命令行对应哪个类可以查看源码配置文件
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driver.classes.default.props
mahout的API
https://builds.apache.org/job/Mahout-Quality/javadoc/
mahout实战参考博客：
http://itindex.net/detail/45259-mahout-%E7%94%B5%E5%BD%B1-%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F
聚类算法
kmeans:无法消除离群点的影响
canopy:两个阈值t1和t2，且t1>t2，简单快速不太准确，可以消除离群点的影响，一般用来决定聚类中心数目k
Canopy聚类算法
http://my.oschina.net/liangtee/blog/125407
mahout canopy算法实战
http://blog.csdn.net/xyilu/article/details/9631677
分类Bayes(训练集，基于概率的)、文本分类算法(监督学习)
朴素贝叶斯分类器两种模型：
多项式模型，以单词打标签，粒度不一样
伯努利模型，以文档打标签
用于新闻分类：体育、娱乐
mahout中提供了一种将指定文件下的文件转换成sequenceFile的方式。
mahout seqdirectory --input /hive/hadoopuser/ --output /mahout/seq/ --charset UTF-8
二进制文件转换为向量
mahout seq2sparse
f.dataguru.cn/thread-244375-1-1.html
http://www.cnblogs.com/panweishadow/p/4320720.html
低版本中还是老的贝叶斯testclassifier
0.11已经是新贝叶斯
#Classification
#new bayes
org.apache.mahout.classifier.naivebayes.training.TrainNaiveBayesJob = trainnb : Train the Vector-based Bayes classifier
org.apache.mahout.classifier.naivebayes.test.TestNaiveBayesDriver = testnb : Test the Vector-based Bayes classifier
cbayes=ComplementaryNaiveBayes
TestNaiveBayesDriver源码
/**
* 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 java.io.IOException;
import java.util.List;
import java.util.Map;
import java.util.regex.Pattern;
import com.google.common.base.Preconditions;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
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.AbstractNaiveBayesClassifier;
import org.apache.mahout.classifier.naivebayes.BayesUtils;
import org.apache.mahout.classifier.naivebayes.ComplementaryNaiveBayesClassifier;
import org.apache.mahout.classifier.naivebayes.NaiveBayesModel;
import org.apache.mahout.classifier.naivebayes.StandardNaiveBayesClassifier;
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;
/**
* 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 COMPLEMENTARY = "class"; //b for bayes, c for complementary
private static final Pattern SLASH = Pattern.compile("/");
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(buildOption("runSequential", "seq", "run sequential?", false, false, String.valueOf(false)));
addOption("labelIndex", "l", "The path to the location of the label index", true);
Map> parsedArgs = parseArguments(args);
if (parsedArgs == null) {
return -1;
}
if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
HadoopUtil.delete(getConf(), getOutputPath());
}
boolean sequential = hasOption("runSequential");
boolean succeeded;
if (sequential) {
runSequential();
} else {
succeeded = runMapReduce();
if (!succeeded) {
return -1;
}
}
//load the labels
Map labelMap = BayesUtils.readLabelIndex(getConf(), new Path(getOption("labelIndex")));
//loop over the results and create the confusion matrix
SequenceFileDirIterable dirIterable =
new SequenceFileDirIterable<>(getOutputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT");
analyzeResults(labelMap, dirIterable, analyzer);
log.info("{} Results: {}", hasOption("testComplementary") ? "Complementary" : "Standard NB", analyzer);
return 0;
}
private void runSequential() throws IOException {
boolean complementary = hasOption("testComplementary");
FileSystem fs = FileSystem.get(getConf());
NaiveBayesModel model = NaiveBayesModel.materialize(new Path(getOption("model")), getConf());
// Ensure that if we are testing in complementary mode, the model has been
// trained complementary. a complementarty model will work for standard classification
// a standard model will not work for complementary classification
if (complementary){
Preconditions.checkArgument((model.isComplemtary()),
"Complementary mode in model is different from test mode");
}
AbstractNaiveBayesClassifier classifier;
if (complementary) {
classifier = new ComplementaryNaiveBayesClassifier(model);
} else {
classifier = new StandardNaiveBayesClassifier(model);
}
try (SequenceFile.Writer writer =
SequenceFile.createWriter(fs, getConf(), new Path(getOutputPath(), "part-r-00000"),
Text.class, VectorWritable.class)) {
SequenceFileDirIterable dirIterable =
new SequenceFileDirIterable<>(getInputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
// loop through the part-r-* files in getInputPath() and get classification scores for all entries
for (Pair pair : dirIterable) {
writer.append(new Text(SLASH.split(pair.getFirst().toString())[1]),
new VectorWritable(classifier.classifyFull(pair.getSecond().get())));
}
}
}
private boolean runMapReduce() throws IOException,
InterruptedException, ClassNotFoundException {
Path model = new Path(getOption("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, getOption("--labels"));
boolean complementary = hasOption("testComplementary");
testJob.getConfiguration().set(COMPLEMENTARY, String.valueOf(complementary));
return testJob.waitForCompletion(true);
}
private static void analyzeResults(Map labelMap,
SequenceFileDirIterable dirIterable,
ResultAnalyzer analyzer) {
for (Pair pair : dirIterable) {
int bestIdx = Integer.MIN_VALUE;
double bestScore = Long.MIN_VALUE;
for (Vector.Element element : pair.getSecond().get().all()) {
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);
}
}
}
}
BayesTestMapper源码