Make feature validation immutable. (#9388)

python-package/xgboost/core.py

@@ -1623,7 +1623,7 @@ def __init__(
         )
         for d in cache:
             # Validate feature only after the feature names are saved into booster.
-            self._validate_dmatrix_features(d)
+            self._assign_dmatrix_features(d)
 
         if isinstance(model_file, Booster):
             assert self.handle is not None
@@ -1746,6 +1746,11 @@ def __setstate__(self, state: Dict) -> None:
         self.__dict__.update(state)
 
     def __getitem__(self, val: Union[int, tuple, slice]) -> "Booster":
+        """Get a slice of the tree-based model.
+
+        .. versionadded:: 1.3.0
+
+        """
         if isinstance(val, int):
             val = slice(val, val + 1)
         if isinstance(val, tuple):
@@ -1784,6 +1789,11 @@ def __getitem__(self, val: Union[int, tuple, slice]) -> "Booster":
         return sliced
 
     def __iter__(self) -> Generator["Booster", None, None]:
+        """Iterator method for getting individual trees.
+
+        .. versionadded:: 2.0.0
+
+        """
         for i in range(0, self.num_boosted_rounds()):
             yield self[i]
 
@@ -1994,7 +2004,7 @@ def update(
         """
         if not isinstance(dtrain, DMatrix):
             raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
-        self._validate_dmatrix_features(dtrain)
+        self._assign_dmatrix_features(dtrain)
 
         if fobj is None:
             _check_call(
@@ -2026,7 +2036,7 @@ def boost(self, dtrain: DMatrix, grad: np.ndarray, hess: np.ndarray) -> None:
             raise ValueError(f"grad / hess length mismatch: {len(grad)} / {len(hess)}")
         if not isinstance(dtrain, DMatrix):
             raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
-        self._validate_dmatrix_features(dtrain)
+        self._assign_dmatrix_features(dtrain)
 
         _check_call(
             _LIB.XGBoosterBoostOneIter(
@@ -2067,7 +2077,7 @@ def eval_set(
                 raise TypeError(f"expected DMatrix, got {type(d[0]).__name__}")
             if not isinstance(d[1], str):
                 raise TypeError(f"expected string, got {type(d[1]).__name__}")
-            self._validate_dmatrix_features(d[0])
+            self._assign_dmatrix_features(d[0])
 
         dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
         evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
@@ -2119,7 +2129,7 @@ def eval(self, data: DMatrix, name: str = "eval", iteration: int = 0) -> str:
         result: str
             Evaluation result string.
         """
-        self._validate_dmatrix_features(data)
+        self._assign_dmatrix_features(data)
         return self.eval_set([(data, name)], iteration)
 
     # pylint: disable=too-many-function-args
@@ -2218,7 +2228,8 @@ def predict(
         if not isinstance(data, DMatrix):
             raise TypeError("Expecting data to be a DMatrix object, got: ", type(data))
         if validate_features:
-            self._validate_dmatrix_features(data)
+            fn = data.feature_names
+            self._validate_features(fn)
         args = {
             "type": 0,
             "training": training,
@@ -2843,14 +2854,13 @@ def trees_to_dataframe(self, fmap: Union[str, os.PathLike] = "") -> DataFrame:
         # pylint: disable=no-member
         return df.sort(["Tree", "Node"]).reset_index(drop=True)
 
-    def _validate_dmatrix_features(self, data: DMatrix) -> None:
+    def _assign_dmatrix_features(self, data: DMatrix) -> None:
         if data.num_row() == 0:
             return
 
         fn = data.feature_names
         ft = data.feature_types
-        # Be consistent with versions before 1.7, "validate" actually modifies the
-        # booster.
+
         if self.feature_names is None:
             self.feature_names = fn
         if self.feature_types is None: