/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.igss.utility;
import com.rapidminer.operator.learner.igss.hypothesis.Hypothesis;
/**
* The instance-averaging utility function WRAcc.
*
* @author Dirk Dach
*/
public class WRAcc extends InstanceAveraging {
/** Constructs new WRAcc with the given default probability. */
public WRAcc(double[] priors, int large) {
super(priors, large);
}
/** Calculates the utility for the given number of examples,positive examples and hypothesis. */
@Override
public double utility(double totalWeight, double totalPositiveWeight, Hypothesis hypo) {
double g = hypo.getCoveredWeight() / totalWeight;
double p = hypo.getPositiveWeight() / hypo.getCoveredWeight();
if (hypo.getPrediction() == Hypothesis.POSITIVE_CLASS) {
return g * (p - this.priors[Hypothesis.POSITIVE_CLASS]);
} else {
return g * (p - this.priors[Hypothesis.NEGATIVE_CLASS]);
}
}
/** Calculates the empirical variance. */
@Override
public double variance(double totalWeight, double totalPositiveWeight, Hypothesis hypo) {
double p0;
if (hypo.getPrediction() == Hypothesis.POSITIVE_CLASS) {
p0 = this.priors[Hypothesis.POSITIVE_CLASS];
} else {
p0 = this.priors[Hypothesis.NEGATIVE_CLASS];
}
double mean = this.utility(totalWeight, totalPositiveWeight, hypo);
double innerTerm = hypo.getPositiveWeight() * Math.pow(1.0 - p0 - mean, 2)
+ (hypo.getCoveredWeight() - hypo.getPositiveWeight()) * Math.pow(0.0 - p0 - mean, 2)
+ (totalWeight - hypo.getCoveredWeight()) * Math.pow(0.0 - mean, 2);
return Math.sqrt(innerTerm) / totalWeight;
}
/** Returns an upper bound for the utility of refinements for the given hypothesis. */
@Override
public double getUpperBound(double totalWeight, double totalPositiveWeight, Hypothesis hypo, double delta) {
double p0;
if (hypo.getPrediction() == Hypothesis.POSITIVE_CLASS) {
p0 = this.priors[Hypothesis.POSITIVE_CLASS];
} else {
p0 = this.priors[Hypothesis.NEGATIVE_CLASS];
}
Utility cov = new Coverage(this.priors, this.large);
Hypothesis h = hypo.clone();
h.setCoveredWeight(hypo.getPositiveWeight()); // all fp become tn
double g = cov.utility(totalWeight, totalPositiveWeight, h);
double conf = cov.confidenceIntervall(totalWeight, delta);
return ((g + conf) * (1.0 - p0));
}
}