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setup.py
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69 lines (64 loc) · 2.11 KB
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"""Setup script for SmallML package."""
from setuptools import setup, find_packages
from pathlib import Path
# Read README
this_directory = Path(__file__).parent
long_description_path = this_directory / "README.md"
# Only read README if it exists (for now it doesn't)
if long_description_path.exists():
long_description = long_description_path.read_text(encoding='utf-8')
else:
long_description = "SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics"
# Read version
version = {}
with open("smallml/version.py") as f:
exec(f.read(), version)
setup(
name="smallml",
version=version['__version__'],
author=version['__author__'],
author_email=version['__email__'],
description=version['__description__'],
long_description=long_description,
long_description_content_type="text/markdown",
url=version['__url__'],
packages=find_packages(exclude=["tests", "examples", "docs"]),
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
],
python_requires=">=3.9",
install_requires=[
"pymc>=5.0.0",
"arviz>=0.22.0",
"numpy>=2.3.0",
"pandas>=2.3.0",
"scikit-learn>=1.7.0",
"scipy>=1.16.0",
],
include_package_data=True,
package_data={
"smallml": ["data/*.pkl"],
},
keywords=[
"machine-learning",
"bayesian",
"transfer-learning",
"small-data",
"hierarchical-models",
"conformal-prediction",
],
project_urls={
"Bug Reports": "https://github.com/seemyon/smallml/issues",
"Source": "https://github.com/seemyon/smallml",
},
)