/**
* 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;
import java.io.File;
import java.io.FilenameFilter;
import java.io.IOException;
import java.nio.charset.Charset;
import java.util.List;
import java.util.Map;
import org.apache.commons.cli2.CommandLine;
import org.apache.commons.cli2.Group;
import org.apache.commons.cli2.Option;
import org.apache.commons.cli2.OptionException;
import org.apache.commons.cli2.builder.ArgumentBuilder;
import org.apache.commons.cli2.builder.DefaultOptionBuilder;
import org.apache.commons.cli2.builder.GroupBuilder;
import org.apache.commons.cli2.commandline.Parser;
import org.apache.mahout.classifier.ClassifierResult;
import org.apache.mahout.classifier.ResultAnalyzer;
import org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm;
import org.apache.mahout.classifier.bayes.algorithm.CBayesAlgorithm;
import org.apache.mahout.classifier.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.datastore.InMemoryBayesDatastore;
import org.apache.mahout.classifier.bayes.exceptions.InvalidDatastoreException;
import org.apache.mahout.classifier.bayes.interfaces.Algorithm;
import org.apache.mahout.classifier.bayes.interfaces.Datastore;
import org.apache.mahout.classifier.bayes.mapreduce.bayes.BayesClassifierDriver;
import org.apache.mahout.classifier.bayes.model.ClassifierContext;
import org.apache.mahout.common.CommandLineUtil;
import org.apache.mahout.common.iterator.FileLineIterable;
import org.apache.mahout.common.TimingStatistics;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import org.apache.mahout.common.nlp.NGrams;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Test the Naive Bayes classifier with improved weighting
* <p/>
* To run the twenty newsgroups example: refer http://cwiki.apache.org/MAHOUT/twentynewsgroups.html
*/
public final class TestClassifier {
private static final Logger log = LoggerFactory.getLogger(TestClassifier.class);
private TestClassifier() {
// do nothing
}
public static void main(String[] args) throws IOException, InvalidDatastoreException {
DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
ArgumentBuilder abuilder = new ArgumentBuilder();
GroupBuilder gbuilder = new GroupBuilder();
Option pathOpt = obuilder.withLongName("model").withRequired(true).withArgument(
abuilder.withName("model").withMinimum(1).withMaximum(1).create()).withDescription(
"The path on HDFS as defined by the -source parameter").withShortName("m")
.create();
Option dirOpt = obuilder.withLongName("testDir").withRequired(true).withArgument(
abuilder.withName("testDir").withMinimum(1).withMaximum(1).create()).withDescription(
"The directory where test documents resides in").withShortName("d").create();
Option helpOpt = DefaultOptionCreator.helpOption();
Option encodingOpt = obuilder.withLongName("encoding").withArgument(
abuilder.withName("encoding").withMinimum(1).withMaximum(1).create()).withDescription(
"The file encoding. Defaults to UTF-8").withShortName("e").create();
Option defaultCatOpt = obuilder.withLongName("defaultCat").withArgument(
abuilder.withName("defaultCat").withMinimum(1).withMaximum(1).create()).withDescription(
"The default category Default Value: unknown").withShortName("default").create();
Option gramSizeOpt = obuilder.withLongName("gramSize").withRequired(false).withArgument(
abuilder.withName("gramSize").withMinimum(1).withMaximum(1).create()).withDescription(
"Size of the n-gram. Default Value: 1").withShortName("ng").create();
Option alphaOpt = obuilder.withLongName("alpha").withRequired(false).withArgument(
abuilder.withName("a").withMinimum(1).withMaximum(1).create()).withDescription(
"Smoothing parameter Default Value: 1.0").withShortName("a").create();
Option verboseOutputOpt = obuilder.withLongName("verbose").withRequired(false).withDescription(
"Output which values were correctly and incorrectly classified").withShortName("v").create();
Option typeOpt = obuilder.withLongName("classifierType").withRequired(false).withArgument(
abuilder.withName("classifierType").withMinimum(1).withMaximum(1).create()).withDescription(
"Type of classifier: bayes|cbayes. Default Value: bayes").withShortName("type").create();
Option dataSourceOpt = obuilder.withLongName("dataSource").withRequired(false).withArgument(
abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create()).withDescription(
"Location of model: hdfs").withShortName("source").create();
Option methodOpt = obuilder.withLongName("method").withRequired(false).withArgument(
abuilder.withName("method").withMinimum(1).withMaximum(1).create()).withDescription(
"Method of Classification: sequential|mapreduce. Default Value: sequential").withShortName("method")
.create();
Group group = gbuilder.withName("Options").withOption(defaultCatOpt).withOption(dirOpt).withOption(
encodingOpt).withOption(gramSizeOpt).withOption(pathOpt).withOption(typeOpt).withOption(dataSourceOpt)
.withOption(helpOpt).withOption(methodOpt).withOption(verboseOutputOpt).withOption(alphaOpt).create();
try {
Parser parser = new Parser();
parser.setGroup(group);
