Example usage for weka.attributeSelection AttributeEvaluator evaluateAttribute

Introduction

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

Prototype

public abstract double evaluateAttribute(int attribute) throws Exception;

Source Link

Document

evaluates an individual attribute

Usage

From source file:com.rapidminer.operator.features.weighting.GenericWekaAttributeWeighting.java

License:Open Source License

public AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
    AttributeWeights weights = new AttributeWeights();

    ASEvaluation evaluator = getWekaAttributeEvaluator(getOperatorClassName(),
            WekaTools.getWekaParametersFromTypes(this, wekaParameters));

    log("Converting to Weka instances.");
    Instances instances = WekaTools.toWekaInstances(exampleSet, "WeightingInstances",
            WekaInstancesAdaptor.WEIGHTING);
    try {//w ww . j  a v  a2  s .c  o m
        log("Building Weka attribute evaluator.");
        evaluator.buildEvaluator(instances);
        //evaluator.buildEvaluator(instances);
    } catch (UnassignedClassException e) {
        throw new UserError(this, e, 105, new Object[] { getOperatorClassName(), e });
    } catch (ArrayIndexOutOfBoundsException e) {
        throw new UserError(this, e, 105, new Object[] { getOperatorClassName(), e });
    } catch (Exception e) {
        throw new UserError(this, e, 905, new Object[] { getOperatorClassName(), e });
    }

    int index = 0;
    if (evaluator instanceof AttributeEvaluator) {
        AttributeEvaluator singleEvaluator = (AttributeEvaluator) evaluator;
        for (Attribute attribute : exampleSet.getAttributes()) {
            try {
                double result = singleEvaluator.evaluateAttribute(index++);
                weights.setWeight(attribute.getName(), result);
            } catch (Exception e) {
                logWarning("Cannot evaluate attribute '" + attribute.getName() + "', use unknown weight.");
            }
        }
    } else {
        logWarning("Cannot evaluate attributes, use unknown weights.");
    }

    return weights;
}

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From source file:mulan.experiments.ENTCS13FeatureSelection.java

License:Open Source License

/**
 * Given an array with feature indices, this method returns a double matrix
 * similar to the one returned by the method rankedAttributes from the Weka
 * {@link weka.attributeSelection.Ranker} class
 *
 * @param featureIndices an array of feature indices
 * @param attributeEval the attribute evaluator to guide the evaluation
 * @return a matrix of sorted attribute indices and evaluations
 *//*from  ww w .ja v a  2s  . c  om*/
public static double[][] sortedEvaluatedAttributeList(int[] featureIndices, AttributeEvaluator attributeEval) {
    double[][] result = new double[featureIndices.length][2];

    try {
        for (int i = 0; i < featureIndices.length; i++) { //the feature indices after problem transformation range from 0 to featureIndices.length
            result[i][0] = featureIndices[i]; //actual feature index
            result[i][1] = attributeEval.evaluateAttribute(i);
        }
        sortAttributeRanking(result, 1, DESCENDING);
    } catch (Exception ex) {
        Logger.getLogger(ENTCS13FeatureSelection.class.getName()).log(Level.SEVERE, null, ex);
    }

    return result;
}

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