Add a test

tests/python-gpu/test_gpu_prediction.py

@@ -3,8 +3,17 @@
 
 import numpy as np
 import xgboost as xgb
+from xgboost.compat import PANDAS_INSTALLED
+
 from hypothesis import given, strategies, assume, settings, note
 
+if PANDAS_INSTALLED:
+    from hypothesis.extra.pandas import column, data_frames, range_indexes
+else:
+    def noop(*args, **kwargs):
+        pass
+    column, data_frames, range_indexes = noop, noop, noop
+
 sys.path.append("tests/python")
 import testing as tm
 from test_predict import run_threaded_predict  # noqa
@@ -259,3 +268,29 @@ def test_predict_leaf_dart(self, param, dataset):
         param['booster'] = 'dart'
         param['tree_method'] = 'gpu_hist'
         self.run_predict_leaf_booster(param, 10, dataset)
+
+    @pytest.mark.skipif(**tm.no_sklearn())
+    @pytest.mark.skipif(**tm.no_pandas())
+    @given(df=data_frames([column('x0', elements=strategies.integers(min_value=0, max_value=3)),
+                           column('x1', elements=strategies.integers(min_value=0, max_value=5))],
+                          index=range_indexes(min_size=20, max_size=50)))
+    @settings(deadline=None)
+    def test_predict_categorical_split(self, df):
+        from sklearn.metrics import mean_squared_error
+
+        df = df.astype('category')
+        x0, x1 = df['x0'].to_numpy(), df['x1'].to_numpy()
+        y = (x0 * 10 - 20) + (x1 - 2)
+        dtrain = xgb.DMatrix(df, label=y, enable_categorical=True)
+
+        params = {'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor',
+                  'enable_experimental_json_serialization': True,
+                  'max_depth': 3, 'learning_rate': 1.0, 'base_score': 0.0, 'eval_metric': 'rmse'}
+
+        eval_history = {}
+        bst = xgb.train(params, dtrain, num_boost_round=5, evals=[(dtrain, 'train')],
+                        verbose_eval=False, evals_result=eval_history)
+
+        pred = bst.predict(dtrain)
+        rmse = mean_squared_error(y_true=y, y_pred=pred, squared=False)
+        np.testing.assert_almost_equal(rmse, eval_history['train']['rmse'][-1], decimal=5)