[MRG] Set min_impurity_split in gradient boosting models

Sebastian Pölsterl · Sebastian Pölsterl · commit 5385982d7b40 · 2016-12-11T11:45:50.000Z
self.min_impurity_split should be passed to DecisionTreeRegressor in BaseGradientBoosting._fit_stage. Fixes #8006
diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py
@@ -767,6 +767,7 @@ def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask,
                 min_samples_split=self.min_samples_split,
                 min_samples_leaf=self.min_samples_leaf,
                 min_weight_fraction_leaf=self.min_weight_fraction_leaf,
+                min_impurity_split=self.min_impurity_split,
                 max_features=self.max_features,
                 max_leaf_nodes=self.max_leaf_nodes,
                 random_state=random_state,
diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py
@@ -961,6 +961,19 @@ def test_max_leaf_nodes_max_depth():
         assert_equal(tree.max_depth, 1)
 
 
+def test_min_impurity_split():
+    # Test if min_impurity_split of base estimators is set
+    # Regression test for #8006
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    all_estimators = [GradientBoostingRegressor,
+                      GradientBoostingClassifier]
+
+    for GBEstimator in all_estimators:
+        est = GBEstimator(min_impurity_split=0.1).fit(X, y)
+        for tree in est.estimators_.flat:
+            assert_equal(tree.min_impurity_split, 0.1)
+
+
 def test_warm_start_wo_nestimators_change():
     # Test if warm_start does nothing if n_estimators is not changed.
     # Regression test for #3513.
