/**
* 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 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.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.mapreduce.bayes.BayesDriver;
import org.apache.mahout.classifier.bayes.mapreduce.cbayes.CBayesDriver;
import org.apache.mahout.common.CommandLineUtil;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
/**
* Train the Naive Bayes classifier with improved weighting
* <p/>
* To run the twenty newsgroups example: refer
* http://cwiki.apache.org/MAHOUT/twentynewsgroups.html
*/
public class TrainClassifier {
private static final Logger log = LoggerFactory
.getLogger(TrainClassifier.class);
private TrainClassifier() {
}
public static void trainNaiveBayes(String dir, String outputDir,
BayesParameters params) throws IOException, InterruptedException,
ClassNotFoundException {
BayesDriver driver = new BayesDriver();
driver.runJob(dir, outputDir, params);
}
public static void trainCNaiveBayes(String dir, String outputDir,
BayesParameters params) throws IOException, InterruptedException,
ClassNotFoundException {
CBayesDriver driver = new CBayesDriver();
driver.runJob(dir, outputDir, params);
}
public static void main(String[] args) throws IOException,
NumberFormatException, IllegalStateException, InterruptedException,
ClassNotFoundException {
DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
ArgumentBuilder abuilder = new ArgumentBuilder();
GroupBuilder gbuilder = new GroupBuilder();
Option helpOpt = DefaultOptionCreator.helpOption(obuilder);
Option inputDirOpt = obuilder
.withLongName("input")
.withRequired(true)
.withArgument(
abuilder.withName("input").withMinimum(1).withMaximum(1).create())
.withDescription(
"The Directory on HDFS containing the collapsed, properly formatted files")
.withShortName("i").create();
Option outputOpt = obuilder.withLongName("output").withRequired(true)
.withArgument(
abuilder.withName("output").withMinimum(1).withMaximum(1).create())
.withDescription("The location of the modelon the HDFS").withShortName(
"o").create();
Option gramSizeOpt = obuilder.withLongName("gramSize").withRequired(true)
.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 typeOpt = obuilder.withLongName("classifierType").withRequired(true)
.withArgument(
abuilder.withName("classifierType").withMinimum(1).withMaximum(1)
.create()).withDescription(
"Type of classifier: bayes|cbayes. Default: bayes").withShortName(
"type").create();
Option dataSourceOpt = obuilder.withLongName("dataSource").withRequired(
true).withArgument(
abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create())
.withDescription("Location of model: hdfs|hbase. Default Value: hdfs")
.withShortName("source").create();
Group group = gbuilder.withName("Options").withOption(gramSizeOpt)
.withOption(helpOpt).withOption(inputDirOpt).withOption(outputOpt)
.withOption(typeOpt).withOption(dataSourceOpt).withOption(alphaOpt)
.create();
try {
Parser parser = new Parser();
parser.setGroup(group);
CommandLine cmdLine = parser.parse(args);
if (cmdLine.hasOption(helpOpt)) {
CommandLineUtil.printHelp(group);
return;
}
String classifierType = (String) cmdLine.getValue(typeOpt);
String dataSourceType = (String) cmdLine.getValue(dataSourceOpt);
BayesParameters params = new BayesParameters(Integer
.parseInt((String) cmdLine.getValue(gramSizeOpt)));
String alpha_i = "1.0";
if (cmdLine.hasOption(alphaOpt)) {
alpha_i = (String) cmdLine.getValue(alphaOpt);
}
params.set("alpha_i", alpha_i);
if (dataSourceType.equals("hbase"))
params.set("dataSource", "hbase");
else
params.set("dataSource", "hdfs");
if (classifierType.equalsIgnoreCase("bayes")) {
log.info("Training Bayes Classifier");
trainNaiveBayes((String) cmdLine.getValue(inputDirOpt),
(String) cmdLine.getValue(outputOpt), params);
} else if (classifierType.equalsIgnoreCase("cbayes")) {
log.info("Training Complementary Bayes Classifier");
// setup the HDFS and copy the files there, then run the trainer
trainCNaiveBayes((String) cmdLine.getValue(inputDirOpt),
(String) cmdLine.getValue(outputOpt), params);
}
} catch (OptionException e) {
log.info("{}", e);
CommandLineUtil.printHelp(group);
return;
}
}
}