Cleanup Python tests. (#7426)

tests/python/test_with_dask.py

@@ -137,16 +137,13 @@ def test_from_dask_array() -> None:
             prediction = prediction.compute()
 
             booster: xgb.Booster = result['booster']
-            single_node_predt = booster.predict(
-                xgb.DMatrix(X.compute())
-            )
+            single_node_predt = booster.predict(xgb.DMatrix(X.compute()))
             np.testing.assert_allclose(prediction, single_node_predt)
 
             config = json.loads(booster.save_config())
             assert int(config['learner']['generic_param']['nthread']) == 5
 
-            from_arr = xgb.dask.predict(
-                client, model=booster, data=X)
+            from_arr = xgb.dask.predict(client, model=booster, data=X)
 
             assert isinstance(from_arr, da.Array)
             assert np.all(single_node_predt == from_arr.compute())
@@ -477,23 +474,6 @@ def test_dask_classifier(model: str, client: "Client") -> None:
     run_dask_classifier(X, y_bin, w, model, None, client, 2)
 
 
-@pytest.mark.skipif(**tm.no_sklearn())
-def test_sklearn_grid_search(client: "Client") -> None:
-    from sklearn.model_selection import GridSearchCV
-    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)
-    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 test_empty_dmatrix_training_continuation(client: "Client") -> None:
     kRows, kCols = 1, 97
     X = dd.from_array(np.random.randn(kRows, kCols))
@@ -714,18 +694,11 @@ def test_auc(client: "Client") -> None:
 
 # No test for Exact, as empty DMatrix handling are mostly for distributed
 # environment and Exact doesn't support it.
-def test_empty_dmatrix_hist() -> None:
+@pytest.mark.parametrize("tree_method", ["hist", "approx"])
+def test_empty_dmatrix(tree_method) -> None:
     with LocalCluster(n_workers=kWorkers) as cluster:
         with Client(cluster) as client:
-            parameters = {'tree_method': 'hist'}
-            run_empty_dmatrix_reg(client, parameters)
-            run_empty_dmatrix_cls(client, parameters)
-
-
-def test_empty_dmatrix_approx() -> None:
-    with LocalCluster(n_workers=kWorkers) as cluster:
-        with Client(cluster) as client:
-            parameters = {'tree_method': 'approx'}
+            parameters = {'tree_method': tree_method}
             run_empty_dmatrix_reg(client, parameters)
             run_empty_dmatrix_cls(client, parameters)
 
@@ -1102,12 +1075,12 @@ class TestWithDask:
         os.remove(after_fname)
 
     def run_updater_test(
-            self,
-            client: "Client",
-            params: Dict,
-            num_rounds: int,
-            dataset: tm.TestDataset,
-            tree_method: str
+        self,
+        client: "Client",
+        params: Dict,
+        num_rounds: int,
+        dataset: tm.TestDataset,
+        tree_method: str
     ) -> None:
         params['tree_method'] = tree_method
         params = dataset.set_params(params)