/**
* 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.naivebayes.test;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.BayesUtils;
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 java.util.Map;
/**
* 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 LABEL_KEY = "labels";
public static final String COMPLEMENTARY = "class"; //b for bayes, c for complementary
public static void main(String[] args) throws Exception {
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("labelIndex", "l", "The path to the location of the label index", true);
Map<String, String> parsedArgs = parseArguments(args);
if (parsedArgs == null) {
return -1;
}
if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
HadoopUtil.delete(getConf(), getOutputPath());
}
Path model = new Path(parsedArgs.get("--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, parsedArgs.get("--labels"));
boolean complementary = parsedArgs.containsKey("--testComplementary");
testJob.getConfiguration().set(COMPLEMENTARY, String.valueOf(complementary));
testJob.waitForCompletion(true);
//load the labels
Map<Integer, String> labelMap = BayesUtils.readLabelIndex(getConf(), new Path(parsedArgs.get("--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 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()) {
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);
}
}
}
}