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:TextClassifierUI.java

private void runButtonActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_runButtonActionPerformed
    // TODO add your handling code here:
    try {//from w ww .  j  a  v a 2  s  .  c o m
        DocClassifier dr = new DocClassifier(trainFiles, testFiles);
        Classifier cl;
        if (naiveBayes.isSelected()) {
            cl = new NaiveBayes();
        } else {
            cl = new IBk(Integer.parseInt(kNearest.getText()));
        }
        Evaluation ev;
        if (useCV.isSelected()) {
            ev = dr.cvClassify(cl, Integer.parseInt(kFold.getText()));
            result.setText(dr.performanceEval(ev));
        } else {
            ev = dr.classify(cl);
            result.setText(dr.performanceEval(ev));
            result.append("\nDOCUMENT\t=>\tPREDICT\n");
            for (String p : dr.getDocPredList()) {
                result.append(p + "\n");
            }
        }
        ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        setVMC(ev.predictions(), vmc, true);
        showVMC(vmc);
    } catch (NumberFormatException e) {
        JOptionPane.showMessageDialog(this, "K Nearest and K-Fold must be positive numbers.",
                "Number Format Error", JOptionPane.ERROR_MESSAGE);
    } catch (Exception e) {
        e.printStackTrace();
        JOptionPane.showMessageDialog(this, "Failed to classify : " + e.getLocalizedMessage(),
                "Unexpected Error", JOptionPane.ERROR_MESSAGE);
    }
}

From source file:adams.flow.sink.WekaCostCurve.java

License:Open Source License

/**
 * Creates the panel to display in the dialog.
 *
 * @return      the panel/*ww w  .j a v  a  2s. c om*/
 */
@Override
protected BasePanel newPanel() {
    BasePanel result;

    result = new BasePanel(new BorderLayout());
    m_VisualizePanel = new ThresholdVisualizePanel();
    result.add(m_VisualizePanel, BorderLayout.CENTER);

    return result;
}

From source file:adams.flow.sink.WekaThresholdCurve.java

License:Open Source License

/**
 * Creates a new panel for the token.//from  ww  w .  j  av a 2 s .  c  om
 *
 * @param token   the token to display in a new panel, can be null
 * @return      the generated panel
 */
public AbstractDisplayPanel createDisplayPanel(Token token) {
    AbstractDisplayPanel result;
    String name;

    if (token != null)
        name = "Threshold curve (" + getEvaluation(token).getHeader().relationName() + ")";
    else
        name = "Threshold curve";

    result = new AbstractComponentDisplayPanel(name) {
        private static final long serialVersionUID = -7362768698548152899L;
        protected ThresholdVisualizePanel m_VisualizePanel;

        @Override
        protected void initGUI() {
            super.initGUI();
            setLayout(new BorderLayout());
            m_VisualizePanel = new ThresholdVisualizePanel();
            add(m_VisualizePanel, BorderLayout.CENTER);
        }

        @Override
        public void display(Token token) {
            try {
                Evaluation eval = getEvaluation(token);
                m_ClassLabelRange.setMax(eval.getHeader().classAttribute().numValues());
                int[] indices = m_ClassLabelRange.getIntIndices();
                for (int index : indices) {
                    ThresholdCurve curve = new ThresholdCurve();
                    Instances data = curve.getCurve(eval.predictions(), index);
                    PlotData2D plot = new PlotData2D(data);
                    plot.setPlotName(eval.getHeader().classAttribute().value(index));
                    plot.m_displayAllPoints = true;
                    boolean[] connectPoints = new boolean[data.numInstances()];
                    for (int cp = 1; cp < connectPoints.length; cp++)
                        connectPoints[cp] = true;
                    plot.setConnectPoints(connectPoints);
                    m_VisualizePanel.addPlot(plot);
                    if (data.attribute(m_AttributeX.toDisplay()) != null)
                        m_VisualizePanel.setXIndex(data.attribute(m_AttributeX.toDisplay()).index());
                    if (data.attribute(m_AttributeY.toDisplay()) != null)
                        m_VisualizePanel.setYIndex(data.attribute(m_AttributeY.toDisplay()).index());
                }
            } catch (Exception e) {
                getLogger().log(Level.SEVERE, "Failed to display token: " + token, e);
            }
        }

        @Override
        public JComponent supplyComponent() {
            return m_VisualizePanel;
        }

        @Override
        public void clearPanel() {
            m_VisualizePanel.removeAllPlots();
        }

        public void cleanUp() {
            m_VisualizePanel.removeAllPlots();
        }
    };

    if (token != null)
        result.display(token);

    return result;
}

From source file:adams.gui.menu.CostCurve.java

License:Open Source License

/**
 * Launches the functionality of the menu item.
 *///w  w w .  j av  a2 s. c  om
@Override
public void launch() {
    File file;
    if (m_Parameters.length == 0) {
        // choose file
        int retVal = m_FileChooser.showOpenDialog(null);
        if (retVal != JFileChooser.APPROVE_OPTION)
            return;
        file = m_FileChooser.getSelectedFile();
    } else {
        file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile();
        m_FileChooser.setSelectedFile(file);
    }

