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# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
# import sphinx_pypi_upload
import sys
if sys.version_info[0] < 3:
import codecs
with codecs.open("README.md", "r", encoding="utf-8") as f:
readme = f.read()
else:
import io
with io.open("README.md", "r", encoding="utf-8") as f:
readme = f.read()
setup(
name="scikit-multilearn",
version="0.2.0",
packages=find_packages(exclude=["docs", "tests", "*.tests"]),
install_requires=[
"scipy>=1.1.0",
"numpy>=1.15.1",
"liac-arff>=2.2.1",
"networkx>=2.1",
"python-louvain>=0.11",
"future>=0.16.0",
"scikit_learn>=0.19.2",
"requests>=2.18.4",
"joblib",
],
extras_require={
"gpl": [
"igraph",
],
"dev": [
"pytest>=3.3.1",
"PyHamcrest>=1.9.0",
"ipython",
"ipython-genutils",
],
"keras": [
"keras",
"tensorflow",
],
"meka": [
"whichcraft>=0.4.1",
],
"test": [
"pytest",
"pyhamcrest",
],
},
author="Piotr Szymański",
author_email="niedakh@gmail.com",
license="BSD",
long_description=readme,
url="http://scikit.ml/",
description="Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem.",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Environment :: Console",
"Environment :: Web Environment",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: BSD License",
"Operating System :: MacOS :: MacOS X",
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX",
"Programming Language :: Python",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Information Analysis",
"Topic :: Scientific/Engineering :: Bio-Informatics",
],
)