Examples of org.apache.mahout.classifier.df.builder.DecisionTreeBuilder

Package org.apache.mahout.classifier.df.builder

Examples of org.apache.mahout.classifier.df.builder.DecisionTreeBuilder

org.apache.mahout.classifier.df.builder.DecisionTreeBuilder
Builds a classification tree or regression tree
A classification tree is built when the criterion variable is the categorical attribute.
A regression tree is built when the criterion variable is the numerical attribute.

    if (ofs.exists(outputPath)) {
      log.error("Output path already exists");
      return;
    }


    DecisionTreeBuilder treeBuilder = new DecisionTreeBuilder();
    if (m != null) {
      treeBuilder.setM(m);
    }
    treeBuilder.setComplemented(complemented);
    if (minSplitNum != null) {
      treeBuilder.setMinSplitNum(minSplitNum);
    }
    if (minVarianceProportion != null) {
      treeBuilder.setMinVarianceProportion(minVarianceProportion);
    }
    
    Builder forestBuilder;
    
    if (isPartial) {

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  private DecisionForest buildForest(Data[] datas) {
    List<Node> trees = Lists.newArrayList();
    for (Data data : datas) {
      // build tree
      DecisionTreeBuilder builder = new DecisionTreeBuilder();
      builder.setM(data.getDataset().nbAttributes() - 1);
      builder.setMinSplitNum(0);
      builder.setComplemented(false);
      trees.add(builder.build(rng, data));
    }
    return new DecisionForest(trees);
  }

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  }
  
  @Test
  public void testTreeVisualize() throws Exception {
    // build tree
    DecisionTreeBuilder builder = new DecisionTreeBuilder();
    builder.setM(data.getDataset().nbAttributes() - 1);
    Node tree = builder.build(rng, data);


    String visualization = TreeVisualizer.toString(tree, data.getDataset(), ATTR_NAMES);


    assertTrue(
        ("\n" +

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  }
  
  @Test
  public void testPredictTrace() throws Exception {
    // build tree
    DecisionTreeBuilder builder = new DecisionTreeBuilder();
    builder.setM(data.getDataset().nbAttributes() - 1);
    Node tree = builder.build(rng, data);
    
    String[] prediction = TreeVisualizer.predictTrace(tree, testData,
        ATTR_NAMES);
    Assert.assertArrayEquals(new String[] {
        "outlook = rainy -> windy = TRUE -> no", "outlook = overcast -> yes",

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      }
    }
    Data lessData = new Data(data.getDataset(), instances);
    
    // build tree
    DecisionTreeBuilder builder = new DecisionTreeBuilder();
    builder.setM(data.getDataset().nbAttributes() - 1);
    builder.setMinSplitNum(0);
    builder.setComplemented(false);
    Node tree = builder.build(rng, lessData);


    String visualization = TreeVisualizer.toString(tree, data.getDataset(), ATTR_NAMES);
    assertTrue(
        ("\noutlook = sunny\n" +
            "|   humidity < 77.5 : yes\n" +

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  @Test
  public void testEmpty() throws Exception {
    Data emptyData = new Data(data.getDataset());
    
    // build tree
    DecisionTreeBuilder builder = new DecisionTreeBuilder();
    Node tree = builder.build(rng, emptyData);


    assertEquals(" : unknown", TreeVisualizer.toString(tree, data.getDataset(), ATTR_NAMES));
  }

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Related Classes of org.apache.mahout.classifier.df.builder.DecisionTreeBuilder

org.apache.mahout.classifier.df.data.Data

org.apache.mahout.classifier.df.data.Instance

org.apache.mahout.classifier.df.DecisionForestTest

org.apache.mahout.classifier.df.mapreduce.BuildForest

org.apache.mahout.classifier.df.node.CategoricalNode

org.apache.mahout.classifier.df.node.Leaf

org.apache.mahout.classifier.df.node.Node

org.apache.mahout.classifier.df.node.NumericalNode

org.apache.mahout.classifier.df.split.OptIgSplit

org.apache.mahout.classifier.df.split.RegressionSplit

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