org.apache.mahout.classifier.bayes.BayesThetaNormalizerDriver.java Source code

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Here is the source code for org.apache.mahout.classifier.bayes.BayesThetaNormalizerDriver.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.classifier.bayes;

import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DefaultStringifier;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.hadoop.util.GenericsUtil;
import org.apache.mahout.classifier.bayes.io.SequenceFileModelReader;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;
import java.util.Map;

/** Create and run the Bayes Theta Normalization Step. */
public class BayesThetaNormalizerDriver {

    private static final Logger log = LoggerFactory.getLogger(BayesThetaNormalizerDriver.class);

    private BayesThetaNormalizerDriver() {
    }

    /**
     * Takes in two arguments: <ol> <li>The input {@link org.apache.hadoop.fs.Path} where the input documents live</li>
     * <li>The output {@link org.apache.hadoop.fs.Path} where to write the the interim filesas a {@link
     * org.apache.hadoop.io.SequenceFile}</li> </ol>
     *
     * @param args The args
     */
    public static void main(String[] args) throws IOException {
        String input = args[0];
        String output = args[1];

        runJob(input, output);
    }

    /**
     * Run the job
     *
     * @param input  the input pathname String
     * @param output the output pathname String
     */
    public static void runJob(String input, String output) throws IOException {
        JobClient client = new JobClient();
        JobConf conf = new JobConf(BayesThetaNormalizerDriver.class);

        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(DoubleWritable.class);
        FileInputFormat.addInputPath(conf, new Path(output + "/trainer-tfIdf/trainer-tfIdf"));
        Path outPath = new Path(output + "/trainer-thetaNormalizer");
        FileOutputFormat.setOutputPath(conf, outPath);
        conf.setNumMapTasks(100);
        //conf.setNumReduceTasks(1);
        conf.setMapperClass(BayesThetaNormalizerMapper.class);
        conf.setInputFormat(SequenceFileInputFormat.class);
        conf.setCombinerClass(BayesThetaNormalizerReducer.class);
        conf.setReducerClass(BayesThetaNormalizerReducer.class);
        conf.setOutputFormat(SequenceFileOutputFormat.class);
        conf.set("io.serializations",
                "org.apache.hadoop.io.serializer.JavaSerialization,org.apache.hadoop.io.serializer.WritableSerialization");
        // Dont ever forget this. People should keep track of how hadoop conf parameters and make or break a piece of code

        FileSystem dfs = FileSystem.get(outPath.toUri(), conf);
        if (dfs.exists(outPath)) {
            dfs.delete(outPath, true);
        }

        Path Sigma_kFiles = new Path(output + "/trainer-weights/Sigma_k/*");
        Map<String, Double> labelWeightSum = SequenceFileModelReader.readLabelSums(dfs, Sigma_kFiles, conf);
        DefaultStringifier<Map<String, Double>> mapStringifier = new DefaultStringifier<Map<String, Double>>(conf,
                GenericsUtil.getClass(labelWeightSum));
        String labelWeightSumString = mapStringifier.toString(labelWeightSum);

        log.info("Sigma_k for Each Label");
        Map<String, Double> c = mapStringifier.fromString(labelWeightSumString);
        log.info("{}", c);
        conf.set("cnaivebayes.sigma_k", labelWeightSumString);

        Path sigma_kSigma_jFile = new Path(output + "/trainer-weights/Sigma_kSigma_j/*");
        double sigma_jSigma_k = SequenceFileModelReader.readSigma_jSigma_k(dfs, sigma_kSigma_jFile, conf);
        DefaultStringifier<Double> stringifier = new DefaultStringifier<Double>(conf, Double.class);
        String sigma_jSigma_kString = stringifier.toString(sigma_jSigma_k);

        log.info("Sigma_kSigma_j for each Label and for each Features");
        double retSigma_jSigma_k = stringifier.fromString(sigma_jSigma_kString);
        log.info("{}", retSigma_jSigma_k);
        conf.set("cnaivebayes.sigma_jSigma_k", sigma_jSigma_kString);

        Path vocabCountFile = new Path(output + "/trainer-tfIdf/trainer-vocabCount/*");
        double vocabCount = SequenceFileModelReader.readVocabCount(dfs, vocabCountFile, conf);
        String vocabCountString = stringifier.toString(vocabCount);

        log.info("Vocabulary Count");
        conf.set("cnaivebayes.vocabCount", vocabCountString);
        double retvocabCount = stringifier.fromString(vocabCountString);
        log.info("{}", retvocabCount);

        client.setConf(conf);

        JobClient.runJob(conf);

    }
}