[dask] Improve documents. (#6687)

* Add tag for versions.
* use autoclass in sphinx build.
Made some class methods to be private to avoid exporting documents.

python-package/xgboost/dask.py

@@ -1,6 +1,7 @@
 # pylint: disable=too-many-arguments, too-many-locals, no-name-in-module
 # pylint: disable=missing-class-docstring, invalid-name
 # pylint: disable=too-many-lines, fixme
+# pylint: disable=too-few-public-methods
 # pylint: disable=import-error
 """Dask extensions for distributed training. See
 https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple
@@ -259,7 +260,7 @@ class DaskDMatrix:
         self.is_quantile: bool = False
 
         self._init = client.sync(
-            self.map_local_data,
+            self._map_local_data,
             client,
             data,
             label=label,
@@ -274,7 +275,7 @@ class DaskDMatrix:
     def __await__(self) -> Generator:
         return self._init.__await__()
 
-    async def map_local_data(
+    async def _map_local_data(
         self,
         client: "distributed.Client",
         data: _DaskCollection,
@@ -393,7 +394,7 @@ class DaskDMatrix:
 
         return self
 
-    def create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
+    def _create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
         '''Create a dictionary of objects that can be pickled for function
         arguments.
 
@@ -627,8 +628,8 @@ class DaskDeviceQuantileDMatrix(DaskDMatrix):
         self.max_bin = max_bin
         self.is_quantile = True
 
-    def create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
-        args = super().create_fn_args(worker_addr)
+    def _create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
+        args = super()._create_fn_args(worker_addr)
         args["max_bin"] = self.max_bin
         return args
 
@@ -864,18 +865,22 @@ async def _train_async(
     futures = []
     for i, worker_addr in enumerate(workers):
         if evals:
-            evals_per_worker = [(e.create_fn_args(worker_addr), name, id(e))
+            # pylint: disable=protected-access
+            evals_per_worker = [(e._create_fn_args(worker_addr), name, id(e))
                                 for e, name in evals]
         else:
             evals_per_worker = []
-        f = client.submit(dispatched_train,
-                          worker_addr,
-                          _rabit_args,
-                          dtrain.create_fn_args(workers[i]),
-                          id(dtrain),
-                          evals_per_worker,
-                          pure=False,
-                          workers=[worker_addr])
+        f = client.submit(
+            dispatched_train,
+            worker_addr,
+            _rabit_args,
+            # pylint: disable=protected-access
+            dtrain._create_fn_args(workers[i]),
+            id(dtrain),
+            evals_per_worker,
+            pure=False,
+            workers=[worker_addr]
+        )
         futures.append(f)
 
     results = await client.gather(futures)
@@ -1755,7 +1760,11 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
 
 
 @xgboost_model_doc(
-    "Implementation of the Scikit-Learn API for XGBoost Ranking.",
+    """Implementation of the Scikit-Learn API for XGBoost Ranking.
+
+    .. versionadded:: 1.4.0
+
+""",
     ["estimators", "model"],
     end_note="""
         Note
@@ -1868,7 +1877,11 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
 
 
 @xgboost_model_doc(
-    "Implementation of the Scikit-Learn API for XGBoost Random Forest Regressor.",
+    """Implementation of the Scikit-Learn API for XGBoost Random Forest Regressor.
+
+    .. versionadded:: 1.4.0
+
+""",
     ["model", "objective"],
     extra_parameters="""
     n_estimators : int
@@ -1904,7 +1917,11 @@ class DaskXGBRFRegressor(DaskXGBRegressor):
 
 
 @xgboost_model_doc(
-    "Implementation of the Scikit-Learn API for XGBoost Random Forest Classifier.",
+    """Implementation of the Scikit-Learn API for XGBoost Random Forest Classifier.
+
+    .. versionadded:: 1.4.0
+
+""",
     ["model", "objective"],
     extra_parameters="""
     n_estimators : int