Example usage for weka.classifiers.evaluation ThresholdCurve getCurve

Introduction

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

Prototype

public Instances getCurve(ArrayList<Prediction> predictions) 

Source Link

Document

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

Usage

From source file:TextClassifierUI.java

private void setVMC(FastVector predictions, ThresholdVisualizePanel vmc, boolean masterPlot) {
    try {/* ww  w.jav a2s . c  o m*/
        ThresholdCurve tc = new ThresholdCurve();
        Instances result = tc.getCurve(predictions);
        // method visualize
        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
        if (masterPlot) {
            vmc.setMasterPlot(tempd);
        } else {
            vmc.addPlot(tempd);
        }
    } catch (Exception ex) {
        System.err.println("Failed to set VMC");
        ex.printStackTrace();
    }
}

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From source file:meka.core.Metrics.java

License:Open Source License

/** Calculate AUPRC: Area Under the Precision-Recall curve. */
public static double P_macroAUPRC(int Y[][], double P[][]) {
    // works with missing
    int L = Y[0].length;
    double AUC[] = new double[L];
    for (int j = 0; j < L; j++) {
        if (allMissing(Y[j])) {
            L--;//ww w  .  j a v  a 2s. c o  m
            continue;
        }
        ThresholdCurve curve = new ThresholdCurve();
        Instances result = curve
                .getCurve(MLUtils.toWekaPredictions(MatrixUtils.getCol(Y, j), MatrixUtils.getCol(P, j)));
        AUC[j] = ThresholdCurve.getPRCArea(result);
    }
    return Utils.mean(AUC);
}

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From source file:meka.core.Metrics.java

License:Open Source License

/** Calculate AUROC: Area Under the ROC curve. */
public static double P_macroAUROC(int Y[][], double P[][]) {
    // works with missing
    int L = Y[0].length;
    double AUC[] = new double[L];
    for (int j = 0; j < L; j++) {
        if (allMissing(Y[j])) {
            L--;/*from  w w  w. jav  a2s .co  m*/
            continue;
        }
        ThresholdCurve curve = new ThresholdCurve();
        Instances result = curve
                .getCurve(MLUtils.toWekaPredictions(MatrixUtils.getCol(Y, j), MatrixUtils.getCol(P, j)));
        AUC[j] = ThresholdCurve.getROCArea(result);
    }
    return Utils.mean(AUC);
}

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From source file:meka.core.Metrics.java

License:Open Source License

/** Get Data for Plotting PR and ROC curves. */
public static Instances curveDataMicroAveraged(int Y[][], double P[][]) {
    //works with missing

    int y[] = MatrixUtils.flatten(Y);
    double p[] = MatrixUtils.flatten(P);

    double[][] aligned = align(y, p);

    y = toIntArray(aligned[0]);//from w  w w.j  ava 2  s.  co  m
    p = aligned[1];

    ThresholdCurve curve = new ThresholdCurve();
    return curve.getCurve(MLUtils.toWekaPredictions(y, p));
}

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From source file:meka.core.Metrics.java

License:Open Source License

/** Get Data for Plotting PR and ROC curves. */
public static Instances curveData(int y[], double p[]) {
    // works with missing
    double[][] aligned = align(y, p);

    y = toIntArray(aligned[0]);/*from   ww  w .j  a  v a  2  s. co  m*/
    p = aligned[1];

    ThresholdCurve curve = new ThresholdCurve();
    return curve.getCurve(MLUtils.toWekaPredictions(y, p));
}

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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  w  w  .j a  v  a2  s .com
 * @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);
}

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