[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, 8 insertions, 2 deletions

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 209f1ee473..cd7310a64f 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -167,13 +167,19 @@ class LinearRegressionModel(LinearRegressionModelBase):
 # return the result of a call to the appropriate JVM stub.
 # _regression_train_wrapper is responsible for setup and error checking.
 def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
+    from pyspark.mllib.classification import LogisticRegressionModel
     first = data.first()
     if not isinstance(first, LabeledPoint):
         raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first)
     if initial_weights is None:
         initial_weights = [0.0] * len(data.first().features)
-    weights, intercept = train_func(data, _convert_to_vector(initial_weights))
-    return modelClass(weights, intercept)
+    if (modelClass == LogisticRegressionModel):
+        weights, intercept, numFeatures, numClasses = train_func(
+            data, _convert_to_vector(initial_weights))
+        return modelClass(weights, intercept, numFeatures, numClasses)
+    else:
+        weights, intercept = train_func(data, _convert_to_vector(initial_weights))
+        return modelClass(weights, intercept)
 
 
 class LinearRegressionWithSGD(object):