/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.cf.taste.impl.recommender;
import java.util.List;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.junit.Test;
/** <p>Tests {@link TreeClusteringRecommender2}.</p> */
@Deprecated
public final class TreeClusteringRecommender2Test extends TasteTestCase {
@Test
public void testHowMany() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double[][] {
{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.4, 0.5, 0.9},
{0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
{0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
});
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
Recommender recommender = new TreeClusteringRecommender2(dataModel, clusterSimilarity, 2);
List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
recommender.refresh(null);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
}
@Test
public void testRescorer() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double[][] {
{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.4, 0.5, 0.9},
});
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
Recommender recommender = new TreeClusteringRecommender2(dataModel, clusterSimilarity, 2);
List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
List<RecommendedItem> rescoredRecommended =
recommender.recommend(1, 2, new ReversingRescorer<Long>());
assertNotNull(originalRecommended);
assertNotNull(rescoredRecommended);
assertEquals(2, originalRecommended.size());
assertEquals(2, rescoredRecommended.size());
assertEquals(originalRecommended.get(0).getItemID(), rescoredRecommended.get(1).getItemID());
assertEquals(originalRecommended.get(1).getItemID(), rescoredRecommended.get(0).getItemID());
}
@Test
public void testEstimatePref() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4},
new Double[][] {
{0.1, 0.3},
{0.2, 0.3, 0.3},
{0.4, 0.3, 0.5},
{0.7, 0.3, 0.8, 0.9},
});
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
Recommender recommender = new TreeClusteringRecommender2(dataModel, clusterSimilarity, 2);
assertEquals(0.9f, recommender.estimatePreference(3, 3), EPSILON);
}
@Test
public void testBestRating() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4},
new Double[][] {
{0.1, 0.3},
{0.2, 0.3, 0.3},
{0.4, 0.3, 0.5},
{0.7, 0.3, 0.8},
});
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
Recommender recommender = new TreeClusteringRecommender2(dataModel, clusterSimilarity, 2);
List<RecommendedItem> recommended = recommender.recommend(1, 1);
assertNotNull(recommended);
assertEquals(1, recommended.size());
RecommendedItem firstRecommended = recommended.get(0);
// item one should be recommended because it has a greater rating/score
assertEquals(2, firstRecommended.getItemID());
assertEquals(0.3, firstRecommended.getValue(), EPSILON);
}
}