Class MLUtils

Loads labeled data in the LIBSVM format into an RDD of LabeledPoint. The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR. Each line represents a labeled sparse feature vector using the following format:

label index1:value1 index2:value2 ...

where the indices are one-based and in ascending order. This method parses each line into a LabeledPoint, where the feature indices are converted to zero-based.

Parameters:

• sc - Spark context
• path - file or directory path in any Hadoop-supported file system URI
• multiclass - whether the input labels contain more than two classes. If false, any label with value greater than 0.5 will be mapped to 1.0, or 0.0 otherwise. So it works for both +1/-1 and 1/0 cases. If true, the double value parsed directly from the label string will be used as the label value.
• numFeatures - number of features, which will be determined from the input data if a nonpositive value is given. This is useful when the dataset is already split into multiple files and you want to load them separately, because some features may not present in certain files, which leads to inconsistent feature dimensions.
• minPartitions - min number of partitions

Returns:

labeled data stored as an RDD of LabeledPoint

>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\n-1\n-1 2:4.0 4:5.0 6:6.0")
>>> tempFile.flush()
>>> 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]
>>> multiclass_examples[1].label
-1.0

Save labeled data in LIBSVM format.

Parameters:

• data - an RDD of LabeledPoint to be saved
• dir - directory to save the data

  >>> from tempfile import NamedTemporaryFile
  >>> from fileinput import input
  >>> from glob import glob
  >>> from pyspark.mllib.util import MLUtils
  >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),                         LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
  >>> tempFile = NamedTemporaryFile(delete=True)
  >>> tempFile.close()
  >>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
  >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
  '0.0 1:1.01 2:2.02 3:3.03\n1.1 1:1.23 3:4.56\n'