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    "info": {
        "author": "Shay Palachy",
        "author_email": "shay.palachy@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "skift |skift_icon|\n##################\n|PyPI-Status| |Downloads| |PyPI-Versions| |Build-Status| |Codecov| |Codefactor| |LICENCE|\n\n.. |skift_icon| image:: https://github.com/shaypal5/skift/blob/be1f8e84d311f926fd39e8ea421525782b4cb39f/skift.png \n\n``scikit-learn`` wrappers for Python ``fastText``.\n\n.. code-block:: python\n\n  >>> from skift import FirstColFtClassifier\n  >>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])\n  >>> sk_clf = FirstColFtClassifier(lr=0.3, epoch=10)\n  >>> sk_clf.fit(df[['txt']], df['lbl'])\n  >>> sk_clf.predict([['woof']])\n  [0]\n\n.. contents::\n\n.. section-numbering::\n\n\nInstallation\n============\n\nDependencies:\n\n* ``numpy``\n* ``scipy``\n* ``scikit-learn``\n* The ``fasttext`` Python package\n\n.. code-block:: bash\n\n  pip install skift\n\n\nConfiguration\n=============\n\nBecause ``fasttext`` reads input data from files, ``skift`` has to dump the input data into temporary files for ``fasttext`` to use. A dedicated folder is created for those files on the filesystem.  By default, this storage is allocated in the system temporary storage location (i.e. /tmp on \\*nix systems).  To override this default location, use the ``SKIFT_TEMP_DIR`` environment variable:\n\n.. code-block:: bash\n\n  export SKIFT_TEMP_DIR=/path/to/desired/temp/folder\n\n**NOTE:** The directory will be created if it does not already exist.\n\n\nFeatures\n========\n\n* Adheres to the ``scikit-learn`` classifier API, including ``predict_proba``.\n* Also caters to the common use case of ``pandas.DataFrame`` inputs.\n* Enables easy stacking of ``fastText`` with other types of ``scikit-learn``-compliant classifiers.\n* Pickle-able classifier objects.\n* Built around the `official fasttext Python package <https://github.com/facebookresearch/fastText/tree/master/python>`_.\n* Pure python.\n* Supports Python 3.5+.\n* Fully tested.\n\n\nWrappers\n=========\n\n``fastText`` works only on text data, which means that it will only use a single column from a dataset which might contain many feature columns of different types. As such, a common use case is to have the ``fastText`` classifier use a single column as input, ignoring other columns. This is especially true when ``fastText`` is to be used as one of several classifiers in a stacking classifier, with other classifiers using non-textual features. \n\n``skift`` includes several ``scikit-learn``-compatible wrappers (for the `official <https://github.com/facebookresearch/fastText/tree/master/python>`_ ``fastText`` Python package) which cater to these use cases.\n\n**NOTICE:** Any additional keyword arguments provided to the classifier constructor, besides those required, will be forwarded to the ``fastText.train_supervised`` method on every call to ``fit``.\n\nStandard wrappers\n-----------------\n\nThese wrappers do not make additional assumptions on input besides those commonly made by ``scikit-learn`` classifies; i.e. that input is a 2d ``ndarray`` object and such.\n\n* ``FirstColFtClassifier`` - An sklearn classifier adapter for fasttext that takes the first column of input ``ndarray`` objects as input.\n\n.. code-block:: python\n\n  >>> from skift import FirstColFtClassifier\n  >>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])\n  >>> sk_clf = FirstColFtClassifier(lr=0.3, epoch=10)\n  >>> sk_clf.fit(df[['txt']], df['lbl'])\n  >>> sk_clf.predict([['woof']])\n  [0]\n\n* ``IdxBasedFtClassifier`` - An sklearn classifier adapter for fasttext that takes input by column index. This is set on object construction by providing the ``input_ix`` parameter to the constructor.