Package org.apache.mahout.cf.taste.impl.model

Examples of org.apache.mahout.cf.taste.impl.model.GenericDataModel

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        if (numRelevantItems > 0) {
          List<User> trainingUsers = new ArrayList<User>(dataModel.getNumUsers());
          for (User user2 : dataModel.getUsers()) {
            processOtherUser(id, relevantItems, trainingUsers, user2);
          }
          DataModel trainingModel = new GenericDataModel(trainingUsers);
          Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

          try {
            trainingModel.getUser(id);
          } catch (NoSuchUserException nsee) {
            continue; // Oops we excluded all prefs for the user -- just move on
          }

          int intersectionSize = 0;
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      if (random.nextDouble() < evaluationPercentage) {
        processOneUser(trainingPercentage, trainingUsers, testUserPrefs, user);
      }
    }

    DataModel trainingModel = new GenericDataModel(trainingUsers);

    Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

    double result = getEvaluation(testUserPrefs, recommender);
    log.info("Evaluation result: " + result);
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        processFile(dataFile, data);
        for (File updateFile : findUpdateFiles()) {
          processFile(updateFile, data);
        }

        delegate = new GenericDataModel(new UserIteratableOverData(data));
        loaded = true;

      } finally {
        reloadLock.unlock();
      }
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  public void testNoRecommendations() throws Exception {
    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1));
    users.add(getUser("test2", 0.2, 0.6));
    users.add(getUser("test3", 0.4, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend("test1", 1);
    assertNotNull(recommended);
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    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    users.add(getUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
    users.add(getUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
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  public void testRescorer() throws Exception {
    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> rescoredRecommended =
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    List<User> users = new ArrayList<User>(4);
    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    assertEquals(0.9, recommender.estimatePreference("test3", "3"));
  }
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    List<User> users = new ArrayList<User>(4);
    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend("test1", 1);
    assertNotNull(recommended);
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    User user3 = new GenericUser<String>("3", Collections.<Preference>emptyList());
    List<User> users = new ArrayList<User>(3);
    users.add(user1);
    users.add(user2);
    users.add(user3);
    return new GenericDataModel(users);
  }
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    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    users.add(getUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
    users.add(getUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
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