Example usage for weka.classifiers.meta CostSensitiveClassifier CostSensitiveClassifier

List of usage examples for weka.classifiers.meta CostSensitiveClassifier CostSensitiveClassifier

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Introduction

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

Prototype

public CostSensitiveClassifier()

Source Link

Document

Default constructor.

Usage

From source file:core.classification.Classifiers.java

License:Open Source License

/**
 * Private constructor for the <code>Classifiers</code> object
 * @param train/*from   w  ww  .  ja  v  a 2 s.co m*/
 */
private Classifiers(boolean train) {
    SCA = new BayesNet();
    SCB = new MultilayerPerceptron();
    SCC1 = new MultilayerPerceptron();
    SCC2 = new MultilayerPerceptron();
    SCC3 = new MultilayerPerceptron();

    RC = new CostSensitiveClassifier();
    YNC = new J48();

    if (train) {
        try {
            this.trainSC();
        } catch (Exception e) {
            System.out.println("The system encountered the following error while training SC:");
            e.printStackTrace();
        }
        try {
            this.trainRC();
        } catch (Exception e) {
            System.out.println("The system encountered the following error while training RC:");
            e.printStackTrace();
        }
        try {
            this.trainYNC();
        } catch (Exception e) {
            System.out.println("The system encountered the following error while training YNC:");
            e.printStackTrace();
        }
    } else {
        try {
            readSC("SCA.model", "SCB.model", "SCC1.model", "SCC2.model", "SCC3.model");
            readRC("RC.model");
            readYNC("YNC.model");
        } catch (Exception e) {
            System.out.println("Error while reading the classifiers: ");
            e.printStackTrace();
        }
    }

    // Strutures Creations
    FastVector labels = new FastVector();
    labels.addElement("0");
    labels.addElement("1");
    labels.addElement("2");
    labels.addElement("3");
    labels.addElement("4");

    FastVector clabels = new FastVector();
    clabels.addElement("1");
    clabels.addElement("2");
    clabels.addElement("3");
    clabels.addElement("4");

    FastVector clabels2 = new FastVector();
    clabels2.addElement("0");
    clabels2.addElement("1");
    clabels2.addElement("2");
    clabels2.addElement("3");
    clabels2.addElement("4");

    FastVector clabels3 = new FastVector();
    clabels3.addElement("Y");
    clabels3.addElement("N");

    // Creating the structure for SC
    FastVector attrs = new FastVector();
    attrs.addElement(new Attribute("RATIO"));
    attrs.addElement(new Attribute("CLASS", clabels));
    dataStructSCA = new Instances("SCA-STRUCT", attrs, 0);
    dataStructSCA.setClassIndex(1);

    FastVector attrsB = new FastVector();
    attrsB.addElement(new Attribute("H2"));
    attrsB.addElement(new Attribute("D2"));
    attrsB.addElement(new Attribute("DX"));
    attrsB.addElement(new Attribute("PCLASS", clabels));
    attrsB.addElement(new Attribute("CLASS", clabels));
    dataStructSCB = new Instances("SCB-STRUCT", attrsB, 0);
    dataStructSCB.setClassIndex(4);

    FastVector attrsC1 = new FastVector();
    FastVector attrsC2 = new FastVector();
    FastVector attrsC3 = new FastVector();
    attrsC1.addElement(new Attribute("LH"));
    attrsC1.addElement(new Attribute("LD"));
    attrsC1.addElement(new Attribute("LDX"));
    attrsC1.addElement(new Attribute("LCLASS", clabels));
    attrsC1.addElement(new Attribute("CLASS", clabels));

    attrsC2.addElement(new Attribute("EH"));
    attrsC2.addElement(new Attribute("ED"));
    attrsC2.addElement(new Attribute("EDX"));
    attrsC2.addElement(new Attribute("ECLASS", clabels));
    attrsC2.addElement(new Attribute("CLASS", clabels));

    attrsC3.addElement(new Attribute("SH"));
    attrsC3.addElement(new Attribute("SD"));
    attrsC3.addElement(new Attribute("SDX"));
    attrsC3.addElement(new Attribute("SCLASS", clabels));
    attrsC3.addElement(new Attribute("CLASS", clabels));

    dataStructSCC1 = new Instances("SCC1-STRUCT", attrsC1, 0);
    dataStructSCC1.setClassIndex(4);

    dataStructSCC2 = new Instances("SCC2-STRUCT", attrsC2, 0);
    dataStructSCC2.setClassIndex(4);

    dataStructSCC3 = new Instances("SCC3-STRUCT", attrsC3, 0);
    dataStructSCC3.setClassIndex(4);