CommandLine cmdLine = parser.parse(args);
if (cmdLine.hasOption(helpOpt)) {
CommandLineUtil.printHelp(group);
return;
}
BayesParameters params = new BayesParameters();
// Setting all default values
int gramSize = 1;
String modelBasePath = (String) cmdLine.getValue(pathOpt);
if (cmdLine.hasOption(gramSizeOpt)) {
gramSize = Integer.parseInt((String) cmdLine.getValue(gramSizeOpt));
}
String classifierType = "bayes";
if (cmdLine.hasOption(typeOpt)) {
classifierType = (String) cmdLine.getValue(typeOpt);
}
String dataSource = "hdfs";
if (cmdLine.hasOption(dataSourceOpt)) {
dataSource = (String) cmdLine.getValue(dataSourceOpt);
}
String defaultCat = "unknown";
if (cmdLine.hasOption(defaultCatOpt)) {
defaultCat = (String) cmdLine.getValue(defaultCatOpt);
}
String encoding = "UTF-8";
if (cmdLine.hasOption(encodingOpt)) {
encoding = (String) cmdLine.getValue(encodingOpt);
}
String alphaI = "1.0";
if (cmdLine.hasOption(alphaOpt)) {
alphaI = (String) cmdLine.getValue(alphaOpt);
}
boolean verbose = cmdLine.hasOption(verboseOutputOpt);
String testDirPath = (String) cmdLine.getValue(dirOpt);
String classificationMethod = "sequential";
if (cmdLine.hasOption(methodOpt)) {
classificationMethod = (String) cmdLine.getValue(methodOpt);
}
params.setGramSize(gramSize);
params.set("verbose", Boolean.toString(verbose));
params.setBasePath(modelBasePath);
params.set("classifierType", classifierType);
params.set("dataSource", dataSource);
params.set("defaultCat", defaultCat);
params.set("encoding", encoding);
params.set("alpha_i", alphaI);
params.set("testDirPath", testDirPath);
if ("sequential".equalsIgnoreCase(classificationMethod)) {
classifySequential(params);
} else if ("mapreduce".equalsIgnoreCase(classificationMethod)) {
classifyParallel(params);
}
} catch (OptionException e) {
CommandLineUtil.printHelp(group);
}
}
public static void classifySequential(BayesParameters params) throws IOException, InvalidDatastoreException {
log.info("Loading model from: {}", params.print());
boolean verbose = Boolean.valueOf(params.get("verbose"));
File dir = new File(params.get("testDirPath"));
File[] subdirs = dir.listFiles(new FilenameFilter() {
@Override
public boolean accept(File file, String s) {
return !s.startsWith(".");
}
});
Algorithm algorithm;
Datastore datastore;
if ("hdfs".equals(params.get("dataSource"))) {
if ("bayes".equalsIgnoreCase(params.get("classifierType"))) {
log.info("Testing Bayes Classifier");
algorithm = new BayesAlgorithm();
datastore = new InMemoryBayesDatastore(params);
} else if ("cbayes".equalsIgnoreCase(params.get("classifierType"))) {
log.info("Testing Complementary Bayes Classifier");
algorithm = new CBayesAlgorithm();
datastore = new InMemoryBayesDatastore(params);
} else {
throw new IllegalArgumentException("Unrecognized classifier type: " + params.get("classifierType"));
}
} else {
throw new IllegalArgumentException("Unrecognized dataSource type: " + params.get("dataSource"));
}
ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
classifier.initialize();
ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
TimingStatistics totalStatistics = new TimingStatistics();
if (subdirs != null) {
for (File file : subdirs) {
if (verbose) {
log.info("--------------");
log.info("Testing: {}", file);
}
TimingStatistics operationStats = new TimingStatistics();
long lineNum = 0;
for (String line : new FileLineIterable(new File(file.getPath()), Charset.forName(params
.get("encoding")), false)) {
Map<String,List<String>> document = new NGrams(line, Integer.parseInt(params.get("gramSize")))
.generateNGrams();
for (Map.Entry<String,List<String>> stringListEntry : document.entrySet()) {
String correctLabel = stringListEntry.getKey();
List<String> strings = stringListEntry.getValue();
TimingStatistics.Call call = operationStats.newCall();
TimingStatistics.Call outercall = totalStatistics.newCall();
ClassifierResult classifiedLabel = classifier.classifyDocument(strings.toArray(new String[strings
.size()]), params.get("defaultCat"));
call.end();
outercall.end();
boolean correct = resultAnalyzer.addInstance(correctLabel, classifiedLabel);
if (verbose) {
// We have one document per line
log.info("Line Number: {} Line(30): {} Expected Label: {} Classified Label: {} Correct: {}",
new Object[] {lineNum, line.length() > 30 ? line.substring(0, 30) : line, correctLabel,
classifiedLabel.getLabel(), correct,});
}
// log.info("{} {}", correctLabel, classifiedLabel);
}
lineNum++;
}
/*
* log.info("{}\t{}\t{}/{}", new Object[] {correctLabel,
* resultAnalyzer.getConfusionMatrix().getAccuracy(correctLabel),
* resultAnalyzer.getConfusionMatrix().getCorrect(correctLabel),
* resultAnalyzer.getConfusionMatrix().getTotal(correctLabel)});
*/
log.info("Classified instances from {}", file.getName());
if (verbose) {
log.info("Performance stats {}", operationStats.toString());
}
}
}
if (verbose) {
log.info("{}", totalStatistics);
}
log.info("{}", resultAnalyzer);
}
public static void classifyParallel(BayesParameters params) throws IOException {
BayesClassifierDriver.runJob(params);
}
}