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What happens if I push all of nixpkgs
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2.2 kB · 96 lines
Nix
at master
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buildPythonPackage rec { pname = "imbalanced-learn"; version = "0.14.0"; pyproject = true;
disabled = pythonOlder "3.8";
src = fetchFromGitHub { owner = "scikit-learn-contrib"; repo = "imbalanced-learn"; tag = version; hash = "sha256-1R7jHOkTO3zK9bkUvvOPQ420ofqIO7J1rqixFEbApR0="; };
build-system = [ setuptools setuptools-scm ];
dependencies = [ joblib numpy scikit-learn scipy threadpoolctl ];
optional-dependencies = { optional = [ keras pandas tensorflow ]; };
pythonImportsCheck = [ "imblearn" ];
nativeCheckInputs = [ pytestCheckHook pandas ];
preCheck = '' export HOME=$TMPDIR # The GitHub source contains too many files picked up by pytest like # examples and documentation files which can't pass. cd $out/${python.sitePackages} '';
disabledTestPaths = [ # require tensorflow and keras, but we don't want to # add them to nativeCheckInputs just for this tests "imblearn/keras" "imblearn/tensorflow" # even with precheck directory change, pytest still tries to test docstrings "imblearn/tests/test_docstring_parameters.py" # Skip dependencies test - pythonImportsCheck already does this "imblearn/utils/tests/test_min_dependencies.py" ];
disabledTests = [ # Broken upstream test https://github.com/scikit-learn-contrib/imbalanced-learn/issues/1131 "test_estimators_compatibility_sklearn" "test_balanced_bagging_classifier_with_function_sampler" ];
meta = { description = "Library offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance"; homepage = "https://github.com/scikit-learn-contrib/imbalanced-learn"; changelog = "https://github.com/scikit-learn-contrib/imbalanced-learn/releases/tag/${version}"; license = lib.licenses.mit; maintainers = with lib.maintainers; [ rmcgibbo philipwilk ]; };}