Merge pull request #359 from rg2410/issue-358

Sample weight sliced to work with cross validation, issue #358

eli5/sklearn/permutation_importance.py

@@ -214,8 +214,12 @@ def _cv_scores_importances(self, X, y, groups=None, **fit_params):
         cv = check_cv(self.cv, y, is_classifier(self.estimator))
         feature_importances = []  # type: List
         base_scores = []  # type: List[float]
+        weights = fit_params.pop('sample_weight', None)
+        fold_fit_params = fit_params.copy()
         for train, test in cv.split(X, y, groups):
-            est = clone(self.estimator).fit(X[train], y[train], **fit_params)
+            if weights is not None:
+                fold_fit_params['sample_weight'] = weights[train]
+            est = clone(self.estimator).fit(X[train], y[train], **fold_fit_params)
             score_func = partial(self.scorer_, est)
             _base_score, _importances = self._get_score_importances(
                 score_func, X[test], y[test])

tests/test_sklearn_permutation_importance.py

@@ -3,7 +3,7 @@
 import numpy as np
 from sklearn.base import is_classifier, is_regressor
 from sklearn.svm import SVR, SVC
-from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.model_selection import train_test_split, cross_val_score
 from sklearn.pipeline import make_pipeline
 from sklearn.feature_selection import SelectFromModel
@@ -165,6 +165,7 @@ def test_explain_weights(iris_train):
         for _expl in res:
             assert "petal width (cm)" in _expl
 
+
 def test_pandas_xgboost_support(iris_train):
     xgboost = pytest.importorskip('xgboost')
     pd = pytest.importorskip('pandas')
@@ -175,3 +176,17 @@ def test_pandas_xgboost_support(iris_train):
     est.fit(X, y)
     # we expect no exception to be raised here when using xgboost with pd.DataFrame
     perm = PermutationImportance(est).fit(X, y) 
+
+
+def test_cv_sample_weight(iris_train):
+    X, y, feature_names, target_names = iris_train
+    weights_ones = np.ones(len(y))
+    model = RandomForestClassifier(random_state=42)
+
+    # we expect no exception to be raised when passing weights with a CV
+    perm_weights = PermutationImportance(model, cv=5, random_state=42).\
+        fit(X, y, sample_weight=weights_ones)
+    perm = PermutationImportance(model, cv=5, random_state=42).fit(X, y)
+
+    # passing a vector of weights filled with one should be the same as passing no weights
+    assert (perm.feature_importances_ == perm_weights.feature_importances_).all()