Example usage for weka.classifiers.trees Id3 toString

Introduction

In this page you can find the example usage for weka.classifiers.trees Id3 toString.

Prototype

@Override
public String toString() 

Source Link

Document

Prints the decision tree using the private toString method from below.

Usage

From source file:tubes.ml.pkg1.TubesML1.java

public void akses() throws Exception {
    Discretize filter;/*from   w  w  w.jav a 2  s. c o  m*/
    int fold = 10;
    int fold3 = 3;
    int trainNum, testNum;
    PrintWriter file = new PrintWriter("model.txt");

    /***dataset 1***/
    file.println("***DATASET 1***");
    fileReader tets = new fileReader("./src/data/iris.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    Instances data = tets.getData();
    filter = new Discretize();
    try {
        filter.setInputFormat(data);
    } catch (Exception ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }

    /*ID3*/
    Instances discreteData;
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        try {

            Instances train = discreteData.trainCV(fold, i);
            Instances test = discreteData.testCV(fold, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;
    J48 jKT = new J48();
    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
            // System.out.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*dataset 2*/
    file.println("***DATASET 2***");
    tets.setFilepath("./src/data/weather.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    data = new Instances(tets.getData());

    /*ID3*/
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold3; i++) {
        try {
            Instances train = discreteData.trainCV(trainNum, i);
            Instances test = discreteData.testCV(testNum, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                //file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    System.out.println(testNum);
    file.println("Test Number");
    file.println(testNum);

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
            System.out.println(jKT.toString());
            file.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*dataset 3*/
    file.println("***DATASET 3***");
    tets.setFilepath("./src/data/weather.nominal.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    data = new Instances(tets.getData());

    /*ID3*/
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold3; i++) {
        try {
            Instances train = discreteData.trainCV(fold, i);
            Instances test = discreteData.testCV(fold, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    System.out.println(testNum);
    file.println("Test Number");
    file.println(testNum);

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
            System.out.println(jKT.toString());
            file.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*RESULTT*/
    System.out.println(jKT.toString());
    file.println("RESULT");
    file.println(jKT.toString());
    file.close();
}

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