Example usage for weka.classifiers Evaluation evaluateModel

Introduction

In this page you can find the example usage for weka.classifiers Evaluation evaluateModel.

Prototype

public static String evaluateModel(Classifier classifier, String[] options) throws Exception 

Source Link

Document

Evaluates a classifier with the options given in an array of strings.

Usage

From source file:tubes1.Main.java

public static Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(trainingSet);
    model.buildClassifier(trainingSet);//from   ww w .j  a va2 s .  co m
    evaluation.evaluateModel(model, testingSet);
    return evaluation;
}

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From source file:tubes2ai.DriverNB.java

public static void run(String data) throws Exception {
    //System.out.println("tes driver");

    ConverterUtils.DataSource source = new ConverterUtils.DataSource(data);
    Instances dataTrain = source.getDataSet();
    //if (dataTrain.classIndex() == -1)
    dataTrain.setClassIndex(0);/*from ww w . j  a  va2s  .c  o m*/
    ArffSaver saver = new ArffSaver();

    //        dataTrain.setClassIndex();
    Discretize discretize = new Discretize();
    discretize.setInputFormat(dataTrain);
    Instances dataTrainDisc = Filter.useFilter(dataTrain, discretize);

    //NaiveBayes NB = new NaiveBayes();
    AIJKNaiveBayes NB = new AIJKNaiveBayes();
    NB.buildClassifier(dataTrainDisc);

    Evaluation eval = new Evaluation(dataTrainDisc);
    eval.evaluateModel(NB, dataTrainDisc);

    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
    /*Instance inst = new DenseInstance(5);
            
    inst.setDataset(dataTrain);
    inst.setValue(0, "sunny");
    inst.setValue(1, "hot");
    inst.setValue(2, "high");
    inst.setValue(3, "FALSE");
    inst.setValue(4, "yes");
    double a = NB.classifyInstance(inst);
    String hasil="";
    if(a==0.0){
    hasil="YES";
    } else{
    hasil="NO";
    }
    //double[] b = NB.distributionForInstance(inst);
    System.out.println("Hasil klasifikasi: "+hasil);
    //System.out.println(b);*/
}

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From source file:tucil.dua.ai.TucilDuaAi.java

public static void fullTrainingSet() throws Exception {
    Classifier j48 = new J48();
    j48.buildClassifier(datas);/* www  . j  a v a 2s.  c  o  m*/

    Evaluation eval = new Evaluation(datas);
    eval.evaluateModel(j48, datas);
    System.out.println("=====Run Information======");
    System.out.println("======Classifier Model======");
    System.out.println(j48.toString());
    System.out.println(eval.toSummaryString("====Stats======\n", false));
    System.out.println(eval.toClassDetailsString("====Detailed Result=====\n"));
    System.out.println(eval.toMatrixString("======Confusion Matrix======\n"));
}

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From source file:tucil2ai.Tucil2AI.java

/**
 *
 * @param cls//w  w  w.ja va2 s .  c o  m
 * @param data
 * @param cross
 * @return
 * @throws Exception
 */
protected static Evaluation evalJ48(Classifier cls, Instances data, boolean cross) throws Exception {
    Evaluation E;
    E = new Evaluation(data);
    if (cross == false) {
        E.evaluateModel(cls, data);
    } else {
        E.crossValidateModel(cls, data, 10, new Random(0x100)); /*crossValidateModel*/
    }
    return E;
}

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From source file:uv.datamining.tp2.WekaModeler.java

public static void generarArbol(File file, float cm) throws Exception {
    ArffLoader loader = new ArffLoader();
    loader.setFile(file);//ww  w  .  j a  v  a 2  s.c  om
    Instances data = loader.getDataSet();
    data.setClassIndex(data.numAttributes() - 1); //columna con el atributo clase
    J48 tree = new J48();
    tree.setConfidenceFactor(cm);
    tree.buildClassifier(data);
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(tree, data);
    System.out.println(eval.toSummaryString());

    weka.core.SerializationHelper.write(
            file.getAbsolutePath().substring(0, file.getAbsolutePath().lastIndexOf(".")) + ".model", tree);

}

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From source file:view.centerPanels.ClassificationPnlCenterPredict.java

private void btnStartActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_btnStartActionPerformed

