/**
* 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.mapreduce.bayes;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapred.JobConf;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureDriver;
import org.apache.mahout.classifier.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesJob;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesTfIdfDriver;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesWeightSummerDriver;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
/** Create and run the Bayes Trainer. */
public class BayesDriver implements BayesJob {
private static final Logger log = LoggerFactory.getLogger(BayesDriver.class);
/**
* Run the job
*
* @param input the input pathname String
* @param output the output pathname String
* @throws ClassNotFoundException
* @throws InterruptedException
*/
@Override
public void runJob(String input, String output, BayesParameters params) throws IOException, InterruptedException, ClassNotFoundException {
JobConf conf = new JobConf(BayesDriver.class);
Path outPath = new Path(output);
FileSystem dfs = FileSystem.get(outPath.toUri(), conf);
if (dfs.exists(outPath)) {
dfs.delete(outPath, true);
}
log.info("Reading features...");
//Read the features in each document normalized by length of each document
BayesFeatureDriver feature = new BayesFeatureDriver();
feature.runJob(input, output, params);
log.info("Calculating Tf-Idf...");
//Calculate the TfIdf for each word in each label
BayesTfIdfDriver tfidf = new BayesTfIdfDriver();
tfidf.runJob(input, output, params);
log.info("Calculating weight sums for labels and features...");
//Calculate the Sums of weights for each label, for each feature and for each feature and for each label
BayesWeightSummerDriver summer = new BayesWeightSummerDriver();
summer.runJob(input, output, params);
log.info("Calculating the weight Normalisation factor for each class...");
//Calculate the normalization factor Sigma_W_ij for each complement class.
BayesThetaNormalizerDriver normalizer = new BayesThetaNormalizerDriver();
normalizer.runJob(input, output, params);
Path docCountOutPath = new Path(output + "/trainer-docCount");
if (dfs.exists(docCountOutPath)) {
dfs.delete(docCountOutPath, true);
}
Path termDocCountOutPath = new Path(output + "/trainer-termDocCount");
if (dfs.exists(termDocCountOutPath)) {
dfs.delete(termDocCountOutPath, true);
}
Path featureCountOutPath = new Path(output + "/trainer-featureCount");
if (dfs.exists(featureCountOutPath)) {
dfs.delete(featureCountOutPath, true);
}
Path wordFreqOutPath = new Path(output + "/trainer-wordFreq");
if (dfs.exists(wordFreqOutPath)) {
dfs.delete(wordFreqOutPath, true);
}
Path vocabCountPath = new Path(output + "/trainer-tfIdf/trainer-vocabCount");
if (dfs.exists(vocabCountPath)) {
dfs.delete(vocabCountPath, true);
}
Path vocabCountOutPath = new Path(output + "/trainer-vocabCount");
if (dfs.exists(vocabCountOutPath)) {
dfs.delete(vocabCountOutPath, true);
}
}
}