82 lines
1.7 KiB
Nix
82 lines
1.7 KiB
Nix
{
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lib,
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buildPythonPackage,
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fetchFromGitHub,
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apricot-select,
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numba,
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numpy,
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nose,
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pytestCheckHook,
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pythonOlder,
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scikit-learn,
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scipy,
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setuptools,
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torchvision,
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tqdm,
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}:
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buildPythonPackage rec {
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pname = "apricot-select";
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version = "0.6.1";
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pyproject = true;
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disabled = pythonOlder "3.7";
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src = fetchFromGitHub {
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owner = "jmschrei";
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repo = "apricot";
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rev = "refs/tags/${version}";
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hash = "sha256-v9BHFxmlbwXVipPze/nV35YijdFBuka3gAl85AlsffQ=";
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};
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postPatch = ''
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sed -i '/"nose"/d' setup.py
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'';
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build-system = [ setuptools ];
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dependencies = [
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numba
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numpy
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scikit-learn
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scipy
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torchvision
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tqdm
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];
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nativeCheckInputs = [
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nose
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pytestCheckHook
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];
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pythonImportsCheck = [ "apricot" ];
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disabledTestPaths = [
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# Tests require nose
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"tests/test_optimizers/test_knapsack_facility_location.py"
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"tests/test_optimizers/test_knapsack_feature_based.py"
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];
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# NOTE: These tests seem to be flaky.
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disabledTests = [
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"test_digits_modular"
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"test_digits_modular_object"
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"test_digits_modular_sparse"
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"test_digits_sqrt_modular"
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"test_digits_sqrt_modular_object"
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"test_digits_sqrt_modular_sparse"
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];
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# NOTE: Tests are disabled by default because they can run for hours and timeout on Hydra.
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doCheck = false;
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passthru.tests.check = apricot-select.overridePythonAttrs { doCheck = true; };
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meta = with lib; {
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description = "Module for submodular optimization for the purpose of selecting subsets of massive data sets";
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homepage = "https://github.com/jmschrei/apricot";
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changelog = "https://github.com/jmschrei/apricot/releases/tag/${version}";
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license = licenses.mit;
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maintainers = with maintainers; [ fab ];
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};
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}
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