\n\n.. code-block:: python\n\n  >>> from skift import IdxBasedFtClassifier\n  >>> df = pandas.DataFrame([[5, 'woof', 0], [83, 'meow', 1]], columns=['count', 'txt', 'lbl'])\n  >>> sk_clf = IdxBasedFtClassifier(input_ix=1, lr=0.4, epoch=6)\n  >>> sk_clf.fit(df[['count', 'txt']], df['lbl'])\n  >>> sk_clf.predict([['woof']])\n  [0]\n\n\n\npandas-dependent wrappers\n-------------------------\n\nThese wrappers assume the ``X`` parameter given to ``fit``, ``predict``, and ``predict_proba`` methods is a ``pandas.DataFrame`` object:\n\n* ``FirstObjFtClassifier`` - An sklearn adapter for fasttext using the first column of ``dtype == object`` as input.\n\n.. code-block:: python\n\n  >>> from skift import FirstObjFtClassifier\n  >>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])\n  >>> sk_clf = FirstObjFtClassifier(lr=0.2)\n  >>> sk_clf.fit(df[['txt']], df['lbl'])\n  >>> sk_clf.predict([['woof']])\n  [0]\n\n* ``ColLblBasedFtClassifier`` - An sklearn adapter for fasttext taking input by column label. This is set on object construction by providing the ``input_col_lbl`` parameter to the constructor.\n\n.. code-block:: python\n\n  >>> from skift import ColLblBasedFtClassifier\n  >>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])\n  >>> sk_clf = ColLblBasedFtClassifier(input_col_lbl='txt', epoch=8)\n  >>> sk_clf.fit(df[['txt']], df['lbl'])\n  >>> sk_clf.predict([['woof']])\n  [0]\n\nContributing\n============\n\nPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.\n\nInstalling for development\n----------------------------\n\nClone:\n\n.. code-block:: bash\n\n  git clone git@github.com:shaypal5/skift.git\n\n\nInstall in development mode, including test dependencies:\n\n.. code-block:: bash\n\n  cd skift\n  pip install -e '.[test]'\n\n\nTo also install ``fasttext``, see instructions in the Installation section.\n\n\nRunning the tests\n-----------------\n\nTo run the tests use:\n\n.. code-block:: bash\n\n  cd skift\n  pytest\n\n\nAdding documentation\n--------------------\n\nThe project is documented using the `numpy docstring conventions`_, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow `these conventions`_.\n\n.. _`numpy docstring conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n.. _`these conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n\nAdditionally, if you update this ``README.rst`` file,  use ``python setup.py checkdocs`` to validate it compiles.\n\n\nCredits\n=======\n\nCreated by Shay Palachy (shay.palachy@gmail.com).\n\nFixes: `uniaz <https://github.com/uniaz>`_, `crouffer <https://github.com/crouffer>`_.\n\n\n.. |PyPI-Status| image:: https://img.shields.io/pypi/v/skift.svg\n  :target: https://pypi.python.org/pypi/skift\n\n.. |PyPI-Versions| image:: https://img.shields.io/pypi/pyversions/skift.svg\n   :target: https://pypi.python.org/pypi/skift\n\n.. |Build-Status| image:: https://travis-ci.org/shaypal5/skift.svg?branch=master\n  :target: https://travis-ci.org/shaypal5/skift\n\n.. |LICENCE| image:: https://github.com/shaypal5/skift/blob/master/mit_license_badge.svg\n  :target: https://github.com/shaypal5/skift/blob/master/LICENSE\n\n.. https://img.shields.io/github/license/shaypal5/skift.svg\n\n.. |Codecov| image:: https://codecov.io/github/shaypal5/skift/coverage.svg?branch=master\n   :target: https://codecov.io/github/shaypal5/skift?branch=master\n\n.. |Downloads| image:: https://pepy.tech/badge/skift\n     :target: https://pepy.tech/project/skift\n     :alt: PePy stats\n\n.. |Codefactor| image:: https://www.codefactor.io/repository/github/shaypal5/skift/badge?style=plastic\n     :target: https://www.codefactor.io/repository/github/shaypal5/skift\n     :alt: Codefactor code quality\n\n\n",
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