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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.
#
"""
Randomly sampled RDDs.
"""
from __future__ import print_function
import sys
from pyspark import SparkContext
from pyspark.mllib.util import MLUtils
if __name__ == "__main__":
if len(sys.argv) not in [1, 2]:
print("Usage: sampled_rdds <libsvm data file>", file=sys.stderr)
exit(-1)
if len(sys.argv) == 2:
datapath = sys.argv[1]
else:
datapath = 'data/mllib/sample_binary_classification_data.txt'
sc = SparkContext(appName="PythonSampledRDDs")
fraction = 0.1 # fraction of data to sample
examples = MLUtils.loadLibSVMFile(sc, datapath)
numExamples = examples.count()
if numExamples == 0:
print("Error: Data file had no samples to load.", file=sys.stderr)
exit(1)
print('Loaded data with %d examples from file: %s' % (numExamples, datapath))
# Example: RDD.sample() and RDD.takeSample()
expectedSampleSize = int(numExamples * fraction)
print('Sampling RDD using fraction %g. Expected sample size = %d.'
% (fraction, expectedSampleSize))
sampledRDD = examples.sample(withReplacement=True, fraction=fraction)
print(' RDD.sample(): sample has %d examples' % sampledRDD.count())
sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize)
print(' RDD.takeSample(): sample has %d examples' % len(sampledArray))
print()
# Example: RDD.sampleByKey()
keyedRDD = examples.map(lambda lp: (int(lp.label), lp.features))
print(' Keyed data using label (Int) as key ==> Orig')
# Count examples per label in original data.
keyCountsA = keyedRDD.countByKey()
# Subsample, and count examples per label in sampled data.
fractions = {}
for k in keyCountsA.keys():
fractions[k] = fraction
sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions)
keyCountsB = sampledByKeyRDD.countByKey()
sizeB = sum(keyCountsB.values())
print(' Sampled %d examples using approximate stratified sampling (by label). ==> Sample'
% sizeB)
# Compare samples
print(' \tFractions of examples with key')
print('Key\tOrig\tSample')
for k in sorted(keyCountsA.keys()):
fracA = keyCountsA[k] / float(numExamples)
if sizeB != 0:
fracB = keyCountsB.get(k, 0) / float(sizeB)
else:
fracB = 0
print('%d\t%g\t%g' % (k, fracA, fracB))
sc.stop()
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