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        "author": "Shay Palachy",
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        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
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            "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": "imbutil\n#######\n|PyPI-Status| |PyPI-Versions| |Build-Status| |Codecov| |LICENCE|\n\nAdditions to the ``imbalanced-learn`` package.\n\n.. code-block:: python\n\n  from imbutil.combine import MinMaxRandomSampler; from imblearn import pipeline;\n  # oversampling minority classes to 100 and undersampling majority classes to 800\n  sampler = MinMaxRandomSampler(min_freq=100, max_freq=800)\n  sampling_clf = pipeline.make_pipeline(sampler, inner_clf)\n\n\n.. contents::\n\n.. section-numbering::\n\n\nInstallation\n============\n\n.. code-block:: bash\n\n  pip install imbutil\n\n\nAdditionally, the ``MinMaxRandomSampler``, in addition to ``RandomUnderSampler`` and ``RandomOverSampler`` from ``imbalanced-learn``, can technically be used with non-numeric data. However, the current implementation of ``imbalanced-learn`` forces a check for numeric data for all samplers. If you want to bypass this limitation, I have a fork of the project which does not force data to be numeric. You can install it with:\n\n.. code-block:: bash\n\n    pip install git+https://github.com/shaypal5/imbalanced-learn.git@f6adc562fafdc2198931873799e725e5abdd65a1\n\n\nBasic Use\n=========\n\n``imbutil`` additions addhere to the structure of the ``imblearn`` package:\n\ncombine\n-------\n\nContaines samplers that both under-sample and over-sample:\n\n``MinMaxRandomSampler`` - Random samples data to bring all class frequencies into a range.\n\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/imbutil.git\n\n\nInstall in development mode, and with test dependencies:\n\n.. code-block:: bash\n\n  cd imbutil\n  pip install -e \".[test]\"\n\n\nRunning the tests\n-----------------\n\nTo run the tests use:\n\n.. code-block:: bash\n\n  cd imbutil\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\n\n.. |PyPI-Status| image:: https://img.shields.io/pypi/v/imbutil.svg\n  :target: https://pypi.org/project/imbutil\n\n.. |PyPI-Versions| image:: https://img.shields.io/pypi/pyversions/imbutil.svg\n   :target: https://pypi.org/project/imbutil\n\n.. |Build-Status| image:: https://travis-ci.org/shaypal5/imbutil.svg?branch=master\n  :target: https://travis-ci.org/shaypal5/imbutil\n\n.. |LICENCE| image:: https://img.shields.io/github/license/shaypal5/imbutil.svg\n  :target: https://github.com/shaypal5/imbutil/blob/master/LICENSE\n\n.. |Codecov| image:: https://codecov.io/github/shaypal5/imbutil/coverage.svg?branch=master\n   :target: https://codecov.io/github/shaypal5/imbutil?branch=master\n\n\n",
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        "name": "imbutil",
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