[SPARK-2550][MLLIB][APACHE SPARK] Support regularization and intercept in pyspark's linear methods.

Related to issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC).

Author: Michael Giannakopoulos <miccagiann@gmail.com>

Closes #1624 from miccagiann/new-branch and squashes the following commits:

c02e5f5 [Michael Giannakopoulos] Merge cleanly with upstream/master.
8dcb888 [Michael Giannakopoulos] Putting the if/else if statements in brackets.
fed8eaa [Michael Giannakopoulos] Adding a space in the message related to the IllegalArgumentException.
44e6ff0 [Michael Giannakopoulos] Adding a blank line before python class LinearRegressionWithSGD.
8eba9c5 [Michael Giannakopoulos] Change function signatures. Exception is thrown from the scala component and not from the python one.
638be47 [Michael Giannakopoulos] Modified code to comply with code standards.
ec50ee9 [Michael Giannakopoulos] Shorten the if-elif-else statement in regression.py file
b962744 [Michael Giannakopoulos] Replaced the enum classes, with strings-keywords for defining the values of 'regType' parameter.
78853ec [Michael Giannakopoulos] Providing intercept and regualizer functionallity for linear methods in only one function.
3ac8874 [Michael Giannakopoulos] Added support for regularizer and intercection parameters for linear regression method.

1 files changed, 28 insertions, 4 deletions

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index b84bc531de..041b119269 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -112,12 +112,36 @@ class LinearRegressionModel(LinearRegressionModelBase):
 
 class LinearRegressionWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0,
-              miniBatchFraction=1.0, initialWeights=None):
-        """Train a linear regression model on the given data."""
+    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
+              initialWeights=None, regParam=1.0, regType=None, intercept=False):
+        """
+        Train a linear regression model on the given data.
+
+        @param data:              The training data.
+        @param iterations:        The number of iterations (default: 100).
+        @param step:              The step parameter used in SGD
+                                  (default: 1.0).
+        @param miniBatchFraction: Fraction of data to be used for each SGD
+                                  iteration.
+        @param initialWeights:    The initial weights (default: None).
+        @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")
+        @param intercept:         Boolean parameter which indicates the use
+                                  or not of the augmented representation for
+                                  training data (i.e. whether bias features
+                                  are activated or not).
+        """
         sc = data.context
+        if regType is None:
+            regType = "none"
         train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
-            d._jrdd, iterations, step, miniBatchFraction, i)
+            d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
         return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)