    //imate vec objekat instances i objekat naiveBayes u ovoj klasi
    //treba da izvucete ono sto je uetu u text fieldove preko liste i da napravite
    //objekat Instance (nema "s"). kad naravite objekat Instance samo ga dodate
    //na instances.add(  )..
    //onda koristite takav Instances i naiveBayes za evaluaciju i u textArea
    //ispisujete kojoj klasi pripada.. Sad ne trba matrica konfuzije potrebno
    //je da ispisete string kojoj klasi pripada
    //pogledajte klasu ClusterinPnlCenterPredict tu je odradjeno 90% onog sto se i ovde trazi
    //        instances.delete();
    for (int i = 0; i < textFields.size(); i++) {
        String text = textFields.get(i).getText().trim();

        //prekace prazna pollja jer za klasterizaciju znaci da se ona ignorisu
        //to za klasifikaciju nije slucaj
        if (!text.equals("")) {

            if (instances.attribute(i).isNominal()) {
                boolean correct = false;
                for (int j = 0; j < instances.attribute(i).numValues(); j++) {
                    if (text.equals(instances.attribute(i).value(j))) {
                        correct = true;/*  w w w  .ja  v  a2 s .  c  om*/
                    }
                }
                if (!correct) {
                    JOptionPane.showMessageDialog(this,
                            "Incorrect format for attribute " + instances.attribute(i).name());
                    break;
                }
            }

            if (instances.attribute(i).isNumeric()) {
                try {
                    double value = Double.parseDouble(text);
                } catch (Exception e) {
                    JOptionPane.showMessageDialog(this,
                            "Incorrect format for attribute " + instances.attribute(i).name());
                    break;
                }
            }

        }
    }

    int numAttributes = instances.numAttributes();

    Instance instance = new Instance(numAttributes);

    for (int i = 0; i < textFields.size(); i++) {
        String text = textFields.get(i).getText().trim();

        try {
            double value = Double.parseDouble(text);
            instance.setValue(i, value);

        } catch (Exception e) {
            instance.setValue(i, text);
        }
    }

    String value = instances.attribute(numAttributes - 1).value(0);
    Attribute Att = instances.attribute(numAttributes - 1);

    instance.setValue(Att, value);
    //        instances.delete();
    //        instance.setValue((numAttributes - 1), instances.attribute(numAttributes - 1).value(0) );

    instances.add(instance);

    double[][] matrix = null;

    System.out.println(instances);
    System.out.println(naiveBayes);

    try {

        //                instances.setClassIndex(instances.numAttributes() - 1);
        Evaluation eval = new Evaluation(instances);

        eval.evaluateModel(naiveBayes, instances);

        textArea.setText(eval.toMatrixString());
        matrix = eval.confusionMatrix();

    } catch (Exception ex) {
        System.out.println("Greska: " + ex);
    }

    double[] array = new double[matrix.length];

    for (int i = 0; i < array.length; i++) {

        double sum = 0;
        for (int j = 0; j < array.length; j++) {

            sum = sum + matrix[j][i];

        }
        array[i] = sum;

    }

    String className = null;
    for (int i = 0; i < array.length; i++) {
        if (array[i] == 1) {
            className = instances.attribute(numAttributes - 1).value(i);
        }
    }

    textArea.setText("This instance belongs to class: \n\n== " + className + " ==");

    instances.delete();
}

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From source file:view.centerPanels.ClassificationPnlCenterTrainNew.java

public ClassificationPnlCenterTrainNew() {
    initComponents();// w  w  w .  j av  a 2s.c  o m

    Instances data = Data.getInstance().getInstances();

    try {
        nb = new NaiveBayes();

        data.setClassIndex(data.numAttributes() - 1);

        Evaluation eval = new Evaluation(data);

        nb.buildClassifier(data);

        eval.evaluateModel(nb, data);

        jTextArea1.setText(eval.toMatrixString());

        //                System.out.println(eval.toMatrixString());
    } catch (Exception ex) {
        JOptionPane.showMessageDialog(this, ex);
    }

    setSize(MainGUI.getInstance().getPnlCenter().getWidth(), MainGUI.getInstance().getPnlCenter().getHeight());
    setVisible(true);

