/*
* 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.
*/
/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2011 Regents of the University of Minnesota
*
* 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.knn.item;
import com.google.common.collect.Lists;
import it.unimi.dsi.fastutil.longs.LongArrayList;
import it.unimi.dsi.fastutil.longs.LongSets;
import org.grouplens.lenskit.GlobalItemRecommender;
import org.grouplens.lenskit.GlobalItemScorer;
import org.grouplens.lenskit.RecommenderBuildException;
import org.grouplens.lenskit.core.LenskitConfiguration;
import org.grouplens.lenskit.core.LenskitRecommender;
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.scored.ScoredId;
import org.grouplens.lenskit.scored.ScoredIds;
import org.grouplens.lenskit.transform.normalize.DefaultUserVectorNormalizer;
import org.grouplens.lenskit.transform.normalize.IdentityVectorNormalizer;
import org.grouplens.lenskit.transform.normalize.UserVectorNormalizer;
import org.grouplens.lenskit.transform.normalize.VectorNormalizer;
import org.grouplens.lenskit.vectors.SparseVector;
import org.junit.Before;
import org.junit.Test;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import static org.grouplens.lenskit.util.test.ExtraMatchers.notANumber;
import static org.hamcrest.CoreMatchers.equalTo;
import static org.hamcrest.CoreMatchers.notNullValue;
import static org.hamcrest.Matchers.*;
import static org.junit.Assert.assertThat;
public class ItemItemGlobalRecommenderTest {
private LenskitRecommender session;
private GlobalItemRecommender gRecommender;
@SuppressWarnings("deprecation")
@Before
public void setup() throws RecommenderBuildException {
List<Rating> rs = new ArrayList<Rating>();
rs.add(Ratings.make(1, 1, 1));
rs.add(Ratings.make(1, 5, 1));
rs.add(Ratings.make(2, 1, 1));
rs.add(Ratings.make(2, 7, 1));
rs.add(Ratings.make(3, 7, 1));
rs.add(Ratings.make(4, 1, 1));
rs.add(Ratings.make(4, 5, 1));
rs.add(Ratings.make(4, 7, 1));
rs.add(Ratings.make(4, 10, 1));
EventCollectionDAO dao = new EventCollectionDAO(rs);
LenskitConfiguration config = new LenskitConfiguration();
config.bind(EventDAO.class).to(dao);
config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
// this is the default
config.bind(UserVectorNormalizer.class)
.to(DefaultUserVectorNormalizer.class);
config.bind(VectorNormalizer.class)
.to(IdentityVectorNormalizer.class);
LenskitRecommenderEngine engine = LenskitRecommenderEngine.build(config);
session = engine.createRecommender();
gRecommender = session.getGlobalItemRecommender();
}
/**
* Check that we score items but do not provide scores for items
* the user has previously rated.
*/
@Test
public void testGlobalItemScorerNoRating() {
long[] queryItems = {1, 10};
long[] items = {5, 10};
ItemItemGlobalScorer scorer = session.get(ItemItemGlobalScorer.class);
assertThat(scorer, notNullValue());
SparseVector scores = scorer.globalScore(LongArrayList.wrap(queryItems), LongArrayList.wrap(items));
assertThat(scores, notNullValue());
assertThat(scores.size(), equalTo(2));
assertThat(scores.get(5), not(notANumber()));
// assertThat(scores.get(10), equalTo(0.0));
}
/**
* Tests {@code globalRecommend(long)}.
*/
// FIXME Give the test methods for global item-item meaningful names
@Test
public void testGlobalItemItemRecommender1() {
List<ScoredId> recs = gRecommender.globalRecommend(LongSets.singleton(1));
assertThat(recs.size(), notNullValue());
recs = gRecommender.globalRecommend(LongSets.singleton(2));
assertThat(recs, empty());
recs = gRecommender.globalRecommend(LongSets.singleton(5));
assertThat(recs.size(), notNullValue());
recs = gRecommender.globalRecommend(LongSets.singleton(1));
assertThat(recs.size(), notNullValue());
recs = gRecommender.globalRecommend(LongSets.singleton(10));
assertThat(recs.size(), notNullValue());
}
/**
* Tests {@code globalRecommend(long, int)}.
