Fix feature names with special characters. (#9923)

tests/python/test_basic_models.py

@@ -28,10 +28,11 @@ def json_model(model_path: str, parameters: dict) -> dict:
 
     if model_path.endswith("ubj"):
         import ubjson
+
         with open(model_path, "rb") as ubjfd:
             model = ubjson.load(ubjfd)
     else:
-        with open(model_path, 'r') as fd:
+        with open(model_path, "r") as fd:
             model = json.load(fd)
 
     return model
@@ -439,25 +440,34 @@ class TestModels:
                       'objective': 'multi:softmax'}
         validate_model(parameters)
 
-    def test_special_model_dump_characters(self):
+    def test_special_model_dump_characters(self) -> None:
         params = {"objective": "reg:squarederror", "max_depth": 3}
-        feature_names = ['"feature 0"', "\tfeature\n1", "feature 2"]
+        feature_names = ['"feature 0"', "\tfeature\n1", """feature "2"."""]
         X, y, w = tm.make_regression(n_samples=128, n_features=3, use_cupy=False)
         Xy = xgb.DMatrix(X, label=y, feature_names=feature_names)
         booster = xgb.train(params, Xy, num_boost_round=3)
+
         json_dump = booster.get_dump(dump_format="json")
         assert len(json_dump) == 3
 
-        def validate(obj: dict) -> None:
+        def validate_json(obj: dict) -> None:
             for k, v in obj.items():
                 if k == "split":
                     assert v in feature_names
                 elif isinstance(v, dict):
-                    validate(v)
+                    validate_json(v)
 
         for j_tree in json_dump:
             loaded = json.loads(j_tree)
-            validate(loaded)
+            validate_json(loaded)
+
+        dot_dump = booster.get_dump(dump_format="dot")
+        for d in dot_dump:
+            assert d.find(r"feature \"2\"") != -1
+
+        text_dump = booster.get_dump(dump_format="text")
+        for d in text_dump:
+            assert d.find(r"feature \"2\"") != -1
 
     def test_categorical_model_io(self):
         X, y = tm.make_categorical(256, 16, 71, False)
@@ -485,6 +495,7 @@ class TestModels:
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_attributes(self):
         from sklearn.datasets import load_iris
+
         X, y = load_iris(return_X_y=True)
         cls = xgb.XGBClassifier(n_estimators=2)
         cls.fit(X, y, early_stopping_rounds=1, eval_set=[(X, y)])
@@ -674,6 +685,7 @@ class TestModels:
     @pytest.mark.skipif(**tm.no_pandas())
     def test_feature_info(self):
         import pandas as pd
+
         rows = 100
         cols = 10
         X = rng.randn(rows, cols)