[SPARK-3773][PySpark][Doc] Sphinx build warning

Remove all warnings on document building
apache · Oct 4, 2014 · 6f65661 · 6f65661
1 parent cf1d32e
commit 6f65661
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Showing 6 changed files with 28 additions and 23 deletions.
diff --git a/python/docs/modules.rst b/python/docs/modules.rst
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
@@ -410,6 +410,7 @@ def sequenceFile(self, path, keyClass=None, valueClass=None, keyConverter=None,
         Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,
         a local file system (available on all nodes), or any Hadoop-supported file system URI.
         The mechanism is as follows:
+
             1. A Java RDD is created from the SequenceFile or other InputFormat, and the key
                and value Writable classes
             2. Serialization is attempted via Pyrolite pickling

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
@@ -89,11 +89,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         @param regParam:          The regularizer parameter (default: 1.0).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater
+                                     - "l2" for using SquaredL2Updater
+                                     - "none" for no regularizer
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features
@@ -158,11 +161,14 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
         @param initialWeights:    The initial weights (default: None).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater
+                                     - "l2" for using SquaredL2Updater,
+                                     - "none" for no regularizer.
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
@@ -22,7 +22,7 @@
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector
 from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
 
-__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel'
+__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel',
            'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD']
 
 
@@ -152,11 +152,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         @param regParam:          The regularizer parameter (default: 1.0).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater,
+                                     - "l2" for using SquaredL2Updater,
+                                     - "none" for no regularizer.
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features

diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
@@ -48,6 +48,7 @@ def __del__(self):
     def predict(self, x):
         """
         Predict the label of one or more examples.
+
         :param x:  Data point (feature vector),
                    or an RDD of data points (feature vectors).
         """

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
@@ -1208,6 +1208,7 @@ def saveAsSequenceFile(self, path, compressionCodecClass=None):
         Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
         system, using the L{org.apache.hadoop.io.Writable} types that we convert from the
         RDD's key and value types. The mechanism is as follows:
+
             1. Pyrolite is used to convert pickled Python RDD into RDD of Java objects.
             2. Keys and values of this Java RDD are converted to Writables and written out.