/*
* 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.LongIterator;
import it.unimi.dsi.fastutil.longs.LongSet;
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 java.util.List;
/**
* An alternate methodology from computing precision/recall scores.
*
* This is computed for each true positive item selected by the testItems selector.
* For each item a set of other items is chosen by the candidate set. A topN
* recommendation is then made over the candidate items (plus the test item). The item is considered a
* hit if the true positive item is in the recommendations. The hit rate over the set
* of testItems is reported.
*
* Under this methodology percussion and recall are roughly equivalent, so only recall
* (hit rate) is returned.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class IndependentRecallTopNMetric extends AbstractMetric<IndependentRecallTopNMetric.Context, IndependentRecallTopNMetric.Result, IndependentRecallTopNMetric.Result> {
private final String prefix;
private final String suffix;
private final int listSize;
private final ItemSelector queryItems;
private final ItemSelector candidates;
private final ItemSelector exclude;
/**
* @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 queryItems the "true positive" items that we compute the hit rate over
* @param candidates items to add to the recommendation, should be a random selection
* @param listSize The size of the recommendation list to evaluate
* @param exclude Items which should not be included in the recommendations.
* Should not include test set.
*/
public IndependentRecallTopNMetric(String pre, String sfx, ItemSelector queryItems, ItemSelector candidates, int listSize, ItemSelector exclude) {
super(Result.class, Result.class);
prefix = pre;
suffix = sfx;
this.queryItems = queryItems;
this.candidates = candidates;
this.listSize = listSize;
this.exclude = exclude;
}
@Override
protected String getPrefix() {
return prefix;
}
@Override
protected String getSuffix() {
return suffix;
}
@Override
public Context createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new Context(ds.getTestData().getItemDAO().getItemIds());
}
@Override
public Result doMeasureUser(TestUser user, Context context) {
double score = 0;
LongSet items = queryItems.select(user);
LongIterator it = items.iterator();
while (it.hasNext()) {
final long l = it.nextLong();
ItemSelector finalCandidates = ItemSelectors.union(ItemSelectors.fixed(l), candidates);
List<ScoredId> recs = user.getRecommendations(listSize, finalCandidates, exclude);
for (ScoredId s : recs) {
if (s.getId() == l) {
score +=1;
}
}
}
int n = items.size();
if (n>0) {
score /= n;
context.mean.add(score);
return new Result(score);
} else {
return null;
}
}
@Override
protected Result getTypedResults(Context context) {
if (context.mean.getCount() > 0) {
return new Result(context.mean.getMean());
} else {
return null;
}
}
public static class Result {
@ResultColumn("IndepRecall")
public final double recall;
public Result(double r) {
recall = r;
}
}
public class Context {
private final LongSet universe;
private final MeanAccumulator mean = new MeanAccumulator();
Context(LongSet universe) {
this.universe = universe;
}
}
/**
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public static class Builder extends TopNMetricBuilder<IndependentRecallTopNMetric> {
private ItemSelector goodItems = ItemSelectors.testItems();
public ItemSelector getGoodItems() {
return goodItems;
}
/**
* Set the set of items that will be considered ‘good’ by the evaluation.
*
* @param goodItems A selector for good items.
*/
public void setGoodItems(ItemSelector goodItems) {
this.goodItems = goodItems;
}
public IndependentRecallTopNMetric build() {
return new IndependentRecallTopNMetric(prefix, suffix, goodItems, candidates, listSize, exclude);
}
}
}