/**
* 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.Random;
import com.google.common.base.Preconditions;
import com.google.common.io.Closeables;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.SparseRowMatrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.DoubleFunction;
import org.apache.mahout.math.stats.Sampler;
public final class ClusteringTestUtils {
private ClusteringTestUtils() {
}
public static void writePointsToFile(Iterable<VectorWritable> points,
Path path,
FileSystem fs,
Configuration conf) throws IOException {
writePointsToFile(points, false, path, fs, conf);
}
public static void writePointsToFile(Iterable<VectorWritable> points,
boolean intWritable,
Path path,
FileSystem fs,
Configuration conf) throws IOException {
SequenceFile.Writer writer = new SequenceFile.Writer(fs,
conf,
path,
intWritable ? IntWritable.class : LongWritable.class,
VectorWritable.class);
try {
int recNum = 0;
for (VectorWritable point : points) {
writer.append(intWritable ? new IntWritable(recNum++) : new LongWritable(recNum++), point);
}
} finally {
Closeables.close(writer, false);
}
}
public static Matrix sampledCorpus(Matrix matrix, Random random,
int numDocs, int numSamples, int numTopicsPerDoc) {
Matrix corpus = new SparseRowMatrix(numDocs, matrix.numCols());
LDASampler modelSampler = new LDASampler(matrix, random);
Vector topicVector = new DenseVector(matrix.numRows());
for (int i = 0; i < numTopicsPerDoc; i++) {
int topic = random.nextInt(topicVector.size());
topicVector.set(topic, topicVector.get(topic) + 1);
}
for (int docId = 0; docId < numDocs; docId++) {
for (int sample : modelSampler.sample(topicVector, numSamples)) {
corpus.set(docId, sample, corpus.get(docId, sample) + 1);
}
}
return corpus;
}
public static Matrix randomStructuredModel(int numTopics, int numTerms) {
return randomStructuredModel(numTopics, numTerms, new DoubleFunction() {
@Override public double apply(double d) {
return 1.0 / (1 + Math.abs(d));
}
});
}
public static Matrix randomStructuredModel(int numTopics, int numTerms, DoubleFunction decay) {
Matrix model = new DenseMatrix(numTopics, numTerms);
int width = numTerms / numTopics;
for (int topic = 0; topic < numTopics; topic++) {
int topicCentroid = width * (1+topic);
for (int i = 0; i < numTerms; i++) {
int distance = Math.abs(topicCentroid - i);
if (distance > numTerms / 2) {
distance = numTerms - distance;
}
double v = decay.apply(distance);
model.set(topic, i, v);
}
}
return model;
}
/**
* Takes in a {@link Matrix} of topic distributions (such as generated by {@link org.apache.mahout.clustering.lda.cvb.CVB0Driver} or
* {@link org.apache.mahout.clustering.lda.cvb.InMemoryCollapsedVariationalBayes0}, and constructs
* a set of samplers over this distribution, which may be sampled from by providing a distribution
* over topics, and a number of samples desired
*/
static class LDASampler {
private final Random random;
private final Sampler[] samplers;
LDASampler(Matrix model, Random random) {
this.random = random;
samplers = new Sampler[model.numRows()];
for (int i = 0; i < samplers.length; i++) {
samplers[i] = new Sampler(random, model.viewRow(i));
}
}
/**
*
* @param topicDistribution vector of p(topicId) for all topicId < model.numTopics()
* @param numSamples the number of times to sample (with replacement) from the model
* @return array of length numSamples, with each entry being a sample from the model. There
* may be repeats
*/
public int[] sample(Vector topicDistribution, int numSamples) {
Preconditions.checkNotNull(topicDistribution);
Preconditions.checkArgument(numSamples > 0, "numSamples must be positive");
Preconditions.checkArgument(topicDistribution.size() == samplers.length,
"topicDistribution must have same cardinality as the sampling model");
int[] samples = new int[numSamples];
Sampler topicSampler = new Sampler(random, topicDistribution);
for (int i = 0; i < numSamples; i++) {
samples[i] = samplers[topicSampler.sample()].sample();
}
return samples;
}
}
}