[SPARK-2550][MLLIB][APACHE SPARK] Support regularization and intercep…

…t in pyspark's linear methods

Related to Jira 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 apache#1775 from miccagiann/linearMethodsReg and squashes the following commits:

cb774c3 [Michael Giannakopoulos] MiniBatchFraction added in related PythonMLLibAPI java stubs.
81fcbc6 [Michael Giannakopoulos] Fixing a typo-error.
8ad263e [Michael Giannakopoulos] Adding regularizer type and intercept parameters to LogisticRegressionWithSGD and SVMWithSGD.

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -271,6 +271,7 @@ class PythonMLLibAPI extends Serializable {
       .setNumIterations(numIterations)
       .setRegParam(regParam)
       .setStepSize(stepSize)
+      .setMiniBatchFraction(miniBatchFraction)
     if (regType == "l2") {
       lrAlg.optimizer.setUpdater(new SquaredL2Updater)
     } else if (regType == "l1") {
@@ -341,16 +342,27 @@ class PythonMLLibAPI extends Serializable {
       stepSize: Double,
       regParam: Double,
       miniBatchFraction: Double,
-      initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
+      initialWeightsBA: Array[Byte],
+      regType: String,
+      intercept: Boolean): java.util.List[java.lang.Object] = {
+    val SVMAlg = new SVMWithSGD()
+    SVMAlg.setIntercept(intercept)
+    SVMAlg.optimizer
+      .setNumIterations(numIterations)
+      .setRegParam(regParam)
+      .setStepSize(stepSize)
+      .setMiniBatchFraction(miniBatchFraction)
+    if (regType == "l2") {
+      SVMAlg.optimizer.setUpdater(new SquaredL2Updater)
+    } else if (regType == "l1") {
+      SVMAlg.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) =>
-        SVMWithSGD.train(
-          data,
-          numIterations,
-          stepSize,
-          regParam,
-          miniBatchFraction,
-          initialWeights),
+        SVMAlg.run(data, initialWeights),
       dataBytesJRDD,
       initialWeightsBA)
   }
@@ -363,15 +375,28 @@ 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 LogRegAlg = new LogisticRegressionWithSGD()
+    LogRegAlg.setIntercept(intercept)
+    LogRegAlg.optimizer
+      .setNumIterations(numIterations)
+      .setRegParam(regParam)
+      .setStepSize(stepSize)
+      .setMiniBatchFraction(miniBatchFraction)
+    if (regType == "l2") {
+      LogRegAlg.optimizer.setUpdater(new SquaredL2Updater)
+    } else if (regType == "l1") {
+      LogRegAlg.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) =>
-        LogisticRegressionWithSGD.train(
-          data,
-          numIterations,
-          stepSize,
-          miniBatchFraction,
-          initialWeights),
+        LogRegAlg.run(data, initialWeights),
       dataBytesJRDD,
       initialWeightsBA)
   }

python/pyspark/mllib/classification.py

@@ -73,11 +73,36 @@ def predict(self, x):
 
 class LogisticRegressionWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None):
-        """Train a logistic 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 logistic 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_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
-            d._jrdd, iterations, step, miniBatchFraction, i)
+            d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
         return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
                                          initialWeights)
 
@@ -115,11 +140,35 @@ def predict(self, x):
 class SVMWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=1.0,
-              miniBatchFraction=1.0, initialWeights=None):
-        """Train a support vector machine on the given data."""
+              miniBatchFraction=1.0, initialWeights=None, regType=None, intercept=False):
+        """
+        Train a support vector machine 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 regParam:          The regularizer parameter (default: 1.0).
+        @param miniBatchFraction: Fraction of data to be used for each SGD
+                                  iteration.
+        @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")
+        @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_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
-            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
+            d._jrdd, iterations, step, regParam, miniBatchFraction, i, regType, intercept)
         return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)