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:Example.java

License:Open Source License

public static void main(String[] argv) throws Exception {
    if (argv.length < 1) {
        System.out.println("Usage: Example <arff file>");
        System.exit(1);//w w  w .j av a2s.  com
    }

    String dataFile = argv[0]; // input arff file

    WLSVM lib = new WLSVM();

    String[] ops = { new String("-t"), dataFile, new String("-x"), // 5 folds CV
            new String("5"), new String("-i"), //
            //---------------
            new String("-S"), // WLSVM options
            new String("0"), // Classification problem
            new String("-K"), // RBF kernel
            new String("2"), new String("-G"), // gamma
            new String("1"), new String("-C"), // C
            new String("7"), new String("-Z"), // normalize input data
            new String("1"), new String("-M"), // cache size in MB
            new String("100") };

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

From source file:CrossValidationMultipleRuns.java

License:Open Source License

/**
 * Performs the cross-validation. See Javadoc of class for information
 * on command-line parameters.//from   ww w  .jav  a  2  s  . c  o m
 *
 * @param args   the command-line parameters
 * @throws Exception   if something goes wrong
 */
public static void main(String[] args) throws Exception {
    // loads data and set class index
    Instances data = DataSource.read(Utils.getOption("t", args));
    String clsIndex = Utils.getOption("c", args);
    if (clsIndex.length() == 0)
        clsIndex = "last";
    if (clsIndex.equals("first"))
        data.setClassIndex(0);
    else if (clsIndex.equals("last"))
        data.setClassIndex(data.numAttributes() - 1);
    else
        data.setClassIndex(Integer.parseInt(clsIndex) - 1);

    // classifier
    String[] tmpOptions;
    String classname;
    tmpOptions = Utils.splitOptions(Utils.getOption("W", args));
    classname = tmpOptions[0];
    tmpOptions[0] = "";
    Classifier cls = (Classifier) Utils.forName(Classifier.class, classname, tmpOptions);

    // other options
    int runs = Integer.parseInt(Utils.getOption("r", args));
    int folds = Integer.parseInt(Utils.getOption("x", args));

    // perform cross-validation
    for (int i = 0; i < runs; i++) {
        // randomize data
        int seed = i + 1;
        Random rand = new Random(seed);
        Instances randData = new Instances(data);
        randData.randomize(rand);
        //if (randData.classAttribute().isNominal())
        //   randData.stratify(folds);

        Evaluation eval = new Evaluation(randData);

        StringBuilder optionsString = new StringBuilder();
        for (String s : cls.getOptions()) {
            optionsString.append(s);
            optionsString.append(" ");
        }

        // output evaluation
        System.out.println();
        System.out.println("=== Setup run " + (i + 1) + " ===");
        System.out.println("Classifier: " + optionsString.toString());
        System.out.println("Dataset: " + data.relationName());
        System.out.println("Folds: " + folds);
        System.out.println("Seed: " + seed);
        System.out.println();

        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(cls);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
            System.out.println(eval.toClassDetailsString());
        }

        System.out.println(
                eval.toSummaryString("=== " + folds + "-fold Cross-validation run " + (i + 1) + " ===", false));
    }
}

From source file:homemadeWEKA.java

public static void reevaluateModel(Instances data_train, Instances data_test, Classifier cls) throws Exception {
    Evaluation eval = new Evaluation(data_train);
    eval.evaluateModel(cls, data_test);
    System.out.println(eval.toSummaryString("\nResults\n\n", false));
}

From source file:ClassificationClass.java

public Evaluation cls_svm(Instances data) {
    Evaluation eval = null;
    try {/*from w  ww. jav  a  2  s. c  o  m*/
        Classifier classifier;
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new SMO();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_knn(Instances data) {
    Evaluation eval = null;
    try {//from w  ww.ja  va  2 s. c om
        Classifier classifier;
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new IBk();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.weightedFMeasure());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_naivebayes(Instances data) {
    Evaluation eval = null;
    try {/*  w w  w  .  j  av  a 2s.c om*/
        Classifier classifier;
        PreparingSteps preparingSteps = new PreparingSteps();
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new NaiveBayes();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.toSummaryString());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_c4_5(Instances data) {
    Evaluation eval = null;
    try {//w w w .  j  a  v  a  2 s . c  o m
        Classifier classifier;
        PreparingSteps preparingSteps = new PreparingSteps();
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new J48();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.toSummaryString());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:Pair.java

License:Open Source License

private double getTestError() throws Exception {

    Evaluation evaluation;
    evaluation = new Evaluation(testData);
    evaluation.evaluateModel(this, testData);
    return evaluation.errorRate();

}

From source file:Pair.java

License:Open Source License

/**
 * Main method for testing this class.//from  ww w .  j  a  va  2 s.  c  om
 *
 * @param argv the options
 */
public static void main(String[] argv) {

    try {
        System.out.println(Evaluation.evaluateModel(new TwoStageTrAdaBoostR2(), argv));
    } catch (Exception e) {
        System.out.println(e.getMessage());
    }
}

From source file:CopiaSeg3.java

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

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

    // Imprime el resultado de Weka explorer:
    String strSummary = validation.toSummaryString();
    System.out.println(strSummary);

    return validation;
}