[SPARK-6255] [MLLIB] Support multiclass classification in Python API

Python API parity check for classification and multiclass classification support, major disparities need to be added for Python:
```scala
LogisticRegressionWithLBFGS
    setNumClasses
    setValidateData
LogisticRegressionModel
    getThreshold
    numClasses
    numFeatures
SVMWithSGD
    setValidateData
SVMModel
    getThreshold
```
For users the greatest benefit in this PR is multiclass classification was supported by Python API.
Users can train multiclass classification model and use it to predict in pyspark.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #5137 from yanboliang/spark-6255 and squashes the following commits:

0bd531e [Yanbo Liang] address comments
444d5e2 [Yanbo Liang] LogisticRegressionModel.predict() optimization
fc7990b [Yanbo Liang] address comments
b0d9c63 [Yanbo Liang] Support Mulinomial LR model predict in Python API
ded847c [Yanbo Liang] Python API parity check for classification (support multiclass classification)

1 files changed, 18 insertions, 4 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 22fa684fd2..662ec5fbed 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
@@ -77,7 +77,13 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector): JList[Object] = {
     try {
       val model = learner.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK), initialWeights)
-      List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava
+      if (model.isInstanceOf[LogisticRegressionModel]) {
+        val lrModel = model.asInstanceOf[LogisticRegressionModel]
+        List(lrModel.weights, lrModel.intercept, lrModel.numFeatures, lrModel.numClasses)
+          .map(_.asInstanceOf[Object]).asJava
+      } else {
+        List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava
+      }
     } finally {
       data.rdd.unpersist(blocking = false)
     }
@@ -190,9 +196,11 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       regType: String,
-      intercept: Boolean): JList[Object] = {
+      intercept: Boolean,
+      validateData: Boolean): JList[Object] = {
     val SVMAlg = new SVMWithSGD()
     SVMAlg.setIntercept(intercept)
+      .setValidateData(validateData)
     SVMAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)
@@ -216,9 +224,11 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector,
       regParam: Double,
       regType: String,
-      intercept: Boolean): JList[Object] = {
+      intercept: Boolean,
+      validateData: Boolean): JList[Object] = {
     val LogRegAlg = new LogisticRegressionWithSGD()
     LogRegAlg.setIntercept(intercept)
+      .setValidateData(validateData)
     LogRegAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)
@@ -242,9 +252,13 @@ private[python] class PythonMLLibAPI extends Serializable {
       regType: String,
       intercept: Boolean,
       corrections: Int,
-      tolerance: Double): JList[Object] = {
+      tolerance: Double,
+      validateData: Boolean,
+      numClasses: Int): JList[Object] = {
     val LogRegAlg = new LogisticRegressionWithLBFGS()
     LogRegAlg.setIntercept(intercept)
+      .setValidateData(validateData)
+      .setNumClasses(numClasses)
     LogRegAlg.optimizer
       .setNumIterations(numIterations)
       .setRegParam(regParam)