Java tutorial
/** * 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. */ package com.eniyitavsiye.mahoutx.hadoop; import java.util.List; import java.util.Map; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.SequenceFile; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.clustering.Cluster; import org.apache.mahout.clustering.canopy.CanopyDriver; import org.apache.mahout.clustering.classify.WeightedVectorWritable; import org.apache.mahout.clustering.conversion.InputDriver; import org.apache.mahout.clustering.kmeans.KMeansDriver; import org.apache.mahout.clustering.kmeans.RandomSeedGenerator; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.common.ClassUtils; import org.apache.mahout.common.HadoopUtil; import org.apache.mahout.common.commandline.DefaultOptionCreator; import org.apache.mahout.common.distance.DistanceMeasure; import org.apache.mahout.common.distance.EuclideanDistanceMeasure; import org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure; import org.apache.mahout.utils.clustering.ClusterDumper; import org.slf4j.Logger; import org.slf4j.LoggerFactory; public final class Job extends AbstractJob { private static final Logger log = LoggerFactory.getLogger(Job.class); private static final String DIRECTORY_CONTAINING_CONVERTED_INPUT = "data"; private Job() { } public static void main(String[] args) throws Exception { if (args.length > 0) { log.info("Running with only user-supplied arguments"); ToolRunner.run(new Configuration(), new Job(), args); } else { log.info("Running with default arguments"); Path output = new Path("output"); Configuration conf = new Configuration(); HadoopUtil.delete(conf, output); run(conf, new Path("testdata"), output, new EuclideanDistanceMeasure(), 6, 0.5, 10); } } @Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption(DefaultOptionCreator.distanceMeasureOption().create()); addOption(DefaultOptionCreator.numClustersOption().create()); addOption(DefaultOptionCreator.t1Option().create()); addOption(DefaultOptionCreator.t2Option().create()); addOption(DefaultOptionCreator.convergenceOption().create()); addOption(DefaultOptionCreator.maxIterationsOption().create()); addOption(DefaultOptionCreator.overwriteOption().create()); Map<String, List<String>> argMap = parseArguments(args); if (argMap == null) { return -1; } Path input = getInputPath(); Path output = getOutputPath(); String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION); if (measureClass == null) { measureClass = SquaredEuclideanDistanceMeasure.class.getName(); } double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION)); int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION)); if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { HadoopUtil.delete(getConf(), output); } DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class); if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) { int k = Integer.parseInt(getOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)); run(getConf(), input, output, measure, k, convergenceDelta, maxIterations); } else { double t1 = Double.parseDouble(getOption(DefaultOptionCreator.T1_OPTION)); double t2 = Double.parseDouble(getOption(DefaultOptionCreator.T2_OPTION)); run(getConf(), input, output, measure, t1, t2, convergenceDelta, maxIterations); } return 0; } /** * Run the kmeans clustering job on an input dataset using the given the * number of clusters k and iteration parameters. All output data will be * written to the output directory, which will be initially deleted if it * exists. The clustered points will reside in the path * <output>/clustered-points. By default, the job expects a file containing * equal length space delimited data that resides in a directory named * "testdata", and writes output to a directory named "output". * * @param conf the Configuration to use * @param input the String denoting the input directory path * @param output the String denoting the output directory path * @param measure the DistanceMeasure to use * @param k the number of clusters in Kmeans * @param convergenceDelta the double convergence criteria for iterations * @param maxIterations the int maximum number of iterations */ public static void run(Configuration conf, Path input, Path output, DistanceMeasure measure, int k, double convergenceDelta, int maxIterations) throws Exception { Path directoryContainingConvertedInput = new Path(output, DIRECTORY_CONTAINING_CONVERTED_INPUT); log.info("Preparing Input"); InputDriver.runJob(input, directoryContainingConvertedInput, "org.apache.mahout.math.RandomAccessSparseVector"); log.info("Running random seed to get initial clusters"); Path clusters = new Path(output, "random-seeds"); clusters = RandomSeedGenerator.buildRandom(conf, directoryContainingConvertedInput, clusters, k, measure); System.out.println("****************************************************************************"); log.info("Running KMeans with k = {}", k); KMeansDriver.run(conf, directoryContainingConvertedInput, clusters, output, measure, convergenceDelta, maxIterations, true, 0.0, false); // run ClusterDumper Path outGlob = new Path(output, "clusters-*-final"); Path clusteredPoints = new Path(output, "clusteredPoints"); log.info("Dumping out clusters from clusters: {} and clusteredPoints: {}", outGlob, clusteredPoints); ClusterDumper clusterDumper = new ClusterDumper(outGlob, clusteredPoints); clusterDumper.printClusters(null); FileSystem fs = FileSystem.get(conf); SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("output/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-00000"), conf); IntWritable key = new IntWritable(); WeightedVectorWritable value = new WeightedVectorWritable(); while (reader.next(key, value)) { System.out.println(value.toString() + " belongs to cluster " + key.toString()); } reader.close(); } /** * Run the kmeans clustering job on an input dataset using the given * distance measure, t1, t2 and iteration parameters. All output data will * be written to the output directory, which will be initially deleted if it * exists. The clustered points will reside in the path * <output>/clustered-points. By default, the job expects the a file * containing synthetic_control.data as obtained from * http://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series * resides in a directory named "testdata", and writes output to a directory * named "output". * * @param conf the Configuration to use * @param input the String denoting the input directory path * @param output the String denoting the output directory path * @param measure the DistanceMeasure to use * @param t1 the canopy T1 threshold * @param t2 the canopy T2 threshold * @param convergenceDelta the double convergence criteria for iterations * @param maxIterations the int maximum number of iterations */ public static void run(Configuration conf, Path input, Path output, DistanceMeasure measure, double t1, double t2, double convergenceDelta, int maxIterations) throws Exception { Path directoryContainingConvertedInput = new Path(output, DIRECTORY_CONTAINING_CONVERTED_INPUT); log.info("Preparing Input"); InputDriver.runJob(input, directoryContainingConvertedInput, "org.apache.mahout.math.RandomAccessSparseVector"); log.info("Running Canopy to get initial clusters"); Path canopyOutput = new Path(output, "canopies"); CanopyDriver.run(new Configuration(), directoryContainingConvertedInput, canopyOutput, measure, t1, t2, false, 0.0, false); log.info("Running KMeans"); KMeansDriver.run(conf, directoryContainingConvertedInput, new Path(canopyOutput, Cluster.INITIAL_CLUSTERS_DIR + "-final"), output, measure, convergenceDelta, maxIterations, true, 0.0, false); // run ClusterDumper ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output, "clusteredPoints")); clusterDumper.printClusters(null); } }