4 files changed, 8 insertions, 13 deletions
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index fe4c4cc509..e2492eef5b 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -16,7 +16,7 @@
 #
 
 from pyspark import SparkContext
-from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _to_java_object_rdd
+from pyspark.mllib.common import callMLlibFunc, callJavaFunc
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector
 
 __all__ = ['KMeansModel', 'KMeans']
@@ -80,10 +80,8 @@ class KMeans(object):
     @classmethod
     def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"):
         """Train a k-means clustering model."""
-        # cache serialized data to avoid objects over head in JVM
-        jcached = _to_java_object_rdd(rdd.map(_convert_to_vector), cache=True)
-        model = callMLlibFunc("trainKMeansModel", jcached, k, maxIterations, runs,
-                              initializationMode)
+        model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
+                              runs, initializationMode)
         centers = callJavaFunc(rdd.context, model.clusterCenters)
         return KMeansModel([c.toArray() for c in centers])
 
diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py
index c6149fe391..33c49e2399 100644
--- a/python/pyspark/mllib/common.py
+++ b/python/pyspark/mllib/common.py
@@ -54,15 +54,13 @@ _picklable_classes = [
 
 
 # this will call the MLlib version of pythonToJava()
-def _to_java_object_rdd(rdd, cache=False):
+def _to_java_object_rdd(rdd):
     """ Return an JavaRDD of Object by unpickling
 
     It will convert each Python object into Java object by Pyrolite, whenever the
     RDD is serialized in batch or not.
     """
     rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
-    if cache:
-        rdd.cache()
     return rdd.ctx._jvm.SerDe.pythonToJava(rdd._jrdd, True)
 
 
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 2bcbf2aaf8..97ec74eda0 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -19,7 +19,7 @@ from collections import namedtuple
 
 from pyspark import SparkContext
 from pyspark.rdd import RDD
-from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, _to_java_object_rdd
+from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
 
 __all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
 
@@ -110,7 +110,7 @@ class ALS(object):
                 ratings = ratings.map(lambda x: Rating(*x))
             else:
                 raise ValueError("rating should be RDD of Rating or tuple/list")
-        return _to_java_object_rdd(ratings, True)
+        return ratings
 
     @classmethod
     def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index f4f5e615fa..210060140f 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -18,7 +18,7 @@
 import numpy as np
 from numpy import array
 
-from pyspark.mllib.common import callMLlibFunc, _to_java_object_rdd
+from pyspark.mllib.common import callMLlibFunc
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector
 
 __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel',
@@ -129,8 +129,7 @@ def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
     if not isinstance(first, LabeledPoint):
         raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first)
     initial_weights = initial_weights or [0.0] * len(data.first().features)
-    weights, intercept = train_func(_to_java_object_rdd(data, cache=True),
-                                    _convert_to_vector(initial_weights))
+    weights, intercept = train_func(data, _convert_to_vector(initial_weights))
     return modelClass(weights, intercept)