Allow sklearn grid search over parameters specified as kwargs (#3791)

tests/python/test_with_sklearn.py

@@ -396,6 +396,27 @@ def test_kwargs():
     assert clf.get_params()['n_estimators'] == 1000
 
 
+def test_kwargs_grid_search():
+    tm._skip_if_no_sklearn()
+    from sklearn.model_selection import GridSearchCV
+    from sklearn import datasets
+
+    params = {'tree_method': 'hist'}
+    clf = xgb.XGBClassifier(n_estimators=1, learning_rate=1.0, **params)
+    assert clf.get_params()['tree_method'] == 'hist'
+    # 'max_leaves' is not a default argument of XGBClassifier
+    # Check we can still do grid search over this parameter
+    search_params = {'max_leaves': range(2, 5)}
+    grid_cv = GridSearchCV(clf, search_params, cv=5)
+    iris = datasets.load_iris()
+    grid_cv.fit(iris.data, iris.target)
+
+    # Expect unique results for each parameter value
+    # This confirms sklearn is able to successfully update the parameter
+    means = grid_cv.cv_results_['mean_test_score']
+    assert len(means) == len(set(means))
+
+
 @raises(TypeError)
 def test_kwargs_error():
     tm._skip_if_no_sklearn()