Example usage for weka.attributeSelection Ranker setNumToSelect

Introduction

In this page you can find the example usage for weka.attributeSelection Ranker setNumToSelect.

Prototype

@Override
public void setNumToSelect(int n) 

Source Link

Document

Specify the number of attributes to select from the ranked list.

Usage

From source file:old.CFS.java

/**
 * uses the meta-classifier//from w w  w.  j  av  a2  s .  co  m
 */
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());
}

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From source file:old.CFS.java

/**
 * uses the filter/*from w w  w . j a  va2s. c o  m*/
 */
protected static void useFilter(Instances data) throws Exception {
    System.out.println("\n2. Filter");
    weka.filters.supervised.attribute.AttributeSelection filter = new weka.filters.supervised.attribute.AttributeSelection();
    ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();

    Ranker search = new Ranker();
    search.setThreshold(-1.7976931348623157E308);
    search.setNumToSelect(1000);
    filter.setEvaluator(eval);

    filter.setSearch(search);
    filter.setInputFormat(data);
    Instances newData = Filter.useFilter(data, filter);
    System.out.println(newData);
}

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From source file:old.CFS.java

/**
 * uses the low level approach/* w  w  w .  jav a2  s .  c  om*/
   * @param data
 */
protected static void useLowLevel(Instances data) throws Exception {
    System.out.println("\n3. Low-level");
    AttributeSelection attsel = new AttributeSelection();
    ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();
    Ranker search = new Ranker();
    search.setThreshold(-1.7976931348623157E308);
    search.setNumToSelect(1000);
    attsel.setEvaluator(eval);
    attsel.setSearch(search);
    attsel.setFolds(10);
    attsel.setXval(true);
    attsel.SelectAttributes(data);
    //    System.out.println(data.toSummaryString());
    //    attsel.selectAttributesCVSplit(data);
    //    attsel.SelectAttributes(data);

    System.out.println(attsel.CrossValidateAttributes());
    //    attsel.SelectAttributes(data);
    //    attsel.selectAttributesCVSplit(data);
    Instances newData = attsel.reduceDimensionality(data);

    int[] indices = attsel.selectedAttributes();
    System.out.println(newData);
    System.out.println("selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
}

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