Update document for sklearn model IO. (#6809)

* Update the use of JSON.
* Remove unnecessary type cast.

python-package/xgboost/core.py

@@ -1949,7 +1949,7 @@ class Booster(object):
                                               ctypes.byref(cptr)))
         return ctypes2buffer(cptr, length.value)
 
-    def load_model(self, fname):
+    def load_model(self, fname: Union[str, bytearray, os.PathLike]) -> None:
         """Load the model from a file or bytearray. Path to file can be local
         or as an URI.
 
@@ -1964,11 +1964,11 @@ class Booster(object):
 
         Parameters
         ----------
-        fname : string, os.PathLike, or a memory buffer
+        fname :
             Input file name or memory buffer(see also save_raw)
 
         """
-        if isinstance(fname, (STRING_TYPES, os.PathLike)):
+        if isinstance(fname, (str, os.PathLike)):
             # assume file name, cannot use os.path.exist to check, file can be
             # from URL.
             fname = os.fspath(os.path.expanduser(fname))

python-package/xgboost/sklearn.py

@@ -4,6 +4,7 @@
 import copy
 import warnings
 import json
+import os
 from typing import Union, Optional, List, Dict, Callable, Tuple, Any
 import numpy as np
 from .core import Booster, DMatrix, XGBoostError
@@ -500,25 +501,7 @@ class XGBModel(XGBModelBase):
             )
         return self._estimator_type  # pylint: disable=no-member
 
-    def save_model(self, fname: str):
-        """Save the model to a file.
-
-        The model is saved in an XGBoost internal format which is universal
-        among the various XGBoost interfaces. Auxiliary attributes of the
-        Python Booster object (such as feature names) will not be saved.
-
-          .. note::
-
-            See:
-
-            https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
-
-        Parameters
-        ----------
-        fname : string
-            Output file name
-
-        """
+    def save_model(self, fname: Union[str, os.PathLike]):
         meta = dict()
         for k, v in self.__dict__.items():
             if k == '_le':
@@ -542,27 +525,18 @@ class XGBModel(XGBModelBase):
         # Delete the attribute after save
         self.get_booster().set_attr(scikit_learn=None)
 
-    def load_model(self, fname):
+    save_model.__doc__ = f"""{Booster.save_model.__doc__}"""
+
+    def load_model(self, fname: Union[str, bytearray, os.PathLike]) -> None:
         # pylint: disable=attribute-defined-outside-init
-        """Load the model from a file.
-
-        The model is loaded from an XGBoost internal format which is universal
-        among the various XGBoost interfaces. Auxiliary attributes of the
-        Python Booster object (such as feature names) will not be loaded.
-
-        Parameters
-        ----------
-        fname : string
-            Input file name.
-
-        """
         if not hasattr(self, '_Booster'):
             self._Booster = Booster({'n_jobs': self.n_jobs})
-        self._Booster.load_model(fname)
-        meta = self._Booster.attr('scikit_learn')
+        self.get_booster().load_model(fname)
+        meta = self.get_booster().attr('scikit_learn')
         if meta is None:
             warnings.warn(
-                'Loading a native XGBoost model with Scikit-Learn interface.')
+                'Loading a native XGBoost model with Scikit-Learn interface.'
+            )
             return
         meta = json.loads(meta)
         states = dict()
@@ -574,9 +548,6 @@ class XGBModel(XGBModelBase):
             if k == 'classes_':
                 self.classes_ = np.array(v)
                 continue
-            if k == 'use_label_encoder':
-                self.use_label_encoder = bool(v)
-                continue
             if k == "_estimator_type":
                 if self._get_type() != v:
                     raise TypeError(
@@ -589,6 +560,8 @@ class XGBModel(XGBModelBase):
         # Delete the attribute after load
         self.get_booster().set_attr(scikit_learn=None)
 
+    load_model.__doc__ = f"""{Booster.load_model.__doc__}"""
+
     def _configure_fit(
         self,
         booster: Optional[Union[Booster, "XGBModel"]],

tests/python/test_with_sklearn.py

@@ -804,10 +804,14 @@ def save_load_model(model_path):
     for train_index, test_index in kf.split(X, y):
         xgb_model = xgb.XGBClassifier(use_label_encoder=False).fit(X[train_index], y[train_index])
         xgb_model.save_model(model_path)
-        xgb_model = xgb.XGBClassifier(use_label_encoder=False)
+
+        xgb_model = xgb.XGBClassifier()
         xgb_model.load_model(model_path)
+
+        assert xgb_model.use_label_encoder is False
         assert isinstance(xgb_model.classes_, np.ndarray)
         assert isinstance(xgb_model._Booster, xgb.Booster)
+
         preds = xgb_model.predict(X[test_index])
         labels = y[test_index]
         err = sum(1 for i in range(len(preds))