/*
* 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.mf.funksvd;
import it.unimi.dsi.fastutil.longs.LongOpenHashSet;
import org.grouplens.lenskit.*;
import org.grouplens.lenskit.baseline.BaselineScorer;
import org.grouplens.lenskit.baseline.UserMeanItemScorer;
import org.grouplens.lenskit.core.LenskitConfiguration;
import org.grouplens.lenskit.core.LenskitRecommenderEngine;
import org.grouplens.lenskit.data.dao.EventCollectionDAO;
import org.grouplens.lenskit.data.dao.EventDAO;
import org.grouplens.lenskit.data.event.Rating;
import org.grouplens.lenskit.data.event.Ratings;
import org.grouplens.lenskit.data.snapshot.PackedPreferenceSnapshot;
import org.grouplens.lenskit.data.snapshot.PreferenceSnapshot;
import org.grouplens.lenskit.scored.ScoredId;
import org.junit.BeforeClass;
import org.junit.Ignore;
import org.junit.Test;
import java.util.ArrayList;
import java.util.List;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
@Ignore("Unstable based on parameters")
public class FunkSVDRecommenderTest {
private static Recommender svdRecommender;
private static ItemRecommender recommender;
private static EventDAO dao;
@SuppressWarnings("deprecation")
@BeforeClass
public static void setup() throws RecommenderBuildException {
List<Rating> rs = new ArrayList<Rating>();
rs.add(Ratings.make(1, 6, 4));
rs.add(Ratings.make(2, 6, 2));
rs.add(Ratings.make(1, 7, 3));
rs.add(Ratings.make(2, 7, 2));
rs.add(Ratings.make(3, 7, 5));
rs.add(Ratings.make(4, 7, 2));
rs.add(Ratings.make(1, 8, 3));
rs.add(Ratings.make(2, 8, 4));
rs.add(Ratings.make(3, 8, 3));
rs.add(Ratings.make(4, 8, 2));
rs.add(Ratings.make(5, 8, 3));
rs.add(Ratings.make(6, 8, 2));
rs.add(Ratings.make(1, 9, 3));
rs.add(Ratings.make(3, 9, 4));
dao = new EventCollectionDAO(rs);
LenskitConfiguration config = new LenskitConfiguration();
config.bind(PreferenceSnapshot.class).to(PackedPreferenceSnapshot.class);
config.bind(ItemScorer.class).to(FunkSVDItemScorer.class);
config.bind(BaselineScorer.class, ItemScorer.class)
.to(UserMeanItemScorer.class);
config.bind(Integer.class).withQualifier(FeatureCount.class).to(100);
// FIXME: Don't use 100 features.
RecommenderEngine engine = LenskitRecommenderEngine.build(config);
svdRecommender = engine.createRecommender();
recommender = svdRecommender.getItemRecommender();
}
/**
* Tests {@code recommend(long)}.
*/
@Test
public void testRecommend1() {
List<ScoredId> recs = recommender.recommend(1);
assertTrue(recs.isEmpty());
recs = recommender.recommend(2);
assertEquals(1, recs.size());
assertTrue(recs.contains(9));
recs = recommender.recommend(3);
assertEquals(1, recs.size());
assertTrue(recs.contains(6));
recs = recommender.recommend(4);
assertEquals(2, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(9, recs.get(1).getId());
recs = recommender.recommend(5);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
recs = recommender.recommend(6);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
}
/**
* Tests {@code recommend(long, int)}.
*/
@Test
public void testRecommend2() {
List<ScoredId> recs = recommender.recommend(6, 4);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
recs = recommender.recommend(6, 3);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
recs = recommender.recommend(6, 2);
assertEquals(2, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
recs = recommender.recommend(6, 1);
assertEquals(1, recs.size());
assertTrue(recs.contains(6));
recs = recommender.recommend(6, 0);
assertTrue(recs.isEmpty());
recs = recommender.recommend(6, -1);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
}
/**
* Tests {@code recommend(long, Set)}.
*/
@Test
public void testRecommend3() {
List<ScoredId> recs = recommender.recommend(5, null);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
LongOpenHashSet candidates = new LongOpenHashSet();
candidates.add(6);
candidates.add(7);
candidates.add(8);
candidates.add(9);
recs = recommender.recommend(5, candidates);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
candidates.remove(8);
recs = recommender.recommend(5, candidates);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
candidates.remove(7);
recs = recommender.recommend(5, candidates);
assertEquals(2, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(9, recs.get(1).getId());
candidates.remove(6);
recs = recommender.recommend(5, candidates);
assertEquals(1, recs.size());
assertEquals(9, recs.get(0).getId());
candidates.remove(9);
recs = recommender.recommend(5, candidates);
assertTrue(recs.isEmpty());
candidates.add(8);
recs = recommender.recommend(5, candidates);
assertTrue(recs.isEmpty());
}
/**
* Tests {@code recommend(long, int, Set, Set)}.
*/
@Test
public void testRecommend4() {
List<ScoredId> recs = recommender.recommend(6, -1, null, null);
assertEquals(4, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(9, recs.get(2).getId());
assertEquals(8, recs.get(3).getId());
LongOpenHashSet exclude = new LongOpenHashSet();
exclude.add(9);
recs = recommender.recommend(6, -1, null, exclude);
assertEquals(3, recs.size());
assertEquals(6, recs.get(0).getId());
assertEquals(7, recs.get(1).getId());
assertEquals(8, recs.get(2).getId());
exclude.add(6);
recs = recommender.recommend(6, -1, null, exclude);
assertEquals(2, recs.size());
assertEquals(7, recs.get(0).getId());
assertEquals(8, recs.get(1).getId());
exclude.add(8);
recs = recommender.recommend(6, -1, null, exclude);
assertEquals(1, recs.size());
assertTrue(recs.contains(7));
}
}