org.apache.mahout.clustering.ClusteringTestUtils.java Source code

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Here is the source code for org.apache.mahout.clustering.ClusteringTestUtils.java

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/**
 * 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;
        }
    }
}