Example usage for weka.classifiers.trees RandomTree setMaxDepth

Introduction

In this page you can find the example usage for weka.classifiers.trees RandomTree setMaxDepth.

Prototype

public void setMaxDepth(int value) 

Source Link

Document

Set the maximum depth of the tree, 0 for unlimited.

Usage

From source file:com.walmart.productgenome.matching.models.EMSRandomForest.java

License:Open Source License

/**
 * Builds a classifier for a set of instances.
 *
 * @param data the instances to train the classifier with
 * @throws Exception if something goes wrong
 */// ww w  . j ava2s.  c o m
public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    m_bagger = new Bagging();
    RandomTree rTree = new RandomTree();

    // set up the random tree options
    m_KValue = m_numFeatures;
    if (m_KValue < 1)
        m_KValue = (int) Utils.log2(data.numAttributes()) + 1;
    rTree.setKValue(m_KValue);
    rTree.setMaxDepth(getMaxDepth());

    // set up the bagger and build the forest
    m_bagger.setClassifier(rTree);
    m_bagger.setSeed(m_randomSeed);
    m_bagger.setNumIterations(m_numTrees);
    m_bagger.setCalcOutOfBag(true);
    m_bagger.setNumExecutionSlots(m_numExecutionSlots);
    m_bagger.buildClassifier(data);
}

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From source file:controller.MineroControler.java

public String clasificardorArbolAleat(String atributo) {
    BufferedReader breader = null;
    Instances datos = null;//from  ww w .  j  a v  a2  s  .c  o  m
    breader = new BufferedReader(fuente_arff);
    try {
        datos = new Instances(breader);
        Attribute atr = datos.attribute(atributo);
        datos.setClass(atr);
        //datos.setClassIndex(0);
    } catch (IOException ex) {
        System.err.println("Problemas al intentar cargar los datos");
        return null;
    }

    RandomTree arbol = new RandomTree(); // Class for constructing a tree that considers K randomly chosen attributes at each node. 

    try {

        arbol.setNumFolds(100);
        arbol.setKValue(0);
        arbol.setMinNum(1);
        arbol.setMaxDepth(0);
        arbol.setSeed(1);
        arbol.buildClassifier(datos);

    } catch (Exception ex) {
        System.err.println("Problemas al ejecutar algorimo de clasificacion" + ex.getLocalizedMessage());
    }
    return arbol.toString();
}

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From source file:KFST.featureSelection.embedded.TreeBasedMethods.DecisionTreeBasedMethod.java

License:Open Source License

/**
 * {@inheritDoc }// w w w  .  j a va  2s  .  co  m
 */
@Override
protected String buildClassifier(Instances dataTrain) {
    try {
        if (TREE_TYPE == TreeType.C45) {
            J48 decisionTreeC45 = new J48();
            decisionTreeC45.setConfidenceFactor((float) confidenceValue);
            decisionTreeC45.setMinNumObj(minNumSampleInLeaf);
            decisionTreeC45.buildClassifier(dataTrain);
            return decisionTreeC45.toString();
        } else if (TREE_TYPE == TreeType.RANDOM_TREE) {
            RandomTree decisionTreeRandomTree = new RandomTree();
            decisionTreeRandomTree.setKValue(randomTreeKValue);
            decisionTreeRandomTree.setMaxDepth(randomTreeMaxDepth);
            decisionTreeRandomTree.setMinNum(randomTreeMinNum);
            decisionTreeRandomTree.setMinVarianceProp(randomTreeMinVarianceProp);
            decisionTreeRandomTree.buildClassifier(dataTrain);
            return decisionTreeRandomTree.toString();
        }
    } catch (Exception ex) {
        Logger.getLogger(DecisionTreeBasedMethod.class.getName()).log(Level.SEVERE, null, ex);
    }
    return "";
}

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From source file:learning.DMRandomForest.java

License:Open Source License

/**
 * Builds a classifier for a set of instances.
 *
 * @param data the instances to train the classifier with
 * @throws Exception if something goes wrong
 *//*from w  w w  .  j a  v  a  2 s . co m*/
public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    m_bagger = new DMBagging();
    RandomTree rTree = new RandomTree();

    // set up the random tree options
    m_KValue = m_numFeatures;
    if (m_KValue < 1)
        m_KValue = (int) Utils.log2(data.numAttributes()) + 1;
    rTree.setKValue(m_KValue);
    rTree.setMaxDepth(getMaxDepth());

    // set up the bagger and build the forest
    m_bagger.setClassifier(rTree);
    m_bagger.setSeed(m_randomSeed);
    m_bagger.setNumIterations(m_numTrees);
    m_bagger.setCalcOutOfBag(true);
    m_bagger.buildClassifier(data);
}

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From source file:org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    double score3 = 0.5;

    weka.classifiers.trees.RandomTree cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);/*from  w  w  w  .  j a  v  a  2s  .c om*/
    cls.setMaxDepth(1);

    try {
        // ds.buildClassifier(data);
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);
    cls.setMaxDepth(2);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score2 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);
    cls.setMaxDepth(3);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    return qualities;
}

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