Example usage for weka.classifiers.meta Bagging setNumIterations

Introduction

In this page you can find the example usage for weka.classifiers.meta Bagging setNumIterations.

Prototype

public void setNumIterations(int numIterations) 

Source Link

Document

Sets the number of bagging iterations

Usage

From source file:jjj.asap.sas.models1.job.BuildBasicMetaCostModels.java

License:Open Source License

@Override
protected void run() throws Exception {

    // validate args
    if (!Bucket.isBucket("datasets", inputBucket)) {
        throw new FileNotFoundException(inputBucket);
    }/* w  w w  .ja v  a  2s . c  o  m*/
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

    // create prototype classifiers
    Map<String, Classifier> prototypes = new HashMap<String, Classifier>();

    // Bagged REPTrees

    Bagging baggedTrees = new Bagging();
    baggedTrees.setNumExecutionSlots(1);
    baggedTrees.setNumIterations(100);
    baggedTrees.setClassifier(new REPTree());
    baggedTrees.setCalcOutOfBag(false);

    prototypes.put("Bagged-REPTrees", baggedTrees);

    // Bagged SMO

    Bagging baggedSVM = new Bagging();
    baggedSVM.setNumExecutionSlots(1);
    baggedSVM.setNumIterations(100);
    baggedSVM.setClassifier(new SMO());
    baggedSVM.setCalcOutOfBag(false);

    prototypes.put("Bagged-SMO", baggedSVM);

    // Meta Cost model for Naive Bayes

    Bagging bagging = new Bagging();
    bagging.setNumExecutionSlots(1);
    bagging.setNumIterations(100);
    bagging.setClassifier(new NaiveBayes());

    CostSensitiveClassifier meta = new CostSensitiveClassifier();
    meta.setClassifier(bagging);
    meta.setMinimizeExpectedCost(true);

    prototypes.put("CostSensitive-MinimizeExpectedCost-NaiveBayes", bagging);

    // init multi-threading
    Job.startService();
    final Queue<Future<Object>> queue = new LinkedList<Future<Object>>();

    // get the input from the bucket
    List<String> names = Bucket.getBucketItems("datasets", this.inputBucket);
    for (String dsn : names) {

        // for each prototype classifier
        for (Map.Entry<String, Classifier> prototype : prototypes.entrySet()) {

            // 
            // speical logic for meta cost
            //

            Classifier alg = AbstractClassifier.makeCopy(prototype.getValue());

            if (alg instanceof CostSensitiveClassifier) {

                int essaySet = Contest.getEssaySet(dsn);

                String matrix = Contest.getRubrics(essaySet).size() == 3 ? "cost3.txt" : "cost4.txt";

                ((CostSensitiveClassifier) alg)
                        .setCostMatrix(new CostMatrix(new FileReader("/asap/sas/trunk/" + matrix)));

            }

            // use InfoGain to discard useless attributes

            AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();

            classifier.setEvaluator(new InfoGainAttributeEval());

            Ranker ranker = new Ranker();
            ranker.setThreshold(0.0001);
            classifier.setSearch(ranker);

            classifier.setClassifier(alg);

            queue.add(Job.submit(
                    new ModelBuilder(dsn, "InfoGain-" + prototype.getKey(), classifier, this.outputBucket)));
        }
    }

    // wait on complete
    Progress progress = new Progress(queue.size(), this.getClass().getSimpleName());
    while (!queue.isEmpty()) {
        try {
            queue.remove().get();
        } catch (Exception e) {
            Job.log("ERROR", e.toString());
        }
        progress.tick();
    }
    progress.done();
    Job.stopService();

}

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From source file:meddle.TrainModelByDomainOS.java

License:Open Source License

/**
 * Given the classifierName, return a classifier
 *
 * @param classifierName//from  ww w . ja va  2s  .com
 *            e.g. J48, Bagging etc.
 */
public static Classifier getClassifier(String classifierName) {
    Classifier classifier = null;
    if (classifierName.equals("J48")) {
        J48 j48 = new J48();
        j48.setUnpruned(true);
        classifier = j48;
    } else if (classifierName.equals("AdaBoostM1")) {
        AdaBoostM1 adm = new AdaBoostM1();
        adm.setNumIterations(10);
        J48 j48 = new J48();
        adm.setClassifier(j48);
        classifier = adm;
    } else if (classifierName.equals("Bagging")) {
        Bagging bagging = new Bagging();
        bagging.setNumIterations(10);
        J48 j48 = new J48();
        bagging.setClassifier(j48);
        classifier = bagging;
    } else if (classifierName.equals("Stacking")) {
        Stacking stacking = new Stacking();
        stacking.setMetaClassifier(new Logistic());
        Classifier cc[] = new Classifier[2];
        cc[0] = new J48();
        cc[1] = new IBk();
        stacking.setClassifiers(cc);
        classifier = stacking;
    } else if (classifierName.equals("AdditiveRegression")) {
        AdditiveRegression ar = new AdditiveRegression();
        ar.setClassifier(new J48());
        classifier = ar;
    } else if (classifierName.equals("LogitBoost")) {
        LogitBoost lb = new LogitBoost();
        lb.setClassifier(new J48());
        classifier = lb;
    }
    return classifier;
}

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