Java tutorial
package edu.umd.cloud9.example.clustering; /* * Cloud9: A Hadoop toolkit for working with big data * * Licensed 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 edu.umd.cloud9.example.bigram; import java.io.BufferedReader; import java.io.IOException; import java.io.InputStreamReader; import java.util.Arrays; import java.util.Iterator; import java.util.StringTokenizer; import java.util.Vector; import org.apache.commons.cli.CommandLine; import org.apache.commons.cli.CommandLineParser; import org.apache.commons.cli.GnuParser; import org.apache.commons.cli.HelpFormatter; import org.apache.commons.cli.OptionBuilder; import org.apache.commons.cli.Options; import org.apache.commons.cli.ParseException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FSDataOutputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; import org.apache.log4j.Logger; import tl.lin.data.pair.PairOfStrings; public class IterateGMM extends Configured implements Tool { private static final Logger LOG = Logger.getLogger(IterateGMM.class); protected static class MyMapper extends Mapper<LongWritable, Text, Text, PairOfStrings> { private static final Text comp = new Text(); private static final PairOfStrings PairValue = new PairOfStrings(); private UnivariateGaussianMixtureModel model = new UnivariateGaussianMixtureModel(); private final Vector<String> lines = new Vector<String>(); private double[] p; public void setup(Context context) throws IOException { // load the information of k clusters String file = context.getConfiguration().get("clusterpath"); FSDataInputStream cluster = FileSystem.get(context.getConfiguration()).open(new Path(file)); BufferedReader reader = new BufferedReader(new InputStreamReader(cluster)); lines.clear(); while (reader.ready()) { String line = reader.readLine(); if (line.indexOf("lld") >= 0) continue; if (line.length() > 5) lines.add(line); } reader.close(); cluster.close(); model.setSize(lines.size()); p = new double[model.size]; for (int i = 0; i < lines.size(); i++) { String[] terms = lines.elementAt(i).split("\\s+"); int j = 0; while (j < terms.length) { if (terms[j].length() > 0) break; j++; } model.pos[i] = Integer.parseInt(terms[j]); model.weight[i] = Double.parseDouble(terms[j + 1]); PVector param = new PVector(2); param.array[0] = Double.parseDouble(terms[j + 2]); param.array[1] = Double.parseDouble(terms[j + 3]); model.param[i] = param; } LOG.info("setup: " + model.toString()); } @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer itr = new StringTokenizer(line); double x = 0; while (itr.hasMoreTokens()) { String curr = itr.nextToken(); x = Double.parseDouble(curr); } // Calculate the LogLikelihood of last iteration double lld = Math.log(model.density(new Point(x))); comp.set("lld"); PairValue.set(String.valueOf(x), String.valueOf(lld)); context.write(comp, PairValue); // E step double sum = 0; for (int k = 0; k < model.size; k++) { double tmp = model.weight[k] * UnivariateGaussianMixtureModel.densityOfGaussian(new Point(x), model.param[k]); p[k] = tmp; sum += tmp; } for (int k = 0; k < model.size; k++) { p[k] /= sum; } for (int i = 0; i < model.size; i++) { comp.set(String.valueOf(model.pos[i])); PairValue.set(String.valueOf(x), String.valueOf(p[i])); context.write(comp, PairValue); } } } protected static class MyReducer extends Reducer<Text, PairOfStrings, Text, Text> { private static final Text result = new Text(); @Override public void reduce(Text key, Iterable<PairOfStrings> values, Context context) throws IOException, InterruptedException { Iterator<PairOfStrings> iter = values.iterator(); if (key.toString().matches("lld")) { // calculate the LogLikelihood for last iteration double lld = 0; while (iter.hasNext()) { lld += Double.parseDouble(iter.next().getRightElement()); } result.set(String.valueOf(lld)); context.write(key, result); } else { // Variables double sum = 0; double mu = 0; double sigma = 0; double diff1 = 0; double diff2 = 0; double diff3 = 0; int tot = 0; // First step of the computation of new mu while (iter.hasNext()) { tot++; PairOfStrings now = iter.next(); double w = Double.parseDouble(now.getRightElement()); double x = Double.parseDouble(now.getLeftElement()); sum += w; mu += x * w; diff1 += x * x * w; diff2 += 2 * x * w; diff3 += w; } mu /= sum; sigma = (diff1 - diff2 * mu + diff3 * mu * mu) / sum; double weight = sum / tot; result.set(String.valueOf(weight) + " " + String.valueOf(mu) + " " + String.valueOf(sigma)); context.write(key, result); } } } protected static class MyPartitioner extends Partitioner<Text, PairOfStrings> { @Override public int getPartition(Text key, PairOfStrings value, int numReduceTasks) { return (key.toString().hashCode() & Integer.MAX_VALUE) % numReduceTasks; } } public IterateGMM() { } private static final String INPUT = "input"; private static final String OUTPUT = "output"; private static final String NUM_REDUCERS = "numReducers"; private static int printUsage() { System.out.println("usage: [input-path] [output-path] [num-reducers]"); ToolRunner.printGenericCommandUsage(System.out); return -1; } /** * Runs this tool. */ @SuppressWarnings({ "static-access" }) public int run(String[] args) throws Exception { Options options = new Options(); options.addOption(OptionBuilder.withArgName("path").hasArg().withDescription("input path").create(INPUT)); options.addOption(OptionBuilder.withArgName("path").hasArg().withDescription("output path").create(OUTPUT)); options.addOption(OptionBuilder.withArgName("num").hasArg().withDescription("number of reducers") .create(NUM_REDUCERS)); CommandLine cmdline; CommandLineParser parser = new GnuParser(); try { cmdline = parser.parse(options, args); } catch (ParseException exp) { System.err.println("Error parsing command line: " + exp.getMessage()); return -1; } if (!cmdline.hasOption(INPUT) || !cmdline.hasOption(OUTPUT)) { System.out.println("args: " + Arrays.toString(args)); HelpFormatter formatter = new HelpFormatter(); formatter.setWidth(120); formatter.printHelp(this.getClass().getName(), options); ToolRunner.printGenericCommandUsage(System.out); return -1; } String inputPath0 = cmdline.getOptionValue(INPUT); String outputPath = cmdline.getOptionValue(OUTPUT); int reduceTasks = cmdline.hasOption(NUM_REDUCERS) ? Integer.parseInt(cmdline.getOptionValue(NUM_REDUCERS)) : 1; LOG.info("Tool: " + IterateGMM.class.getSimpleName()); LOG.info(" - input path: " + inputPath0); String inputPath = inputPath0 + "/points"; LOG.info(" - output path: " + outputPath); LOG.info(" - number of reducers: " + reduceTasks); int iterations = 0; Configuration conf = getConf(); while (iterations == 0 || !FinishIteration(inputPath0, iterations, conf)) { LOG.info("** iterations: " + iterations); try { Job job = Job.getInstance(conf); job.setJobName(IterateGMM.class.getSimpleName()); job.setJarByClass(IterateGMM.class); // set the path of the information of k clusters in this iteration job.getConfiguration().set("clusterpath", inputPath0 + "/cluster" + iterations); job.setNumReduceTasks(reduceTasks); FileInputFormat.setInputPaths(job, new Path(inputPath)); FileOutputFormat.setOutputPath(job, new Path(outputPath)); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(PairOfStrings.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); job.setMapperClass(MyMapper.class); job.setReducerClass(MyReducer.class); job.setPartitionerClass(MyPartitioner.class); // Delete the output directory if it exists already. Path outputDir = new Path(outputPath); FileSystem.get(getConf()).delete(outputDir, true); long startTime = System.currentTimeMillis(); job.waitForCompletion(true); LOG.info("Job Finished in " + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds"); reNameFile(inputPath0, outputPath, iterations + 1, conf, reduceTasks); } catch (Exception exp) { exp.printStackTrace(); } iterations++; } return 0; } /** * Dispatches command-line arguments to the tool via the {@code ToolRunner}. */ public static void main(String[] args) throws Exception { ToolRunner.run(new IterateGMM(), args); } private static final int MAX_ITERATIONS = 30; private static final double logLikelihoodThreshold = 10e-10; public static double getlld(String input, int iterations, Configuration conf) { try { FSDataInputStream cluster = FileSystem.get(conf).open(new Path(input + "/cluster" + iterations)); BufferedReader reader = new BufferedReader(new InputStreamReader(cluster)); UnivariateGaussianMixtureModel model = new UnivariateGaussianMixtureModel(); double lld = 0; while (reader.ready()) { String line = reader.readLine(); if (line.indexOf("lld") >= 0) { String[] terms = line.split("\\s+"); int j = 0; while (j < terms.length) { if (terms[j].indexOf("lld") >= 0) break; j++; } lld = Double.parseDouble(terms[j + 1]); break; } } reader.close(); cluster.close(); return lld; } catch (IOException exp) { exp.printStackTrace(); return 0; } } public static boolean FinishIteration(String input, int iterations, Configuration conf) { if (iterations >= MAX_ITERATIONS) return true; if (iterations <= 1) return false; double logLikelihoodNew = getlld(input, iterations, conf); double logLikelihoodOld = getlld(input, iterations - 1, conf); if (Math.abs((logLikelihoodNew - logLikelihoodOld) / logLikelihoodOld) > logLikelihoodThreshold) return false; else return true; } public static boolean reNameFile(String input, String output, int iterations, Configuration conf, int reduceTasks) { String dstName = input + "/cluster" + iterations; try { FileSystem fs = FileSystem.get(conf); fs.delete(new Path(dstName), true); FSDataOutputStream clusterfile = fs.create(new Path(dstName)); for (int i = 0; i < reduceTasks; i++) { String srcName = output + "/part-r-" + String.format("%05d", i); FSDataInputStream cluster = fs.open(new Path(srcName)); BufferedReader reader = new BufferedReader(new InputStreamReader(cluster)); while (reader.ready()) { String line = reader.readLine() + "\n"; if (line.length() > 5) clusterfile.write(line.getBytes()); } reader.close(); cluster.close(); } clusterfile.flush(); clusterfile.close(); return true; } catch (IOException e) { e.printStackTrace(); return false; } } }