Update Python API doc (#3619)

* Add XGBRanker to Python API doc

* Show inherited members of XGBRegressor in API doc, since XGBRegressor uses default methods from XGBModel

* Add table of contents to Python API doc

* Skip JVM doc download if not available

* Show inherited members for XGBRegressor and XGBRanker

* Expose XGBRanker to Python XGBoost module directory

* Add docstring to XGBRegressor.predict() and XGBRanker.predict()

* Fix rendering errors in Python docstrings

* Fix lint

doc/conf.py

@@ -14,6 +14,7 @@
 from subprocess import call
 from sh.contrib import git
 import urllib.request
+from urllib.error import HTTPError
 from recommonmark.parser import CommonMarkParser
 import sys
 import re
@@ -24,8 +25,11 @@ import guzzle_sphinx_theme
 git_branch = [re.sub(r'origin/', '', x.lstrip(' ')) for x in str(git.branch('-r', '--contains', 'HEAD')).rstrip('\n').split('\n')]
 git_branch = [x for x in git_branch if 'HEAD' not in x]
 print('git_branch = {}'.format(git_branch[0]))
-filename, _ = urllib.request.urlretrieve('https://s3-us-west-2.amazonaws.com/xgboost-docs/{}.tar.bz2'.format(git_branch[0]))
-call('if [ -d tmp ]; then rm -rf tmp; fi; mkdir -p tmp/jvm; cd tmp/jvm; tar xvf {}'.format(filename), shell=True)
+try:
+  filename, _ = urllib.request.urlretrieve('https://s3-us-west-2.amazonaws.com/xgboost-docs/{}.tar.bz2'.format(git_branch[0]))
+  call('if [ -d tmp ]; then rm -rf tmp; fi; mkdir -p tmp/jvm; cd tmp/jvm; tar xvf {}'.format(filename), shell=True)
+except HTTPError:
+  print('JVM doc not found. Skipping...')
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -274,7 +274,7 @@ and then loading the model in another session:
 With regards to ML pipeline save and load, please refer the next section.
 
 Interact with Other Bindings of XGBoost
-------------------------------------
+---------------------------------------
 After we train a model with XGBoost4j-Spark on massive dataset, sometimes we want to do model serving in single machine or integrate it with other single node libraries for further processing. XGBoost4j-Spark supports export model to local by:
 
 .. code-block:: scala

doc/python/python_api.rst

@@ -2,6 +2,10 @@ Python API Reference
 ====================
 This page gives the Python API reference of xgboost, please also refer to Python Package Introduction for more information about python package.
 
+.. contents::
+  :backlinks: none
+  :local:
+
 Core Data Structure
 -------------------
 .. automodule:: xgboost.core
@@ -29,9 +33,15 @@ Scikit-Learn API
 .. automodule:: xgboost.sklearn
 .. autoclass:: xgboost.XGBRegressor
     :members:
+    :inherited-members:
     :show-inheritance:
 .. autoclass:: xgboost.XGBClassifier
     :members:
+    :inherited-members:
+    :show-inheritance:
+.. autoclass:: xgboost.XGBRanker
+    :members:
+    :inherited-members:
     :show-inheritance:
 
 Plotting API