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

Introduction

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

Prototype

public StandardNaiveBayesClassifier(NaiveBayesModel model) 

Source Link

Usage

From source file:mlbench.bayes.test.BayesTest.java

License:Apache License

@SuppressWarnings("deprecation")
public static void main(String[] args) throws MPI_D_Exception, IOException, MPIException {
    parseArgs(args);/*from   ww w.  jav a2s . c  om*/
    HashMap<String, String> conf = new HashMap<String, String>();
    initConf(conf);
    MPI_D.Init(args, MPI_D.Mode.Common, conf);

    if (MPI_D.COMM_BIPARTITE_O != null) {
        rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O);
        size = MPI_D.Comm_size(MPI_D.COMM_BIPARTITE_O);
        NaiveBayesModel model = NaiveBayesModel.materialize(modelPath, config);
        classifier = new StandardNaiveBayesClassifier(model);

        MPI_D.COMM_BIPARTITE_O.Barrier();
        FileSplit[] inputs = DataMPIUtil.HDFSDataLocalLocator.getTaskInputs(MPI_D.COMM_BIPARTITE_O,
                (JobConf) config, inDir, rank);

        for (int i = 0; i < inputs.length; i++) {
            FileSplit fsplit = inputs[i];
            SequenceFileRecordReader<Text, VectorWritable> kvrr = new SequenceFileRecordReader<>(config,
                    fsplit);
            Text key = kvrr.createKey();
            VectorWritable value = kvrr.createValue();
            while (kvrr.next(key, value)) {
                Vector result = classifier.classifyFull(value.get());
                MPI_D.Send(new Text(SLASH.split(key.toString())[1]), new VectorWritable(result));
            }
        }
    } else if (MPI_D.COMM_BIPARTITE_A != null) {
        int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_A);
        config.set(MAPRED_OUTPUT_DIR, outDir);
        config.set("mapred.task.id", DataMPIUtil.getHadoopTaskAttemptID().toString().toString());
        ((JobConf) config).setOutputKeyClass(Text.class);
        ((JobConf) config).setOutputValueClass(VectorWritable.class);
        TaskAttemptContext taskContext = new TaskAttemptContextImpl(config,
                DataMPIUtil.getHadoopTaskAttemptID());
        SequenceFileOutputFormat<Text, VectorWritable> outfile = new SequenceFileOutputFormat<>();
        FileSystem fs = FileSystem.get(config);

        Path output = new Path(config.get(MAPRED_OUTPUT_DIR));
        FileOutputCommitter fcommitter = new FileOutputCommitter(output, taskContext);
        RecordWriter<Text, VectorWritable> outrw = null;
        try {
            fcommitter.setupJob(taskContext);
            outrw = outfile.getRecordWriter(fs, (JobConf) config, getOutputName(rank), null);
        } catch (IOException e) {
            e.printStackTrace();
            System.err.println("ERROR: Please set the HDFS configuration properly\n");
            System.exit(-1);
        }
        Text key = null;
        VectorWritable point = null;
        Vector vector = null;
        Object[] vals = MPI_D.Recv();
        while (vals != null) {
            key = (Text) vals[0];
            point = (VectorWritable) vals[1];
            if (key != null && point != null) {
                vector = point.get();
                outrw.write(key, new VectorWritable(vector));
            }
            vals = MPI_D.Recv();
        }
        outrw.close(null);
        if (fcommitter.needsTaskCommit(taskContext)) {
            fcommitter.commitTask(taskContext);
        }

        MPI_D.COMM_BIPARTITE_A.Barrier();
        if (rank == 0) {
            // load the labels
            Map<Integer, String> labelMap = BayesUtils.readLabelIndex(config, labPath);
            // loop over the results and create the confusion matrix
            SequenceFileDirIterable<Text, VectorWritable> dirIterable = new SequenceFileDirIterable<Text, VectorWritable>(
                    output, PathType.LIST, PathFilters.partFilter(), config);
            ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT");
            analyzeResults(labelMap, dirIterable, analyzer);
        }
    }
    MPI_D.Finalize();
}

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From source file:org.wso2.siddhi.extension.ModelInitializer.java

License:Open Source License

public static StandardNaiveBayesClassifier InitializeNaiveBayes(String modelPath) {
    Configuration configuration = new Configuration();
    NaiveBayesModel naiveBayesModel = null;
    StandardNaiveBayesClassifier NBmodel = null;
    try {/*from w  ww.  j  a v a2s .c om*/
        naiveBayesModel = NaiveBayesModel.materialize(new Path(modelPath), configuration);
        NBmodel = new StandardNaiveBayesClassifier(naiveBayesModel);
    } catch (Exception e) {
        logger.error("Failed to create a Naive Bayes model from the file \"" + modelPath + "\"");
    }
    logger.info(
            "Naive Bayes model execution plan successfully intialized for  \"" + modelPath + "\" model file.");
    return NBmodel;
}

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