"""A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject

# python setup.py sdist bdist_wheel
# python setup.py register
# twine upload dist/quantile_ml-VERSION_NUMBER*

Built using this blog post as well:
https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/
https://tom-christie.github.io/articles/pypi/
http://stackoverflow.com/questions/11848030/how-include-static-files-to-setuptools-python-package
"""

# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path

here = path.abspath(path.dirname(__file__))

# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()

setup(
    name='quantile_ml',

    # Versions should comply with PEP440.  For a discussion on single-sourcing
    # the version across setup.py and the project code, see
    # https://packaging.python.org/en/latest/single_source_version.html
    # version='1.9.6',
    version=open("quantile_ml/_version.py").readlines()[-1].split()[-1].strip("\"'"),


    description='Automated machine learning for production and analytics',
    long_description=long_description,

    # The project's main homepage.
    url='https://github.com/ClimbsRocks/quantile_ml',

    # Author details
    author='Preston Parry',
    author_email='ClimbsBytes@gmail.com',

    # Choose your license
    license='MIT',

    # See https://pypi.python.org/pypi?%3Aaction=list_classifiers
    classifiers=[
        # How mature is this project? Common values are
        #   3 - Alpha
        #   4 - Beta
        #   5 - Production/Stable
        'Development Status :: 5 - Production/Stable',

        # Indicate who your project is intended for
        'Intended Audience :: Developers',
        'Intended Audience :: Information Technology',
        'Intended Audience :: Science/Research',

        'Topic :: Scientific/Engineering :: Artificial Intelligence',
        'Topic :: Scientific/Engineering :: Information Analysis',
        'Topic :: Software Development :: Libraries :: Python Modules',

        # Pick your license as you wish (should match "license" above)
        'License :: OSI Approved :: MIT License',

        # Specify the Python versions you support here. In particular, ensure
        # that you indicate whether you support Python 2, Python 3 or both.
        'Programming Language :: Python :: 2',
        # 'Programming Language :: Python :: 2.6',
        'Programming Language :: Python :: 2.7',
        'Programming Language :: Python :: 3',
        # 'Programming Language :: Python :: 3.3',
        # 'Programming Language :: Python :: 3.4',
        'Programming Language :: Python :: 3.5',
    ],

    # What does your project relate to?
    keywords=['machine learning', 'data science', 'automated machine learning', 'regressor', 'regressors', 'regression', 'classification', 'classifiers', 'classifier', 'estimators', 'predictors', 'XGBoost', 'Random Forest', 'sklearn', 'scikit-learn', 'analytics', 'analysts', 'coefficients', 'feature importances' 'analytics', 'artificial intelligence', 'subpredictors', 'ensembling', 'stacking', 'blending', 'feature engineering', 'feature extraction', 'feature selection', 'production', 'pandas', 'dataframes', 'machinejs', 'deep learning', 'tensorflow', 'deeplearning', 'lightgbm', 'gradient boosting', 'gbm', 'keras', 'production ready', 'test coverage'],

    # You can just specify the packages manually here if your project is
    # simple. Or you can use find_packages().
    packages=['quantile_ml'],

    # Alternatively, if you want to distribute just a my_module.py, uncomment
    # this:
    #   py_modules=["my_module"],

    # List run-time dependencies here.  These will be installed by pip when
    # your project is installed. For an analysis of "install_requires" vs pip's
    # requirements files see:
    # https://packaging.python.org/en/latest/requirements.html
    # We will allow the user to install XGBoost themselves. However, since it can be difficult to install, we will not force them to go through that install challenge if they're just checking out the package and want to get running with it quickly.
    install_requires=['dill', 'h5py', 'keras', 'numpy', 'pandas', 'pathos', 'python-dateutil', 'scikit-learn', 'scipy', 'tensorflow'],

    test_suite='nose.collector',
    tests_require=['nose', 'coveralls']
    # List additional groups of dependencies here (e.g. development
    # dependencies). You can install these using the following syntax,
    # for example:
    # $ pip install -e .[dev,test]
    # extras_require={
    #     'dev': ['check-manifest'],
    #     'test': ['coverage'],
    # },

    # If there are data files included in your packages that need to be
    # installed, specify them here.  If using Python 2.6 or less, then these
    # have to be included in MANIFEST.in as well.
    # include_package_data=True,
    # package_data={
    #     'corpora': ['corpora/aggregatedCorpusCleanedAndFiltered.csv']
    # }

    # Although 'package_data' is the preferred approach, in some case you may
    # need to place data files outside of your packages. See:
    # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa
    # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
    # data_files=[('my_data', ['data/data_file'])],

    # To provide executable scripts, use entry points in preference to the
    # "scripts" keyword. Entry points provide cross-platform support and allow
    # pip to create the appropriate form of executable for the target platform.
    # entry_points={
    #     'console_scripts': [
    #         'sample=sample:main',
    #     ],
    # },
)
