Example usage for weka.classifiers.meta AttributeSelectedClassifier setSearch

List of usage examples for weka.classifiers.meta AttributeSelectedClassifier setSearch

Introduction

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

Prototype

public void setSearch(ASSearch search) 

Source Link

Document

Sets the search method

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);
    }//from  w w  w .  java2s.  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:jjj.asap.sas.models1.job.BuildBasicModels.java

License:Open Source License

@Override
protected void run() throws Exception {

    // validate args
    if (!Bucket.isBucket("datasets", inputBucket)) {
        throw new FileNotFoundException(inputBucket);
    }/*from w w  w. ja  va2s.  co m*/
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

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

    // bayes

    BayesNet net = new BayesNet();
    net.setEstimator(new BMAEstimator());
    prototypes.put("BayesNet", net);

    prototypes.put("NaiveBayes", new NaiveBayes());

    // functions

    prototypes.put("RBFNetwork", new RBFNetwork());
    prototypes.put("SMO", new SMO());

    // 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()) {

            // 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(AbstractClassifier.makeCopy(prototype.getValue()));

            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:jjj.asap.sas.models1.job.BuildBasicModels2.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  .j a va2 s  . c o m*/
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

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

    // models

    prototypes.put("NBTree", new NBTree());
    prototypes.put("Logistic", new Logistic());

    // 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()) {

            // 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(AbstractClassifier.makeCopy(prototype.getValue()));

            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:jjj.asap.sas.models1.job.BuildRBFKernelModels.java

License:Open Source License

@Override
protected void run() throws Exception {

    // validate args
    if (!Bucket.isBucket("datasets", inputBucket)) {
        throw new FileNotFoundException(inputBucket);
    }/*from  w ww.j ava  2  s .c  om*/
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

    // 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) {

        SMO smo = new SMO();
        smo.setFilterType(new SelectedTag(SMO.FILTER_NONE, SMO.TAGS_FILTER));
        smo.setBuildLogisticModels(true);
        RBFKernel kernel = new RBFKernel();
        kernel.setGamma(0.05);
        smo.setKernel(kernel);

        AttributeSelectedClassifier asc = new AttributeSelectedClassifier();
        asc.setEvaluator(new InfoGainAttributeEval());
        Ranker ranker = new Ranker();
        ranker.setThreshold(0.01);
        asc.setSearch(ranker);
        asc.setClassifier(smo);

        queue.add(Job.submit(new ModelBuilder(dsn, "InfoGain-SMO-RBFKernel", asc, 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:mlpoc.MLPOC.java

/**
 * uses the meta-classifier//from  w  w  w  .jav a 2  s  .c o m
 */
protected static void useClassifier(Instances data) throws Exception {
    System.out.println("\n1. Meta-classfier");
    AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    J48 base = new J48();
    classifier.setClassifier(base);
    classifier.setEvaluator(eval);
    classifier.setSearch(search);
    Evaluation evaluation = new Evaluation(data);
    evaluation.crossValidateModel(classifier, data, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
}

From source file:old.CFS.java

/**
 * uses the meta-classifier/* w  w  w . j a v  a2  s .  c  om*/
 */
protected static void useClassifier(Instances data) throws Exception {
    System.out.println("\n1. Meta-classfier");
    AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
    ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();
    Ranker search = new Ranker();
    search.setThreshold(-1.7976931348623157E308);
    search.setNumToSelect(1000);
    J48 base = new J48();
    classifier.setClassifier(base);
    classifier.setEvaluator(eval);
    classifier.setSearch(search);
    Evaluation evaluation = new Evaluation(data);
    evaluation.crossValidateModel(classifier, data, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
}