[docs] Revert #16919 and fix documentation build (#17270)

doc/source/conf.py

@@ -99,58 +99,8 @@ sys.modules["tensorflow"].VERSION = "9.9.9"
 sys.modules["tensorflow.keras.callbacks"] = ChildClassMock()
 sys.modules["pytorch_lightning"] = ChildClassMock()
 
-
+assert "ray" not in sys.modules, (
-def import_or_mock(module):
+    "If ray is already imported, we will not render documentation correctly!")
-    try:
-        # Same as `import module`
-        __import__(module, globals(), locals(), [], 0)
-    except ImportError:
-        sys.modules[module] = ChildClassMock()
-
-
-xgb_lgbm_modules = [
-    "xgboost", "xgboost.core", "xgboost.callback", "xgboost.sklearn",
-    "xgboost_ray", "lightgbm.main", "lightgbm.callback", "lightgbm.compat",
-    "lightgbm_ray"
-]
-for module in xgb_lgbm_modules:
-    import_or_mock(module)
-
-# replace docstring refs in XGBoost documentation XGBoost-Ray inherits from
-# if this is not done, an error during make will be thrown, as there is no
-# such ref in ray docs
-replaces_callback_api = [
-    sys.modules["xgboost_ray"].RayXGBClassifier.fit,
-    sys.modules["xgboost_ray"].RayXGBRegressor.fit
-]
-for m in replaces_callback_api:
-    m.__doc__ = m.__doc__.replace(":ref:`callback_api`", "Callback API")
-
-# fix "more than one target found for cross-reference 'RayParams'"
-# as both lightgbm-ray and xgboost-ray have classes with the same
-# names, we need to refer to the explicitly in ray docs, otherwise
-# make will throw an error
-replaces_ray_params = [
-    sys.modules["xgboost_ray"].train, sys.modules["xgboost_ray"].predict,
-    sys.modules["xgboost_ray"].RayXGBClassifier.fit,
-    sys.modules["xgboost_ray"].RayXGBClassifier.predict,
-    sys.modules["xgboost_ray"].RayXGBClassifier.predict_proba,
-    sys.modules["xgboost_ray"].RayXGBRegressor.fit,
-    sys.modules["xgboost_ray"].RayXGBRegressor.predict
-]
-for m in replaces_ray_params:
-    m.__doc__ = m.__doc__.replace("RayParams", "xgboost_ray.RayParams")
-
-replaces_ray_params = [
-    sys.modules["lightgbm_ray"].train, sys.modules["lightgbm_ray"].predict,
-    sys.modules["lightgbm_ray"].RayLGBMClassifier.fit,
-    sys.modules["lightgbm_ray"].RayLGBMClassifier.predict,
-    sys.modules["lightgbm_ray"].RayLGBMClassifier.predict_proba,
-    sys.modules["lightgbm_ray"].RayLGBMRegressor.fit,
-    sys.modules["lightgbm_ray"].RayLGBMRegressor.predict
-]
-for m in replaces_ray_params:
-    m.__doc__ = m.__doc__.replace("RayParams", "lightgbm_ray.RayParams")
 
 
 class SimpleClass(object):

doc/source/lightgbm-ray.rst

@@ -531,8 +531,8 @@ the `examples folder <https://github.com/ray-project/lightgbm_ray/tree/master/ex
 API reference
 -------------
 
-.. autoclass:: lightgbm_ray.RayParams
+.. .. autoclass:: lightgbm_ray.RayParams
-    :members:
+..     :members:
 
 .. autoclass:: xgboost_ray.RayDMatrix
     :members:

doc/source/xgboost-ray.rst

@@ -165,7 +165,7 @@ Things to keep in mind:
 
 * ``n_jobs`` parameter controls the number of actors spawned.
   You can pass a ``RayParams`` object to the
-  ``fit``\ /\ ``predict``\ /\ ``predict_proba`` methods as the ``ray_params`` argument 
+  ``fit``\ /\ ``predict``\ /\ ``predict_proba`` methods as the ``ray_params`` argument
   for greater control over resource allocation. Doing
   so will override the value of ``n_jobs`` with the value of
   ``ray_params.num_actors`` attribute. For more information, refer
@@ -611,14 +611,14 @@ API reference
 scikit-learn API
 ^^^^^^^^^^^^^^^^
 
-.. autoclass:: xgboost_ray.RayXGBClassifier
+.. .. autoclass:: xgboost_ray.RayXGBClassifier
-    :members:
+..     :members:
 
-.. autoclass:: xgboost_ray.RayXGBRegressor
+.. .. autoclass:: xgboost_ray.RayXGBRegressor
-    :members:
+..     :members:
 
-.. autoclass:: xgboost_ray.RayXGBRFClassifier
+.. .. autoclass:: xgboost_ray.RayXGBRFClassifier
-    :members:
+..     :members:
 
-.. autoclass:: xgboost_ray.RayXGBRFRegressor
+.. .. autoclass:: xgboost_ray.RayXGBRFRegressor
-    :members:
+..     :members: