1 files changed, 50 insertions, 19 deletions
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 0e5f4520b9..e24c144f45 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -19,7 +19,10 @@ import numpy as np
 
 from pyspark.mllib.linalg import Vectors, SparseVector
 from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib._common import _convert_vector
+from pyspark.mllib._common import _convert_vector, _deserialize_labeled_point
+from pyspark.rdd import RDD
+from pyspark.serializers import NoOpSerializer
+
 
 
 class MLUtils:
@@ -105,24 +108,18 @@ class MLUtils:
         >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
         >>> multiclass_examples = MLUtils.loadLibSVMFile(sc, tempFile.name, True).collect()
         >>> tempFile.close()
-        >>> examples[0].label
-        1.0
-        >>> examples[0].features.size
-        6
-        >>> print examples[0].features
-        [0: 1.0, 2: 2.0, 4: 3.0]
-        >>> examples[1].label
-        0.0
-        >>> examples[1].features.size
-        6
-        >>> print examples[1].features
-        []
-        >>> examples[2].label
-        0.0
-        >>> examples[2].features.size
-        6
-        >>> print examples[2].features
-        [1: 4.0, 3: 5.0, 5: 6.0]
+        >>> type(examples[0]) == LabeledPoint
+        True
+        >>> print examples[0]
+        (1.0,(6,[0,2,4],[1.0,2.0,3.0]))
+        >>> type(examples[1]) == LabeledPoint
+        True
+        >>> print examples[1]
+        (0.0,(6,[],[]))
+        >>> type(examples[2]) == LabeledPoint
+        True
+        >>> print examples[2]
+        (0.0,(6,[1,3,5],[4.0,5.0,6.0]))
         >>> multiclass_examples[1].label
         -1.0
         """
@@ -158,6 +155,40 @@ class MLUtils:
         lines.saveAsTextFile(dir)
 
 
+    @staticmethod
+    def loadLabeledPoints(sc, path, minPartitions=None):
+        """
+        Load labeled points saved using RDD.saveAsTextFile.
+
+        @param sc: Spark context
+        @param path: file or directory path in any Hadoop-supported file
+                     system URI
+        @param minPartitions: min number of partitions
+        @return: labeled data stored as an RDD of LabeledPoint
+
+        >>> from tempfile import NamedTemporaryFile
+        >>> from pyspark.mllib.util import MLUtils
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
+                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> tempFile = NamedTemporaryFile(delete=True)
+        >>> tempFile.close()
+        >>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
+        >>> loaded = MLUtils.loadLabeledPoints(sc, tempFile.name).collect()
+        >>> type(loaded[0]) == LabeledPoint
+        True
+        >>> print examples[0]
+        (1.1,(3,[0,2],[-1.23,4.56e-07]))
+        >>> type(examples[1]) == LabeledPoint
+        True
+        >>> print examples[1]
+        (0.0,[1.01,2.02,3.03])
+        """
+        minPartitions = minPartitions or min(sc.defaultParallelism, 2)
+        jSerialized = sc._jvm.PythonMLLibAPI().loadLabeledPoints(sc._jsc, path, minPartitions)
+        serialized = RDD(jSerialized, sc, NoOpSerializer())
+        return serialized.map(lambda bytes: _deserialize_labeled_point(bytearray(bytes)))
+
+
 def _test():
     import doctest
     from pyspark.context import SparkContext