/**
* 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.IOException;
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.hadoop.fs.Path;
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;
/**
* Train the Naive Bayes classifier with improved weighting.
* A properly formatted file for input is one which has one document per line
* with the first entry as the label and the rest as evidence.
*
* @see org.apache.mahout.classifier.BayesFileFormatter
*/
public final class TrainClassifier {
private static final Logger log = LoggerFactory.getLogger(TrainClassifier.class);
private TrainClassifier() { }
public static void trainNaiveBayes(Path dir, Path outputDir, BayesParameters params) throws IOException {
BayesDriver driver = new BayesDriver();
driver.runJob(dir, outputDir, params);
}
public static void trainCNaiveBayes(Path dir, Path outputDir, BayesParameters params) throws IOException {
CBayesDriver driver = new CBayesDriver();
driver.runJob(dir, outputDir, params);
}
public static void main(String[] args) throws Exception {
DefaultOptionBuilder obuilder = new DefaultOptionBuilder();
ArgumentBuilder abuilder = new ArgumentBuilder();
GroupBuilder gbuilder = new GroupBuilder();
Option helpOpt = DefaultOptionCreator.helpOption();
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: "
+ "One doc per line, first entry on the line is the label, rest is the evidence")
.withShortName("i").create();
Option outputOpt = obuilder.withLongName("output").withRequired(true).withArgument(
abuilder.withName("output").withMinimum(1).withMaximum(1).create()).withDescription(
"The location of the model on the HDFS").withShortName("o").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 minDfOpt = obuilder.withLongName("minDf").withRequired(false).withArgument(
abuilder.withName("minDf").withMinimum(1).withMaximum(1).create()).withDescription(
"Minimum Term Document Frequency: 1 ").withShortName("mf").create();
Option minSupportOpt = obuilder.withLongName("minSupport").withRequired(false).withArgument(
abuilder.withName("minSupport").withMinimum(1).withMaximum(1).create()).withDescription(
"Minimum Support (Term Frequency): 1 ").withShortName("ms").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(false).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(false).withArgument(
abuilder.withName("dataSource").withMinimum(1).withMaximum(1).create()).withDescription(
"Location of model: hdfs. Default Value: hdfs").withShortName("source").create();
Option skipCleanupOpt = obuilder.withLongName("skipCleanup").withRequired(false).withDescription(
"Skip cleanup of feature extraction output").withShortName("sc").create();
Group group = gbuilder.withName("Options").withOption(gramSizeOpt).withOption(helpOpt).withOption(
inputDirOpt).withOption(outputOpt).withOption(typeOpt).withOption(dataSourceOpt).withOption(alphaOpt)
.withOption(minDfOpt).withOption(minSupportOpt).withOption(skipCleanupOpt).create();
try {
Parser parser = new Parser();
parser.setGroup(group);
parser.setHelpOption(helpOpt);
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();
// Setting all the default parameter values
params.setGramSize(1);
params.setMinDF(1);
params.set("alpha_i","1.0");
params.set("dataSource", "hdfs");
if (cmdLine.hasOption(gramSizeOpt)) {
params.setGramSize(Integer.parseInt((String) cmdLine.getValue(gramSizeOpt)));
}
if (cmdLine.hasOption(minDfOpt)) {
params.setMinDF(Integer.parseInt((String) cmdLine.getValue(minDfOpt)));
}
if (cmdLine.hasOption(minSupportOpt)) {
params.setMinSupport(Integer.parseInt((String) cmdLine.getValue(minSupportOpt)));
}
if (cmdLine.hasOption(skipCleanupOpt)) {
params.setSkipCleanup(true);
}
if (cmdLine.hasOption(alphaOpt)) {
params.set("alpha_i",(String) cmdLine.getValue(alphaOpt));
}
if (cmdLine.hasOption(dataSourceOpt)) {
params.set("dataSource", dataSourceType);
}
Path inputPath = new Path((String) cmdLine.getValue(inputDirOpt));
Path outputPath = new Path((String) cmdLine.getValue(outputOpt));
if ("cbayes".equalsIgnoreCase(classifierType)) {
log.info("Training Complementary Bayes Classifier");
trainCNaiveBayes(inputPath, outputPath, params);
} else {
log.info("Training Bayes Classifier");
// setup the HDFS and copy the files there, then run the trainer
trainNaiveBayes(inputPath, outputPath, params);
}
} catch (OptionException e) {
log.error("Error while parsing options", e);
CommandLineUtil.printHelp(group);
}
}
}