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

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Here is the source code for com.netease.news.classifier.naivebayes.ThetaMapper.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 java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.classifier.naivebayes.BayesUtils;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

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

    public static final String ALPHA_I = ThetaMapper.class.getName() + ".alphaI";
    static final String TRAIN_COMPLEMENTARY = ThetaMapper.class.getName() + ".trainComplementary";

    private AbstractThetaTrainer trainer;

    @Override
    protected void setup(Context ctx) throws IOException, InterruptedException {
        super.setup(ctx);
        Configuration conf = ctx.getConfiguration();

        float alphaI = conf.getFloat(ALPHA_I, 1.0f);
        Map<String, Vector> scores = BayesUtils.readScoresFromCache(conf);

        if (conf.getBoolean(TRAIN_COMPLEMENTARY, false)) {
            trainer = new ComplementaryThetaTrainer(scores.get(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE),
                    scores.get(TrainNaiveBayesJob.WEIGHTS_PER_LABEL), alphaI);
        } else {
            trainer = new StandardThetaTrainer(scores.get(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE),
                    scores.get(TrainNaiveBayesJob.WEIGHTS_PER_LABEL), alphaI);
        }
    }

    @Override
    protected void map(IntWritable key, VectorWritable value, Context ctx)
            throws IOException, InterruptedException {
        trainer.train(key.get(), value.get());
    }

    @Override
    protected void cleanup(Context ctx) throws IOException, InterruptedException {
        ctx.write(new Text(TrainNaiveBayesJob.LABEL_THETA_NORMALIZER),
                new VectorWritable(trainer.retrievePerLabelThetaNormalizer()));
        super.cleanup(ctx);
    }
}