/**
* 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.
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package org.apache.mahout.classifier.bayes.mapreduce.cbayes;
import java.io.IOException;
import java.util.Map;
import org.apache.hadoop.conf.Configurable;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DefaultStringifier;
import org.apache.hadoop.io.DoubleWritable;
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.BayesParameters;
import org.apache.mahout.classifier.bayes.SequenceFileModelReader;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.StringTuple;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/** Create and run the Bayes Trainer. */
public class CBayesThetaNormalizerDriver implements BayesJob {
private static final Logger log = LoggerFactory.getLogger(CBayesThetaNormalizerDriver.class);
@Override
public void runJob(Path input, Path output, BayesParameters params) throws IOException {
Configurable client = new JobClient();
JobConf conf = new JobConf(CBayesThetaNormalizerDriver.class);
conf.setJobName("Complementary Bayes Theta Normalizer Driver running over input: " + input);
conf.setOutputKeyClass(StringTuple.class);
conf.setOutputValueClass(DoubleWritable.class);
FileInputFormat.addInputPath(conf, new Path(output, "trainer-weights/Sigma_j"));
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(CBayesThetaNormalizerMapper.class);
conf.setInputFormat(SequenceFileInputFormat.class);
conf.setCombinerClass(CBayesThetaNormalizerReducer.class);
conf.setReducerClass(CBayesThetaNormalizerReducer.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
HadoopUtil.delete(conf, outPath);
Path sigmaKFiles = new Path(output, "trainer-weights/Sigma_k/*");
Map<String,Double> labelWeightSum = SequenceFileModelReader.readLabelSums(sigmaKFiles, 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 sigmaKSigmaJFile = new Path(output, "trainer-weights/Sigma_kSigma_j/*");
double sigmaJSigmaK = SequenceFileModelReader.readSigmaJSigmaK(sigmaKSigmaJFile, conf);
DefaultStringifier<Double> stringifier = new DefaultStringifier<Double>(conf, Double.class);
String sigmaJSigmaKString = stringifier.toString(sigmaJSigmaK);
log.info("Sigma_kSigma_j for each Label and for each Features");
double retSigmaJSigmaK = stringifier.fromString(sigmaJSigmaKString);
log.info("{}", retSigmaJSigmaK);
conf.set("cnaivebayes.sigma_jSigma_k", sigmaJSigmaKString);
Path vocabCountFile = new Path(output, "trainer-tfIdf/trainer-vocabCount/*");
double vocabCount = SequenceFileModelReader.readVocabCount(vocabCountFile, conf);
String vocabCountString = stringifier.toString(vocabCount);
log.info("Vocabulary Count");
conf.set("cnaivebayes.vocabCount", vocabCountString);
double retvocabCount = stringifier.fromString(vocabCountString);
log.info("{}", retvocabCount);
conf.set("bayes.parameters", params.toString());
conf.set("output.table", output.toString());
client.setConf(conf);
JobClient.runJob(conf);
}
}