com.netease.news.classifier.naivebayes.WeightsMapper.java Source code

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Here is the source code for com.netease.news.classifier.naivebayes.WeightsMapper.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 com.netease.news.classifier.naivebayes;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.Functions;

import com.google.common.base.Preconditions;

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

    static final String NUM_LABELS = WeightsMapper.class.getName() + ".numLabels";

    private Vector weightsPerFeature;
    private Vector weightsPerLabel;

    @Override
    protected void setup(Context ctx) throws IOException, InterruptedException {
        super.setup(ctx);
        int numLabels = Integer.parseInt(ctx.getConfiguration().get(NUM_LABELS));
        Preconditions.checkArgument(numLabels > 0);
        weightsPerLabel = new DenseVector(numLabels);
    }

    @Override
    protected void map(IntWritable index, VectorWritable value, Context ctx)
            throws IOException, InterruptedException {
        Vector instance = value.get();
        if (weightsPerFeature == null) {
            weightsPerFeature = new RandomAccessSparseVector(instance.size(), instance.getNumNondefaultElements());
        }

        int label = index.get();
        weightsPerFeature.assign(instance, Functions.PLUS);
        weightsPerLabel.set(label, weightsPerLabel.get(label) + instance.zSum());
    }

    @Override
    protected void cleanup(Context ctx) throws IOException, InterruptedException {
        if (weightsPerFeature != null) {
            ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE), new VectorWritable(weightsPerFeature));
            ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_LABEL), new VectorWritable(weightsPerLabel));
        }
        super.cleanup(ctx);
    }
}