Example usage for weka.core Instances relationName

List of usage examples for weka.core Instances relationName

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Introduction

In this page you can find the example usage for weka.core Instances relationName.

Prototype


publicString relationName()

Source Link

Document

Returns the relation's name.

Usage

From source file:cezeri.evaluater.FactoryEvaluation.java

public static Evaluation performCrossValidate(Classifier model, Instances datax, int folds, boolean show_text,
        boolean show_plot, TFigureAttribute attr) {
    Random rand = new Random(1);
    Instances randData = new Instances(datax);
    randData.randomize(rand);/* ww w .  j a  va2 s  .  co  m*/
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }
    Evaluation eval = null;
    try {
        // perform cross-validation
        eval = new Evaluation(randData);
        //            double[] simulated = new double[0];
        //            double[] observed = new double[0];
        //            double[] sim = new double[0];
        //            double[] obs = new double[0];
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n, rand);
            Instances validation = randData.testCV(folds, n);
            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(model);
            clsCopy.buildClassifier(train);

            //                sim = eval.evaluateModel(clsCopy, validation);
            //                obs = validation.attributeToDoubleArray(validation.classIndex());
            //                if (show_plot) {
            //                    double[][] d = new double[2][sim.length];
            //                    d[0] = obs;
            //                    d[1] = sim;
            //                    CMatrix f1 = CMatrix.getInstance(d);
            //                    f1.transpose().plot(attr);
            //                }
            //                if (show_text) {
            //                    // output evaluation
            //                    System.out.println();
            //                    System.out.println("=== Setup for each Cross Validation fold===");
            //                    System.out.println("Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            //                    System.out.println("Dataset: " + randData.relationName());
            //                    System.out.println("Folds: " + folds);
            //                    System.out.println("Seed: " + 1);
            //                    System.out.println();
            //                    System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
            //                }
            simulated = FactoryUtils.concatenate(simulated, eval.evaluateModel(clsCopy, validation));
            observed = FactoryUtils.concatenate(observed,
                    validation.attributeToDoubleArray(validation.classIndex()));
            //                simulated = FactoryUtils.mean(simulated,eval.evaluateModel(clsCopy, validation));
            //                observed = FactoryUtils.mean(observed,validation.attributeToDoubleArray(validation.classIndex()));
        }

        if (show_plot) {
            double[][] d = new double[2][simulated.length];
            d[0] = observed;
            d[1] = simulated;
            CMatrix f1 = CMatrix.getInstance(d);
            attr.figureCaption = "overall performance";
            f1.transpose().plot(attr);
        }
        if (show_text) {
            // output evaluation
            System.out.println();
            System.out.println("=== Setup for Overall Cross Validation===");
            System.out.println(
                    "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            System.out.println("Dataset: " + randData.relationName());
            System.out.println("Folds: " + folds);
            System.out.println("Seed: " + 1);
            System.out.println();
            System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
        }
    } catch (Exception ex) {
        Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:cezeri.evaluater.FactoryEvaluation.java

public static Evaluation performCrossValidateTestAlso(Classifier model, Instances datax, Instances test,
        boolean show_text, boolean show_plot) {
    TFigureAttribute attr = new TFigureAttribute();
    Random rand = new Random(1);
    Instances randData = new Instances(datax);
    randData.randomize(rand);// ww w.  jav  a 2  s . c  om

    Evaluation eval = null;
    int folds = randData.numInstances();
    try {
        eval = new Evaluation(randData);
        for (int n = 0; n < folds; n++) {
            //                randData.randomize(rand);
            //                Instances train = randData;                
            Instances train = randData.trainCV(folds, n);
            //                Instances train = randData.trainCV(folds, n, rand);
            Classifier clsCopy = Classifier.makeCopy(model);
            clsCopy.buildClassifier(train);
            Instances validation = randData.testCV(folds, n);
            //                Instances validation = test.testCV(test.numInstances(), n%test.numInstances());
            //                CMatrix.fromInstances(train).showDataGrid();
            //                CMatrix.fromInstances(validation).showDataGrid();

            simulated = FactoryUtils.concatenate(simulated, eval.evaluateModel(clsCopy, validation));
            observed = FactoryUtils.concatenate(observed,
                    validation.attributeToDoubleArray(validation.classIndex()));
        }

        if (show_plot) {
            double[][] d = new double[2][simulated.length];
            d[0] = observed;
            d[1] = simulated;
            CMatrix f1 = CMatrix.getInstance(d);
            attr.figureCaption = "overall performance";
            f1.transpose().plot(attr);
        }
        if (show_text) {
            // output evaluation
            System.out.println();
            System.out.println("=== Setup for Overall Cross Validation===");
            System.out.println(
                    "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            System.out.println("Dataset: " + randData.relationName());
            System.out.println("Folds: " + folds);
            System.out.println("Seed: " + 1);
            System.out.println();
            System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
        }
    } catch (Exception ex) {
        Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:cezeri.evaluater.FactoryEvaluation.java

