/**
* 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.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
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.math.Vector;
import org.apache.mahout.math.VectorWritable;
/**
* 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
*
* @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 iterate(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));
}
}
private 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);
Writable key = new Text(k);
writer.append(key, classifier);
writer.close();
}
private 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();
reader.next(key, classifierOut);
reader.close();
return classifierOut;
}
}