Example usage for weka.classifiers.evaluation ThresholdCurve getROCArea

Introduction

In this page you can find the example usage for weka.classifiers.evaluation ThresholdCurve getROCArea.

Prototype

public static double getROCArea(Instances tcurve) 

Source Link

Document

Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.

Usage

From source file:meka.gui.explorer.classify.ShowROC.java

License:Open Source License

/**
 * Creates a panel displaying the ROC data.
 *
 * @param data          the threshold curve data
 * @param title         the title of the plot
 * @return              the panel//  ww w  .j av  a2  s  . c o m
 * @throws Exception    if plot generation fails
 */
protected ThresholdVisualizePanel createPanel(Instances data, String title) throws Exception {
    ThresholdVisualizePanel result = super.createPanel(data, title);
    result.setROCString("AUC: " + Utils.doubleToString(ThresholdCurve.getROCArea(data), 3));
    result.setUpComboBoxes(result.getInstances());
    return result;
}

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From source file:meka.gui.guichooser.ROC.java

License:Open Source License

/**
 * Called by the menu items action listener.
 *///ww  w.  j  a v a 2  s .c  om
@Override
protected void launch() {
    m_FileChooser = GUIHelper.newConverterFileChooser();
    // choose file
    int retVal = m_FileChooser.showOpenDialog(null);
    if (retVal != JFileChooser.APPROVE_OPTION)
        return;
    File file = m_FileChooser.getSelectedFile();

    // create plot
    Instances data;
    try {
        data = m_FileChooser.getLoader().getDataSet();
    } catch (Exception e) {
        JOptionPane.showMessageDialog(null, "Error loading file '" + file + "':\n" + e, "Error",
                JOptionPane.ERROR_MESSAGE);
        e.printStackTrace();
        return;
    }
    data.setClassIndex(data.numAttributes() - 1);
    ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
    vmc.setROCString("(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(data), 4) + ")");
    vmc.setName(data.relationName());
    PlotData2D tempd = new PlotData2D(data);
    tempd.setPlotName(data.relationName());
    tempd.m_displayAllPoints = true;
    // specify which points are connected
    boolean[] cp = new boolean[data.numInstances()];
    for (int n = 1; n < cp.length; n++)
        cp[n] = true;
    try {
        tempd.setConnectPoints(cp);
        vmc.addPlot(tempd);
        if (data.attribute(ThresholdCurve.FP_RATE_NAME) != null)
            vmc.setXIndex(data.attribute(ThresholdCurve.FP_RATE_NAME).index());
        if (data.attribute(ThresholdCurve.TP_RATE_NAME) != null)
            vmc.setYIndex(data.attribute(ThresholdCurve.TP_RATE_NAME).index());
    } catch (Exception e) {
        JOptionPane.showMessageDialog(null, "Error adding plot:\n" + e, "Error", JOptionPane.ERROR_MESSAGE);
        e.printStackTrace();
        return;
    }

    MekaFrame frame = new MekaFrame();
    frame.setTitle(getName());
    frame.setDefaultCloseOperation(MekaFrame.DISPOSE_ON_CLOSE);
    frame.getContentPane().setLayout(new BorderLayout());
    frame.getContentPane().add(vmc);
    frame.setSize(800, 600);
    frame.setLocationRelativeTo(null);
    frame.setVisible(true);
}

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From source file:miRdup.WekaModule.java

License:Open Source License

public static void rocCurve(Evaluation eval) {
    try {//from w w  w.  j a  v a  2s  .  co m
        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);
        result.toString();
        // plot curve
        ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")");
        vmc.setName(result.relationName());
        PlotData2D tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.addInstanceNumberAttribute();
        // specify which points are connected
        boolean[] cp = new boolean[result.numInstances()];
        for (int n = 1; n < cp.length; n++) {
            cp[n] = true;
        }
        tempd.setConnectPoints(cp);
        // add plot
        vmc.addPlot(tempd);

        //
        result.toString();

        // display curve
        String plotName = vmc.getName();
        final javax.swing.JFrame jf = new javax.swing.JFrame("Weka Classifier Visualize: " + plotName);
        jf.setSize(500, 400);
        jf.getContentPane().setLayout(new BorderLayout());
        jf.getContentPane().add(vmc, BorderLayout.CENTER);
        jf.addWindowListener(new java.awt.event.WindowAdapter() {
            public void windowClosing(java.awt.event.WindowEvent e) {
                jf.dispose();
            }
        });

        jf.setVisible(true);
        System.out.println("");
    } catch (Exception e) {
        e.printStackTrace();
    }

}

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From source file:mlflex.WekaInMemoryLearner.java

License:Open Source License

/** This method calculates the area under the curve for a set of predictions and is designed to support classification of more than two classes. This code was derived from Weka's source code.
 *
 * @param predictions Predictions that have been made
 * @return Area under the curve, weighted by the proportion of instances for each class
 * @throws Exception/*  www  . j a va 2s. com*/
 */
//
public static double CalculateWeightedAreaUnderRoc(Predictions predictions) throws Exception {
    ArrayList<String> uniqueActualClasses = predictions.GetUniqueActualClasses();

    if (uniqueActualClasses.size() == 0)
        return Double.NaN;

    if (predictions.Size() == 1) {
        if (predictions.Get(0).WasCorrect())
            return 1.0;

        return 0.5;
    }

    if (uniqueActualClasses.size() == 1)
        return 0.5;

    ArrayList<String> dependentVariableClasses = Utilities.ProcessorVault.DependentVariableDataProcessor
            .GetUniqueDependentVariableValues();

    FastVector predictionVector = new FastVector();
    for (Prediction prediction : predictions)
        predictionVector.addElement(
                new NominalPrediction(dependentVariableClasses.indexOf(prediction.DependentVariableValue),
                        Lists.ConvertToDoubleArray(prediction.ClassProbabilities)));

    double aucTotal = 0;

    for (int i = 0; i < dependentVariableClasses.size(); i++) {
        String dependentVariableClass = dependentVariableClasses.get(i);

        Instances result = new ThresholdCurve().getCurve(predictionVector, i);
        double auc = ThresholdCurve.getROCArea(result);

        if (!Instance.isMissingValue(auc))
            aucTotal += (auc * new PredictionResults(predictions)
                    .GetNumActualsWithDependentVariableClass(dependentVariableClass));
    }

    return aucTotal / predictions.Size();
}

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From source file:mulan.evaluation.measure.MacroAUC.java

License:Open Source License

public double getValue() {
    double[] labelAUC = new double[numOfLabels];
    for (int i = 0; i < numOfLabels; i++) {
        ThresholdCurve tc = new ThresholdCurve();
        Instances result = tc.getCurve(m_Predictions[i], 1);
        labelAUC[i] = ThresholdCurve.getROCArea(result);
    }/* ww w . j ava 2  s .com*/
    return Utils.mean(labelAUC);
}

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From source file:mulan.evaluation.measure.MicroAUC.java

License:Open Source License

public double getValue() {
    ThresholdCurve tc = new ThresholdCurve();
    Instances result = tc.getCurve(all_Predictions, 1);
    return ThresholdCurve.getROCArea(result);
}

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