Example usage for weka.attributeSelection ChiSquaredAttributeEval evaluateAttribute

List of usage examples for weka.attributeSelection ChiSquaredAttributeEval evaluateAttribute

Introduction

In this page you can find the example usage for weka.attributeSelection ChiSquaredAttributeEval evaluateAttribute.

Prototype

@Override
public double evaluateAttribute(int attribute) throws Exception 

Source Link

Document

evaluates an individual attribute by measuring its chi-squared value.

Usage

From source file:org.uclab.mm.kcl.ddkat.dataselector.FeatureEvaluator.java

License:Apache License

/**
 * Constructor to instantiate a new FeatureEvaluator object.
 *
 * @param json the data string//from ww  w  .ja  v  a2s . c  o m
 * @param data the data set
 * @throws Exception the exception
 */

public FeatureEvaluator(String json, Instances data) throws Exception {
    //   public FeatureEvaluator(String json, Instances data, String filePath) throws Exception {

    this.featureTitles = new ArrayList<String>();
    this.featureScores = new ArrayList<Double>();
    this.featureWeights = new ArrayList<Double>();
    this.featurePriorities = new ArrayList<Double>();

    OrderedJSONObject jsonObject = new OrderedJSONObject(json.toString());
    JSONArray jsontokenArray = jsonObject.getJSONArray("unprocessed_data");
    String csvString = "";
    String str;
    for (int i = 0; i < jsontokenArray.length(); i++) {
        str = jsontokenArray.get(i).toString();
        str = str.substring(1, str.length() - 1);
        csvString += str + "\n";
    }

    String filePath = BASE_DIR + "FeaturesEvaluationDataSet.csv";
    File file = new File(filePath);
    // if file does not exists, then create it
    if (!file.exists())
        file.createNewFile();

    FileUtils.writeStringToFile(file, csvString);

    CSVLoader loader = new CSVLoader();
    loader.setSource(new File(filePath));
    data = loader.getDataSet();

    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    int numUnlabeledAttributes = data.numAttributes() - 1;
    double[] minmaxValues = new double[2];
    double min, max;

    String[] options = new String[1];
    options[0] = "-T -1.7976931348623157E308 -N -1"; // confidenceFactor = 0.25, minNumObject = 2
    Ranker atrank = new Ranker();
    atrank.setOptions(options);

    weka.attributeSelection.AttributeSelection atsel = new weka.attributeSelection.AttributeSelection();

    //  Information Gain Attribute Evaluator
    InfoGainAttributeEval infoGainAttrEval = new InfoGainAttributeEval();
    atsel.setEvaluator(infoGainAttrEval);
    atsel.setSearch(atrank);
    atsel.SelectAttributes(data);
    double[] infoGainRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        infoGainRanks[i] = Math.round(10000 * infoGainAttrEval.evaluateAttribute(i)) / 10000d;
    }
    minmaxValues = computerMinMaxValues(infoGainRanks);
    min = minmaxValues[0];
    max = minmaxValues[1];
    double[] scaledInfoGainRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        scaledInfoGainRanks[i] = Math.round(10000 * ((infoGainRanks[i] - min) / (max - min))) / 10000d;
    }

    //  Gain Ratio Attribute Evaluator
    GainRatioAttributeEval gainRatioAttrEval = new GainRatioAttributeEval();
    atsel.setEvaluator(gainRatioAttrEval);
    atsel.setSearch(atrank);
    atsel.SelectAttributes(data);
    double[] gainRatioRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        gainRatioRanks[i] = Math.round(10000 * gainRatioAttrEval.evaluateAttribute(i)) / 10000d;
    }
    minmaxValues = computerMinMaxValues(gainRatioRanks);
    min = minmaxValues[0];
    max = minmaxValues[1];
    double[] scaledGainRatioRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        scaledGainRatioRanks[i] = Math.round(10000 * ((gainRatioRanks[i] - min) / (max - min))) / 10000d;
    }

    //  Chi Squared Attribute Evaluator
    ChiSquaredAttributeEval chiSquaredAttrEval = new ChiSquaredAttributeEval();
    atsel.setEvaluator(chiSquaredAttrEval);
    atsel.setSearch(atrank);
    atsel.SelectAttributes(data);
    double[] chiSquaredRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        chiSquaredRanks[i] = Math.round(10000 * chiSquaredAttrEval.evaluateAttribute(i)) / 10000d;
    }
    minmaxValues = computerMinMaxValues(chiSquaredRanks);
    min = minmaxValues[0];
    max = minmaxValues[1];
    double[] scaledChiSquaredRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        scaledChiSquaredRanks[i] = Math.round(10000 * ((chiSquaredRanks[i] - min) / (max - min))) / 10000d;
    }

