformer):\n        n_skmol_features = len(featurizer_skmol.desc_list)\n    else:\n        raise ValueError(f\"Unexpected featurizer type {type(featurizer_skmol)}\")\n    expected_n_features = n_cddd_features + n_skmol_features\n    assert result.shape[1] == expected_n_features\n"
  },
  {
    "path": "uv.toml",
    "content": "required-version = \">=0.5.24\"\n"
  }
]