[doc] Use cross references in sphinx doc. (#7522)

* Use cross references instead of URL.
* Fix auto doc for callback.

doc/parameter.rst

@@ -16,10 +16,13 @@ Before running XGBoost, we must set three types of parameters: general parameter
   :backlinks: none
   :local:
 
+
+.. _global_config:
+
 ********************
 Global Configuration
 ********************
-The following parameters can be set in the global scope, using ``xgb.config_context()`` (Python) or ``xgb.set.config()`` (R).
+The following parameters can be set in the global scope, using :py:func:`xgboost.config_context()` (Python) or ``xgb.set.config()`` (R).
 
 * ``verbosity``: Verbosity of printing messages. Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug).
 * ``use_rmm``: Whether to use RAPIDS Memory Manager (RMM) to allocate GPU memory. This option is only applicable when XGBoost is built (compiled) with the RMM plugin enabled. Valid values are ``true`` and ``false``.

doc/python/callbacks.rst

@@ -2,10 +2,11 @@
 Callback Functions
 ##################
 
-This document gives a basic walkthrough of callback function used in XGBoost Python
-package.  In XGBoost 1.3, a new callback interface is designed for Python package, which
-provides the flexibility of designing various extension for training.  Also, XGBoost has a
-number of pre-defined callbacks for supporting early stopping, checkpoints etc.
+This document gives a basic walkthrough of :ref:`callback API <callback_api>` used in
+XGBoost Python package.  In XGBoost 1.3, a new callback interface is designed for Python
+package, which provides the flexibility of designing various extension for training.
+Also, XGBoost has a number of pre-defined callbacks for supporting early stopping,
+checkpoints etc.
 
 
 Using builtin callbacks
@@ -14,8 +15,8 @@ Using builtin callbacks
 By default, training methods in XGBoost have parameters like ``early_stopping_rounds`` and
 ``verbose``/``verbose_eval``, when specified the training procedure will define the
 corresponding callbacks internally.  For example, when ``early_stopping_rounds`` is
-specified, ``EarlyStopping`` callback is invoked inside iteration loop.  You can also pass
-this callback function directly into XGBoost:
+specified, :py:class:`EarlyStopping <xgboost.callback.EarlyStopping>` callback is invoked
+inside iteration loop.  You can also pass this callback function directly into XGBoost:
 
 .. code-block:: python
 
@@ -54,6 +55,7 @@ this callback function directly into XGBoost:
 Defining your own callback
 --------------------------
 
-XGBoost provides an callback interface class: ``xgboost.callback.TrainingCallback``, user
-defined callbacks should inherit this class and override corresponding methods.  There's a
-working example in `demo/guide-python/callbacks.py <https://github.com/dmlc/xgboost/tree/master/demo/guide-python/callbacks.py>`_
+XGBoost provides an callback interface class: :py:class:`TrainingCallback
+<xgboost.callback.TrainingCallback>`, user defined callbacks should inherit this class and
+override corresponding methods.  There's a working example in
+:ref:`sphx_glr_python_examples_callbacks.py`.

doc/python/python_api.rst

@@ -77,15 +77,29 @@ Plotting API
 
 Callback API
 ------------
-.. autofunction:: xgboost.callback.TrainingCallback
+.. automodule:: xgboost.callback
+.. autoclass:: xgboost.callback.TrainingCallback
+    :members:
 
-.. autofunction:: xgboost.callback.EvaluationMonitor
+.. autoclass:: xgboost.callback.EvaluationMonitor
+    :members:
+    :inherited-members:
+    :show-inheritance:
 
-.. autofunction:: xgboost.callback.EarlyStopping
+.. autoclass:: xgboost.callback.EarlyStopping
+    :members:
+    :inherited-members:
+    :show-inheritance:
 
-.. autofunction:: xgboost.callback.LearningRateScheduler
+.. autoclass:: xgboost.callback.LearningRateScheduler
+    :members:
+    :inherited-members:
+    :show-inheritance:
 
-.. autofunction:: xgboost.callback.TrainingCheckPoint
+.. autoclass:: xgboost.callback.TrainingCheckPoint
+    :members:
+    :inherited-members:
+    :show-inheritance:
 
 .. _dask_api:
 

doc/treemethod.rst

@@ -1,6 +1,6 @@
-####################
-XGBoost Tree Methods
-####################
+############
+Tree Methods
+############
 
 For training boosted tree models, there are 2 parameters used for choosing algorithms,
 namely ``updater`` and ``tree_method``.  XGBoost has 4 builtin tree methods, namely

doc/tutorials/custom_metric_obj.rst

@@ -146,7 +146,8 @@ We will be able to see XGBoost printing something like:
 Notice that the parameter ``disable_default_eval_metric`` is used to suppress the default metric
 in XGBoost.
 
-For fully reproducible source code and comparison plots, see `custom_rmsle.py <https://github.com/dmlc/xgboost/tree/master/demo/guide-python/custom_rmsle.py>`_.
+For fully reproducible source code and comparison plots, see
+:ref:`sphx_glr_python_examples_custom_rmsle.py`.
 
 *********************
 Reverse Link Function
@@ -261,8 +262,7 @@ available in XGBoost:
 We use ``multi:softmax`` to illustrate the differences of transformed prediction.  With
 ``softprob`` the output prediction array has shape ``(n_samples, n_classes)`` while for
 ``softmax`` it's ``(n_samples, )``. A demo for multi-class objective function is also
-available at `demo/guide-python/custom_softmax.py
-<https://github.com/dmlc/xgboost/tree/master/demo/guide-python/custom_softmax.py>`_
+available at :ref:`sphx_glr_python_examples_custom_softmax.py`.
 
 
 **********************