[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, 21 insertions, 7 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 122925d096..7d912737b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -23,6 +23,8 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.mllib.classification._
 import org.apache.spark.mllib.clustering._
+import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
+import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.linalg.{Matrix, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.random.{RandomRDDGenerators => RG}
 import org.apache.spark.mllib.recommendation._
@@ -252,15 +254,27 @@ class PythonMLLibAPI extends Serializable {
       numIterations: Int,
       stepSize: Double,
       miniBatchFraction: Double,
-      initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
+      initialWeightsBA: Array[Byte], 
+      regParam: Double,
+      regType: String,
+      intercept: Boolean): java.util.List[java.lang.Object] = {
+    val lrAlg = new LinearRegressionWithSGD()
+    lrAlg.setIntercept(intercept)
+    lrAlg.optimizer
+      .setNumIterations(numIterations)
+      .setRegParam(regParam)
+      .setStepSize(stepSize)
+    if (regType == "l2") {
+      lrAlg.optimizer.setUpdater(new SquaredL2Updater)
+    } else if (regType == "l1") {
+      lrAlg.optimizer.setUpdater(new L1Updater)
+    } else if (regType != "none") {
+      throw new java.lang.IllegalArgumentException("Invalid value for 'regType' parameter."
+        + " Can only be initialized using the following string values: [l1, l2, none].")
+    }
     trainRegressionModel(
       (data, initialWeights) =>
-        LinearRegressionWithSGD.train(
-          data,
-          numIterations,
-          stepSize,
-          miniBatchFraction,
-          initialWeights),
+        lrAlg.run(data, initialWeights),
       dataBytesJRDD,
       initialWeightsBA)
   }