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What happens if I push all of nixpkgs
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1.6 kB · 85 lines
Nix
at flake-libs
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586{ lib, buildPythonPackage, fetchFromGitHub,
# dependencies networkx, numpy, scipy, scikit-learn, pandas, pyparsing, torch, statsmodels, tqdm, joblib, opt-einsum, xgboost, google-generativeai,
# tests pytestCheckHook, pytest-cov-stub, coverage, mock, black,}:buildPythonPackage rec { pname = "pgmpy"; version = "0.1.26"; pyproject = true;
src = fetchFromGitHub { owner = "pgmpy"; repo = "pgmpy"; tag = "v${version}"; hash = "sha256-RusVREhEXYaJuQXTaCQ7EJgbo4+wLB3wXXCAc3sBGtU="; };
dependencies = [ networkx numpy scipy scikit-learn pandas pyparsing torch statsmodels tqdm joblib opt-einsum xgboost google-generativeai ];
disabledTests = [ # flaky: # AssertionError: -45.78899127622197 != -45.788991276221964 "test_score"
# self.assertTrue(np.isclose(coef, dep_coefs[i], atol=1e-4)) # AssertionError: False is not true "test_pillai"
# requires optional dependency daft "test_to_daft" ];
nativeCheckInputs = [ pytestCheckHook # xdoctest pytest-cov-stub coverage mock black ];
meta = { description = "Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks"; homepage = "https://github.com/pgmpy/pgmpy"; changelog = "https://github.com/pgmpy/pgmpy/releases/tag/v${version}"; license = lib.licenses.mit; maintainers = with lib.maintainers; [ happysalada ]; };}