Example usage for org.apache.mahout.classifier.naivebayes ComplementaryNaiveBayesClassifier ComplementaryNaiveBayesClassifier

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.naivebayes ComplementaryNaiveBayesClassifier ComplementaryNaiveBayesClassifier.

Prototype

public ComplementaryNaiveBayesClassifier(NaiveBayesModel model) 

Source Link

Usage

From source file:com.luca.filipponi.tweetAnalysis.SentimentClassifier.CustomTestNaiveBayesDriver.java

License:Apache License

@Override
public int run(String[] args) throws Exception {
    addInputOption();/*from w  w w . j  a va 2s .  c o  m*/
    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<String, List<String>> parsedArgs = parseArguments(args);
    if (parsedArgs == null) {
        return -1;
    }
    if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
        HadoopUtil.delete(getConf(), getOutputPath());
    }

    boolean complementary = hasOption("testComplementary");
    boolean sequential = hasOption("runSequential");
    if (sequential) {
        FileSystem fs = FileSystem.get(getConf());
        NaiveBayesModel model = NaiveBayesModel.materialize(new Path(getOption("model")), getConf());
        AbstractNaiveBayesClassifier classifier;
        if (complementary) {
            classifier = new ComplementaryNaiveBayesClassifier(model);
        } else {
            classifier = new StandardNaiveBayesClassifier(model);
        }
        SequenceFile.Writer writer = new SequenceFile.Writer(fs, getConf(), getOutputPath(), Text.class,
                VectorWritable.class);
        SequenceFile.Reader reader = new SequenceFile.Reader(fs, getInputPath(), getConf());
        Text key = new Text();
        VectorWritable vw = new VectorWritable();
        while (reader.next(key, vw)) {
            writer.append(new Text(SLASH.split(key.toString())[1]),
                    new VectorWritable(classifier.classifyFull(vw.get())));
        }
        writer.close();
        reader.close();
    } else {
        boolean succeeded = runMapReduce(parsedArgs);
        if (!succeeded) {
            return -1;
        }
    }

    //load the labels
    Map<Integer, String> labelMap = BayesUtils.readLabelIndex(getConf(), new Path(getOption("labelIndex")));

    //loop over the results and create the confusion matrix
    SequenceFileDirIterable<Text, VectorWritable> dirIterable = new SequenceFileDirIterable<Text, VectorWritable>(
            getOutputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
    ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT");
    analyzeResults(labelMap, dirIterable, analyzer);

    log.info("{} Results: {}", complementary ? "Complementary" : "Standard NB", analyzer);
    return 0;
}

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From source file:com.missionsky.scp.dataanalysis.mahout.TestNaiveBayesDriver.java

License:Apache License

@Override
public int run(String[] args) throws Exception {
    addInputOption();/*from  w w w  .  ja v a  2 s .  c o m*/
    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<String, List<String>> parsedArgs = parseArguments(args);
    if (parsedArgs == null) {
        return -1;
    }
    if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
        HadoopUtil.delete(getConf(), getOutputPath());
    }

    boolean complementary = hasOption("testComplementary");
    boolean sequential = hasOption("runSequential");
    if (sequential) {
        FileSystem fs = FileSystem.get(getConf());
        NaiveBayesModel model = NaiveBayesModel.materialize(new Path(getOption("model")), getConf());
        AbstractNaiveBayesClassifier classifier;
        if (complementary) {
            classifier = new ComplementaryNaiveBayesClassifier(model);
        } else {
            classifier = new StandardNaiveBayesClassifier(model);
        }
        SequenceFile.Writer writer = new SequenceFile.Writer(fs, getConf(), getOutputPath(), Text.class,
                VectorWritable.class);
        Reader reader = new Reader(fs, getInputPath(), getConf());
        Text key = new Text();
        VectorWritable vw = new VectorWritable();
        while (reader.next(key, vw)) {
            writer.append(new Text(SLASH.split(key.toString())[1]),
                    new VectorWritable(classifier.classifyFull(vw.get())));
        }
        writer.close();
        reader.close();
    } else {
        boolean succeeded = runMapReduce(parsedArgs);
        if (!succeeded) {
            return -1;
        }
    }

    //load the labels
    Map<Integer, String> labelMap = BayesUtils.readLabelIndex(getConf(), new Path(getOption("labelIndex")));

    //loop over the results and create the confusion matrix
    SequenceFileDirIterable<Text, VectorWritable> dirIterable = new SequenceFileDirIterable<Text, VectorWritable>(
            getOutputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
    ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT");
    analyzeResults(labelMap, dirIterable, analyzer);

    log.info("{} Results: {}", complementary ? "Complementary" : "Standard NB", analyzer);
    return 0;
}

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From source file:com.netease.news.classifier.naivebayes.BayesTestMapper.java

License:Apache License

@Override
protected void setup(Context context) throws IOException, InterruptedException {
    super.setup(context);
    Configuration conf = context.getConfiguration();
    Path modelPath = HadoopUtil.getSingleCachedFile(conf);
    NaiveBayesModel model = NaiveBayesModel.materialize(modelPath, conf);
    boolean compl = Boolean.parseBoolean(conf.get(TestNaiveBayesDriver.COMPLEMENTARY));
    if (compl) {//  w  ww  . j  a v  a 2  s  . c  om
        classifier = new ComplementaryNaiveBayesClassifier(model);
    } else {
        classifier = new StandardNaiveBayesClassifier(model);
    }
}

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