org.apache.mahout.classifier.naivebayes.trainer.NaiveBayesThetaComplementaryMapper.java Source code

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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.classifier.naivebayes.trainer;

import java.io.IOException;
import java.net.URI;
import java.util.Iterator;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.classifier.naivebayes.BayesConstants;
import org.apache.mahout.common.Pair;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileIterable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.Vector.Element;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.map.OpenObjectIntHashMap;

public class NaiveBayesThetaComplementaryMapper extends Mapper<IntWritable, VectorWritable, Text, VectorWritable> {

    private final OpenObjectIntHashMap<String> labelMap = new OpenObjectIntHashMap<String>();
    private Vector featureSum;
    private Vector labelSum;
    private Vector perLabelThetaNormalizer;
    private double alphaI = 1.0;
    private double vocabCount;
    private double totalSum;

    @Override
    protected void map(IntWritable key, VectorWritable value, Context context)
            throws IOException, InterruptedException {
        Vector vector = value.get();
        int label = key.get();
        double sigmaK = labelSum.get(label);
        Iterator<Element> it = vector.iterateNonZero();
        while (it.hasNext()) {
            Element e = it.next();
            double numerator = featureSum.get(e.index()) - e.get() + alphaI;
            double denominator = totalSum - sigmaK + vocabCount;
            double weight = Math.log(numerator / denominator);
            perLabelThetaNormalizer.set(label, perLabelThetaNormalizer.get(label) + weight);
        }
    }

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        super.setup(context);
        Configuration conf = context.getConfiguration();
        URI[] localFiles = DistributedCache.getCacheFiles(conf);
        if (localFiles == null || localFiles.length < 2) {
            throw new IllegalArgumentException("missing paths from the DistributedCache");
        }
        alphaI = conf.getFloat(NaiveBayesTrainer.ALPHA_I, 1.0f);
        Path weightFile = new Path(localFiles[0].getPath());
        for (Pair<Text, VectorWritable> record : new SequenceFileIterable<Text, VectorWritable>(weightFile, true,
                conf)) {
            Text key = record.getFirst();
            VectorWritable value = record.getSecond();
            if (key.toString().equals(BayesConstants.FEATURE_SUM)) {
                featureSum = value.get();
            } else if (key.toString().equals(BayesConstants.LABEL_SUM)) {
                labelSum = value.get();
            }
        }
        perLabelThetaNormalizer = labelSum.like();
        totalSum = labelSum.zSum();
        vocabCount = featureSum.getNumNondefaultElements();

        Path labelMapFile = new Path(localFiles[1].getPath());
        // key is word value is id
        for (Pair<Writable, IntWritable> record : new SequenceFileIterable<Writable, IntWritable>(labelMapFile,
                true, conf)) {
            labelMap.put(record.getFirst().toString(), record.getSecond().get());
        }
    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        context.write(new Text(BayesConstants.LABEL_THETA_NORMALIZER), new VectorWritable(perLabelThetaNormalizer));
        super.cleanup(context);
    }
}