powershap/pyproject.toml at main · predict-idlab/powershap · GitHub

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[project]
name = "powershap"
version = "0.1.0.1"
description = "Feature selection using statistical significance of shap values"
authors = [{ name = "Jarne Verhaeghe, Jeroen Van Der Donckt" }]
requires-python = ">=3.9,<=3.13"
readme = "README.md"
license = "MIT"
keywords = [
    "feature selection",
    "shap",
    "data-science",
    "machine learning",
]
classifiers = [

    "Development Status :: 5 - Production/Stable",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python",
    "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",
    "Operating System :: POSIX :: Linux",
    "Operating System :: MacOS :: MacOS X",
    "Operating System :: Microsoft :: Windows",

]

dependencies = [
    "catboost>=1.0.5",
    "statsmodels>=0.14.0",
    "pandas>=1.3",
    "shap>=0.45.0,<0.46",
    "scikit-learn>=1.6",
]

[project.urls]
Repository = "https://github.com/predict-idlab/powershap"

[dependency-groups]
dev = [
    "pytest>=7.1.1,<8",
    "pytest-cov>=3.0.0,<4",
    "lightgbm>=3.3.2",
    "xgboost>=1.6.0",
    "black>=22.12.0,<23",
    "isort>=5.11.4,<6",
    "ruff>=0.0.219,<0.0.220",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.ruff]
line-length = 100
extend-select = ["Q"]
ignore = ["E402", "F403", "F811", "F401"]

[tool.black]
color = true
line-length = 100
skip-string-normalization = true
skip-magic-trailing-comma = true

[tool.isort]
line_length = 100
known_first_party = ["tsdownsample"]
multi_line_output = 3
include_trailing_comma = true
force_grid_wrap = 0
combine_as_imports = true
color_output = false
skip = "tests/toml_test.py"