Package org.apache.mahout.df.builder

Examples of org.apache.mahout.df.builder.DefaultTreeBuilder

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    return 0;
  }
 
  private DecisionForest buildForest() throws IOException {
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
    treeBuilder.setM(m);
   
    Dataset dataset = Dataset.load(getConf(), datasetPath);
   
    ForestPredictions callback = isOob ? new ForestPredictions(dataset.nbInstances(), dataset.nblabels())
        : null;
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    return 0;
  }
 
  private DecisionForest buildForest() throws IOException, ClassNotFoundException, InterruptedException {
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
    treeBuilder.setM(m);
   
    Dataset dataset = Dataset.load(getConf(), datasetPath);
   
    ForestPredictions callback = isOob ? new ForestPredictions(dataset.nbInstances(), dataset.nblabels())
        : null;
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  /**
   * Make sure that the builder passes the good parameters to the job
   *
   */
  public void testConfigure() {
    TreeBuilder treeBuilder = new DefaultTreeBuilder();
    Path dataPath = new Path("notUsedDataPath");
    Path datasetPath = new Path("notUsedDatasetPath");
    Long seed = 5L;

    new PartialBuilderChecker(treeBuilder, dataPath, datasetPath, seed);
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    return 0;
  }
 
  private DecisionForest buildForest() throws IOException, ClassNotFoundException, InterruptedException {
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
    treeBuilder.setM(m);
   
    Dataset dataset = Dataset.load(getConf(), datasetPath);
   
    ForestPredictions callback = isOob ? new ForestPredictions(dataset.nbInstances(), dataset.nblabels())
        : null;
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    Data test = train.rsplit(rng, (int) (data.size() * 0.1));
   
    int[] trainLabels = train.extractLabels();
    int[] testLabels = test.extractLabels();
   
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
   
    SequentialBuilder forestBuilder = new SequentialBuilder(rng, treeBuilder, train);
   
    // grow a forest with m = log2(M)+1
    ForestPredictions errorM = new ForestPredictions(train.size(), nblabels); // oob error when using m =
                                                                              // log2(M)+1
    treeBuilder.setM(m);
   
    long time = System.currentTimeMillis();
    log.info("Growing a forest with m={}", m);
    DecisionForest forestM = forestBuilder.build(nbtrees, errorM);
    sumTimeM += System.currentTimeMillis() - time;
    numNodesM += forestM.nbNodes();
   
    double oobM = ErrorEstimate.errorRate(trainLabels, errorM.computePredictions(rng)); // oob error estimate
                                                                                        // when m = log2(M)+1
   
    // grow a forest with m=1
    ForestPredictions errorOne = new ForestPredictions(train.size(), nblabels); // oob error when using m = 1
    treeBuilder.setM(1);
   
    time = System.currentTimeMillis();
    log.info("Growing a forest with m=1");
    DecisionForest forestOne = forestBuilder.build(nbtrees, errorOne);
    sumTimeOne += System.currentTimeMillis() - time;
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    if (ofs.exists(outputPath)) {
      log.error("Output path already exists");
      return;
    }

    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
    treeBuilder.setM(m);
   
    Dataset dataset = Dataset.load(getConf(), datasetPath);
   
    ForestPredictions callback = isOob ? new ForestPredictions(dataset.nbInstances(), dataset.nblabels())
        : null;
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   * Make sure that the builder passes the good parameters to the job
   *
   */
  @Test
  public void testConfigure() {
    TreeBuilder treeBuilder = new DefaultTreeBuilder();
    Path dataPath = new Path("notUsedDataPath");
    Path datasetPath = new Path("notUsedDatasetPath");
    Long seed = 5L;

    new PartialBuilderChecker(treeBuilder, dataPath, datasetPath, seed);
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  /**
   * Make sure that the builder passes the good parameters to the job
   *
   */
  public void testConfigure() {
    TreeBuilder treeBuilder = new DefaultTreeBuilder();
    Path dataPath = new Path("notUsedDataPath");
    Path datasetPath = new Path("notUsedDatasetPath");
    Long seed = 5L;

    new PartialBuilderChecker(treeBuilder, dataPath, datasetPath, seed);
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    Data test = train.rsplit(rng, (int) (data.size() * 0.1));
   
    int[] trainLabels = train.extractLabels();
    int[] testLabels = test.extractLabels();
   
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
   
    SequentialBuilder forestBuilder = new SequentialBuilder(rng, treeBuilder, train);

    // grow a forest with m = log2(M)+1
    ForestPredictions errorM = new ForestPredictions(dataSize, nblabels); // oob error when using m = log2(M)+1
    treeBuilder.setM(m);

    long time = System.currentTimeMillis();
    log.info("Growing a forest with m=" + m);
    DecisionForest forestM = forestBuilder.build(nbtrees, errorM);
    sumTimeM += System.currentTimeMillis() - time;

    double oobM = ErrorEstimate.errorRate(trainLabels, errorM.computePredictions(rng)); // oob error estimate when m = log2(M)+1

    // grow a forest with m=1
    ForestPredictions errorOne = new ForestPredictions(dataSize, nblabels); // oob error when using m = 1
    treeBuilder.setM(1);

    time = System.currentTimeMillis();
    log.info("Growing a forest with m=1");
    DecisionForest forestOne = forestBuilder.build(nbtrees, errorOne);
    sumTimeOne += System.currentTimeMillis() - time;
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    return 0;
  }

  private DecisionForest buildForest() throws IOException, ClassNotFoundException, InterruptedException {
    DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder();
    treeBuilder.setM(m);

   Dataset dataset = Dataset.load(getConf(), datasetPath);

    ForestPredictions callback = (isOob) ? new ForestPredictions(dataset
        .nbInstances(), dataset.nblabels()) : null;
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Related Classes of org.apache.mahout.df.builder.DefaultTreeBuilder

  • org.apache.mahout.df.BreimanExample
  • org.apache.mahout.df.data.Data
  • org.apache.mahout.df.data.Instance
  • org.apache.mahout.df.mapred.BuildForest
  • org.apache.mahout.df.mapred.partial.PartialBuilderTest
  • org.apache.mahout.df.mapreduce.BuildForest
  • org.apache.mahout.df.mapreduce.partial.PartialBuilderTest
  • org.apache.mahout.df.node.CategoricalNode
  • org.apache.mahout.df.node.Leaf
  • org.apache.mahout.df.node.Node
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