Example usage for weka.classifiers Evaluation evaluateModel

List of usage examples for weka.classifiers Evaluation evaluateModel

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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:MLP.java

MLP() {

    try {//from   w  ww.j  a  v  a  2 s. c  o m
        FileReader trainreader = new FileReader("C:\\new.arff");
        FileReader testreader = new FileReader("C:\\new.arff");

        Instances train = new Instances(trainreader);
        Instances test = new Instances(testreader);
        train.setClassIndex(train.numAttributes() - 1);
        test.setClassIndex(test.numAttributes() - 1);

        MultilayerPerceptron mlp = new MultilayerPerceptron();
        mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H 4"));

        mlp.buildClassifier(train);

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(mlp, test);
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        trainreader.close();
        testreader.close();

    } catch (Exception ex) {

        ex.printStackTrace();

    }

}

From source file:activeSegmentation.learning.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 v  a  2 s . c  o  m*/
 */
@Override
public double[] testModel(IDataSet instances) {

    try {

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

        double[] predict = evaluator.evaluateModel(classifier, instances.getDataset());

        System.out.println(evaluator.toSummaryString());
        return predict;

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

    return null;
}

From source file:algoritmogeneticocluster.NewClass.java

public static Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(trainingSet);

    model.buildClassifier(trainingSet);//from  www .j a  v  a 2  s.  c  o m
    evaluation.evaluateModel(model, testingSet);

    return evaluation;
}

From source file:ann.ANN.java

public void percentageSplit(Classifier model, double percent, Instances data) {
    try {/*  ww w . ja v a  2  s  .c  o m*/
        int trainSize = (int) Math.round(data.numInstances() * percent / 100);
        int testSize = data.numInstances() - trainSize;
        Instances train = new Instances(data, trainSize);
        Instances test = new Instances(data, testSize);
        ;

        for (int i = 0; i < trainSize; i++) {
            train.add(data.instance(i));
        }
        for (int i = trainSize; i < data.numInstances(); i++) {
            test.add(data.instance(i));
        }

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(model, test);
        System.out.println("================================");
        System.out.println("========Percentage  Split=======");
        System.out.println("================================");
        System.out.println(eval.toSummaryString("\n=== Summary ===\n", false));
        System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString("=== Confusion Matrix ===\n"));
    } catch (Exception ex) {
        System.out.println("File tidak berhasil di-load");
    }
}

From source file:ANN.MultilayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\iris.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);/* w w  w .  ja v  a 2s  .c o  m*/
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    System.out.println();
    //                System.out.println(i + " "+0.8);
    MultilayerPerceptron slp = new MultilayerPerceptron(train, 0.1, 5000, 14);
    slp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(slp, train);
    System.out.println(eval.toSummaryString());
    System.out.print(eval.toMatrixString());
}

From source file:ANN.MultiplePerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\iris.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);//  ww  w .j a  v a 2s.c o m
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    MultiplePerceptron mlp = new MultiplePerceptron(train, 20, 0.3);
    mlp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(mlp, train);
    System.out.println(eval.toSummaryString());
    System.out.print(eval.toMatrixString());
}

From source file:ANN_Single.SinglelayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\diabetes.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);/*from w  w w  .j a  v  a2s  . c  om*/
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    System.out.println();
    //                System.out.println(i + " "+0.8);
    SinglelayerPerceptron slp = new SinglelayerPerceptron(train, 0.1, 5000);
    slp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    //                eval.crossValidateModel(slp, train, 10, new Random(1));
    eval.evaluateModel(slp, train);
    System.out.println(eval.toSummaryString());
    System.out.print(eval.toMatrixString());
}

