Package org.apache.mahout.cf.taste.recommender

Examples of org.apache.mahout.cf.taste.recommender.RecommendedItem

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  public void testRecommender() throws Exception {
    Recommender recommender = new ItemUserAverageRecommender(getDataModel());
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.35151517f, firstRecommended.getValue(), EPSILON);
    recommender.refresh(null);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.35151517f, firstRecommended.getValue(), EPSILON);
  }
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    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(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);
  }
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    for (Entry<Long,List<RecommendedItem>> entry : recommendations.entrySet()) {
      long userID = entry.getKey();
      List<RecommendedItem> items = entry.getValue();
      assertNotNull(items);
      RecommendedItem item1 = items.get(0);

      if (userID == 1L) {
        assertEquals(1, items.size());
        assertEquals(4L, item1.getItemID());
        assertEquals(4.3, item1.getValue(), 0.05);
      }
      if (userID == 2L) {
        assertEquals(1, items.size());
        assertEquals(2L, item1.getItemID());
        assertEquals(3.3, item1.getValue(), 0.05);
      }
      if (userID == 3L) {
        assertEquals(2, items.size());
        assertEquals(3L, item1.getItemID());
        assertEquals(4.1, item1.getValue(), 0.05);
        RecommendedItem item2 = items.get(1);
        assertEquals(1L, item2.getItemID());
        assertEquals(3.7, item2.getValue(), 0.05);
      }
      if (userID == 4L) {
        assertEquals(2, items.size());
        assertEquals(2L, item1.getItemID());
        assertEquals(4.0, item1.getValue(), 0.05);
        RecommendedItem item2 = items.get(1);
        assertEquals(3L, item2.getItemID());
        assertEquals(3.5, item2.getValue(), 0.05);
      }
    }

    Map<Pair<Long, Long>, Double> similarities = readSimilarities(new File(similaritiesOutputDir, "part-r-00000"));
    assertEquals(6, similarities.size());
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    Map<Long,List<RecommendedItem>> recommendations = readRecommendations(new File(outputDir, "part-r-00000"));

    List<RecommendedItem> recommendedToCow = recommendations.get(3L);
    assertEquals(2, recommendedToCow.size());

    RecommendedItem item1 = recommendedToCow.get(0);
    RecommendedItem item2 = recommendedToCow.get(1);

    assertEquals(1L, item1.getItemID());
    assertEquals(3L, item2.getItemID());

    /* predicted pref must be the sum of similarities:
    *    item1: coocc(burger, hotdog) + coocc(burger, icecream) = 3
    *    item2: coocc(berries, hotdog) + coocc(berries, icecream) = 2 */
    assertEquals(3, item1.getValue(), 0.05);
    assertEquals(2, item2.getValue(), 0.05);
  }
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     assertEquals(1, recommendations.size());
     assertTrue(recommendations.containsKey(4L));
     assertEquals(1, recommendations.get(4L).size());

     /* berries should have been recommended to the donkey */
     RecommendedItem recommendedItem = recommendations.get(4L).get(0);
     assertEquals(3L, recommendedItem.getItemID());
     assertEquals(3.5, recommendedItem.getValue(), 0.05);
   }
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  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(0.3f, firstRecommended.getValue());
    recommender.refresh(null);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.3f, firstRecommended.getValue());
  }
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  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(0.3f, firstRecommended.getValue(), EPSILON);
  }
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    recommended = new ArrayList<RecommendedItem>();
    try {
      do {
        long itemID = in.readLong();
        float value = in.readFloat();
        RecommendedItem recommendedItem = new GenericRecommendedItem(itemID, value);
        recommended.add(recommendedItem);
      } while (true);
    } catch (EOFException eofe) {
      // continue; done
    }
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                                   Multimap<Long,RecommendedItem> testData) throws TasteException {
    DoubleWeightedMean score = new DoubleWeightedMean();
    int count = 0;
    for (Map.Entry<Long,RecommendedItem> entry : testData.entries()) {
      long userID = entry.getKey();
      RecommendedItem itemPref = entry.getValue();
      try {
        float estimate = recommender.estimatePreference(userID, itemPref.getItemID());
        Preconditions.checkState(LangUtils.isFinite(estimate));
        score.increment(1.0 - estimate, itemPref.getValue());
      } catch (NoSuchItemException nsie) {
        // continue
      } catch (NoSuchUserException nsue) {
        // continue
      }
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   */
  public static void selectTopNIntoQueue(Queue<MutableRecommendedItem> topN,
                                         Iterator<RecommendedItem> values,
                                         int n) {
    while (values.hasNext()) {
      RecommendedItem value = values.next();
      if (value != null) {
        long itemID = value.getItemID();
        float valueScore = value.getValue();
        if (topN.size() > n) {
          if (valueScore > topN.peek().getValue()) {
            MutableRecommendedItem recycled = topN.poll();
            recycled.set(itemID, valueScore);
            topN.add(recycled);
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Related Classes of org.apache.mahout.cf.taste.recommender.RecommendedItem

  • net.myrrix.common.ByValueAscComparatorTest
  • net.myrrix.common.TopN
  • net.myrrix.online.eval.EstimatedStrengthEvaluator
  • net.myrrix.online.eval.PrecisionRecallEvaluator
  • org.apache.mahout.cf.taste.hadoop.item.RecommenderJobTest
  • org.apache.mahout.cf.taste.hadoop.MutableRecommendedItem
  • org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable
  • org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator
  • org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommenderTest
  • org.apache.mahout.cf.taste.impl.recommender.GenericRecommendedItem
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