[GitHub] spark pull request: [SPARK-1752][MLLIB] Standardize text format fo...

[mengxr <gi...@git.apache.org>]

Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/685#discussion_r13300795
  
    --- Diff: python/pyspark/mllib/util.py ---
    @@ -160,6 +157,40 @@ def saveAsLibSVMFile(data, dir):
             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)))
    +
    --- End diff --
    
    It doesn't pair with `saveAsTextFile`, because there are many vector types in pyspark. Users need to call `map(Vector.stringify).saveAsTextFile`


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