/**
* 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 org.apache.mahout.clustering;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileStatus;
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.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.iterator.sequencefile.PathFilters;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileValueIterator;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import com.google.common.io.Closeables;
/**
* This is an experimental clustering iterator which works with a
* ClusteringPolicy and a prior ClusterClassifier which has been initialized
* with a set of models. To date, it has been tested with k-means and Dirichlet
* clustering. See examples DisplayKMeans and DisplayDirichlet which have been
* switched over to use it.
*/
public class ClusterIterator {
public ClusterIterator(ClusteringPolicy policy) {
this.policy = policy;
}
private final ClusteringPolicy policy;
/**
* Iterate over data using a prior-trained ClusterClassifier, for a number of
* iterations
*
* @param data
* a {@code List<Vector>} of input vectors
* @param classifier
* a prior ClusterClassifier
* @param numIterations
* the int number of iterations to perform
* @return the posterior ClusterClassifier
*/
public ClusterClassifier iterate(Iterable<Vector> data,
ClusterClassifier classifier, int numIterations) {
for (int iteration = 1; iteration <= numIterations; iteration++) {
for (Vector vector : data) {
// classification yields probabilities
Vector probabilities = classifier.classify(vector);
// policy selects weights for models given those probabilities
Vector weights = policy.select(probabilities);
// training causes all models to observe data
for (Iterator<Vector.Element> it = weights.iterateNonZero(); it
.hasNext();) {
int index = it.next().index();
classifier.train(index, vector, weights.get(index));
}
}
// compute the posterior models
classifier.close();
// update the policy
policy.update(classifier);
}
return classifier;
}
/**
* Iterate over data using a prior-trained ClusterClassifier, for a number of
* iterations using a sequential implementation
*
* @param inPath
* a Path to input VectorWritables
* @param priorPath
* a Path to the prior classifier
* @param outPath
* a Path of output directory
* @param numIterations
* the int number of iterations to perform
* @throws IOException
*/
public void iterateSeq(Path inPath, Path priorPath, Path outPath,
int numIterations) throws IOException {
ClusterClassifier classifier = readClassifier(priorPath);
Configuration conf = new Configuration();
for (int iteration = 1; iteration <= numIterations; iteration++) {
for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(
inPath, PathType.LIST, PathFilters.logsCRCFilter(), conf)) {
Vector vector = vw.get();
// classification yields probabilities
Vector probabilities = classifier.classify(vector);
// policy selects weights for models given those probabilities
Vector weights = policy.select(probabilities);
// training causes all models to observe data
for (Iterator<Vector.Element> it = weights.iterateNonZero(); it
.hasNext();) {
int index = it.next().index();
classifier.train(index, vector, weights.get(index));
}
}
// compute the posterior models
classifier.close();
// update the policy
policy.update(classifier);
// output the classifier
writeClassifier(classifier, new Path(outPath, "classifier-" + iteration),
String.valueOf(iteration));
}
}
/**
* Iterate over data using a prior-trained ClusterClassifier, for a number of
* iterations using a mapreduce implementation
*
* @param inPath
* a Path to input VectorWritables
* @param priorPath
* a Path to the prior classifier
* @param outPath
* a Path of output directory
* @param numIterations
* the int number of iterations to perform
*/
public static void iterateMR(Path inPath, Path priorPath, Path outPath,
int numIterations) throws IOException, InterruptedException,
ClassNotFoundException {
Configuration conf = new Configuration();
for (int iteration = 1; iteration <= numIterations; iteration++) {
conf.set("org.apache.mahout.clustering.prior.path", priorPath.toString());
Job job = new Job(conf, "Cluster Iterator running iteration " + iteration
+ " over priorPath: " + priorPath);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(Cluster.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Cluster.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setMapperClass(CIMapper.class);
job.setReducerClass(CIReducer.class);
FileInputFormat.addInputPath(job, inPath);
FileOutputFormat.setOutputPath(job, outPath);
job.setJarByClass(ClusterIterator.class);
HadoopUtil.delete(conf, outPath);
if (!job.waitForCompletion(true)) {
throw new InterruptedException("Cluster Iteration " + iteration
+ " failed processing " + priorPath);
}
FileSystem fs = FileSystem.get(outPath.toUri(), conf);
if (isConverged(outPath, conf, fs)) {
break;
}
}
}
/**
* Return if all of the Clusters in the parts in the filePath have converged
* or not
*
* @param filePath
* the file path to the single file containing the clusters
* @return true if all Clusters are converged
* @throws IOException
* if there was an IO error
*/
private static boolean isConverged(Path filePath, Configuration conf, FileSystem fs)
throws IOException {
for (FileStatus part : fs.listStatus(filePath, PathFilters.partFilter())) {
SequenceFileValueIterator<Cluster> iterator = new SequenceFileValueIterator<Cluster>(
part.getPath(), true, conf);
while (iterator.hasNext()) {
Cluster value = iterator.next();
if (!value.isConverged()) {
Closeables.closeQuietly(iterator);
return false;
}
}
}
return true;
}
public static void writeClassifier(ClusterClassifier classifier,
Path outPath, String k) throws IOException {
Configuration config = new Configuration();
FileSystem fs = FileSystem.get(outPath.toUri(), config);
SequenceFile.Writer writer = new SequenceFile.Writer(fs, config, outPath,
Text.class, ClusterClassifier.class);
try {
Writable key = new Text(k);
writer.append(key, classifier);
} finally {
Closeables.closeQuietly(writer);
}
}
public static ClusterClassifier readClassifier(Path inPath)
throws IOException {
Configuration config = new Configuration();
FileSystem fs = FileSystem.get(inPath.toUri(), config);
SequenceFile.Reader reader = new SequenceFile.Reader(fs, inPath, config);
Writable key = new Text();
ClusterClassifier classifierOut = new ClusterClassifier();
try {
reader.next(key, classifierOut);
} finally {
Closeables.closeQuietly(reader);
}
return classifierOut;
}
}