setup.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""The setup script."""
import os
import codecs
import re

from setuptools import setup, find_packages


description = """
Interesting fake multivariate data is harder to generate than it should be.
 Textbooks typically give definitions, two standard examples (multinomial and
 multivariate normal) and then proceed to proving theorems and propositions.
 True, one dimensional distributions can be combined, but here as well the
 source of examples is also sparse, e.g. products of distributions or copulas
 (typically Gaussian or t-copulas) applied to these 1-d examples.

For machine learning experimentation, it is useful to have an unlimited supply
 of interesting fake data, where by interesting I mean that we know certain
 properties of the data and want to test if the algorithm can pick this up. A
 great potential source of such data is graphical models.

In the current release, we generate fake data with discrete Bayesian networks
 (also known as directed graphical models).
"""

install_requirements = [
    'networkx>=2.4',
    'pandas>=0.25',
    'numpy',
    'scikit-learn>=0.21.3',
    'scipy>=1.3',
    'xarray',
]

extras_require = {
        'probability_polytope':  ["pypoman"]
    }


setup(
    name='fake_data_for_learning',
    version='0.4.4',
    long_description=description,
    long_description_content_type='text/markdown',
    project_urls={
        "Homepage": "https://munichpavel.github.io/fake-data-for-learning/",
        "Bug Tracker": "https://github.com/munichpavel/fake-data-for-learning/issues",
        "Documentation": "https://munichpavel.github.io/fake-data-docs/html/index.html",
        "Source Code": "https://github.com/munichpavel/fake-data-for-learning/",
    },
    packages=find_packages(include=['fake_data_for_learning']),
    include_package_data=False,
    python_requires='>3.6',
    install_requires=install_requirements,
    extras_require=extras_require,
    license="MIT license",
    zip_safe=False,
    classifiers=[
        'Development Status :: 2 - Pre-Alpha',
        'Intended Audience :: Developers',
        'License :: OSI Approved :: MIT License',
        'Natural Language :: English',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
    ],
    author="Paul Larsen",
    author_email='munichpavel@gmail.com'
   )