}

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From source file:wekimini.learning.ModelEvaluator.java

public void evaluateAll(final List<Path> paths, final boolean isTraining, final int numFolds,
        PropertyChangeListener listener) {
    final List<Instances> data = new LinkedList<>();
    for (Path p : paths) {
        Instances i = w.getSupervisedLearningManager().getTrainingDataForPath(p, false);
        data.add(i);/*from  w ww.  ja v a 2 s .co  m*/
    }

    setResults(new String[paths.size()]);
    if (evalWorker != null && evalWorker.getState() != SwingWorker.StateValue.DONE) {
        return;
    }

    evalWorker = new SwingWorker<Integer, Void>() {

        //trainingWorker.
        @Override
        public Integer doInBackground() {
            // train(); //TODO: Add status updates
            int progress = 0;
            //setProgress(progress);
            int numToEvaluate = 0;
            for (Path p : paths) {
                if (p.canBuild()) {
                    numToEvaluate++;
                }
            }

            int numEvaluated = 0;
            int numErr = 0;
            setEvalStatus(new EvaluationStatus(numToEvaluate, numEvaluated, numErr, false));

            for (int i = 0; i < paths.size(); i++) {
                Path p = paths.get(i);
                if (p.canBuild()) {
                    try {
                        System.out.println("Evaluating with " + numFolds);
                        //EVALUATE HERE: TODO 
                        Instances instances = w.getSupervisedLearningManager().getTrainingDataForPath(p, false);
                        Evaluation eval = new Evaluation(instances);
                        Classifier c = ((LearningModelBuilder) p.getModelBuilder()).getClassifier();
                        if (!isTraining) {
                            Random r = new Random();
                            eval.crossValidateModel(c, instances, numFolds, r);
                        } else {
                            Classifier c2 = Classifier.makeCopy(c);
                            c2.buildClassifier(instances);
                            eval.evaluateModel(c2, instances);
                        }
                        String result;
                        if (p.getModelBuilder() instanceof ClassificationModelBuilder) {
                            result = dFormat.format(eval.pctCorrect()) + "%"; //WON"T WORK FOR NN
                        } else {
                            result = dFormat.format(eval.errorRate()) + " (RMS)";
                        }
                        if (!isTraining) {
                            KadenzeLogging.getLogger().crossValidationComputed(w, i, numFolds, result);
                        } else {
                            KadenzeLogging.getLogger().trainingAccuracyComputed(w, i, result);
                        }
                        setResults(i, result);
                        finishedModel(i, result);
                        numEvaluated++;

                        if (isCancelled()) {
                            cancelMe(p);
                            setResults(i, "Cancelled");
                            return 0;
                        }

                    } catch (InterruptedException ex) {
                        cancelMe(p);
                        setResults(i, "Cancelled");
                        return 0; //Not sure this will be called...
                    } catch (Exception ex) {
                        numErr++;
                        Util.showPrettyErrorPane(null, "Error encountered during evaluation "
                                + p.getCurrentModelName() + ": " + ex.getMessage());
                        logger.log(Level.SEVERE, ex.getMessage());
                    }
                    setEvalStatus(new EvaluationStatus(numToEvaluate, numEvaluated, numErr, false));
                } else {
                    logger.log(Level.WARNING, "Could not evaluate path");
                }

            }
            wasCancelled = false;
            hadError = evaluationStatus.numErrorsEncountered > 0;
            return 0;
        }

        @Override
        public void done() {
            if (isCancelled()) {
                EvaluationStatus t = new EvaluationStatus(evaluationStatus.numToEvaluate,
                        evaluationStatus.numEvaluated, evaluationStatus.numErrorsEncountered, true);
                setEvalStatus(t);
            }
            finished();
        }
    };
    evalWorker.addPropertyChangeListener(listener);
    evalWorker.execute();
}

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From source file:wlsvm.WLSVM.java

License:Open Source License

/**
 * //  ww  w  . j ava 2  s.c  o m
 * @param argv
 * @throws Exception
 */

public static void main(String[] argv) throws Exception {
    if (argv.length < 1) {
        System.out.println("Usage: Test <arff file>");
        System.exit(1);
    }
    String dataFile = argv[0];

    WLSVM lib = new WLSVM();

    String[] ops = { new String("-t"), dataFile, new String("-x"), new String("5"), new String("-i"),
            //WLSVM options
            new String("-S"), new String("0"), new String("-K"), new String("2"), new String("-G"),
            new String("1"), new String("-C"), new String("7"),
            //new String("-B"),    
            //new String("1"),
            new String("-M"), new String("100"),
            //new String("-W"), 
            //new String("1.0 1.0")
    };

    System.out.println(Evaluation.evaluateModel(lib, ops));

}

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