Author: srowen
Date: Tue Sep 28 15:05:24 2010
New Revision: 1002206
URL: http://svn.apache.org/viewvc?rev=1002206&view=rev
Log:
Simple test for the Knn recommender
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommenderTest.java
Added:
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommenderTest.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommenderTest.java?rev=1002206&view=auto
==============================================================================
---
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommenderTest.java
(added)
+++
mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommenderTest.java
Tue Sep 28 15:05:24 2010
@@ -0,0 +1,122 @@
+/**
+ * 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.knn;
+
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.TasteTestCase;
+import org.apache.mahout.cf.taste.impl.recommender.ReversingRescorer;
+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.ItemSimilarity;
+import org.junit.Test;
+
+import java.util.List;
+
+public final class KnnItemBasedRecommenderTest extends TasteTestCase {
+
+ @Test
+ public void testRecommender() throws Exception {
+ Recommender recommender = buildRecommender();
+ List<RecommendedItem> recommended = recommender.recommend(1, 1);
+ assertNotNull(recommended);
+ assertEquals(1, recommended.size());
+ RecommendedItem firstRecommended = recommended.get(0);
+ assertEquals(2, firstRecommended.getItemID());
+ assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
+ recommender.refresh(null);
+ assertEquals(2, firstRecommended.getItemID());
+ assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
+ }
+
+ @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},
+ });
+ ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
+ Optimizer optimizer = new ConjugateGradientOptimizer();
+ Recommender recommender = new KnnItemBasedRecommender(dataModel,
similarity, optimizer, 5);
+ 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, 0.5, 0.5, 0.9},
+ });
+ ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
+ Optimizer optimizer = new ConjugateGradientOptimizer();
+ Recommender recommender = new KnnItemBasedRecommender(dataModel,
similarity, optimizer, 5);
+ 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 {
+ Recommender recommender = buildRecommender();
+ assertEquals(0.1f, recommender.estimatePreference(1, 2), EPSILON);
+ }
+
+ @Test
+ public void testBestRating() throws Exception {
+ Recommender recommender = buildRecommender();
+ 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(0.1f, firstRecommended.getValue(), EPSILON);
+ }
+
+ private static Recommender buildRecommender() throws TasteException {
+ DataModel dataModel = getDataModel();
+ ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
+ Optimizer optimizer = new ConjugateGradientOptimizer();
+ return new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
+ }
+
+
+}
