/** * 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 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 Theta Normalization Step. */ public class BayesThetaNormalizerDriver implements BayesJob { private static final Logger log = LoggerFactory.getLogger(BayesThetaNormalizerDriver.class); @Override public void runJob(Path input, Path output, BayesParameters params) throws IOException { Configurable client = new JobClient(); JobConf conf = new JobConf(BayesThetaNormalizerDriver.class); conf.setJobName("Bayes Theta Normalizer Driver running over input: " + input); conf.setOutputKeyClass(StringTuple.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 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 sigmaJSigmaKFile = new Path(output, "trainer-weights/Sigma_kSigma_j/*"); double sigmaJSigmaK = SequenceFileModelReader.readSigmaJSigmaK(sigmaJSigmaKFile, 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); } }