From source file:ANN_single2.SinglelayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\Team.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);/*www  .  j  a  v a  2s.c o  m*/
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    for (int i = 100; i < 3000; i += 100) {
        for (double j = 0.01; j < 1; j += 0.01) {
            System.out.println(i + " " + j);
            SinglelayerPerceptron slp = new SinglelayerPerceptron(i, j, 0.00);
            slp.buildClassifier(train);
            Evaluation eval = new Evaluation(train);
            //                eval.crossValidateModel(slp, train,10, new Random(1));
            eval.evaluateModel(slp, train);
            System.out.println(eval.toSummaryString());
            System.out.println(eval.toMatrixString());
        }
    }
}

From source file:asap.CrossValidation.java

/**
 *
 * @param dataInput//from  ww  w  .  ja  v  a 2  s.  c o m
 * @param classIndex
 * @param removeIndices
 * @param cls
 * @param seed
 * @param folds
 * @param modelOutputFile
 * @return
 * @throws Exception
 */
public static String performCrossValidation(String dataInput, String classIndex, String removeIndices,
        AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception {

    PerformanceCounters.startTimer("cross-validation ST");

    PerformanceCounters.startTimer("cross-validation init ST");

    // loads data and set class index
    Instances data = DataSource.read(dataInput);
    String clsIndex = classIndex;

    switch (clsIndex) {
    case "first":
        data.setClassIndex(0);
        break;
    case "last":
        data.setClassIndex(data.numAttributes() - 1);
        break;
    default:
        try {
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);
        } catch (NumberFormatException e) {
            data.setClassIndex(data.attribute(clsIndex).index());
        }
        break;
    }

    Remove removeFilter = new Remove();
    removeFilter.setAttributeIndices(removeIndices);
    removeFilter.setInputFormat(data);
    data = Filter.useFilter(data, removeFilter);

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval = new Evaluation(randData);
    Instances trainSets[] = new Instances[folds];
    Instances testSets[] = new Instances[folds];
    Classifier foldCls[] = new Classifier[folds];

    for (int n = 0; n < folds; n++) {
        trainSets[n] = randData.trainCV(folds, n);
        testSets[n] = randData.testCV(folds, n);
        foldCls[n] = AbstractClassifier.makeCopy(cls);
    }

    PerformanceCounters.stopTimer("cross-validation init ST");
    PerformanceCounters.startTimer("cross-validation folds+train ST");
    //paralelize!!:--------------------------------------------------------------
    for (int n = 0; n < folds; n++) {
        Instances train = trainSets[n];
        Instances test = testSets[n];

        // the above code is used by the StratifiedRemoveFolds filter, the
        // code below by the Explorer/Experimenter:
        // Instances train = randData.trainCV(folds, n, rand);
        // build and evaluate classifier
        Classifier clsCopy = foldCls[n];
        clsCopy.buildClassifier(train);
        eval.evaluateModel(clsCopy, test);
    }

    cls.buildClassifier(data);
    //until here!-----------------------------------------------------------------

    PerformanceCounters.stopTimer("cross-validation folds+train ST");
    PerformanceCounters.startTimer("cross-validation post ST");
    // output evaluation
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (!modelOutputFile.isEmpty()) {
        SerializationHelper.write(modelOutputFile, cls);
    }

    PerformanceCounters.stopTimer("cross-validation post ST");
    PerformanceCounters.stopTimer("cross-validation ST");

    return out;
}

From source file:asap.NLPSystem.java

private String _buildClassifier() {
    Evaluation eval;
    try {//  ww  w. j a v  a  2s.  c o m
        eval = new Evaluation(trainingSet);
    } catch (Exception ex) {
        Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
        return "Error creating evaluation instance for given data!";
    }

    try {
        classifier.buildClassifier(trainingSet);
    } catch (Exception ex) {
        Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
    }

    try {
        trainingPredictions = eval.evaluateModel(classifier, trainingSet);
        trainingPearsonsCorrelation = eval.correlationCoefficient();
    } catch (Exception ex) {
        Logger.getLogger(NLPSystem.class.getName()).log(Level.SEVERE, null, ex);
    }

    classifierBuilt = true;
    return "Classifier built (" + trainingPearsonsCorrelation + ").";
}