Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1878#discussion_r16094836
--- Diff: examples/src/main/python/mllib/random_and_sampled_rdds.py ---
@@ -0,0 +1,88 @@
+#
+# 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 generated and sampled RDDs.
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.mllib.random import RandomRDDGenerators
+from pyspark.mllib.util import MLUtils
+
+
+
+if __name__ == "__main__":
+ if len(sys.argv) not in [1, 2]:
+ print >> sys.stderr, "Usage: logistic_regression <libsvm data
file>"
+ exit(-1)
+ if len(sys.argv) == 2:
+ datapath = sys.argv[1]
+ else:
+ datapath = 'data/mllib/sample_binary_classification_data.txt'
+
+ sc = SparkContext(appName="PythonRandomAndSampledRDDs")
+
+ points = MLUtils.loadLibSVMFile(sc, datapath)
+
+ numExamples = 10000 # number of examples to generate
+ fraction = 0.1 # fraction of data to sample
+
+ # Example: RandomRDDGenerators
+ normalRDD = RandomRDDGenerators.normalRDD(sc, numExamples)
+ print 'Generated RDD of %d examples sampled from a unit normal
distribution' % normalRDD.count()
+ normalVectorRDD = RandomRDDGenerators.normalVectorRDD(sc, numRows =
numExamples, numCols = 2)
+ print 'Generated RDD of %d examples of length-2 vectors.' %
normalVectorRDD.count()
+
+ print ''
+
+ # Example: RDD.sample() and RDD.takeSample()
+ exactSampleSize = int(numExamples * fraction)
+ print 'Sampling RDD using fraction %g. Expected sample size = %d.' \
+ % (fraction, exactSampleSize)
+ sampledRDD = normalRDD.sample(withReplacement = True, fraction =
fraction)
+ print ' RDD.sample(): sample has %d examples' % sampledRDD.count()
+ sampledArray = normalRDD.takeSample(withReplacement = True, num =
exactSampleSize)
+ print ' RDD.takeSample(): sample has %d examples' % len(sampledArray)
+
+ print ''
+
+ # Example: RDD.sampleByKey()
+ examples = MLUtils.loadLibSVMFile(sc, datapath)
+ sizeA = examples.count()
+ print 'Loaded data with %d examples from file: %s' % (sizeA, datapath)
+ 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)#, exact = True)
--- End diff --
remove `#, exact = True)` because we don't support it in Python
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