[SPARK-4821] [mllib] [python] [docs] Fix for pyspark.mllib.rand doc

+ small doc edit
+ include edit to make IntelliJ happy

CC: davies  mengxr

Note to davies  -- this does not fix the "WARNING: Literal block expected; none found." warnings since that seems to involve spacing which IntelliJ does not like.  (Those warnings occur when generating the Python docs.)

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #3669 from jkbradley/python-warnings and squashes the following commits:

4587868 [Joseph K. Bradley] fixed warning
8cb073c [Joseph K. Bradley] Updated based on davies recommendation
c51eca4 [Joseph K. Bradley] Updated rst file for pyspark.mllib.rand doc.  Small doc edit.  Small include edit to make IntelliJ happy.

python/docs/pyspark.streaming.rst

@@ -1,5 +1,5 @@
 pyspark.streaming module
-==================
+========================
 
 Module contents
 ---------------

python/pyspark/mllib/__init__.py

@@ -32,29 +32,4 @@ import sys
 import rand as random
 random.__name__ = 'random'
 random.RandomRDDs.__module__ = __name__ + '.random'
-
-
-class RandomModuleHook(object):
-    """
-    Hook to import pyspark.mllib.random
-    """
-    fullname = __name__ + '.random'
-
-    def find_module(self, name, path=None):
-        # skip all other modules
-        if not name.startswith(self.fullname):
-            return
-        return self
-
-    def load_module(self, name):
-        if name == self.fullname:
-            return random
-
-        cname = name.rsplit('.', 1)[-1]
-        try:
-            return getattr(random, cname)
-        except AttributeError:
-            raise ImportError
-
-
-sys.meta_path.append(RandomModuleHook())
+sys.modules[__name__ + '.random'] = random

python/pyspark/mllib/feature.py

@@ -53,10 +53,10 @@ class Normalizer(VectorTransformer):
     """
     :: Experimental ::
 
-    Normalizes samples individually to unit L\ :sup:`p`\ norm
+    Normalizes samples individually to unit L\ :sup:`p`\  norm
 
-    For any 1 <= `p` <= float('inf'), normalizes samples using
-    sum(abs(vector). :sup:`p`) :sup:`(1/p)` as norm.
+    For any 1 <= `p` < float('inf'), normalizes samples using
+    sum(abs(vector) :sup:`p`) :sup:`(1/p)` as norm.
 
     For `p` = float('inf'), max(abs(vector)) will be used as norm for normalization.