mlda.attributes.ProportionNumericAttributesWithOutliers.java Source code

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Here is the source code for mlda.attributes.ProportionNumericAttributesWithOutliers.java

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/*
 * This file is part of the MLDA.
 *
 * (c)  Jose Maria Moyano Murillo
 *      Eva Lucrecia Gibaja Galindo
 *      Sebastian Ventura Soto <sventura@uco.es>
 *
 * For the full copyright and license information, please view the LICENSE
 * file that was distributed with this source code.
 */

package mlda.attributes;

import java.util.Arrays;
import java.util.Set;

import mlda.base.MLDataMetric;
import mulan.data.MultiLabelInstances;
import weka.core.Attribute;
import weka.core.Instances;
import weka.core.Utils;

/**
* Class implementing the Proportion of numeric attributes with outliers
*
* @author Jose Maria Moyano Murillo
*/
public class ProportionNumericAttributesWithOutliers extends MLDataMetric {

    /**
     * Constructor
     */
    public ProportionNumericAttributesWithOutliers() {
        super("Proportion of numeric attributes with outliers");
    }

    /**
     * Calculate metric value
     * 
     * @param mlData Multi-label dataset to which calculate the metric
     * @return Value of the metric
     */
    public double calculate(MultiLabelInstances mlData) {
        Instances instances = mlData.getDataSet();
        int nInstances = mlData.getNumInstances();

        double alpha = 0.05;
        int numToTrimAtSide = (int) (nInstances * alpha / 2);
        int nNumeric = 0;
        int nOutliers = 0;
        Set<Attribute> attributeSet = mlData.getFeatureAttributes();

        double variance, varianceTrimmed;
        double[] values;
        double[] trimmed = new double[nInstances - (numToTrimAtSide * 2)];
        double ratio;

        for (Attribute att : attributeSet) {
            if (att.isNumeric()) {
                nNumeric++;
                variance = instances.variance(att);
                values = instances.attributeToDoubleArray(att.index());
                Arrays.sort(values);

                for (int i = 0; i < trimmed.length; i++) {
                    trimmed[i] = values[i + numToTrimAtSide];
                }
                varianceTrimmed = Utils.variance(trimmed);
                ratio = varianceTrimmed / variance;

                if (ratio < 0.7) {
                    nOutliers++;
                }
            }
        }

        if (nNumeric > 0) {
            this.value = ((double) nOutliers) / nNumeric;
        } else {
            this.value = Double.NaN;
        }

        return value;
    }

}