    //  Symmetrical Uncert Attribute Evaluator
    SymmetricalUncertAttributeEval symmetricalUncertAttrEval = new SymmetricalUncertAttributeEval();
    atsel.setEvaluator(symmetricalUncertAttrEval);
    atsel.setSearch(atrank);
    atsel.SelectAttributes(data);
    double[] symmetricalUncertRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        symmetricalUncertRanks[i] = Math.round(10000 * symmetricalUncertAttrEval.evaluateAttribute(i)) / 10000d;
    }
    minmaxValues = computerMinMaxValues(symmetricalUncertRanks);
    min = minmaxValues[0];
    max = minmaxValues[1];
    double[] scaledSymmetricalUncertRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        scaledSymmetricalUncertRanks[i] = Math.round(10000 * ((symmetricalUncertRanks[i] - min) / (max - min)))
                / 10000d;
    }

    //  Significance Attribute Evaluator
    SignificanceAttributeEval significanceAttrEval = new SignificanceAttributeEval();
    atsel.setEvaluator(significanceAttrEval);
    atsel.setSearch(atrank);
    atsel.SelectAttributes(data);
    double[] significanceRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        significanceRanks[i] = Math.round(10000 * significanceAttrEval.evaluateAttribute(i)) / 10000d;
    }
    minmaxValues = computerMinMaxValues(significanceRanks);
    min = minmaxValues[0];
    max = minmaxValues[1];
    double[] scaledSignificanceRanks = new double[numUnlabeledAttributes];
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        scaledSignificanceRanks[i] = Math.round(10000 * ((significanceRanks[i] - min) / (max - min))) / 10000d;
    }

    double attributeSum;

    double[] combinedRanks = new double[numUnlabeledAttributes];
    double combinedranksSum = 0;

    for (int i = 0; i < numUnlabeledAttributes; i++) {
        attributeSum = scaledInfoGainRanks[i] + scaledGainRatioRanks[i] + scaledChiSquaredRanks[i]
                + scaledSymmetricalUncertRanks[i] + scaledSignificanceRanks[i];
        combinedRanks[i] = Math.round(10000 * attributeSum) / 10000d;
        combinedranksSum = combinedranksSum + combinedRanks[i];
    }

    double[][] tempArray = new double[numUnlabeledAttributes][2];
    String[] attributesTitles = new String[numUnlabeledAttributes];
    double[] attributesScores = new double[numUnlabeledAttributes];
    double[] attributesWeights = new double[numUnlabeledAttributes];
    double[] attributesPriorities = new double[numUnlabeledAttributes];

    for (int j = 0; j < numUnlabeledAttributes; j++) {
        tempArray[j][0] = j;
        tempArray[j][1] = combinedRanks[j];
    }

    double temp;
    for (int i = 0; i < numUnlabeledAttributes; i++) {
        for (int j = 1; j < (numUnlabeledAttributes - i); j++) {
            if (combinedRanks[j - 1] < combinedRanks[j]) {
                //swap the elements!
                temp = combinedRanks[j - 1];
                combinedRanks[j - 1] = combinedRanks[j];
                combinedRanks[j] = temp;
            }
        }
    }

    for (int j = 0; j < numUnlabeledAttributes; j++) {
        for (int k = 0; k < numUnlabeledAttributes; k++) {
            if (combinedRanks[j] == tempArray[k][1]) {
                attributesTitles[j] = data.attribute((int) tempArray[k][0]).toString();
                String res[] = attributesTitles[j].split("\\s+");
                attributesTitles[j] = res[1];

                this.featureTitles.add(attributesTitles[j]);
                break;
            }
        }
        attributesScores[j] = Math.round(10000 * (combinedRanks[j] / 9)) / 100d;
        attributesWeights[j] = Math.round(10000 * (combinedRanks[j] / combinedranksSum)) / 100d;
        attributesPriorities[j] = Math.round(attributesScores[j] * attributesWeights[j]) / 100d;
        this.featureScores.add(attributesScores[j]);
        this.featureWeights.add(attributesWeights[j]);
        this.featurePriorities.add(attributesPriorities[j]);

        System.out.println(attributesTitles[j] + " is " + attributesScores[j] + " % Important");
    }

}