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    "info": {
        "author": "Andr\u00e9 Artelt",
        "author_email": "aartelt@techfak.uni-bielefeld.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "****\nceml\n****\n--------------------------------------------------------\nCounterfactuals for Explaining Machine Learning models\n--------------------------------------------------------\n\nceml is a Python toolbox for computing counterfactuals. Counterfactuals can be use to explain the predictions of machine learing models.\n\nIt supports many common machine learning frameworks:\n\n    - scikit-learn\n    - PyTorch\n    - Keras\n    - Tensorflow\n\nFurthermore, ceml is easy to use and can be extended very easily. See the following user guide for more information on how to use and extend ceml.\n\nInstallation\n------------\n\n**Note: Python 3.6 or higher is required!**\n\nPyPi\n++++\n\n.. code-block:: bash\n\n    pip install ceml\n\n**Note**: The package hosted on PyPi uses the cpu only. If you want to use the gpu, you have to install ceml manually - see next section.\n\nGit\n+++\nDownload or clone the repository:\n\n.. code:: bash\n\n    git clone https://github.com/andreArtelt/ceml.git\n    cd ceml\n\nInstall all requirements (listed in ``requirements.txt``):\n\n.. code:: bash\n\n    pip install -r requirements.txt\n\n**Note**: If you want to use a gpu/tpu, you have to install the gpu version of jax, tensorflow and pytorch manually. Do not use ``pip install -r requirements.txt``.\n\nInstall the toolbox itself:\n\n.. code:: bash\n\n    pip install\n\n\nQuick example\n-------------\n\n.. code-block:: python\n\n    #!/usr/bin/env python3\n    # -*- coding: utf-8 -*-\n    from sklearn.datasets import load_iris\n    from sklearn.model_selection import train_test_split\n    from sklearn.metrics import accuracy_score\n    from sklearn.tree import DecisionTreeClassifier\n\n    from ceml.sklearn import generate_counterfactual\n\n\n    if __name__ == \"__main__\":\n        # Load data\n        X, y = load_iris(True)\n        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)\n\n        # Whitelist of features - list of features we can change/use when computing a counterfactual \n        features_whitelist = None   # We can use all features\n\n        # Create and fit model\n        model = DecisionTreeClassifier(max_depth=3)\n        model.fit(X_train, y_train)\n\n        # Select data point for explaining its prediction\n        x = X_test[1,:]\n        print(\"Prediction on x: {0}\".format(model.predict([x])))\n\n        # Compute counterfactual\n        print(\"\\nCompute counterfactual ....\")\n        print(generate_counterfactual(model, x, y_target=0, features_whitelist=features_whitelist))\n\nDocumentation\n-------------\n\nDocumentation is available on readthedocs:`https://ceml.readthedocs.io/en/latest/ <https://ceml.readthedocs.io/en/latest/>`_\n\nLicense\n-------\n\nMIT license - See `LICENSE.md <LICENSE.md>`_\n\nHow to cite?\n------------\n    You can cite ceml by using the following BibTeX entry:\n\n    .. code-block::\n\n        @misc{ceml,\n                author = {Andr\u00e9 Artelt},\n                title = {CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox},\n                year = {2019},\n                publisher = {GitHub},\n                journal = {GitHub repository},\n                howpublished = {\\url{https://www.github.com/andreArtelt/ceml}}\n            }\n\n\nThird party components\n----------------------\n\n    - `numpy <https://github.com/numpy/numpy>`_\n    - `scipy <https://github.com/scipy/scipy>`_\n    - `jax <https://github.com/google/jax>`_\n    - `scikit-learn <https://github.com/scikit-learn/scikit-learn>`_\n    - `sklearn-lvq <https://github.com/MrNuggelz/sklearn-lvq>`_\n    - `PyTorch <https://github.com/pytorch/pytorch>`_\n    - `tensorflow <https://github.com/tensorflow>`_\n\n\n",
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