[MRG] Fixes out of bound check in *SearchCV for callable refit (scikit-learn#13417)

Author: thomasjpfan

sklearn/model_selection/_search.py

@@ -698,7 +698,8 @@ def evaluate_candidates(candidate_params):
                 self.best_index_ = self.refit(results)
                 if not isinstance(self.best_index_, (int, np.integer)):
                     raise TypeError('best_index_ returned is not an integer')
-                if self.best_index_ < 0 or self.best_index_ >= len(results):
+                if (self.best_index_ < 0 or
+                   self.best_index_ >= len(results["params"])):
                     raise IndexError('best_index_ index out of range')
             else:
                 self.best_index_ = results["rank_test_%s"

sklearn/model_selection/tests/test_search.py

@@ -651,7 +651,9 @@ def refit_callable_invalid_type(cv_results):
         clf.fit(X, y)
 
 
-def test_refit_callable_out_bound():
+@pytest.mark.parametrize('out_bound_value', [-1, 2])
+@pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV])
+def test_refit_callable_out_bound(out_bound_value, search_cv):
     """
     Test implementation catches the errors when 'best_index_' returns an
     out of bound result.
@@ -660,14 +662,13 @@ def refit_callable_out_bound(cv_results):
         """
         A dummy function tests when returned 'best_index_' is out of bounds.
         """
-        return -1
+        return out_bound_value
 
     X, y = make_classification(n_samples=100, n_features=4,
                                random_state=42)
 
-    clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]},
-                       scoring='precision', refit=refit_callable_out_bound,
-                       cv=5)
+    clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]},
+                    scoring='precision', refit=refit_callable_out_bound, cv=5)
     with pytest.raises(IndexError, match='best_index_ index out of range'):
         clf.fit(X, y)