Example usage for weka.classifiers.trees RandomForest computeAverageImpurityDecreasePerAttribute

Introduction

In this page you can find the example usage for weka.classifiers.trees RandomForest computeAverageImpurityDecreasePerAttribute.

Prototype

public double[] computeAverageImpurityDecreasePerAttribute(double[] nodeCounts) throws WekaException 

Source Link

Document

Computes the average impurity decrease per attribute over the trees

Usage

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

License:Open Source License

/**
 * {@inheritDoc }//from   w  ww  .  j a v  a  2 s  .  c o  m
 */
@Override
protected String buildClassifier(Instances dataTrain) {
    try {
        RandomForest decisionTreeRandomForest = new RandomForest();
        decisionTreeRandomForest.setNumFeatures(randomForestNumFeatures);
        decisionTreeRandomForest.setMaxDepth(randomForestMaxDepth);
        decisionTreeRandomForest.setNumIterations(randomForestNumIterations);
        decisionTreeRandomForest.setComputeAttributeImportance(true);
        decisionTreeRandomForest.buildClassifier(dataTrain);

        /**
         * Creating an array of indices of the features based on descending
         * order of features' importance
         */
        double[] nodeCounts = new double[numFeatures + 1];
        double[] impurityScores = decisionTreeRandomForest
                .computeAverageImpurityDecreasePerAttribute(nodeCounts);
        int[] sortedIndices = Utils.sort(impurityScores);
        String sortedIndicesToString = "";
        for (int i = sortedIndices.length - 1; i >= 0; i--) {
            if (sortedIndices[i] != numFeatures) {
                sortedIndicesToString += String.valueOf(sortedIndices[i]) + " ";
            }
        }

        return sortedIndicesToString.trim();
        //return decisionTreeRandomForest.toString();
    } catch (Exception ex) {
        Logger.getLogger(RandomForestMethod.class.getName()).log(Level.SEVERE, null, ex);
    }
    return "";
}

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