Update doc for feature constraints and n_gpus. (#4596)

* Update doc for feature constraints. 

* Fix some warnings.

* Clean up doc for `n_gpus`.

python-package/xgboost/dask.py

@@ -101,24 +101,25 @@ def _run_with_rabit(rabit_args, func, *args):
 
 
 def run(client, func, *args):
-    """
-    Launch arbitrary function on dask workers. Workers are connected by rabit, allowing
-    distributed training. The environment variable OMP_NUM_THREADS is defined on each worker
-    according to dask - this means that calls to xgb.train() will use the threads allocated by
-    dask by default, unless the user overrides the nthread parameter.
+    """Launch arbitrary function on dask workers. Workers are connected by rabit,
+    allowing distributed training. The environment variable OMP_NUM_THREADS is
+    defined on each worker according to dask - this means that calls to
+    xgb.train() will use the threads allocated by dask by default, unless the
+    user overrides the nthread parameter.
 
-    Note: Windows platforms are not officially supported. Contributions are welcome here.
+    Note: Windows platforms are not officially
+      supported. Contributions are welcome here.
 
     :param client: Dask client representing the cluster
-    :param func: Python function to be executed by each worker. Typically contains xgboost
-    training code.
+    :param func: Python function to be executed by each worker. Typically
+       contains xgboost training code.
     :param args: Arguments to be forwarded to func
     :return: Dict containing the function return value for each worker
+
     """
     if platform.system() == 'Windows':
-        logging.warning(
-            'Windows is not officially supported for dask/xgboost integration. Contributions '
-            'welcome.')
+        logging.warning('Windows is not officially supported for dask/xgboost'
+                        'integration. Contributions welcome.')
     workers = list(client.scheduler_info()['workers'].keys())
     env = client.run(_start_tracker, len(workers), workers=[workers[0]])
     rabit_args = [('%s=%s' % item).encode() for item in env[workers[0]].items()]

python-package/xgboost/plotting.py

@@ -184,18 +184,16 @@ def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
     no_color : str, default '#FF0000'
         Edge color when doesn't meet the node condition.
     condition_node_params : dict (optional)
-        condition node configuration,
-        {'shape':'box',
-               'style':'filled,rounded',
-               'fillcolor':'#78bceb'
-        }
+      condition node configuration,
+      {'shape':'box',
+       'style':'filled,rounded',
+       'fillcolor':'#78bceb'}
 
     leaf_node_params : dict (optional)
         leaf node configuration
         {'shape':'box',
-               'style':'filled',
-               'fillcolor':'#e48038'
-        }
+         'style':'filled',
+         'fillcolor':'#e48038'}
 
     kwargs :
         Other keywords passed to graphviz graph_attr

python-package/xgboost/sklearn.py

@@ -105,8 +105,8 @@ class XGBModel(XGBModelBase):
         Value in the data which needs to be present as a missing value. If
         None, defaults to np.nan.
     importance_type: string, default "gain"
-        The feature importance type for the feature_importances_ property: either "gain",
-        "weight", "cover", "total_gain" or "total_cover".
+        The feature importance type for the feature_importances\\_ property:
+        either "gain", "weight", "cover", "total_gain" or "total_cover".
     \\*\\*kwargs : dict, optional
         Keyword arguments for XGBoost Booster object.  Full documentation of parameters can
         be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.