#
# Licensed to the Apache Software Foundation (ASF) under one or more
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
Correlations using MLlib.
"""

import sys

from pyspark import SparkContext
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.stat import Statistics
from pyspark.mllib.util import MLUtils


if __name__ == "__main__":
    if len(sys.argv) not in [1, 2]:
        print >> sys.stderr, "Usage: correlations (<file>)"
        exit(-1)
    sc = SparkContext(appName="PythonCorrelations")
    if len(sys.argv) == 2:
        filepath = sys.argv[1]
    else:
        filepath = 'data/mllib/sample_linear_regression_data.txt'
    corrType = 'pearson'

    points = MLUtils.loadLibSVMFile(sc, filepath)\
        .map(lambda lp: LabeledPoint(lp.label, lp.features.toArray()))

    print
    print 'Summary of data file: ' + filepath
    print '%d data points' % points.count()

    # Statistics (correlations)
    print
    print 'Correlation (%s) between label and each feature' % corrType
    print 'Feature\tCorrelation'
    numFeatures = points.take(1)[0].features.size
    labelRDD = points.map(lambda lp: lp.label)
    for i in range(numFeatures):
        featureRDD = points.map(lambda lp: lp.features[i])
        corr = Statistics.corr(labelRDD, featureRDD, corrType)
        print '%d\t%g' % (i, corr)
    print

    sc.stop()
