/*
* 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.apache.commons.lang3.tuple.Pair;
import org.grouplens.lenskit.Recommender;
import org.grouplens.lenskit.data.pref.PreferenceDomain;
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.transform.quantize.PreferenceDomainQuantizer;
import org.grouplens.lenskit.transform.quantize.Quantizer;
import org.grouplens.lenskit.util.statistics.MutualInformationAccumulator;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;
import org.grouplens.lenskit.vectors.Vectors;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Evaluate a recommender's prediction accuracy by computing the mutual
* information between the ratings and the prediction. This tells us the amount
* of information our predictions can tell the user about our ratings.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class EntropyPredictMetric extends AbstractMetric<EntropyPredictMetric.Context, EntropyPredictMetric.EntropyResult, EntropyPredictMetric.EntropyResult> {
private static final Logger logger = LoggerFactory.getLogger(EntropyPredictMetric.class);
public EntropyPredictMetric() {
super(EntropyResult.class, EntropyResult.class);
}
@Override
public Context createContext(Attributed algorithm, TTDataSet dataSet, Recommender rec) {
return new Context(dataSet.getTrainingData().getPreferenceDomain());
}
@Override
public EntropyResult doMeasureUser(TestUser user, Context context) {
SparseVector ratings = user.getTestRatings();
SparseVector predictions = user.getPredictions();
if (predictions == null) {
return null;
}
Quantizer q = context.quantizer;
// TODO Re-use accumulators
MutualInformationAccumulator accum = new MutualInformationAccumulator(q.getCount());
for (Pair<VectorEntry,VectorEntry> e: Vectors.fastIntersect(ratings, predictions)) {
accum.count(q.index(e.getLeft().getValue()),
q.index(e.getRight().getValue()));
}
if (accum.getCount() > 0) {
double ratingEntropy = accum.getV1Entropy();
double predEntropy = accum.getV2Entropy();
double info = accum.getMutualInformation();
context.addUser(info, ratingEntropy, predEntropy);
return new EntropyResult(info, ratingEntropy, predEntropy);
} else {
return null;
}
}
@Override
protected EntropyResult getTypedResults(Context context) {
if (context.nusers <= 0) {
return null;
} else {
return new EntropyResult(context.informationSum / context.nusers,
context.ratingEntropySum / context.nusers,
context.predictionEntropySum / context.nusers);
}
}
public static class EntropyResult {
@ResultColumn("MutualInformation")
public final double mutualInformation;
@ResultColumn("RatingEntropy")
public final double ratingEntropy;
@ResultColumn("PredictionEntropy")
public final double predictionEntropy;
public EntropyResult(double mi, double re, double pe){
mutualInformation = mi;
ratingEntropy = re;
predictionEntropy = pe;
}
}
public class Context {
private Quantizer quantizer;
private double informationSum = 0.0;
private double ratingEntropySum = 0.0;
private double predictionEntropySum = 0.0;
private int nusers = 0;
private Context(PreferenceDomain preferenceDomain) {
quantizer = new PreferenceDomainQuantizer(preferenceDomain);
}
private void addUser(double info, double rent, double pent) {
informationSum += info;
ratingEntropySum += rent;
predictionEntropySum += pent;
nusers += 1;
}
}
}