Author: srowen
Date: Fri Oct 12 09:11:02 2012
New Revision: 1397481
URL: http://svn.apache.org/viewvc?rev=1397481&view=rev
Log:
MAHOUT-1100 fix AIOOBE, add test for TreeClusteringRecommender2
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2Test.java
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java?rev=1397481&r1=1397480&r2=1397481&view=diff
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java
(original)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java
Fri Oct 12 09:11:02 2012
@@ -358,7 +358,7 @@ public final class TreeClusteringRecomme
// catch that case here and put it back into our queue
for (FastIDSet cluster : clusters) {
double similarity = clusterSimilarity.getSimilarity(merged, cluster);
- if (similarity > queue.get(queue.size() - 1).getSimilarity()) {
+ if (queue.size() > 0 && similarity > queue.get(queue.size() -
1).getSimilarity()) {
ListIterator<ClusterClusterPair> queueIterator =
queue.listIterator();
while (queueIterator.hasNext()) {
if (similarity > queueIterator.next().getSimilarity()) {
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2Test.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2Test.java?rev=1397481&view=auto
==============================================================================
---
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2Test.java
(added)
+++
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2Test.java
Fri Oct 12 09:11:02 2012
@@ -0,0 +1,148 @@
+/**
+ * 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.Ignore;
+import org.junit.Test;
+
+/** <p>Tests {@link TreeClusteringRecommender2}.</p> */
+public final class TreeClusteringRecommender2Test extends TasteTestCase {
+
+ // Ignore this test since there is an infinite loop in buildClusters:
+ // mergeClosestClusters never returns true for this dataset
+ @Ignore
+ @Test
+ public void testNoRecommendations() throws Exception {
+ DataModel dataModel = getDataModel(
+ new long[] {1, 2, 3},
+ new Double[][] {
+ {0.1},
+ {0.2, 0.6},
+ {0.4, 0.9},
+ });
+ 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(0, recommended.size());
+ recommender.refresh(null);
+ assertNotNull(recommended);
+ assertEquals(0, recommended.size());
+ }
+
+ @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);
+ }
+
+}
