Java tutorial
/** * 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 com.missionsky.scp.dataanalysis.mahout; import java.io.IOException; import java.util.List; import java.util.Map; import java.util.regex.Pattern; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.SequenceFile; import org.apache.hadoop.io.SequenceFile.Reader; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.classifier.ClassifierResult; import org.apache.mahout.classifier.ResultAnalyzer; import org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier; import org.apache.mahout.classifier.naivebayes.BayesUtils; import org.apache.mahout.classifier.naivebayes.ComplementaryNaiveBayesClassifier; import org.apache.mahout.classifier.naivebayes.NaiveBayesModel; import org.apache.mahout.classifier.naivebayes.StandardNaiveBayesClassifier; import org.apache.mahout.classifier.naivebayes.test.BayesTestMapper; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.common.HadoopUtil; import org.apache.mahout.common.Pair; import org.apache.mahout.common.commandline.DefaultOptionCreator; import org.apache.mahout.common.iterator.sequencefile.PathFilters; import org.apache.mahout.common.iterator.sequencefile.PathType; import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirIterable; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.missionsky.scp.dataanalysis.ForestTest; import com.missionsky.scp.dataanalysis.utils.RemoteHadoopUtil; /** * Test the (Complementary) Naive Bayes model that was built during training * by running the iterating the test set and comparing it to the model */ public class TestNaiveBayesDriver extends AbstractJob { private static final Logger log = LoggerFactory.getLogger(TestNaiveBayesDriver.class); public static final String COMPLEMENTARY = "class"; //b for bayes, c for complementary private static final Pattern SLASH = Pattern.compile("/"); public static void main(String[] args) throws Exception { RemoteHadoopUtil.setConf(ForestTest.class, Thread.currentThread(), "/hadoop"); ToolRunner.run(new Configuration(), new TestNaiveBayesDriver(), args); } @Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption(addOption(DefaultOptionCreator.overwriteOption().create())); addOption("model", "m", "The path to the model built during training", true); addOption( buildOption("testComplementary", "c", "test complementary?", false, false, String.valueOf(false))); addOption(buildOption("runSequential", "seq", "run sequential?", false, false, String.valueOf(false))); addOption("labelIndex", "l", "The path to the location of the label index", true); Map<String, List<String>> parsedArgs = parseArguments(args); if (parsedArgs == null) { return -1; } if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { HadoopUtil.delete(getConf(), getOutputPath()); } boolean complementary = hasOption("testComplementary"); boolean sequential = hasOption("runSequential"); if (sequential) { FileSystem fs = FileSystem.get(getConf()); NaiveBayesModel model = NaiveBayesModel.materialize(new Path(getOption("model")), getConf()); AbstractNaiveBayesClassifier classifier; if (complementary) { classifier = new ComplementaryNaiveBayesClassifier(model); } else { classifier = new StandardNaiveBayesClassifier(model); } SequenceFile.Writer writer = new SequenceFile.Writer(fs, getConf(), getOutputPath(), Text.class, VectorWritable.class); Reader reader = new Reader(fs, getInputPath(), getConf()); Text key = new Text(); VectorWritable vw = new VectorWritable(); while (reader.next(key, vw)) { writer.append(new Text(SLASH.split(key.toString())[1]), new VectorWritable(classifier.classifyFull(vw.get()))); } writer.close(); reader.close(); } else { boolean succeeded = runMapReduce(parsedArgs); if (!succeeded) { return -1; } } //load the labels Map<Integer, String> labelMap = BayesUtils.readLabelIndex(getConf(), new Path(getOption("labelIndex"))); //loop over the results and create the confusion matrix SequenceFileDirIterable<Text, VectorWritable> dirIterable = new SequenceFileDirIterable<Text, VectorWritable>( getOutputPath(), PathType.LIST, PathFilters.partFilter(), getConf()); ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT"); analyzeResults(labelMap, dirIterable, analyzer); log.info("{} Results: {}", complementary ? "Complementary" : "Standard NB", analyzer); return 0; } private boolean runMapReduce(Map<String, List<String>> parsedArgs) throws IOException, InterruptedException, ClassNotFoundException { Path model = new Path(getOption("model")); HadoopUtil.cacheFiles(model, getConf()); //the output key is the expected value, the output value are the scores for all the labels Job testJob = prepareJob(getInputPath(), getOutputPath(), SequenceFileInputFormat.class, BayesTestMapper.class, Text.class, VectorWritable.class, SequenceFileOutputFormat.class); //testJob.getConfiguration().set(LABEL_KEY, getOption("--labels")); //boolean complementary = parsedArgs.containsKey("testComplementary"); //always result to false as key in hash map is "--testComplementary" boolean complementary = hasOption("testComplementary"); //or complementary = parsedArgs.containsKey("--testComplementary"); testJob.getConfiguration().set(COMPLEMENTARY, String.valueOf(complementary)); return testJob.waitForCompletion(true); } private static void analyzeResults(Map<Integer, String> labelMap, SequenceFileDirIterable<Text, VectorWritable> dirIterable, ResultAnalyzer analyzer) { for (Pair<Text, VectorWritable> pair : dirIterable) { int bestIdx = Integer.MIN_VALUE; double bestScore = Long.MIN_VALUE; for (Vector.Element element : pair.getSecond().get().all()) { if (element.get() > bestScore) { bestScore = element.get(); bestIdx = element.index(); } } if (bestIdx != Integer.MIN_VALUE) { ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore); analyzer.addInstance(pair.getFirst().toString(), classifierResult); } } } }