Support _estimator_type. (#6582)

* Use `_estimator_type`.

For more info, see: https://scikit-learn.org/stable/developers/develop.html#estimator-types

* Model trained from dask can be loaded by single node skl interface.

python-package/xgboost/sklearn.py

@@ -16,6 +16,12 @@ from .compat import (SKLEARN_INSTALLED, XGBModelBase,
                      XGBClassifierBase, XGBRegressorBase, XGBoostLabelEncoder)
 
 
+class XGBRankerMixIn:           # pylint: disable=too-few-public-methods
+    """MixIn for ranking, defines the _estimator_type usually defined in scikit-learn base
+    classes."""
+    _estimator_type = "ranker"
+
+
 def _objective_decorator(func):
     """Decorate an objective function
 
@@ -407,6 +413,14 @@ class XGBModel(XGBModelBase):
         """Gets the number of xgboost boosting rounds."""
         return self.n_estimators
 
+    def _get_type(self) -> str:
+        if not hasattr(self, '_estimator_type'):
+            raise TypeError(
+                "`_estimator_type` undefined.  "
+                "Please use appropriate mixin to define estimator type."
+            )
+        return self._estimator_type  # pylint: disable=no-member
+
     def save_model(self, fname: str):
         """Save the model to a file.
 
@@ -442,7 +456,7 @@ class XGBModel(XGBModelBase):
                 meta[k] = v
             except TypeError:
                 warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
-        meta['type'] = type(self).__name__
+        meta['_estimator_type'] = self._get_type()
         meta_str = json.dumps(meta)
         self.get_booster().set_attr(scikit_learn=meta_str)
         self.get_booster().save_model(fname)
@@ -484,11 +498,12 @@ class XGBModel(XGBModelBase):
             if k == 'use_label_encoder':
                 self.use_label_encoder = bool(v)
                 continue
-            if k == 'type' and type(self).__name__ != v:
-                msg = 'Current model type: {}, '.format(type(self).__name__) + \
-                      'type of model in file: {}'.format(v)
-                raise TypeError(msg)
-            if k == 'type':
+            if k == "_estimator_type":
+                if self._get_type() != v:
+                    raise TypeError(
+                        "Loading an estimator with different type. "
+                        f"Expecting: {self._get_type()}, got: {v}"
+                    )
                 continue
             states[k] = v
         self.__dict__.update(states)
@@ -1211,7 +1226,7 @@ class XGBRFRegressor(XGBRegressor):
 
         then your group array should be ``[3, 4]``.
 ''')
-class XGBRanker(XGBModel):
+class XGBRanker(XGBModel, XGBRankerMixIn):
     # pylint: disable=missing-docstring,too-many-arguments,invalid-name
     @_deprecate_positional_args
     def __init__(self, *, objective='rank:pairwise', **kwargs):