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nixpkgs/pkgs/development/python-modules/shap/default.nix
Profpatsch 4a7f99d55d treewide: with stdenv.lib; in meta -> with lib;
Part of: https://github.com/NixOS/nixpkgs/issues/108938

meta = with stdenv.lib;

is a widely used pattern. We want to slowly remove
the `stdenv.lib` indirection and encourage people
to use `lib` directly. Thus let’s start with the meta
field.

This used a rewriting script to mostly automatically
replace all occurances of this pattern, and add the
`lib` argument to the package header if it doesn’t
exist yet.

The script in its current form is available at
https://cs.tvl.fyi/depot@2f807d7f141068d2d60676a89213eaa5353ca6e0/-/blob/users/Profpatsch/nixpkgs-rewriter/default.nix
2021-01-11 10:38:22 +01:00

71 lines
1.6 KiB
Nix

{ lib, stdenv
, buildPythonPackage
, fetchFromGitHub
, isPy27
, pytestCheckHook
, numpy
, scipy
, scikitlearn
, pandas
, tqdm
, slicer
, numba
, matplotlib
, nose
, ipython
}:
buildPythonPackage rec {
pname = "shap";
version = "0.36.0";
disabled = isPy27;
src = fetchFromGitHub {
owner = "slundberg";
repo = pname;
rev = "v${version}";
sha256 = "1wxnxvbz6avzzfqjfbcqd4v879hvpq4021v31fhdpccr2q317rr9";
};
propagatedBuildInputs = [
numpy
scipy
scikitlearn
pandas
tqdm
slicer
numba
];
preCheck = ''
export HOME=$TMPDIR
# when importing the local copy the extension is not found
rm -r shap
'';
checkInputs = [ pytestCheckHook matplotlib nose ipython ];
# Those tests access the network
disabledTests = [
"test_kernel_shap_with_a1a_sparse_zero_background"
"test_kernel_shap_with_a1a_sparse_nonzero_background"
"test_kernel_shap_with_high_dim_sparse"
"test_sklearn_random_forest_newsgroups"
"test_sum_match_random_forest"
"test_sum_match_extra_trees"
"test_single_row_random_forest"
"test_sum_match_gradient_boosting_classifier"
"test_single_row_gradient_boosting_classifier"
"test_HistGradientBoostingClassifier_proba"
"test_HistGradientBoostingClassifier_multidim"
"test_sum_match_gradient_boosting_regressor"
"test_single_row_gradient_boosting_regressor"
];
meta = with lib; {
description = "A unified approach to explain the output of any machine learning model";
homepage = "https://github.com/slundberg/shap";
license = licenses.mit;
maintainers = with maintainers; [ evax ];
platforms = platforms.unix;
};
}