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setup.py
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setup.py
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#!/usr/bin/env python
from setuptools import setup, find_packages
import versioneer
def unique_flatten_dict(d):
return list(set(sum( d.values(), [] )))
core_requires = [
'numpy',
'pandas',
'setuptools',
'typing-extensions',
'pyarrow>=0.15.0',
'psutil',
]
stubs = [
'pandas-stubs', 'types-requests', 'ipython', 'tqdm-stubs'
]
dev_extras = {
'docs': ['sphinx==3.4.3', 'docutils==0.16', 'sphinx_autodoc_typehints==1.11.1', 'sphinx-rtd-theme==0.5.1', 'Jinja2<3.1'],
'test': ['flake8>=5.0', 'mock', 'mypy', 'pytest'] + stubs,
'testai': [
'numba>=0.57.1' # https://github.com/numba/numba/issues/8615
],
'build': ['build']
}
base_extras_light = {
'dirty-cat': [
'dirty-cat',
'scipy',
'scikit-learn<=1.3.2',]
}
base_extras_heavy = {
'rapids': ['cuml', 'cudf', 'cupy'],
}
base_extras = {**base_extras_light, **base_extras_heavy}
extras_require = {
**base_extras_light,
**base_extras_heavy,
**dev_extras,
#kitchen sink for GPU users -- recommended
'all': unique_flatten_dict(base_extras),
#kitchen sink for contributors, skips rapids
'dev': unique_flatten_dict(base_extras_light) + unique_flatten_dict(dev_extras),
}
setup(
name='cu-cat',
version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(),
packages = find_packages(),
platforms='any',
description = 'An end-to-end gpu Python library that encodes categorical variables into machine-learnable numerics',
long_description=open("./README.md").read(),
long_description_content_type='text/markdown',
url='https://github.com/graphistry/cu-cat',
download_url= 'https://github.com/graphistry/cu-cat',
python_requires='>=3.8',
author='The Graphistry Team',
author_email='pygraphistry@graphistry.com',
install_requires=core_requires,
extras_require=extras_require,
license='BSD',
# dependency_links=['https://pypi.nvidia.com'],
keywords=['cudf', 'cuml', 'GPU', 'Rapids']
)