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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.
#
from __future__ import print_function
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
# $example on$
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.clustering import StreamingKMeans
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="StreamingKMeansExample") # SparkContext
ssc = StreamingContext(sc, 1)
# $example on$
# we make an input stream of vectors for training,
# as well as a stream of vectors for testing
def parse(lp):
label = float(lp[lp.find('(') + 1: lp.find(')')])
vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(','))
return LabeledPoint(label, vec)
trainingData = sc.textFile("data/mllib/kmeans_data.txt")\
.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
testingData = sc.textFile("data/mllib/streaming_kmeans_data_test.txt").map(parse)
trainingQueue = [trainingData]
testingQueue = [testingData]
trainingStream = ssc.queueStream(trainingQueue)
testingStream = ssc.queueStream(testingQueue)
# We create a model with random clusters and specify the number of clusters to find
model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(3, 1.0, 0)
# Now register the streams for training and testing and start the job,
# printing the predicted cluster assignments on new data points as they arrive.
model.trainOn(trainingStream)
result = model.predictOnValues(testingStream.map(lambda lp: (lp.label, lp.features)))
result.pprint()
ssc.start()
ssc.stop(stopSparkContext=True, stopGraceFully=True)
# $example off$
print("Final centers: " + str(model.latestModel().centers))
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