/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* MacroAUC.java
* Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
*/
package mulan.evaluation.measure;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.Instances;
import weka.core.Utils;
/**
* Implementation of the macro-averaged AUC measure.
*
* @author Grigorios Tsoumakas
* @version 2010.12.10
*/
public class MacroAUC extends LabelBasedAUC {
/**
* Creates a new instance of this class
*
* @param numOfLabels the number of labels
*/
public MacroAUC(int numOfLabels) {
super(numOfLabels);
}
public String getName() {
return "Macro-averaged AUC";
}
public double getValue() {
double[] labelAUC = new double[numOfLabels];
for (int i = 0; i < numOfLabels; i++) {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_Predictions[i], 1);
labelAUC[i] = ThresholdCurve.getROCArea(result);
}
return Utils.mean(labelAUC);
}
}