/*
* 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.topn;
import it.unimi.dsi.fastutil.longs.LongArrayList;
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.scored.ScoredId;
import org.grouplens.lenskit.util.statistics.MeanAccumulator;
import org.grouplens.lenskit.vectors.SparseVector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.List;
import static java.lang.Math.log;
/**
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class NDCGTopNMetric extends AbstractMetric<MeanAccumulator, NDCGTopNMetric.Result, NDCGTopNMetric.Result> {
private static final Logger logger = LoggerFactory.getLogger(NDCGTopNMetric.class);
private final int listSize;
private final ItemSelector candidates;
private final ItemSelector exclude;
private final String prefix;
private final String suffix;
/**
* Construct a new nDCG Top-N metric.
* @param pre the prefix label for this evaluation, or {@code null} for no prefix.
* @param sfx the suffix label for this evaluation, or {@code null} for no suffix.
* @param listSize The number of recommendations to fetch.
* @param candidates The candidate selector.
* @param exclude The exclude selector.
*/
public NDCGTopNMetric(String pre, String sfx, int listSize, ItemSelector candidates, ItemSelector exclude) {
super(Result.class, Result.class);
suffix = sfx;
prefix = pre;
this.listSize = listSize;
this.candidates = candidates;
this.exclude = exclude;
}
@Override
public MeanAccumulator createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new MeanAccumulator();
}
@Override
protected String getPrefix() {
return prefix;
}
@Override
protected String getSuffix() {
return suffix;
}
/**
* Compute the DCG of a list of items with respect to a value vector.
*/
static double computeDCG(LongList items, SparseVector values) {
final double lg2 = log(2);
double gain = 0;
int rank = 0;
LongIterator iit = items.iterator();
while (iit.hasNext()) {
final long item = iit.nextLong();
final double v = values.get(item, 0);
rank++;
if (rank < 2) {
gain += v;
} else {
gain += v * lg2 / log(rank);
}
}
return gain;
}
@Override
public Result doMeasureUser(TestUser user, MeanAccumulator context) {
List<ScoredId> recommendations;
recommendations = user.getRecommendations(listSize, candidates, exclude);
if (recommendations == null) {
return null;
}
SparseVector ratings = user.getTestRatings();
LongList ideal = ratings.keysByValue(true);
if (ideal.size() > listSize) {
ideal = ideal.subList(0, listSize);
}
double idealGain = computeDCG(ideal, ratings);
LongList actual = new LongArrayList(recommendations.size());
for (ScoredId id: recommendations) {
actual.add(id.getId());
}
double gain = computeDCG(actual, ratings);
double score = gain / idealGain;
context.add(score);
return new Result(score);
}
@Override
protected Result getTypedResults(MeanAccumulator context) {
return new Result(context.getMean());
}
public static class Result {
@ResultColumn("TopN.nDCG")
public final double nDCG;
public Result(double v) {
nDCG = v;
}
}
/**
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public static class Builder extends TopNMetricBuilder<NDCGTopNMetric>{
@Override
public NDCGTopNMetric build() {
return new NDCGTopNMetric(prefix, suffix, listSize, candidates, exclude);
}
}
}