Example usage for weka.attributeSelection Ranker setGenerateRanking

Introduction

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

Prototype

@Override
public void setGenerateRanking(boolean doRank) 

Source Link

Document

This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.

Usage

From source file:net.semanticmetadata.lire.classifiers.HashingSearchBasedClassifierMod.java

License:Open Source License

private static HashMap<String, Double> calculateInformationGain(String wekaFileLocation,
        double[] featureInformationGain, int featureSpace[], HashMap<String, Integer> featureSpaceHashMap,
        ArrayList<String> featureOrder, HashMap<String, Double> featureInformationGainHashMap) {

    Instances data = null;/*from  ww  w.  j a v a2s. co m*/
    try {
        data = new Instances(new BufferedReader(new FileReader(wekaFileLocation)));
    } catch (IOException e) {
        e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates.
    }
    AttributeSelection attsel = new AttributeSelection(); // package weka.attributeSelection!
    InfoGainAttributeEval eval = new InfoGainAttributeEval();
    Ranker search = new Ranker();
    search.setThreshold(-1.7976931348623157E308);
    search.setNumToSelect(-1);
    search.setGenerateRanking(true);
    attsel.setEvaluator(eval);
    attsel.setSearch(search);
    try {

        attsel.SelectAttributes(data);
    } catch (Exception e) {
        e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates.
    }
    // obtain the attribute indices that were selected
    int[] indices = new int[0];
    double[][] rankedAttribuesArray = new double[0][0];
    try {
        rankedAttribuesArray = attsel.rankedAttributes();
    } catch (Exception e) {
        e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates.
    }
    try {
        indices = attsel.selectedAttributes();
    } catch (Exception e) {
        e.printStackTrace(); //To change body of catch statement use File | Settings | File Templates.
    }

    for (int i = 0; i < rankedAttribuesArray.length; i++) {

        int currentFeature = Integer.parseInt(data.attribute((int) rankedAttribuesArray[i][0]).name()
                .substring(0, data.attribute((int) rankedAttribuesArray[i][0]).name().indexOf("_")));
        //System.out.println("DDDDDDDDDDDDDD"+currentFeature);
        // System.out.print(data.attribute((int) rankedAttribuesArray[i][0]).name() + "/" + rankedAttribuesArray[i][0] + "/");
        //     System.out.println(rankedAttribuesArray[i][1]);
        // data.attribute((int) rankedAttribuesArray[i][0]).name().substring(0,data.attribute((int) rankedAttribuesArray[i][0]).name().indexOf("_"));
        // featureInformationGain[currentFeature] = featureInformationGain[currentFeature] + rankedAttribuesArray[i][1];
        featureInformationGainHashMap.put(featureOrder.get(currentFeature),
                featureInformationGainHashMap.get(featureOrder.get(currentFeature))
                        + rankedAttribuesArray[i][1]);
    }

    //Caalculate the mean of the information gain (better comparable)
    // for (int i = 0; i < featureInformationGain.length; i++) {
    //     featureInformationGain[i] = (featureInformationGain[i] / featureSpace[i]) * 100;
    // }

    //Calculate the mean of the information gain (better comparable)
    for (int i = 0; i < featureOrder.size(); i++) {
        //  featureInformationGainHashMap.put(featureOrder.get(i), (featureInformationGainHashMap.get(featureOrder.get(i)) / featureSpaceHashMap.get(featureOrder.get(i))) * 100);
        featureInformationGainHashMap.put(featureOrder.get(i),
                (featureInformationGainHashMap.get(featureOrder.get(i))));
    }

    // for(int i=0;i<0;i++){
    //     System.out.println(data.attribute(indices[i]).toString());
    // }
    System.out.println("Scoring finished, starting with classification! Scores: ");
    for (int i = 0; i < featureOrder.size(); i++) {
        System.out.println(featureOrder.get(i) + " " + featureInformationGainHashMap.get(featureOrder.get(i)));
        // featureInformationGainHashMap.put(featureOrder.get(i),(featureInformationGainHashMap.get(featureOrder.get(i))/featureSpaceHashMap.get(featureOrder.get(i)))*100);
    }
    // return featureInformationGain;
    File deleteFile = new File(wekaFileLocation);
    deleteFile.delete();
    return featureInformationGainHashMap;
}

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