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{
lib,
buildPythonPackage,
fetchFromGitHub,
# dependencies
networkx,
numpy,
scipy,
scikit-learn,
pandas,
pyparsing,
torch,
statsmodels,
tqdm,
joblib,
opt-einsum,
xgboost,
google-generativeai,
# tests
pytestCheckHook,
pytest-cov,
coverage,
mock,
black,
}:
buildPythonPackage rec {
pname = "pgmpy";
version = "0.1.26";
pyproject = true;
src = fetchFromGitHub {
owner = "pgmpy";
repo = "pgmpy";
rev = "refs/tags/v${version}";
hash = "sha256-RusVREhEXYaJuQXTaCQ7EJgbo4+wLB3wXXCAc3sBGtU=";
};
dependencies = [
networkx
numpy
scipy
scikit-learn
pandas
pyparsing
torch
statsmodels
tqdm
joblib
opt-einsum
xgboost
google-generativeai
];
disabledTests = [
# flaky:
# AssertionError: -45.78899127622197 != -45.788991276221964
"test_score"
# self.assertTrue(np.isclose(coef, dep_coefs[i], atol=1e-4))
# AssertionError: False is not true
"test_pillai"
# requires optional dependency daft
"test_to_daft"
];
nativeCheckInputs = [
pytestCheckHook
# xdoctest
pytest-cov
coverage
mock
black
];
meta = {
description = "Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks";
homepage = "https://github.com/pgmpy/pgmpy";
changelog = "https://github.com/pgmpy/pgmpy/releases/tag/v${version}";
license = lib.licenses.mit;
maintainers = with lib.maintainers; [ happysalada ];
};
}
|
