/**
* 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.training;
import java.io.IOException;
import java.util.List;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.naivebayes.BayesUtils;
import org.apache.mahout.classifier.naivebayes.NaiveBayesModel;
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.common.mapreduce.VectorSumReducer;
import org.apache.mahout.math.VectorWritable;
import com.google.common.base.Splitter;
/** Trains a Naive Bayes Classifier (parameters for both Naive Bayes and Complementary Naive Bayes) */
public final class TrainNaiveBayesJob extends AbstractJob {
private static final String TRAIN_COMPLEMENTARY = "trainComplementary";
private static final String ALPHA_I = "alphaI";
private static final String LABEL_INDEX = "labelIndex";
public static final String WEIGHTS_PER_FEATURE = "__SPF";
public static final String WEIGHTS_PER_LABEL = "__SPL";
public static final String LABEL_THETA_NORMALIZER = "_LTN";
public static final String SUMMED_OBSERVATIONS = "summedObservations";
public static final String WEIGHTS = "weights";
public static final String THETAS = "thetas";
public static void main(String[] args) throws Exception {
ToolRunner.run(new Configuration(), new TrainNaiveBayesJob(), args);
}
@Override
public int run(String[] args) throws Exception {
addInputOption();
addOutputOption();
addOption(ALPHA_I, "a", "smoothing parameter", String.valueOf(1.0f));
addOption(buildOption(TRAIN_COMPLEMENTARY, "c", "train complementary?", false, false, String.valueOf(false)));
addOption(LABEL_INDEX, "li", "The path to store the label index in", false);
addOption(DefaultOptionCreator.overwriteOption().create());
Map<String, List<String>> parsedArgs = parseArguments(args);
if (parsedArgs == null) {
return -1;
}
if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
HadoopUtil.delete(getConf(), getOutputPath());
HadoopUtil.delete(getConf(), getTempPath());
}
Path labPath;
String labPathStr = getOption(LABEL_INDEX);
if (labPathStr != null) {
labPath = new Path(labPathStr);
} else {
labPath = getTempPath(LABEL_INDEX);
}
long labelSize = createLabelIndex(labPath);
float alphaI = Float.parseFloat(getOption(ALPHA_I));
boolean trainComplementary = hasOption(TRAIN_COMPLEMENTARY);
HadoopUtil.setSerializations(getConf());
HadoopUtil.cacheFiles(labPath, getConf());
// Add up all the vectors with the same labels, while mapping the labels into our index
Job indexInstances = prepareJob(getInputPath(),
getTempPath(SUMMED_OBSERVATIONS),
SequenceFileInputFormat.class,
IndexInstancesMapper.class,
IntWritable.class,
VectorWritable.class,
VectorSumReducer.class,
IntWritable.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
indexInstances.setCombinerClass(VectorSumReducer.class);
boolean succeeded = indexInstances.waitForCompletion(true);
if (!succeeded) {
return -1;
}
// Sum up all the weights from the previous step, per label and per feature
Job weightSummer = prepareJob(getTempPath(SUMMED_OBSERVATIONS),
getTempPath(WEIGHTS),
SequenceFileInputFormat.class,
WeightsMapper.class,
Text.class,
VectorWritable.class,
VectorSumReducer.class,
Text.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
weightSummer.getConfiguration().set(WeightsMapper.NUM_LABELS, String.valueOf(labelSize));
weightSummer.setCombinerClass(VectorSumReducer.class);
succeeded = weightSummer.waitForCompletion(true);
if (!succeeded) {
return -1;
}
// Put the per label and per feature vectors into the cache
HadoopUtil.cacheFiles(getTempPath(WEIGHTS), getConf());
if (trainComplementary){
// Calculate the per label theta normalizers, write out to LABEL_THETA_NORMALIZER vector
// see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.2, Weight Magnitude Errors
Job thetaSummer = prepareJob(getTempPath(SUMMED_OBSERVATIONS),
getTempPath(THETAS),
SequenceFileInputFormat.class,
ThetaMapper.class,
Text.class,
VectorWritable.class,
VectorSumReducer.class,
Text.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
thetaSummer.setCombinerClass(VectorSumReducer.class);
thetaSummer.getConfiguration().setFloat(ThetaMapper.ALPHA_I, alphaI);
thetaSummer.getConfiguration().setBoolean(ThetaMapper.TRAIN_COMPLEMENTARY, trainComplementary);
succeeded = thetaSummer.waitForCompletion(true);
if (!succeeded) {
return -1;
}
}
// Put the per label theta normalizers into the cache
HadoopUtil.cacheFiles(getTempPath(THETAS), getConf());
// Validate our model and then write it out to the official output
getConf().setFloat(ThetaMapper.ALPHA_I, alphaI);
getConf().setBoolean(NaiveBayesModel.COMPLEMENTARY_MODEL, trainComplementary);
NaiveBayesModel naiveBayesModel = BayesUtils.readModelFromDir(getTempPath(), getConf());
naiveBayesModel.validate();
naiveBayesModel.serialize(getOutputPath(), getConf());
return 0;
}
private long createLabelIndex(Path labPath) throws IOException {
long labelSize = 0;
Iterable<Pair<Text,IntWritable>> iterable =
new SequenceFileDirIterable<Text, IntWritable>(getInputPath(),
PathType.LIST,
PathFilters.logsCRCFilter(),
getConf());
labelSize = BayesUtils.writeLabelIndex(getConf(), labPath, iterable);
return labelSize;
}
}