Example usage for weka.classifiers.evaluation ThresholdCurve getCurve

List of usage examples for weka.classifiers.evaluation ThresholdCurve getCurve

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Introduction

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

Prototype

public Instances getCurve(ArrayList<Prediction> predictions, int classIndex)

Source Link

Document

Calculates the performance stats for the desired class and return results as a set of Instances.

Usage

From source file:adams.data.conversion.WekaEvaluationToThresholdCurve.java

License:Open Source License

/**
 * Performs the actual conversion./* w  ww  .j  av  a 2  s .  c o m*/
 *
 * @return      the converted data
 * @throws Exception   if something goes wrong with the conversion
 */
@Override
protected Object doConvert() throws Exception {
    Evaluation eval;
    ThresholdCurve curve;
    Instances cost;

    eval = (Evaluation) m_Input;
    m_ClassLabelIndex.setMax(eval.getHeader().classAttribute().numValues());
    curve = new ThresholdCurve();
    cost = curve.getCurve(eval.predictions(), m_ClassLabelIndex.getIntIndex());

    return cost;
}

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

License:Open Source License

/**
 * Plots the token (the panel and dialog have already been created at
 * this stage).// w  w w  .  j a  v  a 2 s  .c om
 *
 * @param token   the token to display
 */
@Override
protected void display(Token token) {
    Evaluation eval;
    Attribute classAtt;
    Attribute classAttToUse;
    int classValue;
    ThresholdCurve tc;
    Instances result;
    ArrayList<String> newNames;
    CostBenefitAnalysis cbAnalysis;
    PlotData2D tempd;
    boolean[] cp;
    int n;

    try {
        if (token.getPayload() instanceof WekaEvaluationContainer)
            eval = (Evaluation) ((WekaEvaluationContainer) token.getPayload())
                    .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        else
            eval = (Evaluation) token.getPayload();
        if (eval.predictions() == null) {
            getLogger().severe("No predictions available from Evaluation object!");
            return;
        }
        classAtt = eval.getHeader().classAttribute();
        m_ClassIndex.setData(classAtt);
        classValue = m_ClassIndex.getIntIndex();
        tc = new ThresholdCurve();
        result = tc.getCurve(eval.predictions(), classValue);

        // Create a dummy class attribute with the chosen
        // class value as index 0 (if necessary).
        classAttToUse = eval.getHeader().classAttribute();
        if (classValue != 0) {
            newNames = new ArrayList<>();
            newNames.add(classAtt.value(classValue));
            for (int k = 0; k < classAtt.numValues(); k++) {
                if (k != classValue)
                    newNames.add(classAtt.value(k));
            }
            classAttToUse = new Attribute(classAtt.name(), newNames);
        }
        // assemble plot data
        tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.m_alwaysDisplayPointsOfThisSize = 10;
        // specify which points are connected
        cp = new boolean[result.numInstances()];
        for (n = 1; n < cp.length; n++)
            cp[n] = true;
        tempd.setConnectPoints(cp);
        // add plot
        m_CostBenefitPanel.setCurveData(tempd, classAttToUse);
    } catch (Exception e) {
        handleException("Failed to display token: " + token, e);
    }
}

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

License:Open Source License

/**
 * Creates a new panel for the token.//from  www.ja  va2  s  .co m
 *
 * @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 = "Cost curve (" + getEvaluation(token).getHeader().relationName() + ")";
    else
        name = "Cost curve";

    result = new AbstractComponentDisplayPanel(name) {
        private static final long serialVersionUID = -3513994354297811163L;
        protected CostBenefitAnalysis m_VisualizePanel;

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

        @Override
        public void display(Token token) {
            try {
                Evaluation eval = getEvaluation(token);
                Attribute classAtt = eval.getHeader().classAttribute();
                m_ClassIndex.setData(classAtt);
                int classValue = m_ClassIndex.getIntIndex();
                ThresholdCurve tc = new ThresholdCurve();
                Instances result = tc.getCurve(eval.predictions(), classValue);

                // Create a dummy class attribute with the chosen
                // class value as index 0 (if necessary).
                Attribute classAttToUse = eval.getHeader().classAttribute();
                if (classValue != 0) {
                    ArrayList<String> newNames = new ArrayList<>();
                    newNames.add(classAtt.value(classValue));
                    for (int k = 0; k < classAtt.numValues(); k++) {
                        if (k != classValue)
                            newNames.add(classAtt.value(k));
                    }
                    classAttToUse = new Attribute(classAtt.name(), newNames);
                }
                // assemble plot data
                PlotData2D tempd = new PlotData2D(result);
                tempd.setPlotName(result.relationName());
                tempd.m_alwaysDisplayPointsOfThisSize = 10;
                // 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
                m_VisualizePanel.setCurveData(tempd, classAttToUse);
            } catch (Exception e) {
                getLogger().log(Level.SEVERE, "Failed to display token: " + token, e);
            }
        }

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

        @Override
        public void clearPanel() {
        }

        public void cleanUp() {
        }
    };

