/**
* 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.iterator;
import java.io.IOException;
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.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.clustering.Cluster;
import org.apache.mahout.clustering.classify.ClusterClassifier;
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 a clustering iterator which works with a set of Vector data and a prior ClusterClassifier which has been
* initialized with a set of models. Its implementation is algorithm-neutral and works for any iterative clustering
* algorithm (currently k-means and fuzzy-k-means) that processes all the input vectors in each iteration.
* The cluster classifier is configured with a ClusteringPolicy to select the desired clustering algorithm.
*/
public final class ClusterIterator {
public static final String PRIOR_PATH_KEY = "org.apache.mahout.clustering.prior.path";
private ClusterIterator() {
}
/**
* 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 static ClusterClassifier iterate(Iterable<Vector> data, ClusterClassifier classifier, int numIterations) {
ClusteringPolicy policy = classifier.getPolicy();
for (int iteration = 1; iteration <= numIterations; iteration++) {
for (Vector vector : data) {
// update the policy based upon the prior
policy.update(classifier);
// 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 (Vector.Element e : weights.nonZeroes()) {
int index = e.index();
classifier.train(index, vector, weights.get(index));
}
}
// compute the posterior models
classifier.close();
}
return classifier;
}
/**
* Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a sequential
* implementation
*
* @param conf
* the Configuration
* @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 iterateSeq(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations)
throws IOException {
ClusterClassifier classifier = new ClusterClassifier();
classifier.readFromSeqFiles(conf, priorPath);
Path clustersOut = null;
int iteration = 1;
while (iteration <= numIterations) {
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 = classifier.getPolicy().select(probabilities);
// training causes all models to observe data
for (Vector.Element e : weights.nonZeroes()) {
int index = e.index();
classifier.train(index, vector, weights.get(index));
}
}
// compute the posterior models
classifier.close();
// update the policy
classifier.getPolicy().update(classifier);
// output the classifier
clustersOut = new Path(outPath, Cluster.CLUSTERS_DIR + iteration);
classifier.writeToSeqFiles(clustersOut);
FileSystem fs = FileSystem.get(outPath.toUri(), conf);
iteration++;
if (isConverged(clustersOut, conf, fs)) {
break;
}
}
Path finalClustersIn = new Path(outPath, Cluster.CLUSTERS_DIR + (iteration - 1) + Cluster.FINAL_ITERATION_SUFFIX);
FileSystem.get(clustersOut.toUri(), conf).rename(clustersOut, finalClustersIn);
}
/**
* Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a mapreduce
* implementation
*
* @param conf
* the Configuration
* @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(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations)
throws IOException, InterruptedException, ClassNotFoundException {
ClusteringPolicy policy = ClusterClassifier.readPolicy(priorPath);
Path clustersOut = null;
int iteration = 1;
while (iteration <= numIterations) {
conf.set(PRIOR_PATH_KEY, priorPath.toString());
String jobName = "Cluster Iterator running iteration " + iteration + " over priorPath: " + priorPath;
Job job = new Job(conf, jobName);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(ClusterWritable.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(ClusterWritable.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setMapperClass(CIMapper.class);
job.setReducerClass(CIReducer.class);
FileInputFormat.addInputPath(job, inPath);
clustersOut = new Path(outPath, Cluster.CLUSTERS_DIR + iteration);
priorPath = clustersOut;
FileOutputFormat.setOutputPath(job, clustersOut);
job.setJarByClass(ClusterIterator.class);
if (!job.waitForCompletion(true)) {
throw new InterruptedException("Cluster Iteration " + iteration + " failed processing " + priorPath);
}
ClusterClassifier.writePolicy(policy, clustersOut);
FileSystem fs = FileSystem.get(outPath.toUri(), conf);
iteration++;
if (isConverged(clustersOut, conf, fs)) {
break;
}
}
Path finalClustersIn = new Path(outPath, Cluster.CLUSTERS_DIR + (iteration - 1) + Cluster.FINAL_ITERATION_SUFFIX);
FileSystem.get(clustersOut.toUri(), conf).rename(clustersOut, finalClustersIn);
}
/**
* 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<ClusterWritable> iterator = new SequenceFileValueIterator<>(
part.getPath(), true, conf);
while (iterator.hasNext()) {
ClusterWritable value = iterator.next();
if (!value.getValue().isConverged()) {
Closeables.close(iterator, true);
return false;
}
}
}
return true;
}
}