Example usage for weka.classifiers Evaluation Evaluation

Introduction

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

Prototype

public Evaluation(Instances data) throws Exception

Source Link

Usage

From source file:naivebayes.TucilWeka.java

public static Evaluation fullTraining(Instances data) {
    //10-fold cross validation
    Evaluation eval = null;//from   w ww .j  a  v a2s . com
    Instances train = new Instances(data);
    Instances test = new Instances(data);
    try {
        Classifier classify = new NBTubesAI();
        //Membuat klasifier dari data training
        classify.buildClassifier(train);
        eval = new Evaluation(train);
        eval.evaluateModel(classify, test);
    } catch (Exception ex) {
        Logger.getLogger(TucilWeka.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:NaiveBayesPckge.NaiveBayesMain.java

public static void printEvaluationSplit(Instances instance) throws Exception {
    Evaluation eval = new Evaluation(instance);

    System.out.println("Split Test Result :");
    eval.evaluateModel(naive, instance);
    System.out.println(eval.toSummaryString()); // Summary of Training
    //System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());

}

From source file:NaiveBayesPckge.NaiveBayesMain.java

public static void printEvaluation(Instances instance) throws Exception {
    Evaluation eval = new Evaluation(instance);
    Evaluation eval2 = new Evaluation(instance);

    System.out.println("Full training Result :");
    eval.evaluateModel(naive, instance);
    System.out.println(eval.toSummaryString()); // Summary of Training
    //System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());

    System.out.println("10 cross validation Result :");
    Random rand = new Random(1);
    eval2.crossValidateModel(naive, instance, 10, rand);
    System.out.println(eval2.toSummaryString()); // Summary of Training
    //System.out.println(eval2.toClassDetailsString());
    System.out.println(eval2.toMatrixString());

    double errorRates = eval.incorrect() / eval.numInstances() * 100;
    double accuracy = eval.correct() / eval.numInstances() * 100;

    //        System.out.println("Accuracy: " + df.format(accuracy) + " %");
    //        System.out.println("Error rate: " + df.format(errorRates) + " %"); // Printing Training Mean root squared error
}

From source file:net.sf.jclal.classifier.MOAClassifier.java

License:Open Source License

/**
 *{@inheritDoc}// www.j ava  2 s  .  com
 */
@Override
public AbstractEvaluation testModel(IDataset instances) {

    try {

        // test the current classifier with the test set
        Evaluation evaluator = new Evaluation(new Instances(instances.getDataset(), 0));

        evaluator.evaluateModel(classifier, instances.getDataset());

        SingleLabelEvaluation sleval = new SingleLabelEvaluation();

        sleval.setEvaluation(evaluator);

        return sleval;

    } catch (IllegalArgumentException e) {

        Logger.getLogger(MOAClassifier.class.getName()).log(Level.SEVERE, null, e);

    } catch (InvalidDataFormatException e) {

        Logger.getLogger(MOAClassifier.class.getName()).log(Level.SEVERE, null, e);
    } catch (Exception e) {

        Logger.getLogger(MOAClassifier.class.getName()).log(Level.SEVERE, null, e);
    }

    return null;

}

From source file:net.sf.jclal.classifier.WekaClassifier.java

License:Open Source License

/**
 * Evaluates the classifier using the test dataset and stores the evaluation.
 *
 * @param instances The instances to test
 * @return The evaluation//from  w  w  w.  j  a va 2 s . c o m
 */
@Override
public AbstractEvaluation testModel(IDataset instances) {

    try {

        // test the current classifier with the test set
        Evaluation evaluator = new Evaluation(new Instances(instances.getDataset(), 0));

        evaluator.evaluateModel(classifier, instances.getDataset());

        SingleLabelEvaluation sleval = new SingleLabelEvaluation();

        sleval.setEvaluation(evaluator);

        return sleval;

    } catch (Exception e) {
        Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, e);
    }

    return null;
}

From source file:net.sf.jclal.classifier.WekaComitteClassifier.java

License:Open Source License

/**
 * Evaluates the classifier using the test dataset
 *
 * @param instances The test instances./*from   ww w .  j  a v a2 s.  c  o m*/
 * @return The evaluation of the model.
 */
@Override
public AbstractEvaluation testModel(IDataset instances) {

    try {

        // test phase with the actual model
        Evaluation evaluator;

        evaluator = new Evaluation(new Instances(instances.getDataset(), 0));

        Instances testData = instances.getDataset();

        for (Instance in : testData) {

            double temp[] = distributionForInstance(in);

            evaluator.evaluationForSingleInstance(temp, in, true);

        }

        SingleLabelEvaluation sleval = new SingleLabelEvaluation();

        sleval.setEvaluation(evaluator);

        return sleval;

    } catch (Exception e) {
        Logger.getLogger(WekaComitteClassifier.class.getName()).log(Level.SEVERE, null, e);
    }

    return null;
}

From source file:neuralnetwork.NeuralNetwork.java

/**
 * @param args the command line arguments
 * @throws java.lang.Exception/*from  w  w w.j  a  v a 2 s  . co  m*/
 */
public static void main(String[] args) throws Exception {

    ConverterUtils.DataSource source;
    source = new ConverterUtils.DataSource("C:\\Users\\Harvey\\Documents\\iris.csv");
    Instances data = source.getDataSet();

    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    data.randomize(new Debug.Random(1));

    RemovePercentage trainFilter = new RemovePercentage();
    trainFilter.setPercentage(70);
    trainFilter.setInputFormat(data);
    Instances train = Filter.useFilter(data, trainFilter);

    trainFilter.setInvertSelection(true);
    trainFilter.setInputFormat(data);
    Instances test = Filter.useFilter(data, trainFilter);

    Standardize filter = new Standardize();
    filter.setInputFormat(train);

    Instances newTrain = Filter.useFilter(test, filter);
    Instances newTest = Filter.useFilter(train, filter);

    Classifier nNet = new NeuralNet();
    nNet.buildClassifier(newTrain);
    Evaluation eval = new Evaluation(newTest);
    eval.evaluateModel(nNet, newTest);
    System.out.println(eval.toSummaryString("\nResults\n-------------\n", false));
}

From source file:Neural_Network.NuralN.java

public int[] testNet() {

    System.out.println();//from  w  ww  .j a v a  2 s  .  co m
    int[] results = new int[2];
    if (!trained) {
        System.out.println("Neural netowrk is not trained....");
    } else {
        try {
            loadTestData();
            Evaluation tempEvaluator = new Evaluation(testSet);
            tempEvaluator.evaluateModel(nN, testSet);

            results[0] = (int) tempEvaluator.correct();
            results[1] = (int) tempEvaluator.incorrect();
            tested = true;
            // "Test completed;

        } catch (IOException e) {
            //"Test file missing
            System.out.println(e.toString());
        } catch (Exception e) {
            System.err.println(e.toString());
        }
    }
    return results;
}

From source file:newclassifier.NewClassifier.java

public void crossValidation() throws Exception {
    cls.buildClassifier(data);/*  ww  w .j a va  2  s .c  o  m*/
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(cls, data, 10, new Random(1));
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

From source file:newclassifier.NewClassifier.java

public void percentSplit(float percent) throws Exception {
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances train = new Instances(data, 0, trainSize);
    Instances test = new Instances(data, trainSize, testSize);
    cls.buildClassifier(train);/*from w  w  w .  ja v a 2  s .  c  o  m*/

    data = new Instances(test);
    Evaluation eval = new Evaluation(data);
    ;
    eval.evaluateModel(cls, data);
}