pyproject.toml

[project]
name = "causalml"
version = "0.18.0"
description = "Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms"
readme = { file = "README.md", content-type = "text/markdown" }

authors = [
    { "name" = "Huigang Chen" },
    { "name" = "Totte Harinen" },
    { "name" = "Jeong-Yoon Lee" },
    { "name" = "Jing Pan" },
    { "name" = "Mike Yung" },
    { "name" = "Zhenyu Zhao" }
]
maintainers = [
    { name = "Jeong-Yoon Lee" }
]
classifiers = [
    "Programming Language :: Python",
    "License :: OSI Approved :: Apache Software License",
    "Operating System :: OS Independent",
]

requires-python = ">=3.11"
dependencies = [
    "forestci==0.6",
    "pathos==0.2.9",
    "numpy>=1.25.2",
    "scipy>=1.16.0",
    "matplotlib",
    "pandas>=0.24.1",
    "scikit-learn>=1.6.0",
    "statsmodels>=0.14.5",
    "seaborn",
    "xgboost",
    "pydot>=3.0.0",
    "tqdm",
    "shap",
    "dill",
    "lightgbm",
    "packaging",
    "graphviz",
    "black>=26.1.0",
]

[project.optional-dependencies]
polars = ["polars>=1.0.0"]
test = [
    "pytest>=4.6",
    "pytest-cov>=4.0",
    "causalml[polars]"
]
tf = [
    "tensorflow>=2.4.0"
]
torch = [
    "torch",
    "pyro-ppl"
]
jax = [
    "jax>=0.4.1",
    "flax>=0.11.0",
    "optax>=0.1.4",
    "orbax-checkpoint>=0.10.0",
]
# The Sphinx toolchain only.  Everything the docs import -- numpy, scikit-learn,
# matplotlib and the rest -- already comes from the package's own dependencies,
# and the neural backends are mocked in docs/conf.py rather than installed.
# nbsphinx also needs the `pandoc` binary, which is not a Python package.
docs = [
    "sphinx>=8.2,<10",
    "pydata-sphinx-theme>=0.20.0",
    "sphinxcontrib-bibtex",
    "nbsphinx",
    # nbsphinx renders every notebook code cell as `ipython3`, and that Pygments
    # lexer ships with IPython.  8.10.0 clears GHSA-29gw-9793-fvw7.
    "ipython>=8.10.0",
    # Sphinx pulls Pygments in regardless; < 2.20.0 carries a ReDoS in AdlLexer
    # (GHSA-5239-wwwm-4pmq).
    "pygments>=2.20.0",
]

[build-system]
requires = [
    "setuptools>=18.0",
    "wheel",
    "Cython",
    "numpy>=1.18.5",
    "scikit-learn>=1.6.0",
]

[project.urls]
homepage = "https://github.com/uber/causalml"

[tool.pytest.ini_options]
markers = [
    # The benchmark dataset loaders fetch from their original hosts. Tests that
    # touch the network are opt-in (`pytest -m network`) so the ordinary suite
    # stays offline and a source URL moving cannot redden an unrelated PR.
    "network: needs a benchmark dataset, downloaded or already cached",
]
filterwarnings = [
    # CausalML's own #854 argument-order deprecation must never fire during the
    # suite: it means the library called a shimmed method positionally, and a
    # user calling the API correctly would see the warning with nothing to fix.
    # Scoped to this one message rather than all FutureWarnings, so a warning
    # from numpy, pandas or scikit-learn cannot fail the build on a dependency
    # release. Tests that assert the warning use `pytest.warns`, which is
    # unaffected.
    'error:Passing .treatment. and/or .y. to \w+\(\) by position is deprecated:FutureWarning',
]

[tool.cibuildwheel]
build = ["cp311-*", "cp312-*"]
build-verbosity = 1
manylinux-x86_64-image = "manylinux_2_28"
manylinux-aarch64-image = "manylinux_2_28"
# Skip 32-bit builds
skip = ["*-win32", "*-manylinux_i686", "*-musllinux*"]