mljar-supervised/setup.py at master · mljar/mljar-supervised · GitHub

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from setuptools import setup, find_packages
from codecs import open
from os import path

here = path.abspath(path.dirname(__file__))

# Get the long description from the README file
with open(path.join(here, "README.md"), encoding="utf-8") as f:
    long_description = f.read()

TFM_REQUIREMENTS = [
    "structured-data-models==0.2.1",
    "torch>=2.7,<3",
    "huggingface_hub>=1,<3",
    "psutil>=5.0",
]

setup(
    name="mljar-supervised",
    version="1.4.0b2",
    description="Automated Machine Learning for Humans",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/mljar/mljar-supervised",
    author="MLJAR, Sp. z o.o.",
    author_email="contact@mljar.com",
    license="MIT",
    packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
    install_requires=open("requirements.txt").readlines(),
    extras_require={
        # Compatibility alias: feature discovery is included in every install.
        "feature-discovery": [],
        "apps": ["mercury>=3.2.12,<4"],
        "kumo": TFM_REQUIREMENTS,
        "tabiclv2": TFM_REQUIREMENTS,
        "tfm": TFM_REQUIREMENTS,
        "realmlp": ["pytabkit>=1.7.2,<1.8", "torch>=2.7,<3", "psutil>=5.0"],
        "tabm": ["pytabkit>=1.7.2,<1.8", "torch>=2.7,<3", "psutil>=5.0"],
        "all": TFM_REQUIREMENTS + [
            "pytabkit>=1.7.2,<1.8",
        ],
    },
    include_package_data=True,
    python_requires='>=3.11',
    classifiers=[
        "Programming Language :: Python",
        "Programming Language :: Python :: 3.11",
        "Programming Language :: Python :: 3.12",
    ],
    keywords=[
        "automated machine learning",
        "automl",
        "machine learning",
        "data science",
        "data mining",
        "mljar",
        "random forest",
        "decision tree",
        "xgboost",
        "lightgbm",
        "catboost",
        "neural network",
        "extra trees",
        "linear model",
        "features selection",
        "features engineering"
    ],
)