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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
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()
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