Example usage for weka.classifiers.mi MIBoost MIBoost

Introduction

In this page you can find the example usage for weka.classifiers.mi MIBoost MIBoost.

Prototype

MIBoost

Source Link

Usage

From source file:tutorial.exampleMIMLBinaryRelevance.java

License:Open Source License

public static void main(String[] args) {

    try {//  ww  w .  j av a 2  s .  c  o  m
        // String arffFileNameTrain = Utils.getOption("f", args);
        // String arffFileNameTest = Utils.getOption("g",args);
        // String xmlFileName = Utils.getOption("x", args);

        String arffFileNameTrain = "data" + File.separator + "miml_text_data_random_80train.arff";
        String arffFileNameTest = "data" + File.separator + "miml_text_data_random_20test.arff";
        String xmlFileName = "data" + File.separator + "miml_text_data.xml";

        // Parameter checking
        if (arffFileNameTrain.isEmpty()) {
            System.out.println("Arff pathName must be specified.");
            showUse();
        }
        if (arffFileNameTest.isEmpty()) {
            System.out.println("Arff pathName must be specified.");
            showUse();
        }
        if (xmlFileName.isEmpty()) {
            System.out.println("Xml pathName must be specified.");
            showUse();
        }

        // Loads the dataset
        System.out.println("Loading the dataset....");

        MIMLInstances mimlTrain = new MIMLInstances(arffFileNameTrain, xmlFileName);
        MIMLInstances mimlTest = new MIMLInstances(arffFileNameTest, xmlFileName);
        Classifier baseLearner = new MIBoost();

        MIMLBinaryRelevance MIMLBR = new MIMLBinaryRelevance(baseLearner);

        MIMLBR.setDebug(true);
        MIMLBR.build(mimlTrain);

        // Evaluates a single instance
        Bag bag = mimlTrain.getBag(1);
        MultiLabelOutput prediction = MIMLBR.makePrediction(bag);
        System.out.println("\nPrediction on a single instance:\n\t" + prediction.toString());

        // Performs a cross validation evaluation
        Evaluator evalCV = new Evaluator();
        MultipleEvaluation resultsCV;
        int numFolds = 2;
        System.out.println("\nPerforming " + numFolds + "-fold cross-validation:\n");
        resultsCV = evalCV.crossValidate(MIMLBR, mimlTrain, numFolds);
        System.out.println("\nResults on cross validation evaluation:\n" + resultsCV);

        // Performs a train-test evaluation
        Evaluator evalTT = new Evaluator();
        System.out.println("\nPerforming train-test evaluation:\n");
        Evaluation resultsTT = evalTT.evaluate(MIMLBR, mimlTest, mimlTrain);
        System.out.println("\nResults on train test evaluation:\n" + resultsTT);

        System.out.println("The program has finished.");

    } catch (IndexOutOfBoundsException ioobe) {
        System.err.println("Exception: Incorrect index of Bag.");
    } catch (Exception e) {
        e.printStackTrace();
    }
}

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