[SPARK-6256] [MLlib] MLlib Python API parity check for regression

MLlib Python API parity check for Regression, major disparities need to be added for Python list following:
```scala
LinearRegressionWithSGD
    setValidateData
LassoWithSGD
    setIntercept
    setValidateData
RidgeRegressionWithSGD
    setIntercept
    setValidateData
```
setFeatureScaling is mllib private function which is not needed to expose in pyspark.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #4997 from yanboliang/spark-6256 and squashes the following commits:

102f498 [Yanbo Liang] fix intercept issue & add doc test
1fb7b4f [Yanbo Liang] change 'intercept' to 'addIntercept'
de5ecbc [Yanbo Liang] MLlib Python API parity check for regression

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

@@ -111,9 +111,11 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector,
       regParam: Double,
       regType: String,
-      intercept: Boolean): JList[Object] = {
+      intercept: Boolean,
+      validateData: Boolean): JList[Object] = {
     val lrAlg = new LinearRegressionWithSGD()
     lrAlg.setIntercept(intercept)
+      .setValidateData(validateData)
     lrAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)
@@ -135,8 +137,12 @@ private[python] class PythonMLLibAPI extends Serializable {
       stepSize: Double,
       regParam: Double,
       miniBatchFraction: Double,
-      initialWeights: Vector): JList[Object] = {
+      initialWeights: Vector,
+      intercept: Boolean,
+      validateData: Boolean): JList[Object] = {
     val lassoAlg = new LassoWithSGD()
+    lassoAlg.setIntercept(intercept)
+      .setValidateData(validateData)
     lassoAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)
@@ -157,8 +163,12 @@ private[python] class PythonMLLibAPI extends Serializable {
       stepSize: Double,
       regParam: Double,
       miniBatchFraction: Double,
-      initialWeights: Vector): JList[Object] = {
+      initialWeights: Vector,
+      intercept: Boolean,
+      validateData: Boolean): JList[Object] = {
     val ridgeAlg = new RidgeRegressionWithSGD()
+    ridgeAlg.setIntercept(intercept)
+      .setValidateData(validateData)
     ridgeAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)

python/pyspark/mllib/regression.py

@@ -140,6 +140,13 @@ class LinearRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    ...    miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2",
+    ...    intercept=True, validateData=True)
+    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    True
+    >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
+    True
     """
     def save(self, sc, path):
         java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel(
@@ -173,7 +180,8 @@ class LinearRegressionWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
-              initialWeights=None, regParam=0.0, regType=None, intercept=False):
+              initialWeights=None, regParam=0.0, regType=None, intercept=False,
+              validateData=True):
         """
         Train a linear regression model on the given data.
 
@@ -195,15 +203,18 @@ class LinearRegressionWithSGD(object):
 
                                      (default: None)
 
-        @param intercept:         Boolean parameter which indicates the use
+        :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). (default: False)
+        :param validateData:      Boolean parameter which indicates if the
+                                  algorithm should validate data before training.
+                                  (default: True)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
                                  float(step), float(miniBatchFraction), i, float(regParam),
-                                 regType, bool(intercept))
+                                 regType, bool(intercept), bool(validateData))
 
         return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
 
@@ -253,6 +264,13 @@ class LassoModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
+    ...     validateData=True)
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
+    True
+    >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
+    True
     """
     def save(self, sc, path):
         java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel(
@@ -273,11 +291,13 @@ class LassoWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
-              miniBatchFraction=1.0, initialWeights=None):
+              miniBatchFraction=1.0, initialWeights=None, intercept=False,
+              validateData=True):
         """Train a Lasso regression model on the given data."""
         def train(rdd, i):
             return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
-                                 float(regParam), float(miniBatchFraction), i)
+                                 float(regParam), float(miniBatchFraction), i, bool(intercept),
+                                 bool(validateData))
 
         return _regression_train_wrapper(train, LassoModel, data, initialWeights)
 
@@ -327,6 +347,13 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
+    ...     validateData=True)
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
+    True
+    >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
+    True
     """
     def save(self, sc, path):
         java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(
@@ -347,11 +374,13 @@ class RidgeRegressionWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
-              miniBatchFraction=1.0, initialWeights=None):
+              miniBatchFraction=1.0, initialWeights=None, intercept=False,
+              validateData=True):
         """Train a ridge regression model on the given data."""
         def train(rdd, i):
             return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
-                                 float(regParam), float(miniBatchFraction), i)
+                                 float(regParam), float(miniBatchFraction), i, bool(intercept),
+                                 bool(validateData))
 
         return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)