Unify evaluation functions. (#6037)

tests/python-gpu/test_gpu_prediction.py

@@ -198,6 +198,9 @@ class TestGPUPredict(unittest.TestCase):
            tm.dataset_strategy, shap_parameter_strategy, strategies.booleans())
     @settings(deadline=None)
     def test_shap(self, num_rounds, dataset, param, all_rows):
+        if param['max_depth'] == 0 and param['max_leaves'] == 0:
+            return
+
         param.update({"predictor": "gpu_predictor", "gpu_id": 0})
         param = dataset.set_params(param)
         dmat = dataset.get_dmat()

tests/python-gpu/test_monotonic_constraints.py

@@ -7,6 +7,7 @@ import pytest
 import xgboost as xgb
 sys.path.append("tests/python")
 import testing as tm
+import test_monotone_constraints as tmc
 
 rng = np.random.RandomState(1994)
 
@@ -30,6 +31,7 @@ def assert_constraint(constraint, tree_method):
     bst = xgb.train(param, dtrain)
     dpredict = xgb.DMatrix(X[X[:, 0].argsort()])
     pred = bst.predict(dpredict)
+
     if constraint > 0:
         assert non_decreasing(pred)
     elif constraint < 0:
@@ -38,11 +40,24 @@ def assert_constraint(constraint, tree_method):
 
 class TestMonotonicConstraints(unittest.TestCase):
     @pytest.mark.skipif(**tm.no_sklearn())
-    def test_exact(self):
-        assert_constraint(1, 'exact')
-        assert_constraint(-1, 'exact')
-
-    @pytest.mark.skipif(**tm.no_sklearn())
-    def test_gpu_hist(self):
+    def test_gpu_hist_basic(self):
         assert_constraint(1, 'gpu_hist')
         assert_constraint(-1, 'gpu_hist')
+
+    def test_gpu_hist_depthwise(self):
+        params = {
+            'tree_method': 'gpu_hist',
+            'grow_policy': 'depthwise',
+            'monotone_constraints': '(1, -1)'
+        }
+        model = xgb.train(params, tmc.training_dset)
+        tmc.is_correctly_constrained(model)
+
+    def test_gpu_hist_lossguide(self):
+        params = {
+            'tree_method': 'gpu_hist',
+            'grow_policy': 'lossguide',
+            'monotone_constraints': '(1, -1)'
+        }
+        model = xgb.train(params, tmc.training_dset)
+        tmc.is_correctly_constrained(model)