python/mllib/power_iteration_clustering_example.py

0001 #
0002 # Licensed to the Apache Software Foundation (ASF) under one or more
0003 # contributor license agreements.  See the NOTICE file distributed with
0004 # this work for additional information regarding copyright ownership.
0005 # The ASF licenses this file to You under the Apache License, Version 2.0
0006 # (the "License"); you may not use this file except in compliance with
0007 # the License.  You may obtain a copy of the License at
0008 #
0009 #    http://www.apache.org/licenses/LICENSE-2.0
0010 #
0011 # Unless required by applicable law or agreed to in writing, software
0012 # distributed under the License is distributed on an "AS IS" BASIS,
0013 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014 # See the License for the specific language governing permissions and
0015 # limitations under the License.
0016 #
0017
0018 from __future__ import print_function
0019
0020 from pyspark import SparkContext
0021 # $example on$
0022 from pyspark.mllib.clustering import PowerIterationClustering, PowerIterationClusteringModel
0023 # $example off$
0024
0025 if __name__ == "__main__":
0026     sc = SparkContext(appName="PowerIterationClusteringExample")  # SparkContext
0027
0028     # $example on$
0029     # Load and parse the data
0030     data = sc.textFile("data/mllib/pic_data.txt")
0031     similarities = data.map(lambda line: tuple([float(x) for x in line.split(' ')]))
0032
0033     # Cluster the data into two classes using PowerIterationClustering
0034     model = PowerIterationClustering.train(similarities, 2, 10)
0035
0036     model.assignments().foreach(lambda x: print(str(x.id) + " -> " + str(x.cluster)))
0037
0038     # Save and load model
0039     model.save(sc, "target/org/apache/spark/PythonPowerIterationClusteringExample/PICModel")
0040     sameModel = PowerIterationClusteringModel\
0041         .load(sc, "target/org/apache/spark/PythonPowerIterationClusteringExample/PICModel")
0042     # $example off$
0043
0044     sc.stop()