setup.py

from distutils.core import setup

setup(
    name="settree",
    packages=["settree"],
    version="0.2.1",
    author="Roy Hirsch",
    license="MIT",
    description="A framework for learning tree-based models over sets",
    long_description="Set-Tree\nExtending decision trees to process sets\n\n"
    'This is the official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21).\n'
    "This repository contains a prototypical implementaion of Set-Tree and GBeST (Gradient Boosted Set-Tree) algorithms\n"
    "The Set-Tree package can be downloaded from PIP: pip install settree\n"
    "We also supply the code and datasets for reproducing our experimetns under exps folder.\n\n"
    "In many machine learning applications, each record represents a set of items. A set is an unordered group of items,"
    " the number of items may differ between different sets. Problems comprised from sets of items are present in diverse fields,"
    " from particle physics and cosmology to statistics and computer graphics."
    " In this work, we present a novel tree-based algorithm for processing sets.\n\n"
    "Set-Tree model comprised from two components:\n"
    "Set-compatible split creteria: we specifically support the familly of split creteria defined"
    " by the following equation and parametrized by alpha and beta.\n"
    "Attention-Sets: a mechanism for allplying the split creteria to subsets of the input."
    " The attention-sets are derived forn previous split-creteria and allows the model to learn more complex set-functions.",
    author_email="royhirsch@mail.tau.ac.il",
    url="https://github.com/TAU-MLwell/Set-Tree",
    download_url="https://github.com/TAU-MLwell/Set-Tree/archive/refs/tags/0.2.1.tar.gz",
    install_requires=["numpy>=1.26.4", "scikit-learn>=1.5.1", "scipy>=1.14.0"],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
)