Package org.apache.mahout.classifier.naivebayes

Examples of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier

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    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);
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    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);
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    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 Reader(fs, getInputPath(), getConf());
      Text key = new Text();
      VectorWritable vw = new VectorWritable();
      while (reader.next(key, vw)) {
        writer.append(new Text(key.toString().split("/")[1]),
            new VectorWritable(classifier.classifyFull(vw.get())));
      }
      writer.close();
      reader.close();
    } else {
      boolean succeeded = runMapReduce(parsedArgs);
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    if (complementary){
        Preconditions.checkArgument((model.isComplemtary() == complementary),
            "Complementary mode in model is different from test mode");
    }
   
    AbstractNaiveBayesClassifier classifier;
    if (complementary) {
      classifier = new ComplementaryNaiveBayesClassifier(model);
    } else {
      classifier = new StandardNaiveBayesClassifier(model);
    }
    SequenceFile.Writer writer = SequenceFile.createWriter(fs, getConf(), new Path(getOutputPath(), "part-r-00000"),
        Text.class, VectorWritable.class);

    try {
      SequenceFileDirIterable<Text, VectorWritable> dirIterable =
          new SequenceFileDirIterable<Text, VectorWritable>(getInputPath(), PathType.LIST, PathFilters.partFilter(), getConf());
      // loop through the part-r-* files in getInputPath() and get classification scores for all entries
      for (Pair<Text, VectorWritable> pair : dirIterable) {
        writer.append(new Text(SLASH.split(pair.getFirst().toString())[1]),
            new VectorWritable(classifier.classifyFull(pair.getSecond().get())));
      }
    } finally {
      Closeables.close(writer, false);
    }
  }
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Related Classes of org.apache.mahout.classifier.naivebayes.AbstractNaiveBayesClassifier

  • org.apache.mahout.classifier.naivebayes.test.TestNaiveBayesDriver
  • org.apache.mahout.math.Vector
  • org.apache.mahout.math.Vector.Element
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