*/
@Test
public void testGlobalItemItemRecommender2() {
List<ScoredId> recs = gRecommender.globalRecommend(LongSets.singleton(1), 2);
assertThat(recs, hasSize(2));
recs = gRecommender.globalRecommend(LongSets.singleton(2), 1);
assertThat(recs, empty());
recs = gRecommender.globalRecommend(LongSets.singleton(5), 3);
assertThat(recs, hasSize(3));
}
/**
* Tests {@code globalRecommend(long, Set)}.
*/
@Test
public void testGlobalItemItemRecommender3() {
HashSet<Long> candidates = new HashSet<Long>();
List<ScoredId> recs = gRecommender.globalRecommend(LongSets.singleton(1), candidates);
assertThat(recs, hasSize(0));
candidates.add(1L);
candidates.add(5L);
recs = gRecommender.globalRecommend(LongSets.singleton(1), candidates);
assertThat(Lists.transform(recs, ScoredIds.idFunction()),
contains(5L));
}
/**
* Tests {@code globalRecommend(long, int, Set, Set)}.
*/
@Test
public void testGlobalItemItemRecommender4() {
HashSet<Long> candidates = new HashSet<Long>();
HashSet<Long> excludes = new HashSet<Long>();
List<ScoredId> recs = gRecommender.globalRecommend(LongSets.singleton(1), 1, candidates, excludes);
assertThat(recs, hasSize(0));
candidates.add(7L);
candidates.add(5L);
excludes.add(5L);
recs = gRecommender.globalRecommend(LongSets.singleton(1), 2, candidates, excludes);
assertThat(recs, hasSize(1));
recs = gRecommender.globalRecommend(LongSets.singleton(1), -1, candidates, excludes);
assertThat(recs, hasSize(1));
}
/**
* Tests {@code globalRecommend(Set, int)}.
*/
@Test
public void testGlobalItemItemRecommender5() {
HashSet<Long> basket = new HashSet<Long>();
basket.add(1L);
basket.add(7L);
List<ScoredId> recs = gRecommender.globalRecommend(basket, -1);
assertThat(recs, hasSize(2));
recs = gRecommender.globalRecommend(basket, 1);
assertThat(Lists.transform(recs, ScoredIds.idFunction()),
contains(5L));
}
/**
* Tests {@code globalRecommend(Set, Set)}.
*/
@Test
public void testGlobalItemItemRecommender6() {
HashSet<Long> basket = new HashSet<Long>();
basket.add(1L);
HashSet<Long> candidates = new HashSet<Long>();
candidates.add(5L);
candidates.add(10L);
List<ScoredId> recs = gRecommender.globalRecommend(basket, candidates);
assertThat(Lists.transform(recs, ScoredIds.idFunction()),
contains(5L, 10L));
candidates.add(7L);
recs = gRecommender.globalRecommend(basket, candidates);
assertThat(recs, hasSize(3));
}
/**
* Tests {@code globalRecommend(Set, int, Set, Set)}.
*/
@Test
public void testGlobalItemItemRecommender7() {
HashSet<Long> basket = new HashSet<Long>();
basket.add(5L);
basket.add(10L);
HashSet<Long> candidates = new HashSet<Long>();
candidates.add(1L);
candidates.add(7L);
HashSet<Long> excludes = new HashSet<Long>();
List<ScoredId> recs = gRecommender.globalRecommend(basket, 1, candidates, excludes);
assertThat(recs, hasSize(1));
excludes.add(5L);
recs = gRecommender.globalRecommend(basket, 2, candidates, excludes);
assertThat(Lists.transform(recs, ScoredIds.idFunction()),
contains(1L, 7L));
excludes.add(1L);
recs = gRecommender.globalRecommend(basket, 2, candidates, excludes);
assertThat(Lists.transform(recs, ScoredIds.idFunction()),
contains(7L));
}
}