/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.rules;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
/**
* Calculates the accuracy benefit.
*
* @author Sebastian Land, Ingo Mierswa
* @version $Id: AccuracyCriterion.java,v 1.5 2008/05/09 19:23:13 ingomierswa Exp $
*/
public class AccuracyCriterion extends AbstractCriterion {
public double[] getBenefit(ExampleSet coveredSet, ExampleSet uncoveredSet, String labelName) {
double labelSum = 0;
double totalSum = 0;
Attribute weightAttribute = coveredSet.getAttributes().getWeight();
Attribute labelAttribute = coveredSet.getAttributes().getLabel();
double labelValue = labelAttribute.getMapping().getIndex(labelName);
for (Example e : coveredSet) {
double weight = 1.0d;
if (weightAttribute != null)
weight = e.getValue(weightAttribute);
totalSum += weight;
if (e.getValue(labelAttribute) == labelValue)
labelSum += weight;
}
return new double[] { labelSum / totalSum, totalSum };
}
public double[] getOnlineBenefit(Example example, int labelIndex) {
double accuracy = labelWeights[labelIndex] / weight;
double reverseAccuracy = (totalLabelWeights[labelIndex] - labelWeights[labelIndex]) / (totalWeight - weight);
return new double[] {accuracy, weight, reverseAccuracy, totalWeight - weight};
}
}