Cover approx tree method for categorical data tests. (#7569)

* Add tree to df tests.
* Add plotting tests.
* Add histogram tests.

tests/python/test_parse_tree.py

@@ -12,7 +12,6 @@ rng = np.random.RandomState(1994)
 
 
 class TestTreesToDataFrame:
-
     def build_model(self, max_depth, num_round):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         param = {'max_depth': max_depth, 'objective': 'binary:logistic',
@@ -48,3 +47,26 @@ class TestTreesToDataFrame:
         # test for equality of covers
         cover_from_df = df.Cover.sum()
         assert np.allclose(cover_from_dump, cover_from_df)
+
+    def run_tree_to_df_categorical(self, tree_method: str) -> None:
+        X, y = tm.make_categorical(100, 10, 31, False)
+        Xy = xgb.DMatrix(X, y, enable_categorical=True)
+        booster = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=10)
+        df = booster.trees_to_dataframe()
+        for _, x in df.iterrows():
+            if x["Feature"] != "Leaf":
+                assert len(x["Category"]) >= 1
+
+    def test_tree_to_df_categorical(self) -> None:
+        self.run_tree_to_df_categorical("approx")
+
+    def run_split_value_histograms(self, tree_method) -> None:
+        X, y = tm.make_categorical(1000, 10, 13, False)
+        reg = xgb.XGBRegressor(tree_method=tree_method, enable_categorical=True)
+        reg.fit(X, y)
+
+        with pytest.raises(ValueError, match="doesn't"):
+            reg.get_booster().get_split_value_histogram("3", bins=5)
+
+    def test_split_value_histograms(self):
+        self.run_split_value_histograms("approx")