Example usage for weka.gui.visualize ThresholdVisualizePanel ThresholdVisualizePanel

List of usage examples for weka.gui.visualize ThresholdVisualizePanel ThresholdVisualizePanel

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Introduction

In this page you can find the example usage for weka.gui.visualize ThresholdVisualizePanel ThresholdVisualizePanel.

Prototype

public ThresholdVisualizePanel()

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Document

default constructor

Usage

From source file:meka.gui.explorer.classify.AbstractShowThresholdCurve.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/*from   w ww. j a  va2s  .c  o m*/
 * @throws Exception    if plot generation fails
 */
protected ThresholdVisualizePanel createPanel(Instances data, String title) throws Exception {
    ThresholdVisualizePanel result = new ThresholdVisualizePanel();
    PlotData2D plot = new PlotData2D(data);
    plot.setPlotName(title);
    plot.m_displayAllPoints = true;
    boolean[] connectPoints = new boolean[data.numInstances()];
    for (int cp = 1; cp < connectPoints.length; cp++)
        connectPoints[cp] = true;
    plot.setConnectPoints(connectPoints);
    result.addPlot(plot);
    setComboBoxIndices(data, result);
    return result;
}

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

License:Open Source License

/**
 * Creates a panel displaying the data./*from   w ww  . j  av a2  s  .c  o  m*/
 *
 * @param data          the plot data
 * @return              the panel
 * @throws Exception    if plot generation fails
 */
protected VisualizePanel createPanel(Instances data) throws Exception {
    VisualizePanel result = new ThresholdVisualizePanel();
    PlotData2D plot = new PlotData2D(data);
    plot.setPlotName("Incremental performance");
    plot.m_displayAllPoints = true;
    boolean[] connectPoints = new boolean[data.numInstances()];
    for (int cp = 1; cp < connectPoints.length; cp++)
        connectPoints[cp] = true;
    plot.setConnectPoints(connectPoints);
    result.addPlot(plot);
    if (data.attribute(SAMPLES) != null)
        result.setXIndex(data.attribute(SAMPLES).index());
    if (data.attribute(ACCURACY) != null)
        result.setYIndex(data.attribute(ACCURACY).index());
    return result;
}

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

License:Open Source License

/**
 * Creates a panel displaying the data.//from   w w w  . j  ava2 s. c o m
 *
 * @param data          the plot data
 * @return              the panel
 * @throws Exception    if plot generation fails
 */
protected VisualizePanel createPanel(Instances data) throws Exception {
    VisualizePanel result = new ThresholdVisualizePanel();
    PlotData2D plot = new PlotData2D(data);
    plot.setPlotName("Macro-averaged Performance");
    plot.m_displayAllPoints = true;
    boolean[] connectPoints = new boolean[data.numInstances()];
    for (int cp = 1; cp < connectPoints.length; cp++)
        connectPoints[cp] = true;
    plot.setConnectPoints(connectPoints);
    result.addPlot(plot);
    if (data.attribute(SAMPLES) != null)
        result.setXIndex(data.attribute(SAMPLES).index());
    if (data.attribute(ACCURACY) != null)
        result.setYIndex(data.attribute(ACCURACY).index());
    return result;
}

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

License:Open Source License

/**
 * Creates a panel displaying the data.//w ww.  j a  va  2 s . co  m
 *
 * @param data          the plot data
 * @return              the panel
 * @throws Exception    if plot generation fails
 */
protected VisualizePanel createPanel(Instances data) throws Exception {
    VisualizePanel result = new ThresholdVisualizePanel();
    PlotData2D plot = new PlotData2D(data);
    plot.setPlotName("Micro-averaged Performance");
    plot.m_displayAllPoints = true;
    boolean[] connectPoints = new boolean[data.numInstances()];
    for (int cp = 1; cp < connectPoints.length; cp++)
        connectPoints[cp] = true;
    plot.setConnectPoints(connectPoints);
    result.addPlot(plot);
    if (data.attribute(SAMPLES) != null)
        result.setXIndex(data.attribute(SAMPLES).index());
    if (data.attribute(ACCURACY) != null)
        result.setYIndex(data.attribute(ACCURACY).index());
    return result;
}

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

License:Open Source License

/**
 * Called by the menu items action listener.
 *///from  ww w .  j  av a2  s.  com
@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 PRC = " + Utils.doubleToString(ThresholdCurve.getPRCArea(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.RECALL_NAME) != null)
            vmc.setXIndex(data.attribute(ThresholdCurve.RECALL_NAME).index());
        if (data.attribute(ThresholdCurve.PRECISION_NAME) != null)
            vmc.setYIndex(data.attribute(ThresholdCurve.PRECISION_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);
}

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

License:Open Source License

/**
 * Called by the menu items action listener.
 *//* w  w w .  j a v  a 2 s.  c  o m*/
@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);
}

From source file:miRdup.WekaModule.java

License:Open Source License

public static void rocCurve(Evaluation eval) {
    try {// w  ww.ja 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();
    }

}

From source file:trainableSegmentation.Weka_Segmentation.java

License:GNU General Public License

/**
 * Display the threshold curve window (for precision/recall, ROC, etc.).
 *
 * @param data input instances/*from w ww . j  a  v  a 2 s.c o m*/
 * @param classifier classifier to evaluate
 */
public static void displayGraphs(Instances data, AbstractClassifier classifier) {
    ThresholdCurve tc = new ThresholdCurve();

    FastVector predictions = null;
    try {
        final EvaluationUtils eu = new EvaluationUtils();
        predictions = eu.getTestPredictions(classifier, data);
    } catch (Exception e) {
        IJ.log("Error while evaluating data!");
        e.printStackTrace();
        return;
    }

    Instances result = tc.getCurve(predictions);
    ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
    vmc.setName(result.relationName() + " (display only)");
    PlotData2D tempd = new PlotData2D(result);
    tempd.setPlotName(result.relationName());
    tempd.addInstanceNumberAttribute();
    try {
        vmc.addPlot(tempd);
    } catch (Exception e) {
        IJ.log("Error while adding plot to visualization panel!");
        e.printStackTrace();
        return;
    }
    String plotName = vmc.getName();
    JFrame jf = new JFrame("Weka Classifier Visualize: " + plotName);
    jf.setSize(500, 400);
    jf.getContentPane().setLayout(new BorderLayout());
    jf.getContentPane().add(vmc, BorderLayout.CENTER);
    jf.setVisible(true);
}