Implement max_cat_threshold for CPU. (#7957)

tests/python/test_updaters.py

@@ -31,6 +31,14 @@ hist_parameter_strategy = strategies.fixed_dictionaries({
     x['max_depth'] > 0 or x['grow_policy'] == 'lossguide'))
 
 
+cat_parameter_strategy = strategies.fixed_dictionaries(
+    {
+        "max_cat_to_onehot": strategies.integers(1, 128),
+        "max_cat_threshold": strategies.integers(1, 128),
+    }
+)
+
+
 def train_result(param, dmat, num_rounds):
     result = {}
     xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
@@ -253,7 +261,7 @@ class TestTreeMethod:
         # Test with partition-based split
         run(self.USE_PART)
 
-    def run_categorical_basic(self, rows, cols, rounds, cats, tree_method):
+    def run_categorical_ohe(self, rows, cols, rounds, cats, tree_method):
         onehot, label = tm.make_categorical(rows, cols, cats, True)
         cat, _ = tm.make_categorical(rows, cols, cats, False)
 
@@ -328,9 +336,55 @@ class TestTreeMethod:
            strategies.integers(1, 2), strategies.integers(4, 7))
     @settings(deadline=None, print_blob=True)
     @pytest.mark.skipif(**tm.no_pandas())
-    def test_categorical(self, rows, cols, rounds, cats):
-        self.run_categorical_basic(rows, cols, rounds, cats, "approx")
-        self.run_categorical_basic(rows, cols, rounds, cats, "hist")
+    def test_categorical_ohe(self, rows, cols, rounds, cats):
+        self.run_categorical_ohe(rows, cols, rounds, cats, "approx")
+        self.run_categorical_ohe(rows, cols, rounds, cats, "hist")
+
+    @given(
+        tm.categorical_dataset_strategy,
+        exact_parameter_strategy,
+        hist_parameter_strategy,
+        cat_parameter_strategy,
+        strategies.integers(4, 32),
+        strategies.sampled_from(["hist", "approx"]),
+    )
+    @settings(deadline=None, print_blob=True)
+    @pytest.mark.skipif(**tm.no_pandas())
+    def test_categorical(
+        self,
+        dataset: tm.TestDataset,
+        exact_parameters: Dict[str, Any],
+        hist_parameters: Dict[str, Any],
+        cat_parameters: Dict[str, Any],
+        n_rounds: int,
+        tree_method: str,
+    ) -> None:
+        cat_parameters.update(exact_parameters)
+        cat_parameters.update(hist_parameters)
+        cat_parameters["tree_method"] = tree_method
+
+        results = train_result(cat_parameters, dataset.get_dmat(), n_rounds)
+        tm.non_increasing(results["train"]["rmse"])
+
+    @given(
+        hist_parameter_strategy,
+        cat_parameter_strategy,
+        strategies.sampled_from(["hist", "approx"]),
+    )
+    @settings(deadline=None, print_blob=True)
+    def test_categorical_ames_housing(
+        self,
+        hist_parameters: Dict[str, Any],
+        cat_parameters: Dict[str, Any],
+        tree_method: str,
+    ) -> None:
+        cat_parameters.update(hist_parameters)
+        dataset = tm.TestDataset(
+            "ames_housing", tm.get_ames_housing, "reg:squarederror", "rmse"
+        )
+        cat_parameters["tree_method"] = tree_method
+        results = train_result(cat_parameters, dataset.get_dmat(), 16)
+        tm.non_increasing(results["train"]["rmse"])
 
     @given(
         strategies.integers(10, 400),