setup.cfg

[metadata]
name = data_iq
version = 0.1.0
description = Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
long_description = file: README.md
long_description_content_type = text/markdown
url = https://arxiv.org/abs/2202.08836
author = Nabeel Seedat
license = MIT
license_file = LICENSE
classifiers =
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent
    Programming Language :: Python :: 3
    Topic :: Scientific/Engineering :: Artificial Intelligence
keywords = data-centric AI, data quality, uncertainty

[options]
packages = find:
install_requires =
    autograd-lib
    numpy
    torch>=1.9.1
    torchvision>=0.11.1
    importlib-metadata;python_version<"3.8"
include_package_data = True
package_dir =
    =.
zip_safe = False

[options.packages.find]
where = .

[options.extras_require]
testing =
    SciencePlots
    aum
    autograd-lib
    bandit
    catboost>=1.1
    click
    flake8
    ipykernel
    isort
    lightgbm
    matplotlib
    nbconvert
    nltk
    notebook
    numpy
    pandas>=1.3.5
    pre-commit
    protobuf>=4.21.8
    py
    pytest
    pytest-benchmark
    pytest-cov
    pytest-xdist[psutil]
    pytest-xprocess
    scikit-learn>=1.0.2
    scipy>=1.7.3
    sdv
    seaborn
    tqdm
    umap-learn
    uq360
    xgboost>=1.6.2
    xlrd