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

    return result;
}

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

License:Open Source License

/**
 * Plots the token (the panel and dialog have already been created at
 * this stage)./*from  w  w w .j a  va 2s  .c  om*/
 *
 * @param token   the token to display
 */
@Override
protected void display(Token token) {
    ThresholdCurve curve;
    Evaluation eval;
    PlotData2D plot;
    boolean[] connectPoints;
    int cp;
    Instances data;
    int[] indices;

    try {
        if (token.getPayload() instanceof WekaEvaluationContainer)
            eval = (Evaluation) ((WekaEvaluationContainer) token.getPayload())
                    .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
        else
            eval = (Evaluation) token.getPayload();
        if (eval.predictions() == null) {
            getLogger().severe("No predictions available from Evaluation object!");
            return;
        }
        m_ClassLabelRange.setData(eval.getHeader().classAttribute());
        indices = m_ClassLabelRange.getIntIndices();
        for (int index : indices) {
            curve = new ThresholdCurve();
            data = curve.getCurve(eval.predictions(), index);
            plot = new PlotData2D(data);
            plot.setPlotName(eval.getHeader().classAttribute().value(index));
            plot.m_displayAllPoints = true;
            connectPoints = new boolean[data.numInstances()];
            for (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) {
        handleException("Failed to display token: " + token, e);
    }
}

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

License:Open Source License

/**
 * Creates a new panel for the token.//from w ww  .j a  v  a  2  s.co m
 *
 * @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:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Returns the area under ROC for those predictions that have been collected
 * in the evaluateClassifier(Classifier, Instances) method. Returns
 * Instance.missingValue() if the area is not available.
 * //w  w w.jav a  2  s.  c o m
 * @param classIndex the index of the class to consider as "positive"
 * @return the area under the ROC curve or not a number
 */
public double areaUnderROC(int classIndex) {

    // Check if any predictions have been collected
    if (m_Predictions == null) {
        return Instance.missingValue();
    } else {
        ThresholdCurve tc = new ThresholdCurve();
        Instances result = tc.getCurve(m_Predictions, classIndex);
        double rocArea = ThresholdCurve.getROCArea(result);
        if (rocArea < 0.5) {
            rocArea = 1 - rocArea;
        }

        int tpIndex = result.attribute(ThresholdCurve.TP_RATE_NAME).index();
        int fpIndex = result.attribute(ThresholdCurve.FP_RATE_NAME).index();
        double[] tpRate = result.attributeToDoubleArray(tpIndex);
        double[] fpRate = result.attributeToDoubleArray(fpIndex);

        try {
            FileWriter fw;
            if (classIndex == 0)
                fw = new FileWriter("C://1.csv", true);
            else
                fw = new FileWriter("C://1.csv", true);

            BufferedWriter bw = new BufferedWriter(fw);
            int length = fpRate.length;
            for (int i = 255; i >= 0; i--) {

                int index = i * (length - 1) / 255;
                bw.write(fpRate[index] + ",");
            }
            bw.write("\n");
            for (int i = 255; i >= 0; i--) {
                int index = i * (length - 1) / 255;
                bw.write(tpRate[index] + ",");
            }
            bw.write("\n");

            bw.close();
            fw.close();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

        return rocArea;
    }
}

From source file:com.evaluation.ConfidenceLabelBasedMeasures.java

License:Open Source License

private void computeMeasures(MultiLabelOutput[] output, boolean[][] trueLabels) {
    int numLabels = trueLabels[0].length;

    // AUC//from w w  w. jav  a 2  s .  c om
    FastVector[] m_Predictions = new FastVector[numLabels];
    for (int j = 0; j < numLabels; j++)
        m_Predictions[j] = new FastVector();
    FastVector all_Predictions = new FastVector();

    int numInstances = output.length;
    for (int instanceIndex = 0; instanceIndex < numInstances; instanceIndex++) {
        double[] confidences = output[instanceIndex].getConfidences();
        for (int labelIndex = 0; labelIndex < numLabels; labelIndex++) {

            int classValue;
            boolean actual = trueLabels[instanceIndex][labelIndex];
            if (actual)
                classValue = 1;
            else
                classValue = 0;

            double[] dist = new double[2];
            dist[1] = confidences[labelIndex];
            dist[0] = 1 - dist[1];

            m_Predictions[labelIndex].addElement(new NominalPrediction(classValue, dist, 1));
            all_Predictions.addElement(new NominalPrediction(classValue, dist, 1));
        }
    }

    labelAUC = new double[numLabels];
    for (int i = 0; i < numLabels; i++) {
        ThresholdCurve tc = new ThresholdCurve();
        Instances result = tc.getCurve(m_Predictions[i], 1);
        labelAUC[i] = ThresholdCurve.getROCArea(result);
    }
    auc[Averaging.MACRO.ordinal()] = Utils.mean(labelAUC);
    ThresholdCurve tc = new ThresholdCurve();
    Instances result = tc.getCurve(all_Predictions, 1);
    auc[Averaging.MICRO.ordinal()] = ThresholdCurve.getROCArea(result);
}

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

void getRocCurve() {
    try {// ww  w . j  av  a2 s . com
        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 {//from www .j  av a2 s.  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 ww  .  j a  va2s  .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);
    }
}