/*
* 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.predict.ordrec;
import org.grouplens.lenskit.ItemScorer;
import org.grouplens.lenskit.RecommenderBuildException;
import org.grouplens.lenskit.data.dao.EventCollectionDAO;
import org.grouplens.lenskit.data.dao.EventDAO;
import org.grouplens.lenskit.data.dao.PrefetchingUserEventDAO;
import org.grouplens.lenskit.data.dao.UserEventDAO;
import org.grouplens.lenskit.data.event.Rating;
import org.grouplens.lenskit.data.event.Ratings;
import org.grouplens.lenskit.transform.quantize.Quantizer;
import org.grouplens.lenskit.transform.quantize.ValueArrayQuantizer;
import org.grouplens.lenskit.basic.PrecomputedItemScorer;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.junit.Before;
import org.junit.Test;
import java.util.ArrayList;
import java.util.List;
import static org.hamcrest.CoreMatchers.equalTo;
import static org.junit.Assert.assertThat;
/**
* This class use to test OrdRecRatingPrediction class.
*/
public class OrdRecRatingPredictorTest {
private EventDAO dao;
private UserEventDAO userDAO;
private Quantizer qtz;
@SuppressWarnings("deprecation")
@Before
public void setup() throws RecommenderBuildException {
// build ratings
List<Rating> rs = new ArrayList<Rating>();
rs.add(Ratings.make(42, 1, 2));
rs.add(Ratings.make(42, 2, 1));
rs.add(Ratings.make(42, 3, 3));
rs.add(Ratings.make(42, 4, 3));
rs.add(Ratings.make(42, 5, 1));
rs.add(Ratings.make(42, 6, 2));
rs.add(Ratings.make(42, 7, 2));
rs.add(Ratings.make(42, 8, 3));
rs.add(Ratings.make(42, 9, 1));
dao = new EventCollectionDAO(rs);
userDAO = new PrefetchingUserEventDAO(dao);
qtz = new ValueArrayQuantizer(new double[]{1.0, 2.0, 3.0});
}
/**
* This test is to test the basic performance of OrdRecRatingPredictor,
* The rating value is 1, 2, 3. The score for rating 1 is around 2, for rating 2
* is around 5, for rating 8 is around 3. So for the Ordrec predictor, given a specific
* score value, and test if it can return a matched rating.
*/
@Test
public void testOrdRecPrediction1() {
ItemScorer scorer = PrecomputedItemScorer.newBuilder()
.addScore(42, 1, 5)
.addScore(42, 2, 2)
.addScore(42, 3, 8)
.addScore(42, 4, 8.2)
.addScore(42, 5, 2.1)
.addScore(42, 6, 4.9)
.addScore(42, 7, 5)
.addScore(42, 8, 8)
.addScore(42, 9, 2)
.addScore(42, 10, 1.9)
.addScore(42, 11, 4.8)
.addScore(42, 12, 8.2)
.build();
OrdRecRatingPredictor ordrec = new OrdRecRatingPredictor(scorer, userDAO, qtz);
MutableSparseVector predictItem = MutableSparseVector.create(10, 11, 12);
ordrec.predict(42, predictItem);
assertThat(predictItem.get(10), equalTo(1.0));
assertThat(predictItem.get(11), equalTo(2.0));
assertThat(predictItem.get(12), equalTo(3.0));
}
/**
* This test is to test the basic performance of OrdRecRatingPredictor,
* The rating value is 1, 2, 3. The score for rating 1 is around 1, for rating 2
* is around 2, for rating 3 is around 3. So for the Ordrec predictor, given a specific
* score value, and test if it can return a matched rating.
*/
@Test
public void testOrdRecPrediction2() {
ItemScorer scorer = PrecomputedItemScorer.newBuilder()
.addScore(42, 1, 2)
.addScore(42, 2, 1)
.addScore(42, 3, 3)
.addScore(42, 4, 3)
.addScore(42, 5, 1)
.addScore(42, 6, 2)
.addScore(42, 7, 2)
.addScore(42, 8, 3)
.addScore(42, 9, 1)
.addScore(42, 10, 1.1)
.addScore(42, 11, 1.9)
.addScore(42, 12, 3.1)
.build();
OrdRecRatingPredictor ordrec = new OrdRecRatingPredictor(scorer, userDAO, qtz);
MutableSparseVector predictItem = MutableSparseVector.create(10, 11, 12);
ordrec.predict(42, predictItem);
assertThat(predictItem.get(10), equalTo(1.0));
assertThat(predictItem.get(11), equalTo(2.0));
assertThat(predictItem.get(12), equalTo(3.0));
}
/**
* This test is to test the basic performance of OrdRecRatingPredictor,
* The rating value is 1, 2, 3. The score for rating 1 is around 0.2, for rating 2
* is around 1, for rating 3 is around 1.8. So for the Ordrec predictor, given a specific
* score value, and test if it can return a matched rating.
*/
@Test
public void testOrdRecPrediction3() {
ItemScorer scorer = PrecomputedItemScorer.newBuilder()
.addScore(42, 1, 1)
.addScore(42, 2, 0.2)
.addScore(42, 3, 1.8)
.addScore(42, 4, 1.8)
.addScore(42, 5, 0.2)
.addScore(42, 6, 1)
.addScore(42, 7, 1)
.addScore(42, 8, 1.8)
.addScore(42, 9, 0.2)
.addScore(42, 10, 0.21)
.addScore(42, 11, 1.01)
.addScore(42, 12, 1.75)
.build();
OrdRecRatingPredictor ordrec = new OrdRecRatingPredictor(scorer, userDAO, qtz);
MutableSparseVector predictItem = MutableSparseVector.create(10, 11, 12);
ordrec.predict(42, predictItem);
assertThat(predictItem.get(10), equalTo(1.0));
assertThat(predictItem.get(11), equalTo(2.0));
assertThat(predictItem.get(12), equalTo(3.0));
}
}