Example usage for weka.classifiers.meta AdditiveRegression AdditiveRegression

Introduction

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

Prototype

public AdditiveRegression() 

Source Link

Document

Default constructor specifying DecisionStump as the classifier

Usage

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

License:Open Source License

@Override
protected void run() throws Exception {

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

    // create prototype classifiers
    List<Classifier> models = new ArrayList<Classifier>();

    LinearRegression m5 = new LinearRegression();
    m5.setAttributeSelectionMethod(M5);

    LinearRegression lr = new LinearRegression();
    lr.setAttributeSelectionMethod(NONE);

    RandomSubSpace rss = new RandomSubSpace();
    rss.setClassifier(lr);
    rss.setNumIterations(30);

    AdditiveRegression boostedStumps = new AdditiveRegression();
    boostedStumps.setClassifier(new DecisionStump());
    boostedStumps.setNumIterations(1000);

    AdditiveRegression boostedTrees = new AdditiveRegression();
    boostedTrees.setClassifier(new REPTree());
    boostedTrees.setNumIterations(100);

    models.add(m5);
    models.add(boostedStumps);
    models.add(boostedTrees);
    models.add(rss);

    // 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 (Classifier model : models) {

            String tag = null;
            if (model instanceof SingleClassifierEnhancer) {
                tag = model.getClass().getSimpleName() + "-"
                        + ((SingleClassifierEnhancer) model).getClassifier().getClass().getSimpleName();
            } else {
                tag = model.getClass().getSimpleName();
            }

            queue.add(Job.submit(new RegressionModelBuilder(dsn, tag, AbstractClassifier.makeCopy(model),
                    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();

}

---

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

License:Open Source License

@Override
protected void run() throws Exception {

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

    // create prototype classifiers
    List<Classifier> models = new ArrayList<Classifier>();

    //SGD sgd = new SGD();
    //sgd.setDontNormalize(true);
    //sgd.setLossFunction(new SelectedTag(SGD.SQUAREDLOSS,SGD.TAGS_SELECTION));

    LinearRegression m5 = new LinearRegression();
    m5.setAttributeSelectionMethod(M5);

    //models.add(sgd);
    models.add(m5);

    LinearRegression lr = new LinearRegression();
    lr.setAttributeSelectionMethod(NONE);

    RandomSubSpace rss = new RandomSubSpace();
    rss.setClassifier(lr);
    rss.setNumIterations(30);

    models.add(rss);

    AdditiveRegression boostedStumps = new AdditiveRegression();
    boostedStumps.setClassifier(new DecisionStump());
    boostedStumps.setNumIterations(1000);

    AdditiveRegression boostedTrees = new AdditiveRegression();
    boostedTrees.setClassifier(new REPTree());
    boostedTrees.setNumIterations(100);

    models.add(boostedStumps);
    models.add(boostedTrees);

    models.add(new PLSClassifier());

    // 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 (Classifier model : models) {

            String tag = null;
            if (model instanceof SingleClassifierEnhancer) {
                tag = model.getClass().getSimpleName() + "-"
                        + ((SingleClassifierEnhancer) model).getClassifier().getClass().getSimpleName();
            } else {
                tag = model.getClass().getSimpleName();
            }

            queue.add(Job.submit(new RegressionModelBuilder(dsn, tag, AbstractClassifier.makeCopy(model),
                    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();

}

---

From source file:meddle.TrainModelByDomainOS.java

License:Open Source License

/**
 * Given the classifierName, return a classifier
 *
 * @param classifierName//from  w ww . jav  a2s.c om
 *            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;
}

---