    // create plot
    Instances result;
    try {
        result = m_FileChooser.getLoader().getDataSet();
    } catch (Exception e) {
        GUIHelper.showErrorMessage(getOwner(),
                "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e));
        return;
    }
    result.setClassIndex(result.numAttributes() - 1);
    ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
    PlotData2D plot = new PlotData2D(result);
    plot.setPlotName(result.relationName());
    plot.m_displayAllPoints = true;
    boolean[] connectPoints = new boolean[result.numInstances()];
    for (int cp = 1; cp < connectPoints.length; cp++)
        connectPoints[cp] = true;
    try {
        plot.setConnectPoints(connectPoints);
        vmc.addPlot(plot);
    } catch (Exception e) {
        GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e));
        return;
    }

    ChildFrame frame = createChildFrame(vmc, GUIHelper.getDefaultDialogDimension());
    frame.setTitle(frame.getTitle() + " - " + file);
}

From source file:adams.gui.menu.ROC.java

License:Open Source License

/**
 * Launches the functionality of the menu item.
 *///from  w w w. j a v  a2  s.  c  o m
@Override
public void launch() {
    File file;
    if (m_Parameters.length == 0) {
        // choose file
        int retVal = m_FileChooser.showOpenDialog(null);
        if (retVal != JFileChooser.APPROVE_OPTION)
            return;
        file = m_FileChooser.getSelectedFile();
    } else {
        file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile();
        m_FileChooser.setSelectedFile(file);
    }

    // create plot
    Instances result;
    try {
        result = m_FileChooser.getLoader().getDataSet();
    } catch (Exception e) {
        GUIHelper.showErrorMessage(getOwner(),
                "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e));
        return;
    }
    result.setClassIndex(result.numAttributes() - 1);
    ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
    vmc.setROCString("(Area under ROC = " + Utils.doubleToString(ThresholdCurve.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;
    try {
        tempd.setConnectPoints(cp);
        vmc.addPlot(tempd);
    } catch (Exception e) {
        GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e));
        return;
    }

    ChildFrame frame = createChildFrame(vmc, GUIHelper.getDefaultDialogDimension());
    frame.setTitle(frame.getTitle() + " - " + file);
}

From source file:com.sliit.views.DataVisualizerPanel.java

void getRocCurve() {
    try {/* w w  w .j  av a  2  s.co m*/
        Instances data;
        data = new Instances(new BufferedReader(new FileReader(datasetPathText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        // train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // 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);

        // 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);
    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.sliit.views.KNNView.java

void getRocCurve() {
    try {// w  w  w.j  ava2s . co  m
        Instances data;
        data = new Instances(new BufferedReader(new java.io.FileReader(PredictorPanel.modalText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        // train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // 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);

        rocPanel.removeAll();
        rocPanel.add(vmc, "vmc", 0);
        rocPanel.revalidate();

    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.sliit.views.SVMView.java

/**
 * draw ROC curve/*from   w  w w .ja va  2 s. c  o m*/
 */
void getRocCurve() {
    try {
        Instances data;
        data = new Instances(new BufferedReader(new FileReader(PredictorPanel.modalText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        //train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // 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);

        //            rocPanel.removeAll();
        //            rocPanel.add(vmc, "vmc", 0);
        //            rocPanel.revalidate();
    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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

License:Open Source License

/**
 * Computes the AUC for the supplied learner.
 * @return the AUC as a double value./*from   ww  w .  ja va  2  s.  c om*/
 */
@SuppressWarnings("unused")
private static double validate5x2CV() {
    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);
            }

            Evaluation eval = new Evaluation(randData);

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

                // the above code is used by the StratifiedRemoveFolds filter, the
                // code below by the Explorer/Experimenter:
                // Instances train = randData.trainCV(folds, n, rand);

                // build and evaluate classifier
                String[] options = { "-U", "-A" };
                J48 classifier = new J48();
                //HTree classifier = new HTree();

                classifier.setOptions(options);
                classifier.buildClassifier(train);
                eval.evaluateModel(classifier, test);

                // generate curve
                ThresholdCurve tc = new ThresholdCurve();
                int classIndex = 0;
                Instances result = tc.getCurve(eval.predictions(), classIndex);

                // plot curve
                vmc = new ThresholdVisualizePanel();
                AUC_SUM += ThresholdCurve.getROCArea(result);
                System.out.println("AUC: " + ThresholdCurve.getROCArea(result) + " \tAUC SUM: " + AUC_SUM);
            }
        }

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

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

License:Open Source License

/**
 * Computes the AUC for the supplied learner.
 * @param learner the learning algorithm to use.
 * @return the AUC as a double value./*from   ww  w .j  a v  a 2s . c  om*/
 */
@SuppressWarnings("unused")
private static double validate(Classifier learner) {
    try {

        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(learner, data, 2, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // plot curve
        vmc = new ThresholdVisualizePanel();
        double AUC = ThresholdCurve.getROCArea(result);
        vmc.setROCString(
                "(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(result), 9) + ")");
        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);

        return AUC;
    } catch (Exception e) {
        System.out.println("Exception validating data!");
        return 0;
    }
}