/**
* 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 java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.classifier.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureDriver;
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.apache.mahout.common.HadoopUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/** Create and run the Bayes Trainer. */
public class BayesDriver implements BayesJob {
private static final Logger log = LoggerFactory.getLogger(BayesDriver.class);
@Override
public void runJob(Path input, Path output, BayesParameters params) throws IOException {
Configuration conf = new Configuration();
HadoopUtil.delete(conf, output);
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);
if (params.isSkipCleanup()) {
return;
}
Path docCountOutPath = new Path(output, "trainer-docCount");
HadoopUtil.delete(conf, docCountOutPath);
Path termDocCountOutPath = new Path(output, "trainer-termDocCount");
HadoopUtil.delete(conf, termDocCountOutPath);
Path featureCountOutPath = new Path(output, "trainer-featureCount");
HadoopUtil.delete(conf, featureCountOutPath);
Path wordFreqOutPath = new Path(output, "trainer-wordFreq");
HadoopUtil.delete(conf, wordFreqOutPath);
Path vocabCountPath = new Path(output, "trainer-tfIdf/trainer-vocabCount");
HadoopUtil.delete(conf, vocabCountPath);
Path vocabCountOutPath = new Path(output, "trainer-vocabCount");
HadoopUtil.delete(conf, vocabCountOutPath);
}
}