    FastVector attrs2 = new FastVector();
    attrs2.addElement(new Attribute("H2"));
    attrs2.addElement(new Attribute("D2"));
    attrs2.addElement(new Attribute("DX"));
    attrs2.addElement(new Attribute("CLASS", clabels));
    attrs2.addElement(new Attribute("PCLASS", clabels));
    attrs2.addElement(new Attribute("RELID", clabels2));
    dataStructRC = new Instances("RC-STRUCT", attrs2, 0);
    dataStructRC.setClassIndex(5);

    FastVector attrs3 = new FastVector();
    attrs3.addElement(new Attribute("PCLASS", clabels));
    attrs3.addElement(new Attribute("CCLASS", clabels));
    attrs3.addElement(new Attribute("RAREA"));
    attrs3.addElement(new Attribute("H"));
    attrs3.addElement(new Attribute("D"));
    attrs3.addElement(new Attribute("V"));
    attrs3.addElement(new Attribute("YN", clabels3));
    dataStructYC = new Instances("YC-STRUCT", attrs3, 0);
    dataStructYC.setClassIndex(6);
}

From source file:etc.aloe.cscw2013.TrainingImpl.java

License:Open Source License

@Override
public WekaModel train(ExampleSet examples) {
    System.out.println("SMO Options: " + SMO_OPTIONS);
    SMO smo = new SMO();
    try {/* w  w  w  .ja  v a  2s.c o m*/
        smo.setOptions(Utils.splitOptions(SMO_OPTIONS));
    } catch (Exception ex) {
        System.err.println("Unable to configure SMO.");
        System.err.println("\t" + ex.getMessage());
        return null;
    }

    //Build logistic models if desired
    smo.setBuildLogisticModels(isBuildLogisticModel());

    Classifier classifier = smo;

    if (useCostTraining) {
        CostSensitiveClassifier cost = new CostSensitiveClassifier();
        cost.setClassifier(smo);
        CostMatrix matrix = new CostMatrix(2);
        matrix.setElement(0, 0, 0);
        matrix.setElement(0, 1, falsePositiveCost);
        matrix.setElement(1, 0, falseNegativeCost);
        matrix.setElement(1, 1, 0);
        cost.setCostMatrix(matrix);

        classifier = cost;

        System.out.print("Wrapping SMO in CostSensitiveClassifier " + matrix.toMatlab());

        if (useReweighting) {
            cost.setMinimizeExpectedCost(false);
            System.out.println(" using re-weighting.");
        } else {
            cost.setMinimizeExpectedCost(true);
            System.out.println(" using min-cost criterion.");
        }
    }

    try {
        System.out.print("Training SMO on " + examples.size() + " examples... ");
        classifier.buildClassifier(examples.getInstances());
        System.out.println("done.");

        WekaModel model = new WekaModel(classifier);
        return model;
    } catch (Exception ex) {
        System.err.println("Unable to train SMO.");
        System.err.println("\t" + ex.getMessage());
        return null;
    }
}

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);
    }//from  ww  w  . ja  va 2 s  .  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();

}

From source file:org.conqat.engine.commons.machine_learning.BaseWekaClassifier.java

License:Apache License

/**
 * Returns a new classifier based on the given algorithm.
 *//*from w  w  w.ja  va 2 s . c o  m*/
protected weka.classifiers.Classifier getClassifier(EClassificationAlgorithm algorithm) {
    switch (algorithm) {
    case DECISION_TREE_REP:
        return new REPTree();
    case SUPPORT_VECTOR_MACHINE_SMO:
        return new SMO();
    case COST_SENSITIVE_CLASSIFIER:
        return new CostSensitiveClassifier();
    case DECISION_TREE_J48:
        return new J48();
    default:
        throw new AssertionError("Cannot create a classifier without a specified algorithm.");
    }

}