[dask] Enable gridsearching with skl.

python-package/xgboost/dask.py

@@ -30,7 +30,8 @@
 from .core import DMatrix, Booster, _expect
 from .training import train as worker_train
 from .tracker import RabitTracker
-from .sklearn import XGBModel, XGBClassifierBase, xgboost_model_doc
+from .sklearn import XGBModel, XGBRegressorBase, XGBClassifierBase
+from .sklearn import xgboost_model_doc
 
 # Current status is considered as initial support, many features are
 # not properly supported yet.
@@ -586,7 +587,7 @@ def client(self, clt):
 
 @xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
                    ['estimators', 'model'])
-class DaskXGBRegressor(DaskScikitLearnBase):
+class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
     # pylint: disable=missing-docstring
     def fit(self,
             X,

tests/python/test_with_dask.py

@@ -135,6 +135,25 @@ def test_dask_classifier():
             assert prediction.shape[0] == kRows
 
 
+@pytest.mark.skipif(**tm.no_sklearn())
+def test_sklearn_grid_search():
+    from sklearn.model_selection import GridSearchCV
+    with LocalCluster(n_workers=4) as cluster:
+        with Client(cluster) as client:
+            X, y = generate_array()
+            reg = xgb.dask.DaskXGBRegressor(learning_rate=0.1,
+                                            tree_method='hist')
+            reg.client = client
+            model = GridSearchCV(reg, {'max_depth': [2, 4],
+                                       'n_estimators': [5, 10]},
+                                 cv=2, verbose=1, iid=True)
+            model.fit(X, y)
+            # Expect unique results for each parameter value This confirms
+            # sklearn is able to successfully update the parameter
+            means = model.cv_results_['mean_test_score']
+            assert len(means) == len(set(means))
+
+
 def run_empty_dmatrix(client, parameters):
 
     def _check_outputs(out, predictions):