import setuptools

long_description = """
This package allows both automated and customized treatment of missing
values in datasets using Python. The treatments that are implemented in this
package are:

* Listwise deletion
* Pairwise deletion
* Dropping variables
* Random sample imputation
* Random hot-deck imputation
* LOCF
* NOCB
* Most frequent substitution
* Mean and median substitution
* Constant value imputation
* Random value imputation
* Interpolation
* Interpolation with seasonal adjustment
* Linear regression imputation
* Stochastic regression imputation
* Logistic regression imputation
* K-nearest neighbors imputation
* Sequential regression multiple imputation
* Multiple imputation by chained equations

All these treatments can be applied to whole datasets or parts of them and
 allow for extensive customization. The package can also recommend a
  treatment for a given dataset, inform about the treatments that are
   applicable to it, and automatically apply the best treatment.
"""

setuptools.setup(
    name='imputena',
    version='1.0',
    description='Package that allows both automated and customized treatment '
            'of missing values in datasets using Python.',
    long_description=long_description,
    long_description_content_type="text/markdown",
    packages=setuptools.find_packages(exclude=('test',)),
    url='http://github.com/macarro/imputena',
    author='Miguel Macarro',
    author_email='migmackle@alum.us.es',
    license='MIT',
    classifiers=[
        'Development Status :: 5 - Production/Stable',
        'Natural Language :: English',
        'Programming Language :: Python :: 3.5',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
        'Programming Language :: Python :: 3.8',
        'License :: OSI Approved :: MIT License',
        'Operating System :: OS Independent',
        'Topic :: Scientific/Engineering :: Mathematics',
        'Intended Audience :: Science/Research',
        'Intended Audience :: Healthcare Industry',
        'Intended Audience :: Financial and Insurance Industry'
    ],
    python_requires='>=3.5',
    install_requires=[
        'pandas',
        'numpy',
        'statsmodels',
        'sklearn'
    ],
)