private static Evaluation doTest(boolean isTrained, Classifier model, Instances train, Instances test,
        boolean show_text, boolean show_plot, TFigureAttribute attr) {
    Instances data = new Instances(train);
    Random rand = new Random(1);
    data.randomize(rand);/*from  w  w  w  .j  av a  2  s  . c  o  m*/
    Evaluation eval = null;
    try {
        //            double[] simulated = null;
        eval = new Evaluation(train);
        if (isTrained) {
            simulated = eval.evaluateModel(model, test);
        } else {
            Classifier clsCopy = Classifier.makeCopy(model);
            clsCopy.buildClassifier(train);
            simulated = eval.evaluateModel(clsCopy, test);
        }
        if (show_plot) {
            observed = test.attributeToDoubleArray(test.classIndex());
            double[][] d = new double[2][simulated.length];
            d[0] = observed;
            d[1] = simulated;
            CMatrix f1 = CMatrix.getInstance(d);
            String[] items = { "Observed", "Simulated" };
            attr.items = items;
            attr.figureCaption = model.getClass().getCanonicalName();
            f1.transpose().plot(attr);
            //                if (attr.axis[0].isEmpty() && attr.axis[1].isEmpty()) {
            //                    f1.transpose().plot(attr);
            //                } else {
            //                    f1.transpose().plot(model.getClass().getCanonicalName(), attr.items, attr.axis);
            //                }
        }
        if (show_text) {
            System.out.println();
            System.out.println("=== Setup for Test ===");
            System.out.println(
                    "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            System.out.println("Dataset: " + test.relationName());
            System.out.println();
            System.out.println(eval.toSummaryString("=== Test Results ===", false));
        }
    } catch (Exception ex) {
        Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:cezeri.feature.selection.FeatureSelectionInfluence.java

public static Evaluation getEvaluation(Instances randData, Classifier model, int folds) {
    Evaluation eval = null;/*from   w  w  w . j  a  va2  s .  com*/
    try {
        eval = new Evaluation(randData);
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n);
            Instances test = randData.testCV(folds, n);
            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(model);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
            //                double[] prediction = eval.evaluateModel(clsCopy, test);
            //                double[] original = getAttributeValues(test);
            //                double[][] d = new double[2][prediction.length];
            //                d[0] = prediction;
            //                d[1] = original;
            //                CMatrix f1 = new CMatrix(d);
        }

        // output evaluation
        System.out.println();
        System.out.println("=== Setup ===");
        System.out.println(
                "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
        System.out.println("Dataset: " + randData.relationName());
        System.out.println("Folds: " + folds);
        System.out.println();
        System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
        System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ==="));
        System.out.println(eval.toMatrixString("Confusion Matrix"));

        double acc = eval.correct() / eval.numInstances() * 100;
        System.out.println("correct:" + eval.correct() + "  " + acc + "%");
    } catch (Exception ex) {

        Logger.getLogger(FeatureSelectionInfluence.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:cezeri.utils.FactoryInstance.java

/**
 *
 * @param m tm dataset//from   www .ja v  a2 s. c o  m
 * @param val class value deeri val olanlar filtrele
 * @return
 */
public static Instances[] getSpecificInstancesBasedOnClassValue(Instances m, String[] cl) {
    Instances[] ret = new Instances[cl.length];
    for (int i = 0; i < ret.length; i++) {
        ret[i] = FactoryInstance.generateInstances(m.relationName() + "_class=" + cl[i], m.numAttributes());
        //            ret[i] = m.resampleWithWeights(new Random());
        ret[i].delete();
    }
    for (int i = 0; i < m.numInstances(); i++) {
        Instance ins = m.instance(i);
        for (int j = 0; j < cl.length; j++) {
            if (("" + (int) ins.classValue()).equals(cl[j])) {
                ret[j].add(ins);
            }
        }
    }
    return ret;
}

From source file:cezeri.utils.FactoryInstance.java

public static Instances getSubsetData(Instances data, String[] attList) {
    Instances temp = new Instances(data);
    for (int i = 0; i < data.numAttributes(); i++) {
        if (!temp.attribute(0).equals(temp.classAttribute())) {
            temp.deleteAttributeAt(0);/* w w  w.j ava 2 s  .  c  o m*/
        }
    }
    double[][] m = new double[attList.length + 1][data.numInstances()];
    for (int i = 0; i < attList.length; i++) {
        int n = attList.length - 1 - i;
        String str = attList[n];
        Attribute t = data.attribute(str);
        double[] d = data.attributeToDoubleArray(t.index());
        m[n] = d;
        temp.insertAttributeAt(t, 0);
    }
    m[attList.length] = data.attributeToDoubleArray(data.classIndex());
    m = CMatrix.getInstance(m).transpose().get2DArrayDouble();

    FastVector att = new FastVector();
    for (int i = 0; i < temp.numAttributes(); i++) {
        att.addElement(temp.attribute(i));
    }
    Instances ret = new Instances(temp.relationName(), att, m.length);
    for (int i = 0; i < m.length; i++) {
        Instance ins = new Instance(m[0].length);
        for (int j = 0; j < m[0].length; j++) {
            ins.setValue(j, m[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(temp.classIndex());

    return ret;
}

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

void getRocCurve() {
    try {/*  w w w . j a v a2  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  ww  . j  a v a  2s .  c  om*/
        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// w w  w  .  j  av a 2 s.  c  om
 */
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:com.yahoo.labs.samoa.instances.WekaToSamoaInstanceConverter.java

License:Apache License

/**
* Samoa instances information./* w  w  w. j a v  a2 s  .com*/
*
* @param instances the instances
* @return the instances
*/
public Instances samoaInstancesInformation(weka.core.Instances instances) {
    Instances samoaInstances;
    List<Attribute> attInfo = new ArrayList<Attribute>();
    for (int i = 0; i < instances.numAttributes(); i++) {
        attInfo.add(samoaAttribute(i, instances.attribute(i)));
    }
    samoaInstances = new Instances(instances.relationName(), attInfo, 0);
    samoaInstances.setClassIndex(instances.classIndex());
    return samoaInstances;
}