/*
* 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 it.unimi.dsi.fastutil.longs.LongIterator;
import it.unimi.dsi.fastutil.longs.LongList;
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.util.statistics.MeanAccumulator;
import org.grouplens.lenskit.vectors.SparseVector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class HLUtilityPredictMetric extends AbstractMetric<MeanAccumulator, HLUtilityPredictMetric.Result, HLUtilityPredictMetric.Result> {
private static final Logger logger = LoggerFactory.getLogger(HLUtilityPredictMetric.class);
private double alpha;
public HLUtilityPredictMetric(double newAlpha) {
super(Result.class, Result.class);
alpha = newAlpha;
}
public HLUtilityPredictMetric() {
this(5);
}
@Override
public MeanAccumulator createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new MeanAccumulator();
}
double computeHLU(LongList items, SparseVector values) {
double utility = 0;
int rank = 0;
LongIterator itemIterator = items.iterator();
while (itemIterator.hasNext()) {
final double v = values.get(itemIterator.nextLong());
rank++;
utility += v / Math.pow(2, (rank - 1) / (alpha - 1));
}
return utility;
}
public static class Result {
@ResultColumn("HLUtility")
public final double utility;
public Result(double util) {
utility = util;
}
}
@Override
public Result doMeasureUser(TestUser user, MeanAccumulator context) {
SparseVector predictions = user.getPredictions();
if (predictions == null) {
return null;
}
SparseVector ratings = user.getTestRatings();
LongList ideal = ratings.keysByValue(true);
LongList actual = predictions.keysByValue(true);
double idealUtility = computeHLU(ideal, ratings);
double actualUtility = computeHLU(actual, ratings);
double u = actualUtility / idealUtility;
context.add(u);
return new Result(u);
}
@Override
protected Result getTypedResults(MeanAccumulator context) {
return new Result(context.getMean());
}
}