Example usage for weka.classifiers.meta AdaBoostM1 toString

Introduction

In this page you can find the example usage for weka.classifiers.meta AdaBoostM1 toString.

Prototype

@Override
public String toString() 

Source Link

Document

Returns description of the boosted classifier.

Usage

From source file:classify.Classifier.java

/**
 * @param args the command line arguments
 *//*from   w  ww  .  j av  a  2 s  . c  om*/
public static void main(String[] args) {
    //read in data
    try {
        DataSource input = new DataSource("no_missing_values.csv");
        Instances data = input.getDataSet();
        //Instances data = readFile("newfixed.txt");
        missingValuesRows(data);

        setAttributeValues(data);
        data.setClassIndex(data.numAttributes() - 1);

        //boosting
        AdaBoostM1 boosting = new AdaBoostM1();
        boosting.setNumIterations(25);
        boosting.setClassifier(new DecisionStump());

        //build the classifier
        boosting.buildClassifier(data);

        //evaluate using 10-fold cross validation
        Evaluation e1 = new Evaluation(data);
        e1.crossValidateModel(boosting, data, 10, new Random(1));

        DecimalFormat nf = new DecimalFormat("0.000");

        System.out.println("Results of Boosting with Decision Stumps:");
        System.out.println(boosting.toString());
        System.out.println("Results of Cross Validation:");
        System.out.println("Number of correctly classified instances: " + e1.correct() + " ("
                + nf.format(e1.pctCorrect()) + "%)");
        System.out.println("Number of incorrectly classified instances: " + e1.incorrect() + " ("
                + nf.format(e1.pctIncorrect()) + "%)");

        System.out.println("TP Rate: " + nf.format(e1.weightedTruePositiveRate() * 100) + "%");
        System.out.println("FP Rate: " + nf.format(e1.weightedFalsePositiveRate() * 100) + "%");
        System.out.println("Precision: " + nf.format(e1.weightedPrecision() * 100) + "%");
        System.out.println("Recall: " + nf.format(e1.weightedRecall() * 100) + "%");

        System.out.println();
        System.out.println("Confusion Matrix:");
        for (int i = 0; i < e1.confusionMatrix().length; i++) {
            for (int j = 0; j < e1.confusionMatrix()[0].length; j++) {
                System.out.print(e1.confusionMatrix()[i][j] + "   ");
            }
            System.out.println();
        }
        System.out.println();
        System.out.println();
        System.out.println();

        //logistic regression
        Logistic l = new Logistic();
        l.buildClassifier(data);

        e1 = new Evaluation(data);

        e1.crossValidateModel(l, data, 10, new Random(1));
        System.out.println("Results of Logistic Regression:");
        System.out.println(l.toString());
        System.out.println("Results of Cross Validation:");
        System.out.println("Number of correctly classified instances: " + e1.correct() + " ("
                + nf.format(e1.pctCorrect()) + "%)");
        System.out.println("Number of incorrectly classified instances: " + e1.incorrect() + " ("
                + nf.format(e1.pctIncorrect()) + "%)");

        System.out.println("TP Rate: " + nf.format(e1.weightedTruePositiveRate() * 100) + "%");
        System.out.println("FP Rate: " + nf.format(e1.weightedFalsePositiveRate() * 100) + "%");
        System.out.println("Precision: " + nf.format(e1.weightedPrecision() * 100) + "%");
        System.out.println("Recall: " + nf.format(e1.weightedRecall() * 100) + "%");

        System.out.println();
        System.out.println("Confusion Matrix:");
        for (int i = 0; i < e1.confusionMatrix().length; i++) {
            for (int j = 0; j < e1.confusionMatrix()[0].length; j++) {
                System.out.print(e1.confusionMatrix()[i][j] + "   ");
            }
            System.out.println();
        }

    } catch (Exception ex) {
        //data couldn't be read, so end program
        System.out.println("Exception thrown, program ending.");
    }
}

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