Example usage for weka.classifiers.trees J48 setMinNumObj

Introduction

In this page you can find the example usage for weka.classifiers.trees J48 setMinNumObj.

Prototype

public void setMinNumObj(int v) 

Source Link

Document

Set the value of minNumObj.

Usage

From source file:com.edwardraff.WekaMNIST.java

License:Open Source License

public static void main(String[] args) throws IOException, Exception {
    String folder = args[0];//from   ww  w  .  j av a  2s  .  co  m
    String trainPath = folder + "MNISTtrain.arff";
    String testPath = folder + "MNISTtest.arff";

    System.out.println("Weka Timings");
    Instances mnistTrainWeka = new Instances(new BufferedReader(new FileReader(new File(trainPath))));
    mnistTrainWeka.setClassIndex(mnistTrainWeka.numAttributes() - 1);
    Instances mnistTestWeka = new Instances(new BufferedReader(new FileReader(new File(testPath))));
    mnistTestWeka.setClassIndex(mnistTestWeka.numAttributes() - 1);

    //normalize range like into [0, 1]
    Normalize normalizeFilter = new Normalize();
    normalizeFilter.setInputFormat(mnistTrainWeka);

    mnistTestWeka = Normalize.useFilter(mnistTestWeka, normalizeFilter);
    mnistTrainWeka = Normalize.useFilter(mnistTrainWeka, normalizeFilter);

    long start, end;

    System.out.println("RBF SVM (Full Cache)");
    SMO smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 0/*0 causes Weka to cache the whole matrix...*/, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("RBF SVM (No Cache)");
    smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 1, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("Decision Tree C45");
    J48 wekaC45 = new J48();
    wekaC45.setUseLaplace(false);
    wekaC45.setCollapseTree(false);
    wekaC45.setUnpruned(true);
    wekaC45.setMinNumObj(2);
    wekaC45.setUseMDLcorrection(true);

    evalModel(wekaC45, mnistTrainWeka, mnistTestWeka);

    System.out.println("Random Forest 50 trees");
    int featuresToUse = (int) Math.sqrt(28 * 28);//Weka uses different defaults, so lets make sure they both use the published way

    RandomForest wekaRF = new RandomForest();
    wekaRF.setNumExecutionSlots(1);
    wekaRF.setMaxDepth(0/*0 for unlimited*/);
    wekaRF.setNumFeatures(featuresToUse);
    wekaRF.setNumTrees(50);

    evalModel(wekaRF, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (brute)");
    IBk wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new LinearNNSearch());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Ball Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new BallTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Cover Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new CoverTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("Logistic Regression LBFGS lambda = 1e-4");
    Logistic logisticLBFGS = new Logistic();
    logisticLBFGS.setRidge(1e-4);
    logisticLBFGS.setMaxIts(500);

    evalModel(logisticLBFGS, mnistTrainWeka, mnistTestWeka);

    System.out.println("k-means (Loyd)");
    int origClassIndex = mnistTrainWeka.classIndex();
    mnistTrainWeka.setClassIndex(-1);
    mnistTrainWeka.deleteAttributeAt(origClassIndex);
    {
        long totalTime = 0;
        for (int i = 0; i < 10; i++) {
            SimpleKMeans wekaKMeans = new SimpleKMeans();
            wekaKMeans.setNumClusters(10);
            wekaKMeans.setNumExecutionSlots(1);
            wekaKMeans.setFastDistanceCalc(true);

            start = System.currentTimeMillis();
            wekaKMeans.buildClusterer(mnistTrainWeka);
            end = System.currentTimeMillis();
            totalTime += (end - start);
        }
        System.out.println("\tClustering took: " + (totalTime / 10.0) / 1000.0 + " on average");
    }
}

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From source file:kfst.classifier.WekaClassifier.java

License:Open Source License

/**
 * This method builds and evaluates the decision tree(DT) classifier.
 * The j48 are used as the DT classifier implemented in the Weka software.
 *
 * @param pathTrainData the path of the train set
 * @param pathTestData the path of the test set
 * @param confidenceValue The confidence factor used for pruning
 * @param minNumSampleInLeaf The minimum number of instances per leaf
 * /*from  w  ww.  j  a v a 2  s .  c om*/
 * @return the classification accuracy
 */
public static double dTree(String pathTrainData, String pathTestData, double confidenceValue,
        int minNumSampleInLeaf) {
    double resultValue = 0;
    try {
        BufferedReader readerTrain = new BufferedReader(new FileReader(pathTrainData));
        Instances dataTrain = new Instances(readerTrain);
        readerTrain.close();
        dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

        BufferedReader readerTest = new BufferedReader(new FileReader(pathTestData));
        Instances dataTest = new Instances(readerTest);
        readerTest.close();
        dataTest.setClassIndex(dataTest.numAttributes() - 1);

        J48 decisionTree = new J48();
        decisionTree.setConfidenceFactor((float) confidenceValue);
        decisionTree.setMinNumObj(minNumSampleInLeaf);
        decisionTree.buildClassifier(dataTrain);
        Evaluation eval = new Evaluation(dataTest);
        eval.evaluateModel(decisionTree, dataTest);
        resultValue = 100 - (eval.errorRate() * 100);
    } catch (Exception ex) {
        Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, ex);
    }
    return resultValue;
}

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

License:Open Source License

/**
 * {@inheritDoc }//from  w  w w .  ja  v a  2s . c o 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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