Example usage for weka.classifiers.trees.j48 Distribution matrix

Introduction

In this page you can find the example usage for weka.classifiers.trees.j48 Distribution matrix.

Prototype

public final double[][] matrix() 

Source Link

Document

Returns matrix with distribution of class values.

Usage

From source file:cs.man.ac.uk.classifiers.GetAUC.java

License:Open Source License

/**
 * Computes the AUC for the supplied stream learner.
 * @return the AUC as a double value.//from   w  w  w. j a  v  a  2  s.  co  m
 */
private static double validate5x2CVStream() {
    try {
        // Other options
        int runs = 5;
        int folds = 2;
        double AUC_SUM = 0;

        // perform cross-validation
        for (int i = 0; i < runs; i++) {
            // randomize data
            int seed = i + 1;
            Random rand = new Random(seed);
            Instances randData = new Instances(data);
            randData.randomize(rand);

            if (randData.classAttribute().isNominal()) {
                System.out.println("Stratifying...");
                randData.stratify(folds);
            }

            for (int n = 0; n < folds; n++) {
                Instances train = randData.trainCV(folds, n);
                Instances test = randData.testCV(folds, n);

                Distribution testDistribution = new Distribution(test);

                ArffSaver trainSaver = new ArffSaver();
                trainSaver.setInstances(train);
                trainSaver.setFile(new File(trainPath));
                trainSaver.writeBatch();

                ArffSaver testSaver = new ArffSaver();
                testSaver.setInstances(test);

                double[][] dist = testDistribution.matrix();
                int negativeClassSize = (int) dist[0][0];
                int positiveClassSize = (int) dist[0][1];
                double balance = (double) positiveClassSize / (double) negativeClassSize;

                String tempTestPath = testPath.replace(".arff",
                        "_" + positiveClassSize + "_" + negativeClassSize + "_" + balance + "_1.0.arff");// [Test-n-Set-n]_[+]_[-]_[K]_[L];
                testSaver.setFile(new File(tempTestPath));
                testSaver.writeBatch();

                ARFFFile file = new ARFFFile(tempTestPath, CLASS_INDEX, new DebugLogger(false));
                file.createMetaData();

                HoeffdingTreeTester streamClassifier = new HoeffdingTreeTester(trainPath, tempTestPath,
                        CLASS_INDEX, new String[] { "0", "1" }, new DebugLogger(true));

                streamClassifier.train();

                System.in.read();

                //AUC_SUM += streamClassifier.getROCExternalData("",(int)testDistribution.perClass(1),(int)testDistribution.perClass(0));
                streamClassifier.testStatic(homeDirectory + "/FuckSakeTest.txt");

                String[] files = Common.getFilePaths(scratch);
                for (int j = 0; j < files.length; j++)
                    Common.fileDelete(files[j]);
            }
        }

        return AUC_SUM / ((double) runs * (double) folds);
    } catch (Exception e) {
        System.out.println("Exception validating data!");
        e.printStackTrace();
        return 0;
    }
}

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From source file:org.knime.knip.suise.node.port.WekaClassifierPortObject.java

License:Open Source License

/**
 * {@inheritDoc}/*  w  ww. j av  a2s .  c  o  m*/
 */
@Override
public JComponent[] getViews() {

    JPanel panel = new JPanel(new GridLayout(1, 2));

    // classifier info
    JPanel classifierInfoPanel = new JPanel();
    String text = m_classifier.getClass().getSimpleName() + "\n";
    text += m_classifier.toString();
    classifierInfoPanel.add(new JTextArea(text));
    classifierInfoPanel.setBorder(BorderFactory.createTitledBorder("Weka Classifier Details"));
    panel.add(new JScrollPane(classifierInfoPanel));

    // training instances info
    if (m_trainingInstances.numInstances() > 0) {
        JPanel dataInfoPanel = new JPanel();
        text = "Training instances\n\n";
        Distribution distr = new Distribution(1, m_trainingInstances.numClasses());
        for (Instance i : m_trainingInstances) {
            try {
                distr.add(0, i);
            } catch (Exception e) {
                throw new RuntimeException(e);
            }
        }

        text += "Class-distribution: " + Arrays.toString(distr.matrix()[0]) + " (labels: "
                + Arrays.toString(m_modelspec.getClassLabels()) + ")" + "\n\n";
        text += m_trainingInstances.toSummaryString();

        dataInfoPanel.add(new JTextArea(text));
        dataInfoPanel.setBorder(BorderFactory.createTitledBorder("Training Instances Details"));
        panel.add(new JScrollPane(dataInfoPanel));
    }

    panel.setName("Weka Outport");
    return new JComponent[] { panel };
}

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