/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2014 LensKit Contributors. See CONTRIBUTORS.md.
* Work on LensKit has been funded by the National Science Foundation under
* grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 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., 51
* Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
package org.grouplens.lenskit.eval.metrics.predict;
import org.grouplens.lenskit.Recommender;
import org.grouplens.lenskit.eval.Attributed;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.AbstractMetric;
import org.grouplens.lenskit.eval.metrics.ResultColumn;
import org.grouplens.lenskit.eval.traintest.TestUser;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Simple evaluator that records user, rating and prediction counts and computes
* recommender coverage over the queried items.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class CoveragePredictMetric extends AbstractMetric<CoveragePredictMetric.Context, CoveragePredictMetric.AggregateCoverage, CoveragePredictMetric.Coverage> {
private static final Logger logger = LoggerFactory.getLogger(CoveragePredictMetric.class);
public CoveragePredictMetric() {
super(AggregateCoverage.class, Coverage.class);
}
@Override
public Context createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new Context();
}
@Override
public Coverage doMeasureUser(TestUser user, Context context) {
SparseVector ratings = user.getTestRatings();
SparseVector predictions = user.getPredictions();
if (predictions == null) {
return null;
}
int n = 0;
int good = 0;
for (VectorEntry e : ratings) {
n += 1;
if (predictions.containsKey(e.getKey())) {
good += 1;
}
}
context.addUser(n, good);
return new Coverage(n, good);
}
@Override
protected AggregateCoverage getTypedResults(Context context) {
return new AggregateCoverage(context.nusers, context.npreds, context.ngood);
}
public static class Coverage {
@ResultColumn(value="NAttempted", order=1)
public final int nattempted;
@ResultColumn(value="NGood", order=2)
public final int ngood;
private Coverage(int na, int ng) {
nattempted = na;
ngood = ng;
}
@ResultColumn(value="Coverage", order=3)
public Double getCoverage() {
if (nattempted > 0) {
return ((double) ngood) / nattempted;
} else {
return null;
}
}
}
public static class AggregateCoverage extends Coverage {
@ResultColumn(value="NUsers", order=0)
public final int nusers;
private AggregateCoverage(int nu, int na, int ng) {
super(na, ng);
nusers = nu;
}
}
public class Context {
private int npreds = 0;
private int ngood = 0;
private int nusers = 0;
private void addUser(int np, int ng) {
npreds += np;
ngood += ng;
nusers += 1;
}
}
}