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;
}
}
}